system

Abstract

The system according to the embodiment aims to identify pre-disease signs and future disease risks based on the results of health checkups and to take appropriate measures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a monitoring unit. The collection unit collects health checkup results. The analysis unit analyzes the data collected by the collection unit to identify pre-disease signs or future disease risks. The notification unit notifies employees of additional detailed examinations based on the risks identified by the analysis unit. The monitoring unit monitors the health status of employees notified by the notification unit.

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 technologies do not adequately identify pre-disease signs and future disease risks based on health checkup results and take appropriate measures, so there is room for improvement.

[0005] The system according to the embodiment aims to identify pre-disease signs and future disease risks based on the results of health checkups and to take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a monitoring unit. The collection unit collects the results of health checkups. The analysis unit analyzes the data collected by the collection unit and identifies signs of pre-disease or future disease risks. The notification unit notifies employees of additional detailed examinations based on the risks identified by the analysis unit. The monitoring unit monitors the health status of employees notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify pre-disease signs and future disease risks based on the results of health checkups, and take appropriate 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 checkup analysis system according to an embodiment of the present invention performs pre-disease analysis by requesting a generating AI to analyze the results of annual health checkups. This system promotes health management by detecting future disease risks early and, if necessary, encouraging employees to undergo additional detailed examinations. First, health checkup results are collected and input into the generating AI. The generating AI analyzes this data to identify pre-disease signs and future disease risks. For example, it analyzes blood test results and electrocardiogram data to detect abnormal patterns. Next, based on the risks identified by the generating AI, the company notifies employees to undergo additional detailed examinations. This notification is sent via email or an internal portal. For example, if the generating AI identifies a risk of high blood pressure, it notifies the employee to undergo additional blood pressure testing. Furthermore, the generating AI continuously monitors employees' health status and periodically analyzes health checkup results. This allows employees' health status to be monitored in real time and countermeasures to be taken early. This system enables efficient management of employee health status and early detection and prevention of pre-disease conditions. In addition, companies can promote health management by understanding the health status of their employees and implementing appropriate health management. As a result, the health checkup analysis system can efficiently manage the health status of employees and realize early detection and prevention of illness.

[0029] A health checkup analysis system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a monitoring unit. The collection unit collects health checkup results. Examples of health checkup results include, but are not limited to, blood tests, electrocardiograms, and X-rays. The collection unit digitally collects the health checkup results and stores them in a database. The collection unit can also scan paper health checkup results and convert them into digital data. For example, the collection unit can scan paper health checkup results using a scanner and convert them into text information using OCR technology. The analysis unit analyzes the data collected by the collection unit to identify pre-disease signs and future disease risks. The analysis unit can use, for example, a generative AI to analyze blood test results and electrocardiogram data and detect abnormal patterns. The generative AI can analyze health checkup results with high accuracy using deep learning models and natural language processing technology. For example, the generative AI can input blood test results and detect abnormal biomarker values. The generative AI can also analyze electrocardiogram data and detect abnormal waveforms. The notification unit notifies the employee of an additional detailed examination based on the risk identified by the analysis unit. The notification unit notifies the employee, for example, by email or via an in-house portal. For example, if the generation AI identifies a risk of high blood pressure, the notification unit notifies the employee to undergo an additional blood pressure measurement examination. The monitoring unit continuously monitors the health status of the employee notified by the notification unit. For example, the monitoring unit periodically collects health checkup results and analyzes them using the generation AI. This makes it possible to grasp the employee's health status in real time and take early measures. As a result, the health checkup analysis system according to the embodiment can efficiently manage the health status of employees and achieve early detection and prevention of pre-disease.

[0030] The analysis unit can analyze blood test results and electrocardiogram data to detect abnormal patterns. The analysis unit, for example, uses a generating AI to analyze blood test results. For example, the generating AI receives blood test results and detects abnormal biomarker values. The analysis unit can also use a generating AI to analyze electrocardiogram data. For example, the generating AI receives electrocardiogram data and detects abnormal waveforms. The analysis unit can also use a generating AI to comprehensively analyze multiple health checkup results and identify abnormal patterns. For example, the generating AI comprehensively analyzes blood test results and electrocardiogram data to identify abnormal patterns. This allows for early detection of abnormal patterns by analyzing blood test and electrocardiogram data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the analysis unit can input blood test results to the generating AI and have the generating AI detect abnormal biomarker values.

[0031] The notification unit can notify employees via email or an in-house portal. For example, the notification unit can notify employees of additional detailed examinations based on the risks identified by the generation AI. For example, if the generation AI identifies a risk of high blood pressure, the notification unit can notify the employee to undergo an additional blood pressure test. Furthermore, if the generation AI identifies a risk of diabetes, the notification unit can also notify the employee to undergo an additional blood glucose test. Furthermore, the notification unit can also notify employees to improve their lifestyle habits based on the risks identified by the generation AI. For example, if the generation AI identifies a risk of high cholesterol, the notification unit can notify the employee to improve their diet. This allows information to be quickly conveyed to employees by notifying them via email or an in-house portal. Some or all of the above-described processing in the notification unit can be performed using, or without, the generation AI. For example, the notification unit can input the risks identified by the generation AI and have the generation AI generate notification content.

[0032] The monitoring unit can monitor the employee's health status and analyze the results of the health checkup. For example, the monitoring unit periodically collects the results of the health checkup and analyzes them using the generating AI. For example, the monitoring unit collects monthly health checkup results and analyzes them using the generating AI. The monitoring unit can also monitor the employee's health status in real time using a wearable device. For example, the monitoring unit collects data such as heart rate and step count from a smartwatch worn by the employee and analyzes it using the generating AI. The monitoring unit can also continuously monitor the employee's health status and immediately notify the employee if an abnormality is detected. For example, if the generating AI detects an abnormal heart rate, the monitoring unit notifies the employee to consult a doctor. This allows for continuous monitoring of the employee's health status, allowing for early detection of abnormalities and appropriate measures to be taken. Some or all of the above-mentioned processing in the monitoring unit can be performed using, or without, the generating AI. For example, the monitoring unit can input data collected from the wearable device into the generating AI and have the generating AI detect abnormalities.

[0033] The analysis unit can use the generative AI to identify pre-disease signs and future disease risks. The analysis unit, for example, uses the generative AI to identify pre-disease signs and future disease risks. The generative AI uses deep learning models and natural language processing technology to analyze health checkup results with high accuracy. For example, the generative AI inputs blood test results and detects abnormal biomarker values. The generative AI can also analyze electrocardiogram data and detect abnormal waveforms. Furthermore, the generative AI can comprehensively analyze multiple health checkup results to identify pre-disease signs and future disease risks. For example, the generative AI comprehensively analyzes blood test results and electrocardiogram data to identify pre-disease signs and future disease risks. This allows the generative AI to identify pre-disease signs and future disease risks with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generative AI, or may be performed without the generative AI. For example, the analysis unit inputs health checkup results to the generative AI and has the generative AI identify pre-disease signs and future disease risks.

[0034] The notification unit can notify the employee of an additional detailed examination based on the risk identified by the generation AI. The notification unit notifies the employee of an additional detailed examination based on the risk identified by the generation AI, for example. For example, if the generation AI identifies a risk of high blood pressure, the notification unit notifies the employee to undergo an additional blood pressure measurement test. Also, if the generation AI identifies a risk of diabetes, the notification unit can notify the employee to undergo an additional blood glucose measurement test. Furthermore, the notification unit can notify the employee to improve their lifestyle habits based on the risk identified by the generation AI. For example, if the generation AI identifies a risk of high cholesterol, the notification unit notifies the employee to improve their diet. In this way, by notifying the employee based on the risk identified by the generation AI, it is possible to encourage the employee to undergo appropriate examination. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the risk identified by the generation AI and cause the generation AI to generate notification content.

[0035] When collecting health checkup results, the collection unit can analyze the user's past health checkup history and select the optimal collection method. The collection unit, for example, uses a generation AI to analyze the user's past health checkup history. The generation AI uses the past health checkup results as input and executes an algorithm to select the optimal collection method. For example, the generation AI selects the optimal collection method based on the results of the user's past health checkups. The generation AI can also prioritize collection of specific test items from the user's past health checkup history. Furthermore, the generation AI can analyze the user's past health checkup history and customize the collection method. For example, the generation AI executes an algorithm to prioritize collection of specific test items based on the user's past health checkup results. This allows the optimal collection method to be selected by analyzing the past health checkup history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past health checkup history into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting health checkup results, the collection unit can filter the results based on the user's current living situation and job content. The collection unit, for example, uses a generation AI to analyze the user's current living situation and job content. The generation AI receives data related to the user's living situation and job content and executes an algorithm for filtering. For example, the generation AI prioritizes collecting relevant health checkup results, taking into account the user's current living situation. The generation AI can also filter specific health checkup results based on the user's job content. The generation AI can also customize the health checkup results to be collected based on the user's living situation and job content. For example, the generation AI executes an algorithm that prioritizes collecting specific health checkup results based on the user's living situation and job content. This allows for filtering based on the user's current living situation and job content to collect highly relevant data. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data related to the user's living situation and job content to the generation AI and have the generation AI perform filtering.

[0037] When collecting health checkup results, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to analyze the user's geographical location information. The generation AI inputs geographical location information such as GPS data and address information and executes an algorithm for prioritized collection of highly relevant data. For example, the generation AI prioritizes collection of data related to region-specific health risks based on the user's geographical location information. The generation AI can also prioritize collection of data from nearby medical institutions by taking into account the user's geographical location information. Furthermore, the generation AI can prioritize collection of health checkup results related to environmental factors based on the user's geographical location information. For example, the generation AI executes an algorithm that prioritizes collection of health checkup results related to specific environmental factors based on the user's geographical location information. This allows for prioritized collection of data related to region-specific health risks by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to 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 results. The collection unit, for example, uses a generation AI to analyze the user's social media activity. The generation AI receives data such as social media post content and activity frequency and executes an algorithm to collect related data. For example, the generation AI analyzes the user's social media activity and collects data based on health-related posts. The generation AI can also identify health concerns from the user's social media activity and collect related data. Furthermore, the generation AI can prioritize collecting data related to health risks based on the user's social media activity. For example, the generation AI executes an algorithm that prioritizes collecting data related to specific health risks based on the user's social media activity. This allows health-related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data related to the user's social media activity to the generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the importance of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the importance. For example, the generation AI evaluates the importance of the health checkup results based on abnormal values ​​of specific biomarkers or the presence of symptoms. The analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, the analysis unit performs a detailed analysis of health checkup results with high importance. The analysis unit can also perform a simplified analysis of health checkup results with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of the health checkup results. For example, the analysis unit executes an algorithm that performs a more detailed analysis as the importance increases. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on health checkup results into the generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the health checkup results. The analysis unit, for example, uses a generation AI to classify the categories of the health checkup results. The generation AI takes the health checkup result data as input and executes an algorithm for categorizing the categories. For example, the generation AI classifies the results into categories such as blood test, electrocardiogram, and X-ray. The analysis unit applies different analysis algorithms depending on the classified category. For example, the analysis unit applies a specific analysis algorithm to blood test results. The analysis unit can also apply a different analysis algorithm to electrocardiogram data. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category of health checkup results. For example, the analysis unit applies a regression analysis algorithm to blood test results and a clustering algorithm to electrocardiogram data. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input health check result data into the generation AI and have the generation AI perform category classification and apply analysis algorithms.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission date of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the submission date of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the submission date. For example, the generation AI evaluates the submission date of the health checkup results based on the submission date and submission frequency. The analysis unit determines the analysis priority based on the evaluated submission date. For example, the analysis unit prioritizes analysis of the most recent health checkup results. The analysis unit can also postpone analysis of health checkup results submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority depending on the submission date of the health checkup results. For example, the analysis unit executes an algorithm that prioritizes analysis of more recently submitted data. As a result, by determining the priority based on the submission date, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input health check result data into the generation AI and have the generation AI evaluate the timing of submission and determine analysis priorities.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the relevance of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the relevance. For example, the generation AI evaluates the relevance of the health checkup results based on data correlations and common characteristics. The analysis unit adjusts the order of analysis based on the evaluated relevance. For example, the analysis unit prioritizes analysis of highly relevant health checkup results. The analysis unit can also postpone analysis of less relevant health checkup results. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of the health checkup results. For example, the analysis unit executes an algorithm that prioritizes analysis of more relevant results. This enables efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs health checkup result data to the generation AI and causes the generation AI to evaluate the relevance and adjust the order of analysis.

[0043] The notification unit can adjust the level of detail of the notification based on the importance of the identified risk when sending a notification. The notification unit, for example, uses a generation AI to evaluate the importance of the identified risk. The generation AI inputs risk data and executes an algorithm for evaluating the importance. For example, the generation AI evaluates the importance of the identified risk based on the probability of occurrence and impact of the risk. The notification unit adjusts the level of detail of the notification based on the evaluated importance. For example, the notification unit provides detailed notifications for risks with high importance. The notification unit can also provide simplified notifications for risks with low importance. Furthermore, the notification unit can gradually adjust the level of detail of the notification depending on the importance of the identified risk. For example, the notification unit executes an algorithm that provides more detailed notifications as the importance increases. This enables efficient notification by adjusting the level of detail of the notification depending on the importance of the risk. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data on identified risks into the generation AI and have the generation AI evaluate the importance and adjust the level of detail of the notification.

[0044] The notification unit can apply different notification algorithms depending on the category of the identified risk when notifying. The notification unit, for example, uses a generation AI to classify the category of the identified risk. The generation AI takes risk data as input and executes an algorithm for categorizing the risk. For example, the generation AI classifies the risk into categories such as high blood pressure, diabetes, and heart disease. The notification unit applies different notification algorithms depending on the classified category. For example, the notification unit applies a specific notification algorithm to the risk of high blood pressure. The notification unit can also apply a different notification algorithm to the risk of diabetes. Furthermore, the notification unit can select and apply an optimal notification algorithm for each identified risk category. For example, the notification unit applies a rule-based notification algorithm to the risk of high blood pressure and a machine learning model to the risk of diabetes. This improves the accuracy of notifications by applying the optimal notification algorithm for each category. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input data of the identified risk to the generation AI and have the generation AI classify the category and apply the notification algorithm.

[0045] At the time of notification, the notification unit can determine the priority of notifications based on the time when the identified risk was discovered. The notification unit, for example, uses a generation AI to evaluate the time when the identified risk was discovered. The generation AI inputs risk data and executes an algorithm for evaluating the time of discovery. For example, the generation AI evaluates the time when the identified risk was discovered based on the risk's discovery date and discovery frequency. The notification unit determines the priority of notifications based on the evaluated time of discovery. For example, the notification unit prioritizes notifying the most recent risk. The notification unit can also postpone notifying risks that were discovered earlier. Furthermore, the notification unit can gradually adjust the priority of notifications depending on the time when the identified risk was discovered. For example, the notification unit executes an algorithm that prioritizes notifying the most recently discovered risk. As a result, by determining the priority based on the time of discovery, the most recent risk can be prioritized. Some or all of the above-described processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data on identified risks into the generation AI and have the generation AI evaluate the time of discovery and determine the priority of notifications.

[0046] The notification unit can adjust the order of notifications based on the relevance of the identified risks at the time of notification. The notification unit, for example, uses a generation AI to evaluate the relevance of the identified risks. The generation AI uses risk data as input and executes an algorithm for evaluating the relevance. For example, the generation AI evaluates the relevance of the identified risks based on risk correlations and common characteristics. The notification unit adjusts the order of notifications based on the evaluated relevance. For example, the notification unit prioritizes notifying highly relevant risks. The notification unit can also postpone notifying less relevant risks. Furthermore, the notification unit can gradually adjust the order of notifications according to the relevance of the identified risks. For example, the notification unit executes an algorithm that prioritizes notifications for more relevant risks. This enables efficient notification by adjusting the order of notifications based on relevance. Some or all of the above-described processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data of the identified risks to the generation AI and cause the generation AI to evaluate the relevance and adjust the order of notifications.

[0047] During monitoring, the monitoring unit can analyze the user's past health checkup results and select the optimal monitoring method. The monitoring unit, for example, uses a generation AI to analyze the user's past health checkup results. The generation AI inputs the data of the past health checkup results and executes an algorithm to select the optimal monitoring method. For example, the generation AI selects the optimal monitoring method based on the results of the user's past health checkups. The generation AI can also prioritize specific monitoring items from the user's past health checkup results. Furthermore, the generation AI can analyze the user's past health checkup results and customize the monitoring method. For example, the generation AI executes an algorithm to prioritize specific monitoring items based on the user's past health checkup results. This allows the optimal monitoring method to be selected by analyzing the past health checkup results. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's past health checkup results to the generation AI and have the generation AI select the optimal monitoring method.

[0048] During monitoring, the monitoring unit can customize the monitoring means based on the user's current living situation. The monitoring unit, for example, uses a generation AI to analyze the user's current living situation. The generation AI inputs data related to the user's living situation and executes an algorithm to customize the monitoring means. For example, the generation AI takes into account the user's current living situation and provides highly relevant monitoring means. The generation AI can also customize specific monitoring means based on the user's job content. Furthermore, the generation AI can adjust the monitoring means based on the user's living situation and job content. For example, the generation AI executes an algorithm to provide specific monitoring means based on the user's living situation and job content. This allows for more appropriate monitoring by customizing the monitoring means based on the current living situation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data related to the user's living situation to the generation AI and have the generation AI customize the monitoring means.

[0049] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. The monitoring unit, for example, uses a generation AI to analyze the user's geographical location information. The generation AI inputs geographical location information such as GPS data and address information and executes an algorithm to select the optimal monitoring method. For example, the generation AI prioritizes monitoring related to region-specific health risks based on the user's geographical location information. The generation AI can also perform monitoring based on data from nearby medical institutions by taking into account the user's geographical location information. Furthermore, the generation AI can prioritize monitoring related to environmental factors based on the user's geographical location information. For example, the generation AI executes an algorithm that prioritizes monitoring related to specific environmental factors based on the user's geographical location information. This allows for prioritizing monitoring related to region-specific health risks by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the user's geographical location information to the generation AI and have the generation AI select the optimal monitoring method.

[0050] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. The monitoring unit, for example, uses a generation AI to analyze the user's social media activity. The generation AI inputs data such as the content of social media posts and the frequency of activity and executes an algorithm to suggest monitoring measures. For example, the generation AI analyzes the user's social media activity and suggests monitoring measures based on health-related posts. The generation AI can also identify health concerns from the user's social media activity and suggest related monitoring measures. Furthermore, the generation AI can prioritize suggesting monitoring measures related to health risks based on the user's social media activity. For example, the generation AI executes an algorithm that prioritizes suggesting monitoring measures related to specific health risks based on the user's social media activity. In this way, health-related monitoring measures can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data regarding the user's social media activity to the generation AI and have the generation AI suggest monitoring measures.

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

[0052] The collection unit can collect the user's dietary records and provide them to the analysis unit. For example, the collection unit can collect dietary details entered by the user through a smartphone app and store them in a database. The collection unit can also analyze photos of meals taken by the user and automatically recognize the dietary details. Furthermore, the collection unit can provide the user's dietary records to the analysis unit and analyze the correlation between the dietary details and health checkup results. This allows the correlation between the user's dietary habits and health status to be understood, enabling appropriate health management.

[0053] The analysis unit can analyze the user's exercise data and evaluate its correlation with health checkup results. For example, the analysis unit can analyze the exercise data collected by the user through a wearable device and evaluate the amount and intensity of exercise. The analysis unit can also perform an integrated analysis of the exercise data and health checkup results to identify the impact of exercise habits on health status. Furthermore, the analysis unit can suggest an appropriate exercise plan to the user based on the exercise data. This allows the user to understand the correlation between the user's exercise habits and health status and perform appropriate health management.

[0054] The monitoring unit can collect the user's sleep data and provide it to the analysis unit. For example, the monitoring unit can collect sleep data collected by the user through a wearable device and store it in a database. The monitoring unit can also provide the user's sleep data to the analysis unit and analyze the correlation between sleep patterns and health checkup results. Furthermore, the monitoring unit can propose an appropriate sleep improvement plan based on the user's sleep data. This allows the correlation between the user's sleep habits and health condition to be understood, enabling appropriate health management.

[0055] The notification unit can suggest the most suitable medical institution by taking into account the user's geographical location information. For example, the notification unit can list nearby medical institutions based on the user's current location and suggest an appropriate medical institution. The notification unit can also preferentially suggest medical institutions with specialists based on the user's health checkup results. Furthermore, the notification unit can also suggest the most suitable medical institution by taking into account the means of transportation and required travel time based on the user's geographical location information. In this way, an appropriate medical institution can be suggested by taking into account the user's geographical location information.

[0056] The analysis unit can analyze the user's genetic information and evaluate the correlation with health checkup results. For example, the analysis unit collects the user's genetic information and stores it in a database. The analysis unit can also perform an integrated analysis of the genetic information and health checkup results to identify genetic risks. Furthermore, the analysis unit can suggest appropriate preventive measures to the user based on the genetic information. This allows the correlation between the user's genetic information and health condition to be understood, enabling appropriate health management.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects the results of the health checkup. The results of the health checkup include blood tests, electrocardiograms, X-rays, etc. The collection department collects these results in digital format and stores them in a database. The collection department can also scan health checkup results submitted on paper and convert them into digital data. For example, a scanner can be used to scan the paper health checkup results and then OCR technology can be used to convert them into text information. Step 2: The analysis unit analyzes the data collected by the collection unit to identify pre-disease signs and future disease risks. The analysis unit uses generative AI to analyze blood test results and electrocardiogram data to detect abnormal patterns. The generative AI uses deep learning models and natural language processing technology to analyze health checkup results with high accuracy. For example, it inputs blood test results and detects abnormal biomarker values. It can also analyze electrocardiogram data to detect abnormal waveforms. Step 3: The notification unit notifies employees of additional detailed examinations based on the risks identified by the analysis unit. The notification unit notifies employees via email or an internal portal. For example, if the generation AI identifies a risk of high blood pressure, the unit notifies the employee to undergo additional blood pressure testing. Step 4: The Monitoring Department continuously monitors the health status of employees notified by the Notification Department. The Monitoring Department periodically collects the results of health checkups and analyzes them using Generative AI. This allows the health status of employees to be understood in real time and measures to be taken early.

[0059] (Example 2) A health checkup analysis system according to an embodiment of the present invention performs pre-disease analysis by requesting a generating AI to analyze the results of annual health checkups. This system promotes health management by detecting future disease risks early and, if necessary, encouraging employees to undergo additional detailed examinations. First, health checkup results are collected and input into the generating AI. The generating AI analyzes this data to identify pre-disease signs and future disease risks. For example, it analyzes blood test results and electrocardiogram data to detect abnormal patterns. Next, based on the risks identified by the generating AI, the company notifies employees to undergo additional detailed examinations. This notification is sent via email or an internal portal. For example, if the generating AI identifies a risk of high blood pressure, it notifies the employee to undergo additional blood pressure testing. Furthermore, the generating AI continuously monitors employees' health status and periodically analyzes health checkup results. This allows employees' health status to be monitored in real time and countermeasures to be taken early. This system enables efficient management of employee health status and early detection and prevention of pre-disease conditions. In addition, companies can promote health management by understanding the health status of their employees and implementing appropriate health management. As a result, the health checkup analysis system can efficiently manage the health status of employees and realize early detection and prevention of illness.

[0060] A health checkup analysis system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a monitoring unit. The collection unit collects health checkup results. Examples of health checkup results include, but are not limited to, blood tests, electrocardiograms, and X-rays. The collection unit digitally collects the health checkup results and stores them in a database. The collection unit can also scan paper health checkup results and convert them into digital data. For example, the collection unit can scan paper health checkup results using a scanner and convert them into text information using OCR technology. The analysis unit analyzes the data collected by the collection unit to identify pre-disease signs and future disease risks. The analysis unit can use, for example, a generative AI to analyze blood test results and electrocardiogram data and detect abnormal patterns. The generative AI can analyze health checkup results with high accuracy using deep learning models and natural language processing technology. For example, the generative AI can input blood test results and detect abnormal biomarker values. The generative AI can also analyze electrocardiogram data and detect abnormal waveforms. The notification unit notifies the employee of an additional detailed examination based on the risk identified by the analysis unit. The notification unit notifies the employee, for example, by email or via an in-house portal. For example, if the generation AI identifies a risk of high blood pressure, the notification unit notifies the employee to undergo an additional blood pressure measurement examination. The monitoring unit continuously monitors the health status of the employee notified by the notification unit. For example, the monitoring unit periodically collects health checkup results and analyzes them using the generation AI. This makes it possible to grasp the employee's health status in real time and take early measures. As a result, the health checkup analysis system according to the embodiment can efficiently manage the health status of employees and achieve early detection and prevention of pre-disease.

[0061] The analysis unit can analyze blood test results and electrocardiogram data to detect abnormal patterns. The analysis unit, for example, uses a generating AI to analyze blood test results. For example, the generating AI receives blood test results and detects abnormal biomarker values. The analysis unit can also use a generating AI to analyze electrocardiogram data. For example, the generating AI receives electrocardiogram data and detects abnormal waveforms. The analysis unit can also use a generating AI to comprehensively analyze multiple health checkup results and identify abnormal patterns. For example, the generating AI comprehensively analyzes blood test results and electrocardiogram data to identify abnormal patterns. This allows for early detection of abnormal patterns by analyzing blood test and electrocardiogram data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the analysis unit can input blood test results to the generating AI and have the generating AI detect abnormal biomarker values.

[0062] The notification unit can notify employees via email or an in-house portal. For example, the notification unit can notify employees of additional detailed examinations based on the risks identified by the generation AI. For example, if the generation AI identifies a risk of high blood pressure, the notification unit can notify the employee to undergo an additional blood pressure test. Furthermore, if the generation AI identifies a risk of diabetes, the notification unit can also notify the employee to undergo an additional blood glucose test. Furthermore, the notification unit can also notify employees to improve their lifestyle habits based on the risks identified by the generation AI. For example, if the generation AI identifies a risk of high cholesterol, the notification unit can notify the employee to improve their diet. This allows information to be quickly conveyed to employees by notifying them via email or an in-house portal. Some or all of the above-described processing in the notification unit can be performed using, or without, the generation AI. For example, the notification unit can input the risks identified by the generation AI and have the generation AI generate notification content.

[0063] The monitoring unit can monitor the employee's health status and analyze the results of the health checkup. For example, the monitoring unit periodically collects the results of the health checkup and analyzes them using the generating AI. For example, the monitoring unit collects monthly health checkup results and analyzes them using the generating AI. The monitoring unit can also monitor the employee's health status in real time using a wearable device. For example, the monitoring unit collects data such as heart rate and step count from a smartwatch worn by the employee and analyzes it using the generating AI. The monitoring unit can also continuously monitor the employee's health status and immediately notify the employee if an abnormality is detected. For example, if the generating AI detects an abnormal heart rate, the monitoring unit notifies the employee to consult a doctor. This allows for continuous monitoring of the employee's health status, allowing for early detection of abnormalities and appropriate measures to be taken. Some or all of the above-mentioned processing in the monitoring unit can be performed using, or without, the generating AI. For example, the monitoring unit can input data collected from the wearable device into the generating AI and have the generating AI detect abnormalities.

[0064] The analysis unit can use the generative AI to identify pre-disease signs and future disease risks. The analysis unit, for example, uses the generative AI to identify pre-disease signs and future disease risks. The generative AI uses deep learning models and natural language processing technology to analyze health checkup results with high accuracy. For example, the generative AI inputs blood test results and detects abnormal biomarker values. The generative AI can also analyze electrocardiogram data and detect abnormal waveforms. Furthermore, the generative AI can comprehensively analyze multiple health checkup results to identify pre-disease signs and future disease risks. For example, the generative AI comprehensively analyzes blood test results and electrocardiogram data to identify pre-disease signs and future disease risks. This allows the generative AI to identify pre-disease signs and future disease risks with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generative AI, or may be performed without the generative AI. For example, the analysis unit inputs health checkup results to the generative AI and has the generative AI identify pre-disease signs and future disease risks.

[0065] The notification unit can notify the employee of an additional detailed examination based on the risk identified by the generation AI. The notification unit notifies the employee of an additional detailed examination based on the risk identified by the generation AI, for example. For example, if the generation AI identifies a risk of high blood pressure, the notification unit notifies the employee to undergo an additional blood pressure measurement test. Also, if the generation AI identifies a risk of diabetes, the notification unit can notify the employee to undergo an additional blood glucose measurement test. Furthermore, the notification unit can notify the employee to improve their lifestyle habits based on the risk identified by the generation AI. For example, if the generation AI identifies a risk of high cholesterol, the notification unit notifies the employee to improve their diet. In this way, by notifying the employee based on the risk identified by the generation AI, it is possible to encourage the employee to undergo appropriate examination. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the risk identified by the generation AI and cause the generation AI to generate notification content.

[0066] The collection unit can estimate the user's emotions and adjust the timing of collecting health checkup results based on the estimated user emotions. The collection unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The collection unit adjusts the timing of collecting health checkup results based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects health checkup results during a time when the user is able to relax. The collection unit can also collect health checkup results immediately if the user is relaxed. Furthermore, if the user is busy, the collection unit can adjust the timing of collecting health checkup results to suit the user's schedule. This allows the collection timing to be adjusted according to the user's emotions, thereby collecting health checkup results at a more appropriate time. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0067] When collecting health checkup results, the collection unit can analyze the user's past health checkup history and select the optimal collection method. The collection unit, for example, uses a generation AI to analyze the user's past health checkup history. The generation AI uses the past health checkup results as input and executes an algorithm to select the optimal collection method. For example, the generation AI selects the optimal collection method based on the results of the user's past health checkups. The generation AI can also prioritize collection of specific test items from the user's past health checkup history. Furthermore, the generation AI can analyze the user's past health checkup history and customize the collection method. For example, the generation AI executes an algorithm to prioritize collection of specific test items based on the user's past health checkup results. This allows the optimal collection method to be selected by analyzing the past health checkup history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past health checkup history into the generation AI and have the generation AI select the optimal collection method.

[0068] When collecting health checkup results, the collection unit can filter the results based on the user's current living situation and job content. The collection unit, for example, uses a generation AI to analyze the user's current living situation and job content. The generation AI receives data related to the user's living situation and job content and executes an algorithm for filtering. For example, the generation AI prioritizes collecting relevant health checkup results, taking into account the user's current living situation. The generation AI can also filter specific health checkup results based on the user's job content. The generation AI can also customize the health checkup results to be collected based on the user's living situation and job content. For example, the generation AI executes an algorithm that prioritizes collecting specific health checkup results based on the user's living situation and job content. This allows for filtering based on the user's current living situation and job content to collect highly relevant data. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data related to the user's living situation and job content to the generation AI and have the generation AI perform filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of the health checkup results to be collected based on the estimated user's emotions. The collection unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The collection unit determines the priority of the health checkup results to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related health checkup results. Also, if the user is relaxed, the collection unit can collect overall health checkup results evenly. Furthermore, if the user is tired, the collection unit can prioritize collecting fatigue-related health checkup results. This allows important data to be collected preferentially by determining the priority according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the health checkup results to be collected.

[0070] When collecting health checkup results, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to analyze the user's geographical location information. The generation AI inputs geographical location information such as GPS data and address information and executes an algorithm for prioritized collection of highly relevant data. For example, the generation AI prioritizes collection of data related to region-specific health risks based on the user's geographical location information. The generation AI can also prioritize collection of data from nearby medical institutions by taking into account the user's geographical location information. Furthermore, the generation AI can prioritize collection of health checkup results related to environmental factors based on the user's geographical location information. For example, the generation AI executes an algorithm that prioritizes collection of health checkup results related to specific environmental factors based on the user's geographical location information. This allows for prioritized collection of data related to region-specific health risks by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0071] The collection unit can analyze the user's social media activity and collect related data when collecting health checkup results. The collection unit, for example, uses a generation AI to analyze the user's social media activity. The generation AI receives data such as social media post content and activity frequency and executes an algorithm to collect related data. For example, the generation AI analyzes the user's social media activity and collects data based on health-related posts. The generation AI can also identify health concerns from the user's social media activity and collect related data. Furthermore, the generation AI can prioritize collecting data related to health risks based on the user's social media activity. For example, the generation AI executes an algorithm that prioritizes collecting data related to specific health risks based on the user's social media activity. This allows health-related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data related to the user's social media activity to the generation AI and cause the generation AI to collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit provides simple, highly visible analysis results when the user is nervous. The analysis unit can also provide detailed analysis results when the user is relaxed. The analysis unit can also provide analysis results that focus on the main points when the user is in a hurry. This allows the analysis presentation method to be adjusted according to the user's emotions, making it easier to understand analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the importance of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the importance. For example, the generation AI evaluates the importance of the health checkup results based on abnormal values ​​of specific biomarkers or the presence of symptoms. The analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, the analysis unit performs a detailed analysis of health checkup results with high importance. The analysis unit can also perform a simplified analysis of health checkup results with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of the health checkup results. For example, the analysis unit executes an algorithm that performs a more detailed analysis as the importance increases. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on health checkup results into the generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the health checkup results. The analysis unit, for example, uses a generation AI to classify the categories of the health checkup results. The generation AI takes the health checkup result data as input and executes an algorithm for categorizing the categories. For example, the generation AI classifies the results into categories such as blood test, electrocardiogram, and X-ray. The analysis unit applies different analysis algorithms depending on the classified category. For example, the analysis unit applies a specific analysis algorithm to blood test results. The analysis unit can also apply a different analysis algorithm to electrocardiogram data. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category of health checkup results. For example, the analysis unit applies a regression analysis algorithm to blood test results and a clustering algorithm to electrocardiogram data. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input health check result data into the generation AI and have the generation AI perform category classification and apply analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, the analysis unit provides a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission date of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the submission date of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the submission date. For example, the generation AI evaluates the submission date of the health checkup results based on the submission date and submission frequency. The analysis unit determines the analysis priority based on the evaluated submission date. For example, the analysis unit prioritizes analysis of the most recent health checkup results. The analysis unit can also postpone analysis of health checkup results submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority depending on the submission date of the health checkup results. For example, the analysis unit executes an algorithm that prioritizes analysis of more recently submitted data. As a result, by determining the priority based on the submission date, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input health check result data into the generation AI and have the generation AI evaluate the timing of submission and determine analysis priorities.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health checkup results. The analysis unit, for example, uses a generation AI to evaluate the relevance of the health checkup results. The generation AI inputs health checkup result data and executes an algorithm for evaluating the relevance. For example, the generation AI evaluates the relevance of the health checkup results based on data correlations and common characteristics. The analysis unit adjusts the order of analysis based on the evaluated relevance. For example, the analysis unit prioritizes analysis of highly relevant health checkup results. The analysis unit can also postpone analysis of less relevant health checkup results. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of the health checkup results. For example, the analysis unit executes an algorithm that prioritizes analysis of more relevant results. This enables efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs health checkup result data to the generation AI and causes the generation AI to evaluate the relevance and adjust the order of analysis.

[0078] The notification unit can estimate the user's emotions and adjust the notification presentation method based on the estimated user's emotions. The notification unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The notification unit adjusts the notification presentation method based on the estimated user's emotions. For example, the notification unit can provide a simple, highly visible notification if the user is nervous. The notification unit can also provide a detailed notification if the user is relaxed. The notification unit can also provide a notification that focuses on the main points if the user is in a hurry. This allows more appropriate notifications to be provided by adjusting the notification presentation method according to the user's emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit may input user emotion data into the generation AI and have the generation AI adjust the notification expression method.

[0079] The notification unit can adjust the level of detail of the notification based on the importance of the identified risk when sending a notification. The notification unit, for example, uses a generation AI to evaluate the importance of the identified risk. The generation AI inputs risk data and executes an algorithm for evaluating the importance. For example, the generation AI evaluates the importance of the identified risk based on the probability of occurrence and impact of the risk. The notification unit adjusts the level of detail of the notification based on the evaluated importance. For example, the notification unit provides detailed notifications for risks with high importance. The notification unit can also provide simplified notifications for risks with low importance. Furthermore, the notification unit can gradually adjust the level of detail of the notification depending on the importance of the identified risk. For example, the notification unit executes an algorithm that provides more detailed notifications as the importance increases. This enables efficient notification by adjusting the level of detail of the notification depending on the importance of the risk. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data on identified risks into the generation AI and have the generation AI evaluate the importance and adjust the level of detail of the notification.

[0080] The notification unit can apply different notification algorithms depending on the category of the identified risk when notifying. The notification unit, for example, uses a generation AI to classify the category of the identified risk. The generation AI takes risk data as input and executes an algorithm for categorizing the risk. For example, the generation AI classifies the risk into categories such as high blood pressure, diabetes, and heart disease. The notification unit applies different notification algorithms depending on the classified category. For example, the notification unit applies a specific notification algorithm to the risk of high blood pressure. The notification unit can also apply a different notification algorithm to the risk of diabetes. Furthermore, the notification unit can select and apply an optimal notification algorithm for each identified risk category. For example, the notification unit applies a rule-based notification algorithm to the risk of high blood pressure and a machine learning model to the risk of diabetes. This improves the accuracy of notifications by applying the optimal notification algorithm for each category. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input data of the identified risk to the generation AI and have the generation AI classify the category and apply the notification algorithm.

[0081] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. The notification unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The notification unit adjusts the length of the notification based on the estimated user's emotions. For example, the notification unit can provide a short, to-the-point notification if the user is in a hurry. The notification unit can also provide a detailed notification if the user is relaxed. The notification unit can also provide a visually stimulating notification if the user is excited. This allows for more appropriate notifications to be provided by adjusting the length of the notification according to the user's emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit may input user emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0082] At the time of notification, the notification unit can determine the priority of notifications based on the time when the identified risk was discovered. The notification unit, for example, uses a generation AI to evaluate the time when the identified risk was discovered. The generation AI inputs risk data and executes an algorithm for evaluating the time of discovery. For example, the generation AI evaluates the time when the identified risk was discovered based on the risk's discovery date and discovery frequency. The notification unit determines the priority of notifications based on the evaluated time of discovery. For example, the notification unit prioritizes notifying the most recent risk. The notification unit can also postpone notifying risks that were discovered earlier. Furthermore, the notification unit can gradually adjust the priority of notifications depending on the time when the identified risk was discovered. For example, the notification unit executes an algorithm that prioritizes notifying the most recently discovered risk. As a result, by determining the priority based on the time of discovery, the most recent risk can be prioritized. Some or all of the above-described processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data on identified risks into the generation AI and have the generation AI evaluate the time of discovery and determine the priority of notifications.

[0083] The notification unit can adjust the order of notifications based on the relevance of the identified risks at the time of notification. The notification unit, for example, uses a generation AI to evaluate the relevance of the identified risks. The generation AI uses risk data as input and executes an algorithm for evaluating the relevance. For example, the generation AI evaluates the relevance of the identified risks based on risk correlations and common characteristics. The notification unit adjusts the order of notifications based on the evaluated relevance. For example, the notification unit prioritizes notifying highly relevant risks. The notification unit can also postpone notifying less relevant risks. Furthermore, the notification unit can gradually adjust the order of notifications according to the relevance of the identified risks. For example, the notification unit executes an algorithm that prioritizes notifications for more relevant risks. This enables efficient notification by adjusting the order of notifications based on relevance. Some or all of the above-described processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input data of the identified risks to the generation AI and cause the generation AI to evaluate the relevance and adjust the order of notifications.

[0084] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. The monitoring unit, for example, uses a generation AI to estimate the user's emotions. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The monitoring unit adjusts the monitoring method based on the estimated user's emotions. For example, the monitoring unit can provide a simple, highly visible monitoring method if the user is nervous. The monitoring unit can also provide a detailed monitoring method if the user is relaxed. Furthermore, the monitoring unit can provide a monitoring method that focuses on the key points if the user is in a hurry. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI and cause the generation AI to adjust the monitoring method.

[0085] During monitoring, the monitoring unit can analyze the user's past health checkup results and select the optimal monitoring method. The monitoring unit, for example, uses a generation AI to analyze the user's past health checkup results. The generation AI inputs the data of the past health checkup results and executes an algorithm to select the optimal monitoring method. For example, the generation AI selects the optimal monitoring method based on the results of the user's past health checkups. The generation AI can also prioritize specific monitoring items from the user's past health checkup results. Furthermore, the generation AI can analyze the user's past health checkup results and customize the monitoring method. For example, the generation AI executes an algorithm to prioritize specific monitoring items based on the user's past health checkup results. This allows the optimal monitoring method to be selected by analyzing the past health checkup results. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's past health checkup results to the generation AI and have the generation AI select the optimal monitoring method.

[0086] During monitoring, the monitoring unit can customize the monitoring means based on the user's current living situation. The monitoring unit, for example, uses a generation AI to analyze the user's current living situation. The generation AI inputs data related to the user's living situation and executes an algorithm to customize the monitoring means. For example, the generation AI takes into account the user's current living situation and provides highly relevant monitoring means. The generation AI can also customize specific monitoring means based on the user's job content. Furthermore, the generation AI can adjust the monitoring means based on the user's living situation and job content. For example, the generation AI executes an algorithm to provide specific monitoring means based on the user's living situation and job content. This allows for more appropriate monitoring by customizing the monitoring means based on the current living situation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data related to the user's living situation to the generation AI and have the generation AI customize the monitoring means.

[0087] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit estimates the user's emotions using, for example, a generation AI. The generation AI estimates the user's emotions with high accuracy using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. The generation AI can also analyze the user's text data and calculate an emotion score. For example, the generation AI analyzes the content of the user's emails and chats and calculates an emotion score. The monitoring unit determines monitoring priorities based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit prioritizes stress-related monitoring. Also, if the user is relaxed, the monitoring unit can perform overall monitoring evenly. Furthermore, if the user is tired, the monitoring unit can prioritize fatigue-related monitoring. In this way, by determining monitoring priorities according to the user's emotions, important monitoring can be prioritized. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI determine the monitoring priority.

[0088] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. The monitoring unit, for example, uses a generation AI to analyze the user's geographical location information. The generation AI inputs geographical location information such as GPS data and address information and executes an algorithm to select the optimal monitoring method. For example, the generation AI prioritizes monitoring related to region-specific health risks based on the user's geographical location information. The generation AI can also perform monitoring based on data from nearby medical institutions by taking into account the user's geographical location information. Furthermore, the generation AI can prioritize monitoring related to environmental factors based on the user's geographical location information. For example, the generation AI executes an algorithm that prioritizes monitoring related to specific environmental factors based on the user's geographical location information. This allows for prioritizing monitoring related to region-specific health risks by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the user's geographical location information to the generation AI and have the generation AI select the optimal monitoring method.

[0089] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. The monitoring unit, for example, uses a generation AI to analyze the user's social media activity. The generation AI inputs data such as the content of social media posts and the frequency of activity and executes an algorithm to suggest monitoring measures. For example, the generation AI analyzes the user's social media activity and suggests monitoring measures based on health-related posts. The generation AI can also identify health concerns from the user's social media activity and suggest related monitoring measures. Furthermore, the generation AI can prioritize suggesting monitoring measures related to health risks based on the user's social media activity. For example, the generation AI executes an algorithm that prioritizes suggesting monitoring measures related to specific health risks based on the user's social media activity. In this way, health-related monitoring measures can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data regarding the user's social media activity to the generation AI and have the generation AI suggest monitoring measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects health checkup results using the camera 42 or scanner of the smart device 14 and converts them into digital data 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 health checkup results using a generative AI. The notification unit, realized, for example, by the control unit 46A of the smart device 14, notifies employees via email or an in-house portal. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, periodically analyzes the health checkup results and monitors the employees' health status in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects health checkup results using the camera 42 or scanner of the smart glasses 214 and converts them into digital data 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 health checkup results using a generative AI. The notification unit, realized, for example, by the control unit 46A of the smart glasses 214, notifies employees via email or an in-house portal. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, periodically analyzes the health checkup results and monitors the employees' health status in real time. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and monitoring unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects health checkup results using the camera 42 or scanner of the headset-type terminal 314 and converts the results into digital data 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 health checkup results using a generative AI. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and notifies employees via email or an in-house portal. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically analyzes the health checkup results to monitor the health status of employees in real time. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health checkup results using the camera 42 or scanner of the robot 414 and converts them into digital data 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 health checkup results using generative AI. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies employees via email or an in-house portal. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically analyzes the health checkup results to monitor the employee's health status in real time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can collect the user's dietary records and provide them to the analysis unit. For example, the collection unit can collect dietary details entered by the user through a smartphone app and store them in a database. The collection unit can also analyze photos of meals taken by the user and automatically recognize the dietary details. Furthermore, the collection unit can provide the user's dietary records to the analysis unit and analyze the correlation between the dietary details and health checkup results. This allows the correlation between the user's dietary habits and health status to be understood, enabling appropriate health management.

[0092] The analysis unit can analyze the user's exercise data and evaluate its correlation with health checkup results. For example, the analysis unit can analyze the exercise data collected by the user through a wearable device and evaluate the amount and intensity of exercise. The analysis unit can also perform an integrated analysis of the exercise data and health checkup results to identify the impact of exercise habits on health status. Furthermore, the analysis unit can suggest an appropriate exercise plan to the user based on the exercise data. This allows the user to understand the correlation between the user's exercise habits and health status and perform appropriate health management.

[0093] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can notify the user during a time period when the user is able to relax. Also, if the user is relaxed, the notification unit can notify the user immediately. Furthermore, if the user is busy, the notification unit can adjust the timing of notifications to match the user's schedule. In this way, by adjusting the timing of notifications according to the user's emotions, notifications can be sent at more appropriate times.

[0094] The monitoring unit can collect the user's sleep data and provide it to the analysis unit. For example, the monitoring unit can collect sleep data collected by the user through a wearable device and store it in a database. The monitoring unit can also provide the user's sleep data to the analysis unit and analyze the correlation between sleep patterns and health checkup results. Furthermore, the monitoring unit can propose an appropriate sleep improvement plan based on the user's sleep data. This allows the correlation between the user's sleep habits and health condition to be understood, enabling appropriate health management.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing stress-related health checkup results. Also, if the user is relaxed, the analysis unit can analyze the overall health checkup results evenly. Furthermore, if the user is tired, the analysis unit can prioritize analyzing fatigue-related health checkup results. In this way, by determining the priority of analysis according to the user's emotions, important data can be analyzed preferentially.

[0096] The notification unit can suggest the most suitable medical institution by taking into account the user's geographical location information. For example, the notification unit can list nearby medical institutions based on the user's current location and suggest an appropriate medical institution. The notification unit can also preferentially suggest medical institutions with specialists based on the user's health checkup results. Furthermore, the notification unit can also suggest the most suitable medical institution by taking into account the means of transportation and required travel time based on the user's geographical location information. In this way, an appropriate medical institution can be suggested by taking into account the user's geographical location information.

[0097] The collection unit can estimate the user's emotions and determine the priority of the health checkup results to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related health checkup results. Also, if the user is relaxed, the collection unit can collect overall health checkup results evenly. Furthermore, if the user is tired, the collection unit can prioritize collecting fatigue-related health checkup results. Thus, by determining the priority according to the user's emotions, important data can be collected preferentially.

[0098] The analysis unit can analyze the user's genetic information and evaluate the correlation with health checkup results. For example, the analysis unit collects the user's genetic information and stores it in a database. The analysis unit can also perform an integrated analysis of the genetic information and health checkup results to identify genetic risks. Furthermore, the analysis unit can suggest appropriate preventive measures to the user based on the genetic information. This allows the correlation between the user's genetic information and health condition to be understood, enabling appropriate health management.

[0099] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide a simple, highly visible notification. If the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can also provide a notification that focuses on the main points. In this way, by adjusting the notification expression method according to the user's emotions, more appropriate notifications can be provided.

[0100] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible monitoring method. If the user is relaxed, the monitoring unit can also provide a detailed monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can also provide a monitoring method that focuses on the main points. In this way, more appropriate monitoring can be performed by adjusting the monitoring method according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects the results of the health checkup. The results of the health checkup include blood tests, electrocardiograms, X-rays, etc. The collection department collects these results in digital format and stores them in a database. The collection department can also scan health checkup results submitted on paper and convert them into digital data. For example, a scanner can be used to scan the paper health checkup results and then OCR technology can be used to convert them into text information. Step 2: The analysis unit analyzes the data collected by the collection unit to identify pre-disease signs and future disease risks. The analysis unit uses generative AI to analyze blood test results and electrocardiogram data to detect abnormal patterns. The generative AI uses deep learning models and natural language processing technology to analyze health checkup results with high accuracy. For example, it inputs blood test results and detects abnormal biomarker values. It can also analyze electrocardiogram data to detect abnormal waveforms. Step 3: The notification unit notifies employees of additional detailed examinations based on the risks identified by the analysis unit. The notification unit notifies employees via email or an internal portal. For example, if the generation AI identifies a risk of high blood pressure, the unit notifies the employee to undergo additional blood pressure testing. Step 4: The Monitoring Department continuously monitors the health status of employees notified by the Notification Department. The Monitoring Department periodically collects the results of health checkups and analyzes them using Generative AI. This allows the health status of employees to be understood in real time and measures to be taken early.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 the results of the health checkup; an analysis unit that analyzes the data collected by the collection unit and identifies signs of pre-disease or future disease risks; A notification unit that notifies employees of additional workup based on the risk identified by the analysis unit; a monitoring unit that monitors the health status of the employee notified by the notification unit. A system characterized by:

2. The analysis unit Analyzing blood test results and electrocardiogram data to detect abnormal patterns 2. The system of claim 1.

3. The notification unit Notify employees via email or intranet 2. The system of claim 1.

4. The monitoring unit Monitor employee health and analyze health checkup results 2. The system of claim 1.

5. The analysis unit Generative AI identifies pre-disease signs and future disease risks 2. The system of claim 1.

6. The notification unit Notifying employees for additional workup based on risks identified by generative AI 2. The system of claim 1.

7. The collecting unit Estimating a user's emotions and adjusting the timing of collecting health checkup results based on the estimated user's emotions 2. The system of claim 1.

8. The collecting unit When collecting health checkup results, analyze the user's past health checkup history and select the optimal collection method.

2. The system of claim 1.

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