system

Abstract

We provide the system. [Solution] A means of acquiring lifestyle information and medical image information collected from users, A means of using a generative artificial intelligence model to predict the user's future health status based on acquired lifestyle information and medical image information, A means of visualizing and presenting prediction results to the user, 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 method for controlling a persona chatbot, which is 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] While many users understand the importance of preventing lifestyle diseases, there is a current situation where they lack a sense of personal responsibility and neglect specific countermeasures. Therefore, it is an issue to provide means to promote users' recognition of their own health risks in a specific and visual manner and to take appropriate actions.

Means for Solving the Problems

[0005] This invention provides a system that uses a generative artificial intelligence model to predict future health conditions based on lifestyle information and medical image information collected from users. By visualizing and presenting the predicted health condition to the user, it helps them recognize specific health risks and proposes lifestyle improvement actions based on this. Furthermore, by collaborating with external medical service providers to support medical treatment and health management, it realizes seamless healthcare support.

[0006] "Information regarding lifestyle habits" refers to data on behaviors and habits that affect the user's health in their daily life, such as diet, exercise, and sleep.

[0007] "Medical imaging information" refers to medical image data such as MRI and X-rays used to visualize the inside of a patient's body.

[0008] A "generative artificial intelligence model" refers to artificial intelligence technology that uses machine learning algorithms to predict future outcomes from input data.

[0009] "Predicting health status" means probabilistically estimating future health risks and physical condition based on current data.

[0010] "Visualization" refers to transforming complex data or forecast results into forms such as graphs, charts, and images, and presenting them in an intuitively easy-to-understand manner.

[0011] "Lifestyle improvement actions" refer to proposing specific actions or changes that should be taken to improve future health.

[0012] An "external healthcare service provider" refers to a medical institution or organization that provides services related to health management and medical treatment.

[0013] "Healthcare support" refers to various forms of assistance and services provided to users to maintain their health and prevent illness. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. s [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. <lo000083> [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that aims to prevent lifestyle-related diseases by collecting and analyzing information on a user's lifestyle and medical image information, and predicting their future health status. This system is implemented using the user's smartphone or computer (hereinafter referred to as "terminal") and a cloud server (hereinafter referred to as "server") for data processing.

[0036] First, users input information about their daily diet, exercise frequency, sleep duration, and other details through a dedicated app. Similarly, they upload medical image information obtained during health checkups or medical consultations, allowing the necessary data to be accumulated in the system.

[0037] The terminal encrypts and sends this data to the server. The server stores the received data in a cloud database and converts it to a standardized format. After receiving the data, the server checks for any data loss or errors and requests the user to complete the data if necessary.

[0038] Next, the server uses a generative AI machine learning model to analyze the stored data. This model is trained on a vast amount of historical medical and lifestyle data and has the ability to predict future health conditions. The analysis results are quantified as specific health risks (e.g., the likelihood of developing cardiovascular disease or diabetes).

[0039] The server visualizes these prediction results in charts and graphs and sends them to the user's terminal. The terminal then presents the results to the user in a format that is easy to understand and intuitive.

[0040] Furthermore, based on these results, the server generates a specific action plan for the user to improve their current lifestyle. For example, a user predicted to be at high risk of high blood pressure might be advised to reduce their salt intake and recommended to do aerobic exercise a few times a week.

[0041] Finally, the system collaborates with external healthcare service providers to offer necessary appointments and expert advice. This empowers users to take concrete responsibility for their own health and manage it effectively. For example, a user identified as being at risk of high blood pressure can book a consultation with a nutritionist through the app and receive specific guidance on improving their daily diet.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users record information about their daily lifestyle, such as diet, exercise, and sleep, using a dedicated app. They also upload image data, such as MRI and X-ray images obtained from medical institutions, to the app.

[0045] Step 2:

[0046] The terminal encrypts the lifestyle data entered by the user and the medical image data uploaded, ensuring data security before transmitting it to the server.

[0047] Step 3:

[0048] The server decompresses the received data and saves lifestyle data and medical image data to a cloud database. During this process, it standardizes the data format and checks for missing data or abnormal values. If problems are found, it requests data correction from the user via the terminal.

[0049] Step 4:

[0050] The server uses the organized data to input into a generative artificial intelligence model. This model calculates future health risks and predicted health conditions.

[0051] Step 5:

[0052] The server visualizes the predicted results and presents them as graphs and charts. This visualized data is presented in an intuitively easy-to-understand format.

[0053] Step 6:

[0054] The server generates specific lifestyle improvement actions along with visualized prediction results and sends this information to the user's terminal.

[0055] Step 7:

[0056] The terminal displays information sent from the server on its user interface, presenting the user with future health risks and recommended action plans.

[0057] Step 8:

[0058] Based on the information provided, users work to improve their lifestyle. If necessary, they receive professional support, such as scheduling appointments and providing nutritional guidance, through collaboration with external healthcare service providers.

[0059] (Example 1)

[0060] 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."

[0061] The present invention aims to appropriately predict a user's health status using their lifestyle and medical information, enabling them to understand their own health risks and take concrete measures to improve their lifestyle. Furthermore, it aims to solve the problem of data supplementation to improve the accuracy of predictions and to enable health follow-up through collaboration with external medical support services.

[0062] 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.

[0063] This invention includes a server that provides means for acquiring behavioral data and medical information collected from users, means for using a generative artificial intelligence algorithm to predict the user's health based on the acquired behavioral data and medical information, and means for visualizing and providing the predicted health risks to the user. This enables users to understand their own health risks and take concrete improvement measures. Furthermore, by supplementing missing data and coordinating with external medical support services, sustainable health management is achieved.

[0064] "Behavioral data" refers to information about the activities that users engage in in their daily lives, specifically including information such as diet, exercise frequency, and sleep duration.

[0065] "Medical information" refers to data related to a user's health status, such as diagnostic information, health checkup results, and medical imaging data obtained from medical institutions.

[0066] A "generative artificial intelligence algorithm" is a model trained on past medical and lifestyle data, and it is a technology that uses this model to predict a user's future health risks.

[0067] "Visualization" is the process of representing predicted health risks in the form of charts, graphs, and other visual representations so that users can understand them intuitively.

[0068] "Data completion" refers to the procedure of recollecting or supplementing missing information when necessary for prediction is unavailable.

[0069] "External medical support services" are services that provide support to users, such as scheduling appointments, health consultations, and nutritional guidance, through collaboration with medical institutions and specialists.

[0070] This invention is a system that predicts the user's health status and supports appropriate behavioral improvements. This system is primarily implemented using the user's terminal and a cloud server.

[0071] Users input data about their daily lifestyle habits and medical information obtained from healthcare institutions through a dedicated application. This allows users to accumulate their own health data in a database located in the cloud.

[0072] The device encrypts the collected data and sends it to the cloud server. This encryption process is crucial for protecting user privacy. Furthermore, the device is responsible for converting the stored data into an appropriate format.

[0073] The server receives encrypted data and stores it in a cloud database. The server then verifies for missing data and errors and performs appropriate data completion. During this process, it requests additional information from the user as needed.

[0074] The server inputs the stored data into a generating artificial intelligence algorithm to predict health status. The generating AI model is trained on a vast amount of historical data and has the ability to predict future health risks with high accuracy. An example of a prompt message would be a specific instruction such as, "Predict the risk of diabetes 6 months from now based on historical data."

[0075] The predicted health risks are visualized by the server and sent to the user's device in the form of graphs and charts. The device then displays this information in a format that is intuitively understandable to the user.

[0076] Furthermore, the server generates specific behavioral improvement measures based on the prediction results and notifies the user. For example, a user at risk of high blood pressure may be offered suggestions to reduce their salt intake and instructions for regular aerobic exercise.

[0077] Finally, the system allows users to access external medical support services as needed and obtain expert advice. This enables users to take appropriate responsibility for their own health, improve their behavior, and manage their health sustainably.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input data on their daily lifestyle, such as their diet, exercise frequency, and sleep duration, as well as medical information such as medical images, through a dedicated app. The input data is stored as foundational data for predicting the user's health.

[0081] Step 2:

[0082] The terminal encrypts the data entered by the user and sends it to the cloud server. Here, it receives the user's lifestyle data and medical information as input, performs encryption to ensure data security, and generates encrypted data as output. The encrypted data is stored in the cloud database, making it available for subsequent processing by the server.

[0083] Step 3:

[0084] The server receives data stored in a cloud database and performs validation to detect data loss or errors. It receives encrypted data from the cloud as input and performs a validation process to maintain data integrity. If problems are found, the server requests the user to complete the data and outputs feedback to obtain accurate data.

[0085] Step 4:

[0086] The server inputs the validated data into a generating AI model to predict the user's future health status. Here, it performs analysis using a trained AI model based on past medical and lifestyle data. It receives the prepared user data as input and generates a prediction of health risks as output. For example, by giving the instruction "Predict cardiovascular risk based on lifestyle for the next 6 months," the AI ​​model calculates a specific risk.

[0087] Step 5:

[0088] The server visualizes the health risk predictions output from the generated AI model and sends them to the user's terminal. The visualized results are provided in the form of charts and graphs that are easy to understand intuitively. It receives prediction results from the AI ​​model as input and generates visualized information as output. This allows the user to visually understand their own health status.

[0089] Step 6:

[0090] The terminal displays the prediction results received from the server to the user, providing them in an easy-to-understand format. It receives visualization data from the server as input, converts it into a format that is easy for the user to understand, and outputs it. At this stage, the user can effectively understand their own health status and obtain the basic information to take further action.

[0091] Step 7:

[0092] The server generates and notifies the user of a specific lifestyle improvement action plan based on the prediction results. For example, if a risk of high blood pressure is identified, it will recommend reducing salt intake in meals and engaging in moderate exercise for at least 150 minutes per week. This makes it easier for users to take specific measures necessary to maintain their health.

[0093] Step 8:

[0094] The system integrates with external medical support services as needed to provide users with health follow-up services such as scheduling appointments and providing nutritional guidance. Based on the user's health risk information as input, it integrates with external services and provides users with expert advice and support as output. This allows users to receive comprehensive health management and effectively improve their own health status.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern life, many people are at risk of lifestyle-related diseases, leading to serious problems such as increased medical costs and a decline in quality of life. Furthermore, providing appropriate exercise and dietary plans tailored to individual health conditions is difficult, and general health management methods often fail to deliver sufficient results. Therefore, there is a need for a system that accurately predicts each individual's health condition and provides an optimal improvement plan.

[0098] 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.

[0099] In this invention, the server includes means for acquiring lifestyle information and health image information collected from the user; means for using a generative artificial intelligence model to predict the user's future health status based on the acquired lifestyle information and health image information; means for visualizing and presenting the prediction results to the user; and means for proposing individual exercise plans and dietary plans based on the user's health status. This makes it possible to provide specific and effective lifestyle improvement actions tailored to each individual's health status.

[0100] A "user" is an individual who uses the system and provides information for health management purposes.

[0101] "Information regarding lifestyle habits" refers to data such as the user's daily diet, exercise frequency, and sleep duration.

[0102] "Health image information" refers to image data related to the user's health status, and includes diagnostic images obtained at medical institutions.

[0103] A "generative artificial intelligence model" is an algorithm trained on vast amounts of historical medical and lifestyle data, possessing the ability to predict future health conditions.

[0104] "Visualization" is a technique that displays analysis results in a way that is easy for users to understand, such as in the form of charts and graphs.

[0105] "Exercise and dietary plans" refer to specific types and frequencies of exercise, as well as meal content, that are suggested according to the user's health condition.

[0106] The system for implementing this invention consists of a user terminal and a cloud server. The terminal has a dedicated application installed for the user to input lifestyle information and health image information. The user inputs data such as daily meals, exercise frequency, and sleep duration through the application, and uploads health images taken at medical institutions as needed.

[0107] The device encrypts this data and sends it to the cloud server. The cloud server contains a cloud database for storing the data and a data processing module for converting it into a standardized format. After receiving the data, the server checks for missing or incorrect data and, if necessary, requests the user to complete the data via the device.

[0108] A generative AI model is deployed on a server and uses vast amounts of historical medical and lifestyle data as training data to predict the user's future health status. The model's analysis results are expressed in the form of numerical values ​​for specific health risks. The server then visualizes the prediction results in the form of charts and graphs and sends them to the user's terminal. On the terminal, the results are presented through an intuitive interface.

[0109] Furthermore, based on the analysis results, the server uses a generative AI model to suggest exercise and dietary plans tailored to each individual user. For example, a user identified as lacking exercise might be presented with an improvement plan such as "walking three times a week and focusing on a balanced diet."

[0110] For example, users who are not getting enough exercise will be prompted with a message such as, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health condition." By creating a customized plan tailored to the user's health condition in this way, it becomes possible to support more effective health management.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users input lifestyle information such as their daily diet, exercise frequency, and sleep duration using a dedicated application, and upload health images taken at medical institutions. This information is collected on the user's device and encrypted. The input information is encoded using an encryption algorithm for security purposes. The output is encrypted data.

[0114] Step 2:

[0115] The terminal sends encrypted data to the cloud server. The cloud server decodes the received encrypted data and converts it into a standardized format. The input is encrypted user data, and the output is a standardized dataset. The server uses a data processing module to perform data format conversion.

[0116] Step 3:

[0117] The server checks for missing or incorrect data within the standardized data and, if necessary, requests data completion from the user via the terminal. The input is standardized data, and the output is error-checked data. In this step, the data is scrutinized by a data validation algorithm.

[0118] Step 4:

[0119] The server inputs error-checked data into a generative AI model for analysis. The generative AI model uses a predictive algorithm to predict future health conditions based on past data. The input is error-checked data, and the output is a quantified prediction result regarding specific health risks.

[0120] Step 5:

[0121] The server visualizes the prediction results in charts and graphs, formats them in an intuitively understandable way, and then sends them to the terminal. The input is the prediction result, and the output is the visualized data. The server uses a visualization module to convert the data into a graphical format.

[0122] Step 6:

[0123] The terminal presents the user with visualized prediction results received from the server. The user can visually confirm their health risk. The input is visualized data, and the output is the next action based on the user's understanding.

[0124] Step 7:

[0125] The server utilizes the analysis results and generated AI models to suggest exercise and diet plans optimized for the user's health condition. For example, it might say, "We recommend walking three times a week." The input is visualized data and additional analysis results, and the output is an improvement plan.

[0126] Step 8:

[0127] Ultimately, users refer to the suggested exercise and diet plans on the device to help improve their lifestyle. The device displays prompts that encourage specific actions from the user. For example, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health status."

[0128] 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.

[0129] This invention is a system that collects information on the user's lifestyle and medical image information, and based on this, not only predicts the user's future health status but also supports lifestyle improvement by taking into account the user's emotional state. This system is implemented on the user's terminal and on a server that processes the data.

[0130] First, users use a smartphone app to record data on their daily lifestyle habits (diet, exercise, sleep). In addition, they upload test data obtained from medical institutions. The app incorporates an emotion engine that analyzes emotional indicators such as the user's choice of words in everyday conversation, response speed during input, and selected emojis to assess the user's emotional state.

[0131] The device encrypts the collected data and sends it to the server. The server stores the received lifestyle data, medical image information, and emotional data in a cloud database and standardizes the data. In the initial stages of data analysis, the server verifies the integrity of the data and requests supplementation from the user via the device as needed.

[0132] Next, the server uses this data to perform a health status prediction using a generative AI model. After receiving the prediction results, the server considers emotional data to generate improvement actions that are most acceptable and easy for the user to take. For example, if the emotional engine determines that the user is experiencing high levels of stress, the improvement actions may include suggestions for stress-reducing exercise or mindfulness.

[0133] Based on predicted health status and emotions, improvement actions are visualized on the server side and sent to the user's device. The device presents this information in an easy-to-understand manner, allowing the user to accurately understand their situation. It can also collaborate with external healthcare service providers as needed to receive expert support.

[0134] For example, if the emotional engine detects that a user is experiencing excessive stress, this information is presented along with suggestions for exercise habits, and the user can also book a professional counseling service through the app. This comprehensive approach allows users to actively engage in managing their own health while also considering the emotional aspects.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users input lifestyle information such as their daily meals, type and duration of exercise, and sleep duration using a dedicated app. They also upload MRI and X-ray images obtained at medical facilities to the app.

[0138] Step 2:

[0139] Users provide emotional data to the emotion engine through their daily use of the app. This is done by analyzing factors such as the user's typing speed, frequently used words and phrases, and selected emojis.

[0140] Step 3:

[0141] The device encrypts all collected lifestyle data, medical image data, and emotional data before sending them to the server, protecting data privacy and security.

[0142] Step 4:

[0143] The server stores all received data in a cloud database. It also standardizes the data, checks for errors, and, if necessary, requests data completion from the user via the terminal.

[0144] Step 5:

[0145] The server uses an artificial intelligence model generated based on lifestyle information and medical image data to predict the user's future health risks and health status.

[0146] Step 6:

[0147] After generating prediction results, the server incorporates emotional data from the emotion engine to customize lifestyle improvement actions based on the prediction results. During this process, it considers the user's current emotional state and generates suggestions to increase their feasibility.

[0148] Step 7:

[0149] The server sends visual prediction results and improvement actions tailored to the user's emotional state to the user's device in the form of graphs and charts.

[0150] Step 8:

[0151] The device displays the received information on its user interface, presenting it in a way that makes it easy for the user to understand their own health risks and possible corrective actions.

[0152] Step 9:

[0153] Users take actions to improve their lifestyle based on the information provided. During this process, they can access and book appointments with external healthcare service providers through the app as needed, receiving professional support.

[0154] (Example 2)

[0155] 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".

[0156] Modern health management systems typically predict health status based solely on users' lifestyles and medical information. However, since a user's emotional state can significantly impact their health, systems that fail to consider this factor cannot provide adequate support. Furthermore, if health improvement suggestions are not appropriate for the user's emotional state, their effectiveness may be reduced.

[0157] 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.

[0158] In this invention, the server includes means for acquiring information on habits and medical information collected from the user, means for performing emotion analysis and evaluating the user's emotional state, and means for using a generative artificial intelligence model to predict the user's future health state based on the acquired habit information, medical information, and emotional state. This enables more accurate predictions of health state that take into account the user's emotional state and the proposal of highly effective lifestyle improvement actions.

[0159] "Information about habits" refers to data about users' daily behaviors and patterns, such as eating, exercise, and sleep.

[0160] "Medical information" refers to diagnostic results and test data provided by medical institutions to assess the user's health status.

[0161] "Emotional analysis" is a process of analyzing the emotional trends of a user from their daily conversations and behaviors, and evaluating their emotional state.

[0162] A "generative artificial intelligence model" is a machine learning algorithm or data analysis system that predicts a user's future health status based on acquired data.

[0163] "Improvement actions" refer to specific actions or guidelines proposed to improve the health and emotional state of users.

[0164] "Visualization" refers to graphical methods for displaying data and analysis results in a format that is easy for users to understand.

[0165] "External medical service providers" refer to medical facilities or specialized organizations that collaborate with the system to manage and support the health of users.

[0166] This invention is a system that uses a generative AI model to predict a user's health status based on information about their habits and medical information, and proposes improvement actions. This system is primarily implemented using the user's terminal and server.

[0167] Users input daily lifestyle data using devices such as smartphones and tablets. This includes information such as diet, exercise history, and sleep duration. They can also upload diagnostic results and test data obtained from medical institutions through the application.

[0168] The device has a built-in emotion engine that performs sentiment analysis. This engine analyzes the user's everyday conversation language, input speed, selected emojis, etc., to determine the user's emotional state.

[0169] The device uses the AES encryption algorithm to encrypt collected habit data, medical information, and emotional data. This data is securely transmitted to the server via the SSL / TLS protocol.

[0170] The server receives this data, converts it to a standardized data format in the cloud environment, and stores it. This standardized data is then input into the generating AI model as prompts. An example of a prompt might be: "A 40-year-old male, with moderate daily exercise, high current stress level, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?"

[0171] The AI ​​model predicts the user's health status based on this prompt. Based on the prediction, the server considers the user's emotional state and generates the optimal improvement action. This action may include suggestions for stress-reducing exercise or mindfulness practice.

[0172] The generated improvement actions are clearly displayed on the screen using a visualization library and presented to the user via their device. Through this information, users can receive specific actionable guidelines based on their current health and emotional state. Furthermore, they can utilize integration with external medical service providers as needed to receive additional expert support.

[0173] In this way, the present invention can provide advanced support for health management while taking into account the user's emotional state.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input daily lifestyle data into a smartphone app. This includes meal records, exercise details, and sleep duration. Users also upload test data obtained from medical institutions to the app. The entered data is initially organized by the app.

[0177] Step 2:

[0178] The terminal receives collected lifestyle and medical data and encrypts this data using the AES encryption algorithm. It receives lifestyle and medical data as input and outputs encrypted data. This data is securely transmitted to the server using the SSL / TLS protocol.

[0179] Step 3:

[0180] The server receives encrypted data sent from the terminal and temporarily stores it in a dedicated receive buffer. Next, it decrypts the data and verifies its integrity. It receives encrypted data as input and obtains decrypted, standardized data as output.

[0181] Step 4:

[0182] The server stores the received data in a cloud database and standardizes the data. During this process, it converts the data into an appropriate format, making it available for use in the next processing step. It receives decoded data as input and obtains standard data that can be input into the generated AI model as output.

[0183] Step 5:

[0184] The server generates prompts using standardized data. For example, a possible prompt might be: "A 40-year-old male with moderate daily exercise, high current stress levels, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?" It accepts standardized data as input and generates prompts as output.

[0185] Step 6:

[0186] A generative AI model is input with prompt text to predict the user's future health status. The AI ​​model applies machine learning algorithms to generate health prediction results. It receives prompt text as input and health prediction results as output.

[0187] Step 7:

[0188] The server generates improvement actions based on the generated health prediction results, taking into account emotional data. Natural language processing techniques are used to create actions in a way that is easy for the user to understand. The server receives health prediction results and emotional data as input, and outputs improvement actions.

[0189] Step 8:

[0190] The improvement actions and health prediction results generated on the server are visualized and sent to the user's terminal. Using a visualization library, the data is displayed as graphs and charts. Improvement actions and health prediction results are received as input, and visualized data is obtained as output.

[0191] Step 9:

[0192] The device receives transmitted visualization data and presents it to the user in an easy-to-understand manner. This allows the user to accurately understand their health status and the necessary improvement actions. Furthermore, if needed, the device can connect with external medical service providers for additional support. It receives visualization data as input and presents information to the user as output.

[0193] (Application Example 2)

[0194] 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".

[0195] In recent years, personal health management has required comprehensive management that considers not only physical health but also emotions and stress levels. However, conventional systems have separated health predictions based on lifestyle and medical information from the analysis of emotional states, making it difficult to consistently provide personalized health advice. Furthermore, the lack of a system that provides specific purchasing advice based on emotional states often leads to confusion for users when choosing products and services that can help improve their health. This invention aims to solve these problems and provide optimal health management and purchasing support for individuals.

[0196] 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.

[0197] In this invention, the server includes means for collecting information on lifestyle habits, medical image information, and emotional state obtained from the user; means for using a generative artificial intelligence model to predict the user's future health state based on the acquired lifestyle habit information, medical image information, and emotional state information, and to propose healthy products and services; and means for visualizing the prediction results and presenting the user with purchasing advice that takes the emotional state into consideration. As a result, the user can receive specific and emotionally sensitive product suggestions tailored to their health state, enabling them to manage their health more appropriately.

[0198] A "user" is an individual who uses this system and provides the necessary information.

[0199] "Lifestyle information" refers to data about users' behaviors such as diet, exercise, and sleep.

[0200] "Medical image information" refers to image data obtained at a medical institution that shows the state of a person's physical health.

[0201] "Emotional state" refers to the psychological state that indicates the user's mood and stress level.

[0202] A "generative artificial intelligence model" is a computer model used to predict future health conditions based on data analysis.

[0203] "Visualization" is the process of converting and presenting analysis results in a format that is easy for users to understand.

[0204] "Health product recommendations" refer to the introduction of products and services that contribute to improving the health of users.

[0205] The system for implementing this invention consists of a user's terminal and a server that analyzes the data.

[0206] First, users record data about their daily lifestyle habits (e.g., diet, exercise records, sleep patterns) using a smartphone application. The app also analyzes keywords in everyday conversations, user behavior during input, and selected emojis to understand their emotional state. Furthermore, users are required to upload various test results and medical image information obtained from medical institutions.

[0207] The terminal encrypts the collected data and securely transmits it to the server. To efficiently process large amounts of data, a system is in place that utilizes cloud services. The servers use cloud platforms such as Amazon Web Services (AWS®) and Microsoft Azure®.

[0208] The server stores received lifestyle and medical data in a cloud database and uses a machine learning system to standardize and analyze the data. Here, a generative AI model using software such as TENSORFLOW® predicts the user's health status. Furthermore, by providing emotional data as prompts to the AI ​​model, it generates health-related purchasing advice that takes the user's emotional state into account.

[0209] For example, if emotional analysis determines that a user is experiencing stress, the system can suggest health foods or mindfulness-related products that can help reduce stress. This information is sent back to the user's smartphone in an easy-to-understand, visualized format, making it easier for the user to choose appropriate actions based on their situation.

[0210] As a concrete example, the prompt text would be something like, "Based on the following purchase history and sentiment analysis data, please create a list of healthy products," which is then input into the AI, and the generated list is provided to the user.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] Users open a smartphone app and record information about their daily lifestyle (such as diet, exercise records, and sleep patterns) and data about their emotional state (such as keywords, emojis, and typing speed). Users also upload test results and medical images obtained from medical institutions to the app. This input data is temporarily stored on the user's device.

[0214] Step 2:

[0215] The device encrypts the collected data end-to-end before sending it to the server via a cloud service. The input consists of encrypted lifestyle data, emotional data, and medical data, while the output is a secure data transfer to the server. TLS (Transport Layer Security) is used to ensure security.

[0216] Step 3:

[0217] The server stores the received data in a cloud database and performs data standardization. Here, missing data is imputed and the format is unified, ensuring consistency across various data formats. The input is encrypted data, and the output is standardized, analyzable data.

[0218] Step 4:

[0219] The server uses a generative AI model to derive emotional states and health predictions from data. The input is standardized data, and the output is a health state prediction and emotionally conscious purchasing advice. Complex multivariate analysis is performed using tools such as TensorFlow.

[0220] Step 5:

[0221] The server visually organizes the prediction results and generated purchase advice, designing them in a user-friendly format. The input is the output of the AI ​​model, and the output is a visualized set of information. The results are then formatted in graphs and charts.

[0222] Step 6:

[0223] The server encrypts the visualized information and then sends it to the user's terminal using a secure protocol. The input is the visualized information, and the output is the data transfer to the user's terminal. Data security is ensured through the use of TLS.

[0224] Step 7:

[0225] Through the app, users receive predictive results and purchasing advice from the server, and select products and services that suit their health condition. The input is visualized data from the server, and the output is the user's decision and actions. Based on the suggestions, the user selects the next step.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is a system that aims to prevent lifestyle-related diseases by collecting and analyzing information on a user's lifestyle and medical image information, and predicting their future health status. This system is implemented using the user's smartphone or computer (hereinafter referred to as "terminal") and a cloud server (hereinafter referred to as "server") for data processing.

[0243] First, users input information about their daily diet, exercise frequency, sleep duration, and other details through a dedicated app. Similarly, they upload medical image information obtained during health checkups or medical consultations, allowing the necessary data to be accumulated in the system.

[0244] The terminal encrypts and sends this data to the server. The server stores the received data in a cloud database and converts it to a standardized format. After receiving the data, the server checks for any data loss or errors and requests the user to complete the data if necessary.

[0245] Next, the server uses a generative AI machine learning model to analyze the stored data. This model is trained on a vast amount of historical medical and lifestyle data and has the ability to predict future health conditions. The analysis results are quantified as specific health risks (e.g., the likelihood of developing cardiovascular disease or diabetes).

[0246] The server visualizes these prediction results in charts and graphs and sends them to the user's terminal. The terminal then presents the results to the user in a format that is easy to understand and intuitive.

[0247] Furthermore, based on these results, the server generates a specific action plan for the user to improve their current lifestyle. For example, a user predicted to be at high risk of high blood pressure might be advised to reduce their salt intake and recommended to do aerobic exercise a few times a week.

[0248] Finally, the system collaborates with external healthcare service providers to offer necessary appointments and expert advice. This empowers users to take concrete responsibility for their own health and manage it effectively. For example, a user identified as being at risk of high blood pressure can book a consultation with a nutritionist through the app and receive specific guidance on improving their daily diet.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] Users record information about their daily lifestyle, such as diet, exercise, and sleep, using a dedicated app. They also upload image data, such as MRI and X-ray images obtained from medical institutions, to the app.

[0252] Step 2:

[0253] The terminal encrypts the lifestyle data entered by the user and the medical image data uploaded, ensuring data security before transmitting it to the server.

[0254] Step 3:

[0255] The server decompresses the received data and saves lifestyle data and medical image data to a cloud database. During this process, it standardizes the data format and checks for missing data or abnormal values. If problems are found, it requests data correction from the user via the terminal.

[0256] Step 4:

[0257] The server uses the organized data to input into a generative artificial intelligence model. This model calculates future health risks and predicted health conditions.

[0258] Step 5:

[0259] The server visualizes the predicted results and presents them as graphs and charts. This visualized data is presented in an intuitively easy-to-understand format.

[0260] Step 6:

[0261] The server generates specific lifestyle improvement actions along with visualized prediction results and sends this information to the user's terminal.

[0262] Step 7:

[0263] The terminal displays information sent from the server on its user interface, presenting the user with future health risks and recommended action plans.

[0264] Step 8:

[0265] Based on the information provided, users work to improve their lifestyle. If necessary, they receive professional support, such as scheduling appointments and providing nutritional guidance, through collaboration with external healthcare service providers.

[0266] (Example 1)

[0267] 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."

[0268] The present invention aims to appropriately predict a user's health status using their lifestyle and medical information, enabling them to understand their own health risks and take concrete measures to improve their lifestyle. Furthermore, it aims to solve the problem of data supplementation to improve the accuracy of predictions and to enable health follow-up through collaboration with external medical support services.

[0269] 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.

[0270] This invention includes a server that provides means for acquiring behavioral data and medical information collected from users, means for using a generative artificial intelligence algorithm to predict the user's health based on the acquired behavioral data and medical information, and means for visualizing and providing the predicted health risks to the user. This enables users to understand their own health risks and take concrete improvement measures. Furthermore, by supplementing missing data and coordinating with external medical support services, sustainable health management is achieved.

[0271] "Behavioral data" refers to information about the activities that users engage in in their daily lives, specifically including information such as diet, exercise frequency, and sleep duration.

[0272] "Medical information" refers to data related to a user's health status, such as diagnostic information, health checkup results, and medical imaging data obtained from medical institutions.

[0273] A "generative artificial intelligence algorithm" is a model trained on past medical and lifestyle data, and it is a technology that uses this model to predict a user's future health risks.

[0274] "Visualization" is the process of representing predicted health risks in the form of charts, graphs, and other visual representations so that users can understand them intuitively.

[0275] "Data completion" refers to the procedure of recollecting or supplementing missing information when necessary for prediction is unavailable.

[0276] "External medical support services" are services that provide support to users, such as scheduling appointments, health consultations, and nutritional guidance, through collaboration with medical institutions and specialists.

[0277] This invention is a system that predicts the user's health status and supports appropriate behavioral improvements. This system is primarily implemented using the user's terminal and a cloud server.

[0278] Users input data about their daily lifestyle habits and medical information obtained from healthcare institutions through a dedicated application. This allows users to accumulate their own health data in a database located in the cloud.

[0279] The device encrypts the collected data and sends it to the cloud server. This encryption process is crucial for protecting user privacy. Furthermore, the device is responsible for converting the stored data into an appropriate format.

[0280] The server receives the encrypted data and stores it in the cloud database. Then, the server verifies for missing data or errors and performs appropriate data completion. At this time, the server requests the user to provide additional information if necessary.

[0281] The server inputs the stored data into a generative artificial intelligence algorithm to predict the health status. The generative AI model is trained based on a vast amount of past data and has the ability to accurately predict future health risks. As an example of a prompt sentence, a specific instruction such as "Predict the diabetes risk six months from the past data" can be considered.

[0282] The predicted health risk is visualized by the server and sent to the user's terminal in the form of graphs and charts. The terminal displays this in a form that the user can intuitively understand.

[0283] Furthermore, the server generates specific action improvement measures based on the prediction results and notifies the user. For example, users at risk of high blood pressure are provided with proposals to reduce salt intake in their diet and instructions for regular aerobic exercise.

[0284] Finally, the system enables the user to access external medical support services if necessary and obtain professional advice. This allows users to take appropriate responsibility for their health status, improve their behavior, and continuously manage their health.

[0285] The flow of specific processing in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user inputs data related to lifestyle habits such as daily diet content, exercise frequency, sleep time, and medical information such as medical images through a dedicated app. The input data is accumulated as basic data for predicting the user's health.

[0288] Step 2:

[0289] The terminal encrypts the data entered by the user and sends it to the cloud server. Here, it receives the user's lifestyle data and medical information as input, performs encryption to ensure data security, and generates encrypted data as output. The encrypted data is stored in the cloud database, making it available for subsequent processing by the server.

[0290] Step 3:

[0291] The server receives data stored in a cloud database and performs validation to detect data loss or errors. It receives encrypted data from the cloud as input and performs a validation process to maintain data integrity. If problems are found, the server requests the user to complete the data and outputs feedback to obtain accurate data.

[0292] Step 4:

[0293] The server inputs the validated data into a generating AI model to predict the user's future health status. Here, it performs analysis using a trained AI model based on past medical and lifestyle data. It receives the prepared user data as input and generates a prediction of health risks as output. For example, by giving the instruction "Predict cardiovascular risk based on lifestyle for the next 6 months," the AI ​​model calculates a specific risk.

[0294] Step 5:

[0295] The server visualizes the health risk predictions output from the generated AI model and sends them to the user's terminal. The visualized results are provided in the form of charts and graphs that are easy to understand intuitively. It receives prediction results from the AI ​​model as input and generates visualized information as output. This allows the user to visually understand their own health status.

[0296] Step 6:

[0297] The terminal displays the prediction results received from the server to the user, providing them in an easy-to-understand format. It receives visualization data from the server as input, converts it into a format that is easy for the user to understand, and outputs it. At this stage, the user can effectively understand their own health status and obtain the basic information to take further action.

[0298] Step 7:

[0299] The server generates and notifies the user of a specific lifestyle improvement action plan based on the prediction results. For example, if a risk of high blood pressure is identified, it will recommend reducing salt intake in meals and engaging in moderate exercise for at least 150 minutes per week. This makes it easier for users to take specific measures necessary to maintain their health.

[0300] Step 8:

[0301] The system integrates with external medical support services as needed to provide users with health follow-up services such as scheduling appointments and providing nutritional guidance. Based on the user's health risk information as input, it integrates with external services and provides users with expert advice and support as output. This allows users to receive comprehensive health management and effectively improve their own health status.

[0302] (Application Example 1)

[0303] 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."

[0304] In modern life, many people are exposed to the risks of lifestyle diseases, and the accompanying increase in medical costs and decline in quality of life have become serious problems. In addition, it is difficult to provide appropriate exercise and diet plans based on individual health conditions, and there is an issue that the effects cannot be fully achieved with general health management methods. Therefore, there is a demand for a system that can accurately predict the health conditions of each individual and provide an optimal improvement plan.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for acquiring information on lifestyle habits and health image information collected from users, means for using a generative artificial intelligence model for predicting the future health state of the user based on the acquired lifestyle information and health image information, means for visualizing the prediction results and presenting them to the user, and means for proposing individual exercise plans and diet plans based on the health state of the user. As a result, it becomes possible to provide specific and effective actions for improving lifestyle habits according to individual health conditions.

[0307] A "user" is an individual who uses the system and provides information for health management.

[0308] "Information on lifestyle habits" refers to data such as the dietary content, exercise frequency, and sleep time in the daily life of the user.

[0309] "Health image information" refers to image data related to the health state of the user, and includes diagnostic images obtained at medical institutions.

[0310] A "generative artificial intelligence model" is an algorithm trained based on a vast amount of past medical data and lifestyle data, and has the ability to predict future health states.

[0311] "Visualization" is a technology for displaying the analysis results in a form that is easy for the user to understand, such as in the form of charts or graphs.

[0312] "Exercise and dietary plans" refer to specific types and frequencies of exercise, as well as meal content, that are suggested according to the user's health condition.

[0313] The system for implementing this invention consists of a user terminal and a cloud server. The terminal has a dedicated application installed for the user to input lifestyle information and health image information. The user inputs data such as daily meals, exercise frequency, and sleep duration through the application, and uploads health images taken at medical institutions as needed.

[0314] The device encrypts this data and sends it to the cloud server. The cloud server contains a cloud database for storing the data and a data processing module for converting it into a standardized format. After receiving the data, the server checks for missing or incorrect data and, if necessary, requests the user to complete the data via the device.

[0315] A generative AI model is deployed on a server and uses vast amounts of historical medical and lifestyle data as training data to predict the user's future health status. The model's analysis results are expressed in the form of numerical values ​​for specific health risks. The server then visualizes the prediction results in the form of charts and graphs and sends them to the user's terminal. On the terminal, the results are presented through an intuitive interface.

[0316] Furthermore, based on the analysis results, the server uses a generative AI model to suggest exercise and dietary plans tailored to each individual user. For example, a user identified as lacking exercise might be presented with an improvement plan such as "walking three times a week and focusing on a balanced diet."

[0317] For example, users who are not getting enough exercise will be prompted with a message such as, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health condition." By creating a customized plan tailored to the user's health condition in this way, it becomes possible to support more effective health management.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] Users input lifestyle information such as their daily diet, exercise frequency, and sleep duration using a dedicated application, and upload health images taken at medical institutions. This information is collected on the user's device and encrypted. The input information is encoded using an encryption algorithm for security purposes. The output is encrypted data.

[0321] Step 2:

[0322] The terminal sends encrypted data to the cloud server. The cloud server decodes the received encrypted data and converts it into a standardized format. The input is encrypted user data, and the output is a standardized dataset. The server uses a data processing module to perform data format conversion.

[0323] Step 3:

[0324] The server checks for missing or incorrect data within the standardized data and, if necessary, requests data completion from the user via the terminal. The input is standardized data, and the output is error-checked data. In this step, the data is scrutinized by a data validation algorithm.

[0325] Step 4:

[0326] The server inputs error-checked data into a generative AI model for analysis. The generative AI model uses a predictive algorithm to predict future health conditions based on past data. The input is error-checked data, and the output is a quantified prediction result regarding specific health risks.

[0327] Step 5:

[0328] The server visualizes the prediction results in charts and graphs, formats them in an intuitively understandable way, and then sends them to the terminal. The input is the prediction result, and the output is the visualized data. The server uses a visualization module to convert the data into a graphical format.

[0329] Step 6:

[0330] The terminal presents the user with visualized prediction results received from the server. The user can visually confirm their health risk. The input is visualized data, and the output is the next action based on the user's understanding.

[0331] Step 7:

[0332] The server utilizes the analysis results and generated AI models to suggest exercise and diet plans optimized for the user's health condition. For example, it might say, "We recommend walking three times a week." The input is visualized data and additional analysis results, and the output is an improvement plan.

[0333] Step 8:

[0334] Ultimately, users refer to the suggested exercise and diet plans on the device to help improve their lifestyle. The device displays prompts that encourage specific actions from the user. For example, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health status."

[0335] 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.

[0336] This invention is a system that collects information on the user's lifestyle and medical image information, and based on this, not only predicts the user's future health status but also supports lifestyle improvement by taking into account the user's emotional state. This system is implemented on the user's terminal and on a server that processes the data.

[0337] First, users use a smartphone app to record data on their daily lifestyle habits (diet, exercise, sleep). In addition, they upload test data obtained from medical institutions. The app incorporates an emotion engine that analyzes emotional indicators such as the user's choice of words in everyday conversation, response speed during input, and selected emojis to assess the user's emotional state.

[0338] The device encrypts the collected data and sends it to the server. The server stores the received lifestyle data, medical image information, and emotional data in a cloud database and standardizes the data. In the initial stages of data analysis, the server verifies the integrity of the data and requests supplementation from the user via the device as needed.

[0339] Next, the server uses this data to perform a health status prediction using a generative AI model. After receiving the prediction results, the server considers emotional data to generate improvement actions that are most acceptable and easy for the user to take. For example, if the emotional engine determines that the user is experiencing high levels of stress, the improvement actions may include suggestions for stress-reducing exercise or mindfulness.

[0340] Based on predicted health status and emotions, improvement actions are visualized on the server side and sent to the user's device. The device presents this information in an easy-to-understand manner, allowing the user to accurately understand their situation. It can also collaborate with external healthcare service providers as needed to receive expert support.

[0341] For example, if the emotional engine detects that a user is experiencing excessive stress, this information is presented along with suggestions for exercise habits, and the user can also book a professional counseling service through the app. This comprehensive approach allows users to actively engage in managing their own health while also considering the emotional aspects.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] Users input lifestyle information such as their daily meals, type and duration of exercise, and sleep duration using a dedicated app. They also upload MRI and X-ray images obtained at medical facilities to the app.

[0345] Step 2:

[0346] Users provide emotional data to the emotion engine through their daily use of the app. This is done by analyzing factors such as the user's typing speed, frequently used words and phrases, and selected emojis.

[0347] Step 3:

[0348] The device encrypts all collected lifestyle data, medical image data, and emotional data before sending them to the server, protecting data privacy and security.

[0349] Step 4:

[0350] The server stores all received data in a cloud database. It also standardizes the data, checks for errors, and, if necessary, requests data completion from the user via the terminal.

[0351] Step 5:

[0352] The server uses an artificial intelligence model generated based on lifestyle information and medical image data to predict the user's future health risks and health status.

[0353] Step 6:

[0354] After generating prediction results, the server incorporates emotional data from the emotion engine to customize lifestyle improvement actions based on the prediction results. During this process, it considers the user's current emotional state and generates suggestions to increase their feasibility.

[0355] Step 7:

[0356] The server sends visual prediction results and improvement actions tailored to the user's emotional state to the user's device in the form of graphs and charts.

[0357] Step 8:

[0358] The device displays the received information on its user interface, presenting it in a way that makes it easy for the user to understand their own health risks and possible corrective actions.

[0359] Step 9:

[0360] Users take actions to improve their lifestyle based on the information provided. During this process, they can access and book appointments with external healthcare service providers through the app as needed, receiving professional support.

[0361] (Example 2)

[0362] 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".

[0363] Modern health management systems typically predict health status based solely on users' lifestyles and medical information. However, since a user's emotional state can significantly impact their health, systems that fail to consider this factor cannot provide adequate support. Furthermore, if health improvement suggestions are not appropriate for the user's emotional state, their effectiveness may be reduced.

[0364] 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.

[0365] In this invention, the server includes means for acquiring information on habits and medical information collected from the user, means for performing emotion analysis and evaluating the user's emotional state, and means for using a generative artificial intelligence model to predict the user's future health state based on the acquired habit information, medical information, and emotional state. This enables more accurate predictions of health state that take into account the user's emotional state and the proposal of highly effective lifestyle improvement actions.

[0366] "Information about habits" refers to data about users' daily behaviors and patterns, such as eating, exercise, and sleep.

[0367] "Medical information" refers to diagnostic results and test data provided by medical institutions to assess the user's health status.

[0368] "Emotional analysis" is a process of analyzing the emotional trends of a user from their daily conversations and behaviors, and evaluating their emotional state.

[0369] A "generative artificial intelligence model" is a machine learning algorithm or data analysis system that predicts a user's future health status based on acquired data.

[0370] "Improvement actions" refer to specific actions or guidelines proposed to improve the health and emotional state of users.

[0371] "Visualization" refers to graphical methods for displaying data and analysis results in a format that is easy for users to understand.

[0372] "External medical service providers" refer to medical facilities or specialized organizations that collaborate with the system to manage and support the health of users.

[0373] This invention is a system that uses a generative AI model to predict a user's health status based on information about their habits and medical information, and proposes improvement actions. This system is primarily implemented using the user's terminal and server.

[0374] Users input daily lifestyle data using devices such as smartphones and tablets. This includes information such as diet, exercise history, and sleep duration. They can also upload diagnostic results and test data obtained from medical institutions through the application.

[0375] The device has a built-in emotion engine that performs sentiment analysis. This engine analyzes the user's everyday conversation language, input speed, selected emojis, etc., to determine the user's emotional state.

[0376] The device uses the AES encryption algorithm to encrypt collected habit data, medical information, and emotional data. This data is securely transmitted to the server via the SSL / TLS protocol.

[0377] The server receives this data, converts it to a standardized data format in the cloud environment, and stores it. This standardized data is then input into the generating AI model as prompts. An example of a prompt might be: "A 40-year-old male, with moderate daily exercise, high current stress level, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?"

[0378] The AI ​​model predicts the user's health status based on this prompt. Based on the prediction, the server considers the user's emotional state and generates the optimal improvement action. This action may include suggestions for stress-reducing exercise or mindfulness practice.

[0379] The generated improvement actions are clearly displayed on the screen using a visualization library and presented to the user via their device. Through this information, users can receive specific actionable guidelines based on their current health and emotional state. Furthermore, they can utilize integration with external medical service providers as needed to receive additional expert support.

[0380] In this way, the present invention can provide advanced support for health management while taking into account the user's emotional state.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] Users input daily lifestyle data into a smartphone app. This includes meal records, exercise details, and sleep duration. Users also upload test data obtained from medical institutions to the app. The entered data is initially organized by the app.

[0384] Step 2:

[0385] The terminal receives collected lifestyle and medical data and encrypts this data using the AES encryption algorithm. It receives lifestyle and medical data as input and outputs encrypted data. This data is securely transmitted to the server using the SSL / TLS protocol.

[0386] Step 3:

[0387] The server receives encrypted data sent from the terminal and temporarily stores it in a dedicated receive buffer. Next, it decrypts the data and verifies its integrity. It receives encrypted data as input and obtains decrypted, standardized data as output.

[0388] Step 4:

[0389] The server stores the received data in a cloud database and standardizes the data. During this process, it converts the data into an appropriate format, making it available for use in the next processing step. It receives decoded data as input and obtains standard data that can be input into the generated AI model as output.

[0390] Step 5:

[0391] The server generates prompts using standardized data. For example, a possible prompt might be: "A 40-year-old male with moderate daily exercise, high current stress levels, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?" It accepts standardized data as input and generates prompts as output.

[0392] Step 6:

[0393] A generative AI model is input with prompt text to predict the user's future health status. The AI ​​model applies machine learning algorithms to generate health prediction results. It receives prompt text as input and health prediction results as output.

[0394] Step 7:

[0395] The server generates improvement actions based on the generated health prediction results, taking into account emotional data. Natural language processing techniques are used to create actions in a way that is easy for the user to understand. The server receives health prediction results and emotional data as input, and outputs improvement actions.

[0396] Step 8:

[0397] The improvement actions and health prediction results generated on the server are visualized and sent to the user's terminal. Using a visualization library, the data is displayed as graphs and charts. Improvement actions and health prediction results are received as input, and visualized data is obtained as output.

[0398] Step 9:

[0399] The device receives transmitted visualization data and presents it to the user in an easy-to-understand manner. This allows the user to accurately understand their health status and the necessary improvement actions. Furthermore, if needed, the device can connect with external medical service providers for additional support. It receives visualization data as input and presents information to the user as output.

[0400] (Application Example 2)

[0401] 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 as the "terminal".

[0402] In recent years, personal health management has required comprehensive management that considers not only physical health but also emotions and stress levels. However, conventional systems have separated health predictions based on lifestyle and medical information from the analysis of emotional states, making it difficult to consistently provide personalized health advice. Furthermore, the lack of a system that provides specific purchasing advice based on emotional states often leads to confusion for users when choosing products and services that can help improve their health. This invention aims to solve these problems and provide optimal health management and purchasing support for individuals.

[0403] 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.

[0404] In this invention, the server includes means for collecting information on lifestyle habits, medical image information, and emotional state obtained from the user; means for using a generative artificial intelligence model to predict the user's future health state based on the acquired lifestyle habit information, medical image information, and emotional state information, and to propose healthy products and services; and means for visualizing the prediction results and presenting the user with purchasing advice that takes the emotional state into consideration. As a result, the user can receive specific and emotionally sensitive product suggestions tailored to their health state, enabling them to manage their health more appropriately.

[0405] A "user" is an individual who uses this system and provides the necessary information.

[0406] "Lifestyle information" refers to data about users' behaviors such as diet, exercise, and sleep.

[0407] "Medical image information" refers to image data obtained at a medical institution that shows the state of a person's physical health.

[0408] "Emotional state" refers to the psychological state that indicates the user's mood and stress level.

[0409] A "generative artificial intelligence model" is a computer model used to predict future health conditions based on data analysis.

[0410] "Visualization" is the process of converting and presenting analysis results in a format that is easy for users to understand.

[0411] "Health product recommendations" refer to the introduction of products and services that contribute to improving the health of users.

[0412] The system for implementing this invention consists of a user's terminal and a server that analyzes the data.

[0413] First, users record data about their daily lifestyle habits (e.g., diet, exercise records, sleep patterns) using a smartphone application. The app also analyzes keywords in everyday conversations, user behavior during input, and selected emojis to understand their emotional state. Furthermore, users are required to upload various test results and medical image information obtained from medical institutions.

[0414] The terminal encrypts the collected data and securely transmits it to the server. To efficiently process large amounts of data, a system is in place that utilizes cloud services. The servers use cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure.

[0415] The server stores received lifestyle and medical data in a cloud database and uses a machine learning system to standardize and analyze the data. Here, a generative AI model using software such as TensorFlow predicts the user's health status. Furthermore, by providing emotional data as prompts to the AI ​​model, it generates health-related purchasing advice that takes the user's emotional state into account.

[0416] For example, if emotional analysis determines that a user is experiencing stress, the system can suggest health foods or mindfulness-related products that can help reduce stress. This information is sent back to the user's smartphone in an easy-to-understand, visualized format, making it easier for the user to choose appropriate actions based on their situation.

[0417] As a concrete example, the prompt text would be something like, "Based on the following purchase history and sentiment analysis data, please create a list of healthy products," which is then input into the AI, and the generated list is provided to the user.

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] Users open a smartphone app and record information about their daily lifestyle (such as diet, exercise records, and sleep patterns) and data about their emotional state (such as keywords, emojis, and typing speed). Users also upload test results and medical images obtained from medical institutions to the app. This input data is temporarily stored on the user's device.

[0421] Step 2:

[0422] The device encrypts the collected data end-to-end before sending it to the server via a cloud service. The input consists of encrypted lifestyle data, emotional data, and medical data, while the output is a secure data transfer to the server. TLS (Transport Layer Security) is used to ensure security.

[0423] Step 3:

[0424] The server stores the received data in a cloud database and performs data standardization. Here, missing data is imputed and the format is unified, ensuring consistency across various data formats. The input is encrypted data, and the output is standardized, analyzable data.

[0425] Step 4:

[0426] The server uses a generative AI model to derive emotional states and health predictions from data. The input is standardized data, and the output is a health state prediction and emotionally conscious purchasing advice. Complex multivariate analysis is performed using tools such as TensorFlow.

[0427] Step 5:

[0428] The server visually organizes the prediction results and generated purchase advice, designing them in a user-friendly format. The input is the output of the AI ​​model, and the output is a visualized set of information. The results are then formatted in graphs and charts.

[0429] Step 6:

[0430] The server encrypts the visualized information and then sends it to the user's terminal using a secure protocol. The input is the visualized information, and the output is the data transfer to the user's terminal. Data security is ensured through the use of TLS.

[0431] Step 7:

[0432] Through the app, users receive predictive results and purchasing advice from the server, and select products and services that suit their health condition. The input is visualized data from the server, and the output is the user's decision and actions. Based on the suggestions, the user selects the next step.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention is a system that aims to prevent lifestyle-related diseases by collecting and analyzing information on a user's lifestyle and medical image information, and predicting their future health status. This system is implemented using the user's smartphone or computer (hereinafter referred to as "terminal") and a cloud server (hereinafter referred to as "server") for data processing.

[0450] First, users input information about their daily diet, exercise frequency, sleep duration, and other details through a dedicated app. Similarly, they upload medical image information obtained during health checkups or medical consultations, allowing the necessary data to be accumulated in the system.

[0451] The terminal encrypts and sends this data to the server. The server stores the received data in a cloud database and converts it to a standardized format. After receiving the data, the server checks for any data loss or errors and requests the user to complete the data if necessary.

[0452] Next, the server uses a generative AI machine learning model to analyze the stored data. This model is trained on a vast amount of historical medical and lifestyle data and has the ability to predict future health conditions. The analysis results are quantified as specific health risks (e.g., the likelihood of developing cardiovascular disease or diabetes).

[0453] The server visualizes these prediction results in charts and graphs and sends them to the user's terminal. The terminal then presents the results to the user in a format that is easy to understand and intuitive.

[0454] Furthermore, based on these results, the server generates a specific action plan for the user to improve their current lifestyle. For example, a user predicted to be at high risk of high blood pressure might be advised to reduce their salt intake and recommended to do aerobic exercise a few times a week.

[0455] Finally, the system collaborates with external healthcare service providers to offer necessary appointments and expert advice. This empowers users to take concrete responsibility for their own health and manage it effectively. For example, a user identified as being at risk of high blood pressure can book a consultation with a nutritionist through the app and receive specific guidance on improving their daily diet.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] Users record information about their daily lifestyle, such as diet, exercise, and sleep, using a dedicated app. They also upload image data, such as MRI and X-ray images obtained from medical institutions, to the app.

[0459] Step 2:

[0460] The terminal encrypts the lifestyle data entered by the user and the medical image data uploaded, ensuring data security before transmitting it to the server.

[0461] Step 3:

[0462] The server decompresses the received data and saves lifestyle data and medical image data to a cloud database. During this process, it standardizes the data format and checks for missing data or abnormal values. If problems are found, it requests data correction from the user via the terminal.

[0463] Step 4:

[0464] The server uses the organized data to input into a generative artificial intelligence model. This model calculates future health risks and predicted health conditions.

[0465] Step 5:

[0466] The server visualizes the predicted results and presents them as graphs and charts. This visualized data is presented in an intuitively easy-to-understand format.

[0467] Step 6:

[0468] The server generates specific lifestyle improvement actions along with visualized prediction results and sends this information to the user's terminal.

[0469] Step 7:

[0470] The terminal displays information sent from the server on its user interface, presenting the user with future health risks and recommended action plans.

[0471] Step 8:

[0472] Based on the information provided, users work to improve their lifestyle. If necessary, they receive professional support, such as scheduling appointments and providing nutritional guidance, through collaboration with external healthcare service providers.

[0473] (Example 1)

[0474] 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."

[0475] The present invention aims to appropriately predict a user's health status using their lifestyle and medical information, enabling them to understand their own health risks and take concrete measures to improve their lifestyle. Furthermore, it aims to solve the problem of data supplementation to improve the accuracy of predictions and to enable health follow-up through collaboration with external medical support services.

[0476] 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.

[0477] This invention includes a server that provides means for acquiring behavioral data and medical information collected from users, means for using a generative artificial intelligence algorithm to predict the user's health based on the acquired behavioral data and medical information, and means for visualizing and providing the predicted health risks to the user. This enables users to understand their own health risks and take concrete improvement measures. Furthermore, by supplementing missing data and coordinating with external medical support services, sustainable health management is achieved.

[0478] "Behavioral data" refers to information about the activities that users engage in in their daily lives, specifically including information such as diet, exercise frequency, and sleep duration.

[0479] "Medical information" refers to data related to a user's health status, such as diagnostic information, health checkup results, and medical imaging data obtained from medical institutions.

[0480] A "generative artificial intelligence algorithm" is a model trained on past medical and lifestyle data, and it is a technology that uses this model to predict a user's future health risks.

[0481] "Visualization" is the process of representing predicted health risks in the form of charts, graphs, and other visual representations so that users can understand them intuitively.

[0482] "Data completion" refers to the procedure of recollecting or supplementing missing information when necessary for prediction is unavailable.

[0483] "External medical support services" are services that provide support to users, such as scheduling appointments, health consultations, and nutritional guidance, through collaboration with medical institutions and specialists.

[0484] This invention is a system that predicts the user's health status and supports appropriate behavioral improvements. This system is primarily implemented using the user's terminal and a cloud server.

[0485] Users input data about their daily lifestyle habits and medical information obtained from healthcare institutions through a dedicated application. This allows users to accumulate their own health data in a database located in the cloud.

[0486] The device encrypts the collected data and sends it to the cloud server. This encryption process is crucial for protecting user privacy. Furthermore, the device is responsible for converting the stored data into an appropriate format.

[0487] The server receives encrypted data and stores it in a cloud database. The server then verifies for missing data and errors and performs appropriate data completion. During this process, it requests additional information from the user as needed.

[0488] The server inputs the stored data into a generating artificial intelligence algorithm to predict health status. The generating AI model is trained on a vast amount of historical data and has the ability to predict future health risks with high accuracy. An example of a prompt message would be a specific instruction such as, "Predict the risk of diabetes 6 months from now based on historical data."

[0489] The predicted health risks are visualized by the server and sent to the user's device in the form of graphs and charts. The device then displays this information in a format that is intuitively understandable to the user.

[0490] Furthermore, the server generates specific behavioral improvement measures based on the prediction results and notifies the user. For example, a user at risk of high blood pressure may be offered suggestions to reduce their salt intake and instructions for regular aerobic exercise.

[0491] Finally, the system allows users to access external medical support services as needed and obtain expert advice. This enables users to take appropriate responsibility for their own health, improve their behavior, and manage their health sustainably.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] Users input data on their daily lifestyle, such as their diet, exercise frequency, and sleep duration, as well as medical information such as medical images, through a dedicated app. The input data is stored as foundational data for predicting the user's health.

[0495] Step 2:

[0496] The terminal encrypts the data entered by the user and sends it to the cloud server. Here, it receives the user's lifestyle data and medical information as input, performs encryption to ensure data security, and generates encrypted data as output. The encrypted data is stored in the cloud database, making it available for subsequent processing by the server.

[0497] Step 3:

[0498] The server receives data stored in a cloud database and performs validation to detect data loss or errors. It receives encrypted data from the cloud as input and performs a validation process to maintain data integrity. If problems are found, the server requests the user to complete the data and outputs feedback to obtain accurate data.

[0499] Step 4:

[0500] The server inputs the validated data into a generating AI model to predict the user's future health status. Here, it performs analysis using a trained AI model based on past medical and lifestyle data. It receives the prepared user data as input and generates a prediction of health risks as output. For example, by giving the instruction "Predict cardiovascular risk based on lifestyle for the next 6 months," the AI ​​model calculates a specific risk.

[0501] Step 5:

[0502] The server visualizes the health risk predictions output from the generated AI model and sends them to the user's terminal. The visualized results are provided in the form of charts and graphs that are easy to understand intuitively. It receives prediction results from the AI ​​model as input and generates visualized information as output. This allows the user to visually understand their own health status.

[0503] Step 6:

[0504] The terminal displays the prediction results received from the server to the user, providing them in an easy-to-understand format. It receives visualization data from the server as input, converts it into a format that is easy for the user to understand, and outputs it. At this stage, the user can effectively understand their own health status and obtain the basic information to take further action.

[0505] Step 7:

[0506] The server generates and notifies the user of a specific lifestyle improvement action plan based on the prediction results. For example, if a risk of high blood pressure is identified, it will recommend reducing salt intake in meals and engaging in moderate exercise for at least 150 minutes per week. This makes it easier for users to take specific measures necessary to maintain their health.

[0507] Step 8:

[0508] The system integrates with external medical support services as needed to provide users with health follow-up services such as scheduling appointments and providing nutritional guidance. Based on the user's health risk information as input, it integrates with external services and provides users with expert advice and support as output. This allows users to receive comprehensive health management and effectively improve their own health status.

[0509] (Application Example 1)

[0510] 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."

[0511] In modern life, many people are at risk of lifestyle-related diseases, leading to serious problems such as increased medical costs and a decline in quality of life. Furthermore, providing appropriate exercise and dietary plans tailored to individual health conditions is difficult, and general health management methods often fail to deliver sufficient results. Therefore, there is a need for a system that accurately predicts each individual's health condition and provides an optimal improvement plan.

[0512] 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.

[0513] In this invention, the server includes means for acquiring lifestyle information and health image information collected from the user; means for using a generative artificial intelligence model to predict the user's future health status based on the acquired lifestyle information and health image information; means for visualizing and presenting the prediction results to the user; and means for proposing individual exercise plans and dietary plans based on the user's health status. This makes it possible to provide specific and effective lifestyle improvement actions tailored to each individual's health status.

[0514] A "user" is an individual who uses the system and provides information for health management purposes.

[0515] "Information regarding lifestyle habits" refers to data such as the user's daily diet, exercise frequency, and sleep duration.

[0516] "Health image information" refers to image data related to the user's health status, and includes diagnostic images obtained at medical institutions.

[0517] A "generative artificial intelligence model" is an algorithm trained on vast amounts of historical medical and lifestyle data, possessing the ability to predict future health conditions.

[0518] "Visualization" is a technique that displays analysis results in a way that is easy for users to understand, such as in the form of charts and graphs.

[0519] "Exercise and dietary plans" refer to specific types and frequencies of exercise, as well as meal content, that are suggested according to the user's health condition.

[0520] The system for implementing this invention consists of a user terminal and a cloud server. The terminal has a dedicated application installed for the user to input lifestyle information and health image information. The user inputs data such as daily meals, exercise frequency, and sleep duration through the application, and uploads health images taken at medical institutions as needed.

[0521] The device encrypts this data and sends it to the cloud server. The cloud server contains a cloud database for storing the data and a data processing module for converting it into a standardized format. After receiving the data, the server checks for missing or incorrect data and, if necessary, requests the user to complete the data via the device.

[0522] A generative AI model is deployed on a server and uses vast amounts of historical medical and lifestyle data as training data to predict the user's future health status. The model's analysis results are expressed in the form of numerical values ​​for specific health risks. The server then visualizes the prediction results in the form of charts and graphs and sends them to the user's terminal. On the terminal, the results are presented through an intuitive interface.

[0523] Furthermore, based on the analysis results, the server uses a generative AI model to suggest exercise and dietary plans tailored to each individual user. For example, a user identified as lacking exercise might be presented with an improvement plan such as "walking three times a week and focusing on a balanced diet."

[0524] For example, users who are not getting enough exercise will be prompted with a message such as, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health condition." By creating a customized plan tailored to the user's health condition in this way, it becomes possible to support more effective health management.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] Users input lifestyle information such as their daily diet, exercise frequency, and sleep duration using a dedicated application, and upload health images taken at medical institutions. This information is collected on the user's device and encrypted. The input information is encoded using an encryption algorithm for security purposes. The output is encrypted data.

[0528] Step 2:

[0529] The terminal sends encrypted data to the cloud server. The cloud server decodes the received encrypted data and converts it into a standardized format. The input is encrypted user data, and the output is a standardized dataset. The server uses a data processing module to perform data format conversion.

[0530] Step 3:

[0531] The server checks for missing or incorrect data within the standardized data and, if necessary, requests data completion from the user via the terminal. The input is standardized data, and the output is error-checked data. In this step, the data is scrutinized by a data validation algorithm.

[0532] Step 4:

[0533] The server inputs error-checked data into a generative AI model for analysis. The generative AI model uses a predictive algorithm to predict future health conditions based on past data. The input is error-checked data, and the output is a quantified prediction result regarding specific health risks.

[0534] Step 5:

[0535] The server visualizes the prediction results in charts and graphs, formats them in an intuitively understandable way, and then sends them to the terminal. The input is the prediction result, and the output is the visualized data. The server uses a visualization module to convert the data into a graphical format.

[0536] Step 6:

[0537] The terminal presents the user with visualized prediction results received from the server. The user can visually confirm their health risk. The input is visualized data, and the output is the next action based on the user's understanding.

[0538] Step 7:

[0539] The server utilizes the analysis results and generated AI models to suggest exercise and diet plans optimized for the user's health condition. For example, it might say, "We recommend walking three times a week." The input is visualized data and additional analysis results, and the output is an improvement plan.

[0540] Step 8:

[0541] Ultimately, users refer to the suggested exercise and diet plans on the device to help improve their lifestyle. The device displays prompts that encourage specific actions from the user. For example, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health status."

[0542] 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.

[0543] This invention is a system that collects information on the user's lifestyle and medical image information, and based on this, not only predicts the user's future health status but also supports lifestyle improvement by taking into account the user's emotional state. This system is implemented on the user's terminal and on a server that processes the data.

[0544] First, users use a smartphone app to record data on their daily lifestyle habits (diet, exercise, sleep). In addition, they upload test data obtained from medical institutions. The app incorporates an emotion engine that analyzes emotional indicators such as the user's choice of words in everyday conversation, response speed during input, and selected emojis to assess the user's emotional state.

[0545] The device encrypts the collected data and sends it to the server. The server stores the received lifestyle data, medical image information, and emotional data in a cloud database and standardizes the data. In the initial stages of data analysis, the server verifies the integrity of the data and requests supplementation from the user via the device as needed.

[0546] Next, the server uses this data to perform a health status prediction using a generative AI model. After receiving the prediction results, the server considers emotional data to generate improvement actions that are most acceptable and easy for the user to take. For example, if the emotional engine determines that the user is experiencing high levels of stress, the improvement actions may include suggestions for stress-reducing exercise or mindfulness.

[0547] Based on predicted health status and emotions, improvement actions are visualized on the server side and sent to the user's device. The device presents this information in an easy-to-understand manner, allowing the user to accurately understand their situation. It can also collaborate with external healthcare service providers as needed to receive expert support.

[0548] For example, if the emotional engine detects that a user is experiencing excessive stress, this information is presented along with suggestions for exercise habits, and the user can also book a professional counseling service through the app. This comprehensive approach allows users to actively engage in managing their own health while also considering the emotional aspects.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] Users input lifestyle information such as their daily meals, type and duration of exercise, and sleep duration using a dedicated app. They also upload MRI and X-ray images obtained at medical facilities to the app.

[0552] Step 2:

[0553] Users provide emotional data to the emotion engine through their daily use of the app. This is done by analyzing factors such as the user's typing speed, frequently used words and phrases, and selected emojis.

[0554] Step 3:

[0555] The device encrypts all collected lifestyle data, medical image data, and emotional data before sending them to the server, protecting data privacy and security.

[0556] Step 4:

[0557] The server stores all received data in a cloud database. It also standardizes the data, checks for errors, and, if necessary, requests data completion from the user via the terminal.

[0558] Step 5:

[0559] The server uses an artificial intelligence model generated based on lifestyle information and medical image data to predict the user's future health risks and health status.

[0560] Step 6:

[0561] After generating prediction results, the server incorporates emotional data from the emotion engine to customize lifestyle improvement actions based on the prediction results. During this process, it considers the user's current emotional state and generates suggestions to increase their feasibility.

[0562] Step 7:

[0563] The server sends visual prediction results and improvement actions tailored to the user's emotional state to the user's device in the form of graphs and charts.

[0564] Step 8:

[0565] The device displays the received information on its user interface, presenting it in a way that makes it easy for the user to understand their own health risks and possible corrective actions.

[0566] Step 9:

[0567] Users take actions to improve their lifestyle based on the information provided. During this process, they can access and book appointments with external healthcare service providers through the app as needed, receiving professional support.

[0568] (Example 2)

[0569] 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."

[0570] Modern health management systems typically predict health status based solely on users' lifestyles and medical information. However, since a user's emotional state can significantly impact their health, systems that fail to consider this factor cannot provide adequate support. Furthermore, if health improvement suggestions are not appropriate for the user's emotional state, their effectiveness may be reduced.

[0571] 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.

[0572] In this invention, the server includes means for acquiring information on habits and medical information collected from the user, means for performing emotion analysis and evaluating the user's emotional state, and means for using a generative artificial intelligence model to predict the user's future health state based on the acquired habit information, medical information, and emotional state. This enables more accurate predictions of health state that take into account the user's emotional state and the proposal of highly effective lifestyle improvement actions.

[0573] "Information about habits" refers to data about users' daily behaviors and patterns, such as eating, exercise, and sleep.

[0574] "Medical information" refers to diagnostic results and test data provided by medical institutions to assess the user's health status.

[0575] "Emotional analysis" is a process of analyzing the emotional trends of a user from their daily conversations and behaviors, and evaluating their emotional state.

[0576] A "generative artificial intelligence model" is a machine learning algorithm or data analysis system that predicts a user's future health status based on acquired data.

[0577] "Improvement actions" refer to specific actions or guidelines proposed to improve the health and emotional state of users.

[0578] "Visualization" refers to graphical methods for displaying data and analysis results in a format that is easy for users to understand.

[0579] "External medical service providers" refer to medical facilities or specialized organizations that collaborate with the system to manage and support the health of users.

[0580] This invention is a system that uses a generative AI model to predict a user's health status based on information about their habits and medical information, and proposes improvement actions. This system is primarily implemented using the user's terminal and server.

[0581] Users input daily lifestyle data using devices such as smartphones and tablets. This includes information such as diet, exercise history, and sleep duration. They can also upload diagnostic results and test data obtained from medical institutions through the application.

[0582] The device has a built-in emotion engine that performs sentiment analysis. This engine analyzes the user's everyday conversation language, input speed, selected emojis, etc., to determine the user's emotional state.

[0583] The device uses the AES encryption algorithm to encrypt collected habit data, medical information, and emotional data. This data is securely transmitted to the server via the SSL / TLS protocol.

[0584] The server receives this data, converts it to a standardized data format in the cloud environment, and stores it. This standardized data is then input into the generating AI model as prompts. An example of a prompt might be: "A 40-year-old male, with moderate daily exercise, high current stress level, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?"

[0585] The AI ​​model predicts the user's health status based on this prompt. Based on the prediction, the server considers the user's emotional state and generates the optimal improvement action. This action may include suggestions for stress-reducing exercise or mindfulness practice.

[0586] The generated improvement actions are clearly displayed on the screen using a visualization library and presented to the user via their device. Through this information, users can receive specific actionable guidelines based on their current health and emotional state. Furthermore, they can utilize integration with external medical service providers as needed to receive additional expert support.

[0587] In this way, the present invention can provide advanced support for health management while taking into account the user's emotional state.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] Users input daily lifestyle data into a smartphone app. This includes meal records, exercise details, and sleep duration. Users also upload test data obtained from medical institutions to the app. The entered data is initially organized by the app.

[0591] Step 2:

[0592] The terminal receives collected lifestyle and medical data and encrypts this data using the AES encryption algorithm. It receives lifestyle and medical data as input and outputs encrypted data. This data is securely transmitted to the server using the SSL / TLS protocol.

[0593] Step 3:

[0594] The server receives encrypted data sent from the terminal and temporarily stores it in a dedicated receive buffer. Next, it decrypts the data and verifies its integrity. It receives encrypted data as input and obtains decrypted, standardized data as output.

[0595] Step 4:

[0596] The server stores the received data in a cloud database and standardizes the data. During this process, it converts the data into an appropriate format, making it available for use in the next processing step. It receives decoded data as input and obtains standard data that can be input into the generated AI model as output.

[0597] Step 5:

[0598] The server generates prompts using standardized data. For example, a possible prompt might be: "A 40-year-old male with moderate daily exercise, high current stress levels, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?" It accepts standardized data as input and generates prompts as output.

[0599] Step 6:

[0600] A generative AI model is input with prompt text to predict the user's future health status. The AI ​​model applies machine learning algorithms to generate health prediction results. It receives prompt text as input and health prediction results as output.

[0601] Step 7:

[0602] The server generates improvement actions based on the generated health prediction results, taking into account emotional data. Natural language processing techniques are used to create actions in a way that is easy for the user to understand. The server receives health prediction results and emotional data as input, and outputs improvement actions.

[0603] Step 8:

[0604] The improvement actions and health prediction results generated on the server are visualized and sent to the user's terminal. Using a visualization library, the data is displayed as graphs and charts. Improvement actions and health prediction results are received as input, and visualized data is obtained as output.

[0605] Step 9:

[0606] The device receives transmitted visualization data and presents it to the user in an easy-to-understand manner. This allows the user to accurately understand their health status and the necessary improvement actions. Furthermore, if needed, the device can connect with external medical service providers for additional support. It receives visualization data as input and presents information to the user as output.

[0607] (Application Example 2)

[0608] 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."

[0609] In recent years, personal health management has required comprehensive management that considers not only physical health but also emotions and stress levels. However, conventional systems have separated health predictions based on lifestyle and medical information from the analysis of emotional states, making it difficult to consistently provide personalized health advice. Furthermore, the lack of a system that provides specific purchasing advice based on emotional states often leads to confusion for users when choosing products and services that can help improve their health. This invention aims to solve these problems and provide optimal health management and purchasing support for individuals.

[0610] 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.

[0611] In this invention, the server includes means for collecting information on lifestyle habits, medical image information, and emotional state obtained from the user; means for using a generative artificial intelligence model to predict the user's future health state based on the acquired lifestyle habit information, medical image information, and emotional state information, and to propose healthy products and services; and means for visualizing the prediction results and presenting the user with purchasing advice that takes the emotional state into consideration. As a result, the user can receive specific and emotionally sensitive product suggestions tailored to their health state, enabling them to manage their health more appropriately.

[0612] A "user" is an individual who uses this system and provides the necessary information.

[0613] "Lifestyle information" refers to data about users' behaviors such as diet, exercise, and sleep.

[0614] "Medical image information" refers to image data obtained at a medical institution that shows the state of a person's physical health.

[0615] "Emotional state" refers to the psychological state that indicates the user's mood and stress level.

[0616] A "generative artificial intelligence model" is a computer model used to predict future health conditions based on data analysis.

[0617] "Visualization" is the process of converting and presenting analysis results in a format that is easy for users to understand.

[0618] "Health product recommendations" refer to the introduction of products and services that contribute to improving the health of users.

[0619] The system for implementing this invention consists of a user's terminal and a server that analyzes the data.

[0620] First, users record data about their daily lifestyle habits (e.g., diet, exercise records, sleep patterns) using a smartphone application. The app also analyzes keywords in everyday conversations, user behavior during input, and selected emojis to understand their emotional state. Furthermore, users are required to upload various test results and medical image information obtained from medical institutions.

[0621] The terminal encrypts the collected data and securely transmits it to the server. To efficiently process large amounts of data, a system is in place that utilizes cloud services. The servers use cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure.

[0622] The server stores received lifestyle and medical data in a cloud database and uses a machine learning system to standardize and analyze the data. Here, a generative AI model using software such as TensorFlow predicts the user's health status. Furthermore, by providing emotional data as prompts to the AI ​​model, it generates health-related purchasing advice that takes the user's emotional state into account.

[0623] For example, if emotional analysis determines that a user is experiencing stress, the system can suggest health foods or mindfulness-related products that can help reduce stress. This information is sent back to the user's smartphone in an easy-to-understand, visualized format, making it easier for the user to choose appropriate actions based on their situation.

[0624] As a concrete example, the prompt text would be something like, "Based on the following purchase history and sentiment analysis data, please create a list of healthy products," which is then input into the AI, and the generated list is provided to the user.

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] Users open a smartphone app and record information about their daily lifestyle (such as diet, exercise records, and sleep patterns) and data about their emotional state (such as keywords, emojis, and typing speed). Users also upload test results and medical images obtained from medical institutions to the app. This input data is temporarily stored on the user's device.

[0628] Step 2:

[0629] The device encrypts the collected data end-to-end before sending it to the server via a cloud service. The input consists of encrypted lifestyle data, emotional data, and medical data, while the output is a secure data transfer to the server. TLS (Transport Layer Security) is used to ensure security.

[0630] Step 3:

[0631] The server stores the received data in a cloud database and performs data standardization. Here, missing data is imputed and the format is unified, ensuring consistency across various data formats. The input is encrypted data, and the output is standardized, analyzable data.

[0632] Step 4:

[0633] The server uses a generative AI model to derive emotional states and health predictions from data. The input is standardized data, and the output is a health state prediction and emotionally conscious purchasing advice. Complex multivariate analysis is performed using tools such as TensorFlow.

[0634] Step 5:

[0635] The server visually organizes the prediction results and generated purchase advice, designing them in a user-friendly format. The input is the output of the AI ​​model, and the output is a visualized set of information. The results are then formatted in graphs and charts.

[0636] Step 6:

[0637] The server encrypts the visualized information and then sends it to the user's terminal using a secure protocol. The input is the visualized information, and the output is the data transfer to the user's terminal. Data security is ensured through the use of TLS.

[0638] Step 7:

[0639] Through the app, users receive predictive results and purchasing advice from the server, and select products and services that suit their health condition. The input is visualized data from the server, and the output is the user's decision and actions. Based on the suggestions, the user selects the next step.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention is a system that aims to prevent lifestyle-related diseases by collecting and analyzing information on a user's lifestyle and medical image information, and predicting their future health status. This system is implemented using the user's smartphone or computer (hereinafter referred to as "terminal") and a cloud server (hereinafter referred to as "server") for data processing.

[0658] First, users input information about their daily diet, exercise frequency, sleep duration, and other details through a dedicated app. Similarly, they upload medical image information obtained during health checkups or medical consultations, allowing the necessary data to be accumulated in the system.

[0659] The terminal encrypts and sends this data to the server. The server stores the received data in a cloud database and converts it to a standardized format. After receiving the data, the server checks for any data loss or errors and requests the user to complete the data if necessary.

[0660] Next, the server uses a generative AI machine learning model to analyze the stored data. This model is trained on a vast amount of historical medical and lifestyle data and has the ability to predict future health conditions. The analysis results are quantified as specific health risks (e.g., the likelihood of developing cardiovascular disease or diabetes).

[0661] The server visualizes these prediction results in charts and graphs and sends them to the user's terminal. The terminal then presents the results to the user in a format that is easy to understand and intuitive.

[0662] Furthermore, based on these results, the server generates a specific action plan for the user to improve their current lifestyle. For example, a user predicted to be at high risk of high blood pressure might be advised to reduce their salt intake and recommended to do aerobic exercise a few times a week.

[0663] Finally, the system collaborates with external healthcare service providers to offer necessary appointments and expert advice. This empowers users to take concrete responsibility for their own health and manage it effectively. For example, a user identified as being at risk of high blood pressure can book a consultation with a nutritionist through the app and receive specific guidance on improving their daily diet.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] Users record information about their daily lifestyle, such as diet, exercise, and sleep, using a dedicated app. They also upload image data, such as MRI and X-ray images obtained from medical institutions, to the app.

[0667] Step 2:

[0668] The terminal encrypts the lifestyle data entered by the user and the medical image data uploaded, ensuring data security before transmitting it to the server.

[0669] Step 3:

[0670] The server decompresses the received data and saves lifestyle data and medical image data to a cloud database. During this process, it standardizes the data format and checks for missing data or abnormal values. If problems are found, it requests data correction from the user via the terminal.

[0671] Step 4:

[0672] The server uses the organized data to input into a generative artificial intelligence model. This model calculates future health risks and predicted health conditions.

[0673] Step 5:

[0674] The server visualizes the predicted results and presents them as graphs and charts. This visualized data is presented in an intuitively easy-to-understand format.

[0675] Step 6:

[0676] The server generates specific lifestyle improvement actions along with visualized prediction results and sends this information to the user's terminal.

[0677] Step 7:

[0678] The terminal displays information sent from the server on its user interface, presenting the user with future health risks and recommended action plans.

[0679] Step 8:

[0680] Based on the information provided, users work to improve their lifestyle. If necessary, they receive professional support, such as scheduling appointments and providing nutritional guidance, through collaboration with external healthcare service providers.

[0681] (Example 1)

[0682] 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".

[0683] The present invention aims to appropriately predict a user's health status using their lifestyle and medical information, enabling them to understand their own health risks and take concrete measures to improve their lifestyle. Furthermore, it aims to solve the problem of data supplementation to improve the accuracy of predictions and to enable health follow-up through collaboration with external medical support services.

[0684] 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.

[0685] This invention includes a server that provides means for acquiring behavioral data and medical information collected from users, means for using a generative artificial intelligence algorithm to predict the user's health based on the acquired behavioral data and medical information, and means for visualizing and providing the predicted health risks to the user. This enables users to understand their own health risks and take concrete improvement measures. Furthermore, by supplementing missing data and coordinating with external medical support services, sustainable health management is achieved.

[0686] "Behavioral data" refers to information about the activities that users engage in in their daily lives, specifically including information such as diet, exercise frequency, and sleep duration.

[0687] "Medical information" refers to data related to a user's health status, such as diagnostic information, health checkup results, and medical imaging data obtained from medical institutions.

[0688] A "generative artificial intelligence algorithm" is a model trained on past medical and lifestyle data, and it is a technology that uses this model to predict a user's future health risks.

[0689] "Visualization" is the process of representing predicted health risks in the form of charts, graphs, and other visual representations so that users can understand them intuitively.

[0690] "Data completion" refers to the procedure of recollecting or supplementing missing information when necessary for prediction is unavailable.

[0691] "External medical support services" are services that provide support to users, such as scheduling appointments, health consultations, and nutritional guidance, through collaboration with medical institutions and specialists.

[0692] This invention is a system that predicts the user's health status and supports appropriate behavioral improvements. This system is primarily implemented using the user's terminal and a cloud server.

[0693] Users input data about their daily lifestyle habits and medical information obtained from healthcare institutions through a dedicated application. This allows users to accumulate their own health data in a database located in the cloud.

[0694] The device encrypts the collected data and sends it to the cloud server. This encryption process is crucial for protecting user privacy. Furthermore, the device is responsible for converting the stored data into an appropriate format.

[0695] The server receives encrypted data and stores it in a cloud database. The server then verifies for missing data and errors and performs appropriate data completion. During this process, it requests additional information from the user as needed.

[0696] The server inputs the stored data into a generating artificial intelligence algorithm to predict health status. The generating AI model is trained on a vast amount of historical data and has the ability to predict future health risks with high accuracy. An example of a prompt message would be a specific instruction such as, "Predict the risk of diabetes 6 months from now based on historical data."

[0697] The predicted health risks are visualized by the server and sent to the user's device in the form of graphs and charts. The device then displays this information in a format that is intuitively understandable to the user.

[0698] Furthermore, the server generates specific behavioral improvement measures based on the prediction results and notifies the user. For example, a user at risk of high blood pressure may be offered suggestions to reduce their salt intake and instructions for regular aerobic exercise.

[0699] Finally, the system allows users to access external medical support services as needed and obtain expert advice. This enables users to take appropriate responsibility for their own health, improve their behavior, and manage their health sustainably.

[0700] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0701] Step 1:

[0702] Users input data on their daily lifestyle, such as their diet, exercise frequency, and sleep duration, as well as medical information such as medical images, through a dedicated app. The input data is stored as foundational data for predicting the user's health.

[0703] Step 2:

[0704] The terminal encrypts the data entered by the user and sends it to the cloud server. Here, it receives the user's lifestyle data and medical information as input, performs encryption to ensure data security, and generates encrypted data as output. The encrypted data is stored in the cloud database, making it available for subsequent processing by the server.

[0705] Step 3:

[0706] The server receives data stored in a cloud database and performs validation to detect data loss or errors. It receives encrypted data from the cloud as input and performs a validation process to maintain data integrity. If problems are found, the server requests the user to complete the data and outputs feedback to obtain accurate data.

[0707] Step 4:

[0708] The server inputs the validated data into a generating AI model to predict the user's future health status. Here, it performs analysis using a trained AI model based on past medical and lifestyle data. It receives the prepared user data as input and generates a prediction of health risks as output. For example, by giving the instruction "Predict cardiovascular risk based on lifestyle for the next 6 months," the AI ​​model calculates a specific risk.

[0709] Step 5:

[0710] The server visualizes the health risk predictions output from the generated AI model and sends them to the user's terminal. The visualized results are provided in the form of charts and graphs that are easy to understand intuitively. It receives prediction results from the AI ​​model as input and generates visualized information as output. This allows the user to visually understand their own health status.

[0711] Step 6:

[0712] The terminal displays the prediction results received from the server to the user, providing them in an easy-to-understand format. It receives visualization data from the server as input, converts it into a format that is easy for the user to understand, and outputs it. At this stage, the user can effectively understand their own health status and obtain the basic information to take further action.

[0713] Step 7:

[0714] The server generates and notifies the user of a specific lifestyle improvement action plan based on the prediction results. For example, if a risk of high blood pressure is identified, it will recommend reducing salt intake in meals and engaging in moderate exercise for at least 150 minutes per week. This makes it easier for users to take specific measures necessary to maintain their health.

[0715] Step 8:

[0716] The system integrates with external medical support services as needed to provide users with health follow-up services such as scheduling appointments and providing nutritional guidance. Based on the user's health risk information as input, it integrates with external services and provides users with expert advice and support as output. This allows users to receive comprehensive health management and effectively improve their own health status.

[0717] (Application Example 1)

[0718] 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".

[0719] In modern life, many people are at risk of lifestyle-related diseases, leading to serious problems such as increased medical costs and a decline in quality of life. Furthermore, providing appropriate exercise and dietary plans tailored to individual health conditions is difficult, and general health management methods often fail to deliver sufficient results. Therefore, there is a need for a system that accurately predicts each individual's health condition and provides an optimal improvement plan.

[0720] 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.

[0721] In this invention, the server includes means for acquiring lifestyle information and health image information collected from the user; means for using a generative artificial intelligence model to predict the user's future health status based on the acquired lifestyle information and health image information; means for visualizing and presenting the prediction results to the user; and means for proposing individual exercise plans and dietary plans based on the user's health status. This makes it possible to provide specific and effective lifestyle improvement actions tailored to each individual's health status.

[0722] A "user" is an individual who uses the system and provides information for health management purposes.

[0723] "Information regarding lifestyle habits" refers to data such as the user's daily diet, exercise frequency, and sleep duration.

[0724] "Health image information" refers to image data related to the user's health status, and includes diagnostic images obtained at medical institutions.

[0725] A "generative artificial intelligence model" is an algorithm trained on vast amounts of historical medical and lifestyle data, possessing the ability to predict future health conditions.

[0726] "Visualization" is a technique that displays analysis results in a way that is easy for users to understand, such as in the form of charts and graphs.

[0727] "Exercise and dietary plans" refer to specific types and frequencies of exercise, as well as meal content, that are suggested according to the user's health condition.

[0728] The system for implementing this invention consists of a user terminal and a cloud server. The terminal has a dedicated application installed for the user to input lifestyle information and health image information. The user inputs data such as daily meals, exercise frequency, and sleep duration through the application, and uploads health images taken at medical institutions as needed.

[0729] The device encrypts this data and sends it to the cloud server. The cloud server contains a cloud database for storing the data and a data processing module for converting it into a standardized format. After receiving the data, the server checks for missing or incorrect data and, if necessary, requests the user to complete the data via the device.

[0730] A generative AI model is deployed on a server and uses vast amounts of historical medical and lifestyle data as training data to predict the user's future health status. The model's analysis results are expressed in the form of numerical values ​​for specific health risks. The server then visualizes the prediction results in the form of charts and graphs and sends them to the user's terminal. On the terminal, the results are presented through an intuitive interface.

[0731] Furthermore, based on the analysis results, the server uses a generative AI model to suggest exercise and dietary plans tailored to each individual user. For example, a user identified as lacking exercise might be presented with an improvement plan such as "walking three times a week and focusing on a balanced diet."

[0732] For example, users who are not getting enough exercise will be prompted with a message such as, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health condition." By creating a customized plan tailored to the user's health condition in this way, it becomes possible to support more effective health management.

[0733] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0734] Step 1:

[0735] Users input lifestyle information such as their daily diet, exercise frequency, and sleep duration using a dedicated application, and upload health images taken at medical institutions. This information is collected on the user's device and encrypted. The input information is encoded using an encryption algorithm for security purposes. The output is encrypted data.

[0736] Step 2:

[0737] The terminal sends encrypted data to the cloud server. The cloud server decodes the received encrypted data and converts it into a standardized format. The input is encrypted user data, and the output is a standardized dataset. The server uses a data processing module to perform data format conversion.

[0738] Step 3:

[0739] The server checks for missing or incorrect data within the standardized data and, if necessary, requests data completion from the user via the terminal. The input is standardized data, and the output is error-checked data. In this step, the data is scrutinized by a data validation algorithm.

[0740] Step 4:

[0741] The server inputs error-checked data into a generative AI model for analysis. The generative AI model uses a predictive algorithm to predict future health conditions based on past data. The input is error-checked data, and the output is a quantified prediction result regarding specific health risks.

[0742] Step 5:

[0743] The server visualizes the prediction results in charts and graphs, formats them in an intuitively understandable way, and then sends them to the terminal. The input is the prediction result, and the output is the visualized data. The server uses a visualization module to convert the data into a graphical format.

[0744] Step 6:

[0745] The terminal presents the user with visualized prediction results received from the server. The user can visually confirm their health risk. The input is visualized data, and the output is the next action based on the user's understanding.

[0746] Step 7:

[0747] The server utilizes the analysis results and generated AI models to suggest exercise and diet plans optimized for the user's health condition. For example, it might say, "We recommend walking three times a week." The input is visualized data and additional analysis results, and the output is an improvement plan.

[0748] Step 8:

[0749] Ultimately, users refer to the suggested exercise and diet plans on the device to help improve their lifestyle. The device displays prompts that encourage specific actions from the user. For example, "Please enter your recent exercise data and dietary information. We will create a personalized training plan based on your health status."

[0750] 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.

[0751] This invention is a system that collects information on the user's lifestyle and medical image information, and based on this, not only predicts the user's future health status but also supports lifestyle improvement by taking into account the user's emotional state. This system is implemented on the user's terminal and on a server that processes the data.

[0752] First, users use a smartphone app to record data on their daily lifestyle habits (diet, exercise, sleep). In addition, they upload test data obtained from medical institutions. The app incorporates an emotion engine that analyzes emotional indicators such as the user's choice of words in everyday conversation, response speed during input, and selected emojis to assess the user's emotional state.

[0753] The device encrypts the collected data and sends it to the server. The server stores the received lifestyle data, medical image information, and emotional data in a cloud database and standardizes the data. In the initial stages of data analysis, the server verifies the integrity of the data and requests supplementation from the user via the device as needed.

[0754] Next, the server uses this data to perform a health status prediction using a generative AI model. After receiving the prediction results, the server considers emotional data to generate improvement actions that are most acceptable and easy for the user to take. For example, if the emotional engine determines that the user is experiencing high levels of stress, the improvement actions may include suggestions for stress-reducing exercise or mindfulness.

[0755] Based on predicted health status and emotions, improvement actions are visualized on the server side and sent to the user's device. The device presents this information in an easy-to-understand manner, allowing the user to accurately understand their situation. It can also collaborate with external healthcare service providers as needed to receive expert support.

[0756] For example, if the emotional engine detects that a user is experiencing excessive stress, this information is presented along with suggestions for exercise habits, and the user can also book a professional counseling service through the app. This comprehensive approach allows users to actively engage in managing their own health while also considering the emotional aspects.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] Users input lifestyle information such as their daily meals, type and duration of exercise, and sleep duration using a dedicated app. They also upload MRI and X-ray images obtained at medical facilities to the app.

[0760] Step 2:

[0761] Users provide emotional data to the emotion engine through their daily use of the app. This is done by analyzing factors such as the user's typing speed, frequently used words and phrases, and selected emojis.

[0762] Step 3:

[0763] The device encrypts all collected lifestyle data, medical image data, and emotional data before sending them to the server, protecting data privacy and security.

[0764] Step 4:

[0765] The server stores all received data in a cloud database. It also standardizes the data, checks for errors, and, if necessary, requests data completion from the user via the terminal.

[0766] Step 5:

[0767] The server uses an artificial intelligence model generated based on lifestyle information and medical image data to predict the user's future health risks and health status.

[0768] Step 6:

[0769] After generating prediction results, the server incorporates emotional data from the emotion engine to customize lifestyle improvement actions based on the prediction results. During this process, it considers the user's current emotional state and generates suggestions to increase their feasibility.

[0770] Step 7:

[0771] The server sends visual prediction results and improvement actions tailored to the user's emotional state to the user's device in the form of graphs and charts.

[0772] Step 8:

[0773] The device displays the received information on its user interface, presenting it in a way that makes it easy for the user to understand their own health risks and possible corrective actions.

[0774] Step 9:

[0775] Users take actions to improve their lifestyle based on the information provided. During this process, they can access and book appointments with external healthcare service providers through the app as needed, receiving professional support.

[0776] (Example 2)

[0777] 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".

[0778] Modern health management systems typically predict health status based solely on users' lifestyles and medical information. However, since a user's emotional state can significantly impact their health, systems that fail to consider this factor cannot provide adequate support. Furthermore, if health improvement suggestions are not appropriate for the user's emotional state, their effectiveness may be reduced.

[0779] 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.

[0780] In this invention, the server includes means for acquiring information on habits and medical information collected from the user, means for performing emotion analysis and evaluating the user's emotional state, and means for using a generative artificial intelligence model to predict the user's future health state based on the acquired habit information, medical information, and emotional state. This enables more accurate predictions of health state that take into account the user's emotional state and the proposal of highly effective lifestyle improvement actions.

[0781] "Information about habits" refers to data about users' daily behaviors and patterns, such as eating, exercise, and sleep.

[0782] "Medical information" refers to diagnostic results and test data provided by medical institutions to assess the user's health status.

[0783] "Emotional analysis" is a process of analyzing the emotional trends of a user from their daily conversations and behaviors, and evaluating their emotional state.

[0784] A "generative artificial intelligence model" is a machine learning algorithm or data analysis system that predicts a user's future health status based on acquired data.

[0785] "Improvement actions" refer to specific actions or guidelines proposed to improve the health and emotional state of users.

[0786] "Visualization" refers to graphical methods for displaying data and analysis results in a format that is easy for users to understand.

[0787] "External medical service providers" refer to medical facilities or specialized organizations that collaborate with the system to manage and support the health of users.

[0788] This invention is a system that uses a generative AI model to predict a user's health status based on information about their habits and medical information, and proposes improvement actions. This system is primarily implemented using the user's terminal and server.

[0789] Users input daily lifestyle data using devices such as smartphones and tablets. This includes information such as diet, exercise history, and sleep duration. They can also upload diagnostic results and test data obtained from medical institutions through the application.

[0790] The device has a built-in emotion engine that performs sentiment analysis. This engine analyzes the user's everyday conversation language, input speed, selected emojis, etc., to determine the user's emotional state.

[0791] The device uses the AES encryption algorithm to encrypt collected habit data, medical information, and emotional data. This data is securely transmitted to the server via the SSL / TLS protocol.

[0792] The server receives this data, converts it to a standardized data format in the cloud environment, and stores it. This standardized data is then input into the generating AI model as prompts. An example of a prompt might be: "A 40-year-old male, with moderate daily exercise, high current stress level, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?"

[0793] The AI ​​model predicts the user's health status based on this prompt. Based on the prediction, the server considers the user's emotional state and generates the optimal improvement action. This action may include suggestions for stress-reducing exercise or mindfulness practice.

[0794] The generated improvement actions are clearly displayed on the screen using a visualization library and presented to the user via their device. Through this information, users can receive specific actionable guidelines based on their current health and emotional state. Furthermore, they can utilize integration with external medical service providers as needed to receive additional expert support.

[0795] In this way, the present invention can provide advanced support for health management while taking into account the user's emotional state.

[0796] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0797] Step 1:

[0798] Users input daily lifestyle data into a smartphone app. This includes meal records, exercise details, and sleep duration. Users also upload test data obtained from medical institutions to the app. The entered data is initially organized by the app.

[0799] Step 2:

[0800] The terminal receives collected lifestyle and medical data and encrypts this data using the AES encryption algorithm. It receives lifestyle and medical data as input and outputs encrypted data. This data is securely transmitted to the server using the SSL / TLS protocol.

[0801] Step 3:

[0802] The server receives encrypted data sent from the terminal and temporarily stores it in a dedicated receive buffer. Next, it decrypts the data and verifies its integrity. It receives encrypted data as input and obtains decrypted, standardized data as output.

[0803] Step 4:

[0804] The server stores the received data in a cloud database and standardizes the data. During this process, it converts the data into an appropriate format, making it available for use in the next processing step. It receives decoded data as input and obtains standard data that can be input into the generated AI model as output.

[0805] Step 5:

[0806] The server generates prompts using standardized data. For example, a possible prompt might be: "A 40-year-old male with moderate daily exercise, high current stress levels, and normal blood pressure in a recent health checkup. What lifestyle improvements would you recommend?" It accepts standardized data as input and generates prompts as output.

[0807] Step 6:

[0808] A generative AI model is input with prompt text to predict the user's future health status. The AI ​​model applies machine learning algorithms to generate health prediction results. It receives prompt text as input and health prediction results as output.

[0809] Step 7:

[0810] The server generates improvement actions based on the generated health prediction results, taking into account emotional data. Natural language processing techniques are used to create actions in a way that is easy for the user to understand. The server receives health prediction results and emotional data as input, and outputs improvement actions.

[0811] Step 8:

[0812] The improvement actions and health prediction results generated on the server are visualized and sent to the user's terminal. Using a visualization library, the data is displayed as graphs and charts. Improvement actions and health prediction results are received as input, and visualized data is obtained as output.

[0813] Step 9:

[0814] The device receives transmitted visualization data and presents it to the user in an easy-to-understand manner. This allows the user to accurately understand their health status and the necessary improvement actions. Furthermore, if needed, the device can connect with external medical service providers for additional support. It receives visualization data as input and presents information to the user as output.

[0815] (Application Example 2)

[0816] 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".

[0817] In recent years, personal health management has required comprehensive management that considers not only physical health but also emotions and stress levels. However, conventional systems have separated health predictions based on lifestyle and medical information from the analysis of emotional states, making it difficult to consistently provide personalized health advice. Furthermore, the lack of a system that provides specific purchasing advice based on emotional states often leads to confusion for users when choosing products and services that can help improve their health. This invention aims to solve these problems and provide optimal health management and purchasing support for individuals.

[0818] 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.

[0819] In this invention, the server includes means for collecting information on lifestyle habits, medical image information, and emotional state obtained from the user; means for using a generative artificial intelligence model to predict the user's future health state based on the acquired lifestyle habit information, medical image information, and emotional state information, and to propose healthy products and services; and means for visualizing the prediction results and presenting the user with purchasing advice that takes the emotional state into consideration. As a result, the user can receive specific and emotionally sensitive product suggestions tailored to their health state, enabling them to manage their health more appropriately.

[0820] A "user" is an individual who uses this system and provides the necessary information.

[0821] "Lifestyle information" refers to data about users' behaviors such as diet, exercise, and sleep.

[0822] "Medical image information" refers to image data obtained at a medical institution that shows the state of a person's physical health.

[0823] "Emotional state" refers to the psychological state that indicates the user's mood and stress level.

[0824] A "generative artificial intelligence model" is a computer model used to predict future health conditions based on data analysis.

[0825] "Visualization" is the process of converting and presenting analysis results in a format that is easy for users to understand.

[0826] "Health product recommendations" refer to the introduction of products and services that contribute to improving the health of users.

[0827] The system for implementing this invention consists of a user's terminal and a server that analyzes the data.

[0828] First, users record data about their daily lifestyle habits (e.g., diet, exercise records, sleep patterns) using a smartphone application. The app also analyzes keywords in everyday conversations, user behavior during input, and selected emojis to understand their emotional state. Furthermore, users are required to upload various test results and medical image information obtained from medical institutions.

[0829] The terminal encrypts the collected data and securely transmits it to the server. To efficiently process large amounts of data, a system is in place that utilizes cloud services. The servers use cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure.

[0830] The server stores received lifestyle and medical data in a cloud database and uses a machine learning system to standardize and analyze the data. Here, a generative AI model using software such as TensorFlow predicts the user's health status. Furthermore, by providing emotional data as prompts to the AI ​​model, it generates health-related purchasing advice that takes the user's emotional state into account.

[0831] For example, if emotional analysis determines that a user is experiencing stress, the system can suggest health foods or mindfulness-related products that can help reduce stress. This information is sent back to the user's smartphone in an easy-to-understand, visualized format, making it easier for the user to choose appropriate actions based on their situation.

[0832] As a concrete example, the prompt text would be something like, "Based on the following purchase history and sentiment analysis data, please create a list of healthy products," which is then input into the AI, and the generated list is provided to the user.

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] Users open a smartphone app and record information about their daily lifestyle (such as diet, exercise records, and sleep patterns) and data about their emotional state (such as keywords, emojis, and typing speed). Users also upload test results and medical images obtained from medical institutions to the app. This input data is temporarily stored on the user's device.

[0836] Step 2:

[0837] The device encrypts the collected data end-to-end before sending it to the server via a cloud service. The input consists of encrypted lifestyle data, emotional data, and medical data, while the output is a secure data transfer to the server. TLS (Transport Layer Security) is used to ensure security.

[0838] Step 3:

[0839] The server stores the received data in a cloud database and performs data standardization. Here, missing data is imputed and the format is unified, ensuring consistency across various data formats. The input is encrypted data, and the output is standardized, analyzable data.

[0840] Step 4:

[0841] The server uses a generative AI model to derive emotional states and health predictions from data. The input is standardized data, and the output is a health state prediction and emotionally conscious purchasing advice. Complex multivariate analysis is performed using tools such as TensorFlow.

[0842] Step 5:

[0843] The server visually organizes the prediction results and generated purchase advice, designing them in a user-friendly format. The input is the output of the AI ​​model, and the output is a visualized set of information. The results are then formatted in graphs and charts.

[0844] Step 6:

[0845] The server encrypts the visualized information and then sends it to the user's terminal using a secure protocol. The input is the visualized information, and the output is the data transfer to the user's terminal. Data security is ensured through the use of TLS.

[0846] Step 7:

[0847] Through the app, users receive predictive results and purchasing advice from the server, and select products and services that suit their health condition. The input is visualized data from the server, and the output is the user's decision and actions. Based on the suggestions, the user selects the next step.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] A means of acquiring lifestyle information and medical image information collected from users,

[0872] A means of using a generative artificial intelligence model to predict the user's future health status based on acquired lifestyle information and medical image information,

[0873] A means of visualizing and presenting prediction results to the user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for suggesting specific lifestyle improvement actions to the user based on visualized prediction results.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means of providing support for the user's health management and medical treatment in cooperation with external medical service providers.

[0879] "Example 1"

[0880] (Claim 1)

[0881] Means for acquiring behavioral data and medical information collected from users,

[0882] A means of using a generative artificial intelligence algorithm to predict the user's health based on acquired behavioral data and medical information,

[0883] A means of visualizing and providing users with predicted health risks,

[0884] A means of acquiring information to compensate for missing data in order to improve prediction accuracy,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for showing users specific behavioral improvement measures based on visualized health risks.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising means for collaborating with external medical support services to provide health follow-up for users.

[0890] "Application Example 1"

[0891] (Claim 1)

[0892] A means of acquiring lifestyle information and health image information collected from users,

[0893] A means of using a generative artificial intelligence model to predict a user's future health status based on acquired lifestyle information and health image information,

[0894] A means of visualizing and presenting prediction results to the user,

[0895] A means of proposing individualized exercise and dietary plans based on the user's health condition,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for suggesting specific lifestyle improvement actions to the user based on visualized prediction results and processing the data using a cloud environment.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for providing support for users' health management and medical treatment in cooperation with external health service providers.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] Means for obtaining information on habits and medical information collected from users,

[0904] A means of conducting emotion analysis and evaluating the emotional state of users,

[0905] A means of using a generative artificial intelligence model to predict a user's future health status based on acquired habit information, medical information, and emotional state,

[0906] A means for generating lifestyle improvement actions that take into account emotional state based on the generated predictions,

[0907] A means of visualizing and presenting the generated improvement actions to the user,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, further comprising means for suggesting specific improvement actions to the user based on visualized prediction results and emotional state.

[0911] (Claim 3)

[0912] The system according to claim 1, further comprising means for providing health management and support to users in cooperation with external medical service organizations.

[0913] "Application example 2 when combining with an emotional engine"

[0914] (Claim 1)

[0915] A means of collecting information on lifestyle habits, medical imaging information, and emotional state obtained from users,

[0916] A means of using a generative artificial intelligence model to predict the user's future health status based on acquired lifestyle information, medical image information, and emotional information, and to propose healthy products and services,

[0917] A means of visualizing prediction results and presenting users with purchasing advice that takes their emotional state into consideration,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, further comprising means for providing users with specific purchasing advice or health product suggestions that take their emotional state into account, based on visualized prediction results.

[0921] (Claim 3)

[0922] The system according to claim 1, further comprising means of collaborating with external health-related service providers to provide support for users' health management and purchasing support. [Explanation of symbols]

[0923] 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 of acquiring lifestyle information and medical image information collected from users, A means of using a generative artificial intelligence model to predict the user's future health status based on acquired lifestyle information and medical image information, A means of visualizing and presenting prediction results to the user, A system that includes this.

2. The system according to claim 1, further comprising means for proposing specific lifestyle improvement actions to the user based on visualized prediction results.

3. The system according to claim 1, further comprising means for providing support for the user's health management and medical treatment in cooperation with external medical service providers.

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