system

Abstract

We provide the system. [Solution] Means for acquiring user lifestyle data, A means for predicting the user's future health status based on the aforementioned lifestyle data, A means for visualizing the predicted health status, A means of providing the user with advice for lifestyle improvement based on the visualized health status, A means to track changes in the user's lifestyle based on the aforementioned advice and continuously update health predictions, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, lifestyle diseases are on the rise, and their prevention is socially emphasized. However, many individuals have a problem that even if they recognize the risk of lifestyle diseases, they do not know how to specifically improve them. Also, it is difficult to predict future health conditions and consider specific improvement measures based on them. Therefore, there is a need to present specific and effective improvement methods for individual lifestyles and provide assistance for realizing a healthy life.

Means for Solving the Problems

[0005] This invention is a system that acquires users' lifestyle data and predicts and visualizes their future health status based on that data. Specifically, it collects users' daily purchase history and activity sensor data and uses this to predict their future health status. By visually presenting the predicted health status, it makes it easier for users to understand the need for lifestyle improvements. Furthermore, based on the prediction results, it provides specific lifestyle improvement advice such as dietary improvement suggestions and exercise plans. In addition, by tracking changes in the user's lifestyle and continuously updating the health prediction, users can check the progress of their health status in real time. This enables users to proactively achieve a healthy lifestyle.

[0006] A "user" refers to an individual who uses this system to improve their lifestyle and health.

[0007] "Lifestyle data" refers to data related to a user's daily behavior, including, in particular, dietary content, purchase history, and activity levels.

[0008] "Predicting future health status" refers to the process of estimating a user's future health status based on past lifestyle data.

[0009] "Visualization" refers to the act of representing health prediction results obtained as numerical values ​​or indicators in graphics or visuals in a way that is easy for users to understand.

[0010] "Lifestyle improvement advice" refers to specific action plans and suggestions provided to users to reduce predicted health risks.

[0011] "Tracking" refers to the act of continuously monitoring and accumulating data on the actions and lifestyle changes that users actually take.

[0012] "Updating health predictions" refers to the process of recalculating predictions about a user's future health status based on new user data, and providing the most up-to-date information. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0034] The system according to the present invention uses the user's lifestyle data to predict their future health status and provides the user with advice for improving their lifestyle. This system involves a server, a terminal, and the user working together to carry out a series of processes including data collection, prediction, presentation, and feedback.

[0035] The server receives lifestyle data sent by users and stores it in a database. This data includes dietary information, exercise levels, and purchase history entered by users through the application. The server processes this data and uses machine learning algorithms to predict future health conditions.

[0036] For example, if the server determines that a user has recently been frequently consuming high-fat foods, it predicts future weight gain and worsening skin condition. The server analyzes this information and generates appropriate lifestyle improvement advice for the user. This advice may include suggestions for improving diet and increasing exercise.

[0037] The terminal receives information transmitted from the server and presents it to the user in a visually easy-to-understand format. Through a graphical user interface (GUI), the terminal displays predicted health status and specific improvement advice. Users can incorporate this information into their daily lives to practice healthy habits.

[0038] Based on the advice provided, users actually change their diet and exercise habits. For example, they are encouraged to increase their vegetable intake, reduce fatty foods, or start exercising a few times a week. The effects of these actions are continuously analyzed as the device collects data again and sends it to the server. The server updates health predictions based on the new data and informs the user of their latest improvements, enabling them to take a more proactive approach to health management.

[0039] As described above, this system helps users maintain a healthy lifestyle by monitoring changes in their lifestyle habits over a long period and providing timely feedback.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] User: Inputs or records information such as meal details, exercise levels, or purchase history on the device through applications used daily.

[0043] Step 2:

[0044] Terminal: Collects lifestyle data from users and sends the data to the server periodically or as instructed by the user.

[0045] Step 3:

[0046] Server: Stores received lifestyle data in a database. This may involve encryption or other processing to protect personal information.

[0047] Step 4:

[0048] Server: Applies machine learning algorithms to analyze stored data. This generates a model that predicts the user's future health status.

[0049] Step 5:

[0050] Server: Generates data to visualize appearance and health risks based on predicted health status. For example, it generates images of the likelihood of weight gain or deterioration of skin condition.

[0051] Step 6:

[0052] Server: It also creates specific lifestyle improvement advice for the user, including necessary exercise plans and dietary adjustments.

[0053] Step 7:

[0054] Server: Sends a visualized future scenario and improvement suggestions to the terminal.

[0055] Step 8:

[0056] Terminal: Displays information received from the server in a graphical user interface. This allows users to easily understand the information visually.

[0057] Step 9:

[0058] User: Use the displayed future predictions and advice to take concrete actions in daily life. For example, review your diet or increase your physical activity.

[0059] Step 10:

[0060] Terminal: Records user actions and changes in lifestyle again, and prepares to send feedback to the server in the next data collection cycle.

[0061] Through this series of processes, the system is operated to continuously evaluate the user's health status and provide timely and customized feedback.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In modern society, the diversification of individual lifestyles complicates maintaining health. Under these circumstances, there is a need to provide predictions of health status and concrete action guidelines based on individual lifestyles. However, conventional systems have struggled to comprehensively analyze diverse user data and dynamically provide personalized health advice. In particular, the lack of a system that provides real-time feedback on behavioral changes leads to a weakening of awareness regarding lifestyle improvements, making effective health management difficult.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for acquiring user lifestyle information, means for predicting health status and generating advice using a machine learning model, and means for dynamically updating data to reflect changes in user behavior via a terminal. This makes it possible to provide detailed and accurate health predictions and lifestyle improvement advice based on the user's lifestyle.

[0067] "User lifestyle information" refers to data about the user's daily activities, including records of eating, exercise, and purchasing behavior.

[0068] "Health status prediction" is a process that estimates a user's future health status based on acquired lifestyle information.

[0069] A "machine learning model" is a model that uses algorithms and methods to analyze large amounts of data and identify patterns and trends.

[0070] "Means of generating advice" refers to the process of creating specific suggestions for improving the user's lifestyle based on their predicted health condition.

[0071] A "terminal" is a device used by users to input lifestyle information and receive advice from a server, and includes smartphones and personal computers.

[0072] "Methods for dynamically updating data" refer to methods of continuously changing and adjusting information within a server based on new information input from users, thereby maintaining the data in an up-to-date state.

[0073] This invention relates to a system that predicts a user's health status based on their lifestyle information and provides advice for lifestyle improvement. The implementation of this system involves a server, a terminal, and a user, each fulfilling their respective roles.

[0074] Hardware and software configuration

[0075] The server uses a high-performance database system to receive and store lifestyle information sent by users. Generative AI models such as "TENSORFLOW®" and "PyTorch" are used to process this data. This allows the server to leverage machine learning algorithms to predict health status.

[0076] The terminal is a device used by users to input lifestyle information, and includes smartphones and personal computers. Through a dedicated application, users send the entered information to the server. The terminal uses a GUI (Graphical User Interface) to visually display the advice received from the server.

[0077] Users input data on their daily diet, exercise, and purchasing behavior via their device, and this data is sent to a server. Based on the advice provided on the device, users adjust their lifestyle habits, and by inputting the results again, a feedback loop is formed.

[0078] Specific example

[0079] For example, if a user thinks, "I've recently started gaining weight, so I want to review my diet," they can use the application to record their daily meals and exercise in detail. Based on this information, the server uses a generative AI model to predict future weight fluctuations and sends specific advice to the user's device, such as "You should reduce your fat intake and aim to exercise three times a week."

[0080] Example of a prompt

[0081] "The user has entered lifestyle data. His recent diet has been high in fat. We will predict his future health and provide advice for improvement."

[0082] In this way, the system aims to provide users with useful information and support them in adopting healthy lifestyle habits.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users input lifestyle information into the application using their device. This input consists of data such as diet, exercise levels, and purchase history. The entered data is automatically sent to the application's database.

[0086] Step 2:

[0087] The terminal sends lifestyle information obtained from the user to the server using a secure protocol (e.g., HTTPS). The entered data is sent to the server, and the server, upon receiving the data, securely stores it in its database.

[0088] Step 3:

[0089] The server preprocesses the received lifestyle information, cleaning and organizing the necessary data. This data is then used as input for the generative AI model. Specifically, it performs operations such as imputing missing values ​​and correcting outliers.

[0090] Step 4:

[0091] The server inputs the organized data into a generating AI model (e.g., an algorithm using TensorFlow or PyTorch) to predict future health conditions. Data processing and computation here include feature extraction and application of predictive models to ultimately obtain prediction results.

[0092] Step 5:

[0093] The server generates advice for the user based on the prediction results. This advice includes guidance on dietary improvements, exercise plans, and other behavioral guidelines. This requires processing based on the results obtained from the generating AI model, and is specifically output as prompt statements.

[0094] Step 6:

[0095] The terminal receives advice sent from the server and presents it to the user through a GUI. Specifically, it uses visually easy-to-understand graphs and charts to display health status predictions and advice.

[0096] Step 7:

[0097] Users adjust their lifestyles based on the advice provided and re-enter new lifestyle information. This process forms a feedback loop, leading to continuous improvement that enhances the accuracy of health predictions.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In modern times, personal health management is becoming an increasingly important issue, but there is insufficient information provided to enable users to consciously make healthy choices, making it difficult to make sound decisions, especially when shopping. Furthermore, there is a lack of means to track changes in lifestyle in real time and provide immediate, appropriate feedback.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring information on the user's lifestyle habits, means for predicting the user's future health condition based on the lifestyle habit information, and means for recognizing product information using smart glasses and displaying a health assessment to the user in real time. This allows the user to be encouraged to make healthy choices on the spot when shopping and to continuously improve their healthy lifestyle habits.

[0103] A "user" refers to an individual who uses the system for health management or product selection.

[0104] "Lifestyle information" refers to data about a user's daily activities and choices, specifically including dietary content, exercise levels, and purchase history.

[0105] "Methods for predicting health status" refer to techniques that use machine learning technology and other methods to predict future health trends of users based on acquired lifestyle information.

[0106] "Means of expression" refers to technologies that visualize and present predicted health conditions in a way that is easy for users to understand.

[0107] "Means of providing suggestions" refers to elements of a system that, based on predicted health conditions, provides advice and guidelines to encourage behavioral change in users.

[0108] "Means of tracking and continuously updating health predictions" refers to a process of collecting new lifestyle information from users and updating health predictions and suggestions accordingly.

[0109] "Smart glasses" are wearable devices that utilize augmented reality technology to overlay information onto the user's real-world field of vision.

[0110] "Means of recognizing product information" refers to a system function that uses technology installed in smart glasses to identify products and their labels in a store and acquire related data.

[0111] "A means of displaying health assessments in real time" refers to a function that immediately presents the health impact of a product to the user after product recognition, supporting them in making better choices.

[0112] To implement this invention, a user's smart glasses, a server for processing data, and a terminal for presenting information to the user are required. The operation of this system is described in detail below.

[0113] The server continuously collects and stores user lifestyle information. This includes purchase history information obtained through smart glasses and activity sensor information from wearable devices. Based on this data, the server uses machine learning algorithms to predict the user's future health status. For this purpose, the server uses a database management system (e.g., MySQL®) and a machine learning framework (e.g., TensorFlow).

[0114] The terminal receives predictive data transmitted from the server and builds a visual interface for displaying it on the user's smart glasses. This interface uses object recognition software (e.g., OpenCV) to recognize product labels and barcodes in stores and displays the acquired information to the user in real time. This display includes specific suggestions for improvement based on the predicted health status.

[0115] When users visit a physical store wearing smart glasses, they can receive real-time health assessments of products as they select them. For example, after scanning a product, they might be presented with information such as, "This snack is high in fat. Other foods are recommended for maintaining good health."

[0116] An example of a specific prompt is: "Propose a system that provides real-time health advice when a user views a product through smart glasses. Design an app that recognizes product labels and displays health advice based on past lifestyle data."

[0117] As a result, the present invention functions as a powerful tool to guide users' lifestyles in a better direction, enabling them to make healthier choices.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives lifestyle information transmitted from the user. This information includes purchase history acquired by smart glasses and user activity sensor data. This data is stored in a database and prepared for future predictions. The input is user activity data and purchase history information, and the output is the raw data stored in the database.

[0121] Step 2:

[0122] The server processes the stored data and uses machine learning algorithms to predict the user's health status. This process uses a model built with TensorFlow to analyze trends from the user's past lifestyle data. The input is the accumulated past lifestyle data, and the output is a prediction of the user's future health status.

[0123] Step 3:

[0124] The server generates advice for the user based on their predicted health status. Using a generative AI model, it creates specific dietary improvement suggestions and exercise plans. The input is the predicted health status, and the output is the advice provided to the user.

[0125] Step 4:

[0126] The terminal receives advice from the server and displays it in real time on the user's smart glasses. It uses OpenCV to recognize product labels and obtain health assessment information related to those products. The input consists of product recognition information and advice from the server, while the output is the health assessment information displayed on the smart glasses.

[0127] Step 5:

[0128] Users select healthier products based on displays shown on smart glasses. Based on the user's new choices, behavioral data is sent back to the server for continuous health management. The input is the user's selection behavior data, and the output is new data for future predictions and advice updates.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] The system according to the present invention acquires users' lifestyle data, predicts their future health status, and visualizes it. In addition, by incorporating an emotion engine, it provides advice tailored to the user's emotional state. This system supports health management optimized for individual needs by facilitating information exchange between the server, terminal, and user.

[0131] The server receives and stores lifestyle data transmitted from the user's device and uses this data to predict the user's future health status. This lifestyle data includes diet, exercise levels, purchase history, and biosensor data to estimate the user's emotional state. The server's algorithm integrates this data to make predictions that take into account the impact of the user's emotions on their health habits.

[0132] The emotion engine estimates emotions from the user's biometric data and self-reported data, and incorporates this information into a predictive model. This engine is used to understand the user's current mental state and emotional responses, and to generate more appropriate advice. For example, if a user is feeling stressed, the emotion engine will suggest exercises or relaxation techniques to alleviate that stress.

[0133] The device displays prediction results, visualized health status, and emotion-based lifestyle improvement advice to the user as data sent from the server. Visualizations can include graphs showing emotional states and animations that visualize changes in physical condition. This allows users to intuitively understand future health risks and promote improvement activities in an emotion-conscious manner.

[0134] Users make choices in their daily lives based on the information presented by the device. For example, if the emotional engine indicates signs of stress, the user implements the provided relaxation strategies. This feedback loop allows users to improve both their self-management and their health.

[0135] Thus, the present invention provides a system that utilizes an emotion engine to realize individually adapted health management while taking into account the user's emotional background.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] User: Through the application, users input daily meal content and activity records into their device, and collect biosensor data (e.g., heart rate and stress level) using wearable devices.

[0139] Step 2:

[0140] Terminal: Regularly uploads user-entered lifestyle data and collected biosensor data to the server. This data is encrypted and privacy is protected.

[0141] Step 3:

[0142] Server: Receives user data and stores it in a database, then supplies it to the analysis algorithm. Here, the server calculates the user's current health status and future health predictions from both lifestyle data and biosensor data.

[0143] Step 4:

[0144] Emotion Engine (on-server): Analyzes changes in heart rate and stress levels from the user's biosensor data to estimate the user's emotional state. For example, if high stress levels are estimated, this information is used to evaluate lifestyle habits.

[0145] Step 5:

[0146] Server: Integrates lifestyle data and emotional state analysis results to generate data that visualizes the user's future health. This includes graphics that reflect predicted changes in physical condition and risk factors.

[0147] Step 6:

[0148] Server: Generates lifestyle improvement advice that takes into account the user's emotional state. For example, if a user is feeling stressed, it will generate advice recommending relaxation or meditation.

[0149] Step 7:

[0150] Server: Sends prediction results and advice to the user's terminal.

[0151] Step 8:

[0152] Terminal: Visually displays information sent from the server to the user. Users can intuitively understand their future health status and lifestyle improvement advice.

[0153] Step 9:

[0154] User: Based on advice from the system, users can change their diet or incorporate recommended activities in their real lives. This allows for choices that address their emotional needs.

[0155] Step 10:

[0156] Terminal: Prepares to collect new user lifestyle and emotional data and send it to the server. This new data will be used to generate health predictions and advice for the next cycle.

[0157] In this way, the system takes into account the user's lifestyle and emotional state, and provides a continuously personalized health management and feedback process.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] Traditional health management systems predict health based on users' lifestyles, but they fail to provide appropriate advice that takes into account the user's emotional state. This poses challenges, particularly in stress management and maintaining motivation, areas heavily influenced by emotions. Furthermore, the resulting predictive models and advice have limitations in accuracy, failing to achieve sufficient individualization.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for acquiring information about the user's lifestyle, means for predicting the user's future health status based on the information, and means for estimating the user's emotional state using an emotion analysis algorithm and proposing appropriate actions according to the user's emotions using a generative AI model. This enables the provision of personalized health management advice that takes into account the user's emotional state, improving the accuracy of health predictions and promoting changes in the user's behavior.

[0163] "Information regarding lifestyle habits" refers to data on the user's daily actions and activities, specifically including information such as diet, exercise levels, purchase history, and data from biometric monitoring devices.

[0164] "Predicting future health status" is a process that predicts what kind of health status a user will have in the future, based on their current lifestyle and emotional state.

[0165] "Visualization" refers to presenting health status, obtained as numerical data, in a way that is easy for users to understand, using graphs and animations.

[0166] "Providing guidance" means offering advice on specific choices and actions that users should take in their daily lives.

[0167] An "emotional analysis algorithm" is a series of computational procedures for estimating a user's emotional state based on biometric data and self-reported information, and for processing that information.

[0168] A "generative AI model" is an artificial intelligence system that learns from large amounts of data to make predictions, classifications, and suggestions.

[0169] "Biometric monitoring device data" refers to data obtained from devices that measure heart rate, activity levels, etc., and is used to understand the user's physical condition.

[0170] "Nutritional improvement suggestions" refer to advice on recommended foods and meal combinations to support the user's health.

[0171] A "physical activity plan" is an approach that proposes appropriate exercise and fitness activities for the purpose of maintaining or improving the user's health.

[0172] The system according to the present invention acquires information on the user's lifestyle, predicts their future health status, and provides guidance for lifestyle improvement by visually presenting the results. This system is operated primarily through information exchange between a server, a terminal, and the user.

[0173] The server receives lifestyle information transmitted from the user's device. This information includes dietary content, exercise levels, purchase history, and biometric monitoring data such as heart rate and stress levels. The server stores this data in a database and forms an integrated data profile for each user.

[0174] Next, the server uses a generative AI model based on the acquired data to predict the user's future health status. In particular, it uses an algorithm that analyzes emotions to estimate the user's emotional state and incorporates this into the prediction model. This allows the system to output results evaluating the impact of stress on health, for example, if the user is in a high-stress state.

[0175] The device receives predictive data transmitted from the server and presents it visually to the user. This includes graphs and animations that clearly show changes in health status. It also provides lifestyle improvement guidelines based on generated emotions. Specifically, this includes suggestions for improving nutrition and physical activity plans, allowing the user to know what concrete actions they can take to improve their lifestyle.

[0176] Users utilize the information and advice presented on their devices to modify their choices and actions in daily life. For example, if the prediction indicates that high stress may negatively impact their health, the user might incorporate suggested relaxation methods such as yoga or meditation into their daily routine. Furthermore, by returning the results as feedback to the server, they can contribute to the continuous improvement of the predictive model.

[0177] As a concrete example, a possible prompt to input into the generating AI model could be, "Based on the user's current emotional state, what improvements can be suggested regarding their exercise habits?" This would allow the system to provide personalized advice that takes the user's emotional state into account.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] Users record data about their daily activities and behaviors. Input includes meal logs, exercise levels, purchase history, heart rate, and stress levels. This data is transmitted to a server via the user's device. Data may also be automatically acquired using biometric monitoring devices.

[0181] Step 2:

[0182] The server stores the received data in a database and creates a lifestyle profile for each user. The input data is integrated and organized for each user. This integration process includes data cleansing and data matching to ensure consistency between each dataset. The output is the integrated user lifestyle profile.

[0183] Step 3:

[0184] The server uses a generative AI model to predict the user's future health status from an integrated lifestyle profile. Input includes the user's past lifestyle data and emotional state. Data analysis and machine learning algorithms are used to build a predictive model. The output is a prediction of the future health status.

[0185] Step 4:

[0186] The server uses an emotion analysis algorithm to estimate the user's emotional state and incorporates this information into the predictions of a generative AI model. The input consists of the user's biometric data and self-reported data. Data analysis estimates the emotional state, and this information is reflected in the prediction model. The output is a prediction of health status that takes emotions into account.

[0187] Step 5:

[0188] The terminal receives prediction results sent from the server and displays them visually. The input is a prediction of health status that takes emotions into account. Through visualization processing, it is presented to the user as graphs and animations. The output is visualized data that the user can intuitively understand.

[0189] Step 6:

[0190] The device presents the user with lifestyle improvement guidelines generated by a generative AI model. Input includes predictive data from the server and emotion-based advice. The guidelines are presented as specific suggestions for improving diet and exercise. The output is detailed guidance for improving the user's lifestyle.

[0191] Step 7:

[0192] Users take action to improve their lifestyle based on information and advice from their devices. Input includes visualized data and lifestyle improvement guidelines. Users take specific actions such as reviewing their diet or implementing an exercise plan, and new lifestyle data is generated as output, which is then fed back to the server.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] In modern health management, there is a need for systems that provide continuous health predictions and improvement advice that take into account the user's lifestyle and emotional state. However, existing technologies have struggled to adequately consider the user's daily emotional changes and improvement measures based on specific meal plans. Furthermore, there is a demand for more personalized health maintenance by combining this with logistics plans tailored to emotional states.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0197] In this invention, the server includes means for acquiring the user's biometric and emotional data, means for predicting the user's future health status based on the data, and means for visualizing the predicted health and emotional status and providing emotionally-based lifestyle improvement advice. This enables the provision of personalized health management and optimal eating plans and logistics services that take into account the user's emotional status.

[0198] "User biometric data" refers to data that indicates the user's physical activity and health status, and includes heart rate, sleep data, exercise levels, etc.

[0199] "Emotional data" refers to data that indicates a user's current psychological state and emotional changes, and is collected through self-reporting or biosensors.

[0200] "Predicting future health status" refers to the act of predicting future health risks and changes in health based on the user's past data.

[0201] "Visualization" is the act of clearly displaying predicted health and emotional states using graphs and animations.

[0202] "Lifestyle improvement advice" refers to suggestions for actions and plans to improve dietary habits, exercise habits, and other aspects of a user's life, based on their current condition and predicted results.

[0203] A "meal plan" is a series of plans that propose meal content for the purpose of managing the user's nutrition.

[0204] "Providing logistics services" means delivering meals and supplies at the optimal time and with the appropriate content, tailored to the user's health condition and emotional state.

[0205] The system of the present invention is configured to provide personalized health management through the user's biometric and emotional data. Specific embodiments are described below.

[0206] The server first collects biometric data from devices such as smartwatches and fitness trackers, including the user's heart rate, sleep data, and activity level. This data is transmitted to the server in real time via a smartphone app. Additionally, the user's emotional data is collected using a natural language processing engine and analyzed by an emotion engine. The emotion engine uses machine learning algorithms to predict emotional changes and analyze the user's current emotional state.

[0207] The server uses a TensorFlow-based predictive model to forecast the user's future health based on collected data. This forecast includes dietary history and activity sensor data. The forecast results are generated as graphs and animations that can be intuitively understood through visualization tools and sent to the device. Based on these results, the device provides the user with appropriate lifestyle improvement advice.

[0208] For example, if emotional data indicating that a user is feeling stressed is sent to the server, the server can use this information to recommend a relaxing meal plan and automatically arrange logistics services based on that plan.

[0209] An example of a prompt using a generative AI model is: "Generate a meal plan suggested when the user's emotional state is 'stressed'. Past meal history is 'salad, smoothie, grilled chicken'." This allows for the provision of continuous and adaptive health management services to the user.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server receives user biometric data (heart rate, sleep patterns, activity level, etc.) from smartwatches and fitness trackers. This data is transmitted in real time and stored on the server. The input is data from each device, and the output is an integrated indicator of the user's health status.

[0213] Step 2:

[0214] The server collects user emotion data through a smartphone application. Using a natural language processing engine, it estimates emotions from the text information entered by the user into the application. The input is text data, and the output is the category of the analyzed emotional state.

[0215] Step 3:

[0216] The server uses a TensorFlow-based predictive model to forecast future health status based on collected biometric and emotional data. This process also considers past dietary history and activity sensor data. The input is an integrated dataset, and the output is predictive data regarding future health status.

[0217] Step 4:

[0218] The server visualizes predicted health and emotional states. Graphing software and animation tools are used for visualization, displaying the data on the terminal in an intuitively understandable format for the user. The input is the prediction result, and the output is a graph or animation of the visualized health data.

[0219] Step 5:

[0220] The device generates and presents appropriate lifestyle improvement advice to the user based on visualization data and prediction results sent from the server. Specifically, it is guided by prompts created by a generative AI model and proposes meal plans and exercise plans. The input is visualization data and instructions from the generative AI model, and the output is specific advice for the user.

[0221] Step 6:

[0222] The user adjusts their daily habits according to the advice provided on the device and sends the resulting feedback to the server. This feedback is used to track how the user's habit changes are progressing. The input is the user's actual behavioral data, and the output is the updated information sent to the server.

[0223] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0224] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0225] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0229] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0230] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0231] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0232] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0233] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0234] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0235] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0236] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0237] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0238] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0239] The system according to the present invention uses the user's lifestyle data to predict their future health status and provides the user with advice for improving their lifestyle. This system involves a server, a terminal, and the user working together to carry out a series of processes including data collection, prediction, presentation, and feedback.

[0240] The server receives lifestyle data sent by users and stores it in a database. This data includes dietary information, exercise levels, and purchase history entered by users through the application. The server processes this data and uses machine learning algorithms to predict future health conditions.

[0241] For example, if the server determines that a user has recently been frequently consuming high-fat foods, it predicts future weight gain and worsening skin condition. The server analyzes this information and generates appropriate lifestyle improvement advice for the user. This advice may include suggestions for improving diet and increasing exercise.

[0242] The terminal receives information transmitted from the server and presents it to the user in a visually easy-to-understand format. Through a graphical user interface (GUI), the terminal displays predicted health status and specific improvement advice. Users can incorporate this information into their daily lives to practice healthy habits.

[0243] Based on the advice provided, users actually change their diet and exercise habits. For example, they are encouraged to increase their vegetable intake, reduce fatty foods, or start exercising a few times a week. The effects of these actions are continuously analyzed as the device collects data again and sends it to the server. The server updates health predictions based on the new data and informs the user of their latest improvements, enabling them to take a more proactive approach to health management.

[0244] As described above, this system helps users maintain a healthy lifestyle by monitoring changes in their lifestyle habits over a long period and providing timely feedback.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] User: Inputs or records information such as meal details, exercise levels, or purchase history on the device through applications used daily.

[0248] Step 2:

[0249] Terminal: Collects lifestyle data from users and sends the data to the server periodically or as instructed by the user.

[0250] Step 3:

[0251] Server: Stores received lifestyle data in a database. This may involve encryption or other processing to protect personal information.

[0252] Step 4:

[0253] Server: Applies machine learning algorithms to analyze stored data. This generates a model that predicts the user's future health status.

[0254] Step 5:

[0255] Server: Generates data to visualize appearance and health risks based on predicted health status. For example, it generates images of the likelihood of weight gain or deterioration of skin condition.

[0256] Step 6:

[0257] Server: It also creates specific lifestyle improvement advice for the user, including necessary exercise plans and dietary adjustments.

[0258] Step 7:

[0259] Server: Sends a visualized future scenario and improvement suggestions to the terminal.

[0260] Step 8:

[0261] Terminal: Displays information received from the server in a graphical user interface. This allows users to easily understand the information visually.

[0262] Step 9:

[0263] User: Use the displayed future predictions and advice to take concrete actions in daily life. For example, review your diet or increase your physical activity.

[0264] Step 10:

[0265] Terminal: Records user actions and changes in lifestyle again, and prepares to send feedback to the server in the next data collection cycle.

[0266] Through this series of processes, the system is operated to continuously evaluate the user's health status and provide timely and customized feedback.

[0267] (Example 1)

[0268] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] In modern society, the diversification of individual lifestyles complicates maintaining health. Under these circumstances, there is a need to provide predictions of health status and concrete action guidelines based on individual lifestyles. However, conventional systems have struggled to comprehensively analyze diverse user data and dynamically provide personalized health advice. In particular, the lack of a system that provides real-time feedback on behavioral changes leads to a weakening of awareness regarding lifestyle improvements, making effective health management difficult.

[0270] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0271] In this invention, the server includes means for acquiring user lifestyle information, means for predicting health status and generating advice using a machine learning model, and means for dynamically updating data to reflect changes in user behavior via a terminal. This makes it possible to provide detailed and accurate health predictions and lifestyle improvement advice based on the user's lifestyle.

[0272] "User lifestyle information" refers to data about the user's daily activities, including records of eating, exercise, and purchasing behavior.

[0273] "Health status prediction" is a process that estimates a user's future health status based on acquired lifestyle information.

[0274] A "machine learning model" is a model that uses algorithms and methods to analyze large amounts of data and identify patterns and trends.

[0275] "Means of generating advice" refers to the process of creating specific suggestions for improving the user's lifestyle based on their predicted health condition.

[0276] A "terminal" is a device used by users to input lifestyle information and receive advice from a server, and includes smartphones and personal computers.

[0277] "Methods for dynamically updating data" refer to methods of continuously changing and adjusting information within a server based on new information input from users, thereby maintaining the data in an up-to-date state.

[0278] This invention relates to a system that predicts a user's health status based on their lifestyle information and provides advice for lifestyle improvement. The implementation of this system involves a server, a terminal, and a user, each fulfilling their respective roles.

[0279] Hardware and software configuration

[0280] The server uses a high-performance database system to receive and store the lifestyle information sent from the user. For the processing of this data, generative AI models such as "TensorFlow" and "PyTorch" are used, for example. Thereby, the server utilizes machine learning algorithms to execute the prediction of the health condition.

[0281] The terminal is a device for the user to input lifestyle information, and smartphones and personal computers are applicable. Through a dedicated application, the user sends the input information to the server. The terminal uses a GUI (Graphical User Interface) to visually display the advice received from the server.

[0282] The user inputs data such as daily diet, exercise, and purchasing behavior via the terminal, and this data is sent to the server. The user adjusts their lifestyle based on the advice provided on the terminal and forms a feedback loop by inputting the results again.

[0283] Specific example

[0284] For example, when the user thinks "I've started gaining weight recently and want to review my diet", they use the application to record details of their daily diet and exercise amount. Based on this information, the server uses a generative AI model to predict future weight fluctuations and sends specific advice such as "Reduce lipids and aim to exercise three times a week" to the terminal for the user.

[0285] Example of prompt text

[0286] "The user has input lifestyle data. Their recent diet contains a lot of fat. Predict the future health condition and provide advice for improvement."

[0287] In this way, the system aims to provide useful information for the user and support the implementation of a healthy lifestyle.

[0288] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0289] Step 1:

[0290] Users input lifestyle information into the application using their device. This input consists of data such as diet, exercise levels, and purchase history. The entered data is automatically sent to the application's database.

[0291] Step 2:

[0292] The terminal sends lifestyle information obtained from the user to the server using a secure protocol (e.g., HTTPS). The entered data is sent to the server, and the server, upon receiving the data, securely stores it in its database.

[0293] Step 3:

[0294] The server preprocesses the received lifestyle information, cleaning and organizing the necessary data. This data is then used as input for the generative AI model. Specifically, it performs operations such as imputing missing values ​​and correcting outliers.

[0295] Step 4:

[0296] The server inputs the organized data into a generating AI model (e.g., an algorithm using TensorFlow or PyTorch) to predict future health conditions. Data processing and computation here include feature extraction and application of predictive models to ultimately obtain prediction results.

[0297] Step 5:

[0298] The server generates advice for the user based on the prediction results. This advice includes guidance on dietary improvements, exercise plans, and other behavioral guidelines. This requires processing based on the results obtained from the generating AI model, and is specifically output as prompt statements.

[0299] Step 6:

[0300] The terminal receives advice sent from the server and presents it to the user through a GUI. Specifically, it uses visually easy-to-understand graphs and charts to display health status predictions and advice.

[0301] Step 7:

[0302] Users adjust their lifestyles based on the advice provided and re-enter new lifestyle information. This process forms a feedback loop, leading to continuous improvement that enhances the accuracy of health predictions.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] In modern times, personal health management is becoming an increasingly important issue, but there is insufficient information provided to enable users to consciously make healthy choices, making it difficult to make sound decisions, especially when shopping. Furthermore, there is a lack of means to track changes in lifestyle in real time and provide immediate, appropriate feedback.

[0306] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes means for acquiring the user's lifestyle information, means for predicting the user's future health state based on the lifestyle information, and means for using smart glasses to recognize product information and display a health evaluation to the user in real time. As a result, the user can be prompted to make healthy choices on the spot when shopping, and it becomes possible to continuously improve to a healthy lifestyle.

[0308] The "user" refers to an individual who uses the system for health management and product selection.

[0309] The "lifestyle information" is data related to the actions and choices in the user's daily life, specifically including diet content, exercise amount, purchase history, etc.

[0310] The "means for predicting the health state" is a method for inferring the future health trend of the user using machine learning technology and the like based on the acquired lifestyle information.

[0311] The "means for presenting" is a technology for visualizing the predicted health state in an easy-to-understand form for the user and presenting it visually.

[0312] The "means for providing proposals" is an element of the system that shows advice and guidelines for prompting the user to change their behavior based on the predicted health state.

[0313] The "means for tracking and continuously updating health predictions" refers to the process of collecting the user's new lifestyle information and updating the health state predictions and proposal contents accordingly at any time.

[0314] The "smart glasses" is a wearable device that utilizes augmented reality technology to overlay information on the user's real field of vision.

[0315] The "means for recognizing product information" is a system function that uses the technology installed in the smart glasses to identify in-store products and their labels and acquire relevant data.

[0316] "A means of displaying health assessments in real time" refers to a function that immediately presents the health impact of a product to the user after product recognition, supporting them in making better choices.

[0317] To implement this invention, a user's smart glasses, a server for processing data, and a terminal for presenting information to the user are required. The operation of this system is described in detail below.

[0318] The server continuously collects and stores user lifestyle information. This includes purchase history information obtained through smart glasses and activity sensor data from wearable devices. Based on this data, the server uses machine learning algorithms to predict the user's future health status. For this purpose, the server uses a database management system (e.g., MySQL) and a machine learning framework (e.g., TensorFlow).

[0319] The terminal receives predictive data transmitted from the server and builds a visual interface for displaying it on the user's smart glasses. This interface uses object recognition software (e.g., OpenCV) to recognize product labels and barcodes in stores and displays the acquired information to the user in real time. This display includes specific suggestions for improvement based on the predicted health status.

[0320] When users visit a physical store wearing smart glasses, they can receive real-time health assessments of products as they select them. For example, after scanning a product, they might be presented with information such as, "This snack is high in fat. Other foods are recommended for maintaining good health."

[0321] An example of a specific prompt is: "Propose a system that provides real-time health advice when a user views a product through smart glasses. Design an app that recognizes product labels and displays health advice based on past lifestyle data."

[0322] As a result, the present invention functions as a powerful tool to guide users' lifestyles in a better direction, enabling them to make healthier choices.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server receives lifestyle information transmitted from the user. This information includes purchase history acquired by smart glasses and user activity sensor data. This data is stored in a database and prepared for future predictions. The input is user activity data and purchase history information, and the output is the raw data stored in the database.

[0326] Step 2:

[0327] The server processes the stored data and uses machine learning algorithms to predict the user's health status. This process uses a model built with TensorFlow to analyze trends from the user's past lifestyle data. The input is the accumulated past lifestyle data, and the output is a prediction of the user's future health status.

[0328] Step 3:

[0329] The server generates advice for the user based on their predicted health status. Using a generative AI model, it creates specific dietary improvement suggestions and exercise plans. The input is the predicted health status, and the output is the advice provided to the user.

[0330] Step 4:

[0331] The terminal receives advice from the server and displays it in real time on the user's smart glasses. It uses OpenCV to recognize product labels and obtain health assessment information related to those products. The input consists of product recognition information and advice from the server, while the output is the health assessment information displayed on the smart glasses.

[0332] Step 5:

[0333] Users select healthier products based on displays shown on smart glasses. Based on the user's new choices, behavioral data is sent back to the server for continuous health management. The input is the user's selection behavior data, and the output is new data for future predictions and advice updates.

[0334] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0335] The system according to the present invention acquires users' lifestyle data, predicts their future health status, and visualizes it. In addition, by incorporating an emotion engine, it provides advice tailored to the user's emotional state. This system supports health management optimized for individual needs by facilitating information exchange between the server, terminal, and user.

[0336] The server receives and stores lifestyle data transmitted from the user's device and uses this data to predict the user's future health status. This lifestyle data includes diet, exercise levels, purchase history, and biosensor data to estimate the user's emotional state. The server's algorithm integrates this data to make predictions that take into account the impact of the user's emotions on their health habits.

[0337] The emotion engine estimates emotions from the user's biometric data and self-reported data, and incorporates this information into a predictive model. This engine is used to understand the user's current mental state and emotional responses, and to generate more appropriate advice. For example, if a user is feeling stressed, the emotion engine will suggest exercises or relaxation techniques to alleviate that stress.

[0338] The device displays prediction results, visualized health status, and emotion-based lifestyle improvement advice to the user as data sent from the server. Visualizations can include graphs showing emotional states and animations that visualize changes in physical condition. This allows users to intuitively understand future health risks and promote improvement activities in an emotion-conscious manner.

[0339] Users make choices in their daily lives based on the information presented by the device. For example, if the emotional engine indicates signs of stress, the user implements the provided relaxation strategies. This feedback loop allows users to improve both their self-management and their health.

[0340] Thus, the present invention provides a system that utilizes an emotion engine to realize individually adapted health management while taking into account the user's emotional background.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] User: Through the application, users input daily meal content and activity records into their device, and collect biosensor data (e.g., heart rate and stress level) using wearable devices.

[0344] Step 2:

[0345] Terminal: Regularly uploads user-entered lifestyle data and collected biosensor data to the server. This data is encrypted and privacy is protected.

[0346] Step 3:

[0347] Server: Receives user data and stores it in a database, then supplies it to the analysis algorithm. Here, the server calculates the user's current health status and future health predictions from both lifestyle data and biosensor data.

[0348] Step 4:

[0349] Emotion Engine (on-server): Analyzes changes in heart rate and stress levels from the user's biosensor data to estimate the user's emotional state. For example, if high stress levels are estimated, this information is used to evaluate lifestyle habits.

[0350] Step 5:

[0351] Server: Integrates lifestyle data and emotional state analysis results to generate data that visualizes the user's future health. This includes graphics that reflect predicted changes in physical condition and risk factors.

[0352] Step 6:

[0353] Server: Generates lifestyle improvement advice that takes into account the user's emotional state. For example, if a user is feeling stressed, it will generate advice recommending relaxation or meditation.

[0354] Step 7:

[0355] Server: Sends prediction results and advice to the user's terminal.

[0356] Step 8:

[0357] Terminal: Visually displays information sent from the server to the user. Users can intuitively understand their future health status and lifestyle improvement advice.

[0358] Step 9:

[0359] User: Based on advice from the system, users can change their diet or incorporate recommended activities in their real lives. This allows for choices that address their emotional needs.

[0360] Step 10:

[0361] Terminal: Prepares to collect new user lifestyle and emotional data and send it to the server. This new data will be used to generate health predictions and advice for the next cycle.

[0362] In this way, the system takes into account the user's lifestyle and emotional state, and provides a continuously personalized health management and feedback process.

[0363] (Example 2)

[0364] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0365] Traditional health management systems predict health based on users' lifestyles, but they fail to provide appropriate advice that takes into account the user's emotional state. This poses challenges, particularly in stress management and maintaining motivation, areas heavily influenced by emotions. Furthermore, the resulting predictive models and advice have limitations in accuracy, failing to achieve sufficient individualization.

[0366] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0367] In this invention, the server includes means for acquiring information about the user's lifestyle, means for predicting the user's future health status based on the information, and means for estimating the user's emotional state using an emotion analysis algorithm and proposing appropriate actions according to the user's emotions using a generative AI model. This enables the provision of personalized health management advice that takes into account the user's emotional state, improving the accuracy of health predictions and promoting changes in the user's behavior.

[0368] "Information regarding lifestyle habits" refers to data on the user's daily actions and activities, specifically including information such as diet, exercise levels, purchase history, and data from biometric monitoring devices.

[0369] "Predicting future health status" is a process that predicts what kind of health status a user will have in the future, based on their current lifestyle and emotional state.

[0370] "Visualization" refers to presenting health status, obtained as numerical data, in a way that is easy for users to understand, using graphs and animations.

[0371] "Providing guidance" means offering advice on specific choices and actions that users should take in their daily lives.

[0372] An "emotional analysis algorithm" is a series of computational procedures for estimating a user's emotional state based on biometric data and self-reported information, and for processing that information.

[0373] A "generative AI model" is an artificial intelligence system that learns from large amounts of data to make predictions, classifications, and suggestions.

[0374] "Biometric monitoring device data" refers to data obtained from devices that measure heart rate, activity levels, etc., and is used to understand the user's physical condition.

[0375] "Nutritional improvement suggestions" refer to advice on recommended foods and meal combinations to support the user's health.

[0376] A "physical activity plan" is an approach that proposes appropriate exercise and fitness activities for the purpose of maintaining or improving the user's health.

[0377] The system according to the present invention acquires information on the user's lifestyle, predicts their future health status, and provides guidance for lifestyle improvement by visually presenting the results. This system is operated primarily through information exchange between a server, a terminal, and the user.

[0378] The server receives lifestyle information transmitted from the user's device. This information includes dietary content, exercise levels, purchase history, and biometric monitoring data such as heart rate and stress levels. The server stores this data in a database and forms an integrated data profile for each user.

[0379] Next, the server uses a generative AI model based on the acquired data to predict the user's future health status. In particular, it uses an algorithm that analyzes emotions to estimate the user's emotional state and incorporates this into the prediction model. This allows the system to output results evaluating the impact of stress on health, for example, if the user is in a high-stress state.

[0380] The device receives predictive data transmitted from the server and presents it visually to the user. This includes graphs and animations that clearly show changes in health status. It also provides lifestyle improvement guidelines based on generated emotions. Specifically, this includes suggestions for improving nutrition and physical activity plans, allowing the user to know what concrete actions they can take to improve their lifestyle.

[0381] Users utilize the information and advice presented on their devices to modify their choices and actions in daily life. For example, if the prediction indicates that high stress may negatively impact their health, the user might incorporate suggested relaxation methods such as yoga or meditation into their daily routine. Furthermore, by returning the results as feedback to the server, they can contribute to the continuous improvement of the predictive model.

[0382] As a concrete example, a possible prompt to input into the generating AI model could be, "Based on the user's current emotional state, what improvements can be suggested regarding their exercise habits?" This would allow the system to provide personalized advice that takes the user's emotional state into account.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] Users record data about their daily activities and behaviors. Input includes meal logs, exercise levels, purchase history, heart rate, and stress levels. This data is transmitted to a server via the user's device. Data may also be automatically acquired using biometric monitoring devices.

[0386] Step 2:

[0387] The server stores the received data in a database and creates a lifestyle profile for each user. The input data is integrated and organized for each user. This integration process includes data cleansing and data matching to ensure consistency between each dataset. The output is the integrated user lifestyle profile.

[0388] Step 3:

[0389] The server uses a generative AI model to predict the user's future health status from an integrated lifestyle profile. Input includes the user's past lifestyle data and emotional state. Data analysis and machine learning algorithms are used to build a predictive model. The output is a prediction of the future health status.

[0390] Step 4:

[0391] The server uses an emotion analysis algorithm to estimate the user's emotional state and incorporates this information into the predictions of a generative AI model. The input consists of the user's biometric data and self-reported data. Data analysis estimates the emotional state, and this information is reflected in the prediction model. The output is a prediction of health status that takes emotions into account.

[0392] Step 5:

[0393] The terminal receives prediction results sent from the server and displays them visually. The input is a prediction of health status that takes emotions into account. Through visualization processing, it is presented to the user as graphs and animations. The output is visualized data that the user can intuitively understand.

[0394] Step 6:

[0395] The device presents the user with lifestyle improvement guidelines generated by a generative AI model. Input includes predictive data from the server and emotion-based advice. The guidelines are presented as specific suggestions for improving diet and exercise. The output is detailed guidance for improving the user's lifestyle.

[0396] Step 7:

[0397] Users take action to improve their lifestyle based on information and advice from their devices. Input includes visualized data and lifestyle improvement guidelines. Users take specific actions such as reviewing their diet or implementing an exercise plan, and new lifestyle data is generated as output, which is then fed back to the server.

[0398] (Application Example 2)

[0399] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0400] In modern health management, there is a need for systems that provide continuous health predictions and improvement advice that take into account the user's lifestyle and emotional state. However, existing technologies have struggled to adequately consider the user's daily emotional changes and improvement measures based on specific meal plans. Furthermore, there is a demand for more personalized health maintenance by combining this with logistics plans tailored to emotional states.

[0401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0402] In this invention, the server includes means for acquiring the user's biometric and emotional data, means for predicting the user's future health status based on the data, and means for visualizing the predicted health and emotional status and providing emotionally-based lifestyle improvement advice. This enables the provision of personalized health management and optimal eating plans and logistics services that take into account the user's emotional status.

[0403] "User biometric data" refers to data that indicates the user's physical activity and health status, and includes heart rate, sleep data, exercise levels, etc.

[0404] "Emotional data" refers to data that indicates a user's current psychological state and emotional changes, and is collected through self-reporting or biosensors.

[0405] "Predicting future health status" refers to the act of predicting future health risks and changes in health based on the user's past data.

[0406] "Visualization" is the act of clearly displaying predicted health and emotional states using graphs and animations.

[0407] "Lifestyle improvement advice" refers to suggestions for actions and plans to improve dietary habits, exercise habits, and other aspects of a user's life, based on their current condition and predicted results.

[0408] A "meal plan" is a series of plans that propose meal content for the purpose of managing the user's nutrition.

[0409] "Providing logistics services" means delivering meals and supplies at the optimal time and with the appropriate content, tailored to the user's health condition and emotional state.

[0410] The system of the present invention is configured to provide personalized health management through the user's biometric and emotional data. Specific embodiments are described below.

[0411] The server first collects biometric data from devices such as smartwatches and fitness trackers, including the user's heart rate, sleep data, and activity level. This data is transmitted to the server in real time via a smartphone app. Additionally, the user's emotional data is collected using a natural language processing engine and analyzed by an emotion engine. The emotion engine uses machine learning algorithms to predict emotional changes and analyze the user's current emotional state.

[0412] The server uses a TensorFlow-based predictive model to forecast the user's future health based on collected data. This forecast includes dietary history and activity sensor data. The forecast results are generated as graphs and animations that can be intuitively understood through visualization tools and sent to the device. Based on these results, the device provides the user with appropriate lifestyle improvement advice.

[0413] For example, if emotional data indicating that a user is feeling stressed is sent to the server, the server can use this information to recommend a relaxing meal plan and automatically arrange logistics services based on that plan.

[0414] An example of a prompt using a generative AI model is: "Generate a meal plan suggested when the user's emotional state is 'stressed'. Past meal history is 'salad, smoothie, grilled chicken'." This allows for the provision of continuous and adaptive health management services to the user.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The server receives user biometric data (heart rate, sleep patterns, activity level, etc.) from smartwatches and fitness trackers. This data is transmitted in real time and stored on the server. The input is data from each device, and the output is an integrated indicator of the user's health status.

[0418] Step 2:

[0419] The server collects user emotion data through a smartphone application. Using a natural language processing engine, it estimates emotions from the text information entered by the user into the application. The input is text data, and the output is the category of the analyzed emotional state.

[0420] Step 3:

[0421] The server uses a TensorFlow-based predictive model to forecast future health status based on collected biometric and emotional data. This process also considers past dietary history and activity sensor data. The input is an integrated dataset, and the output is predictive data regarding future health status.

[0422] Step 4:

[0423] The server visualizes predicted health and emotional states. Graphing software and animation tools are used for visualization, displaying the data on the terminal in an intuitively understandable format for the user. The input is the prediction result, and the output is a graph or animation of the visualized health data.

[0424] Step 5:

[0425] The device generates and presents appropriate lifestyle improvement advice to the user based on visualization data and prediction results sent from the server. Specifically, it is guided by prompts created by a generative AI model and proposes meal plans and exercise plans. The input is visualization data and instructions from the generative AI model, and the output is specific advice for the user.

[0426] Step 6:

[0427] The user adjusts their daily habits according to the advice provided on the device and sends the resulting feedback to the server. This feedback is used to track how the user's habit changes are progressing. The input is the user's actual behavioral data, and the output is the updated information sent to the server.

[0428] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0429] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0434] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0436] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0438] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0439] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0440] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0441] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0443] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0444] The system according to the present invention uses the user's lifestyle data to predict their future health status and provides the user with advice for improving their lifestyle. This system involves a server, a terminal, and the user working together to carry out a series of processes including data collection, prediction, presentation, and feedback.

[0445] The server receives lifestyle data sent by users and stores it in a database. This data includes dietary information, exercise levels, and purchase history entered by users through the application. The server processes this data and uses machine learning algorithms to predict future health conditions.

[0446] For example, if the server determines that a user has recently been frequently consuming high-fat foods, it predicts future weight gain and worsening skin condition. The server analyzes this information and generates appropriate lifestyle improvement advice for the user. This advice may include suggestions for improving diet and increasing exercise.

[0447] The terminal receives information transmitted from the server and presents it to the user in a visually easy-to-understand format. Through a graphical user interface (GUI), the terminal displays predicted health status and specific improvement advice. Users can incorporate this information into their daily lives to practice healthy habits.

[0448] Based on the advice provided, users actually change their diet and exercise habits. For example, they are encouraged to increase their vegetable intake, reduce fatty foods, or start exercising a few times a week. The effects of these actions are continuously analyzed as the device collects data again and sends it to the server. The server updates health predictions based on the new data and informs the user of their latest improvements, enabling them to take a more proactive approach to health management.

[0449] As described above, this system helps users maintain a healthy lifestyle by monitoring changes in their lifestyle habits over a long period and providing timely feedback.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] User: Inputs or records information such as meal details, exercise levels, or purchase history on the device through applications used daily.

[0453] Step 2:

[0454] Terminal: Collects lifestyle data from users and sends the data to the server periodically or as instructed by the user.

[0455] Step 3:

[0456] Server: Stores received lifestyle data in a database. This may involve encryption or other processing to protect personal information.

[0457] Step 4:

[0458] Server: Applies machine learning algorithms to analyze stored data. This generates a model that predicts the user's future health status.

[0459] Step 5:

[0460] Server: Generates data to visualize appearance and health risks based on predicted health status. For example, it generates images of the likelihood of weight gain or deterioration of skin condition.

[0461] Step 6:

[0462] Server: It also creates specific lifestyle improvement advice for the user, including necessary exercise plans and dietary adjustments.

[0463] Step 7:

[0464] Server: Sends a visualized future scenario and improvement suggestions to the terminal.

[0465] Step 8:

[0466] Terminal: Displays information received from the server in a graphical user interface. This allows users to easily understand the information visually.

[0467] Step 9:

[0468] User: Use the displayed future predictions and advice to take concrete actions in daily life. For example, review your diet or increase your physical activity.

[0469] Step 10:

[0470] Terminal: Records user actions and changes in lifestyle again, and prepares to send feedback to the server in the next data collection cycle.

[0471] Through this series of processes, the system is operated to continuously evaluate the user's health status and provide timely and customized feedback.

[0472] (Example 1)

[0473] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In modern society, the diversification of individual lifestyles complicates maintaining health. Under these circumstances, there is a need to provide predictions of health status and concrete action guidelines based on individual lifestyles. However, conventional systems have struggled to comprehensively analyze diverse user data and dynamically provide personalized health advice. In particular, the lack of a system that provides real-time feedback on behavioral changes leads to a weakening of awareness regarding lifestyle improvements, making effective health management difficult.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0476] In this invention, the server includes means for acquiring user lifestyle information, means for predicting health status and generating advice using a machine learning model, and means for dynamically updating data to reflect changes in user behavior via a terminal. This makes it possible to provide detailed and accurate health predictions and lifestyle improvement advice based on the user's lifestyle.

[0477] "User lifestyle information" refers to data about the user's daily activities, including records of eating, exercise, and purchasing behavior.

[0478] "Health status prediction" is a process that estimates a user's future health status based on acquired lifestyle information.

[0479] A "machine learning model" is a model that uses algorithms and methods to analyze large amounts of data and identify patterns and trends.

[0480] "Means of generating advice" refers to the process of creating specific suggestions for improving the user's lifestyle based on their predicted health condition.

[0481] A "terminal" is a device used by users to input lifestyle information and receive advice from a server, and includes smartphones and personal computers.

[0482] "Methods for dynamically updating data" refer to methods of continuously changing and adjusting information within a server based on new information input from users, thereby maintaining the data in an up-to-date state.

[0483] This invention relates to a system that predicts a user's health status based on their lifestyle information and provides advice for lifestyle improvement. The implementation of this system involves a server, a terminal, and a user, each fulfilling their respective roles.

[0484] Hardware and software configuration

[0485] The server uses a high-performance database system to receive and store lifestyle information sent by users. Generative AI models such as TensorFlow and PyTorch are used to process this data. This allows the server to leverage machine learning algorithms to predict health status.

[0486] The terminal is a device used by users to input lifestyle information, and includes smartphones and personal computers. Through a dedicated application, users send the entered information to the server. The terminal uses a GUI (Graphical User Interface) to visually display the advice received from the server.

[0487] Users input data on their daily diet, exercise, and purchasing behavior via their device, and this data is sent to a server. Based on the advice provided on the device, users adjust their lifestyle habits, and by inputting the results again, a feedback loop is formed.

[0488] Specific example

[0489] For example, if a user thinks, "I've recently started gaining weight, so I want to review my diet," they can use the application to record their daily meals and exercise in detail. Based on this information, the server uses a generative AI model to predict future weight fluctuations and sends specific advice to the user's device, such as "You should reduce your fat intake and aim to exercise three times a week."

[0490] Example of a prompt

[0491] "The user has entered lifestyle data. His recent diet has been high in fat. We will predict his future health and provide advice for improvement."

[0492] In this way, the system aims to provide users with useful information and support them in adopting healthy lifestyle habits.

[0493] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0494] Step 1:

[0495] Users input lifestyle information into the application using their device. This input consists of data such as diet, exercise levels, and purchase history. The entered data is automatically sent to the application's database.

[0496] Step 2:

[0497] The terminal sends lifestyle information obtained from the user to the server using a secure protocol (e.g., HTTPS). The entered data is sent to the server, and the server, upon receiving the data, securely stores it in its database.

[0498] Step 3:

[0499] The server preprocesses the received lifestyle information, cleaning and organizing the necessary data. This data is then used as input for the generative AI model. Specifically, it performs operations such as imputing missing values ​​and correcting outliers.

[0500] Step 4:

[0501] The server inputs the organized data into a generating AI model (e.g., an algorithm using TensorFlow or PyTorch) to predict future health conditions. Data processing and computation here include feature extraction and application of predictive models to ultimately obtain prediction results.

[0502] Step 5:

[0503] The server generates advice for the user based on the prediction results. This advice includes guidance on dietary improvements, exercise plans, and other behavioral guidelines. This requires processing based on the results obtained from the generating AI model, and is specifically output as prompt statements.

[0504] Step 6:

[0505] The terminal receives advice sent from the server and presents it to the user through a GUI. Specifically, it uses visually easy-to-understand graphs and charts to display health status predictions and advice.

[0506] Step 7:

[0507] Users adjust their lifestyles based on the advice provided and re-enter new lifestyle information. This process forms a feedback loop, leading to continuous improvement that enhances the accuracy of health predictions.

[0508] (Application Example 1)

[0509] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] In modern times, personal health management is becoming an increasingly important issue, but there is insufficient information provided to enable users to consciously make healthy choices, making it difficult to make sound decisions, especially when shopping. Furthermore, there is a lack of means to track changes in lifestyle in real time and provide immediate, appropriate feedback.

[0511] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0512] In this invention, the server includes means for acquiring information on the user's lifestyle habits, means for predicting the user's future health condition based on the lifestyle habit information, and means for recognizing product information using smart glasses and displaying a health assessment to the user in real time. This allows the user to be encouraged to make healthy choices on the spot when shopping and to continuously improve their healthy lifestyle habits.

[0513] A "user" refers to an individual who uses the system for health management or product selection.

[0514] "Lifestyle information" refers to data about a user's daily activities and choices, specifically including dietary content, exercise levels, and purchase history.

[0515] "Methods for predicting health status" refer to techniques that use machine learning technology and other methods to predict future health trends of users based on acquired lifestyle information.

[0516] "Means of expression" refers to technologies that visualize and present predicted health conditions in a way that is easy for users to understand.

[0517] "Means of providing suggestions" refers to elements of a system that, based on predicted health conditions, provides advice and guidelines to encourage behavioral change in users.

[0518] "Means of tracking and continuously updating health predictions" refers to a process of collecting new lifestyle information from users and updating health predictions and suggestions accordingly.

[0519] "Smart glasses" are wearable devices that utilize augmented reality technology to overlay information onto the user's real-world field of vision.

[0520] "Means of recognizing product information" refers to a system function that uses technology installed in smart glasses to identify products and their labels in a store and acquire related data.

[0521] "A means of displaying health assessments in real time" refers to a function that immediately presents the health impact of a product to the user after product recognition, supporting them in making better choices.

[0522] To implement this invention, a user's smart glasses, a server for processing data, and a terminal for presenting information to the user are required. The operation of this system is described in detail below.

[0523] The server continuously collects and stores user lifestyle information. This includes purchase history information obtained through smart glasses and activity sensor data from wearable devices. Based on this data, the server uses machine learning algorithms to predict the user's future health status. For this purpose, the server uses a database management system (e.g., MySQL) and a machine learning framework (e.g., TensorFlow).

[0524] The terminal receives predictive data transmitted from the server and builds a visual interface for displaying it on the user's smart glasses. This interface uses object recognition software (e.g., OpenCV) to recognize product labels and barcodes in stores and displays the acquired information to the user in real time. This display includes specific suggestions for improvement based on the predicted health status.

[0525] When users visit a physical store wearing smart glasses, they can receive real-time health assessments of products as they select them. For example, after scanning a product, they might be presented with information such as, "This snack is high in fat. Other foods are recommended for maintaining good health."

[0526] An example of a specific prompt is: "Propose a system that provides real-time health advice when a user views a product through smart glasses. Design an app that recognizes product labels and displays health advice based on past lifestyle data."

[0527] As a result, the present invention functions as a powerful tool to guide users' lifestyles in a better direction, enabling them to make healthier choices.

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The server receives lifestyle information transmitted from the user. This information includes purchase history acquired by smart glasses and user activity sensor data. This data is stored in a database and prepared for future predictions. The input is user activity data and purchase history information, and the output is the raw data stored in the database.

[0531] Step 2:

[0532] The server processes the stored data and uses machine learning algorithms to predict the user's health status. This process uses a model built with TensorFlow to analyze trends from the user's past lifestyle data. The input is the accumulated past lifestyle data, and the output is a prediction of the user's future health status.

[0533] Step 3:

[0534] The server generates advice for the user based on their predicted health status. Using a generative AI model, it creates specific dietary improvement suggestions and exercise plans. The input is the predicted health status, and the output is the advice provided to the user.

[0535] Step 4:

[0536] The terminal receives advice from the server and displays it in real time on the user's smart glasses. It uses OpenCV to recognize product labels and obtain health assessment information related to those products. The input consists of product recognition information and advice from the server, while the output is the health assessment information displayed on the smart glasses.

[0537] Step 5:

[0538] Users select healthier products based on displays shown on smart glasses. Based on the user's new choices, behavioral data is sent back to the server for continuous health management. The input is the user's selection behavior data, and the output is new data for future predictions and advice updates.

[0539] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0540] The system according to the present invention acquires users' lifestyle data, predicts their future health status, and visualizes it. In addition, by incorporating an emotion engine, it provides advice tailored to the user's emotional state. This system supports health management optimized for individual needs by facilitating information exchange between the server, terminal, and user.

[0541] The server receives and stores lifestyle data transmitted from the user's device and uses this data to predict the user's future health status. This lifestyle data includes diet, exercise levels, purchase history, and biosensor data to estimate the user's emotional state. The server's algorithm integrates this data to make predictions that take into account the impact of the user's emotions on their health habits.

[0542] The emotion engine estimates emotions from the user's biometric data and self-reported data, and incorporates this information into a predictive model. This engine is used to understand the user's current mental state and emotional responses, and to generate more appropriate advice. For example, if a user is feeling stressed, the emotion engine will suggest exercises or relaxation techniques to alleviate that stress.

[0543] The device displays prediction results, visualized health status, and emotion-based lifestyle improvement advice to the user as data sent from the server. Visualizations can include graphs showing emotional states and animations that visualize changes in physical condition. This allows users to intuitively understand future health risks and promote improvement activities in an emotion-conscious manner.

[0544] Users make choices in their daily lives based on the information presented by the device. For example, if the emotional engine indicates signs of stress, the user implements the provided relaxation strategies. This feedback loop allows users to improve both their self-management and their health.

[0545] Thus, the present invention provides a system that utilizes an emotion engine to realize individually adapted health management while taking into account the user's emotional background.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] User: Through the application, users input daily meal content and activity records into their device, and collect biosensor data (e.g., heart rate and stress level) using wearable devices.

[0549] Step 2:

[0550] Terminal: Regularly uploads user-entered lifestyle data and collected biosensor data to the server. This data is encrypted and privacy is protected.

[0551] Step 3:

[0552] Server: Receives user data and stores it in a database, then supplies it to the analysis algorithm. Here, the server calculates the user's current health status and future health predictions from both lifestyle data and biosensor data.

[0553] Step 4:

[0554] Emotion Engine (on-server): Analyzes changes in heart rate and stress levels from the user's biosensor data to estimate the user's emotional state. For example, if high stress levels are estimated, this information is used to evaluate lifestyle habits.

[0555] Step 5:

[0556] Server: Integrates lifestyle data and emotional state analysis results to generate data that visualizes the user's future health. This includes graphics that reflect predicted changes in physical condition and risk factors.

[0557] Step 6:

[0558] Server: Generates lifestyle improvement advice that takes into account the user's emotional state. For example, if a user is feeling stressed, it will generate advice recommending relaxation or meditation.

[0559] Step 7:

[0560] Server: Sends prediction results and advice to the user's terminal.

[0561] Step 8:

[0562] Terminal: Visually displays information sent from the server to the user. Users can intuitively understand their future health status and lifestyle improvement advice.

[0563] Step 9:

[0564] User: Based on advice from the system, users can change their diet or incorporate recommended activities in their real lives. This allows for choices that address their emotional needs.

[0565] Step 10:

[0566] Terminal: Prepares to collect new user lifestyle and emotional data and send it to the server. This new data will be used to generate health predictions and advice for the next cycle.

[0567] In this way, the system takes into account the user's lifestyle and emotional state, and provides a continuously personalized health management and feedback process.

[0568] (Example 2)

[0569] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] Traditional health management systems predict health based on users' lifestyles, but they fail to provide appropriate advice that takes into account the user's emotional state. This poses challenges, particularly in stress management and maintaining motivation, areas heavily influenced by emotions. Furthermore, the resulting predictive models and advice have limitations in accuracy, failing to achieve sufficient individualization.

[0571] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0572] In this invention, the server includes means for acquiring information about the user's lifestyle, means for predicting the user's future health status based on the information, and means for estimating the user's emotional state using an emotion analysis algorithm and proposing appropriate actions according to the user's emotions using a generative AI model. This enables the provision of personalized health management advice that takes into account the user's emotional state, improving the accuracy of health predictions and promoting changes in the user's behavior.

[0573] "Information regarding lifestyle habits" refers to data on the user's daily actions and activities, specifically including information such as diet, exercise levels, purchase history, and data from biometric monitoring devices.

[0574] "Predicting future health status" is a process that predicts what kind of health status a user will have in the future, based on their current lifestyle and emotional state.

[0575] "Visualization" refers to presenting health status, obtained as numerical data, in a way that is easy for users to understand, using graphs and animations.

[0576] "Providing guidance" means offering advice on specific choices and actions that users should take in their daily lives.

[0577] An "emotional analysis algorithm" is a series of computational procedures for estimating a user's emotional state based on biometric data and self-reported information, and for processing that information.

[0578] A "generative AI model" is an artificial intelligence system that learns from large amounts of data to make predictions, classifications, and suggestions.

[0579] "Biometric monitoring device data" refers to data obtained from devices that measure heart rate, activity levels, etc., and is used to understand the user's physical condition.

[0580] "Nutritional improvement suggestions" refer to advice on recommended foods and meal combinations to support the user's health.

[0581] A "physical activity plan" is an approach that proposes appropriate exercise and fitness activities for the purpose of maintaining or improving the user's health.

[0582] The system according to the present invention acquires information on the user's lifestyle, predicts their future health status, and provides guidance for lifestyle improvement by visually presenting the results. This system is operated primarily through information exchange between a server, a terminal, and the user.

[0583] The server receives lifestyle information transmitted from the user's device. This information includes dietary content, exercise levels, purchase history, and biometric monitoring data such as heart rate and stress levels. The server stores this data in a database and forms an integrated data profile for each user.

[0584] Next, the server uses a generative AI model based on the acquired data to predict the user's future health status. In particular, it uses an algorithm that analyzes emotions to estimate the user's emotional state and incorporates this into the prediction model. This allows the system to output results evaluating the impact of stress on health, for example, if the user is in a high-stress state.

[0585] The device receives predictive data transmitted from the server and presents it visually to the user. This includes graphs and animations that clearly show changes in health status. It also provides lifestyle improvement guidelines based on generated emotions. Specifically, this includes suggestions for improving nutrition and physical activity plans, allowing the user to know what concrete actions they can take to improve their lifestyle.

[0586] Users utilize the information and advice presented on their devices to modify their choices and actions in daily life. For example, if the prediction indicates that high stress may negatively impact their health, the user might incorporate suggested relaxation methods such as yoga or meditation into their daily routine. Furthermore, by returning the results as feedback to the server, they can contribute to the continuous improvement of the predictive model.

[0587] As a concrete example, a possible prompt to input into the generating AI model could be, "Based on the user's current emotional state, what improvements can be suggested regarding their exercise habits?" This would allow the system to provide personalized advice that takes the user's emotional state into account.

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

[0589] Step 1:

[0590] Users record data about their daily activities and behaviors. Input includes meal logs, exercise levels, purchase history, heart rate, and stress levels. This data is transmitted to a server via the user's device. Data may also be automatically acquired using biometric monitoring devices.

[0591] Step 2:

[0592] The server stores the received data in a database and creates a lifestyle profile for each user. The input data is integrated and organized for each user. This integration process includes data cleansing and data matching to ensure consistency between each dataset. The output is the integrated user lifestyle profile.

[0593] Step 3:

[0594] The server uses a generative AI model to predict the user's future health status from an integrated lifestyle profile. Input includes the user's past lifestyle data and emotional state. Data analysis and machine learning algorithms are used to build a predictive model. The output is a prediction of the future health status.

[0595] Step 4:

[0596] The server uses an emotion analysis algorithm to estimate the user's emotional state and incorporates this information into the predictions of a generative AI model. The input consists of the user's biometric data and self-reported data. Data analysis estimates the emotional state, and this information is reflected in the prediction model. The output is a prediction of health status that takes emotions into account.

[0597] Step 5:

[0598] The terminal receives prediction results sent from the server and displays them visually. The input is a prediction of health status that takes emotions into account. Through visualization processing, it is presented to the user as graphs and animations. The output is visualized data that the user can intuitively understand.

[0599] Step 6:

[0600] The device presents the user with lifestyle improvement guidelines generated by a generative AI model. Input includes predictive data from the server and emotion-based advice. The guidelines are presented as specific suggestions for improving diet and exercise. The output is detailed guidance for improving the user's lifestyle.

[0601] Step 7:

[0602] Users take action to improve their lifestyle based on information and advice from their devices. Input includes visualized data and lifestyle improvement guidelines. Users take specific actions such as reviewing their diet or implementing an exercise plan, and new lifestyle data is generated as output, which is then fed back to the server.

[0603] (Application Example 2)

[0604] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0605] In modern health management, there is a need for systems that provide continuous health predictions and improvement advice that take into account the user's lifestyle and emotional state. However, existing technologies have struggled to adequately consider the user's daily emotional changes and improvement measures based on specific meal plans. Furthermore, there is a demand for more personalized health maintenance by combining this with logistics plans tailored to emotional states.

[0606] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0607] In this invention, the server includes means for acquiring the user's biometric and emotional data, means for predicting the user's future health status based on the data, and means for visualizing the predicted health and emotional status and providing emotionally-based lifestyle improvement advice. This enables the provision of personalized health management and optimal eating plans and logistics services that take into account the user's emotional status.

[0608] "User biometric data" refers to data that indicates the user's physical activity and health status, and includes heart rate, sleep data, exercise levels, etc.

[0609] "Emotional data" refers to data that indicates a user's current psychological state and emotional changes, and is collected through self-reporting or biosensors.

[0610] "Predicting future health status" refers to the act of predicting future health risks and changes in health based on the user's past data.

[0611] "Visualization" is the act of clearly displaying predicted health and emotional states using graphs and animations.

[0612] "Lifestyle improvement advice" refers to suggestions for actions and plans to improve dietary habits, exercise habits, and other aspects of a user's life, based on their current condition and predicted results.

[0613] A "meal plan" is a series of plans that propose meal content for the purpose of managing the user's nutrition.

[0614] "Providing logistics services" means delivering meals and supplies at the optimal time and with the appropriate content, tailored to the user's health condition and emotional state.

[0615] The system of the present invention is configured to provide personalized health management through the user's biometric and emotional data. Specific embodiments are described below.

[0616] The server first collects biometric data from devices such as smartwatches and fitness trackers, including the user's heart rate, sleep data, and activity level. This data is transmitted to the server in real time via a smartphone app. Additionally, the user's emotional data is collected using a natural language processing engine and analyzed by an emotion engine. The emotion engine uses machine learning algorithms to predict emotional changes and analyze the user's current emotional state.

[0617] The server uses a TensorFlow-based predictive model to forecast the user's future health based on collected data. This forecast includes dietary history and activity sensor data. The forecast results are generated as graphs and animations that can be intuitively understood through visualization tools and sent to the device. Based on these results, the device provides the user with appropriate lifestyle improvement advice.

[0618] For example, if emotional data indicating that a user is feeling stressed is sent to the server, the server can use this information to recommend a relaxing meal plan and automatically arrange logistics services based on that plan.

[0619] An example of a prompt using a generative AI model is: "Generate a meal plan suggested when the user's emotional state is 'stressed'. Past meal history is 'salad, smoothie, grilled chicken'." This allows for the provision of continuous and adaptive health management services to the user.

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The server receives user biometric data (heart rate, sleep patterns, activity level, etc.) from smartwatches and fitness trackers. This data is transmitted in real time and stored on the server. The input is data from each device, and the output is an integrated indicator of the user's health status.

[0623] Step 2:

[0624] The server collects user emotion data through a smartphone application. Using a natural language processing engine, it estimates emotions from the text information entered by the user into the application. The input is text data, and the output is the category of the analyzed emotional state.

[0625] Step 3:

[0626] The server uses a TensorFlow-based predictive model to forecast future health status based on collected biometric and emotional data. This process also considers past dietary history and activity sensor data. The input is an integrated dataset, and the output is predictive data regarding future health status.

[0627] Step 4:

[0628] The server visualizes predicted health and emotional states. Graphing software and animation tools are used for visualization, displaying the data on the terminal in an intuitively understandable format for the user. The input is the prediction result, and the output is a graph or animation of the visualized health data.

[0629] Step 5:

[0630] The device generates and presents appropriate lifestyle improvement advice to the user based on visualization data and prediction results sent from the server. Specifically, it is guided by prompts created by a generative AI model and proposes meal plans and exercise plans. The input is visualization data and instructions from the generative AI model, and the output is specific advice for the user.

[0631] Step 6:

[0632] The user adjusts their daily habits according to the advice provided on the device and sends the resulting feedback to the server. This feedback is used to track how the user's habit changes are progressing. The input is the user's actual behavioral data, and the output is the updated information sent to the server.

[0633] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0634] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0635] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0639] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0640] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0641] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0642] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0643] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0644] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0645] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0646] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0647] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0648] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0649] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] The system according to the present invention uses the user's lifestyle data to predict their future health status and provides the user with advice for improving their lifestyle. This system involves a server, a terminal, and the user working together to carry out a series of processes including data collection, prediction, presentation, and feedback.

[0651] The server receives lifestyle data sent by users and stores it in a database. This data includes dietary information, exercise levels, and purchase history entered by users through the application. The server processes this data and uses machine learning algorithms to predict future health conditions.

[0652] For example, if the server determines that a user has recently been frequently consuming high-fat foods, it predicts future weight gain and worsening skin condition. The server analyzes this information and generates appropriate lifestyle improvement advice for the user. This advice may include suggestions for improving diet and increasing exercise.

[0653] The terminal receives information transmitted from the server and presents it to the user in a visually easy-to-understand format. Through a graphical user interface (GUI), the terminal displays predicted health status and specific improvement advice. Users can incorporate this information into their daily lives to practice healthy habits.

[0654] Based on the advice provided, users actually change their diet and exercise habits. For example, they are encouraged to increase their vegetable intake, reduce fatty foods, or start exercising a few times a week. The effects of these actions are continuously analyzed as the device collects data again and sends it to the server. The server updates health predictions based on the new data and informs the user of their latest improvements, enabling them to take a more proactive approach to health management.

[0655] As described above, this system helps users maintain a healthy lifestyle by monitoring changes in their lifestyle habits over a long period and providing timely feedback.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] User: Inputs or records information such as meal details, exercise levels, or purchase history on the device through applications used daily.

[0659] Step 2:

[0660] Terminal: Collects lifestyle data from users and sends the data to the server periodically or as instructed by the user.

[0661] Step 3:

[0662] Server: Stores received lifestyle data in a database. This may involve encryption or other processing to protect personal information.

[0663] Step 4:

[0664] Server: Applies machine learning algorithms to analyze stored data. This generates a model that predicts the user's future health status.

[0665] Step 5:

[0666] Server: Generates data to visualize appearance and health risks based on predicted health status. For example, it generates images of the likelihood of weight gain or deterioration of skin condition.

[0667] Step 6:

[0668] Server: It also creates specific lifestyle improvement advice for the user, including necessary exercise plans and dietary adjustments.

[0669] Step 7:

[0670] Server: Sends a visualized future scenario and improvement suggestions to the terminal.

[0671] Step 8:

[0672] Terminal: Displays information received from the server in a graphical user interface. This allows users to easily understand the information visually.

[0673] Step 9:

[0674] User: Use the displayed future predictions and advice to take concrete actions in daily life. For example, review your diet or increase your physical activity.

[0675] Step 10:

[0676] Terminal: Records user actions and changes in lifestyle again, and prepares to send feedback to the server in the next data collection cycle.

[0677] Through this series of processes, the system is operated to continuously evaluate the user's health status and provide timely and customized feedback.

[0678] (Example 1)

[0679] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] In modern society, the diversification of individual lifestyles complicates maintaining health. Under these circumstances, there is a need to provide predictions of health status and concrete action guidelines based on individual lifestyles. However, conventional systems have struggled to comprehensively analyze diverse user data and dynamically provide personalized health advice. In particular, the lack of a system that provides real-time feedback on behavioral changes leads to a weakening of awareness regarding lifestyle improvements, making effective health management difficult.

[0681] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0682] In this invention, the server includes means for acquiring user lifestyle information, means for predicting health status and generating advice using a machine learning model, and means for dynamically updating data to reflect changes in user behavior via a terminal. This makes it possible to provide detailed and accurate health predictions and lifestyle improvement advice based on the user's lifestyle.

[0683] "User lifestyle information" refers to data about the user's daily activities, including records of eating, exercise, and purchasing behavior.

[0684] "Health status prediction" is a process that estimates a user's future health status based on acquired lifestyle information.

[0685] A "machine learning model" is a model that uses algorithms and methods to analyze large amounts of data and identify patterns and trends.

[0686] "Means of generating advice" refers to the process of creating specific suggestions for improving the user's lifestyle based on their predicted health condition.

[0687] A "terminal" is a device used by users to input lifestyle information and receive advice from a server, and includes smartphones and personal computers.

[0688] "Methods for dynamically updating data" refer to methods of continuously changing and adjusting information within a server based on new information input from users, thereby maintaining the data in an up-to-date state.

[0689] This invention relates to a system that predicts a user's health status based on their lifestyle information and provides advice for lifestyle improvement. The implementation of this system involves a server, a terminal, and a user, each fulfilling their respective roles.

[0690] Hardware and software configuration

[0691] The server uses a high-performance database system to receive and store lifestyle information sent by users. Generative AI models such as TensorFlow and PyTorch are used to process this data. This allows the server to leverage machine learning algorithms to predict health status.

[0692] The terminal is a device used by users to input lifestyle information, and includes smartphones and personal computers. Through a dedicated application, users send the entered information to the server. The terminal uses a GUI (Graphical User Interface) to visually display the advice received from the server.

[0693] Users input data on their daily diet, exercise, and purchasing behavior via their device, and this data is sent to a server. Based on the advice provided on the device, users adjust their lifestyle habits, and by inputting the results again, a feedback loop is formed.

[0694] Specific example

[0695] For example, if a user thinks, "I've recently started gaining weight, so I want to review my diet," they can use the application to record their daily meals and exercise in detail. Based on this information, the server uses a generative AI model to predict future weight fluctuations and sends specific advice to the user's device, such as "You should reduce your fat intake and aim to exercise three times a week."

[0696] Example of a prompt

[0697] "The user has entered lifestyle data. His recent diet has been high in fat. We will predict his future health and provide advice for improvement."

[0698] In this way, the system aims to provide users with useful information and support them in adopting healthy lifestyle habits.

[0699] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0700] Step 1:

[0701] Users input lifestyle information into the application using their device. This input consists of data such as diet, exercise levels, and purchase history. The entered data is automatically sent to the application's database.

[0702] Step 2:

[0703] The terminal sends lifestyle information obtained from the user to the server using a secure protocol (e.g., HTTPS). The entered data is sent to the server, and the server, upon receiving the data, securely stores it in its database.

[0704] Step 3:

[0705] The server preprocesses the received lifestyle information, cleaning and organizing the necessary data. This data is then used as input for the generative AI model. Specifically, it performs operations such as imputing missing values ​​and correcting outliers.

[0706] Step 4:

[0707] The server inputs the organized data into a generating AI model (e.g., an algorithm using TensorFlow or PyTorch) to predict future health conditions. Data processing and computation here include feature extraction and application of predictive models to ultimately obtain prediction results.

[0708] Step 5:

[0709] The server generates advice for the user based on the prediction results. This advice includes guidance on dietary improvements, exercise plans, and other behavioral guidelines. This requires processing based on the results obtained from the generating AI model, and is specifically output as prompt statements.

[0710] Step 6:

[0711] The terminal receives advice sent from the server and presents it to the user through a GUI. Specifically, it uses visually easy-to-understand graphs and charts to display health status predictions and advice.

[0712] Step 7:

[0713] Users adjust their lifestyles based on the advice provided and re-enter new lifestyle information. This process forms a feedback loop, leading to continuous improvement that enhances the accuracy of health predictions.

[0714] (Application Example 1)

[0715] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] In modern times, personal health management is becoming an increasingly important issue, but there is insufficient information provided to enable users to consciously make healthy choices, making it difficult to make sound decisions, especially when shopping. Furthermore, there is a lack of means to track changes in lifestyle in real time and provide immediate, appropriate feedback.

[0717] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0718] In this invention, the server includes means for acquiring information on the user's lifestyle habits, means for predicting the user's future health condition based on the lifestyle habit information, and means for recognizing product information using smart glasses and displaying a health assessment to the user in real time. This allows the user to be encouraged to make healthy choices on the spot when shopping and to continuously improve their healthy lifestyle habits.

[0719] A "user" refers to an individual who uses the system for health management or product selection.

[0720] "Lifestyle information" refers to data about a user's daily activities and choices, specifically including dietary content, exercise levels, and purchase history.

[0721] "Methods for predicting health status" refer to techniques that use machine learning technology and other methods to predict future health trends of users based on acquired lifestyle information.

[0722] "Means of expression" refers to technologies that visualize and present predicted health conditions in a way that is easy for users to understand.

[0723] "Means of providing suggestions" refers to elements of a system that, based on predicted health conditions, provides advice and guidelines to encourage behavioral change in users.

[0724] "Means of tracking and continuously updating health predictions" refers to a process of collecting new lifestyle information from users and updating health predictions and suggestions accordingly.

[0725] "Smart glasses" are wearable devices that utilize augmented reality technology to overlay information onto the user's real-world field of vision.

[0726] "Means of recognizing product information" refers to a system function that uses technology installed in smart glasses to identify products and their labels in a store and acquire related data.

[0727] "A means of displaying health assessments in real time" refers to a function that immediately presents the health impact of a product to the user after product recognition, supporting them in making better choices.

[0728] To implement this invention, a user's smart glasses, a server for processing data, and a terminal for presenting information to the user are required. The operation of this system is described in detail below.

[0729] The server continuously collects and stores user lifestyle information. This includes purchase history information obtained through smart glasses and activity sensor data from wearable devices. Based on this data, the server uses machine learning algorithms to predict the user's future health status. For this purpose, the server uses a database management system (e.g., MySQL) and a machine learning framework (e.g., TensorFlow).

[0730] The terminal receives predictive data transmitted from the server and builds a visual interface for displaying it on the user's smart glasses. This interface uses object recognition software (e.g., OpenCV) to recognize product labels and barcodes in stores and displays the acquired information to the user in real time. This display includes specific suggestions for improvement based on the predicted health status.

[0731] When users visit a physical store wearing smart glasses, they can receive real-time health assessments of products as they select them. For example, after scanning a product, they might be presented with information such as, "This snack is high in fat. Other foods are recommended for maintaining good health."

[0732] An example of a specific prompt is: "Propose a system that provides real-time health advice when a user views a product through smart glasses. Design an app that recognizes product labels and displays health advice based on past lifestyle data."

[0733] As a result, the present invention functions as a powerful tool to guide users' lifestyles in a better direction, enabling them to make healthier choices.

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server receives lifestyle information transmitted from the user. This information includes purchase history acquired by smart glasses and user activity sensor data. This data is stored in a database and prepared for future predictions. The input is user activity data and purchase history information, and the output is the raw data stored in the database.

[0737] Step 2:

[0738] The server processes the stored data and uses machine learning algorithms to predict the user's health status. This process uses a model built with TensorFlow to analyze trends from the user's past lifestyle data. The input is the accumulated past lifestyle data, and the output is a prediction of the user's future health status.

[0739] Step 3:

[0740] The server generates advice for the user based on their predicted health status. Using a generative AI model, it creates specific dietary improvement suggestions and exercise plans. The input is the predicted health status, and the output is the advice provided to the user.

[0741] Step 4:

[0742] The terminal receives advice from the server and displays it in real time on the user's smart glasses. It uses OpenCV to recognize product labels and obtain health assessment information related to those products. The input consists of product recognition information and advice from the server, while the output is the health assessment information displayed on the smart glasses.

[0743] Step 5:

[0744] Users select healthier products based on displays shown on smart glasses. Based on the user's new choices, behavioral data is sent back to the server for continuous health management. The input is the user's selection behavior data, and the output is new data for future predictions and advice updates.

[0745] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0746] The system according to the present invention acquires users' lifestyle data, predicts their future health status, and visualizes it. In addition, by incorporating an emotion engine, it provides advice tailored to the user's emotional state. This system supports health management optimized for individual needs by facilitating information exchange between the server, terminal, and user.

[0747] The server receives and stores lifestyle data transmitted from the user's device and uses this data to predict the user's future health status. This lifestyle data includes diet, exercise levels, purchase history, and biosensor data to estimate the user's emotional state. The server's algorithm integrates this data to make predictions that take into account the impact of the user's emotions on their health habits.

[0748] The emotion engine estimates emotions from the user's biometric data and self-reported data, and incorporates this information into a predictive model. This engine is used to understand the user's current mental state and emotional responses, and to generate more appropriate advice. For example, if a user is feeling stressed, the emotion engine will suggest exercises or relaxation techniques to alleviate that stress.

[0749] The device displays prediction results, visualized health status, and emotion-based lifestyle improvement advice to the user as data sent from the server. Visualizations can include graphs showing emotional states and animations that visualize changes in physical condition. This allows users to intuitively understand future health risks and promote improvement activities in an emotion-conscious manner.

[0750] Users make choices in their daily lives based on the information presented by the device. For example, if the emotional engine indicates signs of stress, the user implements the provided relaxation strategies. This feedback loop allows users to improve both their self-management and their health.

[0751] Thus, the present invention provides a system that utilizes an emotion engine to realize individually adapted health management while taking into account the user's emotional background.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] User: Through the application, users input daily meal content and activity records into their device, and collect biosensor data (e.g., heart rate and stress level) using wearable devices.

[0755] Step 2:

[0756] Terminal: Regularly uploads user-entered lifestyle data and collected biosensor data to the server. This data is encrypted and privacy is protected.

[0757] Step 3:

[0758] Server: Receives user data and stores it in a database, then supplies it to the analysis algorithm. Here, the server calculates the user's current health status and future health predictions from both lifestyle data and biosensor data.

[0759] Step 4:

[0760] Emotion Engine (on-server): Analyzes changes in heart rate and stress levels from the user's biosensor data to estimate the user's emotional state. For example, if high stress levels are estimated, this information is used to evaluate lifestyle habits.

[0761] Step 5:

[0762] Server: Integrates lifestyle data and emotional state analysis results to generate data that visualizes the user's future health. This includes graphics that reflect predicted changes in physical condition and risk factors.

[0763] Step 6:

[0764] Server: Generates lifestyle improvement advice that takes into account the user's emotional state. For example, if a user is feeling stressed, it will generate advice recommending relaxation or meditation.

[0765] Step 7:

[0766] Server: Sends prediction results and advice to the user's terminal.

[0767] Step 8:

[0768] Terminal: Visually displays information sent from the server to the user. Users can intuitively understand their future health status and lifestyle improvement advice.

[0769] Step 9:

[0770] User: Based on advice from the system, users can change their diet or incorporate recommended activities in their real lives. This allows for choices that address their emotional needs.

[0771] Step 10:

[0772] Terminal: Prepares to collect new user lifestyle and emotional data and send it to the server. This new data will be used to generate health predictions and advice for the next cycle.

[0773] In this way, the system takes into account the user's lifestyle and emotional state, and provides a continuously personalized health management and feedback process.

[0774] (Example 2)

[0775] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] Traditional health management systems predict health based on users' lifestyles, but they fail to provide appropriate advice that takes into account the user's emotional state. This poses challenges, particularly in stress management and maintaining motivation, areas heavily influenced by emotions. Furthermore, the resulting predictive models and advice have limitations in accuracy, failing to achieve sufficient individualization.

[0777] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0778] In this invention, the server includes means for acquiring information about the user's lifestyle, means for predicting the user's future health status based on the information, and means for estimating the user's emotional state using an emotion analysis algorithm and proposing appropriate actions according to the user's emotions using a generative AI model. This enables the provision of personalized health management advice that takes into account the user's emotional state, improving the accuracy of health predictions and promoting changes in the user's behavior.

[0779] "Information regarding lifestyle habits" refers to data on the user's daily actions and activities, specifically including information such as diet, exercise levels, purchase history, and data from biometric monitoring devices.

[0780] "Predicting future health status" is a process that predicts what kind of health status a user will have in the future, based on their current lifestyle and emotional state.

[0781] "Visualization" refers to presenting health status, obtained as numerical data, in a way that is easy for users to understand, using graphs and animations.

[0782] "Providing guidance" means offering advice on specific choices and actions that users should take in their daily lives.

[0783] An "emotional analysis algorithm" is a series of computational procedures for estimating a user's emotional state based on biometric data and self-reported information, and for processing that information.

[0784] A "generative AI model" is an artificial intelligence system that learns from large amounts of data to make predictions, classifications, and suggestions.

[0785] "Biometric monitoring device data" refers to data obtained from devices that measure heart rate, activity levels, etc., and is used to understand the user's physical condition.

[0786] "Nutritional improvement suggestions" refer to advice on recommended foods and meal combinations to support the user's health.

[0787] A "physical activity plan" is an approach that proposes appropriate exercise and fitness activities for the purpose of maintaining or improving the user's health.

[0788] The system according to the present invention acquires information on the user's lifestyle, predicts their future health status, and provides guidance for lifestyle improvement by visually presenting the results. This system is operated primarily through information exchange between a server, a terminal, and the user.

[0789] The server receives lifestyle information transmitted from the user's device. This information includes dietary content, exercise levels, purchase history, and biometric monitoring data such as heart rate and stress levels. The server stores this data in a database and forms an integrated data profile for each user.

[0790] Next, the server uses a generative AI model based on the acquired data to predict the user's future health status. In particular, it uses an algorithm that analyzes emotions to estimate the user's emotional state and incorporates this into the prediction model. This allows the system to output results evaluating the impact of stress on health, for example, if the user is in a high-stress state.

[0791] The device receives predictive data transmitted from the server and presents it visually to the user. This includes graphs and animations that clearly show changes in health status. It also provides lifestyle improvement guidelines based on generated emotions. Specifically, this includes suggestions for improving nutrition and physical activity plans, allowing the user to know what concrete actions they can take to improve their lifestyle.

[0792] Users utilize the information and advice presented on their devices to modify their choices and actions in daily life. For example, if the prediction indicates that high stress may negatively impact their health, the user might incorporate suggested relaxation methods such as yoga or meditation into their daily routine. Furthermore, by returning the results as feedback to the server, they can contribute to the continuous improvement of the predictive model.

[0793] As a concrete example, a possible prompt to input into the generating AI model could be, "Based on the user's current emotional state, what improvements can be suggested regarding their exercise habits?" This would allow the system to provide personalized advice that takes the user's emotional state into account.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] Users record data about their daily activities and behaviors. Input includes meal logs, exercise levels, purchase history, heart rate, and stress levels. This data is transmitted to a server via the user's device. Data may also be automatically acquired using biometric monitoring devices.

[0797] Step 2:

[0798] The server stores the received data in a database and creates a lifestyle profile for each user. The input data is integrated and organized for each user. This integration process includes data cleansing and data matching to ensure consistency between each dataset. The output is the integrated user lifestyle profile.

[0799] Step 3:

[0800] The server uses a generative AI model to predict the user's future health status from an integrated lifestyle profile. Input includes the user's past lifestyle data and emotional state. Data analysis and machine learning algorithms are used to build a predictive model. The output is a prediction of the future health status.

[0801] Step 4:

[0802] The server uses an emotion analysis algorithm to estimate the user's emotional state and incorporates this information into the predictions of a generative AI model. The input consists of the user's biometric data and self-reported data. Data analysis estimates the emotional state, and this information is reflected in the prediction model. The output is a prediction of health status that takes emotions into account.

[0803] Step 5:

[0804] The terminal receives prediction results sent from the server and displays them visually. The input is a prediction of health status that takes emotions into account. Through visualization processing, it is presented to the user as graphs and animations. The output is visualized data that the user can intuitively understand.

[0805] Step 6:

[0806] The device presents the user with lifestyle improvement guidelines generated by a generative AI model. Input includes predictive data from the server and emotion-based advice. The guidelines are presented as specific suggestions for improving diet and exercise. The output is detailed guidance for improving the user's lifestyle.

[0807] Step 7:

[0808] Users take action to improve their lifestyle based on information and advice from their devices. Input includes visualized data and lifestyle improvement guidelines. Users take specific actions such as reviewing their diet or implementing an exercise plan, and new lifestyle data is generated as output, which is then fed back to the server.

[0809] (Application Example 2)

[0810] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0811] In modern health management, there is a need for systems that provide continuous health predictions and improvement advice that take into account the user's lifestyle and emotional state. However, existing technologies have struggled to adequately consider the user's daily emotional changes and improvement measures based on specific meal plans. Furthermore, there is a demand for more personalized health maintenance by combining this with logistics plans tailored to emotional states.

[0812] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0813] In this invention, the server includes means for acquiring the user's biometric and emotional data, means for predicting the user's future health status based on the data, and means for visualizing the predicted health and emotional status and providing emotionally-based lifestyle improvement advice. This enables the provision of personalized health management and optimal eating plans and logistics services that take into account the user's emotional status.

[0814] "User biometric data" refers to data that indicates the user's physical activity and health status, and includes heart rate, sleep data, exercise levels, etc.

[0815] "Emotional data" refers to data that indicates a user's current psychological state and emotional changes, and is collected through self-reporting or biosensors.

[0816] "Predicting future health status" refers to the act of predicting future health risks and changes in health based on the user's past data.

[0817] "Visualization" is the act of clearly displaying predicted health and emotional states using graphs and animations.

[0818] "Lifestyle improvement advice" refers to suggestions for actions and plans to improve dietary habits, exercise habits, and other aspects of a user's life, based on their current condition and predicted results.

[0819] A "meal plan" is a series of plans that propose meal content for the purpose of managing the user's nutrition.

[0820] "Providing logistics services" means delivering meals and supplies at the optimal time and with the appropriate content, tailored to the user's health condition and emotional state.

[0821] The system of the present invention is configured to provide personalized health management through the user's biometric and emotional data. Specific embodiments are described below.

[0822] The server first collects biometric data from devices such as smartwatches and fitness trackers, including the user's heart rate, sleep data, and activity level. This data is transmitted to the server in real time via a smartphone app. Additionally, the user's emotional data is collected using a natural language processing engine and analyzed by an emotion engine. The emotion engine uses machine learning algorithms to predict emotional changes and analyze the user's current emotional state.

[0823] The server uses a TensorFlow-based predictive model to forecast the user's future health based on collected data. This forecast includes dietary history and activity sensor data. The forecast results are generated as graphs and animations that can be intuitively understood through visualization tools and sent to the device. Based on these results, the device provides the user with appropriate lifestyle improvement advice.

[0824] For example, if emotional data indicating that a user is feeling stressed is sent to the server, the server can use this information to recommend a relaxing meal plan and automatically arrange logistics services based on that plan.

[0825] An example of a prompt using a generative AI model is: "Generate a meal plan suggested when the user's emotional state is 'stressed'. Past meal history is 'salad, smoothie, grilled chicken'." This allows for the provision of continuous and adaptive health management services to the user.

[0826] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0827] Step 1:

[0828] The server receives user biometric data (heart rate, sleep patterns, activity level, etc.) from smartwatches and fitness trackers. This data is transmitted in real time and stored on the server. The input is data from each device, and the output is an integrated indicator of the user's health status.

[0829] Step 2:

[0830] The server collects user emotion data through a smartphone application. Using a natural language processing engine, it estimates emotions from the text information entered by the user into the application. The input is text data, and the output is the category of the analyzed emotional state.

[0831] Step 3:

[0832] The server uses a TensorFlow-based predictive model to forecast future health status based on collected biometric and emotional data. This process also considers past dietary history and activity sensor data. The input is an integrated dataset, and the output is predictive data regarding future health status.

[0833] Step 4:

[0834] The server visualizes predicted health and emotional states. Graphing software and animation tools are used for visualization, displaying the data on the terminal in an intuitively understandable format for the user. The input is the prediction result, and the output is a graph or animation of the visualized health data.

[0835] Step 5:

[0836] The device generates and presents appropriate lifestyle improvement advice to the user based on visualization data and prediction results sent from the server. Specifically, it is guided by prompts created by a generative AI model and proposes meal plans and exercise plans. The input is visualization data and instructions from the generative AI model, and the output is specific advice for the user.

[0837] Step 6:

[0838] The user adjusts their daily habits according to the advice provided on the device and sends the resulting feedback to the server. This feedback is used to track how the user's habit changes are progressing. The input is the user's actual behavioral data, and the output is the updated information sent to the server.

[0839] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0840] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0841] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0842] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0843] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0844] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0845] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0846] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0847] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0848] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0849] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0850] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0851] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0852] 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.

[0853] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0854] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0855] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0856] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0857] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0858] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0859] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0860] The following is further disclosed regarding the embodiments described above.

[0861] (Claim 1)

[0862] Means for acquiring user lifestyle data,

[0863] A means for predicting the user's future health status based on the aforementioned lifestyle data,

[0864] A means for visualizing the predicted health status,

[0865] A means of providing the user with advice for lifestyle improvement based on the visualized health status,

[0866] A means to track changes in the user's lifestyle based on the aforementioned advice and continuously update health predictions,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, wherein the lifestyle data includes user purchase history data and activity sensor data.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the lifestyle improvement advice provided to the user includes suggestions for improving dietary content and an exercise plan.

[0872] "Example 1"

[0873] (Claim 1)

[0874] Means for obtaining user lifestyle information,

[0875] A means for predicting the user's future health status based on the aforementioned lifestyle information,

[0876] A means for analyzing the predicted health status using a machine learning model and generating advice for lifestyle improvement for the user,

[0877] A means of visually presenting the generated advice and providing information to the user,

[0878] A means for tracking changes in the user's lifestyle based on the aforementioned advice and dynamically updating health predictions,

[0879] A means of sending new data obtained from the user via the terminal to a server and storing it in a database,

[0880] A means of identifying areas for improvement based on past data and the current state,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the lifestyle information includes user purchasing behavior data and information obtained from behavioral measurement devices.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the lifestyle improvement advice provided to the user includes guidance on improving nutritional intake and an exercise plan.

[0886] "Application Example 1"

[0887] (Claim 1)

[0888] Means for obtaining user lifestyle information,

[0889] A means for predicting the user's future health status based on the aforementioned lifestyle information,

[0890] The means for representing the predicted health state,

[0891] A means of providing the user with suggestions for improving their lifestyle based on the expressed health status,

[0892] Based on the above proposal, a means for tracking changes in the user's lifestyle and continuously updating health predictions,

[0893] A method for recognizing product information using smart glasses and displaying a health assessment to the user in real time,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the lifestyle information includes the user's purchase history information and activity sensor information.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the lifestyle improvement suggestions provided to the user include suggestions for improving nutritional intake and an exercise plan.

[0899] "Example 2 of combining an emotion engine"

[0900] (Claim 1)

[0901] Means for obtaining information about the user's lifestyle,

[0902] Based on the aforementioned information, a means for predicting the user's future health condition,

[0903] A means for visualizing the aforementioned estimated health condition,

[0904] A means of presenting the user with guidelines for improving their lifestyle based on the visualized health status,

[0905] A means for estimating the user's emotional state using an algorithm for analyzing emotions and adjusting the aforementioned guidelines,

[0906] A method for suggesting appropriate actions based on user emotions using a generative AI model,

[0907] Based on the aforementioned guidelines, a means to track changes in the user's lifestyle and continuously update health predictions,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, wherein the information relating to lifestyle habits includes the user's purchase history information and biometric monitoring device data.

[0911] (Claim 3)

[0912] The system according to claim 1, wherein the lifestyle improvement guidelines provided to the user include suggestions for improving nutritional content and a physical activity plan.

[0913] "Application example 2 of combining emotional engines"

[0914] (Claim 1)

[0915] Means for acquiring user biometric data and emotional data,

[0916] Based on the aforementioned data, a means for predicting the user's future health status,

[0917] A means for visualizing the predicted health and emotional states and providing lifestyle improvement advice based on emotions,

[0918] A means of tracking changes in the user's lifestyle based on the aforementioned advice and implementing an optimal diet and logistics plan,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, wherein the biometric data includes a user's nutritional intake record and bioactivity data.

[0922] (Claim 3)

[0923] The system according to claim 1, wherein the lifestyle improvement advice provided to the user includes the selection of a meal plan and logistics services according to the user's emotional state. [Explanation of symbols]

[0924] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring user lifestyle data, A means for predicting the user's future health status based on the aforementioned lifestyle data, A means for visualizing the predicted health status, A means of providing the user with advice for lifestyle improvement based on the visualized health status, A means to track changes in the user's lifestyle based on the aforementioned advice and continuously update health predictions, A system that includes this.

2. The system according to claim 1, wherein the lifestyle data includes the user's purchase history data and activity sensor data.

3. The system according to claim 1, wherein the lifestyle improvement advice provided to the user includes suggestions for improving dietary content and an exercise plan.

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