System

Abstract

An object of the system according to the embodiment is to analyze a health check result and provide personalized health advice.SOLUTION: A system includes a medical examination result acquisition unit, a data analysis unit, an advice providing unit, and an external application cooperation unit. The medical examination result acquisition unit acquires a medical examination result. The data analysis unit analyzes the medical examination result acquired by the medical examination result acquisition unit. The advice providing unit provides health advice based on a result of the analysis by the data analysis unit. The external application cooperation unit causes the health data to cooperate with an external application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet fully utilized health checkup results to predict health risks or provide personalized health advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze health checkup results and provide personalized health advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a health checkup result acquisition unit, a data analysis unit, an advice provision unit, and an external application linkage unit. The health checkup result acquisition unit acquires health checkup results. The data analysis unit analyzes the health checkup results acquired by the health checkup result acquisition unit. The advice provision unit provides health advice based on the results analyzed by the data analysis unit. The external application linkage unit links health data with an external application. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the results of the health check and provide personalized health advice. [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) The health management system according to an embodiment of the present invention automatically reads the user's health checkup results, analyzes them using a generation AI, and provides health risk predictions and personalized health advice. This allows the health management system to comprehensively grasp the user's health condition and take appropriate measures.

[0029] The health management system according to the embodiment includes a health checkup result acquisition unit, a data analysis unit, an advice provision unit, and an external application linkage unit. The health checkup result acquisition unit acquires a user's health checkup results. For example, it digitizes and reads images of the health checkup results using scanning technology. It can also directly read health checkup results submitted in digital format. Furthermore, the health checkup result acquisition unit can also read printed health checkup results using OCR technology. For example, the health checkup result acquisition unit scans handwritten health checkup results with a high-resolution scanner and converts them into text information using OCR technology. Digital health checkup results submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The data analysis unit uses a generation AI to analyze the health checkup results acquired by the health checkup result acquisition unit. For example, the generation AI analyzes the health checkup results using a text generation AI (e.g., LLM). It can also analyze the contents of the health checkup results using a multimodal generation AI. It can also extract and analyze important parts of the health checkup results. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from health checkup results and perform analysis based on that information. The advice provision unit provides health advice based on the results analyzed by the data analysis unit. For example, the advice provision unit may suggest specific preventive measures and lifestyle improvement methods to the user based on the data analyzed by the generation AI. The advice provision unit may also provide personalized health advice to the user based on the data analyzed by the generation AI. For example, the advice provision unit may provide dietary and exercise advice to the user based on the data analyzed by the generation AI. The external app integration unit integrates health data with external apps. For example, the external app integration unit may integrate with an exercise recording app to understand the user's exercise habits, allowing the generation AI to propose an appropriate exercise plan.The external application linking unit can also link with a sleep recording app to understand the user's sleeping habits, allowing the generation AI to propose an appropriate sleep plan. The external application linking unit can also link with a food recording app to understand the user's eating habits, allowing the generation AI to propose an appropriate meal plan. This allows the health management system according to the embodiment to comprehensively understand the user's health condition and take appropriate measures. For example, the output unit displays health advice to the user via a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The health checkup result acquisition unit can automatically recognize and digitize handwritten notes or annotations. For example, when analyzing images of health checkup results, the generation AI automatically recognizes and digitizes handwritten notes and annotations. For example, handwritten comments by doctors and additional test results are extracted as text data. In addition, to recognize handwritten notes and annotations, the generation AI uses OCR technology to analyze handwritten characters and convert them into digital data. For example, it automatically reads handwritten blood pressure values ​​and weight fluctuations. In addition, when analyzing images of health checkup results, the generation AI recognizes handwritten notes and annotations and stores them in a database. For example, it automatically extracts handwritten advice and precautions from doctors and notifies the user. This allows for more detailed health information to be managed by digitizing handwritten notes and annotations.

[0031] The health checkup result acquisition unit can also accommodate diagnostic results in different formats or languages. For example, when analyzing images of health checkup results, the health checkup result acquisition unit enables the generation AI to accommodate diagnostic results in different formats. For example, it automatically recognizes and digitizes diagnostic results that have different layouts and formats depending on the hospital. In addition, multilingual OCR technology is introduced so that the generation AI can also accommodate diagnostic results in different languages. For example, it automatically analyzes and digitizes diagnostic results written in multiple languages, such as English and Chinese. In addition, when analyzing images of health checkup results, the generation AI automatically identifies diagnostic results in different formats or languages ​​and converts them into a unified data format. For example, it centrally manages diagnostic results issued by different hospitals or countries. This allows the generation AI to accommodate diagnostic results in different formats and languages, making it possible to accommodate a wide range of users.

[0032] The health checkup result acquisition unit also supports voice input and can digitize information dictated by the user. For example, when analyzing images of health checkup results, the generation AI also supports voice input and automatically digitizes the information dictated by the user. For example, when a user reads out the diagnosis results, the generation AI saves the content as text data. In addition, a system is built in which the generation AI analyzes voice input and digitizes the health checkup results. For example, when a user dictates details of the diagnosis results, the generation AI registers the information in a database. In addition, when analyzing images of health checkup results, the generation AI also supports voice input and digitizes the information dictated by the user in real time. For example, when a user dictates supplementary information to the diagnosis results, the generation AI automatically adds that content. As a result, by supporting voice input, information dictated by the user can also be digitized.

[0033] The health checkup result acquisition unit can automatically compare with past diagnostic results and visually display changes. For example, when analyzing images of health checkup results, the generation AI automatically compares them with past diagnostic results and visually displays changes. For example, fluctuations in blood pressure and weight are displayed in graphs. In addition, a system is constructed in which the generation AI retrieves past diagnostic results from a database and compares them with current diagnostic results. For example, fluctuations in cholesterol levels are displayed in a line graph. In addition, when analyzing images of health checkup results, the generation AI provides a function to compare with past diagnostic results and visually display changes. For example, fluctuations in blood sugar levels are displayed in a heat map. This allows users to easily understand changes in their health condition by visually displaying a comparison with past diagnostic results.

[0034] The data analysis unit can make more accurate health risk predictions based on the user's genetic information. For example, when making health risk predictions, the generation AI takes the user's genetic information into account to make more accurate risk predictions. For example, it suggests specific preventative measures to users who are genetically at risk for high blood pressure. In addition, a system is built in which the generation AI analyzes the user's genetic information and reflects this in health risk predictions. For example, it provides dietary and exercise advice to users who are genetically at high risk for diabetes. In addition, when making health risk predictions, the generation AI takes the user's genetic information into account to make individualized risk predictions. For example, it suggests regular checkups and lifestyle improvements to users who are genetically at risk for heart disease. In this way, by taking the user's genetic information into account, more accurate health risk predictions are possible.

[0035] The data analysis unit can make health risk predictions based on the user's living environment. For example, when making health risk predictions, the generation AI takes the user's living environment into consideration. For example, it evaluates the risk of respiratory disease based on the climate and air pollution level of the residential area. In addition, a system is built in which the generation AI analyzes the user's living environment data and reflects this in the health risk prediction. For example, it predicts the risk of heatstroke based on the temperature and humidity of the residential area. In addition, when making health risk predictions, the generation AI takes the user's living environment into consideration and makes individualized risk predictions. For example, it evaluates the risk of asthma based on the air pollution level of the residential area and suggests preventive measures. This makes it possible to make more realistic health risk predictions by taking the user's living environment into consideration.

[0036] The data analysis unit can take into account the health data of the user's family members and perform health risk assessments for the entire family. For example, when predicting health risks, the generation AI takes into account the health data of the user's family members and assesses the health risk of the entire family. For example, if a family member has a high risk of heart disease, the generation AI will assess that the user also has a similar risk. The generation AI also analyzes the health data of the user's family members and builds a system that reflects this in health risk predictions. For example, the generation AI will assess genetic risk based on the family's medical history. The generation AI also takes into account the health data of the user's family members when predicting health risks and makes individualized risk predictions. For example, if a family member has a high risk of diabetes, the generation AI will assess that the user also has a similar risk and suggest preventive measures. This enables health management that takes genetic risk into account by assessing the health risk of the entire family.

[0037] The data analysis unit can assess the risk of occupational disease and overwork by taking into account the user's occupation and daily activity level. For example, when predicting health risks, the generation AI takes into account the user's occupation and daily activity level to assess the risk of occupational disease and overwork. For example, for a user who does a lot of desk work, the generation AI assesses the risk of lower back pain and eye strain. In addition, a system is built in which the generation AI analyzes the user's occupational data and reflects this in health risk predictions. For example, for a user who does a lot of physical labor, the generation AI assesses the risk of muscle fatigue and joint pain. In addition, when predicting health risks, the generation AI takes into account the user's daily activity level to make individualized risk predictions. For example, for a user who is not exercising enough, the generation AI assesses the risk of cardiovascular disease and suggests improving exercise habits. In this way, the risk of occupational disease and overwork can be assessed by taking into account the user's occupation and daily activity level.

[0038] The advice providing unit manages information and history about the user's medications, automatically analyzes medication side effects and interactions, and displays warnings to the user. For example, in the medication record function, the generation AI automatically analyzes the side effects and interactions of medications the user is taking and displays warnings. For example, if a specific medication combination is dangerous, the user will be notified. A system can also be built in which the generation AI analyzes medication side effects and interactions and displays appropriate warnings to the user. For example, when adding a new medication, it checks for interactions with existing medications. Also, in the medication record function, the generation AI automatically analyzes medication side effects and interactions and displays warnings to the user. For example, if a specific medication may cause an allergic reaction, the user will be notified. This automatically analyzes medication side effects and interactions and displays warnings to the user, supporting the safe use of medications.

[0039] The advice providing unit can monitor changes in the user's physical condition and symptoms and evaluate the effectiveness of medication. For example, in the medicine notebook function, the generation AI monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication. For example, it periodically checks the effectiveness of medication the user is taking and adjusts it as necessary. The generation AI also analyzes the user's physical condition data and builds a system to evaluate the effectiveness of medication. For example, it evaluates the effectiveness of medication based on changes in symptoms recorded by the user. Also, in the medicine notebook function, the generation AI monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication. For example, it evaluates the effectiveness of medication based on changes in physical condition recorded by the user and provides appropriate advice. In this way, the system monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication, thereby supporting appropriate medication use.

[0040] The advice providing unit can provide advice to maximize the effectiveness of medication in cooperation with the user's dietary data and exercise data. For example, in the medicine notebook function, the generation AI can provide advice to maximize the effectiveness of medication by linking it with the user's dietary data. For example, it can suggest foods that should be avoided when taking a specific medication. In addition, the generation AI can analyze the user's exercise data and build a system that provides advice to maximize the effectiveness of medication. For example, it can suggest the timing of taking medication after exercise to increase its effectiveness. In addition, in the medicine notebook function, the generation AI can provide advice to maximize the effectiveness of medication by linking it with the user's dietary and exercise data. For example, it can adjust the timing of taking medication based on the timing and content of meals. In this way, the generation AI can provide advice to maximize the effectiveness of medication in cooperation with the user's dietary and exercise data, thereby enhancing the effectiveness of the medication.

[0041] The advice providing unit can work with the user's doctor and pharmacist to share medication information in real time. For example, in the medication notebook function, the generation AI works with the user's doctor and pharmacist to build a system that shares medication information in real time. For example, when a user is prescribed a new medication, it notifies the doctor or pharmacist. The generation AI also shares the user's medication information with the doctor or pharmacist in real time and provides appropriate advice. For example, it shares information about medication side effects and interactions. Also, in the medication notebook function, the generation AI works with the user's doctor and pharmacist to share medication information in real time. For example, when a user changes their medication, it notifies the doctor or pharmacist and provides appropriate advice. In this way, the system works with the user's doctor and pharmacist to share medication information in real time, supporting appropriate medication use.

[0042] The external application linking unit can integrate data between different applications to realize comprehensive health management. For example, in linking with an external application, the generation AI integrates data between different applications to build a system that realizes comprehensive health management. For example, data from exercise, sleep, and meal record applications is centrally managed. The generation AI also analyzes data between different applications to realize comprehensive health management. For example, it combines exercise data and meal data to propose an optimal health plan for the user. In linking with an external application, the generation AI also integrates data between different applications to realize comprehensive health management. For example, it combines sleep data and exercise data to evaluate the user's health condition. This makes it possible to integrate data between different applications to realize comprehensive health management.

[0043] The external application linking unit can anonymize user data and share data while protecting privacy. For example, in linking with an external application, the external application linking unit builds a system in which the generation AI anonymizes user data and shares data while protecting privacy. For example, data is shared in a form that does not include personal information. The generation AI also anonymizes user data and shares data while protecting privacy. For example, the user's name and address are deleted and only health data is shared. In addition, in linking with an external application, the generation AI anonymizes user data and shares data while protecting privacy. For example, the user's data is encrypting and provided to a third party. In this way, the user's privacy is protected by anonymizing the user's data and sharing data while protecting privacy.

[0044] The external application linking unit can provide a community function based on user data and promote interaction between users. For example, in linking with an external application, the generation AI provides a community function based on user data, building a system that promotes interaction between users. For example, it matches users who have the same health goals. In addition, the generation AI analyzes user data and provides a community function that connects users with common interests. For example, it connects users who are practicing the same exercise plan. In addition, in linking with an external application, the generation AI provides a community function based on user data and promotes interaction between users. For example, it provides a forum for sharing health information. In this way, it provides a community function based on user data and promotes interaction between users, thereby increasing motivation for health management.

[0045] The external application linking unit can provide a customized health program based on user data. For example, in linking with an external application, the external application linking unit builds a system in which the generation AI provides a customized health program based on user data. For example, an individual health plan is created based on exercise data and dietary data. The generation AI also analyzes the user's data and provides a customized health program. For example, an optimal sleep schedule is suggested based on the user's sleep data. In addition, in linking with an external application, the generation AI provides a customized health program based on the user's data. For example, individual health goals are set based on the user's exercise habits and dietary content. This enables individual health management by providing a customized health program based on the user's data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The health management system may further include an evaluation unit that monitors the user's exercise data in real time and evaluates the effectiveness of the exercise. For example, it may record the type of exercise performed by the user, the duration, and the calories burned, and evaluate the effectiveness of the exercise based on that data. The evaluation unit may also analyze the user's exercise data and provide advice to maximize the effectiveness of the exercise. For example, it may evaluate whether a particular exercise is effective and adjust the exercise plan as needed. The evaluation unit may also visually display the effectiveness of the exercise based on the user's exercise data. For example, it may display the exercise results in graphs or charts, allowing the user to understand the effectiveness of the exercise at a glance. This allows the user to check the effectiveness of their exercise in real time and implement an appropriate exercise plan.

[0048] The health management system may further include a nutritional evaluation unit that acquires the user's dietary data and evaluates the nutritional balance. For example, the unit may record the contents of the meals the user has eaten and evaluate the nutritional balance based on that data. The nutritional evaluation unit may also analyze the user's dietary data and provide advice for improving nutritional balance. For example, if a specific nutrient is lacking, the unit may suggest ingredients or recipes to supplement that nutrient. The nutritional evaluation unit may also visually display the nutritional balance based on the user's dietary data. For example, the percentage of ingested nutrients may be displayed in a graph, allowing the user to understand their nutritional balance at a glance. This allows the user to review their dietary content and achieve a healthier diet.

[0049] The health management system may further include a sleep evaluation unit that acquires the user's sleep data and evaluates the quality of their sleep. For example, it may record the user's sleep time and sleep depth and evaluate the quality of their sleep based on that data. The sleep evaluation unit may also analyze the user's sleep data and provide advice for improving the quality of their sleep. For example, if the quality of their sleep is low, it may suggest ways to relax or ways to improve the sleep environment. The sleep evaluation unit may also visually display the quality of their sleep based on the user's sleep data. For example, it may display the depth of sleep and sleep cycles in graphs, allowing the user to understand the quality of their sleep at a glance. This allows the user to improve the quality of their sleep and live a healthier life.

[0050] The health management system may further include a genetic assessment unit that acquires the user's genetic information and assesses the user's genetic risk. For example, the genetic assessment unit may analyze the user's genetic information and identify diseases for which the user is genetically at high risk. The genetic assessment unit may also provide advice to reduce the user's genetic risk based on the user's genetic information. For example, if the user is genetically at risk for high blood pressure, the genetic assessment unit may suggest specific preventative measures or lifestyle improvement methods. The genetic assessment unit may also visually display the user's genetic risk based on the user's genetic information. For example, the genetic assessment unit may display diseases for which the user is genetically at high risk in graphs or charts, allowing the user to grasp their genetic risk at a glance. This allows the user to understand their genetic risk and take appropriate measures.

[0051] The health management system may further include an environmental assessment unit that acquires the user's living environment data and evaluates environmental risks. For example, the environmental assessment unit may analyze the climate and air pollution levels of the user's residential area and evaluate environmental risks based on the data. The environmental assessment unit may also provide advice for reducing environmental risks based on the user's living environment data. For example, if the air pollution level in the residential area is high, the environmental assessment unit may suggest appropriate preventive measures and ways to improve lifestyle habits. The environmental assessment unit may also visually display environmental risks based on the user's living environment data. For example, the climate and air pollution levels of the residential area may be displayed in graphs and charts, allowing the user to grasp their living environment risks at a glance. This allows the user to understand their living environment risks and take appropriate measures.

[0052] The health management system may further include an occupational assessment unit that acquires the user's occupational data and evaluates occupational risks. For example, the occupational assessment unit may analyze the user's occupation and daily activity level and evaluate the occupational risks based on that data. The occupational assessment unit may also provide advice to reduce the user's occupational risks based on the user's occupational data. For example, if the user does a lot of desk work, the occupational assessment unit may suggest preventive measures for lower back pain and eye strain. The occupational assessment unit may also visually display the user's occupational risks based on the user's occupational data. For example, the occupational risks may be displayed in graphs or charts, allowing the user to grasp their own occupational risks at a glance. This allows the user to understand their own occupational risks and take appropriate measures.

[0053] The health management system may further include a family assessment unit that acquires health data of the user's family and evaluates the health risk of the entire family. For example, the family assessment unit may analyze the health data of the user's family and evaluate genetic risk. The family assessment unit may also provide advice to reduce the health risk of the entire family based on the health data of the user's family. For example, if a family member is at high risk of heart disease, the family assessment unit may suggest specific preventative measures or lifestyle improvements. The family assessment unit may also visually display the health risk of the entire family based on the health data of the user's family. For example, the family's health risk may be displayed in graphs or charts, allowing the user to grasp the health risk of the entire family at a glance. This allows the user to understand the health risk of the entire family and take appropriate measures.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The health checkup result acquisition unit acquires the user's health checkup results. For example, the image of the health checkup result is digitized and read using scanning technology. Health checkup results submitted in digital format can also be directly read. Furthermore, the health checkup result acquisition unit can read printed health checkup results using OCR technology. For example, handwritten health checkup results are scanned with a high-resolution scanner and converted into text information using OCR technology. Digital health checkup results submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: In the data analysis unit, the generation AI analyzes the health checkup results acquired by the health checkup result acquisition unit. For example, the generation AI analyzes the health checkup results using a text generation AI (e.g., LLM). The generation AI can also analyze the contents of the health checkup results using a multimodal generation AI. The generation AI uses keyword extraction technology to pick out particularly important information from the health checkup results and performs analysis based on that information. Step 3: The advice provider provides health advice based on the results of the analysis by the data analyzer. For example, based on the data analyzed by the AI ​​generator, the advice provider may suggest specific preventive measures or ways to improve lifestyle habits to the user. The AI ​​generator may also provide personalized health advice to the user based on the data analyzed by the AI. For example, it may provide advice on diet and exercise. Step 4: The external application linking unit links the health data with external applications. For example, by linking with an exercise recording application, the user's exercise habits can be identified and the generation AI can propose an appropriate exercise plan. Also, by linking with a sleep recording application, the user's sleep habits can be identified and the generation AI can propose an appropriate sleep plan. Furthermore, by linking with a food recording application, the user's eating habits can be identified and the generation AI can propose an appropriate meal plan.

[0056] (Example 2) The health management system according to an embodiment of the present invention automatically reads the user's health checkup results, analyzes them using a generation AI, and provides health risk predictions and personalized health advice. This allows the health management system to comprehensively grasp the user's health condition and take appropriate measures.

[0057] The health management system according to the embodiment includes a health checkup result acquisition unit, a data analysis unit, an advice provision unit, and an external application linkage unit. The health checkup result acquisition unit acquires a user's health checkup results. For example, it digitizes and reads images of the health checkup results using scanning technology. It can also directly read health checkup results submitted in digital format. Furthermore, the health checkup result acquisition unit can also read printed health checkup results using OCR technology. For example, the health checkup result acquisition unit scans handwritten health checkup results with a high-resolution scanner and converts them into text information using OCR technology. Digital health checkup results submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The data analysis unit uses a generation AI to analyze the health checkup results acquired by the health checkup result acquisition unit. For example, the generation AI analyzes the health checkup results using a text generation AI (e.g., LLM). It can also analyze the contents of the health checkup results using a multimodal generation AI. It can also extract and analyze important parts of the health checkup results. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from health checkup results and perform analysis based on that information. The advice provision unit provides health advice based on the results analyzed by the data analysis unit. For example, the advice provision unit may suggest specific preventive measures and lifestyle improvement methods to the user based on the data analyzed by the generation AI. The advice provision unit may also provide personalized health advice to the user based on the data analyzed by the generation AI. For example, the advice provision unit may provide dietary and exercise advice to the user based on the data analyzed by the generation AI. The external app integration unit integrates health data with external apps. For example, the external app integration unit may integrate with an exercise recording app to understand the user's exercise habits, allowing the generation AI to propose an appropriate exercise plan.The external application linking unit can also link with a sleep recording app to understand the user's sleeping habits, allowing the generation AI to propose an appropriate sleep plan. The external application linking unit can also link with a food recording app to understand the user's eating habits, allowing the generation AI to propose an appropriate meal plan. This allows the health management system according to the embodiment to comprehensively understand the user's health condition and take appropriate measures. For example, the output unit displays health advice to the user via a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0058] The health checkup result acquisition unit can automatically recognize and digitize handwritten notes or annotations. For example, when analyzing images of health checkup results, the generation AI automatically recognizes and digitizes handwritten notes and annotations. For example, handwritten comments by doctors and additional test results are extracted as text data. In addition, to recognize handwritten notes and annotations, the generation AI uses OCR technology to analyze handwritten characters and convert them into digital data. For example, it automatically reads handwritten blood pressure values ​​and weight fluctuations. In addition, when analyzing images of health checkup results, the generation AI recognizes handwritten notes and annotations and stores them in a database. For example, it automatically extracts handwritten advice and precautions from doctors and notifies the user. This allows for more detailed health information to be managed by digitizing handwritten notes and annotations.

[0059] The health checkup result acquisition unit can also accommodate diagnostic results in different formats or languages. For example, when analyzing images of health checkup results, the health checkup result acquisition unit enables the generation AI to accommodate diagnostic results in different formats. For example, it automatically recognizes and digitizes diagnostic results that have different layouts and formats depending on the hospital. In addition, multilingual OCR technology is introduced so that the generation AI can also accommodate diagnostic results in different languages. For example, it automatically analyzes and digitizes diagnostic results written in multiple languages, such as English and Chinese. In addition, when analyzing images of health checkup results, the generation AI automatically identifies diagnostic results in different formats or languages ​​and converts them into a unified data format. For example, it centrally manages diagnostic results issued by different hospitals or countries. This allows the generation AI to accommodate diagnostic results in different formats and languages, making it possible to accommodate a wide range of users.

[0060] The medical examination result acquisition unit can use the emotion estimation function to analyze the emotion of the user when photographing the diagnosis result and display a message to reduce stress and anxiety. The medical examination result acquisition unit, for example, uses the emotion estimation function to analyze the facial expression and voice of the user when photographing the diagnosis result and estimate the emotion. For example, if the user looks anxious, a message to relax is displayed. The emotion of the user when photographing the diagnosis result is analyzed in real time and a message to reduce stress and anxiety is displayed. For example, a positive message or words of encouragement is displayed. The emotion estimation function can also be used to build a system that analyzes the emotion of the user when photographing the diagnosis result and displays an appropriate message. For example, if the user is nervous, a message encouraging them to take a deep breath is displayed. In this way, the user's emotion is analyzed and a message to reduce stress and anxiety is displayed, thereby reducing the psychological burden on the user.

[0061] The health checkup result acquisition unit also supports voice input and can digitize information dictated by the user. For example, when analyzing images of health checkup results, the generation AI also supports voice input and automatically digitizes the information dictated by the user. For example, when a user reads out the diagnosis results, the generation AI saves the content as text data. In addition, a system is built in which the generation AI analyzes voice input and digitizes the health checkup results. For example, when a user dictates details of the diagnosis results, the generation AI registers the information in a database. In addition, when analyzing images of health checkup results, the generation AI also supports voice input and digitizes the information dictated by the user in real time. For example, when a user dictates supplementary information to the diagnosis results, the generation AI automatically adds that content. As a result, by supporting voice input, information dictated by the user can also be digitized.

[0062] The health checkup result acquisition unit can automatically compare with past diagnostic results and visually display changes. For example, when analyzing images of health checkup results, the generation AI automatically compares them with past diagnostic results and visually displays changes. For example, fluctuations in blood pressure and weight are displayed in graphs. In addition, a system is constructed in which the generation AI retrieves past diagnostic results from a database and compares them with current diagnostic results. For example, fluctuations in cholesterol levels are displayed in a line graph. In addition, when analyzing images of health checkup results, the generation AI provides a function to compare with past diagnostic results and visually display changes. For example, fluctuations in blood sugar levels are displayed in a heat map. This allows users to easily understand changes in their health condition by visually displaying a comparison with past diagnostic results.

[0063] The medical checkup result acquisition unit can use the emotion estimation function to analyze the emotion a user has when photographing a diagnosis result and provide an interface for eliciting positive emotions. The medical checkup result acquisition unit, for example, uses the emotion estimation function to analyze the emotion a user has when photographing a diagnosis result and provide an interface for eliciting positive emotions. For example, it displays an animation or message that makes the user smile. Furthermore, a system is constructed that analyzes the emotion a user has when photographing a diagnosis result in real time and provides an interface for eliciting positive emotions. For example, it plays music that helps the user relax. Furthermore, the emotion estimation function is used to analyze the emotion a user has when photographing a diagnosis result and provide an interface for eliciting positive emotions. For example, it displays an encouraging message or positive feedback when the user photographs a diagnosis result. In this way, the user's emotions are analyzed and an interface for eliciting positive emotions is provided, thereby reducing the psychological burden on the user.

[0064] The data analysis unit can make more accurate health risk predictions based on the user's genetic information. For example, when making health risk predictions, the generation AI takes the user's genetic information into account to make more accurate risk predictions. For example, it suggests specific preventative measures to users who are genetically at risk for high blood pressure. In addition, a system is built in which the generation AI analyzes the user's genetic information and reflects this in health risk predictions. For example, it provides dietary and exercise advice to users who are genetically at high risk for diabetes. In addition, when making health risk predictions, the generation AI takes the user's genetic information into account to make individualized risk predictions. For example, it suggests regular checkups and lifestyle improvements to users who are genetically at risk for heart disease. In this way, by taking the user's genetic information into account, more accurate health risk predictions are possible.

[0065] The data analysis unit can make health risk predictions based on the user's living environment. For example, when making health risk predictions, the generation AI takes the user's living environment into consideration. For example, it evaluates the risk of respiratory disease based on the climate and air pollution level of the residential area. In addition, a system is built in which the generation AI analyzes the user's living environment data and reflects this in the health risk prediction. For example, it predicts the risk of heatstroke based on the temperature and humidity of the residential area. In addition, when making health risk predictions, the generation AI takes the user's living environment into consideration and makes individualized risk predictions. For example, it evaluates the risk of asthma based on the air pollution level of the residential area and suggests preventive measures. This makes it possible to make more realistic health risk predictions by taking the user's living environment into consideration.

[0066] The data analysis unit can use the emotion estimation function to provide health advice that takes into account the user's emotional state. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and provide health advice based on the results. For example, relaxation methods are suggested for a user who is highly stressed. A system is also constructed that provides health advice that takes into account the user's emotional state. For example, measures to improve mental health are suggested based on emotion estimation data. The emotion estimation function is also used to analyze the user's emotional state and provide advice for stress management and improving mental health. For example, if the user is feeling anxious, breathing techniques for relaxation are suggested. In this way, stress management and improvement of mental health can be achieved by taking the user's emotional state into consideration.

[0067] The data analysis unit can take into account the health data of the user's family members and perform health risk assessments for the entire family. For example, when predicting health risks, the generation AI takes into account the health data of the user's family members and assesses the health risk of the entire family. For example, if a family member has a high risk of heart disease, the generation AI will assess that the user also has a similar risk. The generation AI also analyzes the health data of the user's family members and builds a system that reflects this in health risk predictions. For example, the generation AI will assess genetic risk based on the family's medical history. The generation AI also takes into account the health data of the user's family members when predicting health risks and makes individualized risk predictions. For example, if a family member has a high risk of diabetes, the generation AI will assess that the user also has a similar risk and suggest preventive measures. This enables health management that takes genetic risk into account by assessing the health risk of the entire family.

[0068] The data analysis unit can assess the risk of occupational disease and overwork by taking into account the user's occupation and daily activity level. For example, when predicting health risks, the generation AI takes into account the user's occupation and daily activity level to assess the risk of occupational disease and overwork. For example, for a user who does a lot of desk work, the generation AI assesses the risk of lower back pain and eye strain. In addition, a system is built in which the generation AI analyzes the user's occupational data and reflects this in health risk predictions. For example, for a user who does a lot of physical labor, the generation AI assesses the risk of muscle fatigue and joint pain. In addition, when predicting health risks, the generation AI takes into account the user's daily activity level to make individualized risk predictions. For example, for a user who is not exercising enough, the generation AI assesses the risk of cardiovascular disease and suggests improving exercise habits. In this way, the risk of occupational disease and overwork can be assessed by taking into account the user's occupation and daily activity level.

[0069] The data analysis unit can use the emotion estimation function to monitor the user's emotional state in real time and provide health advice in response to changes in emotion. The data analysis unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and provide health advice based on that data. For example, if the user is feeling stressed, relaxation methods are suggested. A system is also constructed that monitors the user's emotional state in real time and provides health advice in response to changes in emotion. For example, if the user is feeling anxious, measures to improve mental health are suggested. The emotion estimation function is also used to monitor the user's emotional state in real time and provide health advice in response to changes in emotion. For example, if the user is feeling positive, advice on maintaining a healthy lifestyle is provided. In this way, the system supports the user's health management by monitoring the user's emotional state in real time and providing health advice in response to changes in emotion.

[0070] The advice providing unit manages information and history about the user's medications, automatically analyzes medication side effects and interactions, and displays warnings to the user. For example, in the medication record function, the generation AI automatically analyzes the side effects and interactions of medications the user is taking and displays warnings. For example, if a specific medication combination is dangerous, the user will be notified. A system can also be built in which the generation AI analyzes medication side effects and interactions and displays appropriate warnings to the user. For example, when adding a new medication, it checks for interactions with existing medications. Also, in the medication record function, the generation AI automatically analyzes medication side effects and interactions and displays warnings to the user. For example, if a specific medication may cause an allergic reaction, the user will be notified. This automatically analyzes medication side effects and interactions and displays warnings to the user, supporting the safe use of medications.

[0071] The advice providing unit can monitor changes in the user's physical condition and symptoms and evaluate the effectiveness of medication. For example, in the medicine notebook function, the generation AI monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication. For example, it periodically checks the effectiveness of medication the user is taking and adjusts it as necessary. The generation AI also analyzes the user's physical condition data and builds a system to evaluate the effectiveness of medication. For example, it evaluates the effectiveness of medication based on changes in symptoms recorded by the user. Also, in the medicine notebook function, the generation AI monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication. For example, it evaluates the effectiveness of medication based on changes in physical condition recorded by the user and provides appropriate advice. In this way, the system monitors changes in the user's physical condition and symptoms and evaluates the effectiveness of medication, thereby supporting appropriate medication use.

[0072] The advice providing unit uses the emotion estimation function to propose a medication schedule that takes into account the user's emotional state, thereby reducing stress. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and propose a medication schedule based on the results. For example, if the user is feeling stressed, the timing of taking medication is adjusted. Furthermore, a system is constructed that proposes a medication schedule that takes into account the user's emotional state. For example, based on emotion estimation data, it suggests taking medication during a time when the user is relaxed. Furthermore, the emotion estimation function is used to analyze the user's emotional state and propose a schedule to reduce stress associated with taking medication. For example, if the user is feeling anxious, it suggests taking medication along with advice to relax. In this way, stress associated with taking medication is reduced by proposing a medication schedule that takes into account the user's emotional state.

[0073] The advice providing unit can provide advice to maximize the effectiveness of medication in cooperation with the user's dietary data and exercise data. For example, in the medicine notebook function, the generation AI can provide advice to maximize the effectiveness of medication by linking it with the user's dietary data. For example, it can suggest foods that should be avoided when taking a specific medication. In addition, the generation AI can analyze the user's exercise data and build a system that provides advice to maximize the effectiveness of medication. For example, it can suggest the timing of taking medication after exercise to increase its effectiveness. In addition, in the medicine notebook function, the generation AI can provide advice to maximize the effectiveness of medication by linking it with the user's dietary and exercise data. For example, it can adjust the timing of taking medication based on the timing and content of meals. In this way, the generation AI can provide advice to maximize the effectiveness of medication in cooperation with the user's dietary and exercise data, thereby enhancing the effectiveness of the medication.

[0074] The advice providing unit can work with the user's doctor and pharmacist to share medication information in real time. For example, in the medication notebook function, the generation AI works with the user's doctor and pharmacist to build a system that shares medication information in real time. For example, when a user is prescribed a new medication, it notifies the doctor or pharmacist. The generation AI also shares the user's medication information with the doctor or pharmacist in real time and provides appropriate advice. For example, it shares information about medication side effects and interactions. Also, in the medication notebook function, the generation AI works with the user's doctor and pharmacist to share medication information in real time. For example, when a user changes their medication, it notifies the doctor or pharmacist and provides appropriate advice. In this way, the system works with the user's doctor and pharmacist to share medication information in real time, supporting appropriate medication use.

[0075] The external application linking unit can integrate data between different applications to realize comprehensive health management. For example, in linking with an external application, the generation AI integrates data between different applications to build a system that realizes comprehensive health management. For example, data from exercise, sleep, and meal record applications is centrally managed. The generation AI also analyzes data between different applications to realize comprehensive health management. For example, it combines exercise data and meal data to propose an optimal health plan for the user. In linking with an external application, the generation AI also integrates data between different applications to realize comprehensive health management. For example, it combines sleep data and exercise data to evaluate the user's health condition. This makes it possible to integrate data between different applications to realize comprehensive health management.

[0076] The external application linking unit can anonymize user data and share data while protecting privacy. For example, in linking with an external application, the external application linking unit builds a system in which the generation AI anonymizes user data and shares data while protecting privacy. For example, data is shared in a form that does not include personal information. The generation AI also anonymizes user data and shares data while protecting privacy. For example, the user's name and address are deleted and only health data is shared. In addition, in linking with an external application, the generation AI anonymizes user data and shares data while protecting privacy. For example, the user's data is encrypting and provided to a third party. In this way, the user's privacy is protected by anonymizing the user's data and sharing data while protecting privacy.

[0077] The external application linking unit can use the emotion estimation function to analyze the user's emotional state based on data from the external application and provide lifestyle improvement advice according to the emotion. The external application linking unit, for example, uses the emotion estimation function to analyze the user's emotional state based on data from the external application and provides lifestyle improvement advice based on the results. For example, stress management advice is provided based on exercise data. A system is also constructed that analyzes the user's emotional state in real time based on data from the external application and provides lifestyle improvement advice according to the emotion. For example, relaxation methods are suggested based on sleep data. The emotion estimation function is also used to analyze the user's emotional state based on data from the external application and provide lifestyle improvement advice according to the emotion. For example, a meal plan that elicits positive emotions is suggested based on diet data. In this way, the user's emotional state is analyzed based on data from the external application and lifestyle improvement advice according to the emotion is provided, thereby supporting health management for the user.

[0078] The external application linking unit can provide a community function based on user data and promote interaction between users. For example, in linking with an external application, the generation AI provides a community function based on user data, building a system that promotes interaction between users. For example, it matches users who have the same health goals. In addition, the generation AI analyzes user data and provides a community function that connects users with common interests. For example, it connects users who are practicing the same exercise plan. In addition, in linking with an external application, the generation AI provides a community function based on user data and promotes interaction between users. For example, it provides a forum for sharing health information. In this way, it provides a community function based on user data and promotes interaction between users, thereby increasing motivation for health management.

[0079] The external application linking unit can provide a customized health program based on user data. For example, in linking with an external application, the external application linking unit builds a system in which the generation AI provides a customized health program based on user data. For example, an individual health plan is created based on exercise data and dietary data. The generation AI also analyzes the user's data and provides a customized health program. For example, an optimal sleep schedule is suggested based on the user's sleep data. In addition, in linking with an external application, the generation AI provides a customized health program based on the user's data. For example, individual health goals are set based on the user's exercise habits and dietary content. This enables individual health management by providing a customized health program based on the user's data.

[0080] The external application linking unit can use the emotion estimation function to monitor the user's emotional state in real time based on data from an external application and provide feedback according to the emotion. For example, the external application linking unit uses the emotion estimation function to monitor the user's emotional state in real time based on data from an external application and provide feedback based on the results. For example, it provides stress management advice based on exercise data. Furthermore, a system is constructed that monitors the user's emotional state in real time based on data from an external application and provides feedback according to the emotion. For example, it suggests relaxation methods based on sleep data. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time based on data from an external application and provide feedback according to the emotion. For example, it suggests a meal plan that elicits positive emotions based on diet data. In this way, the user's emotional state is monitored in real time based on data from an external application and feedback according to the emotion is provided, thereby supporting health management for the user.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The health management system may further include an evaluation unit that monitors the user's exercise data in real time and evaluates the effectiveness of the exercise. For example, it may record the type of exercise performed by the user, the duration, and the calories burned, and evaluate the effectiveness of the exercise based on that data. The evaluation unit may also analyze the user's exercise data and provide advice to maximize the effectiveness of the exercise. For example, it may evaluate whether a particular exercise is effective and adjust the exercise plan as needed. The evaluation unit may also visually display the effectiveness of the exercise based on the user's exercise data. For example, it may display the exercise results in graphs or charts, allowing the user to understand the effectiveness of the exercise at a glance. This allows the user to check the effectiveness of their exercise in real time and implement an appropriate exercise plan.

[0083] The health management system may further include a nutritional evaluation unit that acquires the user's dietary data and evaluates the nutritional balance. For example, the unit may record the contents of the meals the user has eaten and evaluate the nutritional balance based on that data. The nutritional evaluation unit may also analyze the user's dietary data and provide advice for improving nutritional balance. For example, if a specific nutrient is lacking, the unit may suggest ingredients or recipes to supplement that nutrient. The nutritional evaluation unit may also visually display the nutritional balance based on the user's dietary data. For example, the percentage of ingested nutrients may be displayed in a graph, allowing the user to understand their nutritional balance at a glance. This allows the user to review their dietary content and achieve a healthier diet.

[0084] The health management system may further include a sleep evaluation unit that acquires the user's sleep data and evaluates the quality of their sleep. For example, it may record the user's sleep time and sleep depth and evaluate the quality of their sleep based on that data. The sleep evaluation unit may also analyze the user's sleep data and provide advice for improving the quality of their sleep. For example, if the quality of their sleep is low, it may suggest ways to relax or ways to improve the sleep environment. The sleep evaluation unit may also visually display the quality of their sleep based on the user's sleep data. For example, it may display the depth of sleep and sleep cycles in graphs, allowing the user to understand the quality of their sleep at a glance. This allows the user to improve the quality of their sleep and live a healthier life.

[0085] The health management system may further include a stress management unit that monitors the user's stress level and provides advice for stress management. For example, the system may monitor the user's heart rate and respiratory rate in real time and evaluate the stress level based on that data. The stress management unit may also analyze the user's stress level and provide advice for reducing stress. For example, if the stress level is high, the stress management unit may suggest relaxation methods or activities for relieving stress. The stress management unit may also provide a stress management plan based on the user's stress level. For example, the stress management unit may suggest regular relaxation sessions or a training program for stress management. This allows the user to manage their own stress level and live a healthier life.

[0086] The health management system may further include an emotion management unit that monitors the user's emotional state and provides health advice according to the emotion. For example, the emotion management unit may analyze the user's facial expressions and voice and evaluate the user's emotional state based on the data. The emotion management unit may also analyze the user's emotional state and provide health advice according to the emotion. For example, if the user is feeling stressed, the emotion management unit may suggest relaxation methods or activities for relieving stress. The emotion management unit may also provide an emotion management plan based on the user's emotional state. For example, the emotion management unit may suggest regular relaxation sessions or a training program for emotion management. This allows the user to manage their own emotional state and live a healthier life.

[0087] The health management system may further include a genetic assessment unit that acquires the user's genetic information and assesses the user's genetic risk. For example, the genetic assessment unit may analyze the user's genetic information and identify diseases for which the user is genetically at high risk. The genetic assessment unit may also provide advice to reduce the user's genetic risk based on the user's genetic information. For example, if the user is genetically at risk for high blood pressure, the genetic assessment unit may suggest specific preventative measures or lifestyle improvement methods. The genetic assessment unit may also visually display the user's genetic risk based on the user's genetic information. For example, the genetic assessment unit may display diseases for which the user is genetically at high risk in graphs or charts, allowing the user to grasp their genetic risk at a glance. This allows the user to understand their genetic risk and take appropriate measures.

[0088] The health management system may further include an environmental assessment unit that acquires the user's living environment data and evaluates environmental risks. For example, the environmental assessment unit may analyze the climate and air pollution levels of the user's residential area and evaluate environmental risks based on the data. The environmental assessment unit may also provide advice for reducing environmental risks based on the user's living environment data. For example, if the air pollution level in the residential area is high, the environmental assessment unit may suggest appropriate preventive measures and ways to improve lifestyle habits. The environmental assessment unit may also visually display environmental risks based on the user's living environment data. For example, the climate and air pollution levels of the residential area may be displayed in graphs and charts, allowing the user to grasp their living environment risks at a glance. This allows the user to understand their living environment risks and take appropriate measures.

[0089] The health management system may further include an occupational assessment unit that acquires the user's occupational data and evaluates occupational risks. For example, the occupational assessment unit may analyze the user's occupation and daily activity level and evaluate the occupational risks based on that data. The occupational assessment unit may also provide advice to reduce the user's occupational risks based on the user's occupational data. For example, if the user does a lot of desk work, the occupational assessment unit may suggest preventive measures for lower back pain and eye strain. The occupational assessment unit may also visually display the user's occupational risks based on the user's occupational data. For example, the occupational risks may be displayed in graphs or charts, allowing the user to grasp their own occupational risks at a glance. This allows the user to understand their own occupational risks and take appropriate measures.

[0090] The health management system may further include a family assessment unit that acquires health data of the user's family and evaluates the health risk of the entire family. For example, the family assessment unit may analyze the health data of the user's family and evaluate genetic risk. The family assessment unit may also provide advice to reduce the health risk of the entire family based on the health data of the user's family. For example, if a family member is at high risk of heart disease, the family assessment unit may suggest specific preventative measures or lifestyle improvements. The family assessment unit may also visually display the health risk of the entire family based on the health data of the user's family. For example, the family's health risk may be displayed in graphs or charts, allowing the user to grasp the health risk of the entire family at a glance. This allows the user to understand the health risk of the entire family and take appropriate measures.

[0091] The health management system may further include an emotion management unit that monitors the user's emotional state and provides health advice according to the emotion. For example, the emotion management unit may analyze the user's facial expressions and voice and evaluate the user's emotional state based on the data. The emotion management unit may also analyze the user's emotional state and provide health advice according to the emotion. For example, if the user is feeling stressed, the emotion management unit may suggest relaxation methods or activities for relieving stress. The emotion management unit may also provide an emotion management plan based on the user's emotional state. For example, the emotion management unit may suggest regular relaxation sessions or a training program for emotion management. This allows the user to manage their own emotional state and live a healthier life.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The health checkup result acquisition unit acquires the user's health checkup results. For example, the image of the health checkup result is digitized and read using scanning technology. Health checkup results submitted in digital format can also be directly read. Furthermore, the health checkup result acquisition unit can read printed health checkup results using OCR technology. For example, handwritten health checkup results are scanned with a high-resolution scanner and converted into text information using OCR technology. Digital health checkup results submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: In the data analysis unit, the generation AI analyzes the health checkup results acquired by the health checkup result acquisition unit. For example, the generation AI analyzes the health checkup results using a text generation AI (e.g., LLM). The generation AI can also analyze the contents of the health checkup results using a multimodal generation AI. The generation AI uses keyword extraction technology to pick out particularly important information from the health checkup results and performs analysis based on that information. Step 3: The advice provider provides health advice based on the results of the analysis by the data analyzer. For example, based on the data analyzed by the AI ​​generator, the advice provider may suggest specific preventive measures or ways to improve lifestyle habits to the user. The AI ​​generator may also provide personalized health advice to the user based on the data analyzed by the AI. For example, it may provide advice on diet and exercise. Step 4: The external application linking unit links the health data with external applications. For example, by linking with an exercise recording application, the user's exercise habits can be identified and the generation AI can propose an appropriate exercise plan. Also, by linking with a sleep recording application, the user's sleep habits can be identified and the generation AI can propose an appropriate sleep plan. Furthermore, by linking with a food recording application, the user's eating habits can be identified and the generation AI can propose an appropriate meal plan.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the robot 414, 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 robot 414 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 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 health checkup result acquisition unit that acquires health checkup results; a data analysis unit that analyzes the health checkup results acquired by the health checkup result acquisition unit; an advice providing unit that provides health advice based on the results of the analysis by the data analysis unit; and an external application linking unit that links the health data with an external application. A system characterized by:

2. The health checkup result acquisition unit Automatically recognize and digitize handwritten notes or annotations 2. The system of claim 1.

3. The health checkup result acquisition unit Supports diagnostic results in different formats or languages 2. The system of claim 1.

4. The health checkup result acquisition unit Analyzes the emotions users feel when taking a photo of their diagnosis and displays messages to reduce stress and anxiety 2. The system of claim 1.

5. The health checkup result acquisition unit It also supports voice input, converting information dictated by the user into data.

2. The system of claim 1.

6. The health checkup result acquisition unit Automatically compares with past diagnostic results and visually displays changes 2. The system of claim 1.

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