system

Abstract

We provide the system. [Solution] A means for preprocessing medical image data and lifestyle data acquired from users, A means for predicting future medical images using a generative model based on the aforementioned preprocessed data, A means for creating an individualized health promotion plan based on the predicted medical images, A means for notifying the user of the aforementioned health promotion plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that awareness regarding the prevention of dementia and Alzheimer's disease is low and efforts to improve lifestyle habits are insufficient. In particular, it is difficult to grasp realistic health risks and no specific preventive measures are shown, so it is difficult to realize behavioral changes in users.

Means for Solving the Problems

[0005] This invention provides a system that uses a generative model to predict future medical images based on medical image data and lifestyle data acquired from the user, creates an individualized health promotion plan based on the predicted state, and notifies the user. This allows the user to visualize their actual health risks and implement specific lifestyle improvement measures. Furthermore, by linking with an external medical consultation platform, it is possible to receive even more specialized medical support.

[0006] "Medical image data" refers to image data, particularly images captured using medical devices, that are acquired for the purpose of evaluating a user's health status.

[0007] "Lifestyle data" refers to information about a user's daily actions and habits, including diet, exercise, sleep, and stress levels.

[0008] "Preprocessing" refers to the preparatory process of converting input data into a format suitable for analysis and model processing.

[0009] A "generative model" refers to a machine learning model that is capable of generating new data based on input data.

[0010] A "health promotion plan" is a guideline that includes specific lifestyle improvement suggestions and action plans tailored to each user's individual health condition and risks.

[0011] "Notifications" are a means of conveying information from a system to a user, and include methods such as email and in-app messaging.

[0012] A "medical consultation platform" refers to an online service that allows users to communicate with medical professionals and obtain medical information and advice. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that allows users to predict future health risks using their own medical image data and lifestyle data, and to implement personalized health promotion plans. The system enables data acquisition and processing, prediction execution, and communication of results to the user.

[0035] First, users input their latest medical imaging data and lifestyle data into the system via a dedicated application or web portal. Medical imaging data includes MRI images taken at medical institutions, while lifestyle data includes information such as daily activities, diet, and stress levels.

[0036] Next, the server preprocesses the received data, preparing it for use in the generative model. This stage involves noise reduction and resolution adjustments. Lifestyle data is also formatted and used in the predictive model.

[0037] The server feeds pre-processed data into a generative model to predict future medical images. This prediction is used to visualize the user's future brain health in detail and to extract high-risk areas and regions requiring attention.

[0038] Based on the predicted results, the server creates an individualized health improvement plan. This plan includes specific lifestyle improvements suggested to the user, such as recommendations for specific exercise programs, nutritional improvements, and stress management techniques.

[0039] Finally, the server notifies the user's device of the generated health improvement plan. Furthermore, by linking with an external medical consultation platform, users can implement the plan in detail while receiving advice from experts. This allows users to better understand their own health risks and make necessary improvements in their daily lives.

[0040] For example, if user A uploads their latest MRI image and provides data indicating a sedentary lifestyle and a high-calorie diet, the generative model will predict an increased risk in specific brain regions in future images. Based on this risk, user A will be recommended moderate exercise three times a week and a specific dietary improvement plan. This suggestion allows user A to implement concrete behavioral changes and create a plan to maintain brain health.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users input medical image data and lifestyle data using a dedicated application or web portal. Medical image data includes MRI images, and lifestyle data provides information on diet, exercise, and stress.

[0044] Step 2:

[0045] The server analyzes medical image data received from users and performs preprocessing such as noise reduction to standardize resolution and format. It also checks for any omissions or errors in lifestyle data and makes corrections as needed.

[0046] Step 3:

[0047] The server inputs pre-processed data into a generative model to predict future medical images. Based on past data, the generative model identifies potential brain changes and risk areas in the future.

[0048] Step 4:

[0049] Based on the prediction results, the server creates a personalized health promotion plan tailored to the user's health condition. This plan is designed to include specific exercise programs, nutritional advice, and stress management methods.

[0050] Step 5:

[0051] The server notifies the user's device of the completed health promotion plan. Through the application, the user can review the plan details and decide on specific actions to apply it to their daily life.

[0052] Step 6:

[0053] The server connects data with the medical consultation platform, supporting users so they can receive expert medical advice as needed. This allows users to receive support tailored to their individual needs.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern healthcare management, preventative measures based on individual lifestyles and health conditions are crucial. However, traditional methods make it difficult to effectively utilize medical imaging and lifestyle data to specifically predict future risks, posing a challenge in developing personalized health promotion plans. Furthermore, there is a lack of means to quickly access expert advice.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for inputting the preprocessed data into a generating AI model and predicting future medical images using prompt statements, and means for constructing an individualized health promotion plan based on the predicted medical images. This makes it possible to predict health risks optimized for each individual and to present specific improvement plans.

[0059] A "user" is an entity that uses the system to input and manage their own medical and health data.

[0060] "Medical image data" refers to image data such as MRI scans acquired at medical institutions, and is information that visually represents an individual's health status.

[0061] "Lifestyle data" refers to information about an individual's lifestyle, including their daily activities, diet, exercise, and stress levels.

[0062] "Preprocessing" is the process of applying noise reduction and resolution adjustments to input data in order to prepare it for analysis.

[0063] A "generative AI model" is an artificial intelligence technology used to predict future states based on large amounts of data.

[0064] A "prompt statement" is a document used to give specific instructions to a generative AI model.

[0065] "Medical image prediction" is the process of estimating the future state of medical images.

[0066] A "health promotion plan" is a plan that includes specific action proposals to improve an individual's predicted health risks.

[0067] A "terminal" is an electronic device used by a user to input and receive data.

[0068] To implement this invention, the user begins by inputting medical image data and lifestyle data using a dedicated application or web portal. The terminal collects this data, encrypts it, and then transmits it to the server. A typical smartphone or computer is used for this process, and the software used includes data entry tools and a web browser.

[0069] The server plays a role in preprocessing the received data. Specifically, it performs noise reduction and resolution adjustment on medical image data to prepare it for analysis by the generating AI model. Lifestyle data is also converted to a standard format and stored in a database. The server utilizes high-performance computing equipment and data analysis software to enable these processes.

[0070] Next, the server uses a generative AI model to predict future medical images. This generative AI model utilizes machine learning techniques and has been trained on a large amount of existing data. This model is given instructions using prompts. For example, the prompt "Predict future health risks based on this MRI image and lifestyle data" is used.

[0071] Based on predicted medical images and analysis results, the server creates a personalized health improvement plan. This plan includes specific and actionable lifestyle improvement advice for the user, such as recommendations for exercise a few times a week and dietary improvement guidelines. The created plan is notified to the user via their device, and the user uses it to review their daily behavior.

[0072] Furthermore, by integrating with external medical consultation platforms and systems, users can effectively implement health promotion plans while receiving advice from experts. This provides support for individual users to understand their own health risks and take concrete improvement actions.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users input medical image data and lifestyle data into their devices using a dedicated application or web portal. This step collects data such as MRI images, daily activities, and dietary information. The input data is formatted and encrypted for secure transmission from the device to the server.

[0076] Step 2:

[0077] The server preprocesses medical image data and lifestyle data received from the terminal. For medical images, it performs noise reduction and resolution adjustments to optimize image quality. Lifestyle data is converted to a standard format and prepared into a data structure suitable for analysis. This results in preprocessed data as output, ready for input to the generative AI model.

[0078] Step 3:

[0079] The server inputs the pre-processed data into a generating AI model. Here, a predictive model using machine learning is applied, and specific instructions are given using prompts. For example, a prompt such as "Predict future health risks based on this MRI image and lifestyle data" is used. The model analyzes the relationship between the medical image and lifestyle data and outputs a prediction of future health risks.

[0080] Step 4:

[0081] The server generates personalized health improvement plans based on prediction results obtained from the generation AI model. The prediction results serve as input, and the server constructs specific action suggestions and lifestyle improvement plans that differ for each user. The output is a plan that includes exercise recommendations and dietary improvement guides for the user.

[0082] Step 5:

[0083] The server sends the created health promotion plan to the terminal and notifies the user. The terminal presents the received plan to the user in an easy-to-understand manner, encouraging behavioral changes in daily life. Furthermore, by linking with an external medical consultation platform, the system supports the implementation of the health promotion plan by allowing users to receive advice from experts.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] In modern society, there is a need to predict individual health conditions and propose specific lifestyle improvements based on those predictions. However, conventional methods make it difficult to visually understand an individual's future health risks and appropriately guide them toward improvement measures. Furthermore, there is a challenge in that users have difficulty gaining the motivation to actively maintain their health by utilizing the data and suggestions provided.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user; means for predicting future medical images using a generative model based on the preprocessed data; means for creating a personalized health promotion plan based on the predicted medical images; means for visualizing the user's health status in three dimensions using augmented reality technology; and means for recommending health-related products and services. This makes it possible for users to visually understand their own health risks and to be more motivated to accept more specific and effective lifestyle improvement measures.

[0089] "Medical image data" refers to image information acquired at medical institutions and is data used for diagnostic purposes, such as MRI and CT scans.

[0090] "Lifestyle data" refers to information about an individual's daily activities, diet, exercise, sleep, stress levels, etc., and is used to assess their health status.

[0091] "Preprocessing" refers to the process of preparing data into a format suitable for a generative model, and includes operations such as noise reduction and resolution adjustment.

[0092] A "generative model" is an artificial intelligence algorithm used to generate new information or predictive results from input data.

[0093] A "health promotion plan" is a set of specific action guidelines recommended for individuals to lead healthier lives, including suggestions for exercise, diet, and stress management.

[0094] Augmented reality technology is a technique that overlays computer-generated information onto the real world environment, and is used to provide visual experiences.

[0095] "Product or service recommendation" refers to the act of suggesting health-related products or services that are individually suited to the user's health condition and predicted risks.

[0096] The system for implementing this invention provides predictions of future health risks and personalized health promotion plans based on medical image data and lifestyle data provided by the user.

[0097] The server receives MRI and CT images collected from medical institutions, as well as data on daily activities submitted by users. The received data undergoes preprocessing, including noise reduction and resolution adjustment, before being fed into a generative model. This generative model, built using TENSORFLOW® and PyTorch, predicts future medical images and health risks from the data.

[0098] Based on the predicted medical condition, the server creates a health improvement plan optimized for the user. This plan includes specific exercise programs, dietary recommendations, and stress management techniques. The plan is visualized on the user's smartphone or smart glasses through augmented reality features using ARKit (iOS) or ARCore (Android®).

[0099] Furthermore, the system includes a recommendation function to provide health products and services tailored to each individual's health condition, making it easy for users to have options for proactively managing their own health.

[0100] As a concrete example, when a user uploads MRI images and lifestyle data to the system, a generative model performs an analysis and visualizes the risk of future cognitive decline. Based on this, the system recommends the user to use a meditation app or a specific brain training program. An example of a prompt to the generative AI model might be, "Please show me the risk of cognitive decline and preventative measures."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] Users upload medical image data (e.g., MRI images) and lifestyle data to the server via a dedicated application or web portal. This allows the server to receive basic information about the user's health status.

[0104] Step 2:

[0105] The server performs preprocessing on the received medical image data, including noise reduction and resolution adjustment. The input is medical image data, and the output is image data formatted for use with the generative model. This step uses the OpenCV image processing library.

[0106] Step 3:

[0107] The server also performs formatting on lifestyle data. This involves using data processing libraries such as Pandas and NumPy to clean the input data and convert it into a format usable by the model. The output data is then used as input for the generative model.

[0108] Step 4:

[0109] The server feeds preprocessed data into a generative model (e.g., a TensorFlow model) to predict future medical images and health risks. The input to this step is a set of preprocessed data, and the output is predicted medical image data and risk assessment results. The model's inference capabilities are utilized for data computation.

[0110] Step 5:

[0111] Based on the predicted results, the server creates a personalized health improvement plan. The input is a predicted risk assessment, and the output is specific lifestyle improvement measures, including recommended exercise programs, dietary improvements, and stress management techniques.

[0112] Step 6:

[0113] The server notifies the terminal of health promotion plans and recommendations for related products and services through augmented reality technology. The input is the health promotion plan and related recommendations, and the output is the plan content visually highlighted. AR technologies such as ARKit and ARCore are used to provide the user with the plan.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention incorporates an emotion engine into a system that predicts a user's future health risks based on medical image data and lifestyle data, and provides an individualized health improvement plan, thereby enabling more effective support for improving lifestyle habits.

[0116] The system receives medical image data and lifestyle data provided by the user and first preprocesses this data. The server standardizes the image data format, performs noise reduction, and prepares the lifestyle data into a consistent data format. In the next step, the server uses a generative model to predict future medical images. This prediction visualizes the user's future health status and enables the identification of risks and brain regions that require attention.

[0117] Furthermore, an emotion engine is activated to monitor the user's emotional state. The user's emotions are recognized through text and behavioral data collected by the application through their daily activities. The emotion engine analyzes this information to determine the user's current emotional state.

[0118] The server develops an individualized health promotion plan based on predicted medical images and emotional data obtained from the emotion engine. This plan includes customized advice and recommended actions that are easy for the user to understand and implement. Furthermore, the plan's effectiveness is enhanced by incorporating content appropriate to specific emotional states based on emotional data.

[0119] For example, if the emotion engine detects that user B is feeling fatigued or stressed, the server will suggest specific exercises and relaxation techniques to reduce stress. Furthermore, it will generate encouraging mental support messages to contribute to improving the user's motivation.

[0120] Finally, the server notifies the user's device of the completed health promotion plan, allowing the user to incorporate this information into their daily life. Additionally, if necessary, guidance to medical consultations tailored to the user's emotional state is provided in conjunction with an external medical consultation platform. This ensures that users always receive support optimized for their specific health needs.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Users upload medical image data and lifestyle data using a dedicated application or web portal. Information regarding their emotions may also be entered at this time.

[0124] Step 2:

[0125] The server verifies the medical image data received from the user, performs preprocessing such as resolution adjustment and noise reduction, and standardizes the format. Lifestyle data is also checked and prepared to conform to the input format.

[0126] Step 3:

[0127] The emotion engine activates and analyzes text data and behavioral patterns to recognize the user's emotions. This determines the emotional state the user is currently experiencing.

[0128] Step 4:

[0129] The server inputs pre-processed data into a generative model to predict future medical images. This prediction outputs risks and changes in specific regions of the brain.

[0130] Step 5:

[0131] The server develops an individualized health improvement plan based on predicted medical images and the user's emotional state. The plan's effectiveness is enhanced by incorporating emotionally responsive feedback and advice.

[0132] Step 6:

[0133] The server notifies the user's device of this health promotion plan. The user can then review the plan and decide on specific actions to incorporate into their daily life.

[0134] Step 7:

[0135] The server uses emotional data obtained through the emotion engine to collaborate with an external medical consultation platform to provide users with appropriate medical advice and support. In this way, users can receive support tailored to their own emotional state.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] Currently, health predictions and improvement plans based on medical data and lifestyle habits do not adequately consider individual circumstances, making it difficult to provide effective and sustainable improvement measures for users. Furthermore, the lack of support for health improvement that takes into account users' emotional states limits the promotion of comprehensive health management.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for preprocessing medical data and behavioral data acquired from the user, means for predicting future medical data using a machine learning model based on the preprocessed data, and means for analyzing the user's emotional state. This enables the formulation and notification of highly personalized health improvement plans, and realizes support that appropriately responds to the user's lifestyle patterns and emotional state.

[0141] A "user" is an entity that utilizes the system and provides its own medical and behavioral data.

[0142] "Medical data" refers to data such as images and test results used to understand a user's health status.

[0143] "Behavioral data" refers to data that records a user's activities and habits in their daily life.

[0144] "Preprocessing" refers to the process of performing various operations to prepare acquired data into a format that can be analyzed.

[0145] A "machine learning model" is a mathematical model that uses algorithms to analyze large amounts of complex data and make predictions and classifications about the future.

[0146] "Emotional state" refers to the user's psychological state and is an expression of an individual's emotions as analyzed by the emotion engine.

[0147] "Analysis means" refers to the technology used to process received data and obtain details about the user's emotional state and health predictions.

[0148] A "health improvement plan" is a specific action plan for improving health that is customized based on the user's individual health and emotional state.

[0149] A "user terminal" is an electronic device used by a user to receive their health improvement plan.

[0150] This invention is a system that utilizes medical and behavioral data to analyze a user's health and emotional state, formulates a personalized health improvement plan, and notifies the user's terminal. Three entities are involved in the implementation of the system: the server, the terminal, and the user.

[0151] The server receives medical and behavioral data from users and first performs preprocessing. Medical data includes image data such as CT and MRI scans, which are converted to the standard DICOM format and denoised to make them analyzable. Behavioral data reflects the user's daily activities and habits, and its format is standardized.

[0152] Next, based on the pre-processed data, the server uses a machine learning model to predict future health conditions. This model is implemented as a generative AI model and utilizes deep learning technology. This visualizes health risks from the user's medical data, making it clear which areas require attention.

[0153] Simultaneously, the terminal collects data through the user's smartphone or wearable device. An emotion engine analyzes the collected text and behavioral data to determine the emotional state. The server then uses the analysis results to determine the emotional state and helps in developing a personalized health improvement plan.

[0154] The server combines prediction results and sentiment analysis results to develop a health improvement plan optimized for the user and notifies the device. This plan includes recommendations for specific actions and activities that can be realistically implemented. It can also, if necessary, collaborate with external medical services to provide users with guidance on professional health consultations.

[0155] As a concrete example, consider a scenario where a user sends recent health checkup data and wearable device records to the system. The server performs a cardiovascular assessment, and the emotion engine detects that the user is experiencing stress. In this case, the health improvement plan would include stress-reducing exercises and mental support messages.

[0156] An example of a prompt message is as follows: "Analyze the following medical and lifestyle data to predict future health status. Also, create a health improvement plan that takes into account the user's emotional state."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] Users acquire medical and behavioral data. Medical data includes scan images such as CT and MRI, while behavioral data includes logs of meals, exercise, and sleep. This data is transmitted to the server via the terminal. The input data is provided in various formats and is collected for subsequent processing.

[0160] Step 2:

[0161] The server standardizes the received medical data. Specifically, it converts scanned images to DICOM format, removes image noise, and performs image processing to improve image quality. As a result, the output is clean, consistent image data suitable for analysis. Behavioral data is also formatted and processed to a state that can be analyzed.

[0162] Step 3:

[0163] The server uses a generative AI model to predict future medical data from the data prepared in the previous step. Using pre-processed medical and behavioral data as input, the generative AI model employs deep learning techniques to predict health risks. The output is data containing risk information and warnings for specific health areas.

[0164] Step 4:

[0165] Users transmit additional behavioral data in their daily lives using terminals and wearable devices. The terminals collect this data and gather information necessary to estimate emotional states from the text data and behavioral records provided by the user.

[0166] Step 5:

[0167] The server utilizes an emotion engine to analyze text data and behavioral records received from the terminal. Based on this input data, it performs natural language processing to extract the user's emotional tendencies. The output is data indicating the user's current emotional state.

[0168] Step 6:

[0169] The server integrates predicted medical and emotional data to develop a personalized health improvement plan. Input data includes health risks and emotional tendencies, which are used to generate specific action items that the user can implement. The output is a customized health improvement plan.

[0170] Step 7:

[0171] The server notifies the user's device of the completed health improvement plan. The user can then incorporate this information into their own life. The notification function sends the plan details via push notifications or email, allowing the user to apply specific improvement measures to their lifestyle.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In modern society, the health status and lifestyles of individual users are diverse, and continuous monitoring of real-time lifestyle data and emotional states is essential to provide each person with an optimal health promotion plan. However, conventional systems have difficulty providing individualized plans that accurately reflect the user's feelings and real-time health status, making it a challenge to realize effective support tailored to individual needs.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for predicting future medical images using a generative model based on the preprocessed data, and means for monitoring the user's emotional state using an emotion recognition engine and adjusting the health promotion plan based on the emotional state. This enables timely and personalized health support tailored to the user's individuality.

[0177] "Medical image data" refers to image information acquired during the process of medical diagnosis and treatment, and is used to understand the user's health status.

[0178] "Lifestyle data" refers to information about daily activities and habits such as diet, exercise, and sleep, and serves as the basis for evaluating a user's health status.

[0179] "Preprocessing" is the process of removing noise and standardizing data before analysis, preparing it for analysis.

[0180] A "generative model" is a computational method or algorithm used to generate new data or predictions from given data.

[0181] An "emotion recognition engine" is a system or software used to determine a user's emotional state at any given time based on their written text and behavioral data.

[0182] A "health promotion plan" is a plan that includes guidelines and recommended actions created to improve the user's health.

[0183] A "portable communication device" is a portable information and communication device, such as a smartphone or tablet, used to notify users of information.

[0184] This invention is a system that uses a user's medical image data and lifestyle data to predict future health risks and provide a personalized health promotion plan. The system integrates an emotion engine and optimizes the health promotion plan by also considering the user's emotional state.

[0185] The server acquires medical image data and lifestyle data from the user and first preprocesses this data. This process involves data standardization and noise reduction, and software libraries such as TensorFlow and OpenCV are used.

[0186] Next, predictions are made using a generative AI model with the pre-processed data. This model operates on the basis of Hugging Face's transformer model and has the ability to generate future medical images for the user.

[0187] Furthermore, the emotion recognition engine monitors the user's emotional state and utilizes IBM Watson® and Microsoft® Azure® Cognitive Services to analyze that information. The user's emotional state is determined from biometric information such as heart rate and daily behavioral data.

[0188] A health promotion plan is developed based on the user's emotional state and generated medical images. This plan is communicated to the user's portable communication device, providing personalized support in real time. For example, users experiencing increased stress may be offered specific relaxation exercises and mental support.

[0189] For example, if a user is found to be fatigued in the morning, suggesting stretching or short meditation sessions during their break can immediately contribute to improving their health.

[0190] An example of a prompt for a generating AI model is, "Generate a healthcare plan including optimal stretching and meditation for a user who has been shown to be fatigued in the morning." Such prompts enable health support tailored to the individual user's condition.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server receives medical image data and lifestyle data from users. Inputs include medical image files (e.g., MRI images) and lifestyle data (e.g., exercise frequency, diet, etc.). This data is first preprocessed to remove noise and standardize the format. The output is clean, standardized medical image data and lifestyle data.

[0194] Step 2:

[0195] The server inputs pre-processed data into a generating AI model to predict future medical images. In this step, the predictive model is used to analyze the data and output it as a predicted medical image. A transformer model algorithm is used to visualize future health risks based on the original image.

[0196] Step 3:

[0197] The emotion recognition engine monitors the user's current emotional state and uses user behavioral data (e.g., heart rate, text messages) as input. In this step, emotion analysis software is used to determine the emotion and generate emotional state data as output. This expresses the user's emotional changes as numerical values ​​or categories.

[0198] Step 4:

[0199] The server creates a personalized health promotion plan based on predicted medical images and emotional state data. Inputs include future health risk information and current emotional state data. This step develops specific behavioral advice and relaxation techniques tailored to the user's health condition, and the output is presented as a personalized health promotion plan.

[0200] Step 5:

[0201] The terminal notifies the user of the generated health promotion plan. The input is the health promotion plan sent from the server, and the user receives this information via a portable communication device (e.g., a smartphone). The purpose of this step is to send a notification to the user and to make the plan easily accessible and implementable for the user.

[0202] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 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.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention provides a system that allows users to predict future health risks using their own medical image data and lifestyle data, and to implement personalized health promotion plans. The system enables data acquisition and processing, prediction execution, and communication of results to the user.

[0219] First, users input their latest medical imaging data and lifestyle data into the system via a dedicated application or web portal. Medical imaging data includes MRI images taken at medical institutions, while lifestyle data includes information such as daily activities, diet, and stress levels.

[0220] Next, the server preprocesses the received data, preparing it for use in the generative model. This stage involves noise reduction and resolution adjustments. Lifestyle data is also formatted and used in the predictive model.

[0221] The server feeds pre-processed data into a generative model to predict future medical images. This prediction is used to visualize the user's future brain health in detail and to extract high-risk areas and regions requiring attention.

[0222] Based on the predicted results, the server creates an individualized health improvement plan. This plan includes specific lifestyle improvements suggested to the user, such as recommendations for specific exercise programs, nutritional improvements, and stress management techniques.

[0223] Finally, the server notifies the user's device of the generated health improvement plan. Furthermore, by linking with an external medical consultation platform, users can implement the plan in detail while receiving advice from experts. This allows users to better understand their own health risks and make necessary improvements in their daily lives.

[0224] For example, if user A uploads their latest MRI image and provides data indicating a sedentary lifestyle and a high-calorie diet, the generative model will predict an increased risk in specific brain regions in future images. Based on this risk, user A will be recommended moderate exercise three times a week and a specific dietary improvement plan. This suggestion allows user A to implement concrete behavioral changes and create a plan to maintain brain health.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users input medical image data and lifestyle data using a dedicated application or web portal. Medical image data includes MRI images, and lifestyle data provides information on diet, exercise, and stress.

[0228] Step 2:

[0229] The server analyzes medical image data received from users and performs preprocessing such as noise reduction to standardize resolution and format. It also checks for any omissions or errors in lifestyle data and makes corrections as needed.

[0230] Step 3:

[0231] The server inputs pre-processed data into a generative model to predict future medical images. Based on past data, the generative model identifies potential brain changes and risk areas in the future.

[0232] Step 4:

[0233] Based on the prediction results, the server creates a personalized health promotion plan tailored to the user's health condition. This plan is designed to include specific exercise programs, nutritional advice, and stress management methods.

[0234] Step 5:

[0235] The server notifies the user's device of the completed health promotion plan. Through the application, the user can review the plan details and decide on specific actions to apply it to their daily life.

[0236] Step 6:

[0237] The server connects data with the medical consultation platform, supporting users so they can receive expert medical advice as needed. This allows users to receive support tailored to their individual needs.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In modern healthcare management, preventative measures based on individual lifestyles and health conditions are crucial. However, traditional methods make it difficult to effectively utilize medical imaging and lifestyle data to specifically predict future risks, posing a challenge in developing personalized health promotion plans. Furthermore, there is a lack of means to quickly access expert advice.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for inputting the preprocessed data into a generating AI model and predicting future medical images using prompt statements, and means for constructing an individualized health promotion plan based on the predicted medical images. This makes it possible to predict health risks optimized for each individual and to present specific improvement plans.

[0243] A "user" is an entity that uses the system to input and manage their own medical and health data.

[0244] "Medical image data" refers to image data such as MRI scans acquired at medical institutions, and is information that visually represents an individual's health status.

[0245] "Lifestyle data" refers to information about an individual's lifestyle, including their daily activities, diet, exercise, and stress levels.

[0246] "Preprocessing" is the process of applying noise reduction and resolution adjustments to input data in order to prepare it for analysis.

[0247] A "generative AI model" is an artificial intelligence technology used to predict future states based on large amounts of data.

[0248] A "prompt statement" is a document used to give specific instructions to a generative AI model.

[0249] "Medical image prediction" is the process of estimating the future state of medical images.

[0250] A "health promotion plan" is a plan that includes specific action proposals to improve an individual's predicted health risks.

[0251] A "terminal" is an electronic device used by a user to input and receive data.

[0252] To implement this invention, the user begins by inputting medical image data and lifestyle data using a dedicated application or web portal. The terminal collects this data, encrypts it, and then transmits it to the server. A typical smartphone or computer is used for this process, and the software used includes data entry tools and a web browser.

[0253] The server plays a role in preprocessing the received data. Specifically, it performs noise reduction and resolution adjustment on medical image data to prepare it for analysis by the generating AI model. Lifestyle data is also converted to a standard format and stored in a database. The server utilizes high-performance computing equipment and data analysis software to enable these processes.

[0254] Next, the server uses a generative AI model to predict future medical images. This generative AI model utilizes machine learning techniques and has been trained on a large amount of existing data. This model is given instructions using prompts. For example, the prompt "Predict future health risks based on this MRI image and lifestyle data" is used.

[0255] Based on predicted medical images and analysis results, the server creates a personalized health improvement plan. This plan includes specific and actionable lifestyle improvement advice for the user, such as recommendations for exercise a few times a week and dietary improvement guidelines. The created plan is notified to the user via their device, and the user uses it to review their daily behavior.

[0256] Furthermore, by integrating with external medical consultation platforms and systems, users can effectively implement health promotion plans while receiving advice from experts. This provides support for individual users to understand their own health risks and take concrete improvement actions.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] Users input medical image data and lifestyle data into their devices using a dedicated application or web portal. This step collects data such as MRI images, daily activities, and dietary information. The input data is formatted and encrypted for secure transmission from the device to the server.

[0260] Step 2:

[0261] The server preprocesses medical image data and lifestyle data received from the terminal. For medical images, it performs noise reduction and resolution adjustments to optimize image quality. Lifestyle data is converted to a standard format and prepared into a data structure suitable for analysis. This results in preprocessed data as output, ready for input to the generative AI model.

[0262] Step 3:

[0263] The server inputs the pre-processed data into a generating AI model. Here, a predictive model using machine learning is applied, and specific instructions are given using prompts. For example, a prompt such as "Predict future health risks based on this MRI image and lifestyle data" is used. The model analyzes the relationship between the medical image and lifestyle data and outputs a prediction of future health risks.

[0264] Step 4:

[0265] The server generates personalized health improvement plans based on prediction results obtained from the generation AI model. The prediction results serve as input, and the server constructs specific action suggestions and lifestyle improvement plans that differ for each user. The output is a plan that includes exercise recommendations and dietary improvement guides for the user.

[0266] Step 5:

[0267] The server sends the created health promotion plan to the terminal and notifies the user. The terminal presents the received plan to the user in an easy-to-understand manner, encouraging behavioral changes in daily life. Furthermore, by linking with an external medical consultation platform, the system supports the implementation of the health promotion plan by allowing users to receive advice from experts.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In modern society, there is a need to predict individual health conditions and propose specific lifestyle improvements based on those predictions. However, conventional methods make it difficult to visually understand an individual's future health risks and appropriately guide them toward improvement measures. Furthermore, there is a challenge in that users have difficulty gaining the motivation to actively maintain their health by utilizing the data and suggestions provided.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user; means for predicting future medical images using a generative model based on the preprocessed data; means for creating a personalized health promotion plan based on the predicted medical images; means for visualizing the user's health status in three dimensions using augmented reality technology; and means for recommending health-related products and services. This makes it possible for users to visually understand their own health risks and to be more motivated to accept more specific and effective lifestyle improvement measures.

[0273] "Medical image data" refers to image information acquired at medical institutions and is data used for diagnostic purposes, such as MRI and CT scans.

[0274] "Lifestyle data" refers to information about an individual's daily activities, diet, exercise, sleep, stress levels, etc., and is used to assess their health status.

[0275] "Preprocessing" refers to the process of preparing data into a format suitable for a generative model, and includes operations such as noise reduction and resolution adjustment.

[0276] A "generative model" is an artificial intelligence algorithm used to generate new information or predictive results from input data.

[0277] A "health promotion plan" is a set of specific action guidelines recommended for individuals to lead healthier lives, including suggestions for exercise, diet, and stress management.

[0278] Augmented reality technology is a technique that overlays computer-generated information onto the real world environment, and is used to provide visual experiences.

[0279] "Product or service recommendation" refers to the act of suggesting health-related products or services that are individually suited to the user's health condition and predicted risks.

[0280] The system for implementing this invention provides predictions of future health risks and personalized health promotion plans based on medical image data and lifestyle data provided by the user.

[0281] The server receives MRI and CT images collected from medical institutions, as well as data on daily activities submitted by users. The received data is preprocessed, including noise reduction and resolution adjustment, before being fed into a generative model. This generative model, built using TensorFlow and PyTorch, predicts future medical images and health risks from the data.

[0282] Based on the predicted medical condition, the server creates a health improvement plan optimized for the user. This plan includes specific exercise programs, dietary recommendations, and stress management techniques. The plan is visualized on the user's smartphone or smart glasses through augmented reality features using ARKit (iOS) or ARCore (Android).

[0283] Furthermore, the system has a recommendation function for providing health products and services suitable for an individual's health condition, enabling users to easily obtain options for actively managing their own health.

[0284] As a specific example, when a user uploads MRI images and lifestyle data to the system, the generative model performs an analysis to visualize the risk of future cognitive decline. In response, the system recommends to the user the use of a meditation app or a specific brain training program. An example of a prompt sentence for the generative AI model is the sentence "Please show the risk of cognitive decline and preventive measures."

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The user uploads medical image data (e.g., MRI images) and lifestyle data to the server through a dedicated application or web portal. As a result, the server can receive basic information regarding the user's health condition.

[0288] Step 2:

[0289] The server performs preprocessing such as noise removal and resolution adjustment on the received medical image data. The input is medical image data, and the output is image data arranged in a format usable by the generative model. In this step, OpenCV, an image processing library, is used.

[0290] Step 3: <0,

[0291] The server also performs formatting on the lifestyle data in the same way. For that, data processing libraries such as Pandas and NumPy are used to clean the input data and convert it into a format that can be used by the model. The output data is used as the input to the generative model.

[0292] Step 4:

[0293] The server feeds preprocessed data into a generative model (e.g., a TensorFlow model) to predict future medical images and health risks. The input to this step is a set of preprocessed data, and the output is predicted medical image data and risk assessment results. The model's inference capabilities are utilized for data computation.

[0294] Step 5:

[0295] Based on the predicted results, the server creates a personalized health improvement plan. The input is a predicted risk assessment, and the output is specific lifestyle improvement measures, including recommended exercise programs, dietary improvements, and stress management techniques.

[0296] Step 6:

[0297] The server notifies the terminal of health promotion plans and recommendations for related products and services through augmented reality technology. The input is the health promotion plan and related recommendations, and the output is the plan content visually highlighted. AR technologies such as ARKit and ARCore are used to provide the plan to the user.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention incorporates an emotion engine into a system that predicts a user's future health risks based on medical image data and lifestyle data, and provides an individualized health improvement plan, thereby enabling more effective support for improving lifestyle habits.

[0300] The system receives medical image data and lifestyle data provided by the user and first preprocesses this data. The server standardizes the image data format, performs noise reduction, and prepares the lifestyle data into a consistent data format. In the next step, the server uses a generative model to predict future medical images. This prediction visualizes the user's future health status and enables the identification of risks and brain regions that require attention.

[0301] Furthermore, an emotion engine is activated to monitor the user's emotional state. The user's emotions are recognized through text and behavioral data collected by the application through their daily activities. The emotion engine analyzes this information to determine the user's current emotional state.

[0302] The server develops an individualized health promotion plan based on predicted medical images and emotional data obtained from the emotion engine. This plan includes customized advice and recommended actions that are easy for the user to understand and implement. Furthermore, the plan's effectiveness is enhanced by incorporating content appropriate to specific emotional states based on emotional data.

[0303] For example, if the emotion engine detects that user B is feeling fatigued or stressed, the server will suggest specific exercises and relaxation techniques to reduce stress. Furthermore, it will generate encouraging mental support messages to contribute to improving the user's motivation.

[0304] Finally, the server notifies the user's device of the completed health promotion plan, allowing the user to incorporate this information into their daily life. Additionally, if necessary, guidance to medical consultations tailored to the user's emotional state is provided in conjunction with an external medical consultation platform. This ensures that users always receive support optimized for their specific health needs.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The user uses a dedicated application or web portal to upload medical image data and lifestyle data. At this time, information regarding emotions may also be input.

[0308] Step 2:

[0309] The server verifies the medical image data received from the user, performs preprocessing such as resolution adjustment and noise removal, and standardizes the format. The lifestyle data is also checked and arranged to conform to the input format.

[0310] Step 3:

[0311] The emotion engine operates and analyzes text data and behavior patterns to recognize the user's emotions. From this, the emotional state that the user is currently experiencing is determined.

[0312] Step 4:

[0313] The server inputs the preprocessed data into the generation model to predict future medical images. In this prediction, risks and changes in specific regions of the brain are output.

[0314] Step 5:

[0315] The server formulates an individual health promotion plan based on the predicted medical image and the user's emotional state. By incorporating feedback and advice according to emotions, the effectiveness of the plan is enhanced.

[0316] Step 6:

[0317] The server notifies the user's terminal of this health promotion plan. The user can confirm the plan and determine specific actions to incorporate into daily life.

[0318] Step 7:

[0319] The server uses emotional data obtained through the emotion engine to collaborate with an external medical consultation platform to provide users with appropriate medical advice and support. In this way, users can receive support tailored to their own emotional state.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] Currently, health predictions and improvement plans based on medical data and lifestyle habits do not adequately consider individual circumstances, making it difficult to provide effective and sustainable improvement measures for users. Furthermore, the lack of support for health improvement that takes into account users' emotional states limits the promotion of comprehensive health management.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for preprocessing medical data and behavioral data acquired from the user, means for predicting future medical data using a machine learning model based on the preprocessed data, and means for analyzing the user's emotional state. This enables the formulation and notification of highly personalized health improvement plans, and realizes support that appropriately responds to the user's lifestyle patterns and emotional state.

[0325] A "user" is an entity that utilizes the system and provides its own medical and behavioral data.

[0326] "Medical data" refers to data such as images and test results used to understand a user's health status.

[0327] "Behavioral data" refers to data that records a user's activities and habits in their daily life.

[0328] "Preprocessing" refers to the process of performing various operations to prepare acquired data into a format that can be analyzed.

[0329] A "machine learning model" is a mathematical model that uses algorithms to analyze large amounts of complex data and make predictions and classifications about the future.

[0330] "Emotional state" refers to the user's psychological state and is an expression of an individual's emotions as analyzed by the emotion engine.

[0331] "Analysis means" refers to the technology used to process received data and obtain details about the user's emotional state and health predictions.

[0332] A "health improvement plan" is a specific action plan for improving health that is customized based on the user's individual health and emotional state.

[0333] A "user terminal" is an electronic device used by a user to receive their health improvement plan.

[0334] This invention is a system that utilizes medical and behavioral data to analyze a user's health and emotional state, formulates a personalized health improvement plan, and notifies the user's terminal. Three entities are involved in the implementation of the system: the server, the terminal, and the user.

[0335] The server receives medical and behavioral data from users and first performs preprocessing. Medical data includes image data such as CT and MRI scans, which are converted to the standard DICOM format and denoised to make them analyzable. Behavioral data reflects the user's daily activities and habits, and its format is standardized.

[0336] Next, based on the pre-processed data, the server uses a machine learning model to predict future health conditions. This model is implemented as a generative AI model and utilizes deep learning technology. This visualizes health risks from the user's medical data, making it clear which areas require attention.

[0337] Simultaneously, the terminal collects data through the user's smartphone or wearable device. An emotion engine analyzes the collected text and behavioral data to determine the emotional state. The server then uses the analysis results to determine the emotional state and helps in developing a personalized health improvement plan.

[0338] The server combines prediction results and sentiment analysis results to develop a health improvement plan optimized for the user and notifies the device. This plan includes recommendations for specific actions and activities that can be realistically implemented. It can also, if necessary, collaborate with external medical services to provide users with guidance on professional health consultations.

[0339] As a concrete example, consider a scenario where a user sends recent health checkup data and wearable device records to the system. The server performs a cardiovascular assessment, and the emotion engine detects that the user is experiencing stress. In this case, the health improvement plan would include stress-reducing exercises and mental support messages.

[0340] An example of a prompt message is as follows: "Analyze the following medical and lifestyle data to predict future health status. Also, create a health improvement plan that takes into account the user's emotional state."

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] Users acquire medical and behavioral data. Medical data includes scan images such as CT and MRI, while behavioral data includes logs of meals, exercise, and sleep. This data is transmitted to the server via the terminal. The input data is provided in various formats and is collected for subsequent processing.

[0344] Step 2:

[0345] The server standardizes the received medical data. Specifically, it converts scanned images to DICOM format, removes image noise, and performs image processing to improve image quality. As a result, the output is clean, consistent image data suitable for analysis. Behavioral data is also formatted and processed to a state that can be analyzed.

[0346] Step 3:

[0347] The server uses a generative AI model to predict future medical data from the data prepared in the previous step. Using pre-processed medical and behavioral data as input, the generative AI model employs deep learning techniques to predict health risks. The output is data containing risk information and warnings for specific health areas.

[0348] Step 4:

[0349] Users transmit additional behavioral data in their daily lives using terminals and wearable devices. The terminals collect this data and gather information necessary to estimate emotional states from the text data and behavioral records provided by the user.

[0350] Step 5:

[0351] The server utilizes an emotion engine to analyze text data and behavioral records received from the terminal. Based on this input data, it performs natural language processing to extract the user's emotional tendencies. The output is data indicating the user's current emotional state.

[0352] Step 6:

[0353] The server integrates predicted medical and emotional data to develop a personalized health improvement plan. Input data includes health risks and emotional tendencies, which are used to generate specific action items that the user can implement. The output is a customized health improvement plan.

[0354] Step 7:

[0355] The server notifies the user's device of the completed health improvement plan. The user can then incorporate this information into their own life. The notification function sends the plan details via push notifications or email, allowing the user to apply specific improvement measures to their lifestyle.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] In modern society, the health status and lifestyles of individual users are diverse, and continuous monitoring of real-time lifestyle data and emotional states is essential to provide each person with an optimal health promotion plan. However, conventional systems have difficulty providing individualized plans that accurately reflect the user's feelings and real-time health status, making it a challenge to realize effective support tailored to individual needs.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for predicting future medical images using a generative model based on the preprocessed data, and means for monitoring the user's emotional state using an emotion recognition engine and adjusting the health promotion plan based on the emotional state. This enables timely and personalized health support tailored to the user's individuality.

[0361] "Medical image data" refers to image information acquired during the process of medical diagnosis and treatment, and is used to understand the user's health status.

[0362] "Lifestyle data" refers to information about daily activities and habits such as diet, exercise, and sleep, and serves as the basis for evaluating a user's health status.

[0363] "Preprocessing" is the process of removing noise and standardizing data before analysis, preparing it for analysis.

[0364] A "generative model" is a computational method or algorithm used to generate new data or predictions from given data.

[0365] An "emotion recognition engine" is a system or software used to determine a user's emotional state at any given time based on their written text and behavioral data.

[0366] A "health promotion plan" is a plan that includes guidelines and recommended actions created to improve the user's health.

[0367] A "portable communication device" is a portable information and communication device, such as a smartphone or tablet, used to notify users of information.

[0368] This invention is a system that uses a user's medical image data and lifestyle data to predict future health risks and provide a personalized health promotion plan. The system integrates an emotion engine and optimizes the health promotion plan by also considering the user's emotional state.

[0369] The server acquires medical image data and lifestyle data from the user and first preprocesses this data. This process involves data standardization and noise reduction, and software libraries such as TensorFlow and OpenCV are used.

[0370] Next, predictions are made using a generative AI model with the pre-processed data. This model operates on the basis of Hugging Face's transformer model and has the ability to generate future medical images for the user.

[0371] Furthermore, the emotion recognition engine monitors the user's emotional state and utilizes IBM Watson and Microsoft Azure Cognitive Services to analyze that information. The user's emotional state is determined from biometric information such as heart rate and daily behavioral data.

[0372] A health promotion plan is developed based on the user's emotional state and generated medical images. This plan is communicated to the user's portable communication device, providing personalized support in real time. For example, users experiencing increased stress may be offered specific relaxation exercises and mental support.

[0373] For example, if a user is found to be fatigued in the morning, suggesting stretching or short meditation sessions during their break can immediately contribute to improving their health.

[0374] An example of a prompt for a generating AI model is, "Generate a healthcare plan including optimal stretching and meditation for a user who has been shown to be fatigued in the morning." Such prompts enable health support tailored to the individual user's condition.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The server receives medical image data and lifestyle data from users. Inputs include medical image files (e.g., MRI images) and lifestyle data (e.g., exercise frequency, diet, etc.). This data is first preprocessed to remove noise and standardize the format. The output is clean, standardized medical image data and lifestyle data.

[0378] Step 2:

[0379] The server inputs pre-processed data into a generating AI model to predict future medical images. In this step, the predictive model is used to analyze the data and output it as a predicted medical image. A transformer model algorithm is used to visualize future health risks based on the original image.

[0380] Step 3:

[0381] The emotion recognition engine monitors the user's current emotional state and uses user behavioral data (e.g., heart rate, text messages) as input. In this step, emotion analysis software is used to determine the emotion and generate emotional state data as output. This expresses the user's emotional changes as numerical values ​​or categories.

[0382] Step 4:

[0383] The server creates a personalized health promotion plan based on predicted medical images and emotional state data. Inputs include future health risk information and current emotional state data. This step develops specific behavioral advice and relaxation techniques tailored to the user's health condition, and the output is presented as a personalized health promotion plan.

[0384] Step 5:

[0385] The terminal notifies the user of the generated health promotion plan. The input is the health promotion plan sent from the server, and the user receives this information via a portable communication device (e.g., a smartphone). The purpose of this step is to send a notification to the user and to make the plan easily accessible and implementable for the user.

[0386] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0392] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0394] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0396] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0397] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0398] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0399] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0401] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0402] This invention provides a system that allows users to predict future health risks using their own medical image data and lifestyle data, and to implement personalized health promotion plans. The system enables data acquisition and processing, prediction execution, and communication of results to the user.

[0403] First, users input their latest medical imaging data and lifestyle data into the system via a dedicated application or web portal. Medical imaging data includes MRI images taken at medical institutions, while lifestyle data includes information such as daily activities, diet, and stress levels.

[0404] Next, the server preprocesses the received data, preparing it for use in the generative model. This stage involves noise reduction and resolution adjustments. Lifestyle data is also formatted and used in the predictive model.

[0405] The server feeds pre-processed data into a generative model to predict future medical images. This prediction is used to visualize the user's future brain health in detail and to extract high-risk areas and regions requiring attention.

[0406] Based on the predicted results, the server creates an individualized health improvement plan. This plan includes specific lifestyle improvements suggested to the user, such as recommendations for specific exercise programs, nutritional improvements, and stress management techniques.

[0407] Finally, the server notifies the user's device of the generated health improvement plan. Furthermore, by linking with an external medical consultation platform, users can implement the plan in detail while receiving advice from experts. This allows users to better understand their own health risks and make necessary improvements in their daily lives.

[0408] For example, if user A uploads their latest MRI image and provides data indicating a sedentary lifestyle and a high-calorie diet, the generative model will predict an increased risk in specific brain regions in future images. Based on this risk, user A will be recommended moderate exercise three times a week and a specific dietary improvement plan. This suggestion allows user A to implement concrete behavioral changes and create a plan to maintain brain health.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users input medical image data and lifestyle data using a dedicated application or web portal. Medical image data includes MRI images, and lifestyle data provides information on diet, exercise, and stress.

[0412] Step 2:

[0413] The server analyzes medical image data received from users and performs preprocessing such as noise reduction to standardize resolution and format. It also checks for any omissions or errors in lifestyle data and makes corrections as needed.

[0414] Step 3:

[0415] The server inputs pre-processed data into a generative model to predict future medical images. Based on past data, the generative model identifies potential brain changes and risk areas in the future.

[0416] Step 4:

[0417] Based on the prediction results, the server creates a personalized health promotion plan tailored to the user's health condition. This plan is designed to include specific exercise programs, nutritional advice, and stress management methods.

[0418] Step 5:

[0419] The server notifies the user's device of the completed health promotion plan. Through the application, the user can review the plan details and decide on specific actions to apply it to their daily life.

[0420] Step 6:

[0421] The server connects data with the medical consultation platform, supporting users so they can receive expert medical advice as needed. This allows users to receive support tailored to their individual needs.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] In modern healthcare management, preventative measures based on individual lifestyles and health conditions are crucial. However, traditional methods make it difficult to effectively utilize medical imaging and lifestyle data to specifically predict future risks, posing a challenge in developing personalized health promotion plans. Furthermore, there is a lack of means to quickly access expert advice.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for inputting the preprocessed data into a generating AI model and predicting future medical images using prompt statements, and means for constructing an individualized health promotion plan based on the predicted medical images. This makes it possible to predict health risks optimized for each individual and to present specific improvement plans.

[0427] A "user" is an entity that uses the system to input and manage their own medical and health data.

[0428] "Medical image data" refers to image data such as MRI scans acquired at medical institutions, and is information that visually represents an individual's health status.

[0429] "Lifestyle data" refers to information about an individual's lifestyle, including their daily activities, diet, exercise, and stress levels.

[0430] "Preprocessing" is the process of applying noise reduction and resolution adjustments to input data in order to prepare it for analysis.

[0431] A "generative AI model" is an artificial intelligence technology used to predict future states based on large amounts of data.

[0432] A "prompt statement" is a document used to give specific instructions to a generative AI model.

[0433] "Medical image prediction" is the process of estimating the future state of medical images.

[0434] A "health promotion plan" is a plan that includes specific action proposals to improve an individual's predicted health risks.

[0435] A "terminal" is an electronic device used by a user to input and receive data.

[0436] To implement this invention, the user begins by inputting medical image data and lifestyle data using a dedicated application or web portal. The terminal collects this data, encrypts it, and then transmits it to the server. A typical smartphone or computer is used for this process, and the software used includes data entry tools and a web browser.

[0437] The server plays a role in preprocessing the received data. Specifically, it performs noise reduction and resolution adjustment on medical image data to prepare it for analysis by the generating AI model. Lifestyle data is also converted to a standard format and stored in a database. The server utilizes high-performance computing equipment and data analysis software to enable these processes.

[0438] Next, the server uses a generative AI model to predict future medical images. This generative AI model utilizes machine learning techniques and has been trained on a large amount of existing data. This model is given instructions using prompts. For example, the prompt "Predict future health risks based on this MRI image and lifestyle data" is used.

[0439] Based on predicted medical images and analysis results, the server creates a personalized health improvement plan. This plan includes specific and actionable lifestyle improvement advice for the user, such as recommendations for exercise a few times a week and dietary improvement guidelines. The created plan is notified to the user via their device, and the user uses it to review their daily behavior.

[0440] Furthermore, by integrating with external medical consultation platforms and systems, users can effectively implement health promotion plans while receiving advice from experts. This provides support for individual users to understand their own health risks and take concrete improvement actions.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] Users input medical image data and lifestyle data into their devices using a dedicated application or web portal. This step collects data such as MRI images, daily activities, and dietary information. The input data is formatted and encrypted for secure transmission from the device to the server.

[0444] Step 2:

[0445] The server preprocesses medical image data and lifestyle data received from the terminal. For medical images, it performs noise reduction and resolution adjustments to optimize image quality. Lifestyle data is converted to a standard format and prepared into a data structure suitable for analysis. This results in preprocessed data as output, ready for input to the generative AI model.

[0446] Step 3:

[0447] The server inputs the pre-processed data into a generating AI model. Here, a predictive model using machine learning is applied, and specific instructions are given using prompts. For example, a prompt such as "Predict future health risks based on this MRI image and lifestyle data" is used. The model analyzes the relationship between the medical image and lifestyle data and outputs a prediction of future health risks.

[0448] Step 4:

[0449] The server generates personalized health improvement plans based on prediction results obtained from the generation AI model. The prediction results serve as input, and the server constructs specific action suggestions and lifestyle improvement plans that differ for each user. The output is a plan that includes exercise recommendations and dietary improvement guides for the user.

[0450] Step 5:

[0451] The server sends the created health promotion plan to the terminal and notifies the user. The terminal presents the received plan to the user in an easy-to-understand manner, encouraging behavioral changes in daily life. Furthermore, by linking with an external medical consultation platform, the system supports the implementation of the health promotion plan by allowing users to receive advice from experts.

[0452] (Application Example 1)

[0453] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] In modern society, there is a need to predict individual health conditions and propose specific lifestyle improvements based on those predictions. However, conventional methods make it difficult to visually understand an individual's future health risks and appropriately guide them toward improvement measures. Furthermore, there is a challenge in that users have difficulty gaining the motivation to actively maintain their health by utilizing the data and suggestions provided.

[0455] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0456] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user; means for predicting future medical images using a generative model based on the preprocessed data; means for creating a personalized health promotion plan based on the predicted medical images; means for visualizing the user's health status in three dimensions using augmented reality technology; and means for recommending health-related products and services. This makes it possible for users to visually understand their own health risks and to be more motivated to accept more specific and effective lifestyle improvement measures.

[0457] "Medical image data" refers to image information acquired at medical institutions and is data used for diagnostic purposes, such as MRI and CT scans.

[0458] "Lifestyle data" refers to information about an individual's daily activities, diet, exercise, sleep, stress levels, etc., and is used to assess their health status.

[0459] "Preprocessing" refers to the process of preparing data into a format suitable for a generative model, and includes operations such as noise reduction and resolution adjustment.

[0460] A "generative model" is an artificial intelligence algorithm used to generate new information or predictive results from input data.

[0461] A "health promotion plan" is a set of specific action guidelines recommended for individuals to lead healthier lives, including suggestions for exercise, diet, and stress management.

[0462] Augmented reality technology is a technique that overlays computer-generated information onto the real world environment, and is used to provide visual experiences.

[0463] "Product or service recommendation" refers to the act of suggesting health-related products or services that are individually suited to the user's health condition and predicted risks.

[0464] The system for implementing this invention provides predictions of future health risks and personalized health promotion plans based on medical image data and lifestyle data provided by the user.

[0465] The server receives MRI and CT images collected from medical institutions, as well as data on daily activities submitted by users. The received data is preprocessed, including noise reduction and resolution adjustment, before being fed into a generative model. This generative model, built using TensorFlow and PyTorch, predicts future medical images and health risks from the data.

[0466] Based on the predicted medical condition, the server creates a health improvement plan optimized for the user. This plan includes specific exercise programs, dietary recommendations, and stress management techniques. The plan is visualized on the user's smartphone or smart glasses through augmented reality features using ARKit (iOS) or ARCore (Android).

[0467] Furthermore, the system includes a recommendation function to provide health products and services tailored to each individual's health condition, making it easy for users to have options for proactively managing their own health.

[0468] As a concrete example, when a user uploads MRI images and lifestyle data to the system, a generative model performs an analysis and visualizes the risk of future cognitive decline. Based on this, the system recommends the user to use a meditation app or a specific brain training program. An example of a prompt to the generative AI model might be, "Please show me the risk of cognitive decline and preventative measures."

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] Users upload medical image data (e.g., MRI images) and lifestyle data to the server via a dedicated application or web portal. This allows the server to receive basic information about the user's health status.

[0472] Step 2:

[0473] The server performs preprocessing on the received medical image data, including noise reduction and resolution adjustment. The input is medical image data, and the output is image data formatted for use with the generative model. This step uses the OpenCV image processing library.

[0474] Step 3:

[0475] The server also performs formatting on lifestyle data. This involves using data processing libraries such as Pandas and NumPy to clean the input data and convert it into a format usable by the model. The output data is then used as input for the generative model.

[0476] Step 4:

[0477] The server feeds preprocessed data into a generative model (e.g., a TensorFlow model) to predict future medical images and health risks. The input to this step is a set of preprocessed data, and the output is predicted medical image data and risk assessment results. The model's inference capabilities are utilized for data computation.

[0478] Step 5:

[0479] Based on the predicted results, the server creates a personalized health improvement plan. The input is a predicted risk assessment, and the output is specific lifestyle improvement measures, including recommended exercise programs, dietary improvements, and stress management techniques.

[0480] Step 6:

[0481] The server notifies the terminal of health promotion plans and recommendations for related products and services through augmented reality technology. The input is the health promotion plan and related recommendations, and the output is the plan content visually highlighted. AR technologies such as ARKit and ARCore are used to provide the plan to the user.

[0482] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0483] This invention incorporates an emotion engine into a system that predicts a user's future health risks based on medical image data and lifestyle data, and provides an individualized health improvement plan, thereby enabling more effective support for improving lifestyle habits.

[0484] The system receives medical image data and lifestyle data provided by the user and first preprocesses this data. The server standardizes the image data format, performs noise reduction, and prepares the lifestyle data into a consistent data format. In the next step, the server uses a generative model to predict future medical images. This prediction visualizes the user's future health status and enables the identification of risks and brain regions that require attention.

[0485] Furthermore, an emotion engine is activated to monitor the user's emotional state. The user's emotions are recognized through text and behavioral data collected by the application through their daily activities. The emotion engine analyzes this information to determine the user's current emotional state.

[0486] The server develops an individualized health promotion plan based on predicted medical images and emotional data obtained from the emotion engine. This plan includes customized advice and recommended actions that are easy for the user to understand and implement. Furthermore, the plan's effectiveness is enhanced by incorporating content appropriate to specific emotional states based on emotional data.

[0487] For example, if the emotion engine detects that user B is feeling fatigued or stressed, the server will suggest specific exercises and relaxation techniques to reduce stress. Furthermore, it will generate encouraging mental support messages to contribute to improving the user's motivation.

[0488] Finally, the server notifies the user's device of the completed health promotion plan, allowing the user to incorporate this information into their daily life. Additionally, if necessary, guidance to medical consultations tailored to the user's emotional state is provided in conjunction with an external medical consultation platform. This ensures that users always receive support optimized for their specific health needs.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Users upload medical image data and lifestyle data using a dedicated application or web portal. Information regarding their emotions may also be entered at this time.

[0492] Step 2:

[0493] The server verifies the medical image data received from the user, performs preprocessing such as resolution adjustment and noise reduction, and standardizes the format. Lifestyle data is also checked and prepared to conform to the input format.

[0494] Step 3:

[0495] The emotion engine activates and analyzes text data and behavioral patterns to recognize the user's emotions. This determines the emotional state the user is currently experiencing.

[0496] Step 4:

[0497] The server inputs pre-processed data into a generative model to predict future medical images. This prediction outputs risks and changes in specific regions of the brain.

[0498] Step 5:

[0499] The server develops an individualized health improvement plan based on predicted medical images and the user's emotional state. The plan's effectiveness is enhanced by incorporating emotionally responsive feedback and advice.

[0500] Step 6:

[0501] The server notifies the user's device of this health promotion plan. The user can then review the plan and decide on specific actions to incorporate into their daily life.

[0502] Step 7:

[0503] The server uses emotional data obtained through the emotion engine to collaborate with an external medical consultation platform to provide users with appropriate medical advice and support. In this way, users can receive support tailored to their own emotional state.

[0504] (Example 2)

[0505] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] Currently, health predictions and improvement plans based on medical data and lifestyle habits do not adequately consider individual circumstances, making it difficult to provide effective and sustainable improvement measures for users. Furthermore, the lack of support for health improvement that takes into account users' emotional states limits the promotion of comprehensive health management.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0508] In this invention, the server includes means for preprocessing medical data and behavioral data acquired from the user, means for predicting future medical data using a machine learning model based on the preprocessed data, and means for analyzing the user's emotional state. This enables the formulation and notification of highly personalized health improvement plans, and realizes support that appropriately responds to the user's lifestyle patterns and emotional state.

[0509] A "user" is an entity that utilizes the system and provides its own medical and behavioral data.

[0510] "Medical data" refers to data such as images and test results used to understand a user's health status.

[0511] "Behavioral data" refers to data that records a user's activities and habits in their daily life.

[0512] "Preprocessing" refers to the process of performing various operations to prepare acquired data into a format that can be analyzed.

[0513] A "machine learning model" is a mathematical model that uses algorithms to analyze large amounts of complex data and make predictions and classifications about the future.

[0514] "Emotional state" refers to the user's psychological state and is an expression of an individual's emotions as analyzed by the emotion engine.

[0515] "Analysis means" refers to the technology used to process received data and obtain details about the user's emotional state and health predictions.

[0516] A "health improvement plan" is a specific action plan for improving health that is customized based on the user's individual health and emotional state.

[0517] A "user terminal" is an electronic device used by a user to receive their health improvement plan.

[0518] This invention is a system that utilizes medical and behavioral data to analyze a user's health and emotional state, formulates a personalized health improvement plan, and notifies the user's terminal. Three entities are involved in the implementation of the system: the server, the terminal, and the user.

[0519] The server receives medical and behavioral data from users and first performs preprocessing. Medical data includes image data such as CT and MRI scans, which are converted to the standard DICOM format and denoised to make them analyzable. Behavioral data reflects the user's daily activities and habits, and its format is standardized.

[0520] Next, based on the pre-processed data, the server uses a machine learning model to predict future health conditions. This model is implemented as a generative AI model and utilizes deep learning technology. This visualizes health risks from the user's medical data, making it clear which areas require attention.

[0521] Simultaneously, the terminal collects data through the user's smartphone or wearable device. An emotion engine analyzes the collected text and behavioral data to determine the emotional state. The server then uses the analysis results to determine the emotional state and helps in developing a personalized health improvement plan.

[0522] The server combines prediction results and sentiment analysis results to develop a health improvement plan optimized for the user and notifies the device. This plan includes recommendations for specific actions and activities that can be realistically implemented. It can also, if necessary, collaborate with external medical services to provide users with guidance on professional health consultations.

[0523] As a concrete example, consider a scenario where a user sends recent health checkup data and wearable device records to the system. The server performs a cardiovascular assessment, and the emotion engine detects that the user is experiencing stress. In this case, the health improvement plan would include stress-reducing exercises and mental support messages.

[0524] An example of a prompt message is as follows: "Analyze the following medical and lifestyle data to predict future health status. Also, create a health improvement plan that takes into account the user's emotional state."

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] Users acquire medical and behavioral data. Medical data includes scan images such as CT and MRI, while behavioral data includes logs of meals, exercise, and sleep. This data is transmitted to the server via the terminal. The input data is provided in various formats and is collected for subsequent processing.

[0528] Step 2:

[0529] The server standardizes the received medical data. Specifically, it converts scanned images to DICOM format, removes image noise, and performs image processing to improve image quality. As a result, the output is clean, consistent image data suitable for analysis. Behavioral data is also formatted and processed to a state that can be analyzed.

[0530] Step 3:

[0531] The server uses a generative AI model to predict future medical data from the data prepared in the previous step. Using pre-processed medical and behavioral data as input, the generative AI model employs deep learning techniques to predict health risks. The output is data containing risk information and warnings for specific health areas.

[0532] Step 4:

[0533] Users transmit additional behavioral data in their daily lives using terminals and wearable devices. The terminals collect this data and gather information necessary to estimate emotional states from the text data and behavioral records provided by the user.

[0534] Step 5:

[0535] The server utilizes an emotion engine to analyze text data and behavioral records received from the terminal. Based on this input data, it performs natural language processing to extract the user's emotional tendencies. The output is data indicating the user's current emotional state.

[0536] Step 6:

[0537] The server integrates predicted medical and emotional data to develop a personalized health improvement plan. Input data includes health risks and emotional tendencies, which are used to generate specific action items that the user can implement. The output is a customized health improvement plan.

[0538] Step 7:

[0539] The server notifies the user's device of the completed health improvement plan. The user can then incorporate this information into their own life. The notification function sends the plan details via push notifications or email, allowing the user to apply specific improvement measures to their lifestyle.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In modern society, the health status and lifestyles of individual users are diverse, and continuous monitoring of real-time lifestyle data and emotional states is essential to provide each person with an optimal health promotion plan. However, conventional systems have difficulty providing individualized plans that accurately reflect the user's feelings and real-time health status, making it a challenge to realize effective support tailored to individual needs.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for predicting future medical images using a generative model based on the preprocessed data, and means for monitoring the user's emotional state using an emotion recognition engine and adjusting the health promotion plan based on the emotional state. This enables timely and personalized health support tailored to the user's individuality.

[0545] "Medical image data" refers to image information acquired during the process of medical diagnosis and treatment, and is used to understand the user's health status.

[0546] "Lifestyle data" refers to information about daily activities and habits such as diet, exercise, and sleep, and serves as the basis for evaluating a user's health status.

[0547] "Preprocessing" is the process of removing noise and standardizing data before analysis, preparing it for analysis.

[0548] A "generative model" is a computational method or algorithm used to generate new data or predictions from given data.

[0549] An "emotion recognition engine" is a system or software used to determine a user's emotional state at any given time based on their written text and behavioral data.

[0550] A "health promotion plan" is a plan that includes guidelines and recommended actions created to improve the user's health.

[0551] A "portable communication device" is a portable information and communication device, such as a smartphone or tablet, used to notify users of information.

[0552] This invention is a system that uses a user's medical image data and lifestyle data to predict future health risks and provide a personalized health promotion plan. The system integrates an emotion engine and optimizes the health promotion plan by also considering the user's emotional state.

[0553] The server acquires medical image data and lifestyle data from the user and first preprocesses this data. This process involves data standardization and noise reduction, and software libraries such as TensorFlow and OpenCV are used.

[0554] Next, predictions are made using a generative AI model with the pre-processed data. This model operates on the basis of Hugging Face's transformer model and has the ability to generate future medical images for the user.

[0555] Furthermore, the emotion recognition engine monitors the user's emotional state and utilizes IBM Watson and Microsoft Azure Cognitive Services to analyze that information. The user's emotional state is determined from biometric information such as heart rate and daily behavioral data.

[0556] A health promotion plan is developed based on the user's emotional state and generated medical images. This plan is communicated to the user's portable communication device, providing personalized support in real time. For example, users experiencing increased stress may be offered specific relaxation exercises and mental support.

[0557] For example, if a user is found to be fatigued in the morning, suggesting stretching or short meditation sessions during their break can immediately contribute to improving their health.

[0558] An example of a prompt for a generating AI model is, "Generate a healthcare plan including optimal stretching and meditation for a user who has been shown to be fatigued in the morning." Such prompts enable health support tailored to the individual user's condition.

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The server receives medical image data and lifestyle data from users. Inputs include medical image files (e.g., MRI images) and lifestyle data (e.g., exercise frequency, diet, etc.). This data is first preprocessed to remove noise and standardize the format. The output is clean, standardized medical image data and lifestyle data.

[0562] Step 2:

[0563] The server inputs pre-processed data into a generating AI model to predict future medical images. In this step, the predictive model is used to analyze the data and output it as a predicted medical image. A transformer model algorithm is used to visualize future health risks based on the original image.

[0564] Step 3:

[0565] The emotion recognition engine monitors the user's current emotional state and uses user behavioral data (e.g., heart rate, text messages) as input. In this step, emotion analysis software is used to determine the emotion and generate emotional state data as output. This expresses the user's emotional changes as numerical values ​​or categories.

[0566] Step 4:

[0567] The server creates a personalized health promotion plan based on predicted medical images and emotional state data. Inputs include future health risk information and current emotional state data. This step develops specific behavioral advice and relaxation techniques tailored to the user's health condition, and the output is presented as a personalized health promotion plan.

[0568] Step 5:

[0569] The terminal notifies the user of the generated health promotion plan. The input is the health promotion plan sent from the server, and the user receives this information via a portable communication device (e.g., a smartphone). The purpose of this step is to send a notification to the user and to make the plan easily accessible and implementable for the user.

[0570] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0571] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0572] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0576] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0577] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0578] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0579] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0580] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0581] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0582] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0583] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0584] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0585] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0586] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0587] This invention provides a system that allows users to predict future health risks using their own medical image data and lifestyle data, and to implement personalized health promotion plans. The system enables data acquisition and processing, prediction execution, and communication of results to the user.

[0588] First, users input their latest medical imaging data and lifestyle data into the system via a dedicated application or web portal. Medical imaging data includes MRI images taken at medical institutions, while lifestyle data includes information such as daily activities, diet, and stress levels.

[0589] Next, the server preprocesses the received data, preparing it for use in the generative model. This stage involves noise reduction and resolution adjustments. Lifestyle data is also formatted and used in the predictive model.

[0590] The server feeds pre-processed data into a generative model to predict future medical images. This prediction is used to visualize the user's future brain health in detail and to extract high-risk areas and regions requiring attention.

[0591] Based on the predicted results, the server creates an individualized health improvement plan. This plan includes specific lifestyle improvements suggested to the user, such as recommendations for specific exercise programs, nutritional improvements, and stress management techniques.

[0592] Finally, the server notifies the user's device of the generated health improvement plan. Furthermore, by linking with an external medical consultation platform, users can implement the plan in detail while receiving advice from experts. This allows users to better understand their own health risks and make necessary improvements in their daily lives.

[0593] For example, if user A uploads their latest MRI image and provides data indicating a sedentary lifestyle and a high-calorie diet, the generative model will predict an increased risk in specific brain regions in future images. Based on this risk, user A will be recommended moderate exercise three times a week and a specific dietary improvement plan. This suggestion allows user A to implement concrete behavioral changes and create a plan to maintain brain health.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] Users input medical image data and lifestyle data using a dedicated application or web portal. Medical image data includes MRI images, and lifestyle data provides information on diet, exercise, and stress.

[0597] Step 2:

[0598] The server analyzes medical image data received from users and performs preprocessing such as noise reduction to standardize resolution and format. It also checks for any omissions or errors in lifestyle data and makes corrections as needed.

[0599] Step 3:

[0600] The server inputs pre-processed data into a generative model to predict future medical images. Based on past data, the generative model identifies potential brain changes and risk areas in the future.

[0601] Step 4:

[0602] Based on the prediction results, the server creates a personalized health promotion plan tailored to the user's health condition. This plan is designed to include specific exercise programs, nutritional advice, and stress management methods.

[0603] Step 5:

[0604] The server notifies the user's device of the completed health promotion plan. Through the application, the user can review the plan details and decide on specific actions to apply it to their daily life.

[0605] Step 6:

[0606] The server connects data with the medical consultation platform, supporting users so they can receive expert medical advice as needed. This allows users to receive support tailored to their individual needs.

[0607] (Example 1)

[0608] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] In modern healthcare management, preventative measures based on individual lifestyles and health conditions are crucial. However, traditional methods make it difficult to effectively utilize medical imaging and lifestyle data to specifically predict future risks, posing a challenge in developing personalized health promotion plans. Furthermore, there is a lack of means to quickly access expert advice.

[0610] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0611] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for inputting the preprocessed data into a generating AI model and predicting future medical images using prompt statements, and means for constructing an individualized health promotion plan based on the predicted medical images. This makes it possible to predict health risks optimized for each individual and to present specific improvement plans.

[0612] A "user" is an entity that uses the system to input and manage their own medical and health data.

[0613] "Medical image data" refers to image data such as MRI scans acquired at medical institutions, and is information that visually represents an individual's health status.

[0614] "Lifestyle data" refers to information about an individual's lifestyle, including their daily activities, diet, exercise, and stress levels.

[0615] "Preprocessing" is the process of applying noise reduction and resolution adjustments to input data in order to prepare it for analysis.

[0616] A "generative AI model" is an artificial intelligence technology used to predict future states based on large amounts of data.

[0617] A "prompt statement" is a document used to give specific instructions to a generative AI model.

[0618] "Medical image prediction" is the process of estimating the future state of medical images.

[0619] A "health promotion plan" is a plan that includes specific action proposals to improve an individual's predicted health risks.

[0620] A "terminal" is an electronic device used by a user to input and receive data.

[0621] To implement this invention, the user begins by inputting medical image data and lifestyle data using a dedicated application or web portal. The terminal collects this data, encrypts it, and then transmits it to the server. A typical smartphone or computer is used for this process, and the software used includes data entry tools and a web browser.

[0622] The server plays a role in preprocessing the received data. Specifically, it performs noise reduction and resolution adjustment on medical image data to prepare it for analysis by the generating AI model. Lifestyle data is also converted to a standard format and stored in a database. The server utilizes high-performance computing equipment and data analysis software to enable these processes.

[0623] Next, the server uses a generative AI model to predict future medical images. This generative AI model utilizes machine learning techniques and has been trained on a large amount of existing data. This model is given instructions using prompts. For example, the prompt "Predict future health risks based on this MRI image and lifestyle data" is used.

[0624] Based on predicted medical images and analysis results, the server creates a personalized health improvement plan. This plan includes specific and actionable lifestyle improvement advice for the user, such as recommendations for exercise a few times a week and dietary improvement guidelines. The created plan is notified to the user via their device, and the user uses it to review their daily behavior.

[0625] Furthermore, by integrating with external medical consultation platforms and systems, users can effectively implement health promotion plans while receiving advice from experts. This provides support for individual users to understand their own health risks and take concrete improvement actions.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] Users input medical image data and lifestyle data into their devices using a dedicated application or web portal. This step collects data such as MRI images, daily activities, and dietary information. The input data is formatted and encrypted for secure transmission from the device to the server.

[0629] Step 2:

[0630] The server preprocesses medical image data and lifestyle data received from the terminal. For medical images, it performs noise reduction and resolution adjustments to optimize image quality. Lifestyle data is converted to a standard format and prepared into a data structure suitable for analysis. This results in preprocessed data as output, ready for input to the generative AI model.

[0631] Step 3:

[0632] The server inputs the pre-processed data into a generating AI model. Here, a predictive model using machine learning is applied, and specific instructions are given using prompts. For example, a prompt such as "Predict future health risks based on this MRI image and lifestyle data" is used. The model analyzes the relationship between the medical image and lifestyle data and outputs a prediction of future health risks.

[0633] Step 4:

[0634] The server generates personalized health improvement plans based on prediction results obtained from the generation AI model. The prediction results serve as input, and the server constructs specific action suggestions and lifestyle improvement plans that differ for each user. The output is a plan that includes exercise recommendations and dietary improvement guides for the user.

[0635] Step 5:

[0636] The server sends the created health promotion plan to the terminal and notifies the user. The terminal presents the received plan to the user in an easy-to-understand manner, encouraging behavioral changes in daily life. Furthermore, by linking with an external medical consultation platform, the system supports the implementation of the health promotion plan by allowing users to receive advice from experts.

[0637] (Application Example 1)

[0638] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In modern society, there is a need to predict individual health conditions and propose specific lifestyle improvements based on those predictions. However, conventional methods make it difficult to visually understand an individual's future health risks and appropriately guide them toward improvement measures. Furthermore, there is a challenge in that users have difficulty gaining the motivation to actively maintain their health by utilizing the data and suggestions provided.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0641] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user; means for predicting future medical images using a generative model based on the preprocessed data; means for creating a personalized health promotion plan based on the predicted medical images; means for visualizing the user's health status in three dimensions using augmented reality technology; and means for recommending health-related products and services. This makes it possible for users to visually understand their own health risks and to be more motivated to accept more specific and effective lifestyle improvement measures.

[0642] "Medical image data" refers to image information acquired at medical institutions and is data used for diagnostic purposes, such as MRI and CT scans.

[0643] "Lifestyle data" refers to information about an individual's daily activities, diet, exercise, sleep, stress levels, etc., and is used to assess their health status.

[0644] "Preprocessing" refers to the process of preparing data into a format suitable for a generative model, and includes operations such as noise reduction and resolution adjustment.

[0645] A "generative model" is an artificial intelligence algorithm used to generate new information or predictive results from input data.

[0646] A "health promotion plan" is a set of specific action guidelines recommended for individuals to lead healthier lives, including suggestions for exercise, diet, and stress management.

[0647] Augmented reality technology is a technique that overlays computer-generated information onto the real world environment, and is used to provide visual experiences.

[0648] "Product or service recommendation" refers to the act of suggesting health-related products or services that are individually suited to the user's health condition and predicted risks.

[0649] The system for implementing this invention provides predictions of future health risks and personalized health promotion plans based on medical image data and lifestyle data provided by the user.

[0650] The server receives MRI and CT images collected from medical institutions, as well as data on daily activities submitted by users. The received data is preprocessed, including noise reduction and resolution adjustment, before being fed into a generative model. This generative model, built using TensorFlow and PyTorch, predicts future medical images and health risks from the data.

[0651] Based on the predicted medical condition, the server creates a health improvement plan optimized for the user. This plan includes specific exercise programs, dietary recommendations, and stress management techniques. The plan is visualized on the user's smartphone or smart glasses through augmented reality features using ARKit (iOS) or ARCore (Android).

[0652] Furthermore, the system includes a recommendation function to provide health products and services tailored to each individual's health condition, making it easy for users to have options for proactively managing their own health.

[0653] As a concrete example, when a user uploads MRI images and lifestyle data to the system, a generative model performs an analysis and visualizes the risk of future cognitive decline. Based on this, the system recommends the user to use a meditation app or a specific brain training program. An example of a prompt to the generative AI model might be, "Please show me the risk of cognitive decline and preventative measures."

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] Users upload medical image data (e.g., MRI images) and lifestyle data to the server via a dedicated application or web portal. This allows the server to receive basic information about the user's health status.

[0657] Step 2:

[0658] The server performs preprocessing on the received medical image data, including noise reduction and resolution adjustment. The input is medical image data, and the output is image data formatted for use with the generative model. This step uses the OpenCV image processing library.

[0659] Step 3:

[0660] The server also performs formatting on lifestyle data. This involves using data processing libraries such as Pandas and NumPy to clean the input data and convert it into a format usable by the model. The output data is then used as input for the generative model.

[0661] Step 4:

[0662] The server feeds preprocessed data into a generative model (e.g., a TensorFlow model) to predict future medical images and health risks. The input to this step is a set of preprocessed data, and the output is predicted medical image data and risk assessment results. The model's inference capabilities are utilized for data computation.

[0663] Step 5:

[0664] Based on the predicted results, the server creates a personalized health improvement plan. The input is a predicted risk assessment, and the output is specific lifestyle improvement measures, including recommended exercise programs, dietary improvements, and stress management techniques.

[0665] Step 6:

[0666] The server notifies the terminal of health promotion plans and recommendations for related products and services through augmented reality technology. The input is the health promotion plan and related recommendations, and the output is the plan content visually highlighted. AR technologies such as ARKit and ARCore are used to provide the plan to the user.

[0667] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0668] This invention incorporates an emotion engine into a system that predicts a user's future health risks based on medical image data and lifestyle data, and provides an individualized health improvement plan, thereby enabling more effective support for improving lifestyle habits.

[0669] The system receives medical image data and lifestyle data provided by the user and first preprocesses this data. The server standardizes the image data format, performs noise reduction, and prepares the lifestyle data into a consistent data format. In the next step, the server uses a generative model to predict future medical images. This prediction visualizes the user's future health status and enables the identification of risks and brain regions that require attention.

[0670] Furthermore, an emotion engine is activated to monitor the user's emotional state. The user's emotions are recognized through text and behavioral data collected by the application through their daily activities. The emotion engine analyzes this information to determine the user's current emotional state.

[0671] The server develops an individualized health promotion plan based on predicted medical images and emotional data obtained from the emotion engine. This plan includes customized advice and recommended actions that are easy for the user to understand and implement. Furthermore, the plan's effectiveness is enhanced by incorporating content appropriate to specific emotional states based on emotional data.

[0672] For example, if the emotion engine detects that user B is feeling fatigued or stressed, the server will suggest specific exercises and relaxation techniques to reduce stress. Furthermore, it will generate encouraging mental support messages to contribute to improving the user's motivation.

[0673] Finally, the server notifies the user's device of the completed health promotion plan, allowing the user to incorporate this information into their daily life. Additionally, if necessary, guidance to medical consultations tailored to the user's emotional state is provided in conjunction with an external medical consultation platform. This ensures that users always receive support optimized for their specific health needs.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] Users upload medical image data and lifestyle data using a dedicated application or web portal. Information regarding their emotions may also be entered at this time.

[0677] Step 2:

[0678] The server verifies the medical image data received from the user, performs preprocessing such as resolution adjustment and noise reduction, and standardizes the format. Lifestyle data is also checked and prepared to conform to the input format.

[0679] Step 3:

[0680] The emotion engine activates and analyzes text data and behavioral patterns to recognize the user's emotions. This determines the emotional state the user is currently experiencing.

[0681] Step 4:

[0682] The server inputs pre-processed data into a generative model to predict future medical images. This prediction outputs risks and changes in specific regions of the brain.

[0683] Step 5:

[0684] The server develops an individualized health improvement plan based on predicted medical images and the user's emotional state. The plan's effectiveness is enhanced by incorporating emotionally responsive feedback and advice.

[0685] Step 6:

[0686] The server notifies the user's device of this health promotion plan. The user can then review the plan and decide on specific actions to incorporate into their daily life.

[0687] Step 7:

[0688] The server uses emotional data obtained through the emotion engine to collaborate with an external medical consultation platform to provide users with appropriate medical advice and support. In this way, users can receive support tailored to their own emotional state.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Currently, health predictions and improvement plans based on medical data and lifestyle habits do not adequately consider individual circumstances, making it difficult to provide effective and sustainable improvement measures for users. Furthermore, the lack of support for health improvement that takes into account users' emotional states limits the promotion of comprehensive health management.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes means for preprocessing medical data and behavioral data acquired from the user, means for predicting future medical data using a machine learning model based on the preprocessed data, and means for analyzing the user's emotional state. This enables the formulation and notification of highly personalized health improvement plans, and realizes support that appropriately responds to the user's lifestyle patterns and emotional state.

[0694] A "user" is an entity that utilizes the system and provides its own medical and behavioral data.

[0695] "Medical data" refers to data such as images and test results used to understand a user's health status.

[0696] "Behavioral data" refers to data that records a user's activities and habits in their daily life.

[0697] "Preprocessing" refers to the process of performing various operations to prepare acquired data into a format that can be analyzed.

[0698] A "machine learning model" is a mathematical model that uses algorithms to analyze large amounts of complex data and make predictions and classifications about the future.

[0699] "Emotional state" refers to the user's psychological state and is an expression of an individual's emotions as analyzed by the emotion engine.

[0700] "Analysis means" refers to the technology used to process received data and obtain details about the user's emotional state and health predictions.

[0701] A "health improvement plan" is a specific action plan for improving health that is customized based on the user's individual health and emotional state.

[0702] A "user terminal" is an electronic device used by a user to receive their health improvement plan.

[0703] This invention is a system that utilizes medical and behavioral data to analyze a user's health and emotional state, formulates a personalized health improvement plan, and notifies the user's terminal. Three entities are involved in the implementation of the system: the server, the terminal, and the user.

[0704] The server receives medical and behavioral data from users and first performs preprocessing. Medical data includes image data such as CT and MRI scans, which are converted to the standard DICOM format and denoised to make them analyzable. Behavioral data reflects the user's daily activities and habits, and its format is standardized.

[0705] Next, based on the pre-processed data, the server uses a machine learning model to predict future health conditions. This model is implemented as a generative AI model and utilizes deep learning technology. This visualizes health risks from the user's medical data, making it clear which areas require attention.

[0706] Simultaneously, the terminal collects data through the user's smartphone or wearable device. An emotion engine analyzes the collected text and behavioral data to determine the emotional state. The server then uses the analysis results to determine the emotional state and helps in developing a personalized health improvement plan.

[0707] The server combines prediction results and sentiment analysis results to develop a health improvement plan optimized for the user and notifies the device. This plan includes recommendations for specific actions and activities that can be realistically implemented. It can also, if necessary, collaborate with external medical services to provide users with guidance on professional health consultations.

[0708] As a concrete example, consider a scenario where a user sends recent health checkup data and wearable device records to the system. The server performs a cardiovascular assessment, and the emotion engine detects that the user is experiencing stress. In this case, the health improvement plan would include stress-reducing exercises and mental support messages.

[0709] An example of a prompt message is as follows: "Analyze the following medical and lifestyle data to predict future health status. Also, create a health improvement plan that takes into account the user's emotional state."

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] Users acquire medical and behavioral data. Medical data includes scan images such as CT and MRI, while behavioral data includes logs of meals, exercise, and sleep. This data is transmitted to the server via the terminal. The input data is provided in various formats and is collected for subsequent processing.

[0713] Step 2:

[0714] The server standardizes the received medical data. Specifically, it converts scanned images to DICOM format, removes image noise, and performs image processing to improve image quality. As a result, the output is clean, consistent image data suitable for analysis. Behavioral data is also formatted and processed to a state that can be analyzed.

[0715] Step 3:

[0716] The server uses a generative AI model to predict future medical data from the data prepared in the previous step. Using pre-processed medical and behavioral data as input, the generative AI model employs deep learning techniques to predict health risks. The output is data containing risk information and warnings for specific health areas.

[0717] Step 4:

[0718] Users transmit additional behavioral data in their daily lives using terminals and wearable devices. The terminals collect this data and gather information necessary to estimate emotional states from the text data and behavioral records provided by the user.

[0719] Step 5:

[0720] The server utilizes an emotion engine to analyze text data and behavioral records received from the terminal. Based on this input data, it performs natural language processing to extract the user's emotional tendencies. The output is data indicating the user's current emotional state.

[0721] Step 6:

[0722] The server integrates predicted medical and emotional data to develop a personalized health improvement plan. Input data includes health risks and emotional tendencies, which are used to generate specific action items that the user can implement. The output is a customized health improvement plan.

[0723] Step 7:

[0724] The server notifies the user's device of the completed health improvement plan. The user can then incorporate this information into their own life. The notification function sends the plan details via push notifications or email, allowing the user to apply specific improvement measures to their lifestyle.

[0725] (Application Example 2)

[0726] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] In modern society, the health status and lifestyles of individual users are diverse, and continuous monitoring of real-time lifestyle data and emotional states is essential to provide each person with an optimal health promotion plan. However, conventional systems have difficulty providing individualized plans that accurately reflect the user's feelings and real-time health status, making it a challenge to realize effective support tailored to individual needs.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0729] In this invention, the server includes means for preprocessing medical image data and lifestyle data acquired from the user, means for predicting future medical images using a generative model based on the preprocessed data, and means for monitoring the user's emotional state using an emotion recognition engine and adjusting the health promotion plan based on the emotional state. This enables timely and personalized health support tailored to the user's individuality.

[0730] "Medical image data" refers to image information acquired during the process of medical diagnosis and treatment, and is used to understand the user's health status.

[0731] "Lifestyle data" refers to information about daily activities and habits such as diet, exercise, and sleep, and serves as the basis for evaluating a user's health status.

[0732] "Preprocessing" is the process of removing noise and standardizing data before analysis, preparing it for analysis.

[0733] A "generative model" is a computational method or algorithm used to generate new data or predictions from given data.

[0734] An "emotion recognition engine" is a system or software used to determine a user's emotional state at any given time based on their written text and behavioral data.

[0735] A "health promotion plan" is a plan that includes guidelines and recommended actions created to improve the user's health.

[0736] A "portable communication device" is a portable information and communication device, such as a smartphone or tablet, used to notify users of information.

[0737] This invention is a system that uses a user's medical image data and lifestyle data to predict future health risks and provide a personalized health promotion plan. The system integrates an emotion engine and optimizes the health promotion plan by also considering the user's emotional state.

[0738] The server acquires medical image data and lifestyle data from the user and first preprocesses this data. This process involves data standardization and noise reduction, and software libraries such as TensorFlow and OpenCV are used.

[0739] Next, predictions are made using a generative AI model with the pre-processed data. This model operates on the basis of Hugging Face's transformer model and has the ability to generate future medical images for the user.

[0740] Furthermore, the emotion recognition engine monitors the user's emotional state and utilizes IBM Watson and Microsoft Azure Cognitive Services to analyze that information. The user's emotional state is determined from biometric information such as heart rate and daily behavioral data.

[0741] A health promotion plan is developed based on the user's emotional state and generated medical images. This plan is communicated to the user's portable communication device, providing personalized support in real time. For example, users experiencing increased stress may be offered specific relaxation exercises and mental support.

[0742] For example, if a user is found to be fatigued in the morning, suggesting stretching or short meditation sessions during their break can immediately contribute to improving their health.

[0743] An example of a prompt for a generating AI model is, "Generate a healthcare plan including optimal stretching and meditation for a user who has been shown to be fatigued in the morning." Such prompts enable health support tailored to the individual user's condition.

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The server receives medical image data and lifestyle data from users. Inputs include medical image files (e.g., MRI images) and lifestyle data (e.g., exercise frequency, diet, etc.). This data is first preprocessed to remove noise and standardize the format. The output is clean, standardized medical image data and lifestyle data.

[0747] Step 2:

[0748] The server inputs pre-processed data into a generating AI model to predict future medical images. In this step, the predictive model is used to analyze the data and output it as a predicted medical image. A transformer model algorithm is used to visualize future health risks based on the original image.

[0749] Step 3:

[0750] The emotion recognition engine monitors the user's current emotional state and uses user behavioral data (e.g., heart rate, text messages) as input. In this step, emotion analysis software is used to determine the emotion and generate emotional state data as output. This expresses the user's emotional changes as numerical values ​​or categories.

[0751] Step 4:

[0752] The server creates a personalized health promotion plan based on predicted medical images and emotional state data. Inputs include future health risk information and current emotional state data. This step develops specific behavioral advice and relaxation techniques tailored to the user's health condition, and the output is presented as a personalized health promotion plan.

[0753] Step 5:

[0754] The terminal notifies the user of the generated health promotion plan. The input is the health promotion plan sent from the server, and the user receives this information via a portable communication device (e.g., a smartphone). The purpose of this step is to send a notification to the user and to make the plan easily accessible and implementable for the user.

[0755] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0756] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0757] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0758] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0759] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0760] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0761] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0762] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0763] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0764] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0765] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0766] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0767] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0768] 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.

[0769] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0770] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0771] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0772] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0773] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0774] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0775] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] A means for preprocessing medical image data and lifestyle data acquired from users,

[0779] A means for predicting future medical images using a generative model based on the aforementioned preprocessed data,

[0780] A means for creating an individualized health promotion plan based on the predicted medical images,

[0781] A means for notifying the user of the aforementioned health promotion plan,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, which visualizes realistic health risks to the user and provides information to encourage improvements in lifestyle habits.

[0785] (Claim 3)

[0786] The system according to claim 1, which collaborates with an external medical consultation platform to guide users to medical consultations.

[0787] "Example 1"

[0788] (Claim 1)

[0789] A means for preprocessing medical image data and lifestyle data acquired from users,

[0790] A means for inputting the aforementioned preprocessed data into a generating AI model and predicting future medical images using prompt statements,

[0791] A means for constructing an individualized health promotion plan based on the predicted medical images,

[0792] A means of notifying the user of the aforementioned health promotion plan on their device and enabling them to receive expert advice by linking with an external medical consultation platform,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, which presents users with visualized health risks and provides information including suggestions for specific lifestyle improvements.

[0796] (Claim 3)

[0797] The system according to claim 1, which analyzes lifestyle data from users and creates exercise and nutrition improvement plans tailored to their individual health conditions.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] A means for preprocessing medical image data and lifestyle data acquired from users,

[0801] A means for predicting future medical images using a generative model based on the aforementioned preprocessed data,

[0802] A means for creating an individualized health promotion plan based on the predicted medical images,

[0803] A means for notifying the user of the aforementioned health promotion plan,

[0804] A means of visualizing a user's health status in three dimensions using augmented reality technology,

[0805] Means of promoting health-related products and services,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which visualizes realistic health risks to the user and provides information to encourage improvements in lifestyle habits.

[0809] (Claim 3)

[0810] The system according to claim 1, which collaborates with an external health consultation platform to guide users to health consultations.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] A means for preprocessing medical and behavioral data obtained from users,

[0814] A means for predicting future medical data using a machine learning model based on the aforementioned preprocessed data,

[0815] An analytical method for analyzing the emotional state of a user,

[0816] A means for creating an individualized health improvement plan based on the predicted medical data and emotional state,

[0817] A means for notifying the user terminal of the aforementioned health improvement plan,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which visualizes realistic health risks to the user and provides information to encourage improvements in lifestyle patterns.

[0821] (Claim 3)

[0822] The system according to claim 1, which collaborates with an external medical service platform to provide users with information on health consultations.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] A means for preprocessing medical image data and lifestyle data acquired from users,

[0826] A means for predicting future medical images using a generative model based on the aforementioned preprocessed data,

[0827] A means for creating an individualized health promotion plan based on the predicted medical images,

[0828] A means for monitoring the user's emotional state using an emotion recognition engine and adjusting the health promotion plan based on the said emotional state,

[0829] A means for notifying the user's portable communication device of the contents coordinated with the aforementioned health promotion plan,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which visualizes realistic health risks to the user, provides information to encourage lifestyle improvements, and provides health support tailored to the user's emotional state.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for guiding users to appropriate health consultations by linking with an external health consultation platform. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for preprocessing medical image data and lifestyle data acquired from users, A means for predicting future medical images using a generative model based on the aforementioned preprocessed data, A means for creating an individualized health promotion plan based on the predicted medical images, A means for notifying the user of the aforementioned health promotion plan, A system that includes this.

2. The system according to claim 1, which visualizes realistic health risks to the user and provides information to encourage improvements in lifestyle habits.

3. The system according to claim 1, which collaborates with an external medical consultation platform to guide users to medical consultations.

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