System

Abstract

A system is provided.SOLUTION: A means for inputting data related to dietary habits and exercise habits of a user, a means for transmitting the input data, a means for receiving and storing the transmitted data, a means for analyzing current dietary habits and exercise habits based on the stored data, a means for predicting a future health condition of the user based on the analysis result by using a generated AI model, a means for displaying a prediction result, a means for extracting a past success case and creating a user-specific improvement plan based on the generated AI model, a means for notifying the user of the created improvement plan, and a means for acquiring new data related to a progress situation of the improvement plan; A system comprising: means for updating stored data; and means for continuously updating an improvement plan based on the updated data.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, many people continue to have unhealthy eating and exercise habits, increasing the risk of future health problems. However, it is difficult for these people to make appropriate lifestyle improvements on a sustained basis. Conventional systems and methods lack the functionality to customize specific improvement plans for individual users or to predict and warn of future health risks. Therefore, a system is needed that helps users develop a sense of crisis about their own health status and make sustained efforts to improve it. [Means for solving the problem]

[0005] The present invention provides a system that inputs data on a user's dietary and exercise habits and sends it to a server. The server stores the received data, analyzes it, and predicts future health conditions using a generative AI model. The prediction results are notified to the user, helping them to recognize health risks. Furthermore, a personalized improvement plan is generated for the user based on past success stories and notified to the user. Feedback data on the progress of the improvement plan is acquired and stored on the server, allowing the improvement plan to be continuously updated. This allows users to have specific, actionable actions and continuously work to improve their health.

[0006] "User" means an individual or organization that uses the system.

[0007] A "terminal" is a device that allows a user to input and transmit data, and includes smartphones, tablets, personal computers, etc.

[0008] "Server" means a device or system that receives, stores, and analyzes data sent from a terminal and runs a generative AI model.

[0009] "Data" refers to detailed information about a user's diet and exercise habits, including, specifically, what they eat, how often they exercise, when they exercise, and how much calories they take in.

[0010] A "generative AI model" is an artificial intelligence model that predicts future health conditions based on accumulated data and generates appropriate improvement plans.

[0011] "Health status" refers to the user's level of physical and mental health, specifically including weight, blood pressure, blood sugar level, risk of heart disease, etc.

[0012] The "improvement plan" is a lifestyle improvement plan that includes specific action items that the user can carry out on a sustained basis.

[0013] "Notification" refers to the act of providing information to a user through a device, and includes pop-up messages, emails, push notifications, etc.

[0014] "Feedback data" refers to data regarding the progress of the improvement plan implemented by the user and new dietary and exercise habits.

[0015] "Continuous updating" is the process of regularly reviewing improvement plans based on feedback data and optimizing them based on the latest information. [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] The system of the present invention provides a specific action plan to improve the user's health and supports its implementation. The system mainly consists of three components: a terminal, a server, and a generative AI model. These components work together to enable the user to effectively manage their own health.

[0038] Data collection

[0039] First, the user uses a device to input data about their diet and exercise habits. For example, the device records daily meal contents, exercise frequency, and exercise time. This data is then sent from the device to a server. The device can be a device that the user uses daily, such as a smartphone, PC, or tablet.

[0040] Data analysis

[0041] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and heart disease based on the acquired data and past success stories.

[0042] Generate an improvement plan

[0043] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[0044] Plan execution and feedback

[0045] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0046] Specific examples

[0047] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[0048] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into their device. This reduces the user's health risks and enables sustainable health management.

[0049] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] A user accesses a terminal and inputs data about their diet and exercise habits.

[0053] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[0054] Step 2:

[0055] The terminal transmits the input data to the server.

[0056] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[0057] Step 3:

[0058] The server analyzes the received data and stores it in a database.

[0059] The server checks the integrity of the data and stores it in a database for each user.

[0060] Step 4:

[0061] The server analyzes the user's current eating and exercise habits based on the stored data.

[0062] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[0063] Step 5:

[0064] The server uses the generated AI model to predict the user's health status several years in the future.

[0065] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[0066] Step 6:

[0067] The server generates prediction results and sends them to the device.

[0068] A notification that visually gives the user a sense of crisis is displayed on the device.

[0069] Step 7:

[0070] The server extracts past success stories and feeds them into a generative AI model.

[0071] Success stories include lifestyle data of users who have successfully improved their health.

[0072] Step 8:

[0073] The server creates a user-specific improvement plan.

[0074] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[0075] Step 9:

[0076] The server sends the created improvement plan to the terminal.

[0077] On the device, the user is notified of the improvement plan and action items are listed.

[0078] Step 10:

[0079] The user begins to change their daily life based on the improvement plan displayed on the device.

[0080] The user periodically inputs the status of improvements and new data into the terminal.

[0081] Step 11:

[0082] The device sends new data to the server.

[0083] The new data will include progress on improvement plans and changes to diet and exercise habits.

[0084] Step 12:

[0085] The server receives the new data and updates the database.

[0086] The database is updated based on new data.

[0087] Step 13:

[0088] The server continuously updates the improvement plan based on new data.

[0089] The AI ​​model will reassess and regenerate the optimal improvement plan.

[0090] Step 14:

[0091] The server transmits the updated improvement plan to the terminal again and notifies the user.

[0092] The new improvement plan will be displayed on the device and the user will begin their next action.

[0093] In this way, the system continuously monitors the user's health status and provides specific plans for sustained improvement.

[0094] Example 1

[0095] 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."

[0096] In today's modern lifestyle, users face the challenge of continually improving and maintaining their health. In particular, there is a lack of systems that collect detailed data on dietary and exercise habits and provide specific, sustainable improvement plans based on that data. There is also a need for systems that can predict a user's future health status and dynamically optimize improvement plans based on that data.

[0097] 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.

[0098] In this invention, the server includes a means for inputting data relating to the user's health condition, a means for transmitting the input data, and a means for receiving and storing the transmitted data, thereby enabling the server to specifically quantify the user's unique health condition and provide a dynamically optimized improvement plan.

[0099] "User" refers to an individual who utilizes the system to manage and improve their health.

[0100] "Health status" refers to various health-related data such as a user's diet, exercise habits, weight, and blood pressure.

[0101] "Means for inputting data" refers to devices or interfaces that allow users to record information about their health status, such as smartphones, computers, and tablets.

[0102] "Means for transmitting data" refers to a function for transmitting input data to a cloud server via the Internet. Specifically, this includes a network communication module.

[0103] "Means for receiving and storing data" refers to the function of storing transmitted data on a server. Specifically, this includes database servers and storage systems.

[0104] "Diet" refers to the type of food a user eats on a daily basis and how often they eat it.

[0105] "Exercise habits" refers to the type, frequency, duration, etc. of exercise that a user does on a daily basis.

[0106] "Means for analyzing data" refers to the function of evaluating a user's dietary and exercise habits and analyzing their health status based on the stored data, including analytical algorithms and AI models.

[0107] "Generative AI model" refers to an artificial intelligence model that predicts a user's future health condition and suggests an appropriate improvement plan based on acquired data and past success stories.

[0108] "Means for displaying prediction results" refers to a function for visually presenting the prediction results produced by the generative AI model to the user. Specifically, this includes a display or smartphone screen.

[0109] An "improvement plan" refers to a plan that includes specific action items that a user can implement and commit to sustainably.

[0110] "Means of notification" refers to the functionality for informing users of the generated improvement plan and prediction results, including push notifications and in-app messages.

[0111] "Progress" refers to the progress of the actions taken by the user and the results obtained according to the improvement plan.

[0112] "Dynamic optimization" means that the generative AI model continuously adjusts the improvement plan based on newly acquired data to provide the optimal health management plan.

[0113] MODE FOR CARRYING OUT THE INVENTION

[0114] This invention relates to a system that provides a user with a specific plan to improve their health and supports their implementation. This system is mainly composed of a terminal, a server, and a generative AI model, and each element works together to support the user's health management.

[0115] Data collection

[0116] First, users enter data about their diet and exercise habits using a terminal, including their daily diet, exercise frequency, and exercise duration. The terminal can be a common digital device such as a smartphone, PC, or tablet.

[0117] Data transmission

[0118] The device sends the entered data to the cloud server. The sent data is encrypted and sent over a securely protected communication channel. HTTPS or TLS is the most commonly used communication protocol.

[0119] Data storage

[0120] The server stores the received data in a database. This database uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) and is capable of efficiently managing large amounts of data.

[0121] Data analysis

[0122] The server analyzes the stored data, which includes extracting patterns in the user's diet and exercise habits, using Python libraries such as scikit-learn and TensorFlow.

[0123] Prediction result generation

[0124] The generative AI model uses the analysis results to predict the user's future health status, quantifying risks such as weight gain, high blood pressure, and heart disease based on past success stories and acquired data.

[0125] Improvement plan generation

[0126] The server generates a user-specific improvement plan based on the predictions made by the generative AI model, which includes specific and actionable action items, such as "add fruit to breakfast" or "walk 30 minutes five times a week."

[0127] Improvement plan notification

[0128] The generated improvement plan is sent from the server to the device via push notifications or in-app pop-up messages, allowing users to quickly incorporate the improvement plan into their daily lives.

[0129] Plan execution and feedback

[0130] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user enters their progress and new health data (e.g., new dietary habits, exercise habits) into the device. The entered data is then sent back to the server, which analyzes the new data and updates the improvement plan as needed.

[0131] Specific examples

[0132] For example, consider a 35-year-old female user who eats bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to a server. The server stores the data and analyzes it to determine that her diet is high in calories and she is not getting enough exercise. Based on these results, the generative AI model predicts that she will gain 10 kg in weight in three years, increasing her risk of high blood pressure.

[0133] The server generates a specific improvement plan, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the device. The user follows this plan to improve their daily life and enters their progress into the device. This reduces the user's health risks and enables sustainable health management.

[0134] Prompt Sentence Examples

[0135] "If a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week, what health risks would be predicted and what improvement plan would be suggested?"

[0136] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1: Data collection

[0139] The user inputs data about their health condition into the device. Specifically, the user inputs information such as daily diet, exercise frequency, and exercise duration. This input data is used to proceed to the next processing step. The data entered by the user is saved in the form of a string or a number.

[0140] Step 2: Send data

[0141] The device sends the data entered by the user to the cloud server. At this time, the data is encrypted before being sent, ensuring security. The input data is the health condition data obtained in step 1, and the output data is the encrypted data sent to the server. The HTTPS protocol is used for transmission.

[0142] Step 3: Save data

[0143] The server stores the received data in a database. Specifically, it uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) to efficiently manage large amounts of data. The input data is the encrypted data sent in step 2, and the output data is the unencrypted data stored in the database.

[0144] Step 4: Data analysis

[0145] The server performs analysis based on the stored data. The purpose of this analysis is to extract patterns in the user's diet and exercise habits. The input data is unencrypted data obtained from the database, and the output data is a report of the analysis results. Specifically, data analysis is performed using libraries such as Python's scikit-learn and TensorFlow.

[0146] Step 5: Generate prediction results

[0147] The server inputs the analysis results into a generative AI model to predict the user's future health condition. This generative AI model quantifies health risks based on the acquired data and past success stories. The input data is the analysis results from step 4, and the output data is numerical data indicating future health risks.

[0148] Step 6: Generate an improvement plan

[0149] The server generates a user-specific improvement plan based on the prediction results of the generative AI model, including specific action items (e.g., adding fruit to breakfast, walking 30 minutes five times a week). The input data is the health risk data from Step 5, and the output data is a specific improvement plan.

[0150] Step 7: Notification of Improvement Plan

[0151] The server notifies the device of the generated improvement plan. Notification methods include push notifications and in-app pop-up messages. The input data is the improvement plan from step 6, and the output data is the improvement plan information notified to the user.

[0152] Step 8: Execute the plan and enter progress

[0153] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user also inputs the actions they have taken (e.g., the exercise and diet they actually did) into the device. The input data is the action data the user has taken, and the newly entered progress data is output to the device.

[0154] Step 9: Send progress data

[0155] The device sends the progress data entered by the user to the cloud server. The data is encrypted again before being sent. The input data is the progress data entered in step 8, and the encrypted progress data is sent to the server as output data.

[0156] Step 10: Update your improvement plan

[0157] The server analyzes the newly received progress data and updates the improvement plan as necessary, ensuring that the user always receives the most up-to-date health management plan. The input data is the progress data received in step 9, and the output data is an updated improvement plan. Specifically, the server analyzes the new progress data and incorporates new suggestions into the existing improvement plan.

[0158] (Application example 1)

[0159] 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."

[0160] In modern society, effectively managing and improving individual health is an important challenge. However, many existing systems are limited to collecting and analyzing data on users' dietary and exercise habits. As a result, few systems offer comprehensive health improvement plans or even suggest products based on the user's health status. Furthermore, they lack the functionality to predict future health status using generative AI models and continuously update improvement plans. This makes it difficult for users to engage in sustainable, personalized health management.

[0161] 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.

[0162] In this invention, the server includes means for inputting data on the user's dietary and exercise habits, means for transmitting the input data, means for receiving and storing the transmitted data, means for analyzing the user's current dietary and exercise habits based on the stored data, means for predicting the user's future health status based on the analysis results using a generative AI model, means for displaying the prediction results, means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, means for notifying the user of the created improvement plan, means for acquiring new data on the progress of the improvement plan and updating the stored data, means for continuously updating the improvement plan based on the updated data, and means for suggesting products suitable for the user's health status based on the prediction results. This allows the user to obtain a specific and sustainable health improvement plan and further receive product suggestions tailored to their current health status.

[0163] "User" refers to an individual who uses this system.

[0164] "Diet" refers to the foods that a user regularly consumes and the contents of those foods.

[0165] "Exercise habits" refers to the physical activities a user regularly engages in, as well as their frequency and duration.

[0166] "Data" refers to information about a user's diet and exercise habits.

[0167] "Terminal" refers to a device such as a smartphone, PC, or tablet through which a user enters data.

[0168] "Server" refers to a computer system that receives, stores, and analyzes data sent from a terminal.

[0169] A "generative AI model" refers to an artificial intelligence model that predicts future health conditions based on input data and past success stories.

[0170] "Analysis" refers to assessing the current state of your diet and exercise habits based on your data.

[0171] "Prediction" refers to the use of a generative AI model to indicate a user's future health status.

[0172] "Improvement Plan" refers to specific action items provided to the user based on the analysis and prediction results.

[0173] "Notification" refers to informing the user of the generated improvement plan.

[0174] "Product suggestion" refers to presenting products suitable for the user's health condition.

[0175] "Continuous updates" means constantly providing optimal improvement plans based on new data.

[0176] The system for implementing this invention mainly uses a terminal, a server, and a generative AI model to support users' health management. This system allows users to input data about their diet and exercise habits, analyzes their health status based on that data, and provides specific improvement plans. It also has a function to suggest products suitable for the user's health status.

[0177] Hardware and software used

[0178] Hardware

[0179] Device: Use a device such as a smartphone, computer, or tablet.

[0180] Server: A computer system that stores and analyzes data.

[0181] software

[0182] Python 3: Used for program development and data analysis.

[0183] requests library: A library for sending HTTP requests.

[0184] Data processing and calculation

[0185] 1. Data collection and transmission

[0186] The user uses the device to input data about their diet and exercise habits. For example, they input the type of food they eat, how often they exercise, and how long they exercise. This data is sent from the device to the server. For example, suppose a 35-year-old woman has bread and coffee for breakfast and jogs three times a week. This data is input in the format "35-year-old woman, has bread and coffee for breakfast, jogs three times a week."

[0187] 2. Data analysis and prediction

[0188] The server receives and stores data sent from the device. The stored data includes detailed information about the user's diet and exercise habits. The server analyzes this data and uses a generative AI model to predict the user's future health condition. For example, the generative AI model may predict that "in three years, weight will increase by 10 kg, and the risk of high blood pressure will increase."

[0189] 3. Generate and notify improvement plans

[0190] Based on the analysis and prediction results, the server creates a personalized improvement plan for the user, including specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is then sent back to the device and displayed to the user.

[0191] 4. Product proposal

[0192] Furthermore, the server will suggest products suitable for the user's health condition based on the prediction results. For example, if the user's health risk is high blood pressure, it will suggest products such as "low-salt food sets" or "diet supplements."

[0193] 5. Implementing the plan and providing feedback

[0194] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0195] Specific examples

[0196] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to the server. The server analyzes this data, and the generative AI model predicts that "in three years, weight will increase by 10 kg and the risk of high blood pressure will increase." Based on this prediction, an improvement plan is generated that includes "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and is notified to the user. The user is also suggested products such as "low-salt food sets" and "diet supplements." By following this and continuing to input their progress into the device, the user can achieve sustainable health management.

[0197] Prompt Sentence Examples

[0198] "I'm a 35-year-old woman who has bread and coffee for breakfast and jogs three times a week. I'd like to receive an improvement plan and product suggestions."

[0199] In this way, users can obtain specific and actionable health management measures through the system and achieve sustained improvements in their health.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] Users use devices (smartphones, PCs, tablets, etc.) to input data about their diet and exercise habits. This specifically includes daily meal content, exercise frequency, exercise duration, etc. For example, if a user eats bread and coffee for breakfast and jogs three times a week, they enter that information.

[0203] Input: Information about your diet and exercise habits.

[0204] Output: Data is saved to the device.

[0205] Step 2:

[0206] The terminal sends the entered data to the server. The data is sent to the server using an HTTP request. The server receives it and stores it in a database. For example, the data is sent in JSON format.

[0207] Input: Data entered at the terminal.

[0208] Output: Data stored in the server's database.

[0209] Step 3:

[0210] The server analyzes the user's current dietary and exercise habits based on the stored data. Specifically, it analyzes the data and evaluates calorie intake, exercise volume, etc. This analysis can clarify the user's current health status.

[0211] Input: Data stored on the server.

[0212] Output: Analysis results (e.g., assessment of calorie intake and physical activity).

[0213] Step 4:

[0214] The server uses a generative AI model based on the analysis results to predict the user's future health condition. The generative AI model uses past data and success stories to predict weight gain and high blood pressure risk several years from now. For example, it generates a prediction that "weight will increase by 10 kg in three years and the risk of high blood pressure will increase."

[0215] Input: Analysis results.

[0216] Output: Prediction results.

[0217] Step 5:

[0218] The server displays the prediction results and notifies the user via their device, allowing the user to check the results and gain a deeper understanding of their own health condition.

[0219] Input: Prediction results.

[0220] Output: Notification of prediction results to the user.

[0221] Step 6:

[0222] The server extracts past success stories and creates a personalized improvement plan based on a generative AI model, generating specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[0223] Input: Predicted results and success story data.

[0224] Output: Improvement plan.

[0225] Step 7:

[0226] The server notifies the user of the created improvement plan, which the user can then check on their device and put into action.

[0227] Input: Improvement Plan.

[0228] Output: Notification of improvement plan to user.

[0229] Step 8:

[0230] The user follows the improvement plan and incorporates it into their daily life. They input their progress and new dietary and exercise habits into the device, and this data is then sent back to the server.

[0231] Input: User progress data.

[0232] Output: Progress data sent to the server.

[0233] Step 9:

[0234] The server analyzes the new data and updates the stored data to reflect the user's most recent health status, and updates and notifies the user of an improvement plan if necessary.

[0235] Input: New progress data.

[0236] Output: Updated improvement plan and data.

[0237] Step 10:

[0238] Based on the prediction results, the server will suggest products suitable for the user's health condition, such as a "low-salt food set" or "diet supplements." This will also be notified to the user via their device.

[0239] Input: Prediction results and data.

[0240] Output: Product suggestion notification to the user.

[0241] Through these steps, the system can effectively manage and continuously improve the user's health status.

[0242] 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.

[0243] The system of the present invention provides a specific action plan for improving the user's health and supports its implementation. Furthermore, the system recognizes the user's emotional state and adjusts the improvement plan based on the analysis results, thereby achieving more effective health management. The system mainly consists of the following components: a terminal, a server, a generative AI model, and an emotion engine. These elements work together to enable users to effectively manage their own health.

[0244] Data collection

[0245] First, the user uses a device to input data about their diet and exercise habits. For example, they record their daily diet, exercise frequency, and exercise time on the device. This data is then sent from the device to a server. The device can be a smartphone, PC, tablet, or other device.

[0246] Data analysis

[0247] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and cardiovascular disease based on the acquired data and past success stories.

[0248] Generate an improvement plan

[0249] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[0250] Sentiment analysis with emotion engine

[0251] Furthermore, this system incorporates an emotion engine that recognizes the user's emotional state. When the user uses the device, data such as voice, facial expressions, and text messages is collected and sent to the server. The server analyzes this data and evaluates the user's emotional state. For example, it can detect when the user is feeling stressed or has lost motivation.

[0252] Emotionally-driven plan adjustments

[0253] The server adjusts the improvement plan based on the user's emotional state. For example, if the user is feeling stressed, it adds actions to help them relax (e.g., light exercise or meditation). If their motivation is low, it sets incentives to encourage them to take action.

[0254] Plan execution and feedback

[0255] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0256] Specific examples

[0257] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[0258] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into the device. This reduces the user's health risks.

[0259] Furthermore, if the emotion engine detects that the user is feeling stressed, the server will add "yoga for relaxation" to the improvement plan. Also, if motivation is declining, incentives such as "rewarding yourself for achieving your goal" will be set to encourage the user to take action.

[0260] In this way, the system of the present invention effectively manages the user's health condition and provides an individual improvement plan according to the user's emotional state, thereby supporting continuous health improvement.

[0261] The processing flow will be explained below.

[0262] Step 1:

[0263] A user accesses a terminal and inputs data about their diet and exercise habits.

[0264] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[0265] Step 2:

[0266] The terminal transmits the input data to the server.

[0267] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[0268] Step 3:

[0269] The server analyzes the received data and stores it in a database.

[0270] The server checks the integrity of the data and stores it in a database for each user.

[0271] Step 4:

[0272] The server analyzes the user's current eating and exercise habits based on the stored data.

[0273] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[0274] Step 5:

[0275] The server uses the generated AI model to predict the user's health status several years in the future.

[0276] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[0277] Step 6:

[0278] The server generates prediction results and sends them to the device.

[0279] A notification that visually gives the user a sense of crisis is displayed on the device.

[0280] Step 7:

[0281] The server extracts past success stories and feeds them into a generative AI model.

[0282] Success stories include lifestyle data of users who have successfully improved their health.

[0283] Step 8:

[0284] The server creates a user-specific improvement plan.

[0285] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[0286] Step 9:

[0287] The server sends the created improvement plan to the terminal.

[0288] On the device, the user is notified of the improvement plan and action items are listed.

[0289] Step 10:

[0290] The user begins to change their daily life based on the improvement plan displayed on the device.

[0291] The user periodically inputs the status of improvements and new data into the terminal.

[0292] Step 11:

[0293] The device sends new data to the server.

[0294] The new data will include progress on improvement plans and changes to diet and exercise habits.

[0295] Step 12:

[0296] The server receives the new data and updates the database.

[0297] The database is updated based on new data.

[0298] Step 13:

[0299] The server continuously updates the improvement plan based on new data.

[0300] The generative AI model reevaluates and regenerates the optimal improvement plan.

[0301] Step 14:

[0302] The server transmits the updated improvement plan to the terminal again and notifies the user.

[0303] The new improvement plan will be displayed on the device and the user will begin their next action.

[0304] Step 15:

[0305] Emotional data such as voice, facial expressions, and text messages are collected while the user is using the device.

[0306] This data is transmitted from the terminal to the server.

[0307] Step 16:

[0308] The server analyzes the received emotion data and evaluates the user's emotional state.

[0309] For example, states such as feeling stressed or having low motivation are detected.

[0310] Step 17:

[0311] The server adjusts the remediation plan based on the user's emotional state.

[0312] For example, add yoga to help you relax or set incentives to motivate you.

[0313] Step 18:

[0314] The server transmits the adjusted improvement plan to the terminal and notifies the user.

[0315] The new plan is displayed to the user on the device and they are prompted to execute it.

[0316] In this way, the system also takes into account the user's emotional state and supports sustainable and effective health management.

[0317] Example 2

[0318] 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."

[0319] In today's busy lifestyles, it is not easy for individuals to effectively manage their own health. In particular, the process of centrally managing data on dietary and exercise habits and creating and implementing appropriate action plans is complex and requires specialized knowledge. In addition, because a user's emotional state significantly affects the continuity and effectiveness of health management, flexible plan adjustments based on emotional fluctuations are required.

[0320] 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. In this invention, the server includes a means for inputting data on the user's dietary and exercise habits, a means for transmitting the input data, and a means for receiving and storing the transmitted data. This enables the user to efficiently manage their own health condition.

[0321] The server also includes a means for analyzing the user's current eating and exercise habits based on the stored data, a means for predicting the user's future health condition based on the analysis results using a generative AI model, a means for displaying the prediction results, a means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, and a means for notifying the user of the created improvement plan. This allows the user to receive a specific and actionable improvement plan, and encourages them to take action to improve their health.

[0322] The server further includes means for collecting and transmitting data on the user's emotional state, means for analyzing and evaluating the emotional state, means for adjusting the improvement plan based on the emotional state, means for acquiring new data on the progress of the improvement plan and updating the stored data, and means for continuously updating the improvement plan based on the updated data. This enables flexible plan adjustments that adapt to the user's emotional state, enabling continuous health management.

[0323] A "user" is an entity that uses the system to manage their own health condition.

[0324] "Diet" refers to the user's daily diet and eating habits.

[0325] "Exercise habits" refers to a user's daily exercise and fitness activity patterns.

[0326] "Data" refers to health-related information entered into the system by a user and processed by the system.

[0327] "Terminal" refers to an electronic device that allows a user to input data or receive notifications from the system. Examples include smartphones, personal computers, and tablets.

[0328] "Server" refers to a computer system that serves as the center of the system and receives, stores, analyzes, and processes data.

[0329] "Transmission" refers to the act of sending data from a terminal to a server via a network.

[0330] "Storage" refers to the act of storing the data received by the server in a database or the like.

[0331] "Analysis" refers to the process by which the server evaluates information and derives results based on the data it obtains.

[0332] A "generative AI model" refers to a program that uses machine learning algorithms to predict a user's future health status.

[0333] "Prediction" refers to the act of calculating and estimating future health states based on a generative AI model.

[0334] "Display" refers to the act of sending the results from the server to the terminal and showing the results on the terminal in a form that can be visually recognized by the user.

[0335] "Past success stories" refers to patterns and data of successful health improvements that other users have made in the past.

[0336] "Improvement plan" refers to specific action items for improving the user's health condition, created by the server based on the generated AI model and analysis results.

[0337] "Notification" refers to the act of sending information from a server to a terminal and informing the user.

[0338] "Emotional state" refers to the user's mental and emotional state, such as stress, motivation, etc.

[0339] "Collect" refers to the act of the device obtaining data about the user's emotional state.

[0340] "Evaluation" refers to the act of analyzing the emotion data collected by the server and determining the user's current emotional state.

[0341] "Adjustment" refers to the act of appropriately changing the content of the improvement plan based on the results of the emotional state.

[0342] The system of the present invention provides a user with an action plan to improve their health and supports their implementation. The system components include a terminal, a server, a generative AI model, and an emotion engine.

[0343] Operating procedures and data collection

[0344] Users use devices such as smartphones, PCs, and tablets to input data about their diet and exercise habits. For example, they may input that they ate bread and coffee for breakfast or that they jog three times a week. This data is sent from the device to the server. The device sends the data using a secure communication protocol (e.g., HTTPS).

[0345] Data Analysis and Prediction

[0346] The server stores the received data and begins analyzing it. The data is saved in a database (e.g., MySQL or PostgreSQL). Based on the saved data, the server evaluates the user's current diet and exercise habits. It then uses a generative AI model to predict the user's health status several years into the future. This model predicts risks (e.g., weight gain, high blood pressure, cardiovascular disease).

[0347] Generate an improvement plan

[0348] Based on the prediction results, the server generates a user-specific improvement plan. This plan includes specific action items that can be implemented and sustained, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is notified to the device and displayed to the user.

[0349] Emotion data collection and analysis

[0350] When a user uses the device, it collects data such as voice, facial expressions, and text messages. This data is sent to a server, where an emotion engine analyzes it to assess the user's emotional state, such as whether they are stressed or lacking motivation.

[0351] Adjusting the plan

[0352] The server adjusts the improvement plan based on the user's emotional state: if the user is feeling stressed, it adds relaxation actions (e.g., yoga or meditation), and if motivation is low, it sets incentives (e.g., rewards for achieving goals).

[0353] Feedback and progress management

[0354] Users follow the improvement plan displayed on the device and incorporate it into their daily lives. Progress and new data are periodically entered into the device and sent to the server. The server analyzes the new data and updates the improvement plan, supporting continuous health management.

[0355] Examples and prompts

[0356] For example, suppose a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week. The user inputs this data and sends it to the server. The generative AI model predicts that she will gain 10 kg in weight in three years and her risk of high blood pressure will increase. Based on the results, an improvement plan is generated, including "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and notified to the user.

[0357] Example prompt sentence:

[0358] "Please enter your current diet and exercise habits (e.g., bread and coffee for breakfast, jogging three times a week)."

[0359] "Please describe your recent emotional state (e.g., feeling stressed or unmotivated)."

[0360] This allows users to effectively manage their health status and receive personalized improvement plans based on their emotional state, enabling sustainable health management.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Step 1: Data collection

[0363] The user enters data about their diet and exercise habits into the device, such as what they ate for breakfast, how often they exercise, and how long they exercise. The device then encodes the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0364] Input: Data on diet and exercise habits

[0365] Output: The encoded data is sent to the server

[0366] Step 2: Save data

[0367] The server stores the data received from the terminal in a database, such as a relational database like MySQL or PostgreSQL.

[0368] Input: Encoded data

[0369] Output: Data stored in the database

[0370] Step 3: Initial analysis

[0371] The server begins an initial analysis of the stored data. The analytics engine pulls the data from the database and calculates basic statistics such as calorie intake and total exercise time.

[0372] Input: User data stored in the database

[0373] Output: Basic statistics (calorie intake, exercise time, etc.)

[0374] Step 4: Predict your health status

[0375] The server uses a generative AI model to predict the user's future health status. The generative AI model uses the results of the initial analysis as input data to predict risks such as weight gain, high blood pressure, and cardiovascular disease.

[0376] Input: Results of initial analysis

[0377] Output: Risk prediction results (likelihood of weight gain, high blood pressure, cardiovascular disease, etc.)

[0378] Step 5: Generate an improvement plan

[0379] The server generates a user-specific improvement plan based on the prediction results. The plan generation engine references past success stories and creates action items that the user can implement and sustain. For example, "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[0380] Input: Risk prediction results, past success stories

[0381] Output: Improvement plan (specific action items)

[0382] Step 6: Communicate your improvement plan

[0383] The server transmits the generated improvement plan to the terminal, which displays the received improvement plan on a user interface and notifies the user.

[0384] Input: Improvement Plan

[0385] Output: Improvement plan displayed to the user

[0386] Step 7: Collect emotion data

[0387] When a user uses a device, the device collects emotional data (voice, facial expressions, text messages, etc.). For example, it uses a camera or microphone to capture the user's facial expressions and voice, and obtains new text messages. The collected data is sent to a server.

[0388] Input: Emotional data such as voice, facial expressions, and text messages

[0389] Output: The encoded emotion data is sent to the server.

[0390] Step 8: Analyze the sentiment data

[0391] The emotion engine on the server analyzes the transmitted emotion data and evaluates the user's emotional state, for example, determining whether the user is feeling stressed or unmotivated.

[0392] Input: Encoded emotion data

[0393] Output: User's emotional state (stress, motivation, etc.)

[0394] Step 9: Adjust your improvement plan based on your emotions

[0395] The server adjusts the user's specific improvement plan based on their emotional state. For example, if the user is feeling stressed, yoga for relaxation is added to the improvement plan. If the user's motivation is low, incentives such as rewards for achieving goals are set.

[0396] Input: User's emotional state

[0397] Output: Adjusted improvement plan

[0398] Step 10: Implementation and Feedback

[0399] The user acts according to the improvement plan displayed on the device and inputs their progress into the device. The device encodes the input progress data and sends it to the server. The server analyzes the new progress data and updates the improvement plan as necessary. This allows the user's health to continuously improve.

[0400] Input: Progress data

[0401] Output: Updated improvement plan and new health analysis results

[0402] (Application example 2)

[0403] 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."

[0404] Conventional health management systems only provide simple improvement plans based on the user's diet and exercise habits, and do not adjust the plan to take into account the user's emotional state, making it difficult to effectively manage health. Furthermore, there is no way to provide health advice in physical stores, making it difficult for users to make appropriate choices on the spot. Therefore, there is a need for an effective health management system that takes into account the user's emotional state and can be used in physical stores.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting data on the user's dietary and exercise habits; means for transmitting the input data; means for receiving and storing the transmitted data; means for analyzing the user's current dietary and exercise habits based on the stored data; means for predicting the user's future health status based on the analysis results using a generative AI model; means for displaying the prediction results; means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model; means for notifying the user of the created improvement plan; means for acquiring new data on the progress of the improvement plan and updating the stored data; means for continuously updating the improvement plan based on the updated data; means for recognizing the user's emotional state and adjusting the improvement plan based on the data; means for providing health advice for products the user is considering purchasing in a store; and means for checking the user's emotional state in real time and presenting appropriate advice. This enables effective health management that takes the user's emotional state into consideration and appropriate health advice in a physical store.

[0406] A "user" is an individual who uses the system to manage their own health and emotional state.

[0407] "Diet" refers to the types and amounts of food and drink that an individual consumes on a daily basis, as well as the times when they eat meals.

[0408] "Exercise habits" refers to the type, frequency, duration, and intensity of exercise that an individual engages in on a daily basis.

[0409] "Data" refers to information entered by users relating to their diet, exercise habits, and emotional state.

[0410] An "input means" is a device or interface that allows a user to provide data to a system.

[0411] "Transmitting means" refers to a device or interface with communication capabilities for transferring input data to a server.

[0412] "Means for receiving and storing" refers to a device or system that has the function of receiving transmitted data and storing it on a server.

[0413] "Means for analysis" refers to algorithms or programs that use the stored data to evaluate a user's diet and exercise habits.

[0414] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes new predictions and generates new information.

[0415] "Prediction means" refers to a program or function that uses a generative AI model based on the analysis results to predict the user's future health condition.

[0416] "Means for displaying" refers to a display or notification system that visualizes the prediction results and improvement plans to the user.

[0417] "Means of extraction" refers to algorithms or programs that select relevant information from past success stories.

[0418] "Means for creation" refers to programs and functions for designing user-specific improvement plans based on generative AI models.

[0419] The "notification means" refers to a function or device for notifying the user of the created improvement plan.

[0420] "New data on progress" refers to information about the results and status of activities that a user has carried out based on an improvement plan.

[0421] "Means for updating" refers to programs or functions that update stored data and improvement plans based on new data acquired.

[0422] An "emotional state" refers to a user's mental state, examples of which include stress, joy, sadness, etc.

[0423] "Adjusting means" refers to algorithms or programs that adapt an existing improvement plan based on the user's emotional state.

[0424] A "store" is a physical business establishment where users can visit and purchase products.

[0425] "Health advice" refers to instructions or advice that encourage specific actions or choices to maintain or improve a user's health.

[0426] "Means for checking in real time" refers to functions or devices that allow users to instantly check their emotional state on the spot.

[0427] "Presentation means" refers to a display or notification system that provides appropriate advice and information to the user.

[0428] The system of the present invention provides a personalized improvement plan to improve the user's health, continuously adjusts it taking into account the user's emotional state, and also has the ability to provide effective health advice in physical stores.

[0429] Components

[0430] The system consists of the following components:

[0431] 1. Device:

[0432] This includes smartphones, tablets, and laptops.

[0433] It allows users to enter data about their diet, exercise habits, and emotional state.

[0434] 2. Server:

[0435] Cloud-based or on-premise servers are used.

[0436] The transmitted data is received and stored, and then analyzed and predictions are made.

[0437] 3. Generative AI Model:

[0438] It includes deep learning models and machine learning algorithms that predict future health conditions based on user data and past success stories.

[0439] 4. Emotion Engine:

[0440] The user's emotional state is analyzed using technologies such as voice recognition and facial expression recognition.

[0441] Hardware and Software

[0442] Hardware: Smartphone, tablet, laptop (with built-in camera and microphone)

[0443] Software: Python, OpenCV, Keras, SpeechRecognition library, cloud-based server

[0444] Data processing:

[0445] Camera-based face recognition: Face recognition is performed using OpenCV, and image data is provided to the emotion recognition model.

[0446] Speech Recognition: Use the SpeechRecognition library to capture voice data from the microphone and detect the user's emotional state and needs.

[0447] Processing flow

[0448] 1. Data Collection:

[0449] Users use the device to input data about their diet, exercise habits, and emotional state.

[0450] This data is transmitted from the terminal to the server.

[0451] 2. Data Analysis:

[0452] The server stores the received data and begins analyzing it.

[0453] It assesses the details of your diet and exercise habits and uses generative AI models to predict your future health status.

[0454] 3. Generate an improvement plan:

[0455] Based on the prediction results, the server creates a user-specific improvement plan.

[0456] The emotion engine assesses the user's emotional state and adjusts the remediation plan as needed, for example adding relaxation activities if the user is feeling stressed.

[0457] 4. In-store advice:

[0458] In stores, health advice is provided to users using their smartphones or tablets for products they are considering purchasing.

[0459] It uses facial and voice recognition to check the user's emotional state in real time and provide appropriate advice.

[0460] Specific examples

[0461] For example, when a user is about to buy food at a physical store, the system uses the user's facial image to detect stress. Based on the user's dietary data and emotional state, the system provides health advice such as "Purchase fruit or yogurt, which are effective in relieving stress." It can also use voice recognition to provide specific advice in response to the user's questions.

[0462] Prompt Sentence Examples

[0463] "Recognize the user's emotional state from facial images or voice input. Use the results to generate health advice appropriate to that emotional state."

[0464] In this way, the system comprehensively manages the user's health status and supports the user in improving their health by providing individual improvement plans and real-time health advice.

[0465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0466] Step 1:

[0467] Users use devices (such as smartphones or tablets) to input data about their diet, exercise habits, and emotional state. This includes tapping, voice input, and facial recognition data using a camera. Input data includes dietary content, exercise frequency and duration, and emotional state (such as stress levels).

[0468] Step 2:

[0469] The terminal transmits the input data to the server. At this time, an application on the terminal converts the data into text or binary format and transmits the data using a secure communication protocol (e.g., HTTPS).

[0470] Step 3:

[0471] The server receives the data sent from the device and then stores it in the appropriate database. The data stored in the database is organized by user and maintains its association with past data.

[0472] Step 4:

[0473] The server analyzes the user's current dietary and exercise habits based on the stored data. Based on the dietary and exercise information retrieved from the database, the server evaluates the calorie intake and exercise intensity. The evaluation is performed using queries and algorithms.

[0474] Step 5:

[0475] The server uses the analysis results to predict the user's future health condition using a generative AI model. User data and past success stories are input into the prediction, and the generative AI model outputs risk factors (weight gain, high blood pressure, etc.). For example, specific prediction results such as "risk of weight gain of 10 kg in three years" can be obtained.

[0476] Step 6:

[0477] The server prepares data for displaying the prediction results on the user's terminal. For example, the server generates data including the prediction results visualized in text or graph format and sends it to the terminal. This data is displayed on the terminal's display.

[0478] Step 7:

[0479] The server uses a generative AI model based on past success stories to create a personalized improvement plan for each user. This plan includes specific, actionable, and sustainable actions (e.g., "walk 30 minutes five times a week"). The improvement plan is optimized based on the user's analysis results and emotional state.

[0480] Step 8:

[0481] The server notifies the user of the created improvement plan. The notification is sent to the device and notifies the user via push notification or email. The improvement plan is displayed on the device, and the user can take action based on the plan.

[0482] Step 9:

[0483] The user then inputs new data into the device about their progress with the improvement plan, including their daily diet, exercise, and changes in their emotional state. As the user inputs new data, the data is continually updated.

[0484] Step 10:

[0485] The device then sends new data to the server, which then updates the stored data. The server then analyzes the updated data and continuously updates the improvement plan, ensuring that the device continues to provide a plan that is optimal for the user's current condition.

[0486] Step 11:

[0487] The server recognizes the user's emotional state and adjusts the improvement plan based on that data. For example, if it detects stress, it adds relaxation activities to the action plan. If the user's motivation is low, it sets incentives.

[0488] Step 12:

[0489] When a user visits a physical store, the device provides health advice for products they are considering purchasing. The device's camera is used to recognize the user's face, check their emotional state in real time, and display appropriate health advice. For example, a user who is feeling stressed might be offered advice such as, "Purchase fruits that have a relaxing effect."

[0490] 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.

[0491] 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.

[0492] 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.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] In the smart glasses 214, the 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.

[0505] 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."

[0506] The system of the present invention provides a specific action plan to improve the user's health and supports its implementation. The system mainly consists of three components: a terminal, a server, and a generative AI model. These components work together to enable the user to effectively manage their own health.

[0507] Data collection

[0508] First, the user uses a device to input data about their diet and exercise habits. For example, the device records daily meal contents, exercise frequency, and exercise time. This data is then sent from the device to a server. The device can be a device that the user uses daily, such as a smartphone, PC, or tablet.

[0509] Data analysis

[0510] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and heart disease based on the acquired data and past success stories.

[0511] Generate an improvement plan

[0512] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[0513] Plan execution and feedback

[0514] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0515] Specific examples

[0516] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[0517] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into their device. This reduces the user's health risks and enables sustainable health management.

[0518] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[0519] The processing flow will be explained below.

[0520] Step 1:

[0521] A user accesses a terminal and inputs data about their diet and exercise habits.

[0522] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[0523] Step 2:

[0524] The terminal transmits the input data to the server.

[0525] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[0526] Step 3:

[0527] The server analyzes the received data and stores it in a database.

[0528] The server checks the integrity of the data and stores it in a database for each user.

[0529] Step 4:

[0530] The server analyzes the user's current eating and exercise habits based on the stored data.

[0531] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[0532] Step 5:

[0533] The server uses the generated AI model to predict the user's health status several years in the future.

[0534] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[0535] Step 6:

[0536] The server generates prediction results and sends them to the device.

[0537] A notification that visually gives the user a sense of crisis is displayed on the device.

[0538] Step 7:

[0539] The server extracts past success stories and feeds them into a generative AI model.

[0540] Success stories include lifestyle data of users who have successfully improved their health.

[0541] Step 8:

[0542] The server creates a user-specific improvement plan.

[0543] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[0544] Step 9:

[0545] The server sends the created improvement plan to the terminal.

[0546] On the device, the user is notified of the improvement plan and action items are listed.

[0547] Step 10:

[0548] The user begins to change their daily life based on the improvement plan displayed on the device.

[0549] The user periodically inputs the status of improvements and new data into the terminal.

[0550] Step 11:

[0551] The device sends new data to the server.

[0552] The new data will include progress on improvement plans and changes to diet and exercise habits.

[0553] Step 12:

[0554] The server receives the new data and updates the database.

[0555] The database is updated based on new data.

[0556] Step 13:

[0557] The server continuously updates the improvement plan based on new data.

[0558] The AI ​​model will reassess and regenerate the optimal improvement plan.

[0559] Step 14:

[0560] The server transmits the updated improvement plan to the terminal again and notifies the user.

[0561] The new improvement plan will be displayed on the device and the user will begin their next action.

[0562] In this way, the system continuously monitors the user's health status and provides specific plans for sustained improvement.

[0563] Example 1

[0564] 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."

[0565] In today's modern lifestyle, users face the challenge of continually improving and maintaining their health. In particular, there is a lack of systems that collect detailed data on dietary and exercise habits and provide specific, sustainable improvement plans based on that data. There is also a need for systems that can predict a user's future health status and dynamically optimize improvement plans based on that data.

[0566] 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.

[0567] In this invention, the server includes a means for inputting data relating to the user's health condition, a means for transmitting the input data, and a means for receiving and storing the transmitted data, thereby enabling the server to specifically quantify the user's unique health condition and provide a dynamically optimized improvement plan.

[0568] "User" refers to an individual who utilizes the system to manage and improve their health.

[0569] "Health status" refers to various health-related data such as a user's diet, exercise habits, weight, and blood pressure.

[0570] "Means for inputting data" refers to devices or interfaces that allow users to record information about their health status, such as smartphones, computers, and tablets.

[0571] "Means for transmitting data" refers to a function for transmitting input data to a cloud server via the Internet. Specifically, this includes a network communication module.

[0572] "Means for receiving and storing data" refers to the function of storing transmitted data on a server. Specifically, this includes database servers and storage systems.

[0573] "Diet" refers to the type of food a user eats on a daily basis and how often they eat it.

[0574] "Exercise habits" refers to the type, frequency, duration, etc. of exercise that a user does on a daily basis.

[0575] "Means for analyzing data" refers to the function of evaluating a user's dietary and exercise habits and analyzing their health status based on the stored data, including analytical algorithms and AI models.

[0576] "Generative AI model" refers to an artificial intelligence model that predicts a user's future health condition and suggests an appropriate improvement plan based on acquired data and past success stories.

[0577] "Means for displaying prediction results" refers to a function for visually presenting the prediction results produced by the generative AI model to the user. Specifically, this includes a display or smartphone screen.

[0578] An "improvement plan" refers to a plan that includes specific action items that a user can implement and commit to sustainably.

[0579] "Means of notification" refers to the functionality for informing users of the generated improvement plan and prediction results, including push notifications and in-app messages.

[0580] "Progress" refers to the progress of the actions taken by the user and the results obtained according to the improvement plan.

[0581] "Dynamic optimization" means that the generative AI model continuously adjusts the improvement plan based on newly acquired data to provide the optimal health management plan.

[0582] MODE FOR CARRYING OUT THE INVENTION

[0583] This invention relates to a system that provides a user with a specific plan to improve their health and supports their implementation. This system is mainly composed of a terminal, a server, and a generative AI model, and each element works together to support the user's health management.

[0584] Data collection

[0585] First, users enter data about their diet and exercise habits using a terminal, including their daily diet, exercise frequency, and exercise duration. The terminal can be a common digital device such as a smartphone, PC, or tablet.

[0586] Data transmission

[0587] The device sends the entered data to the cloud server. The sent data is encrypted and sent over a securely protected communication channel. HTTPS or TLS is the most commonly used communication protocol.

[0588] Data storage

[0589] The server stores the received data in a database. This database uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) and is capable of efficiently managing large amounts of data.

[0590] Data analysis

[0591] The server analyzes the stored data, which includes extracting patterns in the user's diet and exercise habits, using Python libraries such as scikit-learn and TensorFlow.

[0592] Prediction result generation

[0593] The generative AI model uses the analysis results to predict the user's future health status, quantifying risks such as weight gain, high blood pressure, and heart disease based on past success stories and acquired data.

[0594] Improvement plan generation

[0595] The server generates a user-specific improvement plan based on the predictions made by the generative AI model, which includes specific and actionable action items, such as "add fruit to breakfast" or "walk 30 minutes five times a week."

[0596] Improvement plan notification

[0597] The generated improvement plan is sent from the server to the device via push notifications or in-app pop-up messages, allowing users to quickly incorporate the improvement plan into their daily lives.

[0598] Plan execution and feedback

[0599] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user enters their progress and new health data (e.g., new dietary habits, exercise habits) into the device. The entered data is then sent back to the server, which analyzes the new data and updates the improvement plan as needed.

[0600] Specific examples

[0601] For example, consider a 35-year-old female user who eats bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to a server. The server stores the data and analyzes it to determine that her diet is high in calories and she is not getting enough exercise. Based on these results, the generative AI model predicts that she will gain 10 kg in weight in three years, increasing her risk of high blood pressure.

[0602] The server generates a specific improvement plan, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the device. The user follows this plan to improve their daily life and enters their progress into the device. This reduces the user's health risks and enables sustainable health management.

[0603] Prompt Sentence Examples

[0604] "If a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week, what health risks would be predicted and what improvement plan would be suggested?"

[0605] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1: Data collection

[0608] The user inputs data about their health condition into the device. Specifically, the user inputs information such as daily diet, exercise frequency, and exercise duration. This input data is used to proceed to the next processing step. The data entered by the user is saved in the form of a string or a number.

[0609] Step 2: Send data

[0610] The device sends the data entered by the user to the cloud server. At this time, the data is encrypted before being sent, ensuring security. The input data is the health condition data obtained in step 1, and the output data is the encrypted data sent to the server. The HTTPS protocol is used for transmission.

[0611] Step 3: Save data

[0612] The server stores the received data in a database. Specifically, it uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) to efficiently manage large amounts of data. The input data is the encrypted data sent in step 2, and the output data is the unencrypted data stored in the database.

[0613] Step 4: Data analysis

[0614] The server performs analysis based on the stored data. The purpose of this analysis is to extract patterns in the user's diet and exercise habits. The input data is unencrypted data obtained from the database, and the output data is a report of the analysis results. Specifically, data analysis is performed using libraries such as Python's scikit-learn and TensorFlow.

[0615] Step 5: Generate prediction results

[0616] The server inputs the analysis results into a generative AI model to predict the user's future health condition. This generative AI model quantifies health risks based on the acquired data and past success stories. The input data is the analysis results from step 4, and the output data is numerical data indicating future health risks.

[0617] Step 6: Generate an improvement plan

[0618] The server generates a user-specific improvement plan based on the prediction results of the generative AI model, including specific action items (e.g., adding fruit to breakfast, walking 30 minutes five times a week). The input data is the health risk data from Step 5, and the output data is a specific improvement plan.

[0619] Step 7: Notification of Improvement Plan

[0620] The server notifies the device of the generated improvement plan. Notification methods include push notifications and in-app pop-up messages. The input data is the improvement plan from step 6, and the output data is the improvement plan information notified to the user.

[0621] Step 8: Execute the plan and enter progress

[0622] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user also inputs the actions they have taken (e.g., the exercise and diet they actually did) into the device. The input data is the action data the user has taken, and the newly entered progress data is output to the device.

[0623] Step 9: Send progress data

[0624] The device sends the progress data entered by the user to the cloud server. The data is encrypted again before being sent. The input data is the progress data entered in step 8, and the encrypted progress data is sent to the server as output data.

[0625] Step 10: Update your improvement plan

[0626] The server analyzes the newly received progress data and updates the improvement plan as necessary, ensuring that the user always receives the most up-to-date health management plan. The input data is the progress data received in step 9, and the output data is an updated improvement plan. Specifically, the server analyzes the new progress data and incorporates new suggestions into the existing improvement plan.

[0627] (Application example 1)

[0628] 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."

[0629] In modern society, effectively managing and improving individual health is an important challenge. However, many existing systems are limited to collecting and analyzing data on users' dietary and exercise habits. As a result, few systems offer comprehensive health improvement plans or even suggest products based on the user's health status. Furthermore, they lack the functionality to predict future health status using generative AI models and continuously update improvement plans. This makes it difficult for users to engage in sustainable, personalized health management.

[0630] 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.

[0631] In this invention, the server includes means for inputting data on the user's dietary and exercise habits, means for transmitting the input data, means for receiving and storing the transmitted data, means for analyzing the user's current dietary and exercise habits based on the stored data, means for predicting the user's future health status based on the analysis results using a generative AI model, means for displaying the prediction results, means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, means for notifying the user of the created improvement plan, means for acquiring new data on the progress of the improvement plan and updating the stored data, means for continuously updating the improvement plan based on the updated data, and means for suggesting products suitable for the user's health status based on the prediction results. This allows the user to obtain a specific and sustainable health improvement plan and further receive product suggestions tailored to their current health status.

[0632] "User" refers to an individual who uses this system.

[0633] "Diet" refers to the foods that a user regularly consumes and the contents of those foods.

[0634] "Exercise habits" refers to the physical activities a user regularly engages in, as well as their frequency and duration.

[0635] "Data" refers to information about a user's diet and exercise habits.

[0636] "Terminal" refers to a device such as a smartphone, PC, or tablet through which a user enters data.

[0637] "Server" refers to a computer system that receives, stores, and analyzes data sent from a terminal.

[0638] A "generative AI model" refers to an artificial intelligence model that predicts future health conditions based on input data and past success stories.

[0639] "Analysis" refers to assessing the current state of your diet and exercise habits based on your data.

[0640] "Prediction" refers to the use of a generative AI model to indicate a user's future health status.

[0641] "Improvement Plan" refers to specific action items provided to the user based on the analysis and prediction results.

[0642] "Notification" refers to informing the user of the generated improvement plan.

[0643] "Product suggestion" refers to presenting products suitable for the user's health condition.

[0644] "Continuous updates" means constantly providing optimal improvement plans based on new data.

[0645] The system for implementing this invention mainly uses a terminal, a server, and a generative AI model to support users' health management. This system allows users to input data about their diet and exercise habits, analyzes their health status based on that data, and provides specific improvement plans. It also has a function to suggest products suitable for the user's health status.

[0646] Hardware and software used

[0647] Hardware

[0648] Device: Use a device such as a smartphone, computer, or tablet.

[0649] Server: A computer system that stores and analyzes data.

[0650] software

[0651] Python 3: Used for program development and data analysis.

[0652] requests library: A library for sending HTTP requests.

[0653] Data processing and calculation

[0654] 1. Data collection and transmission

[0655] The user uses the device to input data about their diet and exercise habits. For example, they input the type of food they eat, how often they exercise, and how long they exercise. This data is sent from the device to the server. For example, suppose a 35-year-old woman has bread and coffee for breakfast and jogs three times a week. This data is input in the format "35-year-old woman, has bread and coffee for breakfast, jogs three times a week."

[0656] 2. Data analysis and prediction

[0657] The server receives and stores data sent from the device. The stored data includes detailed information about the user's diet and exercise habits. The server analyzes this data and uses a generative AI model to predict the user's future health condition. For example, the generative AI model may predict that "in three years, weight will increase by 10 kg, and the risk of high blood pressure will increase."

[0658] 3. Generate and notify improvement plans

[0659] Based on the analysis and prediction results, the server creates a personalized improvement plan for the user, including specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is then sent back to the device and displayed to the user.

[0660] 4. Product proposal

[0661] Furthermore, the server will suggest products suitable for the user's health condition based on the prediction results. For example, if the user's health risk is high blood pressure, it will suggest products such as "low-salt food sets" or "diet supplements."

[0662] 5. Implementing the plan and providing feedback

[0663] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0664] Specific examples

[0665] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to the server. The server analyzes this data, and the generative AI model predicts that "in three years, weight will increase by 10 kg and the risk of high blood pressure will increase." Based on this prediction, an improvement plan is generated that includes "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and is notified to the user. The user is also suggested products such as "low-salt food sets" and "diet supplements." By following this and continuing to input their progress into the device, the user can achieve sustainable health management.

[0666] Prompt Sentence Examples

[0667] "I'm a 35-year-old woman who has bread and coffee for breakfast and jogs three times a week. I'd like to receive an improvement plan and product suggestions."

[0668] In this way, users can obtain specific and actionable health management measures through the system and achieve sustained improvements in their health.

[0669] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0670] Step 1:

[0671] Users use devices (smartphones, PCs, tablets, etc.) to input data about their diet and exercise habits. This specifically includes daily meal content, exercise frequency, exercise duration, etc. For example, if a user eats bread and coffee for breakfast and jogs three times a week, they enter that information.

[0672] Input: Information about your diet and exercise habits.

[0673] Output: Data is saved to the device.

[0674] Step 2:

[0675] The terminal sends the entered data to the server. The data is sent to the server using an HTTP request. The server receives it and stores it in a database. For example, the data is sent in JSON format.

[0676] Input: Data entered at the terminal.

[0677] Output: Data stored in the server's database.

[0678] Step 3:

[0679] The server analyzes the user's current dietary and exercise habits based on the stored data. Specifically, it analyzes the data and evaluates calorie intake, exercise volume, etc. This analysis can clarify the user's current health status.

[0680] Input: Data stored on the server.

[0681] Output: Analysis results (e.g., assessment of calorie intake and physical activity).

[0682] Step 4:

[0683] The server uses a generative AI model based on the analysis results to predict the user's future health condition. The generative AI model uses past data and success stories to predict weight gain and high blood pressure risk several years from now. For example, it generates a prediction that "weight will increase by 10 kg in three years and the risk of high blood pressure will increase."

[0684] Input: Analysis results.

[0685] Output: Prediction results.

[0686] Step 5:

[0687] The server displays the prediction results and notifies the user via their device, allowing the user to check the results and gain a deeper understanding of their own health condition.

[0688] Input: Prediction results.

[0689] Output: Notification of prediction results to the user.

[0690] Step 6:

[0691] The server extracts past success stories and creates a personalized improvement plan based on a generative AI model, generating specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[0692] Input: Predicted results and success story data.

[0693] Output: Improvement plan.

[0694] Step 7:

[0695] The server notifies the user of the created improvement plan, which the user can then check on their device and put into action.

[0696] Input: Improvement Plan.

[0697] Output: Notification of improvement plan to user.

[0698] Step 8:

[0699] The user follows the improvement plan and incorporates it into their daily life. They input their progress and new dietary and exercise habits into the device, and this data is then sent back to the server.

[0700] Input: User progress data.

[0701] Output: Progress data sent to the server.

[0702] Step 9:

[0703] The server analyzes the new data and updates the stored data to reflect the user's most recent health status, and updates and notifies the user of an improvement plan if necessary.

[0704] Input: New progress data.

[0705] Output: Updated improvement plan and data.

[0706] Step 10:

[0707] Based on the prediction results, the server will suggest products suitable for the user's health condition, such as a "low-salt food set" or "diet supplements." This will also be notified to the user via their device.

[0708] Input: Prediction results and data.

[0709] Output: Product suggestion notification to the user.

[0710] Through these steps, the system can effectively manage and continuously improve the user's health status.

[0711] 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.

[0712] The system of the present invention provides a specific action plan for improving the user's health and supports its implementation. Furthermore, the system recognizes the user's emotional state and adjusts the improvement plan based on the analysis results, thereby achieving more effective health management. The system mainly consists of the following components: a terminal, a server, a generative AI model, and an emotion engine. These elements work together to enable users to effectively manage their own health.

[0713] Data collection

[0714] First, the user uses a device to input data about their diet and exercise habits. For example, they record their daily diet, exercise frequency, and exercise time on the device. This data is then sent from the device to a server. The device can be a smartphone, PC, tablet, or other device.

[0715] Data analysis

[0716] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and cardiovascular disease based on the acquired data and past success stories.

[0717] Generate an improvement plan

[0718] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[0719] Sentiment analysis with emotion engine

[0720] Furthermore, this system incorporates an emotion engine that recognizes the user's emotional state. When the user uses the device, data such as voice, facial expressions, and text messages is collected and sent to the server. The server analyzes this data and evaluates the user's emotional state. For example, it can detect when the user is feeling stressed or has lost motivation.

[0721] Emotionally-driven plan adjustments

[0722] The server adjusts the improvement plan based on the user's emotional state. For example, if the user is feeling stressed, it adds actions to help them relax (e.g., light exercise or meditation). If their motivation is low, it sets incentives to encourage them to take action.

[0723] Plan execution and feedback

[0724] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0725] Specific examples

[0726] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[0727] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into the device. This reduces the user's health risks.

[0728] Furthermore, if the emotion engine detects that the user is feeling stressed, the server will add "yoga for relaxation" to the improvement plan. Also, if motivation is declining, incentives such as "rewarding yourself for achieving your goal" will be set to encourage the user to take action.

[0729] In this way, the system of the present invention effectively manages the user's health condition and provides an individual improvement plan according to the user's emotional state, thereby supporting continuous health improvement.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] A user accesses a terminal and inputs data about their diet and exercise habits.

[0733] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[0734] Step 2:

[0735] The terminal transmits the input data to the server.

[0736] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[0737] Step 3:

[0738] The server analyzes the received data and stores it in a database.

[0739] The server checks the integrity of the data and stores it in a database for each user.

[0740] Step 4:

[0741] The server analyzes the user's current eating and exercise habits based on the stored data.

[0742] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[0743] Step 5:

[0744] The server uses the generated AI model to predict the user's health status several years in the future.

[0745] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[0746] Step 6:

[0747] The server generates prediction results and sends them to the device.

[0748] A notification that visually gives the user a sense of crisis is displayed on the device.

[0749] Step 7:

[0750] The server extracts past success stories and feeds them into a generative AI model.

[0751] Success stories include lifestyle data of users who have successfully improved their health.

[0752] Step 8:

[0753] The server creates a user-specific improvement plan.

[0754] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[0755] Step 9:

[0756] The server sends the created improvement plan to the terminal.

[0757] On the device, the user is notified of the improvement plan and action items are listed.

[0758] Step 10:

[0759] The user begins to change their daily life based on the improvement plan displayed on the device.

[0760] The user periodically inputs the status of improvements and new data into the terminal.

[0761] Step 11:

[0762] The device sends new data to the server.

[0763] The new data will include progress on improvement plans and changes to diet and exercise habits.

[0764] Step 12:

[0765] The server receives the new data and updates the database.

[0766] The database is updated based on new data.

[0767] Step 13:

[0768] The server continuously updates the improvement plan based on new data.

[0769] The generative AI model reevaluates and regenerates the optimal improvement plan.

[0770] Step 14:

[0771] The server transmits the updated improvement plan to the terminal again and notifies the user.

[0772] The new improvement plan will be displayed on the device and the user will begin their next action.

[0773] Step 15:

[0774] Emotional data such as voice, facial expressions, and text messages are collected while the user is using the device.

[0775] This data is transmitted from the terminal to the server.

[0776] Step 16:

[0777] The server analyzes the received emotion data and evaluates the user's emotional state.

[0778] For example, states such as feeling stressed or having low motivation are detected.

[0779] Step 17:

[0780] The server adjusts the remediation plan based on the user's emotional state.

[0781] For example, add yoga to help you relax or set incentives to motivate you.

[0782] Step 18:

[0783] The server transmits the adjusted improvement plan to the terminal and notifies the user.

[0784] The new plan is displayed to the user on the device and they are prompted to execute it.

[0785] In this way, the system also takes into account the user's emotional state and supports sustainable and effective health management.

[0786] Example 2

[0787] 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."

[0788] In today's busy lifestyles, it is not easy for individuals to effectively manage their own health. In particular, the process of centrally managing data on dietary and exercise habits and creating and implementing appropriate action plans is complex and requires specialized knowledge. In addition, because a user's emotional state significantly affects the continuity and effectiveness of health management, flexible plan adjustments based on emotional fluctuations are required.

[0789] 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. In this invention, the server includes a means for inputting data on the user's dietary and exercise habits, a means for transmitting the input data, and a means for receiving and storing the transmitted data. This enables the user to efficiently manage their own health condition.

[0790] The server also includes a means for analyzing the user's current eating and exercise habits based on the stored data, a means for predicting the user's future health condition based on the analysis results using a generative AI model, a means for displaying the prediction results, a means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, and a means for notifying the user of the created improvement plan. This allows the user to receive a specific and actionable improvement plan, and encourages them to take action to improve their health.

[0791] The server further includes means for collecting and transmitting data on the user's emotional state, means for analyzing and evaluating the emotional state, means for adjusting the improvement plan based on the emotional state, means for acquiring new data on the progress of the improvement plan and updating the stored data, and means for continuously updating the improvement plan based on the updated data. This enables flexible plan adjustments that adapt to the user's emotional state, enabling continuous health management.

[0792] A "user" is an entity that uses the system to manage their own health condition.

[0793] "Diet" refers to the user's daily diet and eating habits.

[0794] "Exercise habits" refers to a user's daily exercise and fitness activity patterns.

[0795] "Data" refers to health-related information entered into the system by a user and processed by the system.

[0796] "Terminal" refers to an electronic device that allows a user to input data or receive notifications from the system. Examples include smartphones, personal computers, and tablets.

[0797] "Server" refers to a computer system that serves as the center of the system and receives, stores, analyzes, and processes data.

[0798] "Transmission" refers to the act of sending data from a terminal to a server via a network.

[0799] "Storage" refers to the act of storing the data received by the server in a database or the like.

[0800] "Analysis" refers to the process by which the server evaluates information and derives results based on the data it obtains.

[0801] A "generative AI model" refers to a program that uses machine learning algorithms to predict a user's future health status.

[0802] "Prediction" refers to the act of calculating and estimating future health states based on a generative AI model.

[0803] "Display" refers to the act of sending the results from the server to the terminal and showing the results on the terminal in a form that can be visually recognized by the user.

[0804] "Past success stories" refers to patterns and data of successful health improvements that other users have made in the past.

[0805] "Improvement plan" refers to specific action items for improving the user's health condition, created by the server based on the generated AI model and analysis results.

[0806] "Notification" refers to the act of sending information from a server to a terminal and informing the user.

[0807] "Emotional state" refers to the user's mental and emotional state, such as stress, motivation, etc.

[0808] "Collect" refers to the act of the device obtaining data about the user's emotional state.

[0809] "Evaluation" refers to the act of analyzing the emotion data collected by the server and determining the user's current emotional state.

[0810] "Adjustment" refers to the act of appropriately changing the content of the improvement plan based on the results of the emotional state.

[0811] The system of the present invention provides a user with an action plan to improve their health and supports their implementation. The system components include a terminal, a server, a generative AI model, and an emotion engine.

[0812] Operating procedures and data collection

[0813] Users use devices such as smartphones, PCs, and tablets to input data about their diet and exercise habits. For example, they may input that they ate bread and coffee for breakfast or that they jog three times a week. This data is sent from the device to the server. The device sends the data using a secure communication protocol (e.g., HTTPS).

[0814] Data Analysis and Prediction

[0815] The server stores the received data and begins analyzing it. The data is saved in a database (e.g., MySQL or PostgreSQL). Based on the saved data, the server evaluates the user's current diet and exercise habits. It then uses a generative AI model to predict the user's health status several years into the future. This model predicts risks (e.g., weight gain, high blood pressure, cardiovascular disease).

[0816] Generate an improvement plan

[0817] Based on the prediction results, the server generates a user-specific improvement plan. This plan includes specific action items that can be implemented and sustained, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is notified to the device and displayed to the user.

[0818] Emotion data collection and analysis

[0819] When a user uses the device, it collects data such as voice, facial expressions, and text messages. This data is sent to a server, where an emotion engine analyzes it to assess the user's emotional state, such as whether they are stressed or lacking motivation.

[0820] Adjusting the plan

[0821] The server adjusts the improvement plan based on the user's emotional state: if the user is feeling stressed, it adds relaxation actions (e.g., yoga or meditation), and if motivation is low, it sets incentives (e.g., rewards for achieving goals).

[0822] Feedback and progress management

[0823] Users follow the improvement plan displayed on the device and incorporate it into their daily lives. Progress and new data are periodically entered into the device and sent to the server. The server analyzes the new data and updates the improvement plan, supporting continuous health management.

[0824] Examples and prompts

[0825] For example, suppose a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week. The user inputs this data and sends it to the server. The generative AI model predicts that she will gain 10 kg in weight in three years and her risk of high blood pressure will increase. Based on the results, an improvement plan is generated, including "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and notified to the user.

[0826] Example prompt sentence:

[0827] "Please enter your current diet and exercise habits (e.g., bread and coffee for breakfast, jogging three times a week)."

[0828] "Please describe your recent emotional state (e.g., feeling stressed or unmotivated)."

[0829] This allows users to effectively manage their health status and receive personalized improvement plans based on their emotional state, enabling sustainable health management.

[0830] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0831] Step 1: Data collection

[0832] The user enters data about their diet and exercise habits into the device, such as what they ate for breakfast, how often they exercise, and how long they exercise. The device then encodes the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0833] Input: Data on diet and exercise habits

[0834] Output: The encoded data is sent to the server

[0835] Step 2: Save data

[0836] The server stores the data received from the terminal in a database, such as a relational database like MySQL or PostgreSQL.

[0837] Input: Encoded data

[0838] Output: Data stored in the database

[0839] Step 3: Initial analysis

[0840] The server begins an initial analysis of the stored data. The analytics engine pulls the data from the database and calculates basic statistics such as calorie intake and total exercise time.

[0841] Input: User data stored in the database

[0842] Output: Basic statistics (calorie intake, exercise time, etc.)

[0843] Step 4: Predict your health status

[0844] The server uses a generative AI model to predict the user's future health status. The generative AI model uses the results of the initial analysis as input data to predict risks such as weight gain, high blood pressure, and cardiovascular disease.

[0845] Input: Results of initial analysis

[0846] Output: Risk prediction results (likelihood of weight gain, high blood pressure, cardiovascular disease, etc.)

[0847] Step 5: Generate an improvement plan

[0848] The server generates a user-specific improvement plan based on the prediction results. The plan generation engine references past success stories and creates action items that the user can implement and sustain. For example, "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[0849] Input: Risk prediction results, past success stories

[0850] Output: Improvement plan (specific action items)

[0851] Step 6: Communicate your improvement plan

[0852] The server transmits the generated improvement plan to the terminal, which displays the received improvement plan on a user interface and notifies the user.

[0853] Input: Improvement Plan

[0854] Output: Improvement plan displayed to the user

[0855] Step 7: Collect emotion data

[0856] When a user uses a device, the device collects emotional data (voice, facial expressions, text messages, etc.). For example, it uses a camera or microphone to capture the user's facial expressions and voice, and obtains new text messages. The collected data is sent to a server.

[0857] Input: Emotional data such as voice, facial expressions, and text messages

[0858] Output: The encoded emotion data is sent to the server.

[0859] Step 8: Analyze the sentiment data

[0860] The emotion engine on the server analyzes the transmitted emotion data and evaluates the user's emotional state, for example, determining whether the user is feeling stressed or unmotivated.

[0861] Input: Encoded emotion data

[0862] Output: User's emotional state (stress, motivation, etc.)

[0863] Step 9: Adjust your improvement plan based on your emotions

[0864] The server adjusts the user's specific improvement plan based on their emotional state. For example, if the user is feeling stressed, yoga for relaxation is added to the improvement plan. If the user's motivation is low, incentives such as rewards for achieving goals are set.

[0865] Input: User's emotional state

[0866] Output: Adjusted improvement plan

[0867] Step 10: Implementation and Feedback

[0868] The user acts according to the improvement plan displayed on the device and inputs their progress into the device. The device encodes the input progress data and sends it to the server. The server analyzes the new progress data and updates the improvement plan as necessary. This allows the user's health to continuously improve.

[0869] Input: Progress data

[0870] Output: Updated improvement plan and new health analysis results

[0871] (Application example 2)

[0872] 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."

[0873] Conventional health management systems only provide simple improvement plans based on the user's diet and exercise habits, and do not adjust the plan to take into account the user's emotional state, making it difficult to effectively manage health. Furthermore, there is no way to provide health advice in physical stores, making it difficult for users to make appropriate choices on the spot. Therefore, there is a need for an effective health management system that takes into account the user's emotional state and can be used in physical stores.

[0874] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting data on the user's dietary and exercise habits; means for transmitting the input data; means for receiving and storing the transmitted data; means for analyzing the user's current dietary and exercise habits based on the stored data; means for predicting the user's future health status based on the analysis results using a generative AI model; means for displaying the prediction results; means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model; means for notifying the user of the created improvement plan; means for acquiring new data on the progress of the improvement plan and updating the stored data; means for continuously updating the improvement plan based on the updated data; means for recognizing the user's emotional state and adjusting the improvement plan based on the data; means for providing health advice for products the user is considering purchasing in a store; and means for checking the user's emotional state in real time and presenting appropriate advice. This enables effective health management that takes the user's emotional state into consideration and appropriate health advice in a physical store.

[0875] A "user" is an individual who uses the system to manage their own health and emotional state.

[0876] "Diet" refers to the types and amounts of food and drink that an individual consumes on a daily basis, as well as the times when they eat meals.

[0877] "Exercise habits" refers to the type, frequency, duration, and intensity of exercise that an individual engages in on a daily basis.

[0878] "Data" refers to information entered by users relating to their diet, exercise habits, and emotional state.

[0879] An "input means" is a device or interface that allows a user to provide data to a system.

[0880] "Transmitting means" refers to a device or interface with communication capabilities for transferring input data to a server.

[0881] "Means for receiving and storing" refers to a device or system that has the function of receiving transmitted data and storing it on a server.

[0882] "Means for analysis" refers to algorithms or programs that use the stored data to evaluate a user's diet and exercise habits.

[0883] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes new predictions and generates new information.

[0884] "Prediction means" refers to a program or function that uses a generative AI model based on the analysis results to predict the user's future health condition.

[0885] "Means for displaying" refers to a display or notification system that visualizes the prediction results and improvement plans to the user.

[0886] "Means of extraction" refers to algorithms or programs that select relevant information from past success stories.

[0887] "Means for creation" refers to programs and functions for designing user-specific improvement plans based on generative AI models.

[0888] The "notification means" refers to a function or device for notifying the user of the created improvement plan.

[0889] "New data on progress" refers to information about the results and status of activities that a user has carried out based on an improvement plan.

[0890] "Means for updating" refers to programs or functions that update stored data and improvement plans based on new data acquired.

[0891] An "emotional state" refers to a user's mental state, examples of which include stress, joy, sadness, etc.

[0892] "Adjusting means" refers to algorithms or programs that adapt an existing improvement plan based on the user's emotional state.

[0893] A "store" is a physical business establishment where users can visit and purchase products.

[0894] "Health advice" refers to instructions or advice that encourage specific actions or choices to maintain or improve a user's health.

[0895] "Means for checking in real time" refers to functions or devices that allow users to instantly check their emotional state on the spot.

[0896] "Presentation means" refers to a display or notification system that provides appropriate advice and information to the user.

[0897] The system of the present invention provides a personalized improvement plan to improve the user's health, continuously adjusts it taking into account the user's emotional state, and also has the ability to provide effective health advice in physical stores.

[0898] Components

[0899] The system consists of the following components:

[0900] 1. Device:

[0901] This includes smartphones, tablets, and laptops.

[0902] It allows users to enter data about their diet, exercise habits, and emotional state.

[0903] 2. Server:

[0904] Cloud-based or on-premise servers are used.

[0905] The transmitted data is received and stored, and then analyzed and predictions are made.

[0906] 3. Generative AI Model:

[0907] It includes deep learning models and machine learning algorithms that predict future health conditions based on user data and past success stories.

[0908] 4. Emotion Engine:

[0909] The user's emotional state is analyzed using technologies such as voice recognition and facial expression recognition.

[0910] Hardware and Software

[0911] Hardware: Smartphone, tablet, laptop (with built-in camera and microphone)

[0912] Software: Python, OpenCV, Keras, SpeechRecognition library, cloud-based server

[0913] Data processing:

[0914] Camera-based face recognition: Face recognition is performed using OpenCV, and image data is provided to the emotion recognition model.

[0915] Speech Recognition: Use the SpeechRecognition library to capture voice data from the microphone and detect the user's emotional state and needs.

[0916] Processing flow

[0917] 1. Data Collection:

[0918] Users use the device to input data about their diet, exercise habits, and emotional state.

[0919] This data is transmitted from the terminal to the server.

[0920] 2. Data Analysis:

[0921] The server stores the received data and begins analyzing it.

[0922] It assesses the details of your diet and exercise habits and uses generative AI models to predict your future health status.

[0923] 3. Generate an improvement plan:

[0924] Based on the prediction results, the server creates a user-specific improvement plan.

[0925] The emotion engine assesses the user's emotional state and adjusts the remediation plan as needed, for example adding relaxation activities if the user is feeling stressed.

[0926] 4. In-store advice:

[0927] In stores, health advice is provided to users using their smartphones or tablets for products they are considering purchasing.

[0928] It uses facial and voice recognition to check the user's emotional state in real time and provide appropriate advice.

[0929] Specific examples

[0930] For example, when a user is about to buy food at a physical store, the system uses the user's facial image to detect stress. Based on the user's dietary data and emotional state, the system provides health advice such as "Purchase fruit or yogurt, which are effective in relieving stress." It can also use voice recognition to provide specific advice in response to the user's questions.

[0931] Prompt Sentence Examples

[0932] "Recognize the user's emotional state from facial images or voice input. Use the results to generate health advice appropriate to that emotional state."

[0933] In this way, the system comprehensively manages the user's health status and supports the user in improving their health by providing individual improvement plans and real-time health advice.

[0934] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0935] Step 1:

[0936] Users use devices (such as smartphones or tablets) to input data about their diet, exercise habits, and emotional state. This includes tapping, voice input, and facial recognition data using a camera. Input data includes dietary content, exercise frequency and duration, and emotional state (such as stress levels).

[0937] Step 2:

[0938] The terminal transmits the input data to the server. At this time, an application on the terminal converts the data into text or binary format and transmits the data using a secure communication protocol (e.g., HTTPS).

[0939] Step 3:

[0940] The server receives the data sent from the device and then stores it in the appropriate database. The data stored in the database is organized by user and maintains its association with past data.

[0941] Step 4:

[0942] The server analyzes the user's current dietary and exercise habits based on the stored data. Based on the dietary and exercise information retrieved from the database, the server evaluates the calorie intake and exercise intensity. The evaluation is performed using queries and algorithms.

[0943] Step 5:

[0944] The server uses the analysis results to predict the user's future health condition using a generative AI model. User data and past success stories are input into the prediction, and the generative AI model outputs risk factors (weight gain, high blood pressure, etc.). For example, specific prediction results such as "risk of weight gain of 10 kg in three years" can be obtained.

[0945] Step 6:

[0946] The server prepares data for displaying the prediction results on the user's terminal. For example, the server generates data including the prediction results visualized in text or graph format and sends it to the terminal. This data is displayed on the terminal's display.

[0947] Step 7:

[0948] The server uses a generative AI model based on past success stories to create a personalized improvement plan for each user. This plan includes specific, actionable, and sustainable actions (e.g., "walk 30 minutes five times a week"). The improvement plan is optimized based on the user's analysis results and emotional state.

[0949] Step 8:

[0950] The server notifies the user of the created improvement plan. The notification is sent to the device and notifies the user via push notification or email. The improvement plan is displayed on the device, and the user can take action based on the plan.

[0951] Step 9:

[0952] The user then inputs new data into the device about their progress with the improvement plan, including their daily diet, exercise, and changes in their emotional state. As the user inputs new data, the data is continually updated.

[0953] Step 10:

[0954] The device then sends new data to the server, which then updates the stored data. The server then analyzes the updated data and continuously updates the improvement plan, ensuring that the device continues to provide a plan that is optimal for the user's current condition.

[0955] Step 11:

[0956] The server recognizes the user's emotional state and adjusts the improvement plan based on that data. For example, if it detects stress, it adds relaxation activities to the action plan. If the user's motivation is low, it sets incentives.

[0957] Step 12:

[0958] When a user visits a physical store, the device provides health advice for products they are considering purchasing. The device's camera is used to recognize the user's face, check their emotional state in real time, and display appropriate health advice. For example, a user who is feeling stressed might be offered advice such as, "Purchase fruits that have a relaxing effect."

[0959] 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.

[0960] 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.

[0961] 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.

[0962] [Third embodiment]

[0963] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0964] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0965] 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).

[0966] 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.

[0967] 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.

[0968] 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).

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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."

[0975] The system of the present invention provides a specific action plan to improve the user's health and supports its implementation. The system mainly consists of three components: a terminal, a server, and a generative AI model. These components work together to enable the user to effectively manage their own health.

[0976] Data collection

[0977] First, the user uses a device to input data about their diet and exercise habits. For example, the device records daily meal contents, exercise frequency, and exercise time. This data is then sent from the device to a server. The device can be a device that the user uses daily, such as a smartphone, PC, or tablet.

[0978] Data analysis

[0979] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and heart disease based on the acquired data and past success stories.

[0980] Generate an improvement plan

[0981] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[0982] Plan execution and feedback

[0983] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[0984] Specific examples

[0985] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[0986] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into their device. This reduces the user's health risks and enables sustainable health management.

[0987] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[0988] The processing flow will be explained below.

[0989] Step 1:

[0990] A user accesses a terminal and inputs data about their diet and exercise habits.

[0991] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[0992] Step 2:

[0993] The terminal transmits the input data to the server.

[0994] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[0995] Step 3:

[0996] The server analyzes the received data and stores it in a database.

[0997] The server checks the integrity of the data and stores it in a database for each user.

[0998] Step 4:

[0999] The server analyzes the user's current eating and exercise habits based on the stored data.

[1000] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[1001] Step 5:

[1002] The server uses the generated AI model to predict the user's health status several years in the future.

[1003] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[1004] Step 6:

[1005] The server generates prediction results and sends them to the device.

[1006] A notification that visually gives the user a sense of crisis is displayed on the device.

[1007] Step 7:

[1008] The server extracts past success stories and feeds them into a generative AI model.

[1009] Success stories include lifestyle data of users who have successfully improved their health.

[1010] Step 8:

[1011] The server creates a user-specific improvement plan.

[1012] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[1013] Step 9:

[1014] The server sends the created improvement plan to the terminal.

[1015] On the device, the user is notified of the improvement plan and action items are listed.

[1016] Step 10:

[1017] The user begins to change their daily life based on the improvement plan displayed on the device.

[1018] The user periodically inputs the status of improvements and new data into the terminal.

[1019] Step 11:

[1020] The device sends new data to the server.

[1021] The new data will include progress on improvement plans and changes to diet and exercise habits.

[1022] Step 12:

[1023] The server receives the new data and updates the database.

[1024] The database is updated based on new data.

[1025] Step 13:

[1026] The server continuously updates the improvement plan based on new data.

[1027] The AI ​​model will reassess and regenerate the optimal improvement plan.

[1028] Step 14:

[1029] The server transmits the updated improvement plan to the terminal again and notifies the user.

[1030] The new improvement plan will be displayed on the device and the user will begin their next action.

[1031] In this way, the system continuously monitors the user's health status and provides specific plans for sustained improvement.

[1032] Example 1

[1033] 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."

[1034] In today's modern lifestyle, users face the challenge of continually improving and maintaining their health. In particular, there is a lack of systems that collect detailed data on dietary and exercise habits and provide specific, sustainable improvement plans based on that data. There is also a need for systems that can predict a user's future health status and dynamically optimize improvement plans based on that data.

[1035] 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.

[1036] In this invention, the server includes a means for inputting data relating to the user's health condition, a means for transmitting the input data, and a means for receiving and storing the transmitted data, thereby enabling the server to specifically quantify the user's unique health condition and provide a dynamically optimized improvement plan.

[1037] "User" refers to an individual who utilizes the system to manage and improve their health.

[1038] "Health status" refers to various health-related data such as a user's diet, exercise habits, weight, and blood pressure.

[1039] "Means for inputting data" refers to devices or interfaces that allow users to record information about their health status, such as smartphones, computers, and tablets.

[1040] "Means for transmitting data" refers to a function for transmitting input data to a cloud server via the Internet. Specifically, this includes a network communication module.

[1041] "Means for receiving and storing data" refers to the function of storing transmitted data on a server. Specifically, this includes database servers and storage systems.

[1042] "Diet" refers to the type of food a user eats on a daily basis and how often they eat it.

[1043] "Exercise habits" refers to the type, frequency, duration, etc. of exercise that a user does on a daily basis.

[1044] "Means for analyzing data" refers to the function of evaluating a user's dietary and exercise habits and analyzing their health status based on the stored data, including analytical algorithms and AI models.

[1045] "Generative AI model" refers to an artificial intelligence model that predicts a user's future health condition and suggests an appropriate improvement plan based on acquired data and past success stories.

[1046] "Means for displaying prediction results" refers to a function for visually presenting the prediction results produced by the generative AI model to the user. Specifically, this includes a display or smartphone screen.

[1047] An "improvement plan" refers to a plan that includes specific action items that a user can implement and commit to sustainably.

[1048] "Means of notification" refers to the functionality for informing users of the generated improvement plan and prediction results, including push notifications and in-app messages.

[1049] "Progress" refers to the progress of the actions taken by the user and the results obtained according to the improvement plan.

[1050] "Dynamic optimization" means that the generative AI model continuously adjusts the improvement plan based on newly acquired data to provide the optimal health management plan.

[1051] MODE FOR CARRYING OUT THE INVENTION

[1052] This invention relates to a system that provides a user with a specific plan to improve their health and supports their implementation. This system is mainly composed of a terminal, a server, and a generative AI model, and each element works together to support the user's health management.

[1053] Data collection

[1054] First, users enter data about their diet and exercise habits using a terminal, including their daily diet, exercise frequency, and exercise duration. The terminal can be a common digital device such as a smartphone, PC, or tablet.

[1055] Data transmission

[1056] The device sends the entered data to the cloud server. The sent data is encrypted and sent over a securely protected communication channel. HTTPS or TLS is the most commonly used communication protocol.

[1057] Data storage

[1058] The server stores the received data in a database. This database uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) and is capable of efficiently managing large amounts of data.

[1059] Data analysis

[1060] The server analyzes the stored data, which includes extracting patterns in the user's diet and exercise habits, using Python libraries such as scikit-learn and TensorFlow.

[1061] Prediction result generation

[1062] The generative AI model uses the analysis results to predict the user's future health status, quantifying risks such as weight gain, high blood pressure, and heart disease based on past success stories and acquired data.

[1063] Improvement plan generation

[1064] The server generates a user-specific improvement plan based on the predictions made by the generative AI model, which includes specific and actionable action items, such as "add fruit to breakfast" or "walk 30 minutes five times a week."

[1065] Improvement plan notification

[1066] The generated improvement plan is sent from the server to the device via push notifications or in-app pop-up messages, allowing users to quickly incorporate the improvement plan into their daily lives.

[1067] Plan execution and feedback

[1068] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user enters their progress and new health data (e.g., new dietary habits, exercise habits) into the device. The entered data is then sent back to the server, which analyzes the new data and updates the improvement plan as needed.

[1069] Specific examples

[1070] For example, consider a 35-year-old female user who eats bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to a server. The server stores the data and analyzes it to determine that her diet is high in calories and she is not getting enough exercise. Based on these results, the generative AI model predicts that she will gain 10 kg in weight in three years, increasing her risk of high blood pressure.

[1071] The server generates a specific improvement plan, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the device. The user follows this plan to improve their daily life and enters their progress into the device. This reduces the user's health risks and enables sustainable health management.

[1072] Prompt Sentence Examples

[1073] "If a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week, what health risks would be predicted and what improvement plan would be suggested?"

[1074] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[1075] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1076] Step 1: Data collection

[1077] The user inputs data about their health condition into the device. Specifically, the user inputs information such as daily diet, exercise frequency, and exercise duration. This input data is used to proceed to the next processing step. The data entered by the user is saved in the form of a string or a number.

[1078] Step 2: Send data

[1079] The device sends the data entered by the user to the cloud server. At this time, the data is encrypted before being sent, ensuring security. The input data is the health condition data obtained in step 1, and the output data is the encrypted data sent to the server. The HTTPS protocol is used for transmission.

[1080] Step 3: Save data

[1081] The server stores the received data in a database. Specifically, it uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) to efficiently manage large amounts of data. The input data is the encrypted data sent in step 2, and the output data is the unencrypted data stored in the database.

[1082] Step 4: Data analysis

[1083] The server performs analysis based on the stored data. The purpose of this analysis is to extract patterns in the user's diet and exercise habits. The input data is unencrypted data obtained from the database, and the output data is a report of the analysis results. Specifically, data analysis is performed using libraries such as Python's scikit-learn and TensorFlow.

[1084] Step 5: Generate prediction results

[1085] The server inputs the analysis results into a generative AI model to predict the user's future health condition. This generative AI model quantifies health risks based on the acquired data and past success stories. The input data is the analysis results from step 4, and the output data is numerical data indicating future health risks.

[1086] Step 6: Generate an improvement plan

[1087] The server generates a user-specific improvement plan based on the prediction results of the generative AI model, including specific action items (e.g., adding fruit to breakfast, walking 30 minutes five times a week). The input data is the health risk data from Step 5, and the output data is a specific improvement plan.

[1088] Step 7: Notification of Improvement Plan

[1089] The server notifies the device of the generated improvement plan. Notification methods include push notifications and in-app pop-up messages. The input data is the improvement plan from step 6, and the output data is the improvement plan information notified to the user.

[1090] Step 8: Execute the plan and enter progress

[1091] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user also inputs the actions they have taken (e.g., the exercise and diet they actually did) into the device. The input data is the action data the user has taken, and the newly entered progress data is output to the device.

[1092] Step 9: Send progress data

[1093] The device sends the progress data entered by the user to the cloud server. The data is encrypted again before being sent. The input data is the progress data entered in step 8, and the encrypted progress data is sent to the server as output data.

[1094] Step 10: Update your improvement plan

[1095] The server analyzes the newly received progress data and updates the improvement plan as necessary, ensuring that the user always receives the most up-to-date health management plan. The input data is the progress data received in step 9, and the output data is an updated improvement plan. Specifically, the server analyzes the new progress data and incorporates new suggestions into the existing improvement plan.

[1096] (Application example 1)

[1097] 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."

[1098] In modern society, effectively managing and improving individual health is an important challenge. However, many existing systems are limited to collecting and analyzing data on users' dietary and exercise habits. As a result, few systems offer comprehensive health improvement plans or even suggest products based on the user's health status. Furthermore, they lack the functionality to predict future health status using generative AI models and continuously update improvement plans. This makes it difficult for users to engage in sustainable, personalized health management.

[1099] 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.

[1100] In this invention, the server includes means for inputting data on the user's dietary and exercise habits, means for transmitting the input data, means for receiving and storing the transmitted data, means for analyzing the user's current dietary and exercise habits based on the stored data, means for predicting the user's future health status based on the analysis results using a generative AI model, means for displaying the prediction results, means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, means for notifying the user of the created improvement plan, means for acquiring new data on the progress of the improvement plan and updating the stored data, means for continuously updating the improvement plan based on the updated data, and means for suggesting products suitable for the user's health status based on the prediction results. This allows the user to obtain a specific and sustainable health improvement plan and further receive product suggestions tailored to their current health status.

[1101] "User" refers to an individual who uses this system.

[1102] "Diet" refers to the foods that a user regularly consumes and the contents of those foods.

[1103] "Exercise habits" refers to the physical activities a user regularly engages in, as well as their frequency and duration.

[1104] "Data" refers to information about a user's diet and exercise habits.

[1105] "Terminal" refers to a device such as a smartphone, PC, or tablet through which a user enters data.

[1106] "Server" refers to a computer system that receives, stores, and analyzes data sent from a terminal.

[1107] A "generative AI model" refers to an artificial intelligence model that predicts future health conditions based on input data and past success stories.

[1108] "Analysis" refers to assessing the current state of your diet and exercise habits based on your data.

[1109] "Prediction" refers to the use of a generative AI model to indicate a user's future health status.

[1110] "Improvement Plan" refers to specific action items provided to the user based on the analysis and prediction results.

[1111] "Notification" refers to informing the user of the generated improvement plan.

[1112] "Product suggestion" refers to presenting products suitable for the user's health condition.

[1113] "Continuous updates" means constantly providing optimal improvement plans based on new data.

[1114] The system for implementing this invention mainly uses a terminal, a server, and a generative AI model to support users' health management. This system allows users to input data about their diet and exercise habits, analyzes their health status based on that data, and provides specific improvement plans. It also has a function to suggest products suitable for the user's health status.

[1115] Hardware and software used

[1116] Hardware

[1117] Device: Use a device such as a smartphone, computer, or tablet.

[1118] Server: A computer system that stores and analyzes data.

[1119] software

[1120] Python 3: Used for program development and data analysis.

[1121] requests library: A library for sending HTTP requests.

[1122] Data processing and calculation

[1123] 1. Data collection and transmission

[1124] The user uses the device to input data about their diet and exercise habits. For example, they input the type of food they eat, how often they exercise, and how long they exercise. This data is sent from the device to the server. For example, suppose a 35-year-old woman has bread and coffee for breakfast and jogs three times a week. This data is input in the format "35-year-old woman, has bread and coffee for breakfast, jogs three times a week."

[1125] 2. Data analysis and prediction

[1126] The server receives and stores data sent from the device. The stored data includes detailed information about the user's diet and exercise habits. The server analyzes this data and uses a generative AI model to predict the user's future health condition. For example, the generative AI model may predict that "in three years, weight will increase by 10 kg, and the risk of high blood pressure will increase."

[1127] 3. Generate and notify improvement plans

[1128] Based on the analysis and prediction results, the server creates a personalized improvement plan for the user, including specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is then sent back to the device and displayed to the user.

[1129] 4. Product proposal

[1130] Furthermore, the server will suggest products suitable for the user's health condition based on the prediction results. For example, if the user's health risk is high blood pressure, it will suggest products such as "low-salt food sets" or "diet supplements."

[1131] 5. Implementing the plan and providing feedback

[1132] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[1133] Specific examples

[1134] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to the server. The server analyzes this data, and the generative AI model predicts that "in three years, weight will increase by 10 kg and the risk of high blood pressure will increase." Based on this prediction, an improvement plan is generated that includes "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and is notified to the user. The user is also suggested products such as "low-salt food sets" and "diet supplements." By following this and continuing to input their progress into the device, the user can achieve sustainable health management.

[1135] Prompt Sentence Examples

[1136] "I'm a 35-year-old woman who has bread and coffee for breakfast and jogs three times a week. I'd like to receive an improvement plan and product suggestions."

[1137] In this way, users can obtain specific and actionable health management measures through the system and achieve sustained improvements in their health.

[1138] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1139] Step 1:

[1140] Users use devices (smartphones, PCs, tablets, etc.) to input data about their diet and exercise habits. This specifically includes daily meal content, exercise frequency, exercise duration, etc. For example, if a user eats bread and coffee for breakfast and jogs three times a week, they enter that information.

[1141] Input: Information about your diet and exercise habits.

[1142] Output: Data is saved to the device.

[1143] Step 2:

[1144] The terminal sends the entered data to the server. The data is sent to the server using an HTTP request. The server receives it and stores it in a database. For example, the data is sent in JSON format.

[1145] Input: Data entered at the terminal.

[1146] Output: Data stored in the server's database.

[1147] Step 3:

[1148] The server analyzes the user's current dietary and exercise habits based on the stored data. Specifically, it analyzes the data and evaluates calorie intake, exercise volume, etc. This analysis can clarify the user's current health status.

[1149] Input: Data stored on the server.

[1150] Output: Analysis results (e.g., assessment of calorie intake and physical activity).

[1151] Step 4:

[1152] The server uses a generative AI model based on the analysis results to predict the user's future health condition. The generative AI model uses past data and success stories to predict weight gain and high blood pressure risk several years from now. For example, it generates a prediction that "weight will increase by 10 kg in three years and the risk of high blood pressure will increase."

[1153] Input: Analysis results.

[1154] Output: Prediction results.

[1155] Step 5:

[1156] The server displays the prediction results and notifies the user via their device, allowing the user to check the results and gain a deeper understanding of their own health condition.

[1157] Input: Prediction results.

[1158] Output: Notification of prediction results to the user.

[1159] Step 6:

[1160] The server extracts past success stories and creates a personalized improvement plan based on a generative AI model, generating specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[1161] Input: Predicted results and success story data.

[1162] Output: Improvement plan.

[1163] Step 7:

[1164] The server notifies the user of the created improvement plan, which the user can then check on their device and put into action.

[1165] Input: Improvement Plan.

[1166] Output: Notification of improvement plan to user.

[1167] Step 8:

[1168] The user follows the improvement plan and incorporates it into their daily life. They input their progress and new dietary and exercise habits into the device, and this data is then sent back to the server.

[1169] Input: User progress data.

[1170] Output: Progress data sent to the server.

[1171] Step 9:

[1172] The server analyzes the new data and updates the stored data to reflect the user's most recent health status, and updates and notifies the user of an improvement plan if necessary.

[1173] Input: New progress data.

[1174] Output: Updated improvement plan and data.

[1175] Step 10:

[1176] Based on the prediction results, the server will suggest products suitable for the user's health condition, such as a "low-salt food set" or "diet supplements." This will also be notified to the user via their device.

[1177] Input: Prediction results and data.

[1178] Output: Product suggestion notification to the user.

[1179] Through these steps, the system can effectively manage and continuously improve the user's health status.

[1180] 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.

[1181] The system of the present invention provides a specific action plan for improving the user's health and supports its implementation. Furthermore, the system recognizes the user's emotional state and adjusts the improvement plan based on the analysis results, thereby achieving more effective health management. The system mainly consists of the following components: a terminal, a server, a generative AI model, and an emotion engine. These elements work together to enable users to effectively manage their own health.

[1182] Data collection

[1183] First, the user uses a device to input data about their diet and exercise habits. For example, they record their daily diet, exercise frequency, and exercise time on the device. This data is then sent from the device to a server. The device can be a smartphone, PC, tablet, or other device.

[1184] Data analysis

[1185] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and cardiovascular disease based on the acquired data and past success stories.

[1186] Generate an improvement plan

[1187] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[1188] Sentiment analysis with emotion engine

[1189] Furthermore, this system incorporates an emotion engine that recognizes the user's emotional state. When the user uses the device, data such as voice, facial expressions, and text messages is collected and sent to the server. The server analyzes this data and evaluates the user's emotional state. For example, it can detect when the user is feeling stressed or has lost motivation.

[1190] Emotionally-driven plan adjustments

[1191] The server adjusts the improvement plan based on the user's emotional state. For example, if the user is feeling stressed, it adds actions to help them relax (e.g., light exercise or meditation). If their motivation is low, it sets incentives to encourage them to take action.

[1192] Plan execution and feedback

[1193] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[1194] Specific examples

[1195] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[1196] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into the device. This reduces the user's health risks.

[1197] Furthermore, if the emotion engine detects that the user is feeling stressed, the server will add "yoga for relaxation" to the improvement plan. Also, if motivation is declining, incentives such as "rewarding yourself for achieving your goal" will be set to encourage the user to take action.

[1198] In this way, the system of the present invention effectively manages the user's health condition and provides an individual improvement plan according to the user's emotional state, thereby supporting continuous health improvement.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] A user accesses a terminal and inputs data about their diet and exercise habits.

[1202] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[1203] Step 2:

[1204] The terminal transmits the input data to the server.

[1205] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[1206] Step 3:

[1207] The server analyzes the received data and stores it in a database.

[1208] The server checks the integrity of the data and stores it in a database for each user.

[1209] Step 4:

[1210] The server analyzes the user's current eating and exercise habits based on the stored data.

[1211] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[1212] Step 5:

[1213] The server uses the generated AI model to predict the user's health status several years in the future.

[1214] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[1215] Step 6:

[1216] The server generates prediction results and sends them to the device.

[1217] A notification that visually gives the user a sense of crisis is displayed on the device.

[1218] Step 7:

[1219] The server extracts past success stories and feeds them into a generative AI model.

[1220] Success stories include lifestyle data of users who have successfully improved their health.

[1221] Step 8:

[1222] The server creates a user-specific improvement plan.

[1223] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[1224] Step 9:

[1225] The server sends the created improvement plan to the terminal.

[1226] On the device, the user is notified of the improvement plan and action items are listed.

[1227] Step 10:

[1228] The user begins to change their daily life based on the improvement plan displayed on the device.

[1229] The user periodically inputs the status of improvements and new data into the terminal.

[1230] Step 11:

[1231] The device sends new data to the server.

[1232] The new data will include progress on improvement plans and changes to diet and exercise habits.

[1233] Step 12:

[1234] The server receives the new data and updates the database.

[1235] The database is updated based on new data.

[1236] Step 13:

[1237] The server continuously updates the improvement plan based on new data.

[1238] The generative AI model reevaluates and regenerates the optimal improvement plan.

[1239] Step 14:

[1240] The server transmits the updated improvement plan to the terminal again and notifies the user.

[1241] The new improvement plan will be displayed on the device and the user will begin their next action.

[1242] Step 15:

[1243] Emotional data such as voice, facial expressions, and text messages are collected while the user is using the device.

[1244] This data is transmitted from the terminal to the server.

[1245] Step 16:

[1246] The server analyzes the received emotion data and evaluates the user's emotional state.

[1247] For example, states such as feeling stressed or having low motivation are detected.

[1248] Step 17:

[1249] The server adjusts the remediation plan based on the user's emotional state.

[1250] For example, add yoga to help you relax or set incentives to motivate you.

[1251] Step 18:

[1252] The server transmits the adjusted improvement plan to the terminal and notifies the user.

[1253] The new plan is displayed to the user on the device and they are prompted to execute it.

[1254] In this way, the system also takes into account the user's emotional state and supports sustainable and effective health management.

[1255] Example 2

[1256] 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."

[1257] In today's busy lifestyles, it is not easy for individuals to effectively manage their own health. In particular, the process of centrally managing data on dietary and exercise habits and creating and implementing appropriate action plans is complex and requires specialized knowledge. In addition, because a user's emotional state significantly affects the continuity and effectiveness of health management, flexible plan adjustments based on emotional fluctuations are required.

[1258] 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. In this invention, the server includes a means for inputting data on the user's dietary and exercise habits, a means for transmitting the input data, and a means for receiving and storing the transmitted data. This enables the user to efficiently manage their own health condition.

[1259] The server also includes a means for analyzing the user's current eating and exercise habits based on the stored data, a means for predicting the user's future health condition based on the analysis results using a generative AI model, a means for displaying the prediction results, a means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, and a means for notifying the user of the created improvement plan. This allows the user to receive a specific and actionable improvement plan, and encourages them to take action to improve their health.

[1260] The server further includes means for collecting and transmitting data on the user's emotional state, means for analyzing and evaluating the emotional state, means for adjusting the improvement plan based on the emotional state, means for acquiring new data on the progress of the improvement plan and updating the stored data, and means for continuously updating the improvement plan based on the updated data. This enables flexible plan adjustments that adapt to the user's emotional state, enabling continuous health management.

[1261] A "user" is an entity that uses the system to manage their own health condition.

[1262] "Diet" refers to the user's daily diet and eating habits.

[1263] "Exercise habits" refers to a user's daily exercise and fitness activity patterns.

[1264] "Data" refers to health-related information entered into the system by a user and processed by the system.

[1265] "Terminal" refers to an electronic device that allows a user to input data or receive notifications from the system. Examples include smartphones, personal computers, and tablets.

[1266] "Server" refers to a computer system that serves as the center of the system and receives, stores, analyzes, and processes data.

[1267] "Transmission" refers to the act of sending data from a terminal to a server via a network.

[1268] "Storage" refers to the act of storing the data received by the server in a database or the like.

[1269] "Analysis" refers to the process by which the server evaluates information and derives results based on the data it obtains.

[1270] A "generative AI model" refers to a program that uses machine learning algorithms to predict a user's future health status.

[1271] "Prediction" refers to the act of calculating and estimating future health states based on a generative AI model.

[1272] "Display" refers to the act of sending the results from the server to the terminal and showing the results on the terminal in a form that can be visually recognized by the user.

[1273] "Past success stories" refers to patterns and data of successful health improvements that other users have made in the past.

[1274] "Improvement plan" refers to specific action items for improving the user's health condition, created by the server based on the generated AI model and analysis results.

[1275] "Notification" refers to the act of sending information from a server to a terminal and informing the user.

[1276] "Emotional state" refers to the user's mental and emotional state, such as stress, motivation, etc.

[1277] "Collect" refers to the act of the device obtaining data about the user's emotional state.

[1278] "Evaluation" refers to the act of analyzing the emotion data collected by the server and determining the user's current emotional state.

[1279] "Adjustment" refers to the act of appropriately changing the content of the improvement plan based on the results of the emotional state.

[1280] The system of the present invention provides a user with an action plan to improve their health and supports their implementation. The system components include a terminal, a server, a generative AI model, and an emotion engine.

[1281] Operating procedures and data collection

[1282] Users use devices such as smartphones, PCs, and tablets to input data about their diet and exercise habits. For example, they may input that they ate bread and coffee for breakfast or that they jog three times a week. This data is sent from the device to the server. The device sends the data using a secure communication protocol (e.g., HTTPS).

[1283] Data Analysis and Prediction

[1284] The server stores the received data and begins analyzing it. The data is saved in a database (e.g., MySQL or PostgreSQL). Based on the saved data, the server evaluates the user's current diet and exercise habits. It then uses a generative AI model to predict the user's health status several years into the future. This model predicts risks (e.g., weight gain, high blood pressure, cardiovascular disease).

[1285] Generate an improvement plan

[1286] Based on the prediction results, the server generates a user-specific improvement plan. This plan includes specific action items that can be implemented and sustained, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is notified to the device and displayed to the user.

[1287] Emotion data collection and analysis

[1288] When a user uses the device, it collects data such as voice, facial expressions, and text messages. This data is sent to a server, where an emotion engine analyzes it to assess the user's emotional state, such as whether they are stressed or lacking motivation.

[1289] Adjusting the plan

[1290] The server adjusts the improvement plan based on the user's emotional state: if the user is feeling stressed, it adds relaxation actions (e.g., yoga or meditation), and if motivation is low, it sets incentives (e.g., rewards for achieving goals).

[1291] Feedback and progress management

[1292] Users follow the improvement plan displayed on the device and incorporate it into their daily lives. Progress and new data are periodically entered into the device and sent to the server. The server analyzes the new data and updates the improvement plan, supporting continuous health management.

[1293] Examples and prompts

[1294] For example, suppose a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week. The user inputs this data and sends it to the server. The generative AI model predicts that she will gain 10 kg in weight in three years and her risk of high blood pressure will increase. Based on the results, an improvement plan is generated, including "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and notified to the user.

[1295] Example prompt sentence:

[1296] "Please enter your current diet and exercise habits (e.g., bread and coffee for breakfast, jogging three times a week)."

[1297] "Please describe your recent emotional state (e.g., feeling stressed or unmotivated)."

[1298] This allows users to effectively manage their health status and receive personalized improvement plans based on their emotional state, enabling sustainable health management.

[1299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1300] Step 1: Data collection

[1301] The user enters data about their diet and exercise habits into the device, such as what they ate for breakfast, how often they exercise, and how long they exercise. The device then encodes the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1302] Input: Data on diet and exercise habits

[1303] Output: The encoded data is sent to the server

[1304] Step 2: Save data

[1305] The server stores the data received from the terminal in a database, such as a relational database like MySQL or PostgreSQL.

[1306] Input: Encoded data

[1307] Output: Data stored in the database

[1308] Step 3: Initial analysis

[1309] The server begins an initial analysis of the stored data. The analytics engine pulls the data from the database and calculates basic statistics such as calorie intake and total exercise time.

[1310] Input: User data stored in the database

[1311] Output: Basic statistics (calorie intake, exercise time, etc.)

[1312] Step 4: Predict your health status

[1313] The server uses a generative AI model to predict the user's future health status. The generative AI model uses the results of the initial analysis as input data to predict risks such as weight gain, high blood pressure, and cardiovascular disease.

[1314] Input: Results of initial analysis

[1315] Output: Risk prediction results (likelihood of weight gain, high blood pressure, cardiovascular disease, etc.)

[1316] Step 5: Generate an improvement plan

[1317] The server generates a user-specific improvement plan based on the prediction results. The plan generation engine references past success stories and creates action items that the user can implement and sustain. For example, "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[1318] Input: Risk prediction results, past success stories

[1319] Output: Improvement plan (specific action items)

[1320] Step 6: Communicate your improvement plan

[1321] The server transmits the generated improvement plan to the terminal, which displays the received improvement plan on a user interface and notifies the user.

[1322] Input: Improvement Plan

[1323] Output: Improvement plan displayed to the user

[1324] Step 7: Collect emotion data

[1325] When a user uses a device, the device collects emotional data (voice, facial expressions, text messages, etc.). For example, it uses a camera or microphone to capture the user's facial expressions and voice, and obtains new text messages. The collected data is sent to a server.

[1326] Input: Emotional data such as voice, facial expressions, and text messages

[1327] Output: The encoded emotion data is sent to the server.

[1328] Step 8: Analyze the sentiment data

[1329] The emotion engine on the server analyzes the transmitted emotion data and evaluates the user's emotional state, for example, determining whether the user is feeling stressed or unmotivated.

[1330] Input: Encoded emotion data

[1331] Output: User's emotional state (stress, motivation, etc.)

[1332] Step 9: Adjust your improvement plan based on your emotions

[1333] The server adjusts the user's specific improvement plan based on their emotional state. For example, if the user is feeling stressed, yoga for relaxation is added to the improvement plan. If the user's motivation is low, incentives such as rewards for achieving goals are set.

[1334] Input: User's emotional state

[1335] Output: Adjusted improvement plan

[1336] Step 10: Implementation and Feedback

[1337] The user acts according to the improvement plan displayed on the device and inputs their progress into the device. The device encodes the input progress data and sends it to the server. The server analyzes the new progress data and updates the improvement plan as necessary. This allows the user's health to continuously improve.

[1338] Input: Progress data

[1339] Output: Updated improvement plan and new health analysis results

[1340] (Application example 2)

[1341] 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."

[1342] Conventional health management systems only provide simple improvement plans based on the user's diet and exercise habits, and do not adjust the plan to take into account the user's emotional state, making it difficult to effectively manage health. Furthermore, there is no way to provide health advice in physical stores, making it difficult for users to make appropriate choices on the spot. Therefore, there is a need for an effective health management system that takes into account the user's emotional state and can be used in physical stores.

[1343] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting data on the user's dietary and exercise habits; means for transmitting the input data; means for receiving and storing the transmitted data; means for analyzing the user's current dietary and exercise habits based on the stored data; means for predicting the user's future health status based on the analysis results using a generative AI model; means for displaying the prediction results; means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model; means for notifying the user of the created improvement plan; means for acquiring new data on the progress of the improvement plan and updating the stored data; means for continuously updating the improvement plan based on the updated data; means for recognizing the user's emotional state and adjusting the improvement plan based on the data; means for providing health advice for products the user is considering purchasing in a store; and means for checking the user's emotional state in real time and presenting appropriate advice. This enables effective health management that takes the user's emotional state into consideration and appropriate health advice in a physical store.

[1344] A "user" is an individual who uses the system to manage their own health and emotional state.

[1345] "Diet" refers to the types and amounts of food and drink that an individual consumes on a daily basis, as well as the times when they eat meals.

[1346] "Exercise habits" refers to the type, frequency, duration, and intensity of exercise that an individual engages in on a daily basis.

[1347] "Data" refers to information entered by users relating to their diet, exercise habits, and emotional state.

[1348] An "input means" is a device or interface that allows a user to provide data to a system.

[1349] "Transmitting means" refers to a device or interface with communication capabilities for transferring input data to a server.

[1350] "Means for receiving and storing" refers to a device or system that has the function of receiving transmitted data and storing it on a server.

[1351] "Means for analysis" refers to algorithms or programs that use the stored data to evaluate a user's diet and exercise habits.

[1352] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes new predictions and generates new information.

[1353] "Prediction means" refers to a program or function that uses a generative AI model based on the analysis results to predict the user's future health condition.

[1354] "Means for displaying" refers to a display or notification system that visualizes the prediction results and improvement plans to the user.

[1355] "Means of extraction" refers to algorithms or programs that select relevant information from past success stories.

[1356] "Means for creation" refers to programs and functions for designing user-specific improvement plans based on generative AI models.

[1357] The "notification means" refers to a function or device for notifying the user of the created improvement plan.

[1358] "New data on progress" refers to information about the results and status of activities that a user has carried out based on an improvement plan.

[1359] "Means for updating" refers to programs or functions that update stored data and improvement plans based on new data acquired.

[1360] An "emotional state" refers to a user's mental state, examples of which include stress, joy, sadness, etc.

[1361] "Adjusting means" refers to algorithms or programs that adapt an existing improvement plan based on the user's emotional state.

[1362] A "store" is a physical business establishment where users can visit and purchase products.

[1363] "Health advice" refers to instructions or advice that encourage specific actions or choices to maintain or improve a user's health.

[1364] "Means for checking in real time" refers to functions or devices that allow users to instantly check their emotional state on the spot.

[1365] "Presentation means" refers to a display or notification system that provides appropriate advice and information to the user.

[1366] The system of the present invention provides a personalized improvement plan to improve the user's health, continuously adjusts it taking into account the user's emotional state, and also has the ability to provide effective health advice in physical stores.

[1367] Components

[1368] The system consists of the following components:

[1369] 1. Device:

[1370] This includes smartphones, tablets, and laptops.

[1371] It allows users to enter data about their diet, exercise habits, and emotional state.

[1372] 2. Server:

[1373] Cloud-based or on-premise servers are used.

[1374] The transmitted data is received and stored, and then analyzed and predictions are made.

[1375] 3. Generative AI Model:

[1376] It includes deep learning models and machine learning algorithms that predict future health conditions based on user data and past success stories.

[1377] 4. Emotion Engine:

[1378] The user's emotional state is analyzed using technologies such as voice recognition and facial expression recognition.

[1379] Hardware and Software

[1380] Hardware: Smartphone, tablet, laptop (with built-in camera and microphone)

[1381] Software: Python, OpenCV, Keras, SpeechRecognition library, cloud-based server

[1382] Data processing:

[1383] Camera-based face recognition: Face recognition is performed using OpenCV, and image data is provided to the emotion recognition model.

[1384] Speech Recognition: Use the SpeechRecognition library to capture voice data from the microphone and detect the user's emotional state and needs.

[1385] Processing flow

[1386] 1. Data Collection:

[1387] Users use the device to input data about their diet, exercise habits, and emotional state.

[1388] This data is transmitted from the terminal to the server.

[1389] 2. Data Analysis:

[1390] The server stores the received data and begins analyzing it.

[1391] It assesses the details of your diet and exercise habits and uses generative AI models to predict your future health status.

[1392] 3. Generate an improvement plan:

[1393] Based on the prediction results, the server creates a user-specific improvement plan.

[1394] The emotion engine assesses the user's emotional state and adjusts the remediation plan as needed, for example adding relaxation activities if the user is feeling stressed.

[1395] 4. In-store advice:

[1396] In stores, health advice is provided to users using their smartphones or tablets for products they are considering purchasing.

[1397] It uses facial and voice recognition to check the user's emotional state in real time and provide appropriate advice.

[1398] Specific examples

[1399] For example, when a user is about to buy food at a physical store, the system uses the user's facial image to detect stress. Based on the user's dietary data and emotional state, the system provides health advice such as "Purchase fruit or yogurt, which are effective in relieving stress." It can also use voice recognition to provide specific advice in response to the user's questions.

[1400] Prompt Sentence Examples

[1401] "Recognize the user's emotional state from facial images or voice input. Use the results to generate health advice appropriate to that emotional state."

[1402] In this way, the system comprehensively manages the user's health status and supports the user in improving their health by providing individual improvement plans and real-time health advice.

[1403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1404] Step 1:

[1405] Users use devices (such as smartphones or tablets) to input data about their diet, exercise habits, and emotional state. This includes tapping, voice input, and facial recognition data using a camera. Input data includes dietary content, exercise frequency and duration, and emotional state (such as stress levels).

[1406] Step 2:

[1407] The terminal transmits the input data to the server. At this time, an application on the terminal converts the data into text or binary format and transmits the data using a secure communication protocol (e.g., HTTPS).

[1408] Step 3:

[1409] The server receives the data sent from the device and then stores it in the appropriate database. The data stored in the database is organized by user and maintains its association with past data.

[1410] Step 4:

[1411] The server analyzes the user's current dietary and exercise habits based on the stored data. Based on the dietary and exercise information retrieved from the database, the server evaluates the calorie intake and exercise intensity. The evaluation is performed using queries and algorithms.

[1412] Step 5:

[1413] The server uses the analysis results to predict the user's future health condition using a generative AI model. User data and past success stories are input into the prediction, and the generative AI model outputs risk factors (weight gain, high blood pressure, etc.). For example, specific prediction results such as "risk of weight gain of 10 kg in three years" can be obtained.

[1414] Step 6:

[1415] The server prepares data for displaying the prediction results on the user's terminal. For example, the server generates data including the prediction results visualized in text or graph format and sends it to the terminal. This data is displayed on the terminal's display.

[1416] Step 7:

[1417] The server uses a generative AI model based on past success stories to create a personalized improvement plan for each user. This plan includes specific, actionable, and sustainable actions (e.g., "walk 30 minutes five times a week"). The improvement plan is optimized based on the user's analysis results and emotional state.

[1418] Step 8:

[1419] The server notifies the user of the created improvement plan. The notification is sent to the device and notifies the user via push notification or email. The improvement plan is displayed on the device, and the user can take action based on the plan.

[1420] Step 9:

[1421] The user then inputs new data into the device about their progress with the improvement plan, including their daily diet, exercise, and changes in their emotional state. As the user inputs new data, the data is continually updated.

[1422] Step 10:

[1423] The device then sends new data to the server, which then updates the stored data. The server then analyzes the updated data and continuously updates the improvement plan, ensuring that the device continues to provide a plan that is optimal for the user's current condition.

[1424] Step 11:

[1425] The server recognizes the user's emotional state and adjusts the improvement plan based on that data. For example, if it detects stress, it adds relaxation activities to the action plan. If the user's motivation is low, it sets incentives.

[1426] Step 12:

[1427] When a user visits a physical store, the device provides health advice for products they are considering purchasing. The device's camera is used to recognize the user's face, check their emotional state in real time, and display appropriate health advice. For example, a user who is feeling stressed might be offered advice such as, "Purchase fruits that have a relaxing effect."

[1428] 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.

[1429] 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.

[1430] 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.

[1431] [Fourth embodiment]

[1432] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1433] 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.

[1434] 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).

[1435] 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.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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."

[1445] The system of the present invention provides a specific action plan to improve the user's health and supports its implementation. The system mainly consists of three components: a terminal, a server, and a generative AI model. These components work together to enable the user to effectively manage their own health.

[1446] Data collection

[1447] First, the user uses a device to input data about their diet and exercise habits. For example, the device records daily meal contents, exercise frequency, and exercise time. This data is then sent from the device to a server. The device can be a device that the user uses daily, such as a smartphone, PC, or tablet.

[1448] Data analysis

[1449] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and heart disease based on the acquired data and past success stories.

[1450] Generate an improvement plan

[1451] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[1452] Plan execution and feedback

[1453] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[1454] Specific examples

[1455] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[1456] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into their device. This reduces the user's health risks and enables sustainable health management.

[1457] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[1458] The processing flow will be explained below.

[1459] Step 1:

[1460] A user accesses a terminal and inputs data about their diet and exercise habits.

[1461] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[1462] Step 2:

[1463] The terminal transmits the input data to the server.

[1464] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[1465] Step 3:

[1466] The server analyzes the received data and stores it in a database.

[1467] The server checks the integrity of the data and stores it in a database for each user.

[1468] Step 4:

[1469] The server analyzes the user's current eating and exercise habits based on the stored data.

[1470] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[1471] Step 5:

[1472] The server uses the generated AI model to predict the user's health status several years in the future.

[1473] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[1474] Step 6:

[1475] The server generates prediction results and sends them to the device.

[1476] A notification that visually gives the user a sense of crisis is displayed on the device.

[1477] Step 7:

[1478] The server extracts past success stories and feeds them into a generative AI model.

[1479] Success stories include lifestyle data of users who have successfully improved their health.

[1480] Step 8:

[1481] The server creates a user-specific improvement plan.

[1482] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[1483] Step 9:

[1484] The server sends the created improvement plan to the terminal.

[1485] On the device, the user is notified of the improvement plan and action items are listed.

[1486] Step 10:

[1487] The user begins to change their daily life based on the improvement plan displayed on the device.

[1488] The user periodically inputs the status of improvements and new data into the terminal.

[1489] Step 11:

[1490] The device sends new data to the server.

[1491] The new data will include progress on improvement plans and changes to diet and exercise habits.

[1492] Step 12:

[1493] The server receives the new data and updates the database.

[1494] The database is updated based on new data.

[1495] Step 13:

[1496] The server continuously updates the improvement plan based on new data.

[1497] The AI ​​model will reassess and regenerate the optimal improvement plan.

[1498] Step 14:

[1499] The server transmits the updated improvement plan to the terminal again and notifies the user.

[1500] The new improvement plan will be displayed on the device and the user will begin their next action.

[1501] In this way, the system continuously monitors the user's health status and provides specific plans for sustained improvement.

[1502] Example 1

[1503] 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."

[1504] In today's modern lifestyle, users face the challenge of continually improving and maintaining their health. In particular, there is a lack of systems that collect detailed data on dietary and exercise habits and provide specific, sustainable improvement plans based on that data. There is also a need for systems that can predict a user's future health status and dynamically optimize improvement plans based on that data.

[1505] 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.

[1506] In this invention, the server includes a means for inputting data relating to the user's health condition, a means for transmitting the input data, and a means for receiving and storing the transmitted data, thereby enabling the server to specifically quantify the user's unique health condition and provide a dynamically optimized improvement plan.

[1507] "User" refers to an individual who utilizes the system to manage and improve their health.

[1508] "Health status" refers to various health-related data such as a user's diet, exercise habits, weight, and blood pressure.

[1509] "Means for inputting data" refers to devices or interfaces that allow users to record information about their health status, such as smartphones, computers, and tablets.

[1510] "Means for transmitting data" refers to a function for transmitting input data to a cloud server via the Internet. Specifically, this includes a network communication module.

[1511] "Means for receiving and storing data" refers to the function of storing transmitted data on a server. Specifically, this includes database servers and storage systems.

[1512] "Diet" refers to the type of food a user eats on a daily basis and how often they eat it.

[1513] "Exercise habits" refers to the type, frequency, duration, etc. of exercise that a user does on a daily basis.

[1514] "Means for analyzing data" refers to the function of evaluating a user's dietary and exercise habits and analyzing their health status based on the stored data, including analytical algorithms and AI models.

[1515] "Generative AI model" refers to an artificial intelligence model that predicts a user's future health condition and suggests an appropriate improvement plan based on acquired data and past success stories.

[1516] "Means for displaying prediction results" refers to a function for visually presenting the prediction results produced by the generative AI model to the user. Specifically, this includes a display or smartphone screen.

[1517] An "improvement plan" refers to a plan that includes specific action items that a user can implement and commit to sustainably.

[1518] "Means of notification" refers to the functionality for informing users of the generated improvement plan and prediction results, including push notifications and in-app messages.

[1519] "Progress" refers to the progress of the actions taken by the user and the results obtained according to the improvement plan.

[1520] "Dynamic optimization" means that the generative AI model continuously adjusts the improvement plan based on newly acquired data to provide the optimal health management plan.

[1521] MODE FOR CARRYING OUT THE INVENTION

[1522] This invention relates to a system that provides a user with a specific plan to improve their health and supports their implementation. This system is mainly composed of a terminal, a server, and a generative AI model, and each element works together to support the user's health management.

[1523] Data collection

[1524] First, users enter data about their diet and exercise habits using a terminal, including their daily diet, exercise frequency, and exercise duration. The terminal can be a common digital device such as a smartphone, PC, or tablet.

[1525] Data transmission

[1526] The device sends the entered data to the cloud server. The sent data is encrypted and sent over a securely protected communication channel. HTTPS or TLS is the most commonly used communication protocol.

[1527] Data storage

[1528] The server stores the received data in a database. This database uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) and is capable of efficiently managing large amounts of data.

[1529] Data analysis

[1530] The server analyzes the stored data, which includes extracting patterns in the user's diet and exercise habits, using Python libraries such as scikit-learn and TensorFlow.

[1531] Prediction result generation

[1532] The generative AI model uses the analysis results to predict the user's future health status, quantifying risks such as weight gain, high blood pressure, and heart disease based on past success stories and acquired data.

[1533] Improvement plan generation

[1534] The server generates a user-specific improvement plan based on the predictions made by the generative AI model, which includes specific and actionable action items, such as "add fruit to breakfast" or "walk 30 minutes five times a week."

[1535] Improvement plan notification

[1536] The generated improvement plan is sent from the server to the device via push notifications or in-app pop-up messages, allowing users to quickly incorporate the improvement plan into their daily lives.

[1537] Plan execution and feedback

[1538] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user enters their progress and new health data (e.g., new dietary habits, exercise habits) into the device. The entered data is then sent back to the server, which analyzes the new data and updates the improvement plan as needed.

[1539] Specific examples

[1540] For example, consider a 35-year-old female user who eats bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to a server. The server stores the data and analyzes it to determine that her diet is high in calories and she is not getting enough exercise. Based on these results, the generative AI model predicts that she will gain 10 kg in weight in three years, increasing her risk of high blood pressure.

[1541] The server generates a specific improvement plan, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the device. The user follows this plan to improve their daily life and enters their progress into the device. This reduces the user's health risks and enables sustainable health management.

[1542] Prompt Sentence Examples

[1543] "If a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week, what health risks would be predicted and what improvement plan would be suggested?"

[1544] In this way, the system of the present invention provides users with specific and actionable health management tools and supports continuous health improvement.

[1545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1546] Step 1: Data collection

[1547] The user inputs data about their health condition into the device. Specifically, the user inputs information such as daily diet, exercise frequency, and exercise duration. This input data is used to proceed to the next processing step. The data entered by the user is saved in the form of a string or a number.

[1548] Step 2: Send data

[1549] The device sends the data entered by the user to the cloud server. At this time, the data is encrypted before being sent, ensuring security. The input data is the health condition data obtained in step 1, and the output data is the encrypted data sent to the server. The HTTPS protocol is used for transmission.

[1550] Step 3: Save data

[1551] The server stores the received data in a database. Specifically, it uses a high-performance storage system (e.g., Amazon RDS or Google Cloud SQL) to efficiently manage large amounts of data. The input data is the encrypted data sent in step 2, and the output data is the unencrypted data stored in the database.

[1552] Step 4: Data analysis

[1553] The server performs analysis based on the stored data. The purpose of this analysis is to extract patterns in the user's diet and exercise habits. The input data is unencrypted data obtained from the database, and the output data is a report of the analysis results. Specifically, data analysis is performed using libraries such as Python's scikit-learn and TensorFlow.

[1554] Step 5: Generate prediction results

[1555] The server inputs the analysis results into a generative AI model to predict the user's future health condition. This generative AI model quantifies health risks based on the acquired data and past success stories. The input data is the analysis results from step 4, and the output data is numerical data indicating future health risks.

[1556] Step 6: Generate an improvement plan

[1557] The server generates a user-specific improvement plan based on the prediction results of the generative AI model, including specific action items (e.g., adding fruit to breakfast, walking 30 minutes five times a week). The input data is the health risk data from Step 5, and the output data is a specific improvement plan.

[1558] Step 7: Notification of Improvement Plan

[1559] The server notifies the device of the generated improvement plan. Notification methods include push notifications and in-app pop-up messages. The input data is the improvement plan from step 6, and the output data is the improvement plan information notified to the user.

[1560] Step 8: Execute the plan and enter progress

[1561] The user follows the improvement plan displayed on the device and incorporates it into their daily life. The user also inputs the actions they have taken (e.g., the exercise and diet they actually did) into the device. The input data is the action data the user has taken, and the newly entered progress data is output to the device.

[1562] Step 9: Send progress data

[1563] The device sends the progress data entered by the user to the cloud server. The data is encrypted again before being sent. The input data is the progress data entered in step 8, and the encrypted progress data is sent to the server as output data.

[1564] Step 10: Update your improvement plan

[1565] The server analyzes the newly received progress data and updates the improvement plan as necessary, ensuring that the user always receives the most up-to-date health management plan. The input data is the progress data received in step 9, and the output data is an updated improvement plan. Specifically, the server analyzes the new progress data and incorporates new suggestions into the existing improvement plan.

[1566] (Application example 1)

[1567] 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."

[1568] In modern society, effectively managing and improving individual health is an important challenge. However, many existing systems are limited to collecting and analyzing data on users' dietary and exercise habits. As a result, few systems offer comprehensive health improvement plans or even suggest products based on the user's health status. Furthermore, they lack the functionality to predict future health status using generative AI models and continuously update improvement plans. This makes it difficult for users to engage in sustainable, personalized health management.

[1569] 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.

[1570] In this invention, the server includes means for inputting data on the user's dietary and exercise habits, means for transmitting the input data, means for receiving and storing the transmitted data, means for analyzing the user's current dietary and exercise habits based on the stored data, means for predicting the user's future health status based on the analysis results using a generative AI model, means for displaying the prediction results, means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, means for notifying the user of the created improvement plan, means for acquiring new data on the progress of the improvement plan and updating the stored data, means for continuously updating the improvement plan based on the updated data, and means for suggesting products suitable for the user's health status based on the prediction results. This allows the user to obtain a specific and sustainable health improvement plan and further receive product suggestions tailored to their current health status.

[1571] "User" refers to an individual who uses this system.

[1572] "Diet" refers to the foods that a user regularly consumes and the contents of those foods.

[1573] "Exercise habits" refers to the physical activities a user regularly engages in, as well as their frequency and duration.

[1574] "Data" refers to information about a user's diet and exercise habits.

[1575] "Terminal" refers to a device such as a smartphone, PC, or tablet through which a user enters data.

[1576] "Server" refers to a computer system that receives, stores, and analyzes data sent from a terminal.

[1577] A "generative AI model" refers to an artificial intelligence model that predicts future health conditions based on input data and past success stories.

[1578] "Analysis" refers to assessing the current state of your diet and exercise habits based on your data.

[1579] "Prediction" refers to the use of a generative AI model to indicate a user's future health status.

[1580] "Improvement Plan" refers to specific action items provided to the user based on the analysis and prediction results.

[1581] "Notification" refers to informing the user of the generated improvement plan.

[1582] "Product suggestion" refers to presenting products suitable for the user's health condition.

[1583] "Continuous updates" means constantly providing optimal improvement plans based on new data.

[1584] The system for implementing this invention mainly uses a terminal, a server, and a generative AI model to support users' health management. This system allows users to input data about their diet and exercise habits, analyzes their health status based on that data, and provides specific improvement plans. It also has a function to suggest products suitable for the user's health status.

[1585] Hardware and software used

[1586] Hardware

[1587] Device: Use a device such as a smartphone, computer, or tablet.

[1588] Server: A computer system that stores and analyzes data.

[1589] software

[1590] Python 3: Used for program development and data analysis.

[1591] requests library: A library for sending HTTP requests.

[1592] Data processing and calculation

[1593] 1. Data collection and transmission

[1594] The user uses the device to input data about their diet and exercise habits. For example, they input the type of food they eat, how often they exercise, and how long they exercise. This data is sent from the device to the server. For example, suppose a 35-year-old woman has bread and coffee for breakfast and jogs three times a week. This data is input in the format "35-year-old woman, has bread and coffee for breakfast, jogs three times a week."

[1595] 2. Data analysis and prediction

[1596] The server receives and stores data sent from the device. The stored data includes detailed information about the user's diet and exercise habits. The server analyzes this data and uses a generative AI model to predict the user's future health condition. For example, the generative AI model may predict that "in three years, weight will increase by 10 kg, and the risk of high blood pressure will increase."

[1597] 3. Generate and notify improvement plans

[1598] Based on the analysis and prediction results, the server creates a personalized improvement plan for the user, including specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is then sent back to the device and displayed to the user.

[1599] 4. Product proposal

[1600] Furthermore, the server will suggest products suitable for the user's health condition based on the prediction results. For example, if the user's health risk is high blood pressure, it will suggest products such as "low-salt food sets" or "diet supplements."

[1601] 5. Implementing the plan and providing feedback

[1602] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[1603] Specific examples

[1604] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. This data is entered into the device and sent to the server. The server analyzes this data, and the generative AI model predicts that "in three years, weight will increase by 10 kg and the risk of high blood pressure will increase." Based on this prediction, an improvement plan is generated that includes "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and is notified to the user. The user is also suggested products such as "low-salt food sets" and "diet supplements." By following this and continuing to input their progress into the device, the user can achieve sustainable health management.

[1605] Prompt Sentence Examples

[1606] "I'm a 35-year-old woman who has bread and coffee for breakfast and jogs three times a week. I'd like to receive an improvement plan and product suggestions."

[1607] In this way, users can obtain specific and actionable health management measures through the system and achieve sustained improvements in their health.

[1608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1609] Step 1:

[1610] Users use devices (smartphones, PCs, tablets, etc.) to input data about their diet and exercise habits. This specifically includes daily meal content, exercise frequency, exercise duration, etc. For example, if a user eats bread and coffee for breakfast and jogs three times a week, they enter that information.

[1611] Input: Information about your diet and exercise habits.

[1612] Output: Data is saved to the device.

[1613] Step 2:

[1614] The terminal sends the entered data to the server. The data is sent to the server using an HTTP request. The server receives it and stores it in a database. For example, the data is sent in JSON format.

[1615] Input: Data entered at the terminal.

[1616] Output: Data stored in the server's database.

[1617] Step 3:

[1618] The server analyzes the user's current dietary and exercise habits based on the stored data. Specifically, it analyzes the data and evaluates calorie intake, exercise volume, etc. This analysis can clarify the user's current health status.

[1619] Input: Data stored on the server.

[1620] Output: Analysis results (e.g., assessment of calorie intake and physical activity).

[1621] Step 4:

[1622] The server uses a generative AI model based on the analysis results to predict the user's future health condition. The generative AI model uses past data and success stories to predict weight gain and high blood pressure risk several years from now. For example, it generates a prediction that "weight will increase by 10 kg in three years and the risk of high blood pressure will increase."

[1623] Input: Analysis results.

[1624] Output: Prediction results.

[1625] Step 5:

[1626] The server displays the prediction results and notifies the user via their device, allowing the user to check the results and gain a deeper understanding of their own health condition.

[1627] Input: Prediction results.

[1628] Output: Notification of prediction results to the user.

[1629] Step 6:

[1630] The server extracts past success stories and creates a personalized improvement plan based on a generative AI model, generating specific action items such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[1631] Input: Predicted results and success story data.

[1632] Output: Improvement plan.

[1633] Step 7:

[1634] The server notifies the user of the created improvement plan, which the user can then check on their device and put into action.

[1635] Input: Improvement Plan.

[1636] Output: Notification of improvement plan to user.

[1637] Step 8:

[1638] The user follows the improvement plan and incorporates it into their daily life. They input their progress and new dietary and exercise habits into the device, and this data is then sent back to the server.

[1639] Input: User progress data.

[1640] Output: Progress data sent to the server.

[1641] Step 9:

[1642] The server analyzes the new data and updates the stored data to reflect the user's most recent health status, and updates and notifies the user of an improvement plan if necessary.

[1643] Input: New progress data.

[1644] Output: Updated improvement plan and data.

[1645] Step 10:

[1646] Based on the prediction results, the server will suggest products suitable for the user's health condition, such as a "low-salt food set" or "diet supplements." This will also be notified to the user via their device.

[1647] Input: Prediction results and data.

[1648] Output: Product suggestion notification to the user.

[1649] Through these steps, the system can effectively manage and continuously improve the user's health status.

[1650] 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.

[1651] The system of the present invention provides a specific action plan for improving the user's health and supports its implementation. Furthermore, the system recognizes the user's emotional state and adjusts the improvement plan based on the analysis results, thereby achieving more effective health management. The system mainly consists of the following components: a terminal, a server, a generative AI model, and an emotion engine. These elements work together to enable users to effectively manage their own health.

[1652] Data collection

[1653] First, the user uses a device to input data about their diet and exercise habits. For example, they record their daily diet, exercise frequency, and exercise time on the device. This data is then sent from the device to a server. The device can be a smartphone, PC, tablet, or other device.

[1654] Data analysis

[1655] The server stores the received data and begins analysis. The stored data includes detailed information about the user's diet and exercise habits. Based on this, the server evaluates the user's current health condition and uses a generative AI model to predict their health condition several years from now. The generative AI model predicts risks such as weight gain, high blood pressure, and cardiovascular disease based on the acquired data and past success stories.

[1656] Generate an improvement plan

[1657] Based on the prediction results, the server generates a user-specific improvement plan. This improvement plan includes specific action items that the user can implement and continue to work on. For example, "add fruit to breakfast" or "walk 30 minutes five times a week." The improvement plan is notified to the device and displayed to the user.

[1658] Sentiment analysis with emotion engine

[1659] Furthermore, this system incorporates an emotion engine that recognizes the user's emotional state. When the user uses the device, data such as voice, facial expressions, and text messages is collected and sent to the server. The server analyzes this data and evaluates the user's emotional state. For example, it can detect when the user is feeling stressed or has lost motivation.

[1660] Emotionally-driven plan adjustments

[1661] The server adjusts the improvement plan based on the user's emotional state. For example, if the user is feeling stressed, it adds actions to help them relax (e.g., light exercise or meditation). If their motivation is low, it sets incentives to encourage them to take action.

[1662] Plan execution and feedback

[1663] The user follows the improvement plan displayed on the device and incorporates it into their daily life. They periodically enter their progress and new dietary and exercise habits into the device. This data is then sent from the device to the server. The server analyzes the new data and updates the improvement plan as appropriate. By repeating this cycle, the user's health can be continuously improved.

[1664] Specific examples

[1665] For example, suppose a 35-year-old female user has bread and coffee for breakfast and jogs three times a week. The user enters this data into the device and sends it to the server. Based on this data, the server analyzes that her current diet is high in calories and she does not get enough exercise. Based on the analysis results, the generative AI model predicts that she will gain 10 kg in weight in three years and that her risk of high blood pressure will increase. This prediction result is notified to the device and displayed to the user.

[1666] The server uses past success stories as a reference to generate an improvement plan, such as "jog for at least 30 minutes five times a week" or "add fruit and yogurt to breakfast," and notifies the user. The user follows this improvement plan, reviews their daily life, and enters their progress into the device. This reduces the user's health risks.

[1667] Furthermore, if the emotion engine detects that the user is feeling stressed, the server will add "yoga for relaxation" to the improvement plan. Also, if motivation is declining, incentives such as "rewarding yourself for achieving your goal" will be set to encourage the user to take action.

[1668] In this way, the system of the present invention effectively manages the user's health condition and provides an individual improvement plan according to the user's emotional state, thereby supporting continuous health improvement.

[1669] The processing flow will be explained below.

[1670] Step 1:

[1671] A user accesses a terminal and inputs data about their diet and exercise habits.

[1672] The data entered includes dietary intake (e.g., bread and coffee for breakfast), exercise frequency (e.g., jogging three times a week), and exercise duration.

[1673] Step 2:

[1674] The terminal transmits the input data to the server.

[1675] The data to be transmitted is sent to the server as structured data in JSON format or the like via a POST request.

[1676] Step 3:

[1677] The server analyzes the received data and stores it in a database.

[1678] The server checks the integrity of the data and stores it in a database for each user.

[1679] Step 4:

[1680] The server analyzes the user's current eating and exercise habits based on the stored data.

[1681] It evaluates calorie intake, nutritional balance, exercise volume, etc. and generates analytical results.

[1682] Step 5:

[1683] The server uses the generated AI model to predict the user's health status several years in the future.

[1684] Predictions include weight gain, blood pressure fluctuations, and risk of cardiovascular disease.

[1685] Step 6:

[1686] The server generates prediction results and sends them to the device.

[1687] A notification that visually gives the user a sense of crisis is displayed on the device.

[1688] Step 7:

[1689] The server extracts past success stories and feeds them into a generative AI model.

[1690] Success stories include lifestyle data of users who have successfully improved their health.

[1691] Step 8:

[1692] The server creates a user-specific improvement plan.

[1693] The improvement plan includes specific action items (e.g., adding fruit and yogurt to breakfast, running five times a week).

[1694] Step 9:

[1695] The server sends the created improvement plan to the terminal.

[1696] On the device, the user is notified of the improvement plan and action items are listed.

[1697] Step 10:

[1698] The user begins to change their daily life based on the improvement plan displayed on the device.

[1699] The user periodically inputs the status of improvements and new data into the terminal.

[1700] Step 11:

[1701] The device sends new data to the server.

[1702] The new data will include progress on improvement plans and changes to diet and exercise habits.

[1703] Step 12:

[1704] The server receives the new data and updates the database.

[1705] The database is updated based on new data.

[1706] Step 13:

[1707] The server continuously updates the improvement plan based on new data.

[1708] The generative AI model reevaluates and regenerates the optimal improvement plan.

[1709] Step 14:

[1710] The server transmits the updated improvement plan to the terminal again and notifies the user.

[1711] The new improvement plan will be displayed on the device and the user will begin their next action.

[1712] Step 15:

[1713] Emotional data such as voice, facial expressions, and text messages are collected while the user is using the device.

[1714] This data is transmitted from the terminal to the server.

[1715] Step 16:

[1716] The server analyzes the received emotion data and evaluates the user's emotional state.

[1717] For example, states such as feeling stressed or having low motivation are detected.

[1718] Step 17:

[1719] The server adjusts the remediation plan based on the user's emotional state.

[1720] For example, add yoga to help you relax or set incentives to motivate you.

[1721] Step 18:

[1722] The server transmits the adjusted improvement plan to the terminal and notifies the user.

[1723] The new plan is displayed to the user on the device and they are prompted to execute it.

[1724] In this way, the system also takes into account the user's emotional state and supports sustainable and effective health management.

[1725] Example 2

[1726] 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."

[1727] In today's busy lifestyles, it is not easy for individuals to effectively manage their own health. In particular, the process of centrally managing data on dietary and exercise habits and creating and implementing appropriate action plans is complex and requires specialized knowledge. In addition, because a user's emotional state significantly affects the continuity and effectiveness of health management, flexible plan adjustments based on emotional fluctuations are required.

[1728] 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. In this invention, the server includes a means for inputting data on the user's dietary and exercise habits, a means for transmitting the input data, and a means for receiving and storing the transmitted data. This enables the user to efficiently manage their own health condition.

[1729] The server also includes a means for analyzing the user's current eating and exercise habits based on the stored data, a means for predicting the user's future health condition based on the analysis results using a generative AI model, a means for displaying the prediction results, a means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model, and a means for notifying the user of the created improvement plan. This allows the user to receive a specific and actionable improvement plan, and encourages them to take action to improve their health.

[1730] The server further includes means for collecting and transmitting data on the user's emotional state, means for analyzing and evaluating the emotional state, means for adjusting the improvement plan based on the emotional state, means for acquiring new data on the progress of the improvement plan and updating the stored data, and means for continuously updating the improvement plan based on the updated data. This enables flexible plan adjustments that adapt to the user's emotional state, enabling continuous health management.

[1731] A "user" is an entity that uses the system to manage their own health condition.

[1732] "Diet" refers to the user's daily diet and eating habits.

[1733] "Exercise habits" refers to a user's daily exercise and fitness activity patterns.

[1734] "Data" refers to health-related information entered into the system by a user and processed by the system.

[1735] "Terminal" refers to an electronic device that allows a user to input data or receive notifications from the system. Examples include smartphones, personal computers, and tablets.

[1736] "Server" refers to a computer system that serves as the center of the system and receives, stores, analyzes, and processes data.

[1737] "Transmission" refers to the act of sending data from a terminal to a server via a network.

[1738] "Storage" refers to the act of storing the data received by the server in a database or the like.

[1739] "Analysis" refers to the process by which the server evaluates information and derives results based on the data it obtains.

[1740] A "generative AI model" refers to a program that uses machine learning algorithms to predict a user's future health status.

[1741] "Prediction" refers to the act of calculating and estimating future health states based on a generative AI model.

[1742] "Display" refers to the act of sending the results from the server to the terminal and showing the results on the terminal in a form that can be visually recognized by the user.

[1743] "Past success stories" refers to patterns and data of successful health improvements that other users have made in the past.

[1744] "Improvement plan" refers to specific action items for improving the user's health condition, created by the server based on the generated AI model and analysis results.

[1745] "Notification" refers to the act of sending information from a server to a terminal and informing the user.

[1746] "Emotional state" refers to the user's mental and emotional state, such as stress, motivation, etc.

[1747] "Collect" refers to the act of the device obtaining data about the user's emotional state.

[1748] "Evaluation" refers to the act of analyzing the emotion data collected by the server and determining the user's current emotional state.

[1749] "Adjustment" refers to the act of appropriately changing the content of the improvement plan based on the results of the emotional state.

[1750] The system of the present invention provides a user with an action plan to improve their health and supports their implementation. The system components include a terminal, a server, a generative AI model, and an emotion engine.

[1751] Operating procedures and data collection

[1752] Users use devices such as smartphones, PCs, and tablets to input data about their diet and exercise habits. For example, they may input that they ate bread and coffee for breakfast or that they jog three times a week. This data is sent from the device to the server. The device sends the data using a secure communication protocol (e.g., HTTPS).

[1753] Data Analysis and Prediction

[1754] The server stores the received data and begins analyzing it. The data is saved in a database (e.g., MySQL or PostgreSQL). Based on the saved data, the server evaluates the user's current diet and exercise habits. It then uses a generative AI model to predict the user's health status several years into the future. This model predicts risks (e.g., weight gain, high blood pressure, cardiovascular disease).

[1755] Generate an improvement plan

[1756] Based on the prediction results, the server generates a user-specific improvement plan. This plan includes specific action items that can be implemented and sustained, such as "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast." This improvement plan is notified to the device and displayed to the user.

[1757] Emotion data collection and analysis

[1758] When a user uses the device, it collects data such as voice, facial expressions, and text messages. This data is sent to a server, where an emotion engine analyzes it to assess the user's emotional state, such as whether they are stressed or lacking motivation.

[1759] Adjusting the plan

[1760] The server adjusts the improvement plan based on the user's emotional state: if the user is feeling stressed, it adds relaxation actions (e.g., yoga or meditation), and if motivation is low, it sets incentives (e.g., rewards for achieving goals).

[1761] Feedback and progress management

[1762] Users follow the improvement plan displayed on the device and incorporate it into their daily lives. Progress and new data are periodically entered into the device and sent to the server. The server analyzes the new data and updates the improvement plan, supporting continuous health management.

[1763] Examples and prompts

[1764] For example, suppose a 35-year-old female user eats bread and coffee for breakfast and jogs three times a week. The user inputs this data and sends it to the server. The generative AI model predicts that she will gain 10 kg in weight in three years and her risk of high blood pressure will increase. Based on the results, an improvement plan is generated, including "jog for at least 30 minutes five times a week" and "add fruit and yogurt to breakfast," and notified to the user.

[1765] Example prompt sentence:

[1766] "Please enter your current diet and exercise habits (e.g., bread and coffee for breakfast, jogging three times a week)."

[1767] "Please describe your recent emotional state (e.g., feeling stressed or unmotivated)."

[1768] This allows users to effectively manage their health status and receive personalized improvement plans based on their emotional state, enabling sustainable health management.

[1769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1770] Step 1: Data collection

[1771] The user enters data about their diet and exercise habits into the device, such as what they ate for breakfast, how often they exercise, and how long they exercise. The device then encodes the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1772] Input: Data on diet and exercise habits

[1773] Output: The encoded data is sent to the server

[1774] Step 2: Save data

[1775] The server stores the data received from the terminal in a database, such as a relational database like MySQL or PostgreSQL.

[1776] Input: Encoded data

[1777] Output: Data stored in the database

[1778] Step 3: Initial analysis

[1779] The server begins an initial analysis of the stored data. The analytics engine pulls the data from the database and calculates basic statistics such as calorie intake and total exercise time.

[1780] Input: User data stored in the database

[1781] Output: Basic statistics (calorie intake, exercise time, etc.)

[1782] Step 4: Predict your health status

[1783] The server uses a generative AI model to predict the user's future health status. The generative AI model uses the results of the initial analysis as input data to predict risks such as weight gain, high blood pressure, and cardiovascular disease.

[1784] Input: Results of initial analysis

[1785] Output: Risk prediction results (likelihood of weight gain, high blood pressure, cardiovascular disease, etc.)

[1786] Step 5: Generate an improvement plan

[1787] The server generates a user-specific improvement plan based on the prediction results. The plan generation engine references past success stories and creates action items that the user can implement and sustain. For example, "jog for 30 minutes five times a week" or "add fruit and yogurt to breakfast."

[1788] Input: Risk prediction results, past success stories

[1789] Output: Improvement plan (specific action items)

[1790] Step 6: Communicate your improvement plan

[1791] The server transmits the generated improvement plan to the terminal, which displays the received improvement plan on a user interface and notifies the user.

[1792] Input: Improvement Plan

[1793] Output: Improvement plan displayed to the user

[1794] Step 7: Collect emotion data

[1795] When a user uses a device, the device collects emotional data (voice, facial expressions, text messages, etc.). For example, it uses a camera or microphone to capture the user's facial expressions and voice, and obtains new text messages. The collected data is sent to a server.

[1796] Input: Emotional data such as voice, facial expressions, and text messages

[1797] Output: The encoded emotion data is sent to the server.

[1798] Step 8: Analyze the sentiment data

[1799] The emotion engine on the server analyzes the transmitted emotion data and evaluates the user's emotional state, for example, determining whether the user is feeling stressed or unmotivated.

[1800] Input: Encoded emotion data

[1801] Output: User's emotional state (stress, motivation, etc.)

[1802] Step 9: Adjust your improvement plan based on your emotions

[1803] The server adjusts the user's specific improvement plan based on their emotional state. For example, if the user is feeling stressed, yoga for relaxation is added to the improvement plan. If the user's motivation is low, incentives such as rewards for achieving goals are set.

[1804] Input: User's emotional state

[1805] Output: Adjusted improvement plan

[1806] Step 10: Implementation and Feedback

[1807] The user acts according to the improvement plan displayed on the device and inputs their progress into the device. The device encodes the input progress data and sends it to the server. The server analyzes the new progress data and updates the improvement plan as necessary. This allows the user's health to continuously improve.

[1808] Input: Progress data

[1809] Output: Updated improvement plan and new health analysis results

[1810] (Application example 2)

[1811] 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."

[1812] Conventional health management systems only provide simple improvement plans based on the user's diet and exercise habits, and do not adjust the plan to take into account the user's emotional state, making it difficult to effectively manage health. Furthermore, there is no way to provide health advice in physical stores, making it difficult for users to make appropriate choices on the spot. Therefore, there is a need for an effective health management system that takes into account the user's emotional state and can be used in physical stores.

[1813] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting data on the user's dietary and exercise habits; means for transmitting the input data; means for receiving and storing the transmitted data; means for analyzing the user's current dietary and exercise habits based on the stored data; means for predicting the user's future health status based on the analysis results using a generative AI model; means for displaying the prediction results; means for extracting past success stories and creating a user-specific improvement plan based on the generative AI model; means for notifying the user of the created improvement plan; means for acquiring new data on the progress of the improvement plan and updating the stored data; means for continuously updating the improvement plan based on the updated data; means for recognizing the user's emotional state and adjusting the improvement plan based on the data; means for providing health advice for products the user is considering purchasing in a store; and means for checking the user's emotional state in real time and presenting appropriate advice. This enables effective health management that takes the user's emotional state into consideration and appropriate health advice in a physical store.

[1814] A "user" is an individual who uses the system to manage their own health and emotional state.

[1815] "Diet" refers to the types and amounts of food and drink that an individual consumes on a daily basis, as well as the times when they eat meals.

[1816] "Exercise habits" refers to the type, frequency, duration, and intensity of exercise that an individual engages in on a daily basis.

[1817] "Data" refers to information entered by users relating to their diet, exercise habits, and emotional state.

[1818] An "input means" is a device or interface that allows a user to provide data to a system.

[1819] "Transmitting means" refers to a device or interface with communication capabilities for transferring input data to a server.

[1820] "Means for receiving and storing" refers to a device or system that has the function of receiving transmitted data and storing it on a server.

[1821] "Means for analysis" refers to algorithms or programs that use the stored data to evaluate a user's diet and exercise habits.

[1822] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes new predictions and generates new information.

[1823] "Prediction means" refers to a program or function that uses a generative AI model based on the analysis results to predict the user's future health condition.

[1824] "Means for displaying" refers to a display or notification system that visualizes the prediction results and improvement plans to the user.

[1825] "Means of extraction" refers to algorithms or programs that select relevant information from past success stories.

[1826] "Means for creation" refers to programs and functions for designing user-specific improvement plans based on generative AI models.

[1827] The "notification means" refers to a function or device for notifying the user of the created improvement plan.

[1828] "New data on progress" refers to information about the results and status of activities that a user has carried out based on an improvement plan.

[1829] "Means for updating" refers to programs or functions that update stored data and improvement plans based on new data acquired.

[1830] An "emotional state" refers to a user's mental state, examples of which include stress, joy, sadness, etc.

[1831] "Adjusting means" refers to algorithms or programs that adapt an existing improvement plan based on the user's emotional state.

[1832] A "store" is a physical business establishment where users can visit and purchase products.

[1833] "Health advice" refers to instructions or advice that encourage specific actions or choices to maintain or improve a user's health.

[1834] "Means for checking in real time" refers to functions or devices that allow users to instantly check their emotional state on the spot.

[1835] "Presentation means" refers to a display or notification system that provides appropriate advice and information to the user.

[1836] The system of the present invention provides a personalized improvement plan to improve the user's health, continuously adjusts it taking into account the user's emotional state, and also has the ability to provide effective health advice in physical stores.

[1837] Components

[1838] The system consists of the following components:

[1839] 1. Device:

[1840] This includes smartphones, tablets, and laptops.

[1841] It allows users to enter data about their diet, exercise habits, and emotional state.

[1842] 2. Server:

[1843] Cloud-based or on-premise servers are used.

[1844] The transmitted data is received and stored, and then analyzed and predictions are made.

[1845] 3. Generative AI Model:

[1846] It includes deep learning models and machine learning algorithms that predict future health conditions based on user data and past success stories.

[1847] 4. Emotion Engine:

[1848] The user's emotional state is analyzed using technologies such as voice recognition and facial expression recognition.

[1849] Hardware and Software

[1850] Hardware: Smartphone, tablet, laptop (with built-in camera and microphone)

[1851] Software: Python, OpenCV, Keras, SpeechRecognition library, cloud-based server

[1852] Data processing:

[1853] Camera-based face recognition: Face recognition is performed using OpenCV, and image data is provided to the emotion recognition model.

[1854] Speech Recognition: Use the SpeechRecognition library to capture voice data from the microphone and detect the user's emotional state and needs.

[1855] Processing flow

[1856] 1. Data Collection:

[1857] Users use the device to input data about their diet, exercise habits, and emotional state.

[1858] This data is transmitted from the terminal to the server.

[1859] 2. Data Analysis:

[1860] The server stores the received data and begins analyzing it.

[1861] It assesses the details of your diet and exercise habits and uses generative AI models to predict your future health status.

[1862] 3. Generate an improvement plan:

[1863] Based on the prediction results, the server creates a user-specific improvement plan.

[1864] The emotion engine assesses the user's emotional state and adjusts the remediation plan as needed, for example adding relaxation activities if the user is feeling stressed.

[1865] 4. In-store advice:

[1866] In stores, health advice is provided to users using their smartphones or tablets for products they are considering purchasing.

[1867] It uses facial and voice recognition to check the user's emotional state in real time and provide appropriate advice.

[1868] Specific examples

[1869] For example, when a user is about to buy food at a physical store, the system uses the user's facial image to detect stress. Based on the user's dietary data and emotional state, the system provides health advice such as "Purchase fruit or yogurt, which are effective in relieving stress." It can also use voice recognition to provide specific advice in response to the user's questions.

[1870] Prompt Sentence Examples

[1871] "Recognize the user's emotional state from facial images or voice input. Use the results to generate health advice appropriate to that emotional state."

[1872] In this way, the system comprehensively manages the user's health status and supports the user in improving their health by providing individual improvement plans and real-time health advice.

[1873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1874] Step 1:

[1875] Users use devices (such as smartphones or tablets) to input data about their diet, exercise habits, and emotional state. This includes tapping, voice input, and facial recognition data using a camera. Input data includes dietary content, exercise frequency and duration, and emotional state (such as stress levels).

[1876] Step 2:

[1877] The terminal transmits the input data to the server. At this time, an application on the terminal converts the data into text or binary format and transmits the data using a secure communication protocol (e.g., HTTPS).

[1878] Step 3:

[1879] The server receives the data sent from the device and then stores it in the appropriate database. The data stored in the database is organized by user and maintains its association with past data.

[1880] Step 4:

[1881] The server analyzes the user's current dietary and exercise habits based on the stored data. Based on the dietary and exercise information retrieved from the database, the server evaluates the calorie intake and exercise intensity. The evaluation is performed using queries and algorithms.

[1882] Step 5:

[1883] The server uses the analysis results to predict the user's future health condition using a generative AI model. User data and past success stories are input into the prediction, and the generative AI model outputs risk factors (weight gain, high blood pressure, etc.). For example, specific prediction results such as "risk of weight gain of 10 kg in three years" can be obtained.

[1884] Step 6:

[1885] The server prepares data for displaying the prediction results on the user's terminal. For example, the server generates data including the prediction results visualized in text or graph format and sends it to the terminal. This data is displayed on the terminal's display.

[1886] Step 7:

[1887] The server uses a generative AI model based on past success stories to create a personalized improvement plan for each user. This plan includes specific, actionable, and sustainable actions (e.g., "walk 30 minutes five times a week"). The improvement plan is optimized based on the user's analysis results and emotional state.

[1888] Step 8:

[1889] The server notifies the user of the created improvement plan. The notification is sent to the device and notifies the user via push notification or email. The improvement plan is displayed on the device, and the user can take action based on the plan.

[1890] Step 9:

[1891] The user then inputs new data into the device about their progress with the improvement plan, including their daily diet, exercise, and changes in their emotional state. As the user inputs new data, the data is continually updated.

[1892] Step 10:

[1893] The device then sends new data to the server, which then updates the stored data. The server then analyzes the updated data and continuously updates the improvement plan, ensuring that the device continues to provide a plan that is optimal for the user's current condition.

[1894] Step 11:

[1895] The server recognizes the user's emotional state and adjusts the improvement plan based on that data. For example, if it detects stress, it adds relaxation activities to the action plan. If the user's motivation is low, it sets incentives.

[1896] Step 12:

[1897] When a user visits a physical store, the device provides health advice for products they are considering purchasing. The device's camera is used to recognize the user's face, check their emotional state in real time, and display appropriate health advice. For example, a user who is feeling stressed might be offered advice such as, "Purchase fruits that have a relaxing effect."

[1898] 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.

[1899] 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.

[1900] 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 robot 414.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] 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).

[1905] 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, motorcycles, and other devices, 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 half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1906] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1907] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1908] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1909] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1910] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1911] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1912] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1913] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1914] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1915] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1916] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1917] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facili...

Claims

1. means for inputting data regarding the user's dietary and exercise habits; means for transmitting the input data; means for receiving and storing the transmitted data; A means of analyzing your current diet and exercise habits based on the stored data; A means of predicting the user's future health status using a generative AI model based on the analysis results; a means for displaying the prediction results; A means to extract past success stories and create a user-specific improvement plan based on a generative AI model; and a means for notifying the user of the improvement plan that has been created; A means of obtaining new data and updating stored data regarding the progress of the improvement plan; A means to continually update the improvement plan based on updated data; A system including:

2. The system of claim 1 , wherein the data regarding the user's dietary and exercise habits includes meal content and exercise frequency and duration.

3. 10. The system of claim 1, wherein the means for analyzing the stored data evaluates the user's dietary calorie intake and physical activity.

4. 10. The system of claim 1, wherein the generative AI model works with a historical database to predict future health risks.

5. 2. The system according to claim 1, wherein the means for notifying the user of the improvement plan displays specific action items as a list.

6. 10. The system of claim 1, wherein new data regarding the progress of the improvement plan is entered periodically by the user.

7. 10. The system of claim 1, wherein the continuous updates are automatic based on new user data.

8. 2. The system according to claim 1, wherein the means for displaying the prediction result visually gives a sense of danger to the user.

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