System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting life style AI from users; means for analyzing the collected life style data to simulate future health conditions; means for imaging the simulation results; means for generating and presenting personalized health improvement action plans to the users based on the simulation results; and means for tracking and continuously updating the health improvement actions of the users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, it is difficult to visually and concretely understand the impact of lifestyle habits on future health, and there is a lack of tools to plan and implement health improvement actions that are appropriate for each individual user. As a result, many people are unable to fully recognize future health risks and are unable to take appropriate health management measures. [Means for solving the problem]

[0005] The present invention provides an AI analysis tool that includes a means for collecting a user's lifestyle data and simulates future health conditions based on that data. It then uses imaging technology to visually display the simulation results, allowing the user to clearly understand their future health conditions. It also includes a means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user. Furthermore, by having a means for tracking the user's health improvement actions and continuously updating the data, effective healthcare management is realized. This allows the user to implement a specific action plan based on the visual future predictions, enabling them to prevent future health risks.

[0006] "User" refers to an individual who uses the system to input their lifestyle data and receive health prediction results and an action plan.

[0007] "Lifestyle data" refers to data related to a user's daily life, such as diet, exercise, and sleep.

[0008] "Means of collection" refers to devices, sensors, software, etc. that are used to input and acquire lifestyle data from users and store it in the system.

[0009] "AI analysis means" refers to an artificial intelligence model and its processing device that analyzes collected lifestyle data and simulates future health conditions.

[0010] "Simulation results" refers to predictive data regarding future health conditions generated by AI analysis tools.

[0011] "Imaging means" refers to techniques and devices for visually displaying simulation results.

[0012] A "personalized health improvement action plan" refers to a plan that shows specific health improvement measures appropriate for each individual user based on the results of the simulation.

[0013] "Means for tracking health improvement behaviors" refers to mechanisms and devices that record the health improvement behaviors (e.g., exercise, diet) that users have taken and continuously update the data.

[0014] "Local device" refers to a terminal device (e.g., smartphone, wearable device) that is directly operated by the user.

[0015] "Server" refers to the central computer and network system that receives and stores data sent from the terminal, analyzes it, and manages the entire system. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a futuristic health prediction system that utilizes AI and advanced imaging technology. It visually simulates future health conditions based on a user's current lifestyle data and provides an individualized health improvement action plan. This system collects lifestyle data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, it generates a personalized health improvement action plan based on the simulation results and presents it to the user. As a result, the user is able to take specific and continuous actions to improve their health.

[0038] Data collection

[0039] User:

[0040] Users input their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application on their device. Users also wear a wearable device, which automatically collects activity data (e.g., number of steps, heart rate), providing accurate lifestyle data.

[0041] Device:

[0042] The device temporarily stores the lifestyle data entered by the user in a local database. It also acquires activity data in real time from the wearable device and stores it in the local database. The collected data is sent to the server at regular intervals (e.g., every night at midnight).

[0043] server:

[0044] The server receives the lifestyle data sent from the device and stores it in a database. Based on this data, the server can also request additional information from the user.

[0045] Data analysis and simulation

[0046] server:

[0047] The server inputs the collected lifestyle data into an AI analysis module, which then simulates future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.) based on the current data. The simulation results are used to assess future health risks.

[0048] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[0049] Generate a personalized action plan

[0050] server:

[0051] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[0052] Device:

[0053] The device receives the action plan sent from the server and notifies the user within the application. The user then checks the action plan and puts it into action.

[0054] Continuous health management and behavior tracking

[0055] User:

[0056] Based on the action plan presented, users input their daily activities (e.g., diet, exercise) into the application, and the wearable device continues to collect activity data.

[0057] Device:

[0058] The device continuously collects daily lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[0059] server:

[0060] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[0061] Specific examples

[0062] Below is a case where a user A uses "health time travel."

[0063] User:

[0064] Mr. A enters his daily meal record (breakfast, lunch, dinner) into the application, and also adds records of his walking and jogging. He also wears a smartwatch at all times to measure his heart rate and number of steps.

[0065] Device:

[0066] The lifestyle data entered by Mr. A and the activity data automatically collected from the smartwatch are sent to the server every night at midnight.

[0067] server:

[0068] The server inputs Mr. A's lifestyle data into an AI analysis module and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, it generates an action plan appropriate for Mr. A (e.g., "walk for 30 minutes three times a week," "limit daily food intake to 2000 kcal"). This action plan is then sent to the device.

[0069] Device:

[0070] The device notifies Person A of the action plan and displays it as specific tasks. Person A changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[0071] server:

[0072] It continuously re-analyzes the data it receives, updates the action plan as needed, and sends the new plan to the device.

[0073] This allows Mr. A to visually understand his future health condition and take specific and effective actions to improve his health.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] User:

[0077] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate).

[0078] Step 2:

[0079] Device:

[0080] The lifestyle data entered by the user and the activity data acquired from the wearable device are temporarily stored in a local database, and these data are sent to a server at regular intervals (e.g., every night at midnight).

[0081] Step 3:

[0082] server:

[0083] The system receives lifestyle and activity data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[0084] Step 4:

[0085] server:

[0086] The AI ​​analysis module analyzes the lifestyle data received from the database and simulates the user's future health condition, such as weight fluctuations and blood sugar levels over the next three months.

[0087] Step 5:

[0088] server:

[0089] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[0090] Step 6:

[0091] server:

[0092] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[0093] Step 7:

[0094] server:

[0095] The generated action plan is sent to the device.

[0096] Step 8:

[0097] Device:

[0098] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[0099] Step 9:

[0100] User:

[0101] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application, which also collects activity data using a wearable device.

[0102] Step 10:

[0103] Device:

[0104] Daily lifestyle and activity data is continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0105] Step 11:

[0106] server:

[0107] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[0108] Step 12:

[0109] server:

[0110] The updated action plan is sent to the device and notified to the user, allowing them to take the optimal health improvement actions at the appropriate time.

[0111] Example 1

[0112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] Conventional health management systems have not been able to fully utilize collected lifestyle data, making it difficult to provide personalized health improvement action plans. Furthermore, they have not been able to continuously update users' behavioral data or accurately simulate their future health status. This has resulted in insufficient support for users to continuously take specific and effective health improvement actions.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0115] In this invention, the server includes means for collecting lifestyle habit data from a user, artificial intelligence analysis means for simulating future health conditions based on the collected lifestyle habit data, image processing means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for temporarily storing the lifestyle habit data and activity data in a local device and periodically transmitting them to the server. This provides a personalized health improvement action plan, enabling the user to continuously take specific and effective health improvement actions.

[0116] "User" refers to an individual who uses the System to provide lifestyle data.

[0117] "Lifestyle data" refers to information about the user's daily lifestyle (for example, dietary content, amount of exercise, sleep duration, etc.).

[0118] "Artificial intelligence analysis methods" refer to methods that use AI technology to predict and analyze future health conditions based on collected lifestyle data.

[0119] "Image processing means" refers to technology for visually displaying the results of a simulation (e.g., graphs, 3D models).

[0120] A "health improvement action plan" refers to specific behavioral instructions and suggestions proposed to improve a user's health based on AI analysis.

[0121] A "local device" refers to a terminal or wearable device used by a user, which is a device for temporarily storing lifestyle habit data and activity data.

[0122] "Server" refers to a centralized computer system that receives collected lifestyle data, analyzes it, and provides the results to users.

[0123] "Activity data" refers to information about a user's physical activity (e.g., number of steps, heart rate, etc.).

[0124] This invention is a system that utilizes AI and advanced imaging technology to visually simulate future health conditions based on a user's lifestyle data and provide a personalized action plan for improving their health. Specific methods for implementing this system are described below.

[0125] System configuration

[0126] Hardware Configuration

[0127] Users use a smartphone or tablet with a dedicated application installed, and also wear a wearable device (e.g., a smartwatch) to collect activity data.

[0128] The device refers to the user's smartphone or tablet, and lifestyle and activity data is stored in a local database.

[0129] The server is located on the cloud and analyzes data, generates simulation results, and proposes action plans.

[0130] Software Configuration

[0131] The dedicated application allows users to input lifestyle habit data and check the action plan sent from the server.

[0132] The AI ​​analysis module has an algorithm for simulating future health conditions based on data collected from users.

[0133] Image processing techniques include techniques for visually displaying simulation results (e.g., graphs, 3D models).

[0134] Data collection

[0135] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application, and activity data (e.g., number of steps, heart rate) is automatically collected through the wearable device.

[0136] The terminal stores the data entered by the user and the data obtained from the wearable device in a local database and sends it to the server every night at midnight.

[0137] Example prompt: "Enter your exercise record for today. Example: 30 minutes walking, 20 minutes jogging."

[0138] Data analysis and simulation

[0139] The server inputs the collected lifestyle data into an AI analysis module and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[0140] The simulation results are displayed visually using image processing technology so that users can understand them intuitively.

[0141] Example: "Display a graph of weight fluctuations and blood glucose levels for the next three months"

[0142] Generate a personalized action plan

[0143] Based on the simulation results, the server uses an AI analysis module to generate a personalized health improvement action plan, which includes specific instructions (e.g., exercising for 30 minutes three times a week and eating less than 2,000 kcal a day).

[0144] The generated action plan is transmitted from the server to the terminal, which then notifies the user of the plan.

[0145] Example prompt: "Exercise for 30 minutes three times a week. Enter your exercise log for today."

[0146] Continuous health management and behavior tracking

[0147] Users follow the action plan provided to them to change their lifestyle habits and continue to input their daily activities into the application, which also continuously collects activity data using a wearable device.

[0148] The device continuously collects data, stores it in a local database, and transmits it to a server at regular intervals.

[0149] The server compares the received data with past data, monitors changes in health status, reanalyzes the data based on the latest data, and updates the action plan as needed.

[0150] Example prompt: "A new exercise plan has been generated. Please continue your activities according to the new plan."

[0151] This system allows users to visually understand their future health status and take specific and effective actions to improve their health, thereby supporting them in continuously managing and improving their health.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Step 1:

[0154] The user enters lifestyle data (e.g., dietary content, exercise time, sleep time) into a dedicated application. The wearable device (e.g., smartwatch) automatically collects activity data (e.g., number of steps, heart rate).

[0155] Input: User-entered lifestyle data, wearable device activity data

[0156] Output: Data is saved on the device

[0157] Specific operation: The user enters into the app, "I ate bread and eggs for breakfast" and "I jogged for 30 minutes." The smartwatch then measures the number of steps taken and heart rate for the day.

[0158] Step 2:

[0159] The terminal stores the user's input data and activity data acquired from the wearable device in a local database.

[0160] Input: User-entered lifestyle data, wearable device activity data

[0161] Output: Data stored in a local database

[0162] Specific operation: The device automatically saves the user's food log and data received from the smartwatch.

[0163] Step 3:

[0164] The device sends the data stored in the local database to the server at midnight every night.

[0165] Input: Lifestyle and activity data stored in a local database

[0166] Output: Data sent to the server

[0167] Specific operation: Automatically upload data to the server when the date changes.

[0168] Step 4:

[0169] The server inputs the lifestyle data received into the AI ​​analysis module, which analyzes the data and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[0170] Input: Lifestyle and activity data received by the server

[0171] Output: Simulation results

[0172] Specific operation: Based on the received data, the AI ​​analysis module predicts changes in weight and blood sugar levels three months from now.

[0173] Step 5:

[0174] The server visually displays the simulation results using image processing technology.

[0175] Input: Simulation results

[0176] Output: Simulation results in a format that can be visually understood by the user

[0177] Specific operation: Predictions of future health risks (e.g., obesity, diabetes) are displayed in graphs and 3D models.

[0178] Step 6:

[0179] Based on the simulation results, the server's AI analysis module generates a personalized health improvement action plan, which includes specific instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[0180] Input: Simulation results

[0181] Output: Health Improvement Action Plan

[0182] Specific actions: The AI ​​analysis module generates instructions such as "walk for 30 minutes three times a week" or "eat less than 2000 kcal per day."

[0183] Step 7:

[0184] The server sends the generated health improvement action plan to the terminal.

[0185] Input: Health Improvement Action Plan

[0186] Output: Action plan sent to device

[0187] Specific operation: Instructions are sent from the server to the device and notified to the app.

[0188] Step 8:

[0189] The device notifies the user of the action plan and displays it within the application.

[0190] Input: Health improvement action plan sent from the server

[0191] Output: Notified action plan

[0192] Specific action: The app displays a notification saying, "Exercise for 30 minutes three times a week."

[0193] Step 9:

[0194] The user changes their lifestyle according to the action plan provided and enters their daily activities (e.g., diet, exercise) into the application.

[0195] Input: User action data

[0196] Output: Action data based on the action plan

[0197] Specific action: The user enters the exercise record as "30 minutes of walking completed."

[0198] Step 10:

[0199] The device continuously collects lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[0200] Input: Daily lifestyle and activity data

[0201] Output: Updates stored in the local database and data sent to the server

[0202] Specific operation: The device sends the accumulated data to the server at midnight every night.

[0203] Step 11:

[0204] The server continuously analyzes the received data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[0205] Input: Continuously received lifestyle and activity data

[0206] Output: Updated action plan

[0207] Specific operation: The AI ​​analysis module reanalyzes the data, generates updated instructions such as "Extend walking time to 40 minutes," and sends them to the device.

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] Many conventional health management systems collect users' lifestyle data and predict their future health status, but they lack motivation to take specific action to improve their health and provide continuous feedback. As a result, it is difficult for users to sustain health improvement actions, and actual improvements in health are difficult to achieve. In addition, there is no connection with physical stores, and no support is provided through specific services or products for health improvement, making them less practical for users.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes means for collecting lifestyle data from a user, AI analysis means for simulating future health conditions based on the collected lifestyle data, imaging means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for providing coupons for specific services and products based on the action plan. This provides specific instructions and support for the user to continuously take health improvement actions, enabling health improvement through practical services in collaboration with physical stores.

[0213] A "user" is an individual who uses the system to provide lifestyle data and receives health status predictions and action plans.

[0214] "Lifestyle data" refers to data related to the user's daily activities such as diet, exercise, and sleep.

[0215] "AI analysis means" refers to means that include artificial intelligence technology for simulating future health conditions based on collected lifestyle data.

[0216] "Imaging means" refers to a technique for visually displaying the simulation results.

[0217] A "personalized health improvement action plan" is a specific action plan for improving health that is created based on the individual health condition and lifestyle habits of each user.

[0218] The "means for continuously updating data" is a technology that tracks the user's health improvement behavior and sends the latest data to a server.

[0219] The "means of providing coupons" is a means of providing users with discount coupons or benefits for specific services or products based on an action plan.

[0220] The "server" is a centralized computer system that collects lifestyle data, analyzes it, performs simulations, generates action plans, and continuously updates the data.

[0221] MODE FOR CARRYING OUT THE INVENTION

[0222] A system for implementing this invention has the following configuration and functions. It includes a means for collecting lifestyle habit data from a user and an AI analysis means for analyzing the collected data. In addition, it includes an imaging means for visually displaying the analysis results, a means for generating a personalized health improvement action plan and presenting it to the user, and a means for tracking the user's health improvement actions and continuously updating the data. It also includes a means for providing coupons for specific services or products based on the action plan.

[0223] This invention is implemented using various devices and cloud infrastructure, including a smartphone app, a wearable device (e.g., a smartwatch), a cloud server (e.g., AWS or Google Cloud), an AI analysis module (e.g., Python + TensorFlow or PyTorch), a database system (e.g., MySQL or PostgreSQL), and imaging technology (e.g., Unity or Unreal Engine).

[0224] Data collection

[0225] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated smartphone application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as the number of steps taken and heart rate. The smartphone application temporarily stores this data in a local database (SQLite). The data is then periodically sent to a cloud server every night.

[0226] Data analysis and simulation

[0227] The server inputs the received data into an AI analysis module to simulate future health conditions (weight, blood pressure, blood sugar levels, etc.) The simulation results are visualized using Unity or Unreal Engine and displayed in a format that users can intuitively understand.

[0228] Personalized action plan generation

[0229] Based on the simulation results, the server generates a specific health improvement action plan for the user, which may include, for example, a specific fitness program or healthy foods, and then sends the action plan to the user's smartphone and notifies them.

[0230] Continuous data updates and physical store integration

[0231] Based on the designated action plan, users continuously input their lifestyle data and collect activity data using a wearable device. This data is continuously sent to a server, which analyzes the data as needed and updates the action plan as necessary. The server also provides users with coupons for specific services and products based on the action plan.

[0232] Specific examples

[0233] For example, when User A uses this system, he or she enters daily food and exercise records into the app and collects activity data using a smartwatch. The server uses this data to simulate User A's weight and blood pressure three months from now, suggests a fitness program (e.g., "attending yoga classes three times a week") and health foods (e.g., "specific supplements") that are suitable for User A, and provides discount coupons.

[0234] Prompt Sentence Examples

[0235] "Analyze User A's lifestyle data (e.g., diet, exercise, and sleep data), predict their weight and blood pressure three months from now, suggest optimal fitness programs and healthy foods, and visually display the results. Also, provide discount coupons based on the results."

[0236] This allows users to receive specific instructions and support from physical stores to take ongoing health improvement actions.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] Users input their daily lifestyle data (e.g., dietary content, amount of exercise, and sleep time) into a smartphone app. In addition, activity data (e.g., number of steps, heart rate) is automatically collected using a wearable device (e.g., a smartwatch). This data is temporarily stored in a local database (SQLite) on the smartphone. The input here consists of data manually entered by the user and data automatically collected by the wearable device.

[0240] Step 2:

[0241] The device sends the lifestyle data collected from the user to a server at a fixed time every night. The data includes the user's diet, exercise, sleep time, and activity data. This process involves transferring data from the smartphone to a cloud server (e.g., AWS, Google Cloud).

[0242] Step 3:

[0243] The server stores the received lifestyle data in a database (e.g., MySQL, PostgreSQL). To store the received data accurately, the data is preprocessed and converted into a consistent format, which facilitates subsequent data analysis.

[0244] Step 4:

[0245] The server inputs the user's lifestyle data stored in the database into an AI analysis module (Python + TensorFlow or PyTorch) to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels). This analysis is a step in which a generative AI model is used to detect and predict outliers. Prediction data is obtained as a result of the analysis.

[0246] Step 5:

[0247] The simulation results are visually displayed using imaging technology (Unity or Unreal Engine). The server analyzes the simulation results and visualizes them in a format that is easy for users to understand. This allows users to intuitively understand their future health status. The output is data for visual display.

[0248] Step 6:

[0249] The server generates a personalized health improvement action plan based on the simulation results, which includes specific behavioral instructions (e.g., exercising three times a week, adjusting diet). The input here is the simulation results, and the output is an individualized action plan.

[0250] Step 7:

[0251] The generated action plan is sent to the device and notified to the user via a smartphone app. The user receives the notification and begins taking action to improve their lifestyle habits according to the plan. The output here is a user notification.

[0252] Step 8:

[0253] Users change their daily habits based on the action plan presented to them and enter the results into a smartphone app. They also continuously wear a wearable device to collect activity data. The input data are new lifestyle and activity data.

[0254] Step 9:

[0255] The device continuously collects data and sends it to the server at regular intervals. This allows the server to always obtain the latest user data and continuously monitor the health status. The output here is continuously updated data.

[0256] Step 10:

[0257] The server re-analyzes the data and updates the action plan as needed. It also generates coupons for specific services or products and provides them to the user. The user can then use these coupons to receive discounts on fitness programs or healthy foods. The output is the updated action plan and coupons.

[0258] This allows users to receive specific instructions and support to take ongoing health-improving actions.

[0259] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0260] This invention is a futuristic health prediction system that utilizes AI, advanced imaging technology, and an emotion engine. It visually simulates a user's future health status based on their current lifestyle and emotional data, and provides a personalized health improvement action plan. This system collects lifestyle and emotional data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, a personalized health improvement action plan is generated based on the simulation results and emotional data and presented to the user. As a result, the user is able to take specific and continuous health improvement actions.

[0261] Data collection

[0262] User:

[0263] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Additionally, the emotion engine recognizes and collects emotional data from the user's facial expressions and voice.

[0264] Device:

[0265] The device temporarily stores the lifestyle data entered by the user, as well as the activity and emotion data collected from the wearable device and emotion engine in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[0266] server:

[0267] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. Based on this data, the server can also request additional information from the user.

[0268] Data analysis and simulation

[0269] server:

[0270] The server inputs the collected lifestyle and emotional data into an AI analysis module. The AI ​​analysis module uses this data to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.). The simulation results are used to assess future health risks. In addition, emotional data is reflected in the analysis, enabling more accurate and personalized predictions.

[0271] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[0272] Generate a personalized action plan

[0273] server:

[0274] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day), which is then sent to the device.

[0275] Device:

[0276] The device notifies the user of the action plan sent from the server within a dedicated application. The user then confirms the action plan and puts it into action. Emotional data plays an important role in adjusting the content and strength of the action plan.

[0277] Continuous health management and behavior tracking

[0278] User:

[0279] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0280] Device:

[0281] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0282] server:

[0283] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[0284] Specific examples

[0285] Below is a case where User B uses "Health Time Travel."

[0286] User:

[0287] Person B enters his daily food record (e.g. bread and coffee for breakfast, salad and chicken for lunch) into the application and adds records of his walking and jogging. He also wears a smartwatch to measure his heart rate and number of steps. Furthermore, if Person B feels stressed, the emotion engine collects emotional data from his facial expressions and voice.

[0288] Device:

[0289] The lifestyle data entered by Mr. B, the activity data automatically collected from the smartwatch, and the emotion data from the emotion engine are sent to the server every night at midnight.

[0290] server:

[0291] The server inputs Mr. B's lifestyle, activity, and emotional data into an AI analysis module, and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for Mr. B is generated (e.g., "incorporate relaxation into your routine when stress levels rise," walk 30 minutes three times a week, and limit daily food intake to 2,000 kcal). This action plan is then sent to the device.

[0292] Device:

[0293] The device notifies Person B of the action plan and displays it as specific tasks. Person B changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[0294] server:

[0295] The system continuously reanalyzes the received data, updates the action plan as needed, and sends the new plan to the device, allowing Mr. B to visually understand his future health condition and take specific and effective actions to improve his health.

[0296] The present invention allows users to visually and emotionally understand future health risks and implement appropriate action plans to improve their health.

[0297] The processing flow will be explained below.

[0298] Step 1:

[0299] User:

[0300] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. They also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Furthermore, an emotion engine is used to collect emotional data (e.g., stress level, happiness level) from facial expressions and voice.

[0301] Step 2:

[0302] Device:

[0303] The device temporarily stores the lifestyle data entered by the user, as well as data collected from the wearable device and emotion engine, in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[0304] Step 3:

[0305] server:

[0306] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[0307] Step 4:

[0308] server:

[0309] The AI ​​analysis module analyzes the lifestyle and emotional data received from the database to simulate the user's future health condition. For example, it simulates changes in weight and blood sugar levels over the next three months. It also incorporates emotional data into the analysis to evaluate the impact of the user's mental state on their health.

[0310] Step 5:

[0311] server:

[0312] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[0313] Step 6:

[0314] server:

[0315] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, which includes specific behavioral instructions (e.g., exercising 30 minutes three times a week, eating less than 2,000 kcal a day, and stress management techniques).

[0316] Step 7:

[0317] server:

[0318] The generated action plan is sent to the device.

[0319] Step 8:

[0320] Device:

[0321] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[0322] Step 9:

[0323] User:

[0324] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise, stress management) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0325] Step 10:

[0326] Device:

[0327] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0328] Step 11:

[0329] server:

[0330] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as needed.

[0331] Step 12:

[0332] server:

[0333] The updated action plan is sent to the device and notified to the user, allowing them to take optimal health-improving actions at the appropriate time.

[0334] Example 2

[0335] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] Conventional health management systems only consider users' lifestyle habits, which means they are unable to fully consider emotional fluctuations and their impact when predicting future health conditions. This can lead to inaccurate individual health improvement action plans, making it difficult for users to effectively improve their health. Furthermore, action plans are often not updated appropriately in line with ongoing data updates, which can lead to insufficient tracking of improvement actions.

[0337] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0338] In this invention, the server includes means for collecting lifestyle data from a user and emotion data collected from a wearable device and an emotion engine, means for temporarily storing the collected lifestyle data and emotion data in a local device and periodically transmitting them to the server, AI analysis means for simulating future health conditions based on the data transmitted to the server, means for visually displaying the simulation results using advanced imaging technology, means for generating a personalized health improvement action plan based on the simulation results and emotion data and notifying the user, and means for tracking the user's health improvement actions and continuously updating the data. This enables more accurate and personalized health predictions that take emotion data into account, the generation of improvement action plans, and the continuous updating of these plans.

[0339] "User" refers to an individual who uses this system to provide their lifestyle and emotional data and receives a health status simulation and a health improvement action plan.

[0340] "Lifestyle data" refers to information about a user's daily behavior and lifestyle, such as diet, exercise records, and sleep time.

[0341] "Emotional Data" refers to information about a user's emotional state collected from their facial expressions and voice.

[0342] A "wearable device" is an electronic device that can be worn, such as a smartwatch, and collects activity data such as heart rate and number of steps.

[0343] An "emotion engine" refers to a software or hardware system that analyzes a user's facial expressions and voice to generate emotional data.

[0344] A "local device" refers to a device used by a user (such as a smartphone or tablet) that temporarily stores lifestyle data and emotional data.

[0345] "Server" refers to the central management system that receives and stores data sent from the device and performs AI analysis.

[0346] "AI analysis module" refers to a program that uses artificial intelligence technology to analyze collected data and simulate future health conditions.

[0347] "Advanced imaging technology" refers to the latest image processing technology for visually displaying simulation results in an easy-to-understand manner.

[0348] "Health Improvement Action Plan" refers to individual health improvement measures provided to users based on the simulation results and emotional data.

[0349] "Continuous data updating" refers to the process of analyzing health status and revising action plans using new data collected regularly from users.

[0350] The present invention is a system that predicts future health conditions based on a user's lifestyle and emotional data and provides a personalized action plan for improving health. This system requires the participation of a server, a terminal, and the user.

[0351] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as heart rate and number of steps. Furthermore, an emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0352] The device temporarily stores the lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine in a local database. These data are then sent to the server every night at midnight.

[0353] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. The server's AI analysis module uses this data to simulate future health conditions (such as weight, blood pressure, and blood sugar levels). This simulation uses AI analysis technology and advanced imaging technology, allowing users to visually confirm the simulation results.

[0354] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day). The action plan is then sent to the device, which then notifies the user.

[0355] The user continues to input their daily activities (e.g., diet and exercise) into a dedicated application in accordance with the presented action plan. The device continuously collects this daily input data and sends it to the server every night at midnight. The server continuously receives the data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary. The updated action plan is then sent back to the device and the user is notified.

[0356] As a concrete example, let's say user A uses this system. A enters their daily food and walking records into the app and wears a smartwatch to measure their heart rate and number of steps. Furthermore, if A feels stressed, emotional data is collected from their facial expressions and voice. This data is sent to the server, and an AI analysis module simulates A's weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for A (for example, "incorporate relaxation into your routine when stress levels rise," walk for 30 minutes three times a week, and limit daily food intake to 2000 kcal) is generated. This action plan is sent to the device and notified to A.

[0357] Example prompt sentence:

[0358] "User A enters his daily food records (e.g., bread and coffee for breakfast, salad and chicken for lunch) and his walking and jogging records into the app, and uses a smartwatch to measure his heart rate and number of steps. In addition, if User A feels stressed, emotional data is collected. The app simulates his future health condition (e.g., weight, blood sugar level, etc.) and generates a health improvement action plan appropriate for User A (e.g., 30 minutes of exercise three times a week, limiting daily food intake to 2000 kcal)."

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] User:

[0362] Users enter their daily lifestyle data (e.g., diet, exercise records, sleep time) into a dedicated application.

[0363] Input: Meal details, exercise record, sleep time

[0364] Output: Lifestyle data is saved in the application

[0365] Specific actions: If you had bread and coffee for breakfast, enter that information in the app, and also enter the 30-minute jog you went for in the morning.

[0366] Step 2:

[0367] User:

[0368] Users wear a wearable device such as a smartwatch, which automatically collects activity data (e.g., steps taken, heart rate).

[0369] Input: Wearing and operating the wearable device

[0370] Output: Activity data is obtained

[0371] What it does: The user wears the smartwatch throughout the day, and the watch periodically records their heart rate and steps.

[0372] Step 3:

[0373] Emotion Engine:

[0374] The emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0375] Input: User's facial expression data, voice data

[0376] Output: Emotion data

[0377] Specific operation: Facial expressions and tone of voice that occur when the user feels stress are detected, and emotional data is recorded as "stress."

[0378] Step 4:

[0379] Device:

[0380] The lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine are temporarily stored in a local database.

[0381] Input: lifestyle data, activity data, emotion data

[0382] Output: Data stored in a local database

[0383] What it does: The application temporarily stores user input and automatically collected data.

[0384] Step 5:

[0385] Device:

[0386] The collected data is sent to the server every night at midnight.

[0387] Input: Data from the local database

[0388] Output: Data sent to the server

[0389] Specific operation: At midnight, the device automatically sends data to the server via the network.

[0390] Step 6:

[0391] server:

[0392] The lifestyle data, activity data, and emotion data transmitted from the terminal are received and stored in a database.

[0393] Input: Data sent from the terminal

[0394] Output: Data stored in the server database

[0395] Specific operation: When data is sent to the server, it is received and processed and recorded in the server's database.

[0396] Step 7:

[0397] server:

[0398] The collected data is input into an AI analysis module.

[0399] Input: Data stored in the server's database

[0400] Output: Data input to the AI ​​analysis module

[0401] Specific operation: The server periodically passes database data to the AI ​​analysis module.

[0402] Step 8:

[0403] AI analysis module:

[0404] Simulate future health conditions based on data.

[0405] Input: lifestyle data, activity data, emotion data

[0406] Output: Health condition simulation results

[0407] How it works: The AI ​​analysis module analyzes the input data and runs algorithms to predict future changes in weight and blood pressure.

[0408] Step 9:

[0409] server:

[0410] Simulation results are visually displayed using advanced imaging technology.

[0411] Input: Simulation results of AI analysis module

[0412] Output: Visually displayed simulation results

[0413] Specific behavior: Prediction results are generated as graphs and charts and presented in a user-accessible format.

[0414] Step 10:

[0415] AI analysis module:

[0416] Generate a personalized health improvement action plan based on the simulation results and emotional data.

[0417] Input: Simulation results, emotion data

[0418] Output: Health Improvement Action Plan

[0419] Specific actions: A specific action plan (e.g., exercise frequency, dietary content) is generated in text format.

[0420] Step 11:

[0421] server:

[0422] The generated action plan is sent to the device.

[0423] Input: Health Improvement Action Plan

[0424] Output: Action plan sent to device

[0425] Specific behavior: A notification is triggered to the device and an action plan is sent.

[0426] Step 12:

[0427] Device:

[0428] The terminal notifies the user of the action plan sent from the server within a dedicated application.

[0429] Input: Action plan sent from the server

[0430] Output: Action plan communicated to user

[0431] Specific behavior: A push notification will appear on the user's screen.

[0432] Step 13:

[0433] User:

[0434] Follow the action plan provided and continue to enter your daily actions into the application.

[0435] Input: New lifestyle and activity data based on the action plan

[0436] Output: New lifestyle data input into the application

[0437] Specific actions: After exercising, enter the activity record into the application.

[0438] Step 14:

[0439] Device:

[0440] Lifestyle data, activity data, and emotional data are continuously collected and sent to a server every night at midnight.

[0441] Input: New lifestyle data, activity data, emotion data

[0442] Output: Continuous data sent to the server

[0443] Specific operation: Data is automatically sent to the server every night at midnight.

[0444] Step 15:

[0445] server:

[0446] Receive data continuously and compare it with past data to monitor changes in your health.

[0447] Input: Continuously received data

[0448] Output: Analysis results based on changes in health status

[0449] Specific operation: New data is passed to the AI ​​analysis module and reanalyzed.

[0450] Step 16:

[0451] server:

[0452] Update your action plan with the latest data.

[0453] Input: Latest data

[0454] Output: Updated action plan

[0455] Specific operation: The AI ​​analysis module reanalyzes and generates a new action plan.

[0456] Step 17:

[0457] Device:

[0458] The updated action plan is sent to the device and notified to the user.

[0459] Input: Update action plan sent from the server

[0460] Output: Update action plan communicated to the user

[0461] What happens: A notification of a new action plan will appear on your device screen.

[0462] (Application example 2)

[0463] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0464] There are many challenges facing health management for users in modern society. In particular, it is difficult to accurately grasp the impact of lifestyle and emotional fluctuations on health and provide specific improvement measures based on that understanding. Furthermore, users lack the motivation and specific feedback to continuously improve their health. To resolve this situation, it is necessary to use more advanced data analysis and visualization technologies to provide health information in a format that users can intuitively understand, and to obtain feedback in concrete settings such as physical stores.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0466] In this invention, the server includes means for collecting lifestyle habit data and emotional data from a user, means for simulating a future health state based on the collected lifestyle habit data and emotional data, means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and emotional data and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for the user to receive feedback at a physical store. This allows the user to visually and intuitively understand future health risks, receive a specific and feasible health improvement action plan, and further receive feedback at the physical store, enabling continuous health management and behavioral improvement.

[0467] "Lifestyle data" refers to data related to the user's daily life, such as dietary habits, exercise records, and sleep duration.

[0468] "Emotion data" refers to data that indicates the emotional state of the user as recognized from facial expressions, voice, etc.

[0469] "AI analysis methods" are methods that use artificial intelligence technology to analyze collected data and simulate future health conditions.

[0470] "Visualization means" refers to technology for visually displaying simulation results, including graphs and 3D models.

[0471] A "health improvement action plan" is a specific, individualized instruction for health improvement behavior that is generated based on the simulation results and emotional data.

[0472] A "brick and mortar store" is a physical store that customers can visit in person, such as a fitness club or health food store.

[0473] "Feedback means" refers to a means by which users can receive specific feedback about their health status and behavior in a physical store.

[0474] A "local device" is a device carried by a user, such as a smartphone or tablet, that has the function of temporarily storing data.

[0475] "Server" is a central processing unit that stores collected data and performs analysis and generation of action plans.

[0476] "Data update means" refers to a means for keeping the system up to date based on the user's health improvement actions and new data collected.

[0477] This invention is a system that collects lifestyle data and emotional data from a user, simulates future health conditions, and provides a personalized action plan for improving health. Specific embodiments of this system are described below.

[0478] Data collection

[0479] User: The user enters lifestyle data such as daily diet, exercise records, and sleep time into a dedicated smartphone application. The user also wears a wearable device (e.g., a smartwatch) that automatically collects activity data such as heart rate and number of steps. The emotion engine then recognizes and collects emotional data from the user's facial expressions and voice.

[0480] Terminal: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and emotion engines. These data are then periodically sent to a server.

[0481] server:

[0482] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. It may also request additional information from the user.

[0483] Data analysis and simulation

[0484] server:

[0485] The collected lifestyle and emotional data is input into an AI analysis module to simulate future health conditions. The AI ​​analysis module then uses generative AI models, such as deep learning models, to make highly accurate predictions. The simulation results are visualized using 3D models and graphs.

[0486] Generate a personalized action plan

[0487] server:

[0488] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific exercise and nutritional instructions, and presents it to the user. For example, instructions might include "walking for 30 minutes three times a week" or "limiting daily food intake to 2,000 kcal."

[0489] Device:

[0490] The action plan sent from the server is notified within the dedicated application and displayed as specific tasks. The user can then change their lifestyle habits according to the action plan and put it into action.

[0491] Continuous health management and behavior tracking

[0492] User:

[0493] The user follows the action plan and inputs their daily activities into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0494] Device:

[0495] Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to a server.

[0496] server:

[0497] The system continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[0498] Specific examples

[0499] For example, when a user uses "health time travel," the following operations occur:

[0500] Users enter their daily food and exercise records into the application, and their heart rate and number of steps are measured using a wearable device. The emotion engine collects emotional data from facial expressions and voice when users feel stressed. This data is sent to the server every night at midnight, and the server provides the latest simulation results and action plans the following morning.

[0501] Prompt Sentence Examples

[0502] "Based on the user's current lifestyle and emotional data, predict future health conditions and generate individualized health improvement action plans. Build a system that uses inputs such as dietary habits, exercise records, sleep time, heart rate, number of steps, and emotional data (facial expressions and voice) to simulate future weight, blood pressure, and blood sugar levels and provide appropriate behavioral instructions."

[0503] The above is a specific embodiment of the present invention. This system allows users to obtain health information in an intuitively understandable format and to take continuous health improvement actions based on a personalized action plan.

[0504] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0505] Step 1:

[0506] User: The user uses a dedicated smartphone application to input daily lifestyle data (food content, exercise records, sleep time, etc.). In addition, the user wears a wearable device (such as a smartwatch) to automatically collect activity data such as heart rate and number of steps. The emotion engine recognizes and collects emotion data from the user's facial expressions and voice.

[0507] Input: Meal details, exercise records, sleep time, heart rate, number of steps, facial expression data, voice data

[0508] Output: Collected lifestyle data, activity data, and emotion data

[0509] Step 2:

[0510] Device: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and the emotion engine. This data is sent to the server every night at midnight.

[0511] Input: Collected lifestyle data, activity data, and emotional data

[0512] Output: Data stored on local device, data sent to server

[0513] Step 3:

[0514] Server: The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. If necessary, it may request additional information from the user.

[0515] Input: Data sent from the terminal

[0516] Output: Data stored in the database, prompting the user for more information

[0517] Step 4:

[0518] Server: The server retrieves lifestyle and emotional data from the database and inputs it into the AI ​​analysis module. Using a generative AI model, this data is analyzed and future health conditions (weight, blood pressure, blood sugar level, etc.) are simulated.

[0519] Input: Data retrieved from the database

[0520] Output: Simulation results

[0521] Step 5:

[0522] Server: Visually displays the simulation results using 3D models, graphs, etc. Using visualization tools, the server processes the data so that the user can intuitively understand it.

[0523] Input: Simulation results

[0524] Output: Visualized data (3D models, graphs, etc.)

[0525] Step 6:

[0526] Server: Generates a personalized health improvement action plan based on the simulation results and emotional data. This includes specific exercise and nutritional instructions. Examples include "walk for 30 minutes three times a week" and "limit daily food intake to 2000 kcal."

[0527] Input: Simulation results, emotion data

[0528] Output: A personalized health improvement action plan

[0529] Step 7:

[0530] Device: The action plan sent from the server is displayed in a dedicated application and specific tasks are displayed. The user can then change their lifestyle habits according to the action plan and put it into action.

[0531] Input: Action plan sent from the server

[0532] Output: Action plan communicated to user

[0533] Step 8:

[0534] User: Follows the action plan provided and enters daily activities (e.g., meals, exercise) into the application. The wearable device also continuously collects activity data. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0535] Input: Daily behavior data based on the action plan

[0536] Output: Newly collected lifestyle, activity, and emotion data

[0537] Step 9:

[0538] Device: Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to the server.

[0539] Input: Newly collected data

[0540] Output: Data stored on local device, data sent to server

[0541] Step 10:

[0542] Server: Continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[0543] Input: Continuously received data

[0544] Output: Change in health status, updated action plan

[0545] The above are the specific processing steps for carrying out this invention, which allow the user to visually and intuitively understand their future health status, receive a specific and feasible action plan for improving their health, and continue to manage their health and improve their behavior.

[0546] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0547] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0549] [Second embodiment]

[0550] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0551] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0552] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0553] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0554] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0555] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0556] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0557] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0558] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0559] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0560] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0561] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0562] This invention is a futuristic health prediction system that utilizes AI and advanced imaging technology. It visually simulates a user's future health status based on their current lifestyle data and provides an individualized health improvement action plan. This system collects lifestyle data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, it generates a personalized health improvement action plan based on the simulation results and presents it to the user. As a result, the user can take specific and continuous actions to improve their health.

[0563] Data collection

[0564] User:

[0565] Users input their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application on their device. Users also wear a wearable device, which automatically collects activity data (e.g., number of steps, heart rate), allowing for accurate lifestyle data to be obtained.

[0566] Device:

[0567] The device temporarily stores the lifestyle data entered by the user in a local database. It also acquires activity data in real time from the wearable device and stores it in the local database. The collected data is sent to the server at regular intervals (e.g., every night at midnight).

[0568] server:

[0569] The server receives the lifestyle data sent from the device and stores it in a database. Based on this data, the server can also request additional information from the user.

[0570] Data analysis and simulation

[0571] server:

[0572] The server inputs the collected lifestyle data into an AI analysis module, which then simulates future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.) based on the current data. The simulation results are used to assess future health risks.

[0573] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[0574] Generate a personalized action plan

[0575] server:

[0576] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[0577] Device:

[0578] The device receives the action plan sent from the server and notifies the user within the application. The user then checks the action plan and puts it into action.

[0579] Continuous health management and behavior tracking

[0580] User:

[0581] Based on the action plan presented, users input their daily activities (e.g., diet, exercise) into the application, and the wearable device continues to collect activity data.

[0582] Device:

[0583] The device continuously collects daily lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[0584] server:

[0585] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as needed. The updated action plan is sent to the device and notified to the user.

[0586] Specific examples

[0587] Below is a case where a user A uses "health time travel."

[0588] User:

[0589] Mr. A enters his daily meal record (breakfast, lunch, dinner) into the application, and also adds records of his walking and jogging. He also wears a smartwatch at all times to measure his heart rate and number of steps.

[0590] Device:

[0591] The lifestyle data entered by Mr. A and the activity data automatically collected from the smartwatch are sent to the server every night at midnight.

[0592] server:

[0593] The server inputs Mr. A's lifestyle data into an AI analysis module and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, it generates an action plan appropriate for Mr. A (e.g., "walk for 30 minutes three times a week," "limit daily food intake to 2000 kcal"). This action plan is then sent to the device.

[0594] Device:

[0595] The device notifies Person A of the action plan and displays it as specific tasks. Person A changes his / her lifestyle habits according to the plan and periodically enters the results into the application.

[0596] server:

[0597] It continuously re-analyzes the data it receives, updates the action plan as needed, and sends the new plan to the device.

[0598] This allows Mr. A to visually understand his future health condition and take specific and effective actions to improve his health.

[0599] The processing flow will be explained below.

[0600] Step 1:

[0601] User:

[0602] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate).

[0603] Step 2:

[0604] Device:

[0605] The lifestyle data entered by the user and the activity data acquired from the wearable device are temporarily stored in a local database, and these data are sent to a server at regular intervals (e.g., every night at midnight).

[0606] Step 3:

[0607] server:

[0608] The system receives lifestyle and activity data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[0609] Step 4:

[0610] server:

[0611] The AI ​​analysis module analyzes the lifestyle data received from the database and simulates the user's future health condition, such as weight fluctuations and blood sugar levels over the next three months.

[0612] Step 5:

[0613] server:

[0614] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[0615] Step 6:

[0616] server:

[0617] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific behavioral instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[0618] Step 7:

[0619] server:

[0620] The generated action plan is sent to the device.

[0621] Step 8:

[0622] Device:

[0623] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[0624] Step 9:

[0625] User:

[0626] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application, which also collects activity data using a wearable device.

[0627] Step 10:

[0628] Device:

[0629] Daily lifestyle and activity data is continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0630] Step 11:

[0631] server:

[0632] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[0633] Step 12:

[0634] server:

[0635] The updated action plan is sent to the device and notified to the user, allowing them to take the optimal health improvement actions at the appropriate time.

[0636] Example 1

[0637] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0638] Conventional health management systems have not been able to fully utilize collected lifestyle data, making it difficult to provide personalized health improvement action plans. Furthermore, they have not been able to continuously update users' behavioral data or accurately simulate their future health status. This has resulted in insufficient support for users to continuously take specific and effective health improvement actions.

[0639] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0640] In this invention, the server includes means for collecting lifestyle habit data from a user, artificial intelligence analysis means for simulating future health conditions based on the collected lifestyle habit data, image processing means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for temporarily storing the lifestyle habit data and activity data in a local device and periodically transmitting them to the server. This provides a personalized health improvement action plan, enabling the user to continuously take specific and effective health improvement actions.

[0641] "User" refers to an individual who uses the System to provide lifestyle data.

[0642] "Lifestyle data" refers to information about the user's daily lifestyle (for example, dietary content, amount of exercise, sleep duration, etc.).

[0643] "Artificial intelligence analysis methods" refer to methods that use AI technology to predict and analyze future health conditions based on collected lifestyle data.

[0644] "Image processing means" refers to technology for visually displaying the results of a simulation (e.g., graphs, 3D models).

[0645] A "health improvement action plan" refers to specific behavioral instructions and suggestions proposed to improve a user's health based on AI analysis.

[0646] A "local device" refers to a terminal or wearable device used by a user, which is a device for temporarily storing lifestyle habit data and activity data.

[0647] "Server" refers to a centralized computer system that receives collected lifestyle data, analyzes it, and provides the results to users.

[0648] "Activity data" refers to information about a user's physical activity (e.g., number of steps, heart rate, etc.).

[0649] This invention is a system that utilizes AI and advanced imaging technology to visually simulate future health conditions based on a user's lifestyle data and provide a personalized action plan for improving their health. Specific methods for implementing this system are described below.

[0650] System configuration

[0651] Hardware Configuration

[0652] Users use a smartphone or tablet with a dedicated application installed, and also wear a wearable device (e.g., a smartwatch) to collect activity data.

[0653] The device refers to the user's smartphone or tablet, and lifestyle and activity data is stored in a local database.

[0654] The server is located on the cloud and analyzes data, generates simulation results, and proposes action plans.

[0655] Software Configuration

[0656] The dedicated application allows users to input lifestyle habit data and check the action plan sent from the server.

[0657] The AI ​​analysis module has an algorithm for simulating future health conditions based on data collected from users.

[0658] Image processing techniques include techniques for visually displaying simulation results (e.g., graphs, 3D models).

[0659] Data collection

[0660] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application, and activity data (e.g., number of steps, heart rate) is automatically collected through the wearable device.

[0661] The terminal stores the data entered by the user and the data obtained from the wearable device in a local database and sends it to the server every night at midnight.

[0662] Example prompt: "Enter your exercise record for today. Example: 30 minutes walking, 20 minutes jogging."

[0663] Data analysis and simulation

[0664] The server inputs the collected lifestyle data into an AI analysis module and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[0665] The simulation results are displayed visually using image processing technology so that users can understand them intuitively.

[0666] Example: "Display a graph of weight fluctuations and blood glucose levels for the next three months"

[0667] Generate a personalized action plan

[0668] Based on the simulation results, the server uses an AI analysis module to generate a personalized health improvement action plan, which includes specific instructions (e.g., exercising for 30 minutes three times a week and eating less than 2,000 kcal a day).

[0669] The generated action plan is transmitted from the server to the terminal, which then notifies the user of the plan.

[0670] Example prompt: "Exercise for 30 minutes three times a week. Enter your exercise log for today."

[0671] Continuous health management and behavior tracking

[0672] Users follow the action plan provided to them to change their lifestyle habits and continue to input their daily activities into the application, which also continuously collects activity data using a wearable device.

[0673] The device continuously collects data, stores it in a local database, and transmits it to a server at regular intervals.

[0674] The server compares the received data with past data, monitors changes in health status, reanalyzes the data based on the latest data, and updates the action plan as needed.

[0675] Example prompt: "A new exercise plan has been generated. Please continue your activities according to the new plan."

[0676] This system allows users to visually understand their future health status and take specific and effective actions to improve their health, thereby supporting them in continuously managing and improving their health.

[0677] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0678] Step 1:

[0679] The user enters lifestyle data (e.g., dietary content, exercise time, sleep time) into a dedicated application. The wearable device (e.g., smartwatch) automatically collects activity data (e.g., number of steps, heart rate).

[0680] Input: User-entered lifestyle data, wearable device activity data

[0681] Output: Data is saved on the device

[0682] Specific operation: The user enters into the app, "I ate bread and eggs for breakfast" and "I jogged for 30 minutes." The smartwatch then measures the number of steps taken and heart rate for the day.

[0683] Step 2:

[0684] The terminal stores the user's input data and activity data acquired from the wearable device in a local database.

[0685] Input: User-entered lifestyle data, wearable device activity data

[0686] Output: Data stored in a local database

[0687] Specific operation: The device automatically saves the user's food log and data received from the smartwatch.

[0688] Step 3:

[0689] The device sends the data stored in the local database to the server every night at midnight.

[0690] Input: Lifestyle and activity data stored in a local database

[0691] Output: Data sent to the server

[0692] Specific operation: Automatically upload data to the server when the date changes.

[0693] Step 4:

[0694] The server inputs the lifestyle data received into the AI ​​analysis module, which analyzes the data and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[0695] Input: Lifestyle and activity data received by the server

[0696] Output: Simulation results

[0697] Specific operation: Based on the received data, the AI ​​analysis module predicts changes in weight and blood sugar levels three months from now.

[0698] Step 5:

[0699] The server visually displays the simulation results using image processing technology.

[0700] Input: Simulation results

[0701] Output: Simulation results in a format that can be visually understood by the user

[0702] Specific operation: Predictions of future health risks (e.g., obesity, diabetes) are displayed in graphs and 3D models.

[0703] Step 6:

[0704] Based on the simulation results, the server's AI analysis module generates a personalized health improvement action plan, which includes specific instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[0705] Input: Simulation results

[0706] Output: Health Improvement Action Plan

[0707] Specific actions: The AI ​​analysis module generates instructions such as "walk for 30 minutes three times a week" or "eat less than 2000 kcal per day."

[0708] Step 7:

[0709] The server sends the generated health improvement action plan to the terminal.

[0710] Input: Health Improvement Action Plan

[0711] Output: Action plan sent to device

[0712] Specific operation: Instructions are sent from the server to the device and notified to the app.

[0713] Step 8:

[0714] The device notifies the user of the action plan and displays it within the application.

[0715] Input: Health improvement action plan sent from the server

[0716] Output: Notified action plan

[0717] Specific action: The app displays a notification saying, "Exercise for 30 minutes three times a week."

[0718] Step 9:

[0719] The user changes their lifestyle according to the action plan provided and enters their daily activities (e.g., diet, exercise) into the application.

[0720] Input: User action data

[0721] Output: Action data based on the action plan

[0722] Specific action: The user enters the exercise record as "30 minutes of walking completed."

[0723] Step 10:

[0724] The device continuously collects lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[0725] Input: Daily lifestyle and activity data

[0726] Output: Updates stored in the local database and data sent to the server

[0727] Specific operation: The device sends the accumulated data to the server at midnight every night.

[0728] Step 11:

[0729] The server continuously analyzes the received data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[0730] Input: Continuously received lifestyle and activity data

[0731] Output: Updated action plan

[0732] Specific operation: The AI ​​analysis module reanalyzes the data, generates updated instructions such as "Extend walking time to 40 minutes," and sends them to the device.

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0735] Many conventional health management systems collect users' lifestyle data and predict their future health status, but they lack motivation to take specific action to improve their health and provide continuous feedback. As a result, it is difficult for users to sustain health improvement actions, and actual improvements in health are difficult to achieve. In addition, there is no connection with physical stores, and no support is provided through specific services or products for health improvement, making them less practical for users.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes means for collecting lifestyle data from a user, AI analysis means for simulating future health conditions based on the collected lifestyle data, imaging means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for providing coupons for specific services and products based on the action plan. This provides specific instructions and support for the user to continuously take health improvement actions, enabling health improvement through practical services in collaboration with physical stores.

[0738] A "user" is an individual who uses the system to provide lifestyle data and receives health status predictions and action plans.

[0739] "Lifestyle data" refers to data related to the user's daily activities such as diet, exercise, and sleep.

[0740] "AI analysis means" refers to means that include artificial intelligence technology for simulating future health conditions based on collected lifestyle data.

[0741] "Imaging means" refers to a technique for visually displaying the simulation results.

[0742] A "personalized health improvement action plan" is a specific action plan for improving health that is created based on the individual health condition and lifestyle habits of each user.

[0743] The "means for continuously updating data" is a technology that tracks the user's health improvement behavior and sends the latest data to a server.

[0744] The "means of providing coupons" is a means of providing users with discount coupons or benefits for specific services or products based on an action plan.

[0745] The "server" is a centralized computer system that collects lifestyle data, analyzes it, performs simulations, generates action plans, and continuously updates the data.

[0746] MODE FOR CARRYING OUT THE INVENTION

[0747] A system for implementing this invention has the following configuration and functions. It includes a means for collecting lifestyle habit data from a user and an AI analysis means for analyzing the collected data. In addition, it includes an imaging means for visually displaying the analysis results, a means for generating a personalized health improvement action plan and presenting it to the user, and a means for tracking the user's health improvement actions and continuously updating the data. It also includes a means for providing coupons for specific services or products based on the action plan.

[0748] This invention is implemented using various devices and cloud infrastructure, including a smartphone app, a wearable device (e.g., a smartwatch), a cloud server (e.g., AWS or Google Cloud), an AI analysis module (e.g., Python + TensorFlow or PyTorch), a database system (e.g., MySQL or PostgreSQL), and imaging technology (e.g., Unity or Unreal Engine).

[0749] Data collection

[0750] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated smartphone application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as the number of steps taken and heart rate. The smartphone application temporarily stores this data in a local database (SQLite). The data is then periodically sent to a cloud server every night.

[0751] Data analysis and simulation

[0752] The server inputs the received data into an AI analysis module to simulate future health conditions (weight, blood pressure, blood sugar levels, etc.) The simulation results are visualized using Unity or Unreal Engine and displayed in a format that users can intuitively understand.

[0753] Personalized action plan generation

[0754] Based on the simulation results, the server generates a specific health improvement action plan for the user, which may include, for example, a specific fitness program or healthy foods, and then sends the action plan to the user's smartphone and notifies them.

[0755] Continuous data updates and physical store integration

[0756] Based on the designated action plan, users continuously input their lifestyle data and collect activity data using a wearable device. This data is continuously sent to a server, which analyzes the data as needed and updates the action plan as necessary. The server also provides users with coupons for specific services and products based on the action plan.

[0757] Specific examples

[0758] For example, when User A uses this system, he or she enters daily food and exercise records into the app and collects activity data using a smartwatch. The server uses this data to simulate User A's weight and blood pressure three months from now, suggests a fitness program (e.g., "attending yoga classes three times a week") and health foods (e.g., "specific supplements") that are suitable for User A, and provides discount coupons.

[0759] Prompt Sentence Examples

[0760] "Analyze User A's lifestyle data (e.g., diet, exercise, and sleep data), predict their weight and blood pressure three months from now, suggest optimal fitness programs and healthy foods, and visually display the results. Also, provide discount coupons based on the results."

[0761] This allows users to receive specific instructions and support from physical stores to take ongoing health improvement actions.

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] Users input their daily lifestyle data (e.g., dietary content, amount of exercise, and sleep time) into a smartphone app. In addition, activity data (e.g., number of steps, heart rate) is automatically collected using a wearable device (e.g., a smartwatch). This data is temporarily stored in a local database (SQLite) on the smartphone. The input here consists of data manually entered by the user and data automatically collected by the wearable device.

[0765] Step 2:

[0766] The device sends the lifestyle data collected from the user to a server at a fixed time every night. The data includes the user's diet, exercise, sleep time, and activity data. This process involves transferring data from the smartphone to a cloud server (e.g., AWS, Google Cloud).

[0767] Step 3:

[0768] The server stores the received lifestyle data in a database (e.g., MySQL, PostgreSQL). To store the received data accurately, the data is preprocessed and converted into a consistent format, which facilitates subsequent data analysis.

[0769] Step 4:

[0770] The server inputs the user's lifestyle data stored in the database into an AI analysis module (Python + TensorFlow or PyTorch) to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels). This analysis is a step in which a generative AI model is used to detect and predict outliers. Prediction data is obtained as a result of the analysis.

[0771] Step 5:

[0772] The simulation results are visually displayed using imaging technology (Unity or Unreal Engine). The server analyzes the simulation results and visualizes them in a format that is easy for users to understand. This allows users to intuitively understand their future health status. The output is data for visual display.

[0773] Step 6:

[0774] The server generates a personalized health improvement action plan based on the simulation results, which includes specific behavioral instructions (e.g., exercising three times a week, adjusting diet). The input here is the simulation results, and the output is an individualized action plan.

[0775] Step 7:

[0776] The generated action plan is sent to the device and notified to the user via a smartphone app. The user receives the notification and begins taking action to improve their lifestyle habits according to the plan. The output here is a user notification.

[0777] Step 8:

[0778] Users change their daily habits based on the action plan presented to them and enter the results into a smartphone app. They also continuously wear a wearable device to collect activity data. The input data are new lifestyle and activity data.

[0779] Step 9:

[0780] The device continuously collects data and sends it to the server at regular intervals. This allows the server to always obtain the latest user data and continuously monitor the health status. The output here is continuously updated data.

[0781] Step 10:

[0782] The server re-analyzes the data and updates the action plan as needed. It also generates coupons for specific services or products and provides them to the user. The user can then use these coupons to receive discounts on fitness programs or healthy foods. The output is the updated action plan and coupons.

[0783] This allows users to receive specific instructions and support to take ongoing health-improving actions.

[0784] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0785] This invention is a futuristic health prediction system that utilizes AI, advanced imaging technology, and an emotion engine. It visually simulates a user's future health status based on their current lifestyle and emotional data, and provides a personalized health improvement action plan. This system collects lifestyle and emotional data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, a personalized health improvement action plan is generated based on the simulation results and emotional data and presented to the user. As a result, the user is able to take specific and continuous health improvement actions.

[0786] Data collection

[0787] User:

[0788] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Additionally, the emotion engine recognizes and collects emotional data from the user's facial expressions and voice.

[0789] Device:

[0790] The device temporarily stores the lifestyle data entered by the user, as well as the activity and emotion data collected from the wearable device and emotion engine in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[0791] server:

[0792] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. Based on this data, the server can also request additional information from the user.

[0793] Data analysis and simulation

[0794] server:

[0795] The server inputs the collected lifestyle and emotional data into an AI analysis module. The AI ​​analysis module uses this data to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.). The simulation results are used to assess future health risks. In addition, emotional data is reflected in the analysis, enabling more accurate and personalized predictions.

[0796] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[0797] Generate a personalized action plan

[0798] server:

[0799] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day), which is then sent to the device.

[0800] Device:

[0801] The device notifies the user of the action plan sent from the server within a dedicated application. The user then confirms the action plan and puts it into action. Emotional data plays an important role in adjusting the content and strength of the action plan.

[0802] Continuous health management and behavior tracking

[0803] User:

[0804] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0805] Device:

[0806] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0807] server:

[0808] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[0809] Specific examples

[0810] Below is a case where User B uses "Health Time Travel."

[0811] User:

[0812] Person B enters his daily food record (e.g. bread and coffee for breakfast, salad and chicken for lunch) into the application and adds records of his walking and jogging. He also wears a smartwatch to measure his heart rate and number of steps. Furthermore, if Person B feels stressed, the emotion engine collects emotional data from his facial expressions and voice.

[0813] Device:

[0814] The lifestyle data entered by Mr. B, the activity data automatically collected from the smartwatch, and the emotion data from the emotion engine are sent to the server every night at midnight.

[0815] server:

[0816] The server inputs Mr. B's lifestyle, activity, and emotional data into an AI analysis module, and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for Mr. B is generated (e.g., "incorporate relaxation into your routine when stress levels rise," walk 30 minutes three times a week, and limit daily food intake to 2,000 kcal). This action plan is then sent to the device.

[0817] Device:

[0818] The device notifies Person B of the action plan and displays it as specific tasks. Person B changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[0819] server:

[0820] The system continuously reanalyzes the received data, updates the action plan as needed, and sends the new plan to the device, allowing Mr. B to visually understand his future health condition and take specific and effective actions to improve his health.

[0821] The present invention allows users to visually and emotionally understand future health risks and implement appropriate action plans to improve their health.

[0822] The processing flow will be explained below.

[0823] Step 1:

[0824] User:

[0825] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. They also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Furthermore, an emotion engine is used to collect emotional data (e.g., stress level, happiness level) from facial expressions and voice.

[0826] Step 2:

[0827] Device:

[0828] The device temporarily stores the lifestyle data entered by the user, as well as data collected from the wearable device and emotion engine, in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[0829] Step 3:

[0830] server:

[0831] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[0832] Step 4:

[0833] server:

[0834] The AI ​​analysis module analyzes the lifestyle and emotional data received from the database to simulate the user's future health condition. For example, it simulates changes in weight and blood sugar levels over the next three months. It also incorporates emotional data into the analysis to evaluate the impact of the user's mental state on their health.

[0835] Step 5:

[0836] server:

[0837] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[0838] Step 6:

[0839] server:

[0840] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, which includes specific behavioral instructions (e.g., exercising 30 minutes three times a week, eating less than 2,000 kcal a day, and stress management techniques).

[0841] Step 7:

[0842] server:

[0843] The generated action plan is sent to the device.

[0844] Step 8:

[0845] Device:

[0846] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[0847] Step 9:

[0848] User:

[0849] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise, stress management) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[0850] Step 10:

[0851] Device:

[0852] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[0853] Step 11:

[0854] server:

[0855] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as needed.

[0856] Step 12:

[0857] server:

[0858] The updated action plan is sent to the device and notified to the user, allowing them to take optimal health-improving actions at the appropriate time.

[0859] Example 2

[0860] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0861] Conventional health management systems only consider users' lifestyle habits, which means they are unable to fully consider emotional fluctuations and their impact when predicting future health conditions. This can lead to inaccurate individual health improvement action plans, making it difficult for users to effectively improve their health. Furthermore, action plans are often not updated appropriately in line with ongoing data updates, which can lead to insufficient tracking of improvement actions.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0863] In this invention, the server includes means for collecting lifestyle data from a user and emotion data collected from a wearable device and an emotion engine, means for temporarily storing the collected lifestyle data and emotion data in a local device and periodically transmitting them to the server, AI analysis means for simulating future health conditions based on the data transmitted to the server, means for visually displaying the simulation results using advanced imaging technology, means for generating a personalized health improvement action plan based on the simulation results and emotion data and notifying the user, and means for tracking the user's health improvement actions and continuously updating the data. This enables more accurate and personalized health predictions that take emotion data into account, the generation of improvement action plans, and the continuous updating of these plans.

[0864] "User" refers to an individual who uses this system to provide their lifestyle and emotional data and receives a health status simulation and a health improvement action plan.

[0865] "Lifestyle data" refers to information about a user's daily behavior and lifestyle, such as diet, exercise records, and sleep time.

[0866] "Emotional Data" refers to information about a user's emotional state collected from their facial expressions and voice.

[0867] A "wearable device" is an electronic device that can be worn, such as a smartwatch, and collects activity data such as heart rate and number of steps.

[0868] An "emotion engine" refers to a software or hardware system that analyzes a user's facial expressions and voice to generate emotional data.

[0869] A "local device" refers to a device used by a user (such as a smartphone or tablet) that temporarily stores lifestyle data and emotional data.

[0870] "Server" refers to the central management system that receives and stores data sent from the device and performs AI analysis.

[0871] "AI analysis module" refers to a program that uses artificial intelligence technology to analyze collected data and simulate future health conditions.

[0872] "Advanced imaging technology" refers to the latest image processing technology for visually displaying simulation results in an easy-to-understand manner.

[0873] "Health Improvement Action Plan" refers to individual health improvement measures provided to users based on the simulation results and emotional data.

[0874] "Continuous data updating" refers to the process of analyzing health status and revising action plans using new data collected regularly from users.

[0875] The present invention is a system that predicts future health conditions based on a user's lifestyle and emotional data and provides a personalized action plan for improving health. This system requires the participation of a server, a terminal, and the user.

[0876] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as heart rate and number of steps. Furthermore, an emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0877] The device temporarily stores the lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine in a local database. These data are then sent to the server every night at midnight.

[0878] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. The server's AI analysis module uses this data to simulate future health conditions (such as weight, blood pressure, and blood sugar levels). This simulation uses AI analysis technology and advanced imaging technology, allowing users to visually confirm the simulation results.

[0879] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day). The action plan is then sent to the device, which then notifies the user.

[0880] The user continues to input their daily activities (e.g., diet and exercise) into a dedicated application in accordance with the presented action plan. The device continuously collects this daily input data and sends it to the server every night at midnight. The server continuously receives the data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary. The updated action plan is then sent back to the device and the user is notified.

[0881] As a concrete example, let's say user A uses this system. A enters their daily food and walking records into the app and wears a smartwatch to measure their heart rate and number of steps. Furthermore, if A feels stressed, emotional data is collected from their facial expressions and voice. This data is sent to the server, and an AI analysis module simulates A's weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for A (for example, "incorporate relaxation into your routine when stress levels rise," walk for 30 minutes three times a week, and limit daily food intake to 2000 kcal) is generated. This action plan is sent to the device and notified to A.

[0882] Example prompt sentence:

[0883] "User A enters his daily food records (e.g., bread and coffee for breakfast, salad and chicken for lunch) and his walking and jogging records into the app, and uses a smartwatch to measure his heart rate and number of steps. In addition, if User A feels stressed, emotional data is collected. The app simulates his future health condition (e.g., weight, blood sugar level, etc.) and generates a health improvement action plan appropriate for User A (e.g., 30 minutes of exercise three times a week, limiting daily food intake to 2000 kcal)."

[0884] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0885] Step 1:

[0886] User:

[0887] Users enter their daily lifestyle data (e.g., diet, exercise records, sleep time) into a dedicated application.

[0888] Input: Meal details, exercise record, sleep time

[0889] Output: Lifestyle data is saved in the application

[0890] Specific actions: If you had bread and coffee for breakfast, enter that information in the app, and also enter the 30-minute jog you went for in the morning.

[0891] Step 2:

[0892] User:

[0893] Users wear a wearable device such as a smartwatch, which automatically collects activity data (e.g., steps taken, heart rate).

[0894] Input: Wearing and operating the wearable device

[0895] Output: Activity data is obtained

[0896] What it does: The user wears the smartwatch throughout the day, and the watch periodically records their heart rate and steps.

[0897] Step 3:

[0898] Emotion Engine:

[0899] The emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0900] Input: User's facial expression data, voice data

[0901] Output: Emotion data

[0902] Specific operation: Facial expressions and tone of voice that occur when the user feels stress are detected, and emotional data is recorded as "stress."

[0903] Step 4:

[0904] Device:

[0905] The lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine are temporarily stored in a local database.

[0906] Input: lifestyle data, activity data, emotion data

[0907] Output: Data stored in a local database

[0908] What it does: The application temporarily stores user input and automatically collected data.

[0909] Step 5:

[0910] Device:

[0911] The collected data is sent to the server every night at midnight.

[0912] Input: Data from the local database

[0913] Output: Data sent to the server

[0914] Specific operation: At midnight, the device automatically sends data to the server via the network.

[0915] Step 6:

[0916] server:

[0917] The lifestyle data, activity data, and emotion data transmitted from the terminal are received and stored in a database.

[0918] Input: Data sent from the terminal

[0919] Output: Data stored in the server database

[0920] Specific operation: When data is sent to the server, it is received and processed and recorded in the server's database.

[0921] Step 7:

[0922] server:

[0923] The collected data is input into an AI analysis module.

[0924] Input: Data stored in the server's database

[0925] Output: Data input to the AI ​​analysis module

[0926] Specific operation: The server periodically passes database data to the AI ​​analysis module.

[0927] Step 8:

[0928] AI analysis module:

[0929] Simulate future health conditions based on data.

[0930] Input: lifestyle data, activity data, emotion data

[0931] Output: Health condition simulation results

[0932] How it works: The AI ​​analysis module analyzes the input data and runs algorithms to predict future changes in weight and blood pressure.

[0933] Step 9:

[0934] server:

[0935] Simulation results are visually displayed using advanced imaging technology.

[0936] Input: Simulation results of AI analysis module

[0937] Output: Visually displayed simulation results

[0938] Specific behavior: Prediction results are generated as graphs and charts and presented in a user-accessible format.

[0939] Step 10:

[0940] AI analysis module:

[0941] Generate a personalized health improvement action plan based on the simulation results and emotional data.

[0942] Input: Simulation results, emotion data

[0943] Output: Health Improvement Action Plan

[0944] Specific actions: A specific action plan (e.g., exercise frequency, dietary content) is generated in text format.

[0945] Step 11:

[0946] server:

[0947] The generated action plan is sent to the device.

[0948] Input: Health Improvement Action Plan

[0949] Output: Action plan sent to device

[0950] Specific behavior: A notification is triggered to the device and an action plan is sent.

[0951] Step 12:

[0952] Device:

[0953] The terminal notifies the user of the action plan sent from the server within a dedicated application.

[0954] Input: Action plan sent from the server

[0955] Output: Action plan communicated to user

[0956] Specific behavior: A push notification will appear on the user's screen.

[0957] Step 13:

[0958] User:

[0959] Follow the action plan provided and continue to enter your daily actions into the application.

[0960] Input: New lifestyle and activity data based on the action plan

[0961] Output: New lifestyle data input into the application

[0962] Specific actions: After exercising, enter the activity record into the application.

[0963] Step 14:

[0964] Device:

[0965] Lifestyle data, activity data, and emotional data are continuously collected and sent to a server every night at midnight.

[0966] Input: New lifestyle data, activity data, emotion data

[0967] Output: Continuous data sent to the server

[0968] Specific operation: Data is automatically sent to the server every night at midnight.

[0969] Step 15:

[0970] server:

[0971] Receive data continuously and compare it with past data to monitor changes in your health.

[0972] Input: Continuously received data

[0973] Output: Analysis results based on changes in health status

[0974] Specific operation: New data is passed to the AI ​​analysis module and reanalyzed.

[0975] Step 16:

[0976] server:

[0977] Update your action plan with the latest data.

[0978] Input: Latest data

[0979] Output: Updated action plan

[0980] Specific operation: The AI ​​analysis module reanalyzes and generates a new action plan.

[0981] Step 17:

[0982] Device:

[0983] The updated action plan is sent to the device and notified to the user.

[0984] Input: Update action plan sent from the server

[0985] Output: Update action plan communicated to the user

[0986] What happens: A notification of a new action plan will appear on your device screen.

[0987] (Application example 2)

[0988] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0989] There are many challenges facing health management for users in modern society. In particular, it is difficult to accurately grasp the impact of lifestyle and emotional fluctuations on health and provide specific improvement measures based on that understanding. Furthermore, users lack the motivation and specific feedback to continuously improve their health. To resolve this situation, it is necessary to use more advanced data analysis and visualization technologies to provide health information in a format that users can intuitively understand, and to obtain feedback in concrete settings such as physical stores.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0991] In this invention, the server includes means for collecting lifestyle habit data and emotional data from a user, means for simulating a future health state based on the collected lifestyle habit data and emotional data, means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and emotional data and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for the user to receive feedback at a physical store. This allows the user to visually and intuitively understand future health risks, receive a specific and feasible health improvement action plan, and further receive feedback at the physical store, enabling continuous health management and behavioral improvement.

[0992] "Lifestyle data" refers to data related to the user's daily life, such as dietary habits, exercise records, and sleep duration.

[0993] "Emotion data" refers to data that indicates the emotional state of the user as recognized from facial expressions, voice, etc.

[0994] "AI analysis methods" are methods that use artificial intelligence technology to analyze collected data and simulate future health conditions.

[0995] "Visualization means" refers to technology for visually displaying simulation results, including graphs and 3D models.

[0996] A "health improvement action plan" is a specific, individualized instruction for health improvement behavior that is generated based on the simulation results and emotional data.

[0997] A "brick and mortar store" is a physical store that customers can visit in person, such as a fitness club or health food store.

[0998] "Feedback means" refers to a means by which users can receive specific feedback about their health status and behavior in a physical store.

[0999] A "local device" is a device carried by a user, such as a smartphone or tablet, that has the function of temporarily storing data.

[1000] "Server" is a central processing unit that stores collected data and performs analysis and generation of action plans.

[1001] "Data update means" refers to a means for keeping the system up to date based on the user's health improvement actions and new data collected.

[1002] This invention is a system that collects lifestyle data and emotional data from a user, simulates future health conditions, and provides a personalized action plan for improving health. Specific embodiments of this system are described below.

[1003] Data collection

[1004] User: The user enters lifestyle data such as daily diet, exercise records, and sleep time into a dedicated smartphone application. The user also wears a wearable device (e.g., a smartwatch) that automatically collects activity data such as heart rate and number of steps. The emotion engine then recognizes and collects emotional data from the user's facial expressions and voice.

[1005] Terminal: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and emotion engines. These data are then periodically sent to a server.

[1006] server:

[1007] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. It may also request additional information from the user.

[1008] Data analysis and simulation

[1009] server:

[1010] The collected lifestyle and emotional data is input into an AI analysis module to simulate future health conditions. The AI ​​analysis module then uses generative AI models, such as deep learning models, to make highly accurate predictions. The simulation results are visualized using 3D models and graphs.

[1011] Generate a personalized action plan

[1012] server:

[1013] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific exercise and nutritional instructions, and presents it to the user. For example, instructions might include "walking for 30 minutes three times a week" or "limiting daily food intake to 2,000 kcal."

[1014] Device:

[1015] The action plan sent from the server is notified within the dedicated application and displayed as specific tasks. The user can then change their lifestyle habits according to the action plan and put it into action.

[1016] Continuous health management and behavior tracking

[1017] User:

[1018] The user follows the action plan and inputs their daily activities into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1019] Device:

[1020] Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to a server.

[1021] server:

[1022] The system continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[1023] Specific examples

[1024] For example, when a user uses "health time travel," the following operations occur:

[1025] Users enter their daily food and exercise records into the application, and their heart rate and number of steps are measured using a wearable device. The emotion engine collects emotional data from facial expressions and voice when users feel stressed. This data is sent to the server every night at midnight, and the server provides the latest simulation results and action plans the following morning.

[1026] Prompt Sentence Examples

[1027] "Based on the user's current lifestyle and emotional data, predict future health conditions and generate individualized health improvement action plans. Build a system that uses inputs such as dietary habits, exercise records, sleep time, heart rate, number of steps, and emotional data (facial expressions and voice) to simulate future weight, blood pressure, and blood sugar levels and provide appropriate behavioral instructions."

[1028] The above is a specific embodiment of the present invention. This system allows users to obtain health information in an intuitively understandable format and to take continuous health improvement actions based on a personalized action plan.

[1029] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1030] Step 1:

[1031] User: The user uses a dedicated smartphone application to input daily lifestyle data (food content, exercise records, sleep time, etc.). In addition, the user wears a wearable device (such as a smartwatch) to automatically collect activity data such as heart rate and number of steps. The emotion engine recognizes and collects emotion data from the user's facial expressions and voice.

[1032] Input: Meal details, exercise records, sleep time, heart rate, number of steps, facial expression data, voice data

[1033] Output: Collected lifestyle data, activity data, and emotion data

[1034] Step 2:

[1035] Device: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and the emotion engine. This data is sent to the server every night at midnight.

[1036] Input: Collected lifestyle data, activity data, and emotional data

[1037] Output: Data stored on local device, data sent to server

[1038] Step 3:

[1039] Server: The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. If necessary, it may request additional information from the user.

[1040] Input: Data sent from the terminal

[1041] Output: Data stored in the database, prompting the user for more information

[1042] Step 4:

[1043] Server: The server retrieves lifestyle and emotional data from the database and inputs it into the AI ​​analysis module. Using a generative AI model, this data is analyzed and future health conditions (weight, blood pressure, blood sugar level, etc.) are simulated.

[1044] Input: Data retrieved from the database

[1045] Output: Simulation results

[1046] Step 5:

[1047] Server: Visually displays the simulation results using 3D models, graphs, etc. Using visualization tools, the server processes the data so that the user can intuitively understand it.

[1048] Input: Simulation results

[1049] Output: Visualized data (3D models, graphs, etc.)

[1050] Step 6:

[1051] Server: Generates a personalized health improvement action plan based on the simulation results and emotional data. This includes specific exercise and nutritional instructions. Examples include "walk for 30 minutes three times a week" and "limit daily food intake to 2000 kcal."

[1052] Input: Simulation results, emotion data

[1053] Output: A personalized health improvement action plan

[1054] Step 7:

[1055] Device: The action plan sent from the server is displayed in a dedicated application and specific tasks are displayed. The user can then change their lifestyle habits according to the action plan and put it into action.

[1056] Input: Action plan sent from the server

[1057] Output: Action plan communicated to user

[1058] Step 8:

[1059] User: Follows the action plan provided and enters daily activities (e.g., meals, exercise) into the application. The wearable device also continuously collects activity data. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1060] Input: Daily behavior data based on the action plan

[1061] Output: Newly collected lifestyle, activity, and emotion data

[1062] Step 9:

[1063] Device: Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to the server.

[1064] Input: Newly collected data

[1065] Output: Data stored on local device, data sent to server

[1066] Step 10:

[1067] Server: Continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[1068] Input: Continuously received data

[1069] Output: Change in health status, updated action plan

[1070] The above are the specific processing steps for carrying out this invention, which allow the user to visually and intuitively understand their future health status, receive a specific and feasible action plan for improving their health, and continue to manage their health and improve their behavior.

[1071] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1073] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1074] [Third embodiment]

[1075] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1076] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1077] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1078] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1079] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1080] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1081] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1082] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1083] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1085] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1086] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1087] This invention is a futuristic health prediction system that utilizes AI and advanced imaging technology. It visually simulates a user's future health status based on their current lifestyle data and provides an individualized health improvement action plan. This system collects lifestyle data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, it generates a personalized health improvement action plan based on the simulation results and presents it to the user. As a result, the user can take specific and continuous actions to improve their health.

[1088] Data collection

[1089] User:

[1090] Users input their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application on their device. Users also wear a wearable device, which automatically collects activity data (e.g., number of steps, heart rate), allowing for accurate lifestyle data to be obtained.

[1091] Device:

[1092] The device temporarily stores the lifestyle data entered by the user in a local database. It also acquires activity data in real time from the wearable device and stores it in the local database. The collected data is sent to the server at regular intervals (e.g., every night at midnight).

[1093] server:

[1094] The server receives the lifestyle data sent from the device and stores it in a database. Based on this data, the server can also request additional information from the user.

[1095] Data analysis and simulation

[1096] server:

[1097] The server inputs the collected lifestyle data into an AI analysis module, which then simulates future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.) based on the current data. The simulation results are used to assess future health risks.

[1098] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[1099] Generate a personalized action plan

[1100] server:

[1101] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[1102] Device:

[1103] The device receives the action plan sent from the server and notifies the user within the application. The user then checks the action plan and puts it into action.

[1104] Continuous health management and behavior tracking

[1105] User:

[1106] Based on the action plan presented, users input their daily activities (e.g., diet, exercise) into the application, and the wearable device continues to collect activity data.

[1107] Device:

[1108] The device continuously collects daily lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[1109] server:

[1110] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as needed. The updated action plan is sent to the device and notified to the user.

[1111] Specific examples

[1112] Below is a case where a user A uses "health time travel."

[1113] User:

[1114] Mr. A enters his daily meal record (breakfast, lunch, dinner) into the application, and also adds records of his walking and jogging. He also wears a smartwatch at all times to measure his heart rate and number of steps.

[1115] Device:

[1116] The lifestyle data entered by Mr. A and the activity data automatically collected from the smartwatch are sent to the server every night at midnight.

[1117] server:

[1118] The server inputs Mr. A's lifestyle data into an AI analysis module and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, it generates an action plan appropriate for Mr. A (e.g., "walk for 30 minutes three times a week," "limit daily food intake to 2000 kcal"). This action plan is then sent to the device.

[1119] Device:

[1120] The device notifies Person A of the action plan and displays it as specific tasks. Person A changes his / her lifestyle habits according to the plan and periodically enters the results into the application.

[1121] server:

[1122] It continuously re-analyzes the data it receives, updates the action plan as needed, and sends the new plan to the device.

[1123] This allows Mr. A to visually understand his future health condition and take specific and effective actions to improve his health.

[1124] The processing flow will be explained below.

[1125] Step 1:

[1126] User:

[1127] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate).

[1128] Step 2:

[1129] Device:

[1130] The lifestyle data entered by the user and the activity data acquired from the wearable device are temporarily stored in a local database, and these data are sent to a server at regular intervals (e.g., every night at midnight).

[1131] Step 3:

[1132] server:

[1133] The system receives lifestyle and activity data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[1134] Step 4:

[1135] server:

[1136] The AI ​​analysis module analyzes the lifestyle data received from the database and simulates the user's future health condition, such as weight fluctuations and blood sugar levels over the next three months.

[1137] Step 5:

[1138] server:

[1139] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[1140] Step 6:

[1141] server:

[1142] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific behavioral instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[1143] Step 7:

[1144] server:

[1145] The generated action plan is sent to the device.

[1146] Step 8:

[1147] Device:

[1148] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[1149] Step 9:

[1150] User:

[1151] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application, which also collects activity data using a wearable device.

[1152] Step 10:

[1153] Device:

[1154] Daily lifestyle and activity data is continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1155] Step 11:

[1156] server:

[1157] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[1158] Step 12:

[1159] server:

[1160] The updated action plan is sent to the device and notified to the user, allowing them to take the optimal health improvement actions at the appropriate time.

[1161] Example 1

[1162] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] Conventional health management systems have not been able to fully utilize collected lifestyle data, making it difficult to provide personalized health improvement action plans. Furthermore, they have not been able to continuously update users' behavioral data or accurately simulate their future health status. This has resulted in insufficient support for users to continuously take specific and effective health improvement actions.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1165] In this invention, the server includes means for collecting lifestyle habit data from a user, artificial intelligence analysis means for simulating future health conditions based on the collected lifestyle habit data, image processing means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for temporarily storing the lifestyle habit data and activity data in a local device and periodically transmitting them to the server. This provides a personalized health improvement action plan, enabling the user to continuously take specific and effective health improvement actions.

[1166] "User" refers to an individual who uses the System to provide lifestyle data.

[1167] "Lifestyle data" refers to information about the user's daily lifestyle (for example, dietary content, amount of exercise, sleep duration, etc.).

[1168] "Artificial intelligence analysis methods" refer to methods that use AI technology to predict and analyze future health conditions based on collected lifestyle data.

[1169] "Image processing means" refers to technology for visually displaying the results of a simulation (e.g., graphs, 3D models).

[1170] A "health improvement action plan" refers to specific behavioral instructions and suggestions proposed to improve a user's health based on AI analysis.

[1171] A "local device" refers to a terminal or wearable device used by a user, which is a device for temporarily storing lifestyle habit data and activity data.

[1172] "Server" refers to a centralized computer system that receives collected lifestyle data, analyzes it, and provides the results to users.

[1173] "Activity data" refers to information about a user's physical activity (e.g., number of steps, heart rate, etc.).

[1174] This invention is a system that utilizes AI and advanced imaging technology to visually simulate future health conditions based on a user's lifestyle data and provide a personalized action plan for improving their health. Specific methods for implementing this system are described below.

[1175] System configuration

[1176] Hardware Configuration

[1177] Users use a smartphone or tablet with a dedicated application installed, and also wear a wearable device (e.g., a smartwatch) to collect activity data.

[1178] The device refers to the user's smartphone or tablet, and lifestyle and activity data is stored in a local database.

[1179] The server is located on the cloud and analyzes data, generates simulation results, and proposes action plans.

[1180] Software Configuration

[1181] The dedicated application allows users to input lifestyle habit data and check the action plan sent from the server.

[1182] The AI ​​analysis module has an algorithm for simulating future health conditions based on data collected from users.

[1183] Image processing techniques include techniques for visually displaying simulation results (e.g., graphs, 3D models).

[1184] Data collection

[1185] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application, and activity data (e.g., number of steps, heart rate) is automatically collected through the wearable device.

[1186] The terminal stores the data entered by the user and the data obtained from the wearable device in a local database and sends it to the server every night at midnight.

[1187] Example prompt: "Enter your exercise record for today. Example: 30 minutes walking, 20 minutes jogging."

[1188] Data analysis and simulation

[1189] The server inputs the collected lifestyle data into an AI analysis module and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[1190] The simulation results are displayed visually using image processing technology so that users can understand them intuitively.

[1191] Example: "Display a graph of weight fluctuations and blood glucose levels for the next three months"

[1192] Generate a personalized action plan

[1193] Based on the simulation results, the server uses an AI analysis module to generate a personalized health improvement action plan, which includes specific instructions (e.g., exercising for 30 minutes three times a week and eating less than 2,000 kcal a day).

[1194] The generated action plan is transmitted from the server to the terminal, which then notifies the user of the plan.

[1195] Example prompt: "Exercise for 30 minutes three times a week. Enter your exercise log for today."

[1196] Continuous health management and behavior tracking

[1197] Users follow the action plan provided to them to change their lifestyle habits and continue to input their daily activities into the application, which also continuously collects activity data using a wearable device.

[1198] The device continuously collects data, stores it in a local database, and transmits it to a server at regular intervals.

[1199] The server compares the received data with past data, monitors changes in health status, reanalyzes the data based on the latest data, and updates the action plan as needed.

[1200] Example prompt: "A new exercise plan has been generated. Please continue your activities according to the new plan."

[1201] This system allows users to visually understand their future health status and take specific and effective actions to improve their health, thereby supporting them in continuously managing and improving their health.

[1202] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1203] Step 1:

[1204] The user enters lifestyle data (e.g., dietary content, exercise time, sleep time) into a dedicated application. The wearable device (e.g., smart watch) automatically collects activity data (e.g., number of steps, heart rate).

[1205] Input: User-entered lifestyle data, wearable device activity data

[1206] Output: Data is saved on the device

[1207] Specific operation: The user enters into the app, "I ate bread and eggs for breakfast" and "I jogged for 30 minutes." The smartwatch then measures the number of steps taken and heart rate for the day.

[1208] Step 2:

[1209] The terminal stores the user's input data and activity data acquired from the wearable device in a local database.

[1210] Input: User-entered lifestyle data, wearable device activity data

[1211] Output: Data stored in a local database

[1212] Specific operation: The device automatically saves the user's food log and data received from the smartwatch.

[1213] Step 3:

[1214] The device sends the data stored in the local database to the server every night at midnight.

[1215] Input: Lifestyle and activity data stored in a local database

[1216] Output: Data sent to the server

[1217] Specific operation: Automatically upload data to the server when the date changes.

[1218] Step 4:

[1219] The server inputs the lifestyle data received into the AI ​​analysis module, which analyzes the data and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[1220] Input: Lifestyle and activity data received by the server

[1221] Output: Simulation results

[1222] Specific operation: Based on the received data, the AI ​​analysis module predicts changes in weight and blood sugar levels three months from now.

[1223] Step 5:

[1224] The server visually displays the simulation results using image processing technology.

[1225] Input: Simulation results

[1226] Output: Simulation results in a format that can be visually understood by the user

[1227] Specific operation: Predictions of future health risks (e.g., obesity, diabetes) are displayed in graphs and 3D models.

[1228] Step 6:

[1229] Based on the simulation results, the server's AI analysis module generates a personalized health improvement action plan, which includes specific instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[1230] Input: Simulation results

[1231] Output: Health Improvement Action Plan

[1232] Specific actions: The AI ​​analysis module generates instructions such as "walk for 30 minutes three times a week" or "eat less than 2000 kcal per day."

[1233] Step 7:

[1234] The server sends the generated health improvement action plan to the terminal.

[1235] Input: Health Improvement Action Plan

[1236] Output: Action plan sent to device

[1237] Specific operation: Instructions are sent from the server to the device and notified to the app.

[1238] Step 8:

[1239] The device notifies the user of the action plan and displays it within the application.

[1240] Input: Health improvement action plan sent from the server

[1241] Output: Notified action plan

[1242] Specific action: The app displays a notification saying, "Exercise for 30 minutes three times a week."

[1243] Step 9:

[1244] The user changes their lifestyle according to the action plan provided and enters their daily activities (e.g., diet, exercise) into the application.

[1245] Input: User action data

[1246] Output: Action data based on the action plan

[1247] Specific action: The user enters the exercise record as "30 minutes of walking completed."

[1248] Step 10:

[1249] The device continuously collects lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[1250] Input: Daily lifestyle and activity data

[1251] Output: Updates stored in the local database and data sent to the server

[1252] Specific operation: The device sends the accumulated data to the server at midnight every night.

[1253] Step 11:

[1254] The server continuously analyzes the received data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[1255] Input: Continuously received lifestyle and activity data

[1256] Output: Updated action plan

[1257] Specific operation: The AI ​​analysis module reanalyzes the data, generates updated instructions such as "Extend walking time to 40 minutes," and sends them to the device.

[1258] (Application example 1)

[1259] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1260] Many conventional health management systems collect users' lifestyle data and predict their future health status, but they lack motivation to take specific action to improve their health and provide continuous feedback. As a result, it is difficult for users to sustain health improvement actions, and actual improvements in health are difficult to achieve. In addition, there is no connection with physical stores, and no support is provided through specific services or products for health improvement, making them less practical for users.

[1261] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1262] In this invention, the server includes means for collecting lifestyle data from a user, AI analysis means for simulating future health conditions based on the collected lifestyle data, imaging means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for providing coupons for specific services and products based on the action plan. This provides specific instructions and support for the user to continuously take health improvement actions, enabling health improvement through practical services in collaboration with physical stores.

[1263] A "user" is an individual who uses the system to provide lifestyle data and receives health status predictions and action plans.

[1264] "Lifestyle data" refers to data related to the user's daily activities such as diet, exercise, and sleep.

[1265] "AI analysis means" refers to means that include artificial intelligence technology for simulating future health conditions based on collected lifestyle data.

[1266] "Imaging means" refers to a technique for visually displaying the simulation results.

[1267] A "personalized health improvement action plan" is a specific action plan for improving health that is created based on the individual health condition and lifestyle habits of each user.

[1268] The "means for continuously updating data" is a technology that tracks the user's health improvement behavior and sends the latest data to a server.

[1269] The "means of providing coupons" is a means of providing users with discount coupons or benefits for specific services or products based on an action plan.

[1270] The "server" is a centralized computer system that collects lifestyle data, analyzes it, performs simulations, generates action plans, and continuously updates the data.

[1271] MODE FOR CARRYING OUT THE INVENTION

[1272] A system for implementing this invention has the following configuration and functions. It includes a means for collecting lifestyle habit data from a user and an AI analysis means for analyzing the collected data. In addition, it includes an imaging means for visually displaying the analysis results, a means for generating a personalized health improvement action plan and presenting it to the user, and a means for tracking the user's health improvement actions and continuously updating the data. It also includes a means for providing coupons for specific services or products based on the action plan.

[1273] This invention is implemented using various devices and cloud infrastructure, including a smartphone app, a wearable device (e.g., a smartwatch), a cloud server (e.g., AWS or Google Cloud), an AI analysis module (e.g., Python + TensorFlow or PyTorch), a database system (e.g., MySQL or PostgreSQL), and imaging technology (e.g., Unity or Unreal Engine).

[1274] Data collection

[1275] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated smartphone application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as the number of steps taken and heart rate. The smartphone application temporarily stores this data in a local database (SQLite). The data is then periodically sent to a cloud server every night.

[1276] Data analysis and simulation

[1277] The server inputs the received data into an AI analysis module to simulate future health conditions (weight, blood pressure, blood sugar levels, etc.) The simulation results are visualized using Unity or Unreal Engine and displayed in a format that users can intuitively understand.

[1278] Personalized action plan generation

[1279] Based on the simulation results, the server generates a specific health improvement action plan for the user, which may include, for example, a specific fitness program or healthy foods, and then sends the action plan to the user's smartphone and notifies them.

[1280] Continuous data updates and physical store integration

[1281] Based on the designated action plan, users continuously input their lifestyle data and collect activity data using a wearable device. This data is continuously sent to a server, which analyzes the data as needed and updates the action plan as necessary. The server also provides users with coupons for specific services and products based on the action plan.

[1282] Specific examples

[1283] For example, when User A uses this system, he or she enters daily food and exercise records into the app and collects activity data using a smartwatch. The server uses this data to simulate User A's weight and blood pressure three months from now, suggests a fitness program (e.g., "attending yoga classes three times a week") and health foods (e.g., "specific supplements") that are suitable for User A, and provides discount coupons.

[1284] Prompt Sentence Examples

[1285] "Analyze User A's lifestyle data (e.g., diet, exercise, and sleep data), predict their weight and blood pressure three months from now, suggest optimal fitness programs and healthy foods, and visually display the results. Also, provide discount coupons based on the results."

[1286] This allows users to receive specific instructions and support from physical stores to take ongoing health improvement actions.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] Users input their daily lifestyle data (e.g., dietary content, amount of exercise, and sleep time) into a smartphone app. In addition, activity data (e.g., number of steps, heart rate) is automatically collected using a wearable device (e.g., a smartwatch). This data is temporarily stored in a local database (SQLite) on the smartphone. The input here consists of data manually entered by the user and data automatically collected by the wearable device.

[1290] Step 2:

[1291] The device sends the lifestyle data collected from the user to a server at a fixed time every night. The data includes the user's diet, exercise, sleep time, and activity data. This process involves transferring data from the smartphone to a cloud server (e.g., AWS, Google Cloud).

[1292] Step 3:

[1293] The server stores the received lifestyle data in a database (e.g., MySQL, PostgreSQL). To store the received data accurately, the data is preprocessed and converted into a consistent format, which facilitates subsequent data analysis.

[1294] Step 4:

[1295] The server inputs the user's lifestyle data stored in the database into an AI analysis module (Python + TensorFlow or PyTorch) to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels). This analysis is a step in which a generative AI model is used to detect and predict outliers. Prediction data is obtained as a result of the analysis.

[1296] Step 5:

[1297] The simulation results are visually displayed using imaging technology (Unity or Unreal Engine). The server analyzes the simulation results and visualizes them in a format that is easy for users to understand. This allows users to intuitively understand their future health status. The output is data for visual display.

[1298] Step 6:

[1299] The server generates a personalized health improvement action plan based on the simulation results, which includes specific behavioral instructions (e.g., exercising three times a week, adjusting diet). The input here is the simulation results, and the output is an individualized action plan.

[1300] Step 7:

[1301] The generated action plan is sent to the device and notified to the user via a smartphone app. The user receives the notification and begins taking action to improve their lifestyle habits according to the plan. The output here is a user notification.

[1302] Step 8:

[1303] Users change their daily habits based on the action plan presented to them and enter the results into a smartphone app. They also continuously wear a wearable device to collect activity data. The input data are new lifestyle and activity data.

[1304] Step 9:

[1305] The device continuously collects data and sends it to the server at regular intervals. This allows the server to always obtain the latest user data and continuously monitor the health status. The output here is continuously updated data.

[1306] Step 10:

[1307] The server re-analyzes the data and updates the action plan as needed. It also generates coupons for specific services or products and provides them to the user. The user can then use these coupons to receive discounts on fitness programs or healthy foods. The output is the updated action plan and coupons.

[1308] This allows users to receive specific instructions and support to take ongoing health-improving actions.

[1309] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1310] This invention is a futuristic health prediction system that utilizes AI, advanced imaging technology, and an emotion engine. It visually simulates a user's future health status based on their current lifestyle and emotional data, and provides a personalized health improvement action plan. This system collects lifestyle and emotional data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, a personalized health improvement action plan is generated based on the simulation results and emotional data and presented to the user. As a result, the user is able to take specific and continuous health improvement actions.

[1311] Data collection

[1312] User:

[1313] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Additionally, the emotion engine recognizes and collects emotional data from the user's facial expressions and voice.

[1314] Device:

[1315] The device temporarily stores the lifestyle data entered by the user, as well as the activity and emotion data collected from the wearable device and emotion engine in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[1316] server:

[1317] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. Based on this data, the server can also request additional information from the user.

[1318] Data analysis and simulation

[1319] server:

[1320] The server inputs the collected lifestyle and emotional data into an AI analysis module. The AI ​​analysis module uses this data to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.). The simulation results are used to assess future health risks. In addition, emotional data is reflected in the analysis, enabling more accurate and personalized predictions.

[1321] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[1322] Generate a personalized action plan

[1323] server:

[1324] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[1325] Device:

[1326] The device notifies the user of the action plan sent from the server within a dedicated application. The user then confirms the action plan and puts it into action. Emotional data plays an important role in adjusting the content and strength of the action plan.

[1327] Continuous health management and behavior tracking

[1328] User:

[1329] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1330] Device:

[1331] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1332] server:

[1333] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as needed. The updated action plan is sent to the device and notified to the user.

[1334] Specific examples

[1335] Below is a case where User B uses "Health Time Travel."

[1336] User:

[1337] Person B enters his daily food record (e.g. bread and coffee for breakfast, salad and chicken for lunch) into the application and adds records of his walking and jogging. He also wears a smartwatch to measure his heart rate and number of steps. Furthermore, if Person B feels stressed, the emotion engine collects emotional data from his facial expressions and voice.

[1338] Device:

[1339] The lifestyle data entered by Mr. B, the activity data automatically collected from the smartwatch, and the emotion data from the emotion engine are sent to the server every night at midnight.

[1340] server:

[1341] The server inputs Mr. B's lifestyle, activity, and emotional data into an AI analysis module, and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for Mr. B is generated (e.g., "incorporate relaxation into your routine when stress levels rise," walk 30 minutes three times a week, and limit daily food intake to 2,000 kcal). This action plan is then sent to the device.

[1342] Device:

[1343] The device notifies Person B of the action plan and displays it as specific tasks. Person B changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[1344] server:

[1345] The system continuously reanalyzes the received data, updates the action plan as needed, and sends the new plan to the device, allowing Mr. B to visually understand his future health condition and take specific and effective actions to improve his health.

[1346] The present invention allows users to visually and emotionally understand future health risks and implement appropriate action plans to improve their health.

[1347] The processing flow will be explained below.

[1348] Step 1:

[1349] User:

[1350] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. They also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Furthermore, an emotion engine is used to collect emotional data (e.g., stress level, happiness level) from facial expressions and voice.

[1351] Step 2:

[1352] Device:

[1353] The device temporarily stores the lifestyle data entered by the user, as well as data collected from the wearable device and emotion engine, in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[1354] Step 3:

[1355] server:

[1356] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[1357] Step 4:

[1358] server:

[1359] The AI ​​analysis module analyzes the lifestyle and emotional data received from the database to simulate the user's future health condition. For example, it simulates changes in weight and blood sugar levels over the next three months. It also incorporates emotional data into the analysis to evaluate the impact of the user's mental state on their health.

[1360] Step 5:

[1361] server:

[1362] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[1363] Step 6:

[1364] server:

[1365] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, which includes specific behavioral instructions (e.g., exercising 30 minutes three times a week, eating less than 2,000 kcal a day, and stress management techniques).

[1366] Step 7:

[1367] server:

[1368] The generated action plan is sent to the device.

[1369] Step 8:

[1370] Device:

[1371] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[1372] Step 9:

[1373] User:

[1374] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise, stress management) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1375] Step 10:

[1376] Device:

[1377] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1378] Step 11:

[1379] server:

[1380] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as needed.

[1381] Step 12:

[1382] server:

[1383] The updated action plan is sent to the device and notified to the user, allowing them to take optimal health-improving actions at the appropriate time.

[1384] Example 2

[1385] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1386] Conventional health management systems only consider users' lifestyle habits, which means they are unable to fully consider emotional fluctuations and their impact when predicting future health conditions. This can lead to inaccurate individual health improvement action plans, making it difficult for users to effectively improve their health. Furthermore, action plans are often not updated appropriately in line with ongoing data updates, which can lead to insufficient tracking of improvement actions.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1388] In this invention, the server includes means for collecting lifestyle data from a user and emotion data collected from a wearable device and an emotion engine, means for temporarily storing the collected lifestyle data and emotion data in a local device and periodically transmitting them to the server, AI analysis means for simulating future health conditions based on the data transmitted to the server, means for visually displaying the simulation results using advanced imaging technology, means for generating a personalized health improvement action plan based on the simulation results and emotion data and notifying the user, and means for tracking the user's health improvement actions and continuously updating the data. This enables more accurate and personalized health condition predictions that take emotion data into account, the generation of improvement action plans, and the continuous updating of these plans.

[1389] "User" refers to an individual who uses this system to provide their lifestyle and emotional data and receives a health status simulation and a health improvement action plan.

[1390] "Lifestyle data" refers to information about a user's daily behavior and lifestyle, such as diet, exercise records, and sleep time.

[1391] "Emotional Data" refers to information about a user's emotional state collected from their facial expressions and voice.

[1392] A "wearable device" is an electronic device that can be worn, such as a smartwatch, and collects activity data such as heart rate and number of steps.

[1393] An "emotion engine" refers to a software or hardware system that analyzes a user's facial expressions and voice to generate emotional data.

[1394] A "local device" refers to a device used by a user (such as a smartphone or tablet) that temporarily stores lifestyle data and emotional data.

[1395] "Server" refers to the central management system that receives and stores data sent from the device and performs AI analysis.

[1396] "AI analysis module" refers to a program that uses artificial intelligence technology to analyze collected data and simulate future health conditions.

[1397] "Advanced imaging technology" refers to the latest image processing technology for visually displaying simulation results in an easy-to-understand manner.

[1398] "Health Improvement Action Plan" refers to individual health improvement measures provided to users based on the simulation results and emotional data.

[1399] "Continuous data updating" refers to the process of analyzing health status and revising action plans using new data collected periodically from users.

[1400] The present invention is a system that predicts future health conditions based on a user's lifestyle and emotional data and provides a personalized action plan for improving health. This system requires the participation of a server, a terminal, and the user.

[1401] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as heart rate and number of steps. Furthermore, an emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1402] The device temporarily stores the lifestyle data entered by the user, the activity data from the wearable device, and the emotion data from the emotion engine in a local database. These data are then sent to the server at midnight every night.

[1403] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. The server's AI analysis module uses this data to simulate future health conditions (such as weight, blood pressure, and blood sugar levels). This simulation uses AI analysis technology and advanced imaging technology, allowing users to visually confirm the simulation results.

[1404] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day). The action plan is then sent to the device, which then notifies the user.

[1405] The user continues to input their daily activities (e.g., diet and exercise) into a dedicated application in accordance with the presented action plan. The device continuously collects this daily input data and sends it to the server every night at midnight. The server continuously receives the data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary. The updated action plan is then sent back to the device and the user is notified.

[1406] As a concrete example, let's say user A uses this system. A enters their daily food and walking records into the app and wears a smartwatch to measure their heart rate and number of steps. Furthermore, if A feels stressed, emotional data is collected from their facial expressions and voice. This data is sent to the server, and an AI analysis module simulates A's weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for A (for example, "incorporate relaxation into your routine when stress levels rise," walk for 30 minutes three times a week, and limit daily food intake to 2000 kcal) is generated. This action plan is sent to the device and notified to A.

[1407] Example prompt sentence:

[1408] "User A enters his daily food records (e.g., bread and coffee for breakfast, salad and chicken for lunch) and his walking and jogging records into the app, and uses a smartwatch to measure his heart rate and number of steps. In addition, if User A feels stressed, emotional data is collected. The app simulates his future health condition (e.g., weight, blood sugar level, etc.) and generates a health improvement action plan appropriate for User A (e.g., 30 minutes of exercise three times a week, limiting daily food intake to 2000 kcal)."

[1409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1410] Step 1:

[1411] User:

[1412] Users enter their daily lifestyle data (e.g., diet, exercise records, sleep time) into a dedicated application.

[1413] Input: Meal details, exercise record, sleep time

[1414] Output: Lifestyle data is saved in the application

[1415] Specific actions: If you had bread and coffee for breakfast, enter that information in the app, and also enter the 30-minute jog you went for in the morning.

[1416] Step 2:

[1417] User:

[1418] Users wear a wearable device such as a smartwatch, which automatically collects activity data (e.g., steps taken, heart rate).

[1419] Input: Wearing and operating the wearable device

[1420] Output: Activity data is obtained

[1421] What it does: The user wears the smartwatch throughout the day, and the watch periodically records their heart rate and steps.

[1422] Step 3:

[1423] Emotion Engine:

[1424] The emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1425] Input: User's facial expression data, voice data

[1426] Output: Emotion data

[1427] Specific operation: Facial expressions and tone of voice that occur when the user feels stress are detected, and emotional data is recorded as "stress."

[1428] Step 4:

[1429] Device:

[1430] Lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine are temporarily stored in a local database.

[1431] Input: lifestyle data, activity data, emotion data

[1432] Output: Data stored in a local database

[1433] What it does: The application temporarily stores user input and automatically collected data.

[1434] Step 5:

[1435] Device:

[1436] The collected data is sent to the server every night at midnight.

[1437] Input: Data from the local database

[1438] Output: Data sent to the server

[1439] Specific operation: At midnight, the device automatically sends data to the server via the network.

[1440] Step 6:

[1441] server:

[1442] The lifestyle data, activity data, and emotion data transmitted from the terminal are received and stored in a database.

[1443] Input: Data sent from the terminal

[1444] Output: Data stored in the server database

[1445] Specific operation: When data is sent to the server, it is received and processed and recorded in the server's database.

[1446] Step 7:

[1447] server:

[1448] The collected data is input into an AI analysis module.

[1449] Input: Data stored in the server's database

[1450] Output: Data input to the AI ​​analysis module

[1451] Specific operation: The server periodically passes database data to the AI ​​analysis module.

[1452] Step 8:

[1453] AI analysis module:

[1454] Simulate future health conditions based on data.

[1455] Input: lifestyle data, activity data, emotion data

[1456] Output: Health condition simulation results

[1457] How it works: The AI ​​analysis module analyzes the input data and runs algorithms to predict future changes in weight and blood pressure.

[1458] Step 9:

[1459] server:

[1460] Simulation results are visually displayed using advanced imaging technology.

[1461] Input: Simulation results of AI analysis module

[1462] Output: Visually displayed simulation results

[1463] Specific behavior: Prediction results are generated as graphs and charts and presented in a user-accessible format.

[1464] Step 10:

[1465] AI analysis module:

[1466] Generate a personalized health improvement action plan based on the simulation results and emotional data.

[1467] Input: Simulation results, emotion data

[1468] Output: Health Improvement Action Plan

[1469] Specific actions: A specific action plan (e.g., exercise frequency, dietary content) is generated in text format.

[1470] Step 11:

[1471] server:

[1472] The generated action plan is sent to the device.

[1473] Input: Health Improvement Action Plan

[1474] Output: Action plan sent to device

[1475] Specific behavior: A notification is triggered to the device and an action plan is sent.

[1476] Step 12:

[1477] Device:

[1478] The terminal notifies the user of the action plan sent from the server within a dedicated application.

[1479] Input: Action plan sent from the server

[1480] Output: Action plan communicated to user

[1481] Specific behavior: A push notification will appear on the user's screen.

[1482] Step 13:

[1483] User:

[1484] Follow the action plan provided and continue to enter your daily actions into the application.

[1485] Input: New lifestyle and activity data based on the action plan

[1486] Output: New lifestyle data input into the application

[1487] Specific actions: After exercising, enter the activity record into the application.

[1488] Step 14:

[1489] Device:

[1490] Lifestyle data, activity data, and emotional data are continuously collected and sent to a server every night at midnight.

[1491] Input: New lifestyle data, activity data, emotion data

[1492] Output: Continuous data sent to the server

[1493] Specific operation: Data is automatically sent to the server every night at midnight.

[1494] Step 15:

[1495] server:

[1496] Receive data continuously and compare it with past data to monitor changes in your health.

[1497] Input: Continuously received data

[1498] Output: Analysis results based on changes in health status

[1499] Specific operation: New data is passed to the AI ​​analysis module and reanalyzed.

[1500] Step 16:

[1501] server:

[1502] Update your action plan with the latest data.

[1503] Input: Latest data

[1504] Output: Updated action plan

[1505] Specific operation: The AI ​​analysis module reanalyzes and generates a new action plan.

[1506] Step 17:

[1507] Device:

[1508] The updated action plan is sent to the device and notified to the user.

[1509] Input: Update action plan sent from the server

[1510] Output: Update action plan communicated to the user

[1511] What happens: A notification of a new action plan will appear on your device screen.

[1512] (Application example 2)

[1513] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1514] There are many challenges facing health management for users in modern society. In particular, it is difficult to accurately grasp the impact of lifestyle and emotional fluctuations on health and provide specific improvement measures based on that understanding. Furthermore, users lack the motivation and specific feedback to continuously improve their health. To resolve this situation, it is necessary to use more advanced data analysis and visualization technologies to provide health information in a format that users can intuitively understand, and to obtain feedback in concrete settings such as physical stores.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1516] In this invention, the server includes means for collecting lifestyle habit data and emotional data from a user, means for simulating a future health state based on the collected lifestyle habit data and emotional data, means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and emotional data and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for the user to receive feedback at a physical store. This allows the user to visually and intuitively understand future health risks, receive a specific and feasible health improvement action plan, and further receive feedback at the physical store, enabling continuous health management and behavioral improvement.

[1517] "Lifestyle data" refers to data related to the user's daily life, such as dietary habits, exercise records, and sleep duration.

[1518] "Emotion data" refers to data that indicates the emotional state of the user as recognized from facial expressions, voice, etc.

[1519] "AI analysis methods" are methods that use artificial intelligence technology to analyze collected data and simulate future health conditions.

[1520] "Visualization means" refers to technology for visually displaying simulation results, including graphs and 3D models.

[1521] A "health improvement action plan" is a specific, individualized instruction for health improvement behavior that is generated based on the simulation results and emotional data.

[1522] A "brick and mortar store" is a physical store that customers can visit in person, such as a fitness club or health food store.

[1523] "Feedback means" refers to a means by which users can receive specific feedback about their health status and behavior in a physical store.

[1524] A "local device" is a device carried by a user, such as a smartphone or tablet, that has the function of temporarily storing data.

[1525] "Server" is a central processing unit that stores collected data and performs analysis and generation of action plans.

[1526] "Data update means" refers to a means for keeping the system up to date based on the user's health improvement actions and new data collected.

[1527] This invention is a system that collects lifestyle data and emotional data from a user, simulates future health conditions, and provides a personalized action plan for improving health. Specific embodiments of this system are described below.

[1528] Data collection

[1529] User: The user enters lifestyle data such as daily diet, exercise records, and sleep time into a dedicated smartphone application. The user also wears a wearable device (e.g., a smartwatch) that automatically collects activity data such as heart rate and number of steps. The emotion engine then recognizes and collects emotional data from the user's facial expressions and voice.

[1530] Terminal: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and emotion engines. These data are then periodically sent to a server.

[1531] server:

[1532] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. It may also request additional information from the user.

[1533] Data analysis and simulation

[1534] server:

[1535] The collected lifestyle and emotional data is input into an AI analysis module to simulate future health conditions. The AI ​​analysis module then uses generative AI models, such as deep learning models, to make highly accurate predictions. The simulation results are visualized using 3D models and graphs.

[1536] Generate a personalized action plan

[1537] server:

[1538] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific exercise and nutritional instructions, and presents it to the user. For example, instructions might include "walking for 30 minutes three times a week" or "limiting daily food intake to 2,000 kcal."

[1539] Device:

[1540] The action plan sent from the server is notified within the dedicated application and displayed as specific tasks. The user can then change their lifestyle habits according to the action plan and put it into action.

[1541] Continuous health management and behavior tracking

[1542] User:

[1543] The user follows the action plan and inputs their daily activities into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1544] Device:

[1545] Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to a server.

[1546] server:

[1547] The system continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[1548] Specific examples

[1549] For example, when a user uses "health time travel," the following operations occur:

[1550] Users enter their daily food and exercise records into the application, and their heart rate and number of steps are measured using a wearable device. The emotion engine collects emotional data from facial expressions and voice when users feel stressed. This data is sent to the server every night at midnight, and the server provides the latest simulation results and action plans the following morning.

[1551] Prompt Sentence Examples

[1552] "Based on the user's current lifestyle and emotional data, predict future health conditions and generate individualized health improvement action plans. Build a system that uses inputs such as dietary habits, exercise records, sleep time, heart rate, number of steps, and emotional data (facial expressions and voice) to simulate future weight, blood pressure, and blood sugar levels and provide appropriate behavioral instructions."

[1553] The above is a specific embodiment of the present invention. This system allows users to obtain health information in an intuitively understandable format and to take continuous health improvement actions based on a personalized action plan.

[1554] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1555] Step 1:

[1556] User: The user uses a dedicated smartphone application to input daily lifestyle data (food content, exercise records, sleep time, etc.). In addition, the user wears a wearable device (such as a smartwatch) to automatically collect activity data such as heart rate and number of steps. The emotion engine recognizes and collects emotion data from the user's facial expressions and voice.

[1557] Input: Meal details, exercise records, sleep time, heart rate, number of steps, facial expression data, voice data

[1558] Output: Collected lifestyle data, activity data, and emotion data

[1559] Step 2:

[1560] Device: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and the emotion engine. This data is sent to the server every night at midnight.

[1561] Input: Collected lifestyle data, activity data, and emotional data

[1562] Output: Data stored on local device, data sent to server

[1563] Step 3:

[1564] Server: The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. If necessary, it may request additional information from the user.

[1565] Input: Data sent from the terminal

[1566] Output: Data stored in the database, prompting the user for more information

[1567] Step 4:

[1568] Server: The server retrieves lifestyle and emotional data from the database and inputs it into the AI ​​analysis module. Using a generative AI model, this data is analyzed and future health conditions (weight, blood pressure, blood sugar level, etc.) are simulated.

[1569] Input: Data retrieved from the database

[1570] Output: Simulation results

[1571] Step 5:

[1572] Server: Visually displays the simulation results using 3D models, graphs, etc. Using visualization tools, the server processes the data so that the user can intuitively understand it.

[1573] Input: Simulation results

[1574] Output: Visualized data (3D models, graphs, etc.)

[1575] Step 6:

[1576] Server: Generates a personalized health improvement action plan based on the simulation results and emotional data. This includes specific exercise and nutritional instructions. Examples include "walk for 30 minutes three times a week" and "limit daily food intake to 2000 kcal."

[1577] Input: Simulation results, emotion data

[1578] Output: A personalized health improvement action plan

[1579] Step 7:

[1580] Device: The action plan sent from the server is displayed in a dedicated application and specific tasks are displayed. The user can then change their lifestyle habits according to the action plan and put it into action.

[1581] Input: Action plan sent from the server

[1582] Output: Action plan communicated to user

[1583] Step 8:

[1584] User: Follows the action plan provided and enters daily activities (e.g., meals, exercise) into the application. The wearable device also continuously collects activity data. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1585] Input: Daily behavior data based on the action plan

[1586] Output: Newly collected lifestyle, activity, and emotion data

[1587] Step 9:

[1588] Device: Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to the server.

[1589] Input: Newly collected data

[1590] Output: Data stored on local device, data sent to server

[1591] Step 10:

[1592] Server: Continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[1593] Input: Continuously received data

[1594] Output: Change in health status, updated action plan

[1595] The above are the specific processing steps for carrying out this invention, which allow the user to visually and intuitively understand their future health status, receive a specific and feasible action plan for improving their health, and continue to manage their health and improve their behavior.

[1596] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1597] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1598] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1599] [Fourth embodiment]

[1600] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1601] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1602] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1604] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1606] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1607] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1608] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1609] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1610] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1611] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1612] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] This invention is a futuristic health prediction system that utilizes AI and advanced imaging technology. It visually simulates a user's future health status based on their current lifestyle data and provides an individualized health improvement action plan. This system collects lifestyle data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, it generates a personalized health improvement action plan based on the simulation results and presents it to the user. As a result, the user can take specific and continuous actions to improve their health.

[1614] Data collection

[1615] User:

[1616] Users input their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application on their device. Users also wear a wearable device, which automatically collects activity data (e.g., number of steps, heart rate), allowing for accurate lifestyle data to be obtained.

[1617] Device:

[1618] The device temporarily stores the lifestyle data entered by the user in a local database. It also acquires activity data in real time from the wearable device and stores it in the local database. The collected data is sent to the server at regular intervals (e.g., every night at midnight).

[1619] server:

[1620] The server receives the lifestyle data sent from the device and stores it in a database. Based on this data, the server can also request additional information from the user.

[1621] Data analysis and simulation

[1622] server:

[1623] The server inputs the collected lifestyle data into an AI analysis module, which then simulates future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.) based on the current data. The simulation results are used to assess future health risks.

[1624] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[1625] Generate a personalized action plan

[1626] server:

[1627] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[1628] Device:

[1629] The device receives the action plan sent from the server and notifies the user within the application. The user then checks the action plan and puts it into action.

[1630] Continuous health management and behavior tracking

[1631] User:

[1632] Based on the action plan presented to them, users input their daily activities (e.g., diet, exercise) into the application, and the wearable device continues to collect activity data.

[1633] Device:

[1634] The device continuously collects daily lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[1635] server:

[1636] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[1637] Specific examples

[1638] Below is a case where a user A uses "health time travel."

[1639] User:

[1640] Mr. A enters his daily meal record (breakfast, lunch, dinner) into the application, and also adds records of his walking and jogging. He also wears a smartwatch at all times to measure his heart rate and number of steps.

[1641] Device:

[1642] The lifestyle data entered by Mr. A and the activity data automatically collected from the smartwatch are sent to the server every night at midnight.

[1643] server:

[1644] The server inputs Mr. A's lifestyle data into an AI analysis module and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, it generates an action plan appropriate for Mr. A (e.g., "walk for 30 minutes three times a week," "limit daily food intake to 2000 kcal"). This action plan is then sent to the device.

[1645] Device:

[1646] The device notifies Person A of the action plan and displays it as specific tasks. Person A changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[1647] server:

[1648] It continuously re-analyzes the data it receives, updates the action plan as needed, and sends the new plan to the device.

[1649] This allows Mr. A to visually understand his future health condition and take specific and effective actions to improve his health.

[1650] The processing flow will be explained below.

[1651] Step 1:

[1652] User:

[1653] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate).

[1654] Step 2:

[1655] Device:

[1656] The lifestyle data entered by the user and the activity data acquired from the wearable device are temporarily stored in a local database, and these data are sent to a server at regular intervals (e.g., every night at midnight).

[1657] Step 3:

[1658] server:

[1659] The system receives lifestyle and activity data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[1660] Step 4:

[1661] server:

[1662] The AI ​​analysis module analyzes the lifestyle data received from the database and simulates the user's future health condition, such as weight fluctuations and blood sugar levels over the next three months.

[1663] Step 5:

[1664] server:

[1665] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[1666] Step 6:

[1667] server:

[1668] Based on the simulation results, the AI ​​analysis module generates a personalized health improvement action plan, including specific behavioral instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[1669] Step 7:

[1670] server:

[1671] The generated action plan is sent to the device.

[1672] Step 8:

[1673] Device:

[1674] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[1675] Step 9:

[1676] User:

[1677] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application, which also collects activity data using a wearable device.

[1678] Step 10:

[1679] Device:

[1680] Daily lifestyle and activity data is continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1681] Step 11:

[1682] server:

[1683] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[1684] Step 12:

[1685] server:

[1686] The updated action plan is sent to the device and notified to the user, allowing them to take the optimal health improvement actions at the appropriate time.

[1687] Example 1

[1688] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1689] Conventional health management systems have not been able to fully utilize collected lifestyle data, making it difficult to provide personalized health improvement action plans. Furthermore, they have not been able to continuously update users' behavioral data or accurately simulate their future health status. This has resulted in insufficient support for users to continuously take specific and effective health improvement actions.

[1690] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1691] In this invention, the server includes means for collecting lifestyle habit data from a user, artificial intelligence analysis means for simulating future health conditions based on the collected lifestyle habit data, image processing means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for temporarily storing the lifestyle habit data and activity data in a local device and periodically transmitting them to the server. This provides a personalized health improvement action plan, enabling the user to continuously take specific and effective health improvement actions.

[1692] "User" refers to an individual who uses the System to provide lifestyle data.

[1693] "Lifestyle data" refers to information about the user's daily lifestyle (for example, dietary content, amount of exercise, sleep duration, etc.).

[1694] "Artificial intelligence analysis methods" refer to methods that use AI technology to predict and analyze future health conditions based on collected lifestyle data.

[1695] "Image processing means" refers to technology for visually displaying the results of a simulation (e.g., graphs, 3D models).

[1696] A "health improvement action plan" refers to specific behavioral instructions and suggestions proposed to improve a user's health based on AI analysis.

[1697] A "local device" refers to a terminal or wearable device used by a user, which is a device for temporarily storing lifestyle habit data and activity data.

[1698] "Server" refers to a centralized computer system that receives collected lifestyle data, analyzes it, and provides the results to users.

[1699] "Activity data" refers to information about a user's physical activity (e.g., number of steps, heart rate, etc.).

[1700] This invention is a system that utilizes AI and advanced imaging technology to visually simulate future health conditions based on a user's lifestyle data and provide a personalized action plan for improving their health. Specific methods for implementing this system are described below.

[1701] System configuration

[1702] Hardware Configuration

[1703] Users use a smartphone or tablet with a dedicated application installed, and also wear a wearable device (e.g., a smartwatch) to collect activity data.

[1704] The device refers to the user's smartphone or tablet, and lifestyle and activity data is stored in a local database.

[1705] The server is located on the cloud and analyzes data, generates simulation results, and proposes action plans.

[1706] Software Configuration

[1707] The dedicated application allows users to input lifestyle habit data and check the action plan sent from the server.

[1708] The AI ​​analysis module has an algorithm for simulating future health conditions based on data collected from users.

[1709] Image processing techniques include techniques for visually displaying simulation results (e.g., graphs, 3D models).

[1710] Data collection

[1711] Users input their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated application, and activity data (e.g., steps taken, heart rate) is automatically collected through the wearable device.

[1712] The terminal stores the data entered by the user and the data obtained from the wearable device in a local database and sends it to the server every night at midnight.

[1713] Example prompt: "Enter your exercise record for today. Example: 30 minutes walking, 20 minutes jogging."

[1714] Data analysis and simulation

[1715] The server inputs the collected lifestyle data into an AI analysis module and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[1716] The simulation results are displayed visually using image processing technology so that users can understand them intuitively.

[1717] Example: "Display a graph of weight fluctuations and blood glucose levels for the next three months"

[1718] Generate a personalized action plan

[1719] Based on the simulation results, the server uses an AI analysis module to generate a personalized health improvement action plan, which includes specific instructions (e.g., exercising for 30 minutes three times a week and eating less than 2,000 kcal per day).

[1720] The generated action plan is transmitted from the server to the terminal, which then notifies the user of the plan.

[1721] Example prompt: "Exercise for 30 minutes three times a week. Enter your exercise log for today."

[1722] Continuous health management and behavior tracking

[1723] Users follow the action plan provided to them to change their lifestyle habits and continue to input their daily activities into the application, which also continuously collects activity data using a wearable device.

[1724] The device continuously collects data, stores it in a local database, and transmits it to a server at regular intervals.

[1725] The server compares the received data with past data, monitors changes in health status, reanalyzes the data based on the latest data, and updates the action plan as needed.

[1726] Example prompt: "A new exercise plan has been generated. Please continue your activities according to the new plan."

[1727] This system allows users to visually understand their future health status and take specific and effective actions to improve their health, thereby supporting them in continuously managing and improving their health.

[1728] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1729] Step 1:

[1730] The user enters lifestyle data (e.g., dietary content, exercise time, sleep time) into a dedicated application. The wearable device (e.g., smartwatch) automatically collects activity data (e.g., number of steps, heart rate).

[1731] Input: User-entered lifestyle data, wearable device activity data

[1732] Output: Data is saved on the device

[1733] Specific operation: The user enters into the app, "I ate bread and eggs for breakfast" and "I jogged for 30 minutes." The smartwatch then measures the number of steps taken and heart rate for the day.

[1734] Step 2:

[1735] The terminal stores the user's input data and activity data acquired from the wearable device in a local database.

[1736] Input: User-entered lifestyle data, wearable device activity data

[1737] Output: Data stored in a local database

[1738] Specific operation: The device automatically saves the user's food log and data received from the smartwatch.

[1739] Step 3:

[1740] The device sends the data stored in the local database to the server every night at midnight.

[1741] Input: Lifestyle and activity data stored in a local database

[1742] Output: Data sent to the server

[1743] Specific operation: Automatically upload data to the server when the date changes.

[1744] Step 4:

[1745] The server inputs the lifestyle data received into the AI ​​analysis module, which analyzes the data and simulates future health conditions (e.g., weight, blood pressure, blood sugar levels).

[1746] Input: Lifestyle and activity data received by the server

[1747] Output: Simulation results

[1748] Specific operation: Based on the received data, the AI ​​analysis module predicts changes in weight and blood sugar levels three months from now.

[1749] Step 5:

[1750] The server visually displays the simulation results using image processing technology.

[1751] Input: Simulation results

[1752] Output: Simulation results in a format that can be visually understood by the user

[1753] Specific operation: Predictions of future health risks (e.g., obesity, diabetes) are displayed in graphs and 3D models.

[1754] Step 6:

[1755] Based on the simulation results, the server's AI analysis module generates a personalized health improvement action plan, which includes specific instructions (e.g., exercising 30 minutes three times a week and eating less than 2,000 kcal per day).

[1756] Input: Simulation results

[1757] Output: Health Improvement Action Plan

[1758] Specific actions: The AI ​​analysis module generates instructions such as "walk for 30 minutes three times a week" or "eat less than 2000 kcal per day."

[1759] Step 7:

[1760] The server sends the generated health improvement action plan to the terminal.

[1761] Input: Health Improvement Action Plan

[1762] Output: Action plan sent to device

[1763] Specific operation: Instructions are sent from the server to the device and notified to the app.

[1764] Step 8:

[1765] The device notifies the user of the action plan and displays it within the application.

[1766] Input: Health improvement action plan sent from the server

[1767] Output: Notified action plan

[1768] Specific action: The app displays a notification saying, "Exercise for 30 minutes three times a week."

[1769] Step 9:

[1770] The user changes their lifestyle according to the action plan provided and enters their daily activities (e.g., diet, exercise) into the application.

[1771] Input: User action data

[1772] Output: Action data based on the action plan

[1773] Specific action: The user enters the exercise record as "30 minutes of walking completed."

[1774] Step 10:

[1775] The device continuously collects lifestyle and activity data, stores it in a local database, and periodically transmits the collected data to a server.

[1776] Input: Daily lifestyle and activity data

[1777] Output: Updates stored in the local database and data sent to the server

[1778] Specific operation: The device sends the accumulated data to the server at midnight every night.

[1779] Step 11:

[1780] The server continuously analyzes the received data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary.

[1781] Input: Continuously received lifestyle and activity data

[1782] Output: Updated action plan

[1783] Specific operation: The AI ​​analysis module reanalyzes the data, generates updated instructions such as "Extend walking time to 40 minutes," and sends them to the device.

[1784] (Application example 1)

[1785] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1786] Many conventional health management systems collect users' lifestyle data and predict their future health status, but they lack motivation to take specific action to improve their health and provide continuous feedback. As a result, it is difficult for users to sustain health improvement actions, and actual improvements in health are difficult to achieve. In addition, there is no connection with physical stores, and no support is provided through specific services or products for health improvement, making them less practical for users.

[1787] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1788] In this invention, the server includes means for collecting lifestyle data from a user, AI analysis means for simulating future health conditions based on the collected lifestyle data, imaging means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for providing coupons for specific services and products based on the action plan. This provides specific instructions and support for the user to continuously take health improvement actions, enabling health improvement through practical services in collaboration with physical stores.

[1789] A "user" is an individual who uses the system to provide lifestyle data and receives health status predictions and action plans.

[1790] "Lifestyle data" refers to data related to the user's daily activities such as diet, exercise, and sleep.

[1791] "AI analysis means" refers to means that include artificial intelligence technology for simulating future health conditions based on collected lifestyle data.

[1792] "Imaging means" refers to a technique for visually displaying the simulation results.

[1793] A "personalized health improvement action plan" is a specific action plan for improving health that is created based on the individual health condition and lifestyle habits of each user.

[1794] The "means for continuously updating data" is a technology that tracks the user's health improvement behavior and sends the latest data to a server.

[1795] The "means of providing coupons" is a means of providing users with discount coupons or benefits for specific services or products based on an action plan.

[1796] The "server" is a centralized computer system that collects lifestyle data, analyzes it, performs simulations, generates action plans, and continuously updates the data.

[1797] MODE FOR CARRYING OUT THE INVENTION

[1798] A system for implementing this invention has the following configuration and functions. It includes a means for collecting lifestyle habit data from a user and an AI analysis means for analyzing the collected data. In addition, it includes an imaging means for visually displaying the analysis results, a means for generating a personalized health improvement action plan and presenting it to the user, and a means for tracking the user's health improvement actions and continuously updating the data. It also includes a means for providing coupons for specific services or products based on the action plan.

[1799] This invention is implemented using various devices and cloud infrastructure, including a smartphone app, a wearable device (e.g., a smartwatch), a cloud server (e.g., AWS or Google Cloud), an AI analysis module (e.g., Python + TensorFlow or PyTorch), a database system (e.g., MySQL or PostgreSQL), and imaging technology (e.g., Unity or Unreal Engine).

[1800] Data collection

[1801] Users enter their daily lifestyle data (e.g., diet, exercise, sleep) into a dedicated smartphone application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as the number of steps taken and heart rate. The smartphone application temporarily stores this data in a local database (SQLite). The data is then periodically sent to a cloud server every night.

[1802] Data analysis and simulation

[1803] The server inputs the received data into an AI analysis module to simulate future health conditions (weight, blood pressure, blood sugar levels, etc.) The simulation results are visualized using Unity or Unreal Engine and displayed in a format that users can intuitively understand.

[1804] Personalized action plan generation

[1805] Based on the simulation results, the server generates a specific health improvement action plan for the user, which may include, for example, a specific fitness program or healthy foods, and then sends the action plan to the user's smartphone and notifies them.

[1806] Continuous data updates and physical store integration

[1807] Based on the designated action plan, users continuously input their lifestyle data and collect activity data using a wearable device. This data is continuously sent to a server, which analyzes the data as needed and updates the action plan as necessary. The server also provides users with coupons for specific services and products based on the action plan.

[1808] Specific examples

[1809] For example, when User A uses this system, he or she enters daily food and exercise records into the app and collects activity data using a smartwatch. The server uses this data to simulate User A's weight and blood pressure three months from now, suggests a fitness program (e.g., "attending yoga classes three times a week") and health foods (e.g., "specific supplements") that are suitable for User A, and provides discount coupons.

[1810] Prompt Sentence Examples

[1811] "Analyze User A's lifestyle data (e.g., diet, exercise, and sleep data), predict their weight and blood pressure three months from now, suggest optimal fitness programs and healthy foods, and visually display the results. Also, provide discount coupons based on the results."

[1812] This allows users to receive specific instructions and support from physical stores to take ongoing health improvement actions.

[1813] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1814] Step 1:

[1815] Users input their daily lifestyle data (e.g., dietary content, amount of exercise, and sleep time) into a smartphone app. In addition, activity data (e.g., number of steps, heart rate) is automatically collected using a wearable device (e.g., a smartwatch). This data is temporarily stored in a local database (SQLite) on the smartphone. The input here consists of data manually entered by the user and data automatically collected by the wearable device.

[1816] Step 2:

[1817] The device sends the lifestyle data collected from the user to a server at a fixed time every night. The data includes the user's diet, exercise, sleep time, and activity data. This process involves transferring data from the smartphone to a cloud server (e.g., AWS, Google Cloud).

[1818] Step 3:

[1819] The server stores the received lifestyle data in a database (e.g., MySQL, PostgreSQL). To store the received data accurately, the data is preprocessed and converted into a consistent format, which facilitates subsequent data analysis.

[1820] Step 4:

[1821] The server inputs the user's lifestyle data stored in the database into an AI analysis module (Python + TensorFlow or PyTorch) to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels). This analysis is a step in which a generative AI model is used to detect and predict outliers. Prediction data is obtained as a result of the analysis.

[1822] Step 5:

[1823] The simulation results are visually displayed using imaging technology (Unity or Unreal Engine). The server analyzes the simulation results and visualizes them in a format that is easy for users to understand. This allows users to intuitively understand their future health status. The output is data for visual display.

[1824] Step 6:

[1825] The server generates a personalized health improvement action plan based on the simulation results, which includes specific behavioral instructions (e.g., exercising three times a week, adjusting diet). The input here is the simulation results, and the output is an individualized action plan.

[1826] Step 7:

[1827] The generated action plan is sent to the device and notified to the user via a smartphone app. The user receives the notification and begins taking action to improve their lifestyle habits according to the plan. The output here is a user notification.

[1828] Step 8:

[1829] Users change their daily habits based on the action plan presented to them and enter the results into a smartphone app. They also continuously wear a wearable device to collect activity data. The input data are new lifestyle and activity data.

[1830] Step 9:

[1831] The device continuously collects data and sends it to the server at regular intervals. This allows the server to always obtain the latest user data and continuously monitor the health status. The output here is continuously updated data.

[1832] Step 10:

[1833] The server re-analyzes the data and updates the action plan as needed. It also generates coupons for specific services or products and provides them to the user. The user can then use these coupons to receive discounts on fitness programs or healthy foods. The output is the updated action plan and coupons.

[1834] This allows users to receive specific instructions and support to take ongoing health-improving actions.

[1835] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1836] This invention is a futuristic health prediction system that utilizes AI, advanced imaging technology, and an emotion engine. It visually simulates a user's future health status based on their current lifestyle and emotional data, and provides a personalized health improvement action plan. This system collects lifestyle and emotional data from the user, inputs it into an AI analysis module for analysis, and visually displays the simulation results. Furthermore, a personalized health improvement action plan is generated based on the simulation results and emotional data and presented to the user. As a result, the user is able to take specific and continuous health improvement actions.

[1837] Data collection

[1838] User:

[1839] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. Users also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Additionally, the emotion engine recognizes and collects emotional data from the user's facial expressions and voice.

[1840] Device:

[1841] The device temporarily stores the lifestyle data entered by the user, as well as the activity and emotion data collected from the wearable device and emotion engine in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[1842] server:

[1843] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. Based on this data, the server can also request additional information from the user.

[1844] Data analysis and simulation

[1845] server:

[1846] The server inputs the collected lifestyle and emotional data into an AI analysis module. The AI ​​analysis module uses this data to simulate future health conditions (e.g., weight, blood pressure, blood sugar levels, etc.). The simulation results are used to assess future health risks. In addition, emotional data is reflected in the analysis, enabling more accurate and personalized predictions.

[1847] Simulation results are visually displayed using imaging techniques, and the display is presented in a format that is intuitively understandable to the user.

[1848] Generate a personalized action plan

[1849] server:

[1850] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, limiting daily diet to 2,000 kcal or less), which is then sent to the device.

[1851] Device:

[1852] The device notifies the user of the action plan sent from the server within a dedicated application. The user then confirms the action plan and puts it into action. Emotional data plays an important role in adjusting the content and strength of the action plan.

[1853] Continuous health management and behavior tracking

[1854] User:

[1855] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1856] Device:

[1857] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1858] server:

[1859] The server continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as needed. The updated action plan is sent to the device and notified to the user.

[1860] Specific examples

[1861] Below is a case where User B uses "Health Time Travel."

[1862] User:

[1863] Person B enters his daily food record (e.g. bread and coffee for breakfast, salad and chicken for lunch) into the application and adds records of his walking and jogging. He also wears a smartwatch to measure his heart rate and number of steps. Furthermore, if Person B feels stressed, the emotion engine collects emotional data from his facial expressions and voice.

[1864] Device:

[1865] The lifestyle data entered by Mr. B, the activity data automatically collected from the smartwatch, and the emotion data from the emotion engine are sent to the server every night at midnight.

[1866] server:

[1867] The server inputs Mr. B's lifestyle, activity, and emotional data into an AI analysis module, and simulates his weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for Mr. B is generated (e.g., "incorporate relaxation into your routine when stress levels rise," walk 30 minutes three times a week, and limit daily food intake to 2,000 kcal). This action plan is then sent to the device.

[1868] Device:

[1869] The device notifies Person B of the action plan and displays it as specific tasks. Person B changes his / her lifestyle habits according to this plan and periodically enters the results into the application.

[1870] server:

[1871] The system continuously reanalyzes the received data, updates the action plan as needed, and sends the new plan to the device, allowing Mr. B to visually understand his future health condition and take specific and effective actions to improve his health.

[1872] The present invention allows users to visually and emotionally understand future health risks and implement appropriate action plans to improve their health.

[1873] The processing flow will be explained below.

[1874] Step 1:

[1875] User:

[1876] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application on their device. They also wear a wearable device (e.g., a smartwatch) that automatically collects activity data (e.g., steps taken, heart rate). Furthermore, an emotion engine is used to collect emotional data (e.g., stress level, happiness level) from facial expressions and voice.

[1877] Step 2:

[1878] Device:

[1879] The device temporarily stores the lifestyle data entered by the user, as well as data collected from the wearable device and emotion engine, in a local database. This data is then sent to the server at regular intervals (e.g., every night at midnight).

[1880] Step 3:

[1881] server:

[1882] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. After storing the data, it prepares it for input into the AI ​​analysis module.

[1883] Step 4:

[1884] server:

[1885] The AI ​​analysis module analyzes the lifestyle and emotional data received from the database to simulate the user's future health condition. For example, it simulates changes in weight and blood sugar levels over the next three months. It also incorporates emotional data into the analysis to evaluate the impact of the user's mental state on their health.

[1886] Step 5:

[1887] server:

[1888] Simulation results are visually displayed using imaging technology, and the generated visual future prediction images are created in a format that users can intuitively understand.

[1889] Step 6:

[1890] server:

[1891] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, which includes specific behavioral instructions (e.g., exercising 30 minutes three times a week, eating less than 2,000 kcal a day, and stress management techniques).

[1892] Step 7:

[1893] server:

[1894] The generated action plan is sent to the device.

[1895] Step 8:

[1896] Device:

[1897] The action plan received from the server is notified to the user within the dedicated application. The user confirms the action plan and begins to change their lifestyle habits.

[1898] Step 9:

[1899] User:

[1900] The user follows the action plan presented to them and inputs their daily activities (e.g., diet, exercise, stress management) into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[1901] Step 10:

[1902] Device:

[1903] Daily lifestyle data, activity data, and emotional data are continuously collected and stored in a local database, and then sent to a server at regular intervals (e.g., every night at midnight).

[1904] Step 11:

[1905] server:

[1906] The server continuously receives and stores the data in a database, compares it with past data, and monitors changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as needed.

[1907] Step 12:

[1908] server:

[1909] The updated action plan is sent to the device and notified to the user, allowing them to take optimal health-improving actions at the appropriate time.

[1910] Example 2

[1911] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1912] Conventional health management systems only consider users' lifestyle habits, which means they are unable to fully consider emotional fluctuations and their impact when predicting future health conditions. This can lead to inaccurate individual health improvement action plans, making it difficult for users to effectively improve their health. Furthermore, action plans are often not updated appropriately in line with ongoing data updates, which can lead to insufficient tracking of improvement actions.

[1913] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1914] In this invention, the server includes means for collecting lifestyle data from a user and emotion data collected from a wearable device and an emotion engine, means for temporarily storing the collected lifestyle data and emotion data in a local device and periodically transmitting them to the server, AI analysis means for simulating future health conditions based on the data transmitted to the server, means for visually displaying the simulation results using advanced imaging technology, means for generating a personalized health improvement action plan based on the simulation results and emotion data and notifying the user, and means for tracking the user's health improvement actions and continuously updating the data. This enables more accurate and personalized health condition predictions that take emotion data into account, the generation of improvement action plans, and the continuous updating of these plans.

[1915] "User" refers to an individual who uses this system to provide their lifestyle and emotional data and receives a health status simulation and a health improvement action plan.

[1916] "Lifestyle data" refers to information about a user's daily behavior and lifestyle, such as diet, exercise records, and sleep time.

[1917] "Emotional Data" refers to information about a user's emotional state collected from their facial expressions and voice.

[1918] A "wearable device" is an electronic device that can be worn, such as a smartwatch, and collects activity data such as heart rate and number of steps.

[1919] An "emotion engine" refers to a software or hardware system that analyzes a user's facial expressions and voice to generate emotional data.

[1920] A "local device" refers to a device used by a user (such as a smartphone or tablet) that temporarily stores lifestyle data and emotional data.

[1921] "Server" refers to the central management system that receives and stores data sent from the device and performs AI analysis.

[1922] "AI analysis module" refers to a program that uses artificial intelligence technology to analyze collected data and simulate future health conditions.

[1923] "Advanced imaging technology" refers to the latest image processing technology for visually displaying simulation results in an easy-to-understand manner.

[1924] "Health Improvement Action Plan" refers to individual health improvement measures provided to users based on the simulation results and emotional data.

[1925] "Continuous data updating" refers to the process of analyzing health status and revising action plans using new data collected periodically from users.

[1926] The present invention is a system that predicts future health conditions based on a user's lifestyle and emotional data and provides a personalized action plan for improving health. This system requires the participation of a server, a terminal, and the user.

[1927] Users input their daily lifestyle data (e.g., dietary habits, exercise records, and sleep duration) into a dedicated application. They also wear a wearable device such as a smartwatch, which automatically collects activity data such as heart rate and number of steps. Furthermore, an emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1928] The device temporarily stores the lifestyle data entered by the user, the activity data from the wearable device, and the emotion data from the emotion engine in a local database. These data are then sent to the server at midnight every night.

[1929] The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. The server's AI analysis module uses this data to simulate future health conditions (such as weight, blood pressure, and blood sugar levels). This simulation uses AI analysis technology and advanced imaging technology, allowing users to visually confirm the simulation results.

[1930] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific instructions (e.g., 30 minutes of exercise three times a week, eating less than 2,000 kcal a day). The action plan is then sent to the device, which then notifies the user.

[1931] The user continues to input their daily activities (e.g., diet and exercise) into a dedicated application in accordance with the presented action plan. The device continuously collects this daily input data and sends it to the server every night at midnight. The server continuously receives the data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary. The updated action plan is then sent back to the device and the user is notified.

[1932] As a concrete example, let's say user A uses this system. A enters their daily food and walking records into the app and wears a smartwatch to measure their heart rate and number of steps. Furthermore, if A feels stressed, emotional data is collected from their facial expressions and voice. This data is sent to the server, and an AI analysis module simulates A's weight fluctuations and blood sugar levels over the next three months. Based on the simulation results, an action plan appropriate for A (for example, "incorporate relaxation into your routine when stress levels rise," walk for 30 minutes three times a week, and limit daily food intake to 2000 kcal) is generated. This action plan is sent to the device and notified to A.

[1933] Example prompt sentence:

[1934] "User A enters his daily food records (e.g., bread and coffee for breakfast, salad and chicken for lunch) and his walking and jogging records into the app, and uses a smartwatch to measure his heart rate and number of steps. In addition, if User A feels stressed, emotional data is collected. The app simulates his future health condition (e.g., weight, blood sugar level, etc.) and generates a health improvement action plan appropriate for User A (e.g., 30 minutes of exercise three times a week, limiting daily food intake to 2000 kcal)."

[1935] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1936] Step 1:

[1937] User:

[1938] Users enter their daily lifestyle data (e.g., diet, exercise records, sleep time) into a dedicated application.

[1939] Input: Meal details, exercise record, sleep time

[1940] Output: Lifestyle data is saved in the application

[1941] Specific actions: If you had bread and coffee for breakfast, enter that information in the app, and also enter the 30-minute jog you went for in the morning.

[1942] Step 2:

[1943] User:

[1944] Users wear a wearable device such as a smartwatch, which automatically collects activity data (e.g., steps taken, heart rate).

[1945] Input: Wearing and operating the wearable device

[1946] Output: Activity data is obtained

[1947] What it does: The user wears the smartwatch throughout the day, and the watch periodically records their heart rate and steps.

[1948] Step 3:

[1949] Emotion Engine:

[1950] The emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1951] Input: User's facial expression data, voice data

[1952] Output: Emotion data

[1953] Specific operation: Facial expressions and tone of voice that occur when the user feels stress are detected, and emotional data is recorded as "stress."

[1954] Step 4:

[1955] Device:

[1956] Lifestyle data entered by the user, activity data from the wearable device, and emotion data from the emotion engine are temporarily stored in a local database.

[1957] Input: lifestyle data, activity data, emotion data

[1958] Output: Data stored in a local database

[1959] What it does: The application temporarily stores user input and automatically collected data.

[1960] Step 5:

[1961] Device:

[1962] The collected data is sent to the server every night at midnight.

[1963] Input: Data from the local database

[1964] Output: Data sent to the server

[1965] Specific operation: At midnight, the device automatically sends data to the server via the network.

[1966] Step 6:

[1967] server:

[1968] The lifestyle data, activity data, and emotion data transmitted from the terminal are received and stored in a database.

[1969] Input: Data sent from the terminal

[1970] Output: Data stored in the server database

[1971] Specific operation: When data is sent to the server, it is received and processed and recorded in the server's database.

[1972] Step 7:

[1973] server:

[1974] The collected data is input into an AI analysis module.

[1975] Input: Data stored in the server's database

[1976] Output: Data input to the AI ​​analysis module

[1977] Specific operation: The server periodically passes database data to the AI ​​analysis module.

[1978] Step 8:

[1979] AI analysis module:

[1980] Simulate future health conditions based on data.

[1981] Input: lifestyle data, activity data, emotion data

[1982] Output: Health condition simulation results

[1983] How it works: The AI ​​analysis module analyzes the input data and runs algorithms to predict future changes in weight and blood pressure.

[1984] Step 9:

[1985] server:

[1986] Simulation results are visually displayed using advanced imaging technology.

[1987] Input: Simulation results of AI analysis module

[1988] Output: Visually displayed simulation results

[1989] Specific behavior: Prediction results are generated as graphs and charts and presented in a user-accessible format.

[1990] Step 10:

[1991] AI analysis module:

[1992] Generate a personalized health improvement action plan based on the simulation results and emotional data.

[1993] Input: Simulation results, emotion data

[1994] Output: Health Improvement Action Plan

[1995] Specific actions: A specific action plan (e.g., exercise frequency, dietary content) is generated in text format.

[1996] Step 11:

[1997] server:

[1998] The generated action plan is sent to the device.

[1999] Input: Health Improvement Action Plan

[2000] Output: Action plan sent to device

[2001] Specific behavior: A notification is triggered to the device and an action plan is sent.

[2002] Step 12:

[2003] Device:

[2004] The terminal notifies the user of the action plan sent from the server within a dedicated application.

[2005] Input: Action plan sent from the server

[2006] Output: Action plan communicated to user

[2007] Specific behavior: A push notification will appear on the user's screen.

[2008] Step 13:

[2009] User:

[2010] Follow the action plan provided and continue to enter your daily actions into the application.

[2011] Input: New lifestyle and activity data based on the action plan

[2012] Output: New lifestyle data input into the application

[2013] Specific actions: After exercising, enter the activity record into the application.

[2014] Step 14:

[2015] Device:

[2016] Lifestyle data, activity data, and emotional data are continuously collected and sent to a server every night at midnight.

[2017] Input: New lifestyle data, activity data, emotion data

[2018] Output: Continuous data sent to the server

[2019] Specific operation: Data is automatically sent to the server every night at midnight.

[2020] Step 15:

[2021] server:

[2022] Receive data continuously and compare it with past data to monitor changes in your health.

[2023] Input: Continuously received data

[2024] Output: Analysis results based on changes in health status

[2025] Specific operation: New data is passed to the AI ​​analysis module and reanalyzed.

[2026] Step 16:

[2027] server:

[2028] Update your action plan with the latest data.

[2029] Input: Latest data

[2030] Output: Updated action plan

[2031] Specific operation: The AI ​​analysis module reanalyzes and generates a new action plan.

[2032] Step 17:

[2033] Device:

[2034] The updated action plan is sent to the device and notified to the user.

[2035] Input: Update action plan sent from the server

[2036] Output: Update action plan communicated to the user

[2037] What happens: A notification of a new action plan will appear on your device screen.

[2038] (Application example 2)

[2039] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2040] There are many challenges facing health management for users in modern society. In particular, it is difficult to accurately grasp the impact of lifestyle and emotional fluctuations on health and provide specific improvement measures based on that understanding. Furthermore, users lack the motivation and specific feedback to continuously improve their health. To resolve this situation, it is necessary to use more advanced data analysis and visualization technologies to provide health information in a format that users can intuitively understand, and to obtain feedback in concrete settings such as physical stores.

[2041] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2042] In this invention, the server includes means for collecting lifestyle habit data and emotional data from a user, means for simulating a future health state based on the collected lifestyle habit data and emotional data, means for visually displaying the simulation results, means for generating a personalized health improvement action plan based on the simulation results and emotional data and presenting it to the user, means for tracking the user's health improvement actions and continuously updating the data, and means for the user to receive feedback at a physical store. This allows the user to visually and intuitively understand future health risks, receive a specific and feasible health improvement action plan, and further receive feedback at the physical store, enabling continuous health management and behavioral improvement.

[2043] "Lifestyle data" refers to data related to the user's daily life, such as dietary habits, exercise records, and sleep duration.

[2044] "Emotion data" refers to data that indicates the emotional state of the user as recognized from facial expressions, voice, etc.

[2045] "AI analysis methods" are methods that use artificial intelligence technology to analyze collected data and simulate future health conditions.

[2046] "Visualization means" refers to technology for visually displaying simulation results, including graphs and 3D models.

[2047] A "health improvement action plan" is a specific, individualized instruction for health improvement behavior that is generated based on the simulation results and emotional data.

[2048] A "brick and mortar store" is a physical store that customers can visit in person, such as a fitness club or health food store.

[2049] "Feedback means" refers to a means by which users can receive specific feedback about their health status and behavior in a physical store.

[2050] A "local device" is a device carried by a user, such as a smartphone or tablet, that has the function of temporarily storing data.

[2051] "Server" is a central processing unit that stores collected data and performs analysis and generation of action plans.

[2052] "Data update means" refers to a means for keeping the system up to date based on the user's health improvement actions and new data collected.

[2053] This invention is a system that collects lifestyle data and emotional data from a user, simulates future health conditions, and provides a personalized action plan for improving health. Specific embodiments of this system are described below.

[2054] Data collection

[2055] User: The user enters lifestyle data such as daily diet, exercise records, and sleep time into a dedicated smartphone application. The user also wears a wearable device (e.g., a smartwatch) that automatically collects activity data such as heart rate and number of steps. The emotion engine then recognizes and collects emotional data from the user's facial expressions and voice.

[2056] Terminal: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and emotion engines. These data are then periodically sent to a server.

[2057] server:

[2058] The server receives the lifestyle data, activity data, and emotion data sent from the device and stores them in a database. It may also request additional information from the user.

[2059] Data analysis and simulation

[2060] server:

[2061] The collected lifestyle and emotional data is input into an AI analysis module to simulate future health conditions. The AI ​​analysis module then uses generative AI models, such as deep learning models, to make highly accurate predictions. The simulation results are visualized using 3D models and graphs.

[2062] Generate a personalized action plan

[2063] server:

[2064] Based on the simulation results and emotional data, the AI ​​analysis module generates a personalized health improvement action plan, including specific exercise and nutritional instructions, and presents it to the user. For example, instructions might include "walking for 30 minutes three times a week" or "limiting daily food intake to 2,000 kcal."

[2065] Device:

[2066] The action plan sent from the server is notified within the dedicated application and displayed as specific tasks. The user can then change their lifestyle habits according to the action plan and put it into action.

[2067] Continuous health management and behavior tracking

[2068] User:

[2069] The user follows the action plan and inputs their daily activities into the application. Activity data is also continuously collected using a wearable device. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[2070] Device:

[2071] Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to a server.

[2072] server:

[2073] The system continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[2074] Specific examples

[2075] For example, when a user uses "health time travel," the following operations occur:

[2076] Users enter their daily food and exercise records into the application, and their heart rate and number of steps are measured using a wearable device. The emotion engine collects emotional data from facial expressions and voice when users feel stressed. This data is sent to the server every night at midnight, and the server provides the latest simulation results and action plans the following morning.

[2077] Prompt Sentence Examples

[2078] "Based on the user's current lifestyle and emotional data, predict future health conditions and generate individualized health improvement action plans. Build a system that uses inputs such as dietary habits, exercise records, sleep time, heart rate, number of steps, and emotional data (facial expressions and voice) to simulate future weight, blood pressure, and blood sugar levels and provide appropriate behavioral instructions."

[2079] The above is a specific embodiment of the present invention. This system allows users to obtain health information in an intuitively understandable format and to take continuous health improvement actions based on a personalized action plan.

[2080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2081] Step 1:

[2082] User: The user uses a dedicated smartphone application to input daily lifestyle data (food content, exercise records, sleep time, etc.). In addition, the user wears a wearable device (such as a smartwatch) to automatically collect activity data such as heart rate and number of steps. The emotion engine recognizes and collects emotion data from the user's facial expressions and voice.

[2083] Input: Meal details, exercise records, sleep time, heart rate, number of steps, facial expression data, voice data

[2084] Output: Collected lifestyle data, activity data, and emotion data

[2085] Step 2:

[2086] Device: Local devices such as smartphones and tablets temporarily store lifestyle data entered by users, as well as activity and emotion data collected from wearable devices and the emotion engine. This data is sent to the server every night at midnight.

[2087] Input: Collected lifestyle data, activity data, and emotional data

[2088] Output: Data stored on local device, data sent to server

[2089] Step 3:

[2090] Server: The server receives the lifestyle, activity, and emotion data sent from the device and stores it in a database. If necessary, it may request additional information from the user.

[2091] Input: Data sent from the terminal

[2092] Output: Data stored in the database, prompting the user for more information

[2093] Step 4:

[2094] Server: The server retrieves lifestyle and emotional data from the database and inputs it into the AI ​​analysis module. Using a generative AI model, this data is analyzed and future health conditions (weight, blood pressure, blood sugar level, etc.) are simulated.

[2095] Input: Data retrieved from the database

[2096] Output: Simulation results

[2097] Step 5:

[2098] Server: Visually displays the simulation results using 3D models, graphs, etc. Using visualization tools, the server processes the data so that the user can intuitively understand it.

[2099] Input: Simulation results

[2100] Output: Visualized data (3D models, graphs, etc.)

[2101] Step 6:

[2102] Server: Generates a personalized health improvement action plan based on the simulation results and emotional data. This includes specific exercise and nutritional instructions. Examples include "walk for 30 minutes three times a week" and "limit daily food intake to 2000 kcal."

[2103] Input: Simulation results, emotion data

[2104] Output: A personalized health improvement action plan

[2105] Step 7:

[2106] Device: The action plan sent from the server is displayed in a dedicated application and specific tasks are displayed. The user can then change their lifestyle habits according to the action plan and put it into action.

[2107] Input: Action plan sent from the server

[2108] Output: Action plan communicated to user

[2109] Step 8:

[2110] User: Follows the action plan provided and enters daily activities (e.g., meals, exercise) into the application. The wearable device also continuously collects activity data. The emotion engine continuously recognizes and collects the user's emotional data and reflects it in the system.

[2111] Input: Daily behavior data based on the action plan

[2112] Output: Newly collected lifestyle, activity, and emotion data

[2113] Step 9:

[2114] Device: Daily lifestyle data, activity data, and emotional data are continuously collected and stored on the local device, and then periodically sent to the server.

[2115] Input: Newly collected data

[2116] Output: Data stored on local device, data sent to server

[2117] Step 10:

[2118] Server: Continuously receives data and compares it with past data to monitor changes in health status. The AI ​​analysis module reanalyzes the data based on the latest data and updates the action plan as necessary. The updated action plan is sent to the device and notified to the user.

[2119] Input: Continuously received data

[2120] Output: Change in health status, updated action plan

[2121] The above are the specific processing steps for carrying out this invention, which allow the user to visually and intuitively understand their future health status, receive a specific and feasible action plan for improving their health, and continue to manage their health and improve their behavior.

[2122] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2124] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2125] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2126] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2127] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2128] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right ...

Claims

1. a means for collecting lifestyle data from users; AI analysis tools that simulate future health conditions based on collected lifestyle data, and an imaging means for visually displaying the simulation results; A means for generating a personalized health improvement action plan based on the simulation results and presenting the plan to the user; A means to track users' health improvement actions and continually update the data; A system including:

2. The system of claim 1 , further comprising means for assessing future health risks and providing specific remedial measures when generating a health improvement action plan.

3. 2. The system according to claim 1, further comprising means for temporarily storing lifestyle habit data input by a user or collected in a local device and periodically transmitting the data to a server.

Images