system

Abstract

The system according to the embodiment aims to promote preventive behavior by presenting the user with a future brain state and providing a personalized preventive program. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a presentation unit, and a provision unit. The collection unit collects a user's current MRI images and lifestyle data. The generation unit analyzes the data collected by the collection unit and generates future MRI images of the user's brain. The presentation unit presents the future MRI images generated by the generation unit to the user. The provision unit provides the user with a personalized prevention program based on the future MRI images presented by the presentation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a sense of ownership in preventing dementia and Alzheimer's disease, which means that preventive actions are not fully implemented.

[0005] The system according to the embodiment aims to promote preventive behavior by presenting the user with a future brain state and providing a personalized preventive program. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a presentation unit, and a provision unit. The collection unit collects a user's current MRI images and lifestyle data. The generation unit analyzes the data collected by the collection unit and generates future MRI images of the user's brain. The presentation unit presents the future MRI images generated by the generation unit to the user. The provision unit provides the user with a personalized prevention program based on the future MRI images presented by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can promote preventive behavior by presenting the future brain state to the user and providing a personalized preventive program. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A prevention support system according to an embodiment of the present invention generates future brain MRI images based on a user's current MRI images and lifestyle data, and provides a personalized prevention program. The prevention support system collects the user's current MRI images and lifestyle data, and a generation AI analyzes these data to generate future brain MRI images. The generated future MRI images are presented to the user, visualizing future brain atrophy and encouraging behavioral change that leads the user to implement disease prevention measures. Furthermore, the generation AI constructs and provides a personalized prevention program for the user to prevent brain disease. For example, the prevention support system collects the user's current MRI images and lifestyle data. For example, detailed data on the user's lifestyle habits, such as diet, exercise, and sleep, is collected. Next, the prevention support system uses the generation AI to analyze the collected data and generate future brain MRI images. The generation AI predicts the user's future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the prediction. For example, the system predicts the progression of brain atrophy if the user continues their current lifestyle, and generates the result as a future MRI image. The prevention support system then presents the generated future MRI images to the user, allowing the user to visually confirm the future state of their brain. For example, by actually seeing the progression of brain atrophy if the user continues their current lifestyle, the user recognizes the importance of disease prevention measures and is encouraged to change their behavior. Furthermore, the prevention support system uses the generative AI to create and provide a personalized prevention program for the user to prevent brain disease. The generative AI proposes an optimal prevention program based on the user's lifestyle data. For example, it provides specific advice such as improving diet, encouraging exercise, and improving sleep quality. This allows the user to implement preventive measures that are tailored to them. This allows the prevention support system to visually confirm the future state of the user's brain and recognize the importance of disease prevention measures. Furthermore, by providing a personalized prevention program, the user can implement effective preventive measures.

[0029] A preventive support system according to an embodiment includes a collection unit, a generation unit, a presentation unit, and a provision unit. The collection unit collects a user's current MRI images and lifestyle data. The user's lifestyle data includes, but is not limited to, diet, exercise, and sleep. For example, the collection unit records the user's diet and collects calorie intake and nutrient balance. The collection unit can also record the type, frequency, and intensity of the user's exercise. The collection unit can also record the user's sleep time and quality. For example, the collection unit collects data by having the user input the user's diet through a smartphone app. The collection unit can also collect the user's exercise data using a wearable device. The collection unit can also collect the user's sleep data using a sleep tracker. The generation unit uses a generation AI to analyze the data collected by the collection unit and generate future MRI images of the user's brain. For example, the generation AI predicts a future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the prediction. For example, the generating AI predicts the progression of brain atrophy if the user continues with their current lifestyle habits and generates the results as future MRI images. The generating unit can also use the generating AI to analyze the user's lifestyle data and predict the future brain state. For example, the generating AI predicts the future brain health state based on the user's dietary data. The generating unit can also use the generating AI to analyze the user's exercise data and predict the future brain health state. For example, the generating AI predicts the future brain health state based on the user's exercise data. The presentation unit presents the future MRI images generated by the generating unit to the user. The presentation unit, for example, visually displays the generated future MRI images to the user to encourage the user to take disease prevention measures. For example, the presentation unit displays the generated future MRI images as a 3D model, allowing the user to visually see the future brain atrophy. The presentation unit can also present the generated future MRI images to the user to make them aware of the importance of disease prevention measures. For example, the presentation unit presents the generated future MRI images to the user, visually showing the progression of brain atrophy if the user continues with their current lifestyle.The providing unit provides a personalized prevention program to the user based on the future MRI images presented by the presenting unit. The providing unit, for example, uses a generating AI to propose an optimal prevention program based on the user's lifestyle data. For example, the providing unit provides specific advice such as improving diet, recommending exercise, and improving sleep quality. The providing unit can also provide a personalized prevention program to the user using the generating AI. For example, the providing unit proposes a prevention program tailored to the user's individual health condition based on the user's lifestyle data. This allows the prevention support system according to the embodiment to visually check the future state of the user's brain and recognize the importance of disease prevention measures. Furthermore, by providing a personalized prevention program, the user can take effective preventive measures.

[0030] The collection unit can collect data on the user's lifestyle habits, including diet, exercise, and sleep. For example, the collection unit records the user's dietary habits and collects calorie intake and nutrient balance. For example, the collection unit prompts the user to input dietary habits through a smartphone app and collects the data. The collection unit can also record the type, frequency, and intensity of the user's exercise. For example, the collection unit can collect the user's exercise data using a wearable device. The collection unit can also record the user's sleep time and sleep quality. For example, the collection unit can collect the user's sleep data using a sleep tracker. By collecting detailed lifestyle habits data of the user, more accurate future MRI images can be generated. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's dietary habits into the generation AI and have the generation AI analyze the dietary data.

[0031] The generation unit can predict a future brain state based on current MRI images and lifestyle data, and generate future MRI images based on the prediction. The generation unit, for example, uses a generation AI to predict a future brain state based on current MRI images and lifestyle data. For example, the generation AI predicts the progression of brain atrophy if the current lifestyle is continued and generates a future MRI image based on the result. The generation unit can also use the generation AI to analyze a user's lifestyle data and predict a future brain state. For example, the generation AI predicts a future brain health state based on the user's diet data. The generation unit can also use the generation AI to analyze a user's exercise data and predict a future brain health state. For example, the generation AI predicts a future brain health state based on the user's exercise data. This predicts a future brain state and generates a future MRI image based on the prediction, thereby encouraging the user to take specific measures to prevent illness. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input current MRI images and lifestyle data into the generation AI and have the generation AI generate future MRI images.

[0032] The presentation unit can present the generated future MRI images to the user and visually show the future brain atrophy using a 3D model. The presentation unit, for example, can visually show the generated future MRI images to the user, thereby encouraging the user to take measures to combat the disease. For example, the presentation unit can display the generated future MRI images as a 3D model, visually showing the future brain atrophy to the user. The presentation unit can also present the generated future MRI images to the user to help them recognize the importance of disease prevention measures. For example, the presentation unit can present the generated future MRI images to the user and visually show the progression of brain atrophy if the user continues with their current lifestyle. This allows the user to visually see the future brain state, making them recognize the importance of disease prevention measures and encouraging behavioral change. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the generated future MRI images to a generation AI and cause the generation AI to generate a 3D model.

[0033] The providing unit can propose an appropriate preventive program based on the user's lifestyle data. The providing unit, for example, uses a generating AI to propose an optimal preventive program based on the user's lifestyle data. For example, the providing unit provides specific advice such as improving diet, recommending exercise, and improving sleep quality. The providing unit can also use the generating AI to provide a personalized preventive program to the user. For example, the providing unit proposes a preventive program tailored to the user's individual health condition based on the user's lifestyle data. This allows for effective disease prevention measures to be implemented by providing a personalized preventive program based on the user's lifestyle data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle data into the generating AI and have the generating AI execute the prevention program proposal.

[0034] The providing unit can provide specific advice, including advice on improving diet, recommending exercise, and improving sleep quality. For example, the providing unit uses a generating AI to provide specific advice, such as advice on improving diet, recommending exercise, and improving sleep quality, based on the user's lifestyle data. For example, the providing unit can analyze the user's diet and suggest improving nutritional balance. The providing unit can also analyze the user's exercise data and suggest an appropriate exercise plan. Furthermore, the providing unit can analyze the user's sleep data and provide advice for improving sleep quality. For example, the providing unit can suggest improving nutritional balance based on the user's diet. For example, the providing unit can analyze the user's diet and advise the user to increase their intake of specific nutrients. The providing unit can also suggest an appropriate exercise plan based on the user's exercise data. For example, the providing unit can analyze the user's exercise data and suggest how many times per week and to what extent the user should exercise. Furthermore, the providing unit can provide advice for improving sleep quality based on the user's sleep data. For example, the providing unit can analyze the user's sleep data and suggest adjusting their bedtime and wake-up time. This allows the provision of specific advice, thereby suggesting preventive measures that are easy for the user to implement. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the user's lifestyle habit data into the generating AI and cause the generating AI to generate specific advice.

[0035] The collection unit can analyze the user's past lifestyle habit data and select the optimal collection method. The collection unit, for example, analyzes the past lifestyle habit data and selects the optimal collection method. For example, the collection unit selects the most efficient collection method based on data previously input by the user. The collection unit can also analyze the user's past data collection history and optimize the collection frequency. The collection unit can also select the optimal collection method by referring to the user's past data input method (voice, text, etc.). For example, the collection unit selects the most efficient collection method based on data previously input by the user. For example, the collection unit analyzes the user's past data collection history and optimizes the collection frequency. The collection unit can also select the optimal collection method by referring to the user's past data input method (voice, text, etc.). This enables efficient data collection by analyzing past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past lifestyle habit data to a generation AI and cause the generation AI to select the optimal collection method.

[0036] When collecting lifestyle habit data, the collection unit can filter the data based on the user's current health condition and medical history. The collection unit filters the data based on, for example, the user's current health condition and medical history. For example, the collection unit collects only necessary data taking into account the user's current health condition. The collection unit can also prioritize collecting relevant data based on the user's medical history. Furthermore, the collection unit can limit the range of collected data when the user's health condition is deteriorating. For example, the collection unit collects only necessary data taking into account the user's current health condition. For example, the collection unit prioritizes collecting relevant data based on the user's medical history. The collection unit can also limit the range of collected data when the user's health condition is deteriorating. In this way, by filtering the data based on the user's health condition and medical history, only necessary data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and medical history to the generation AI and cause the generation AI to filter the data.

[0037] When collecting lifestyle habit data, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method. For example, if the user prefers voice input, the collection unit may prioritize voice input to collect data. Furthermore, if the user prefers text input, the collection unit may prioritize text input to collect data. Furthermore, if the user prefers image input, the collection unit may prioritize image input to collect data. For example, if the user prefers voice input, the collection unit prioritizes voice input to collect data. For example, if the user prefers text input, the collection unit prioritizes text input to collect data. Furthermore, if the user prefers image input, the collection unit prioritizes image input to collect data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting lifestyle habit data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data about the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting lifestyle habit data at home. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is traveling, the collection unit prioritizes collecting data about the travel destination. Furthermore, when the user is at home, the collection unit prioritizes collecting lifestyle habit data at home. In this way, by taking into account the user's geographical location information, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the user's social media activities and collect related data when collecting lifestyle habit data. The collection unit may, for example, analyze the user's social media activities and collect related data. For example, the collection unit may collect dietary data based on photos of meals shared by the user on social media. The collection unit may also collect exercise data based on the user's exercise records on social media. The collection unit may also collect sleep data based on the user's sleep records on social media. For example, the collection unit may collect dietary data based on photos of meals shared by the user on social media. For example, the collection unit may collect exercise data based on the user's exercise records on social media. The collection unit may also collect sleep data based on the user's sleep records on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activities into a generation AI and cause the generation AI to collect related data.

[0040] When collecting lifestyle habit data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method based on, for example, the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also adjust the range of collected data based on the user's past feedback. Furthermore, the collection unit can optimize the collection timing by referring to the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the range of collected data based on the user's past feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0041] The generation unit can adjust the accuracy of generation based on the level of detail of the user's lifestyle data when generating future MRI images. The generation unit adjusts the accuracy of generation based on, for example, the level of detail of the user's lifestyle data. For example, when the user's lifestyle data is detailed, the generation unit generates future MRI images with high accuracy. Furthermore, when the user's lifestyle data is insufficient, the generation unit can generate future MRI images with lower accuracy. Furthermore, when part of the user's lifestyle data is missing, the generation unit can generate future MRI images using complementary data. For example, when the user's lifestyle data is detailed, the generation unit generates future MRI images with high accuracy. For example, when the user's lifestyle data is insufficient, the generation unit generates future MRI images with lower accuracy. Furthermore, when part of the user's lifestyle data is missing, the generation unit generates future MRI images using complementary data. In this way, by adjusting the accuracy of generation based on the level of detail of the lifestyle data, more accurate future MRI images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the accuracy of the generation.

[0042] The generation unit can apply different generation algorithms depending on the user's health risk factors when generating future MRI images. The generation unit applies different generation algorithms depending on, for example, the user's health risk factors. For example, if the user has high blood pressure, the generation unit generates future MRI images taking into account risk factors related to high blood pressure. Furthermore, if the user has diabetes, the generation unit can generate future MRI images taking into account risk factors related to diabetes. Furthermore, if the user is a smoker, the generation unit can generate future MRI images taking into account risk factors related to smoking. For example, if the user has high blood pressure, the generation unit generates future MRI images taking into account risk factors related to high blood pressure. For example, if the user has diabetes, the generation unit generates future MRI images taking into account risk factors related to diabetes. Furthermore, if the user is a smoker, the generation unit generates future MRI images taking into account risk factors related to smoking. In this way, by applying the generation algorithm depending on the health risk factors, more appropriate future MRI images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health risk factors into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] The generation unit can improve the accuracy of generation when generating future MRI images by referring to the user's past MRI images. The generation unit can improve the accuracy of generation by referring to, for example, the user's past MRI images. For example, the generation unit can improve the accuracy of the future MRI images based on the user's past MRI images. The generation unit can also compare the user's past MRI images with the current MRI image to generate the future MRI images. The generation unit can also analyze changes in the user's past MRI images to improve the accuracy of the future MRI images. For example, the generation unit can improve the accuracy of the future MRI images based on the user's past MRI images. For example, the generation unit can compare the user's past MRI images with the current MRI image to generate the future MRI images. The generation unit can also analyze changes in the user's past MRI images to improve the accuracy of the future MRI images. In this way, the accuracy of the future MRI images can be improved by referring to the past MRI images. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past MRI images to the generation AI and cause the generation AI to improve the accuracy of the generation.

[0044] The generation unit can determine the generation priority based on changes in the user's lifestyle when generating future MRI images. The generation unit determines the generation priority based on, for example, changes in the user's lifestyle. For example, if the user's lifestyle changes significantly, the generation unit prioritizes generating MRI images that reflect the changes. Furthermore, if the user's lifestyle is stable, the generation unit can generate MRI images periodically. Furthermore, if the user's lifestyle is irregular, the generation unit can generate MRI images during periods of significant change. For example, if the user's lifestyle changes significantly, the generation unit prioritizes generating MRI images that reflect the changes. For example, if the user's lifestyle is stable, the generation unit generates MRI images periodically. Furthermore, if the user's lifestyle is irregular, the generation unit generates MRI images during periods of significant change. In this way, by determining the generation priority based on changes in the lifestyle, MRI images can be generated at more appropriate times. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input changes in the user's lifestyle habits into the generation AI and have the generation AI determine the generation priorities.

[0045] The generation unit can improve the accuracy of generation by referring to the user's related medical data when generating future MRI images. The generation unit, for example, improves the accuracy of generation by referring to the user's related medical data. For example, the generation unit improves the accuracy of the future MRI images based on the user's medical data. The generation unit can also integrate the user's medical data and lifestyle data to generate future MRI images. The generation unit can also analyze changes in the user's medical data to improve the accuracy of the future MRI images. For example, the generation unit improves the accuracy of the future MRI images based on the user's medical data. For example, the generation unit integrates the user's medical data and lifestyle data to generate future MRI images. The generation unit can also analyze changes in the user's medical data to improve the accuracy of the future MRI images. In this way, the accuracy of the future MRI images can be improved by referring to the related medical data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related medical data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0046] The generation unit can adjust the generated explanation according to the user's level of expertise when generating future MRI images. The generation unit adjusts the generated explanation according to, for example, the user's level of expertise. For example, if the user is a medical professional, the generation unit generates an MRI image including a detailed medical explanation. Furthermore, if the user is a layperson, the generation unit can generate an MRI image including a concise and easy-to-understand explanation. Furthermore, the generation unit can generate an MRI image including an appropriate explanation according to the user's level of expertise. For example, if the user is a medical professional, the generation unit generates an MRI image including a detailed medical explanation. For example, if the user is a layperson, the generation unit generates an MRI image including a concise and easy-to-understand explanation. Furthermore, the generation unit generates an MRI image including an appropriate explanation according to the user's level of expertise. In this way, by adjusting the explanation according to the user's level of expertise, it is possible to provide an MRI image that is easier to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the generated explanation.

[0047] The presentation unit can select the optimal presentation method by referring to the user's past responses when presenting future MRI images. The presentation unit selects the optimal presentation method by referring to, for example, the user's past responses. For example, the presentation unit selects the optimal presentation method based on display methods that the user preferred in the past. The presentation unit can also analyze the user's past responses and select a display method with high visibility. The presentation unit can also improve the presentation method by referring to the user's past feedback. For example, the presentation unit selects the optimal presentation method based on display methods that the user preferred in the past. For example, the presentation unit analyzes the user's past responses and selects a display method with high visibility. The presentation unit can improve the presentation method by referring to the user's past feedback. In this way, the optimal presentation method for the user can be selected by referring to the past responses. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past responses to a generation AI and cause the generation AI to select the optimal presentation method.

[0048] The presentation unit can customize the presentation content according to the user's current health condition when presenting future MRI images. The presentation unit customizes the presentation content according to, for example, the user's current health condition. For example, if the user's health condition is good, the presentation unit provides presentation content including detailed information. Furthermore, if the user's health condition is deteriorating, the presentation unit can provide brief and easy-to-understand presentation content. Furthermore, the presentation unit can customize appropriate presentation content according to the user's health condition. For example, if the user's health condition is good, the presentation unit provides presentation content including detailed information. For example, if the user's health condition is deteriorating, the presentation unit provides brief and easy-to-understand presentation content. Furthermore, the presentation unit customizes appropriate presentation content according to the user's health condition. In this way, by customizing the presentation content according to the user's current health condition, more appropriate information can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current health condition to the generation AI and cause the generation AI to customize the presentation content.

[0049] The presentation unit can improve the presentation method by reflecting the user's feedback when presenting future MRI images. The presentation unit improves the presentation method based on, for example, user feedback. For example, the presentation unit improves the presentation method based on feedback previously provided by the user. The presentation unit can also select a presentation method with high visibility based on the user's feedback. The presentation unit can also optimize the presentation content by referring to the user's feedback. For example, the presentation unit improves the presentation method based on feedback previously provided by the user. For example, the presentation unit selects a presentation method with high visibility based on the user's feedback. The presentation unit can also optimize the presentation content by referring to the user's feedback. In this way, the presentation method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's feedback to the generation AI and cause the generation AI to improve the presentation method.

[0050] The presentation unit can select the optimal presentation method by taking into account the user's device information when presenting future MRI images. The presentation unit selects the presentation method by taking into account, for example, the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a personal computer, the presentation unit can provide a display method including detailed information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the presentation unit provides a display method optimized for a large screen. Furthermore, if the user is using a personal computer, the presentation unit provides a display method including detailed information. In this way, the optimal display method for the user can be provided by taking into account the device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's device information to the generation AI and cause the generation AI to select the optimal presentation method.

[0051] The presentation unit can make the presentation content multilingual according to the user's language setting when presenting future MRI images. The presentation unit can make the presentation content multilingual according to the user's language setting, for example. For example, the presentation unit automatically sets the presentation content based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. The presentation unit can also provide the presentation content in a specific language when the user selects that language. For example, the presentation unit automatically sets the presentation content based on the language setting of the user's device. For example, the presentation unit provides a language switching function when the user uses multiple languages. The presentation unit can also provide the presentation content in a specific language when the user selects that language. This makes it possible to provide information that is easy for the user to understand by providing multilingual support according to the language setting. Some or all of the above-described processing by the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's language setting to a generation AI and cause the generation AI to generate multilingual presentation content.

[0052] The presentation unit can adjust the level of detail of the presentation according to the user's visual comprehension when presenting future MRI images. The presentation unit adjusts the level of detail of the presentation according to the user's visual comprehension, for example. For example, if the user has a high level of visual comprehension, the presentation unit provides presentation content including detailed information. Furthermore, if the user has a low level of visual comprehension, the presentation unit can provide presentation content that is concise and easy to understand. Furthermore, the presentation unit can customize appropriate presentation content according to the user's visual comprehension. For example, if the user has a high level of visual comprehension, the presentation unit provides presentation content including detailed information. For example, if the user has a low level of visual comprehension, the presentation unit provides presentation content that is concise and easy to understand. Furthermore, the presentation unit customizes appropriate presentation content according to the user's visual comprehension. In this way, by adjusting the level of detail of the presentation according to the visual comprehension, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's visual comprehension to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0053] When providing a prevention program, the providing unit can analyze the user's past lifestyle data and select an optimal providing method. The providing unit, for example, analyzes the user's past lifestyle data and selects the optimal providing method. For example, the providing unit provides an optimal prevention program based on the user's past lifestyle data. The providing unit can also analyze the user's past data and select an effective prevention program. Furthermore, the providing unit can customize the providing method by referring to the user's past lifestyle data. For example, the providing unit provides an optimal prevention program based on the user's past lifestyle data. For example, the providing unit analyzes the user's past data and selects an effective prevention program. The providing unit can also customize the providing method by referring to the user's past lifestyle data. In this way, the optimal prevention program can be provided by analyzing the past lifestyle data. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past lifestyle data to a generation AI and cause the generation AI to select an optimal providing method.

[0054] When providing a prevention program, the providing unit can customize the provided content based on the user's current health condition. The providing unit customizes the provided content based on, for example, the user's current health condition. For example, if the user's health condition is good, the providing unit provides a detailed prevention program. Furthermore, if the user's health condition is deteriorating, the providing unit can provide a concise and easy-to-understand prevention program. Furthermore, the providing unit can customize an appropriate prevention program according to the user's health condition. For example, if the user's health condition is good, the providing unit provides a detailed prevention program. For example, if the user's health condition is deteriorating, the providing unit provides a concise and easy-to-understand prevention program. Furthermore, the providing unit customizes an appropriate prevention program according to the user's health condition. In this way, by customizing the provided content based on the user's current health condition, a more appropriate prevention program can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's current health condition to the generating AI and cause the generating AI to customize the provided content.

[0055] The providing unit can improve the provision method by reflecting user feedback when providing a prevention program. The providing unit, for example, improves the provision method based on user feedback. For example, the providing unit improves the provision method based on feedback previously provided by the user. The providing unit can also select an effective prevention program from the user feedback. The providing unit can also optimize the content to be provided by referring to the user feedback. For example, the providing unit improves the provision method based on feedback previously provided by the user. For example, the providing unit selects an effective prevention program from the user feedback. The providing unit can also optimize the content to be provided by referring to the user feedback. In this way, the provision method can be optimized by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback to a generation AI and cause the generation AI to improve the provision method.

[0056] When providing a prevention program, the providing unit can select an optimal delivery method by taking into account the user's geographical location information. The providing unit provides the prevention program, for example, by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides a prevention program related to the area. Furthermore, if the user is traveling, the providing unit can provide a prevention program that can be executed at the travel destination. Furthermore, if the user is at home, the providing unit can provide a prevention program that can be executed at home. For example, if the user is in a specific area, the providing unit provides a prevention program related to the area. For example, if the user is traveling, the providing unit provides a prevention program that can be executed at the travel destination. Furthermore, if the user is at home, the providing unit provides a prevention program that can be executed at home. In this way, by taking into account the geographical location information, it is possible to provide an optimal prevention program for the user. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to select an optimal delivery method.

[0057] The providing unit may analyze the user's social media activity and provide relevant advice when providing the prevention program. The providing unit may, for example, analyze the user's social media activity and provide the relevant advice. For example, the providing unit may provide advice to improve diet based on photos of meals shared by the user on social media. The providing unit may also provide exercise recommendations based on the user's exercise records on social media. The providing unit may also provide advice to improve sleep quality based on the user's sleep records on social media. For example, the providing unit may provide advice to improve diet based on photos of meals shared by the user on social media. For example, the providing unit may provide exercise recommendations based on the user's exercise records on social media. The providing unit may also provide advice to improve sleep quality based on the user's sleep records on social media. In this way, by analyzing social media activity, it is possible to provide advice that is highly relevant to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media activity into a generation AI and cause the generation AI to provide relevant advice.

[0058] When providing a prevention program, the providing unit can customize the content to be provided by reflecting the user's past feedback. The providing unit customizes the content to be provided, for example, based on the user's past feedback. For example, the providing unit improves the content to be provided based on feedback provided by the user in the past. The providing unit can also select an effective prevention program from the user's feedback. The providing unit can also optimize the content to be provided by referring to the user's feedback. For example, the providing unit improves the content to be provided based on feedback provided by the user in the past. For example, the providing unit selects an effective prevention program from the user's feedback. The providing unit can also optimize the content to be provided by referring to the user's feedback. In this way, by reflecting the past feedback, an optimal prevention program for the user can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into a generation AI and cause the generation AI to customize the content to be provided.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The generation unit can also generate future MRI images taking into account the user's genetic information in addition to the user's lifestyle data. For example, the generation unit can predict future brain conditions based on the user's genetic test results, taking into account genetic risk factors. The generation unit can also evaluate genetic risk based on the user's family history. Furthermore, the generation unit can integrate the user's genetic information and lifestyle data to generate more accurate future MRI images. This makes it possible to provide a more personalized prevention program by taking genetic information into account.

[0061] The provision unit can provide a prevention program by taking into account the user's social network information in addition to the user's lifestyle data. For example, the provision unit can suggest a prevention program that can be carried out in groups, taking into account the health conditions and lifestyles of the user's friends and family. The provision unit can also analyze the user's social media activity and encourage collaboration with other users who have the same goals. Furthermore, the provision unit can provide advice to strengthen the user's social support. This makes it possible to provide a prevention program that utilizes the user's social network, thereby increasing the user's motivation.

[0062] The collection unit can collect environmental data about the user in addition to the user's lifestyle data. For example, the collection unit can measure the air quality and noise level in the user's living environment using sensors. The collection unit can also record the user's work environment and commute time. Furthermore, the collection unit can adjust the collection method of the lifestyle data based on the user's environmental data. This enables data collection that takes into account the user's environmental factors, allowing for more accurate future MRI images to be generated.

[0063] The provision unit can provide a prevention program taking into consideration the user's lifestyle data as well as the user's financial situation. For example, the provision unit can analyze the user's income and expenses and propose an economically feasible prevention program. The provision unit can also recommend cost-effective health foods and exercise plans depending on the user's financial situation. Furthermore, the provision unit can introduce health services that can be used free of charge or at low cost based on the user's financial situation. This makes it possible to provide a prevention program that takes into consideration financial constraints, thereby increasing the user's motivation to participate.

[0064] The generation unit can generate future MRI images by taking into account the user's occupational data in addition to the user's lifestyle data. For example, the generation unit can evaluate the user's stress level and physical activity level due to their occupation and predict their future brain state. The generation unit can also generate future MRI images by taking into account risk factors related to the user's occupation. Furthermore, the generation unit can integrate the user's occupational data and lifestyle data to generate more accurate future MRI images. This makes it possible to provide future MRI images that take occupational factors into account, thereby providing a more personalized prevention program.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects the user's current MRI images and lifestyle data. The user's lifestyle data includes diet, exercise, sleep, etc. For example, the collection unit may record dietary information through a smartphone app, collect exercise data using a wearable device, and collect sleep data using a sleep tracker. Step 2: The generation unit uses the generation AI to analyze the data collected by the collection unit and generate future MRI images of the user's brain. The generation AI predicts the future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the predictions. Step 3: The presentation unit presents the future MRI images generated by the generation unit to the user. The presentation unit visually displays the generated future MRI images to encourage the user to take measures against the disease. For example, the presentation unit displays the future MRI images as a 3D model to visually show future brain atrophy. Step 4: The provider provides the user with a personalized prevention program based on the future MRI images presented by the presentation unit. The provider uses the generative AI to propose an optimal prevention program based on the user's lifestyle data. For example, the provider provides specific advice such as improving diet, recommending exercise, and improving sleep quality.

[0067] (Example 2) A prevention support system according to an embodiment of the present invention generates future brain MRI images based on a user's current MRI images and lifestyle data, and provides a personalized prevention program. The prevention support system collects the user's current MRI images and lifestyle data, and a generation AI analyzes these data to generate future brain MRI images. The generated future MRI images are presented to the user, visualizing future brain atrophy and encouraging behavioral change that leads the user to implement disease prevention measures. Furthermore, the generation AI constructs and provides a personalized prevention program for the user to prevent brain disease. For example, the prevention support system collects the user's current MRI images and lifestyle data. For example, detailed data on the user's lifestyle habits, such as diet, exercise, and sleep, is collected. Next, the prevention support system uses the generation AI to analyze the collected data and generate future brain MRI images. The generation AI predicts the user's future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the prediction. For example, the system predicts the progression of brain atrophy if the user continues their current lifestyle, and generates the result as a future MRI image. The prevention support system then presents the generated future MRI images to the user, allowing the user to visually confirm the future state of their brain. For example, by actually seeing the progression of brain atrophy if the user continues their current lifestyle, the user recognizes the importance of disease prevention measures and is encouraged to change their behavior. Furthermore, the prevention support system uses the generative AI to create and provide a personalized prevention program for the user to prevent brain disease. The generative AI proposes an optimal prevention program based on the user's lifestyle data. For example, it provides specific advice such as improving diet, encouraging exercise, and improving sleep quality. This allows the user to implement preventive measures that are tailored to them. This allows the prevention support system to visually confirm the future state of the user's brain and recognize the importance of disease prevention measures. Furthermore, by providing a personalized prevention program, the user can implement effective preventive measures.

[0068] A preventive support system according to an embodiment includes a collection unit, a generation unit, a presentation unit, and a provision unit. The collection unit collects a user's current MRI images and lifestyle data. The user's lifestyle data includes, but is not limited to, diet, exercise, and sleep. For example, the collection unit records the user's diet and collects calorie intake and nutrient balance. The collection unit can also record the type, frequency, and intensity of the user's exercise. The collection unit can also record the user's sleep time and quality. For example, the collection unit collects data by having the user input the user's diet through a smartphone app. The collection unit can also collect the user's exercise data using a wearable device. The collection unit can also collect the user's sleep data using a sleep tracker. The generation unit uses a generation AI to analyze the data collected by the collection unit and generate future MRI images of the user's brain. For example, the generation AI predicts a future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the prediction. For example, the generating AI predicts the progression of brain atrophy if the user continues with their current lifestyle habits and generates the results as future MRI images. The generating unit can also use the generating AI to analyze the user's lifestyle data and predict the future brain state. For example, the generating AI predicts the future brain health state based on the user's dietary data. The generating unit can also use the generating AI to analyze the user's exercise data and predict the future brain health state. For example, the generating AI predicts the future brain health state based on the user's exercise data. The presentation unit presents the future MRI images generated by the generating unit to the user. The presentation unit, for example, visually displays the generated future MRI images to the user to encourage the user to take disease prevention measures. For example, the presentation unit displays the generated future MRI images as a 3D model, allowing the user to visually see the future brain atrophy. The presentation unit can also present the generated future MRI images to the user to make them aware of the importance of disease prevention measures. For example, the presentation unit presents the generated future MRI images to the user, visually showing the progression of brain atrophy if the user continues with their current lifestyle.The providing unit provides a personalized prevention program to the user based on the future MRI images presented by the presenting unit. The providing unit, for example, uses a generating AI to propose an optimal prevention program based on the user's lifestyle data. For example, the providing unit provides specific advice such as improving diet, recommending exercise, and improving sleep quality. The providing unit can also provide a personalized prevention program to the user using the generating AI. For example, the providing unit proposes a prevention program tailored to the user's individual health condition based on the user's lifestyle data. This allows the prevention support system according to the embodiment to visually check the future state of the user's brain and recognize the importance of disease prevention measures. Furthermore, by providing a personalized prevention program, the user can take effective preventive measures.

[0069] The collection unit can collect data on the user's lifestyle habits, including diet, exercise, and sleep. For example, the collection unit records the user's dietary habits and collects calorie intake and nutrient balance. For example, the collection unit prompts the user to input dietary habits through a smartphone app and collects the data. The collection unit can also record the type, frequency, and intensity of the user's exercise. For example, the collection unit can collect the user's exercise data using a wearable device. The collection unit can also record the user's sleep time and sleep quality. For example, the collection unit can collect the user's sleep data using a sleep tracker. By collecting detailed lifestyle habits data of the user, more accurate future MRI images can be generated. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's dietary habits into the generation AI and have the generation AI analyze the dietary data.

[0070] The generation unit can predict a future brain state based on current MRI images and lifestyle data, and generate future MRI images based on the prediction. The generation unit, for example, uses a generation AI to predict a future brain state based on current MRI images and lifestyle data. For example, the generation AI predicts the progression of brain atrophy if the current lifestyle is continued and generates a future MRI image based on the result. The generation unit can also use the generation AI to analyze a user's lifestyle data and predict a future brain state. For example, the generation AI predicts a future brain health state based on the user's diet data. The generation unit can also use the generation AI to analyze a user's exercise data and predict a future brain health state. For example, the generation AI predicts a future brain health state based on the user's exercise data. This predicts a future brain state and generates a future MRI image based on the prediction, thereby encouraging the user to take specific measures to prevent illness. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input current MRI images and lifestyle data into the generation AI and have the generation AI generate future MRI images.

[0071] The presentation unit can present the generated future MRI images to the user and visually show the future brain atrophy using a 3D model. The presentation unit, for example, can visually show the generated future MRI images to the user, thereby encouraging the user to take measures to combat the disease. For example, the presentation unit can display the generated future MRI images as a 3D model, visually showing the future brain atrophy to the user. The presentation unit can also present the generated future MRI images to the user to help them recognize the importance of disease prevention measures. For example, the presentation unit can present the generated future MRI images to the user and visually show the progression of brain atrophy if the user continues with their current lifestyle. This allows the user to visually see the future brain state, making them recognize the importance of disease prevention measures and encouraging behavioral change. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the generated future MRI images to a generation AI and cause the generation AI to generate a 3D model.

[0072] The providing unit can propose an appropriate preventive program based on the user's lifestyle data. The providing unit, for example, uses a generating AI to propose an optimal preventive program based on the user's lifestyle data. For example, the providing unit provides specific advice such as improving diet, recommending exercise, and improving sleep quality. The providing unit can also use the generating AI to provide a personalized preventive program to the user. For example, the providing unit proposes a preventive program tailored to the user's individual health condition based on the user's lifestyle data. This allows for effective disease prevention measures to be implemented by providing a personalized preventive program based on the user's lifestyle data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle data into the generating AI and have the generating AI execute the prevention program proposal.

[0073] The providing unit can provide specific advice, including advice on improving diet, recommending exercise, and improving sleep quality. For example, the providing unit uses a generating AI to provide specific advice, such as advice on improving diet, recommending exercise, and improving sleep quality, based on the user's lifestyle data. For example, the providing unit can analyze the user's diet and suggest improving nutritional balance. The providing unit can also analyze the user's exercise data and suggest an appropriate exercise plan. Furthermore, the providing unit can analyze the user's sleep data and provide advice for improving sleep quality. For example, the providing unit can suggest improving nutritional balance based on the user's diet. For example, the providing unit can analyze the user's diet and advise the user to increase their intake of specific nutrients. The providing unit can also suggest an appropriate exercise plan based on the user's exercise data. For example, the providing unit can analyze the user's exercise data and suggest how many times per week and to what extent the user should exercise. Furthermore, the providing unit can provide advice for improving sleep quality based on the user's sleep data. For example, the providing unit can analyze the user's sleep data and suggest adjusting their bedtime and wake-up time. This allows the provision of specific advice, thereby suggesting preventive measures that are easy for the user to implement. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the user's lifestyle habit data into the generating AI and cause the generating AI to generate specific advice.

[0074] The collection unit can estimate the user's emotion and adjust the timing of collecting lifestyle habit data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using the emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using the emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expression. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows for more accurate data collection by adjusting the collection timing according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0075] The collection unit can analyze the user's past lifestyle habit data and select the optimal collection method. The collection unit, for example, analyzes the past lifestyle habit data and selects the optimal collection method. For example, the collection unit selects the most efficient collection method based on data previously input by the user. The collection unit can also analyze the user's past data collection history and optimize the collection frequency. The collection unit can also select the optimal collection method by referring to the user's past data input method (voice, text, etc.). For example, the collection unit selects the most efficient collection method based on data previously input by the user. For example, the collection unit analyzes the user's past data collection history and optimizes the collection frequency. The collection unit can also select the optimal collection method by referring to the user's past data input method (voice, text, etc.). This enables efficient data collection by analyzing past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past lifestyle habit data to a generation AI and cause the generation AI to select the optimal collection method.

[0076] When collecting lifestyle habit data, the collection unit can filter the data based on the user's current health condition and medical history. The collection unit filters the data based on, for example, the user's current health condition and medical history. For example, the collection unit collects only necessary data taking into account the user's current health condition. The collection unit can also prioritize collecting relevant data based on the user's medical history. Furthermore, the collection unit can limit the range of collected data when the user's health condition is deteriorating. For example, the collection unit collects only necessary data taking into account the user's current health condition. For example, the collection unit prioritizes collecting relevant data based on the user's medical history. The collection unit can also limit the range of collected data when the user's health condition is deteriorating. In this way, by filtering the data based on the user's health condition and medical history, only necessary data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and medical history to the generation AI and cause the generation AI to filter the data.

[0077] When collecting lifestyle habit data, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method. For example, if the user prefers voice input, the collection unit may prioritize voice input to collect data. Furthermore, if the user prefers text input, the collection unit may prioritize text input to collect data. Furthermore, if the user prefers image input, the collection unit may prioritize image input to collect data. For example, if the user prefers voice input, the collection unit prioritizes voice input to collect data. For example, if the user prefers text input, the collection unit prioritizes text input to collect data. Furthermore, if the user prefers image input, the collection unit prioritizes image input to collect data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0078] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using the emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using the emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expression. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows important data to be collected preferentially by determining the priority of data to be collected according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] When collecting lifestyle habit data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data about the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting lifestyle habit data at home. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is traveling, the collection unit prioritizes collecting data about the travel destination. Furthermore, when the user is at home, the collection unit prioritizes collecting lifestyle habit data at home. In this way, by taking into account the user's geographical location information, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit may analyze the user's social media activities and collect related data when collecting lifestyle habit data. The collection unit may, for example, analyze the user's social media activities and collect related data. For example, the collection unit may collect dietary data based on photos of meals shared by the user on social media. The collection unit may also collect exercise data based on the user's exercise records on social media. The collection unit may also collect sleep data based on the user's sleep records on social media. For example, the collection unit may collect dietary data based on photos of meals shared by the user on social media. For example, the collection unit may collect exercise data based on the user's exercise records on social media. The collection unit may also collect sleep data based on the user's sleep records on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activities into a generation AI and cause the generation AI to collect related data.

[0081] When collecting lifestyle habit data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method based on, for example, the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also adjust the range of collected data based on the user's past feedback. Furthermore, the collection unit can optimize the collection timing by referring to the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the range of collected data based on the user's past feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0082] The generation unit can estimate the user's emotion and adjust the generation method of future MRI images based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation method to be adjusted according to the user's emotion, thereby generating more appropriate future MRI images. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the generation method for future MRI images.

[0083] The generation unit can adjust the accuracy of generation based on the level of detail of the user's lifestyle data when generating future MRI images. The generation unit adjusts the accuracy of generation based on, for example, the level of detail of the user's lifestyle data. For example, when the user's lifestyle data is detailed, the generation unit generates future MRI images with high accuracy. Furthermore, when the user's lifestyle data is insufficient, the generation unit can generate future MRI images with lower accuracy. Furthermore, when part of the user's lifestyle data is missing, the generation unit can generate future MRI images using complementary data. For example, when the user's lifestyle data is detailed, the generation unit generates future MRI images with high accuracy. For example, when the user's lifestyle data is insufficient, the generation unit generates future MRI images with lower accuracy. Furthermore, when part of the user's lifestyle data is missing, the generation unit generates future MRI images using complementary data. In this way, by adjusting the accuracy of generation based on the level of detail of the lifestyle data, more accurate future MRI images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the accuracy of the generation.

[0084] The generation unit can apply different generation algorithms depending on the user's health risk factors when generating future MRI images. The generation unit applies different generation algorithms depending on, for example, the user's health risk factors. For example, if the user has high blood pressure, the generation unit generates future MRI images taking into account risk factors related to high blood pressure. Furthermore, if the user has diabetes, the generation unit can generate future MRI images taking into account risk factors related to diabetes. Furthermore, if the user is a smoker, the generation unit can generate future MRI images taking into account risk factors related to smoking. For example, if the user has high blood pressure, the generation unit generates future MRI images taking into account risk factors related to high blood pressure. For example, if the user has diabetes, the generation unit generates future MRI images taking into account risk factors related to diabetes. Furthermore, if the user is a smoker, the generation unit generates future MRI images taking into account risk factors related to smoking. In this way, by applying the generation algorithm depending on the health risk factors, more appropriate future MRI images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health risk factors into the generation AI and cause the generation AI to apply the generation algorithm.

[0085] The generation unit can improve the accuracy of generation when generating future MRI images by referring to the user's past MRI images. The generation unit can improve the accuracy of generation by referring to, for example, the user's past MRI images. For example, the generation unit can improve the accuracy of the future MRI images based on the user's past MRI images. The generation unit can also compare the user's past MRI images with the current MRI image to generate the future MRI images. The generation unit can also analyze changes in the user's past MRI images to improve the accuracy of the future MRI images. For example, the generation unit can improve the accuracy of the future MRI images based on the user's past MRI images. For example, the generation unit can compare the user's past MRI images with the current MRI image to generate the future MRI images. The generation unit can also analyze changes in the user's past MRI images to improve the accuracy of the future MRI images. In this way, the accuracy of the future MRI images can be improved by referring to the past MRI images. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past MRI images to the generation AI and cause the generation AI to improve the accuracy of the generation.

[0086] The generation unit can estimate the user's emotion and adjust the level of detail of the generated MRI image based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the level of detail of the MRI image to be adjusted according to the user's emotion, thereby generating a more appropriate MRI image. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the MRI image.

[0087] The generation unit can determine the generation priority based on changes in the user's lifestyle when generating future MRI images. The generation unit determines the generation priority based on, for example, changes in the user's lifestyle. For example, if the user's lifestyle changes significantly, the generation unit prioritizes generating MRI images that reflect the changes. Furthermore, if the user's lifestyle is stable, the generation unit can generate MRI images periodically. Furthermore, if the user's lifestyle is irregular, the generation unit can generate MRI images during periods of significant change. For example, if the user's lifestyle changes significantly, the generation unit prioritizes generating MRI images that reflect the changes. For example, if the user's lifestyle is stable, the generation unit generates MRI images periodically. Furthermore, if the user's lifestyle is irregular, the generation unit generates MRI images during periods of significant change. In this way, by determining the generation priority based on changes in the lifestyle, MRI images can be generated at more appropriate times. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input changes in the user's lifestyle habits into the generation AI and have the generation AI determine the generation priorities.

[0088] The generation unit can improve the accuracy of generation by referring to the user's related medical data when generating future MRI images. The generation unit, for example, improves the accuracy of generation by referring to the user's related medical data. For example, the generation unit improves the accuracy of the future MRI images based on the user's medical data. The generation unit can also integrate the user's medical data and lifestyle data to generate future MRI images. The generation unit can also analyze changes in the user's medical data to improve the accuracy of the future MRI images. For example, the generation unit improves the accuracy of the future MRI images based on the user's medical data. For example, the generation unit integrates the user's medical data and lifestyle data to generate future MRI images. The generation unit can also analyze changes in the user's medical data to improve the accuracy of the future MRI images. In this way, the accuracy of the future MRI images can be improved by referring to the related medical data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related medical data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0089] The generation unit can adjust the generated explanation according to the user's level of expertise when generating future MRI images. The generation unit adjusts the generated explanation according to, for example, the user's level of expertise. For example, if the user is a medical professional, the generation unit generates an MRI image including a detailed medical explanation. Furthermore, if the user is a layperson, the generation unit can generate an MRI image including a concise and easy-to-understand explanation. Furthermore, the generation unit can generate an MRI image including an appropriate explanation according to the user's level of expertise. For example, if the user is a medical professional, the generation unit generates an MRI image including a detailed medical explanation. For example, if the user is a layperson, the generation unit generates an MRI image including a concise and easy-to-understand explanation. Furthermore, the generation unit generates an MRI image including an appropriate explanation according to the user's level of expertise. In this way, by adjusting the explanation according to the user's level of expertise, it is possible to provide an MRI image that is easier to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the generated explanation.

[0090] The presentation unit can estimate the user's emotion and adjust the presentation method of the MRI image based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the presentation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotion using voice analysis technology. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit can calculate an emotion score based on changes in facial expression. The presentation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the presentation unit can analyze the tone and speed of the voice and calculate the emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on heart rate fluctuations. This allows the presentation method to be adjusted according to the user's emotion, thereby presenting the MRI image in a more appropriate manner. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit may input the user's emotion data into the generation AI and have the generation AI adjust the presentation method.

[0091] The presentation unit can select the optimal presentation method by referring to the user's past responses when presenting future MRI images. The presentation unit selects the optimal presentation method by referring to, for example, the user's past responses. For example, the presentation unit selects the optimal presentation method based on display methods that the user preferred in the past. The presentation unit can also analyze the user's past responses and select a display method with high visibility. The presentation unit can also improve the presentation method by referring to the user's past feedback. For example, the presentation unit selects the optimal presentation method based on display methods that the user preferred in the past. For example, the presentation unit analyzes the user's past responses and selects a display method with high visibility. The presentation unit can improve the presentation method by referring to the user's past feedback. In this way, the optimal presentation method for the user can be selected by referring to the past responses. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past responses to a generation AI and cause the generation AI to select the optimal presentation method.

[0092] The presentation unit can customize the presentation content according to the user's current health condition when presenting future MRI images. The presentation unit customizes the presentation content according to, for example, the user's current health condition. For example, if the user's health condition is good, the presentation unit provides presentation content including detailed information. Furthermore, if the user's health condition is deteriorating, the presentation unit can provide brief and easy-to-understand presentation content. Furthermore, the presentation unit can customize appropriate presentation content according to the user's health condition. For example, if the user's health condition is good, the presentation unit provides presentation content including detailed information. For example, if the user's health condition is deteriorating, the presentation unit provides brief and easy-to-understand presentation content. Furthermore, the presentation unit customizes appropriate presentation content according to the user's health condition. In this way, by customizing the presentation content according to the user's current health condition, more appropriate information can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current health condition to the generation AI and cause the generation AI to customize the presentation content.

[0093] The presentation unit can improve the presentation method by reflecting the user's feedback when presenting future MRI images. The presentation unit improves the presentation method based on, for example, user feedback. For example, the presentation unit improves the presentation method based on feedback previously provided by the user. The presentation unit can also select a presentation method with high visibility based on the user's feedback. The presentation unit can also optimize the presentation content by referring to the user's feedback. For example, the presentation unit improves the presentation method based on feedback previously provided by the user. For example, the presentation unit selects a presentation method with high visibility based on the user's feedback. The presentation unit can also optimize the presentation content by referring to the user's feedback. In this way, the presentation method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's feedback to the generation AI and cause the generation AI to improve the presentation method.

[0094] The presentation unit can estimate the user's emotion and adjust the presentation order of the MRI images based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the presentation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotion using an emotion analysis technology. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit can calculate an emotion score based on changes in facial expression. The presentation unit can also record the user's voice and estimate the emotion using an emotion analysis technology. For example, the presentation unit can analyze the tone and speed of the voice and calculate an emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on heart rate fluctuations. This allows information to be conveyed more effectively by adjusting the presentation order according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit may input the user's emotion data into the generation AI and have the generation AI adjust the presentation order.

[0095] The presentation unit can select the optimal presentation method by taking into account the user's device information when presenting future MRI images. The presentation unit selects the presentation method by taking into account, for example, the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a personal computer, the presentation unit can provide a display method including detailed information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the presentation unit provides a display method optimized for a large screen. Furthermore, if the user is using a personal computer, the presentation unit provides a display method including detailed information. In this way, the optimal display method for the user can be provided by taking into account the device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's device information to the generation AI and cause the generation AI to select the optimal presentation method.

[0096] The presentation unit can make the presentation content multilingual according to the user's language setting when presenting future MRI images. The presentation unit can make the presentation content multilingual according to the user's language setting, for example. For example, the presentation unit automatically sets the presentation content based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. The presentation unit can also provide the presentation content in a specific language when the user selects that language. For example, the presentation unit automatically sets the presentation content based on the language setting of the user's device. For example, the presentation unit provides a language switching function when the user uses multiple languages. The presentation unit can also provide the presentation content in a specific language when the user selects that language. This makes it possible to provide information that is easy for the user to understand by providing multilingual support according to the language setting. Some or all of the above-described processing by the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's language setting to a generation AI and cause the generation AI to generate multilingual presentation content.

[0097] The presentation unit can adjust the level of detail of the presentation according to the user's visual comprehension when presenting future MRI images. The presentation unit adjusts the level of detail of the presentation according to the user's visual comprehension, for example. For example, if the user has a high level of visual comprehension, the presentation unit provides presentation content including detailed information. Furthermore, if the user has a low level of visual comprehension, the presentation unit can provide presentation content that is concise and easy to understand. Furthermore, the presentation unit can customize appropriate presentation content according to the user's visual comprehension. For example, if the user has a high level of visual comprehension, the presentation unit provides presentation content including detailed information. For example, if the user has a low level of visual comprehension, the presentation unit provides presentation content that is concise and easy to understand. Furthermore, the presentation unit customizes appropriate presentation content according to the user's visual comprehension. In this way, by adjusting the level of detail of the presentation according to the visual comprehension, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's visual comprehension to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0098] The providing unit can estimate the user's emotion and adjust the method of providing the prevention program based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using the emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using the emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using the emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the provision method to be adjusted according to the user's emotion, thereby providing a more effective prevention program. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and have the generation AI adjust the provision method.

[0099] When providing a prevention program, the providing unit can analyze the user's past lifestyle data and select an optimal providing method. The providing unit, for example, analyzes the user's past lifestyle data and selects the optimal providing method. For example, the providing unit provides an optimal prevention program based on the user's past lifestyle data. The providing unit can also analyze the user's past data and select an effective prevention program. Furthermore, the providing unit can customize the providing method by referring to the user's past lifestyle data. For example, the providing unit provides an optimal prevention program based on the user's past lifestyle data. For example, the providing unit analyzes the user's past data and selects an effective prevention program. The providing unit can also customize the providing method by referring to the user's past lifestyle data. In this way, the optimal prevention program can be provided by analyzing the past lifestyle data. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past lifestyle data to a generation AI and cause the generation AI to select an optimal providing method.

[0100] When providing a prevention program, the providing unit can customize the provided content based on the user's current health condition. The providing unit customizes the provided content based on, for example, the user's current health condition. For example, if the user's health condition is good, the providing unit provides a detailed prevention program. Furthermore, if the user's health condition is deteriorating, the providing unit can provide a concise and easy-to-understand prevention program. Furthermore, the providing unit can customize an appropriate prevention program according to the user's health condition. For example, if the user's health condition is good, the providing unit provides a detailed prevention program. For example, if the user's health condition is deteriorating, the providing unit provides a concise and easy-to-understand prevention program. Furthermore, the providing unit customizes an appropriate prevention program according to the user's health condition. In this way, by customizing the provided content based on the user's current health condition, a more appropriate prevention program can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's current health condition to the generating AI and cause the generating AI to customize the provided content.

[0101] The providing unit can improve the provision method by reflecting user feedback when providing a prevention program. The providing unit, for example, improves the provision method based on user feedback. For example, the providing unit improves the provision method based on feedback previously provided by the user. The providing unit can also select an effective prevention program from the user feedback. The providing unit can also optimize the content to be provided by referring to the user feedback. For example, the providing unit improves the provision method based on feedback previously provided by the user. For example, the providing unit selects an effective prevention program from the user feedback. The providing unit can also optimize the content to be provided by referring to the user feedback. In this way, the provision method can be optimized by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback to a generation AI and cause the generation AI to improve the provision method.

[0102] The providing unit can estimate the user's emotion and determine the priority of prevention programs based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using an emotion analysis technology. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using an emotion analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows for determining the priority of preventive programs according to the user's emotions, thereby providing more effective preventive measures. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to determine the priority of preventive programs.

[0103] When providing a prevention program, the providing unit can select an optimal delivery method by taking into account the user's geographical location information. The providing unit provides the prevention program, for example, by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides a prevention program related to the area. Furthermore, if the user is traveling, the providing unit can provide a prevention program that can be executed at the travel destination. Furthermore, if the user is at home, the providing unit can provide a prevention program that can be executed at home. For example, if the user is in a specific area, the providing unit provides a prevention program related to the area. For example, if the user is traveling, the providing unit provides a prevention program that can be executed at the travel destination. Furthermore, if the user is at home, the providing unit provides a prevention program that can be executed at home. In this way, by taking into account the geographical location information, it is possible to provide an optimal prevention program for the user. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to select an optimal delivery method.

[0104] The providing unit may analyze the user's social media activity and provide relevant advice when providing the prevention program. The providing unit may, for example, analyze the user's social media activity and provide the relevant advice. For example, the providing unit may provide advice to improve diet based on photos of meals shared by the user on social media. The providing unit may also provide exercise recommendations based on the user's exercise records on social media. The providing unit may also provide advice to improve sleep quality based on the user's sleep records on social media. For example, the providing unit may provide advice to improve diet based on photos of meals shared by the user on social media. For example, the providing unit may provide exercise recommendations based on the user's exercise records on social media. The providing unit may also provide advice to improve sleep quality based on the user's sleep records on social media. In this way, by analyzing social media activity, it is possible to provide advice that is highly relevant to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media activity into a generation AI and cause the generation AI to provide relevant advice.

[0105] When providing a prevention program, the providing unit can customize the content to be provided by reflecting the user's past feedback. The providing unit customizes the content to be provided, for example, based on the user's past feedback. For example, the providing unit improves the content to be provided based on feedback provided by the user in the past. The providing unit can also select an effective prevention program from the user's feedback. The providing unit can also optimize the content to be provided by referring to the user's feedback. For example, the providing unit improves the content to be provided based on feedback provided by the user in the past. For example, the providing unit selects an effective prevention program from the user's feedback. The providing unit can also optimize the content to be provided by referring to the user's feedback. In this way, by reflecting the past feedback, an optimal prevention program for the user can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into a generation AI and cause the generation AI to customize the content to be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, presentation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and generates future MRI images. The presentation unit, for example, presents the generated future MRI images to the user using the display 40A of the smart device 14. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a personalized prevention program to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, presentation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and generates future MRI images. The presentation unit, for example, presents the generated future MRI images to the user using the display of the smart glasses 214. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a personalized prevention program to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, presentation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate future MRI images. The presentation unit presents the generated future MRI images to the user using, for example, the display 343 of the headset-type terminal 314. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a personalized prevention program to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, presentation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate future MRI images. The presentation unit presents the generated future MRI images to the user using, for example, the display of the robot 414. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a personalized prevention program to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] In addition to the user's lifestyle data, the collection unit can also measure the user's stress level. For example, the collection unit can collect the user's heart rate variability and electrodermal activity using a sensor to estimate the user's stress level. The collection unit can also record the user's self-reported stress level. Furthermore, the collection unit can adjust the frequency of collection of lifestyle data based on the user's stress level. This enables data collection that takes the user's stress level into account, allowing for more accurate future MRI images to be generated.

[0108] The generation unit can also generate future MRI images taking into account the user's genetic information in addition to the user's lifestyle data. For example, the generation unit can predict future brain conditions based on the user's genetic test results, taking into account genetic risk factors. The generation unit can also evaluate genetic risk based on the user's family history. Furthermore, the generation unit can integrate the user's genetic information and lifestyle data to generate more accurate future MRI images. This makes it possible to provide a more personalized prevention program by taking genetic information into account.

[0109] In addition to the generated future MRI images, the presentation unit can display motivational messages based on the user's emotional state. For example, if the user is feeling anxious, the presentation unit can display an encouraging message. Alternatively, if the user is feeling positive, the presentation unit can display a message emphasizing a sense of accomplishment. Furthermore, the presentation unit can present a specific action plan to encourage behavioral change according to the user's emotional state. This makes it possible to provide information that takes into consideration the user's emotions, thereby more effectively encouraging behavioral change.

[0110] The provision unit can provide a prevention program by taking into account the user's social network information in addition to the user's lifestyle data. For example, the provision unit can suggest a prevention program that can be carried out in groups, taking into account the health conditions and lifestyles of the user's friends and family. The provision unit can also analyze the user's social media activity and encourage collaboration with other users who have the same goals. Furthermore, the provision unit can provide advice to strengthen the user's social support. This makes it possible to provide a prevention program that utilizes the user's social network, thereby increasing the user's motivation.

[0111] The collection unit can collect environmental data about the user in addition to the user's lifestyle data. For example, the collection unit can measure the air quality and noise level in the user's living environment using sensors. The collection unit can also record the user's work environment and commute time. Furthermore, the collection unit can adjust the collection method of the lifestyle data based on the user's environmental data. This enables data collection that takes into account the user's environmental factors, allowing for more accurate future MRI images to be generated.

[0112] The generator can adjust the frequency of generating future MRI images based on the user's emotional state. For example, if the user is feeling stressed, the generator can increase the generation frequency and provide future MRI images periodically. Alternatively, if the user is relaxed, the generator can decrease the generation frequency and provide future MRI images only when necessary. Furthermore, the generator can adjust the level of detail of the generated MRI images according to the user's emotional state. This makes it possible to provide future MRI images that take the user's emotional state into consideration, thereby increasing the user's sense of security.

[0113] The provision unit can provide a prevention program taking into consideration the user's lifestyle data as well as the user's financial situation. For example, the provision unit can analyze the user's income and expenses and propose an economically feasible prevention program. The provision unit can also recommend cost-effective health foods and exercise plans depending on the user's financial situation. Furthermore, the provision unit can introduce health services that can be used free of charge or at low cost based on the user's financial situation. This makes it possible to provide a prevention program that takes into consideration financial constraints, thereby increasing the user's motivation to participate.

[0114] The collection unit can customize the data collection interface based on the user's emotional state. For example, if the user is feeling stressed, the collection unit can provide a simple and intuitive interface. Alternatively, if the user is relaxed, the collection unit can provide an interface that allows for detailed input. Furthermore, the collection unit can adjust data input reminders according to the user's emotional state. This enables data collection that takes the user's emotional state into consideration, thereby reducing the burden on the user.

[0115] The generation unit can generate future MRI images by taking into account the user's occupational data in addition to the user's lifestyle data. For example, the generation unit can evaluate the user's stress level and physical activity level due to their occupation and predict their future brain state. The generation unit can also generate future MRI images by taking into account risk factors related to the user's occupation. Furthermore, the generation unit can integrate the user's occupational data and lifestyle data to generate more accurate future MRI images. This makes it possible to provide future MRI images that take occupational factors into account, thereby providing a more personalized prevention program.

[0116] The providing unit can adjust the feedback method of the prevention program based on the emotional state of the user. For example, if the user has positive emotions, the providing unit can emphasize positive feedback. Also, if the user is feeling anxious, the providing unit can provide feedback that gives a sense of security. Furthermore, the providing unit can adjust the frequency and timing of feedback according to the emotional state of the user. This enables feedback that takes into account the emotional state of the user, thereby improving the effectiveness of the prevention program.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects the user's current MRI images and lifestyle data. The user's lifestyle data includes diet, exercise, sleep, etc. For example, the collection unit may record dietary information through a smartphone app, collect exercise data using a wearable device, and collect sleep data using a sleep tracker. Step 2: The generation unit uses the generation AI to analyze the data collected by the collection unit and generate future MRI images of the user's brain. The generation AI predicts the future brain state based on the current MRI images and lifestyle data, and generates future MRI images based on the predictions. Step 3: The presentation unit presents the future MRI images generated by the generation unit to the user. The presentation unit visually displays the generated future MRI images to encourage the user to take measures against the disease. For example, the presentation unit displays the future MRI images as a 3D model to visually show future brain atrophy. Step 4: The provider provides the user with a personalized prevention program based on the future MRI images presented by the presentation unit. The provider uses the generative AI to propose an optimal prevention program based on the user's lifestyle data. For example, the provider provides specific advice such as improving diet, recommending exercise, and improving sleep quality.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting the user's current MRI images and lifestyle data; a generation unit that analyzes the data collected by the collection unit and generates a future MRI image of the user's brain; a presentation unit that presents the future MRI image generated by the generation unit to a user; a providing unit that provides a personalized prevention program to a user based on the future MRI image presented by the presenting unit. A system characterized by:

2. The collecting unit Collect data about your lifestyle, including your diet, exercise, and sleep habits 2. The system of claim 1.

3. The generation unit Based on current MRI images and lifestyle data, future brain states are predicted, and future MRI images are generated based on the predictions.

2. The system of claim 1.

4. The presentation unit The generated future MRI images are presented to the user, visualizing future brain atrophy in a 3D model.

2. The system of claim 1.

5. The providing unit Proposing appropriate preventive programs based on the user's lifestyle data 2. The system of claim 1.

6. The providing unit Providing specific advice, including improving your diet, encouraging exercise, and improving sleep quality 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting lifestyle data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past lifestyle data and select the optimal collection method 2. The system of claim 1.

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