system

Abstract

We provide the system. [Solution] A means of receiving and storing individual users' health information and lifestyle data, A means of using artificial intelligence algorithms to analyze stored health information and lifestyle data, A means of providing users with personalized diet and exercise advice based on the analysis results, A means of issuing a warning if an anomaly is detected during analysis and contacting a pre-registered medical institution, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, individual users are required to appropriately manage their own health conditions. However, analysis of health information and lifestyle data, as well as providing specific advice based on the analysis, requires specialized knowledge and is difficult for general users. In addition, there is a lack of a system for early detection of abnormal health conditions and prompt cooperation with medical institutions, so users may miss the opportunity to receive medical treatment at an appropriate time.

Means for Solving the Problems

[0005] This invention receives individual users' health information and lifestyle data, analyzes it using an artificial intelligence algorithm, and provides users with personalized diet and exercise advice. Furthermore, if an abnormality in the user's health condition is detected during the analysis process, it issues a warning to the user and quickly coordinates with pre-registered medical institutions. This enables users to manage their health more accurately and specifically, and to time medical interventions appropriately.

[0006] "User" refers to an individual who uses this system and is the entity that provides their health information and lifestyle data.

[0007] "Health information" refers to data about the user's physical condition, specifically including biometric data such as heart rate, weight, and blood pressure.

[0008] "Lifestyle data" refers to information about the user's daily activities and habits, specifically including the content of their meals, the frequency of their exercise, and the duration of their sleep.

[0009] An "artificial intelligence algorithm" refers to a computational method used to analyze collected health information and lifestyle data to derive useful information and predictions.

[0010] "Advice" refers to information provided to users based on analysis results, specifically guidance and suggestions regarding diet and exercise aimed at maintaining or improving health.

[0011] "Abnormal" refers to a situation derived from the analysis results that is expected to deviate from a normal state of health, and includes situations where medical intervention is necessary.

[0012] "Medical institution" refers to an organization or facility that provides medical services to users, and includes hospitals and clinics. [Brief explanation of the drawing]

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

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

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

[0016] In the following embodiments, the 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, the 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, the 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] This invention effectively utilizes health information and lifestyle data provided by individual users to offer personalized health management advice. The system mainly consists of three roles: server, terminal, and user.

[0035] The server receives health information and lifestyle data transmitted from the user's device and securely stores it in a database. The data is updated regularly and reflected in real time. Next, the received data is analyzed using artificial intelligence algorithms executed within the server. This analysis allows for an assessment of health status and prediction of disease risk. Based on the analysis results, the server generates personalized diet and exercise advice for each user and issues warnings for abnormalities as needed. For example, if the server detects an abnormal fluctuation in heart rate, it can automatically notify pre-registered medical institutions.

[0036] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps taken. This data is often obtained from sensor devices closely integrated into the user's daily life (e.g., smartwatches). The device sends the information entered by the user to a server and immediately notifies the user of the received advice. The notification is made through an application, and the user can view the advice on the screen. For example, specific suggestions such as "Let's walk 10,000 steps today" or "Let's try to eat a low-salt diet" may be presented.

[0037] Users manage their health by inputting their health information and daily habits into the device and incorporating the received advice into their daily lives. Furthermore, users can contribute to the system by providing feedback on the suggested advice. This feedback is sent to the server and used to improve the accuracy of the system and enhance the service.

[0038] Thus, the present invention provides users with personalized health management support and enables prompt medical response.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input their health information and lifestyle data using their devices. Heart rate and step count are automatically collected from sensor devices such as smartwatches.

[0042] Step 2:

[0043] The device temporarily stores collected health information and lifestyle data, and sends it to the server when ready. The transmission is performed automatically and periodically, designed to minimize the burden on the user.

[0044] Step 3:

[0045] The server receives data sent from the terminal and stores it in the database. Data is received in real time, allowing for information updates.

[0046] Step 4:

[0047] An artificial intelligence algorithm within the server operates and analyzes the stored data. The analysis includes assessing health status and predicting potential disease risks, and then generates analysis results.

[0048] Step 5:

[0049] Based on the analysis results, the server generates personalized health management advice for the user. This advice includes specific guidance on diet and exercise.

[0050] Step 6:

[0051] If the server detects an anomaly during analysis, it will immediately issue a warning. It will also automatically contact pre-registered medical institutions as needed.

[0052] Step 7:

[0053] Analysis results and advice sent from the server are received by the device. The device then displays the results to the user through notifications and an in-app dashboard.

[0054] Step 8:

[0055] Users incorporate the advice they receive via their devices into their daily lives as a guide for health management. They also send feedback to the server via their devices regarding the process and results of implementing the advice.

[0056] Step 9:

[0057] The server receives feedback from users and uses it for future analysis. This allows for continuous improvement of the accuracy of advice and the overall system.

[0058] (Example 1)

[0059] 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."

[0060] In modern society, individuals have diverse lifestyles, and the importance of health management is increasing. However, many people rely only on general health information, and there is a lack of advice optimized for individual lifestyles and health conditions. As a result, it is difficult for individuals to effectively manage their health. Therefore, there is a need for a system that provides personalized health advice to individual users based on their specific health conditions and lifestyles.

[0061] 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.

[0062] In this invention, the server includes means for receiving and recording individual users' health status information and lifestyle information, means for using a machine learning model to analyze the recorded health status information and lifestyle information, and means for providing users with personalized nutrition and physical activity guidelines based on the analysis results. This makes it possible to provide health management advice tailored to the characteristics of each user quickly and effectively.

[0063] "Health status information" refers to data related to the user's physical health, and specifically includes numerical information such as heart rate, blood pressure, weight, body temperature, and sleep patterns.

[0064] "Lifestyle information" refers to data about the user's daily activities and habits, and specifically includes information such as the content of their meals, the frequency and type of exercise, and their smoking and drinking habits.

[0065] A "machine learning model" refers to an algorithm used to analyze data. It learns data patterns using statistical methods and is used to make predictions and classifications based on future data.

[0066] "Guidelines on nutrition and physical activity" refer to advice provided to improve or maintain the health of users, and specifically include suggestions for improving dietary content and recommendations for increasing or decreasing exercise levels.

[0067] "Device" refers to a digital device used to receive, display, and provide analysis results of a user's health information, and specifically includes smartphones, tablets, smartwatches, and the like.

[0068] This invention effectively utilizes health status and lifestyle information provided by individual users to offer personalized health management advice. The system mainly consists of three elements: a server, a terminal, and a user.

[0069] The server receives health status and lifestyle information transmitted from the user's device and securely records it in a database. The recorded data is analyzed using machine learning frameworks such as TENSORFLOW® and PyTorch. This allows for an assessment of the user's health status and prediction of disease risk, as well as the generation of personalized nutrition and physical activity guidelines based on the analysis results. For example, the server can analyze the user's heart rate data and provide specific advice such as, "Your heart rate is a little high today. Try taking deep breaths to relax."

[0070] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps via various sensors such as smartwatches and fitness trackers. The device sends this data to a server and immediately notifies the user of the generated guidance. The notification is made through a dedicated application, and the user can check the advice on the app screen. For example, a notification such as "Please do 10 minutes of stretching exercises today" might appear on the device.

[0071] Users input their health information and daily habits into the device and manage their health by incorporating the received advice into their daily lives. Furthermore, users can send their responses and feedback to the suggested advice from the device to the server. This feedback is used to improve the accuracy of the system and enhance the service.

[0072] An example of a prompt message might be, "Male in his 30s, average daily steps 5,000, please provide advice for maintaining good health." This allows the system to generate specific health management guidelines tailored to the user's characteristics.

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

[0074] Step 1:

[0075] User data entry

[0076] Users collect biometric information using devices such as smartwatches and fitness trackers. Through these devices, users input data such as heart rate, steps taken, and sleep patterns into their devices. This input data reflects the user's daily lifestyle.

[0077] Step 2:

[0078] Data transmission by terminal

[0079] The terminal organizes health status and lifestyle information entered by the user and sends it to the server. The data sent is in real time, allowing the server to perform immediate analysis based on this information. Encryption is applied during data transmission to ensure the security of the communication.

[0080] Step 3:

[0081] Server-based data recording

[0082] The server records health status information received from the terminal into a database. The stored data is identified by individual user IDs, making it easily accessible and manageable. This recording process is regularly backed up to maintain data consistency and reliability.

[0083] Step 4:

[0084] Server-based data analysis

[0085] The server analyzes recorded data using a machine learning model. The input data includes user health information, and the server uses this to derive results such as health assessments and risk predictions. The generative AI model learns data patterns using frameworks such as TensorFlow and PyTorch. The output obtained from this analysis is personalized health management advice for each user.

[0086] Step 5:

[0087] Server-based advice generation

[0088] The server generates personalized nutrition and physical activity guidelines based on the analysis results. For example, it might create advice such as, "Today, take a 30-minute walk and reduce your salt intake." These guidelines are then adjusted to the user's specific health condition using prompts.

[0089] Step 6:

[0090] Notifications and reception via device

[0091] The device receives advice generated from the server and immediately notifies the user. The notification is delivered by an application, allowing the user to view the guidance on the screen. The interface in this process is designed to make it easy for the user to take action.

[0092] Step 7:

[0093] User Feedback Submission

[0094] Users incorporate the provided advice into their daily lives and send feedback to the server via their device, detailing the results and their reactions. This feedback is recorded by the server and used to improve the accuracy of system analysis and enhance the service. Users provide information about specific changes in their physical condition and the effectiveness of the advice.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In modern times, individual users are required not only to manage their own health status but also to efficiently select appropriate products based on that status and to purchase them quickly and safely. However, existing health management systems have been insufficient in providing personalized product recommendations and electronic transaction functions based on users' health data, thus lacking convenience. Therefore, the present invention aims to provide a system that solves these problems.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes a device for receiving and storing individual users' health information and lifestyle data, a device for using a machine learning model to analyze the stored health information and lifestyle data, and a device having an electronic transaction function that suggests optimal health-related products to the user based on the analysis data and allows for direct purchase. This enables appropriate product suggestions based on the user's health condition and their rapid purchase.

[0100] "Health information" refers to data on the health status of individual users, specifically biometric data such as heart rate, blood pressure, and body temperature.

[0101] "Lifestyle data" refers to information about users' daily behavioral patterns, diet, and exercise habits.

[0102] A "machine learning model" refers to an algorithm that uses a computer to analyze data and recognize or predict patterns.

[0103] "Personalized dietary and exercise advice" means providing guidance on diet and exercise methods optimized for each user, based on their health information and lifestyle data.

[0104] "Health-related products" are products intended to maintain and improve the health of users, and include supplements and health foods.

[0105] "Electronic transaction functionality" refers to a system that supports a series of operations necessary when purchasing goods or services via the internet.

[0106] "Communication equipment" refers to devices used by users as a means of sending and receiving data, and includes smartphones, tablets, and other similar devices.

[0107] "Feedback" refers to opinions and evaluations provided by users based on their information and experiences, for the purpose of improving services and products.

[0108] The system for implementing this invention consists of three roles: server, terminal, and user.

[0109] The server receives health information and lifestyle data provided by users. This data includes biometric data obtained from wearable devices such as smartwatches, as well as manually entered lifestyle information. The server securely stores this data in a database and performs analysis using machine learning models. As a result of the analysis, personalized health status assessments and suggestions for appropriate health-related products are generated. These suggestions aim to present products that match the user's existing health status.

[0110] The terminal functions as a device that facilitates user input and displays analysis results and suggestions received from the server to the user. The terminal is an application implemented on communication devices such as smartphones and tablets, visualizing the received personalized advice and product suggestions on the screen. Furthermore, the terminal includes electronic transaction functionality for quickly purchasing suggested products, allowing users to easily select items.

[0111] Users input their health information via their device and review the advice and product suggestions they receive. For example, if a user is diagnosed with high blood pressure, a "low-sodium diet package" suggestion will appear on the device. They can then purchase it with a single click. At this time, a prompt message generated by a generative AI model is presented, such as, "Based on your high blood pressure data, we recommend the following low-sodium diet product. If you wish to purchase this product, please click the link below," presenting the options in an easy-to-understand manner for the user. User feedback is also sent to the server and used to improve the accuracy of the analysis and enhance the service.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives health information and lifestyle data from the user's device. The data received as input includes heart rate, steps taken, and dietary information. This data is securely stored in a database on the server.

[0115] Step 2:

[0116] The server inputs the stored data into a machine learning model for analysis. Data processing here includes normalization of numerical data and integration across time axes. The output provides health status assessments and disease risk predictions.

[0117] Step 3:

[0118] The server generates personalized health advice and product recommendations based on the analysis results. Using a generative AI model, it creates personalized prompt messages for each user, such as "Based on your health condition, we recommend this low-sodium food product."

[0119] Step 4:

[0120] The terminal receives advice and suggestions sent from the server and displays them visually to the user. The input is notification data from the server, and the output is a display that the user can review.

[0121] Step 5:

[0122] The user reviews the suggested health-related products on their device and, if they wish to purchase them, selects the items using the provided electronic transaction function and completes the purchase process. Specifically, this involves clicking the "Purchase" button on the screen. As a result, transaction confirmation information is sent to both the device and the server.

[0123] Step 6:

[0124] Users input feedback about the service from their devices and send it to the server. The server receives this input, which allows it to obtain data for improving the accuracy of analysis and enhancing the service.

[0125] 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.

[0126] This invention provides personalized health management advice that takes into account the user's emotional state, in addition to their health information and lifestyle data, by utilizing an emotion engine. The system mainly consists of three main components: a server, a terminal, and an emotion engine.

[0127] The server receives health information, lifestyle data, and emotional information transmitted from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms are used to analyze the data, and based on the results obtained during the analysis, the server provides personalized advice to each user. The server comprehensively analyzes the user's data to assess their health status and generates appropriate diet and exercise advice based on that assessment. Furthermore, if an abnormality is detected during the analysis, it can automatically contact registered medical institutions. For example, if the user is experiencing stress, the advice can be adjusted to promote relaxation.

[0128] The terminal functions as an interface with the user and is responsible for collecting health and emotional data. Users input daily health information and subjective emotional states into the terminal. The terminal sends this information to the server and displays the analysis results and advice received from the server. The displayed advice takes into account the emotional data analyzed by the emotion engine, and specific suggestions such as "Let's take a walk today to refresh yourself" are presented to the user.

[0129] The emotion engine recognizes and analyzes the user's emotional state based on user input data and sensor information. This ensures that the user's psychological state is appropriately considered within the system, enabling advice that is more tailored to individual needs.

[0130] The present invention aims to contribute to maintaining the health and preventing illness of users by providing personalized advice that comprehensively takes into account each user's individual health and psychological state.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses the device to input health information (e.g., heart rate, weight, diet) and subjective emotional state (e.g., feeling good, feeling stressed). The device temporarily stores this data.

[0134] Step 2:

[0135] The device periodically sends health and emotional information to the server. This transmission is automated and designed to ensure data integrity and efficient delivery to the server.

[0136] Step 3:

[0137] The server stores the data received from the terminal in a database. The stored data is prepared for analysis, and the latest health and emotional information is used.

[0138] Step 4:

[0139] The artificial intelligence algorithms on the server analyze incoming data and assess the user's health and psychological state. This assessment includes detecting health risks and emotional fluctuations based on comparisons with past data.

[0140] Step 5:

[0141] Based on the analysis results, the server generates personalized health management advice. The results of the emotion engine analysis are also taken into consideration; for example, if mental refreshment is needed, it will suggest relaxing activities.

[0142] Step 6:

[0143] The server sends the generated advice to the terminal. The advice is displayed on the user's device and presented in a format that is easy for the user to understand.

[0144] Step 7:

[0145] Users follow the advice they receive from their device to manage their health and adjust their lifestyle. The advice is designed to be easily incorporated into their daily routines.

[0146] Step 8:

[0147] Users provide feedback on the implementation status and their impressions of the advice through their devices. This feedback is used to analyze the user's actions in the future.

[0148] Step 9:

[0149] The server receives feedback and uses it to improve the accuracy of its analysis algorithms and enhance its services. This ensures that the health management support provided to users is constantly optimized.

[0150] (Example 2)

[0151] 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".

[0152] Traditional health management systems have been limited to providing advice based on users' health information and lifestyle data, and have a problem of not fully meeting individual needs because they do not adequately consider users' emotional states. Furthermore, while there is a need for a rapid response when an abnormality is detected, the lack of an automated system for contacting medical institutions meant that there was a possibility of delays in emergency action.

[0153] 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.

[0154] In this invention, the server includes means for receiving and storing individual users' health information, lifestyle data, and emotional information; means for using artificial intelligence algorithms and generative AI models to analyze the stored information; and an emotion engine for analyzing the user's emotional state. This enables the provision of personalized advice that takes into account the user's emotional state, and allows for rapid response in emergencies by automatically contacting medical institutions quickly when an anomaly is detected.

[0155] "Health information" refers to data about the user's physical condition, specifically including weight, blood pressure, heart rate, etc.

[0156] "Lifestyle data" refers to data about the user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0157] "Emotional information" refers to data about the user's subjective emotional state, including psychological elements such as stress levels and mood.

[0158] An "artificial intelligence algorithm" is a computational method that analyzes data and processes it based on a specific purpose. In this invention, it is used to analyze health information and lifestyle data.

[0159] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new information from data, and is used to create personalized advice.

[0160] An "emotion engine" is a device or program that analyzes emotional information obtained from users and evaluates their psychological state based on that analysis.

[0161] "Personalized advice" refers to suggestions and guidance tailored to the user's specific health and emotional state, including specific instructions regarding diet, exercise, and stress management.

[0162] "Anomaly detection" is the process of identifying unusual states or values ​​based on collected data and determining safety and health risks.

[0163] "Automatic contact with medical institutions" is a function that, when an abnormality is detected, automatically contacts pre-registered medical facilities to encourage a prompt medical response.

[0164] This invention is a system that provides health management advice to individual users and mainly consists of a server, a terminal, and an emotion engine.

[0165] The server receives health information, lifestyle data, and emotional information from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms and generative AI models are used for data analysis, which generates personalized health management advice for the user. During the server's analysis process, the emotion engine evaluates the user's psychological state. This information is used to provide personalized advice on diet, exercise, and stress management. For example, if the data analysis determines that the user is experiencing high stress levels, the server might generate advice such as, "Try taking a walk today to refresh yourself."

[0166] The terminal receives input data from the user and simultaneously displays the analysis results. Users input daily health information and subjective emotional states into this terminal. After the data is sent to the server, the analysis results returned from the server are displayed on the terminal, providing the user with specific actionable guidelines.

[0167] The emotion engine analyzes the user's psychological state based on the emotional information they input. This allows the system to provide personalized support that takes into account not only the user's physical health but also their emotional state.

[0168] For example, if a user inputs a prompt such as "a time when I tend to feel stressed" into the system, the AI ​​model will generate advice based on that prompt. Because this prompt reflects the user's psychological tendencies, the generated advice will be more accurate and effective.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Users input daily health information and emotional states into the device. Specific data entered includes weight, diet, exercise levels, and stress levels. This information is used as input data for the next processing step.

[0172] Step 2:

[0173] The terminal collects health and emotional information entered by the user and bundles it into data packets. These bundled data packets are then sent to the server via a secure communication protocol. The server receives the data as output.

[0174] Step 3:

[0175] The server stores the received data in its internal database. The stored data undergoes format conversion and is processed into a structured dataset for analysis. The server's next action is to prepare the data for input into the generating AI model.

[0176] Step 4:

[0177] The server inputs the stored dataset into the generating AI model and artificial intelligence algorithm. Prompt statements are also input, and data analysis is performed based on the user's emotional state and lifestyle patterns. Here, an analysis prompt such as "Generate appropriate advice considering the user's stress level and diet" is used.

[0178] Step 5:

[0179] The generative AI model analyzes input data and generates optimal advice for the user. For example, it might produce a specific output such as, "Light walking is recommended to relieve stress."

[0180] Step 6:

[0181] The server sends the generated advice to the device. The device analyzes the received advice and displays it on the screen. At this point, the user can receive personalized health management advice via the device.

[0182] (Application Example 2)

[0183] 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 device 14 will be referred to as the "terminal."

[0184] Existing health management systems do not adequately recommend personalized products and services that take into account the emotional state of users, thereby limiting improvements in users' health and quality of life. Therefore, it is necessary to provide more appropriate products and services based on users' emotional states to achieve more effective health management and improved quality of life.

[0185] 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.

[0186] In this invention, the server includes means for receiving and storing individual users' health information and lifestyle data; means for using artificial intelligence algorithms to analyze the stored health information and lifestyle data; means for providing users with personalized diet and exercise advice based on the analysis results; means for recommending products and services to users based on their emotional state; and means for considering the user's past purchase history when recommending products and services. This enables more personalized recommendations of products and services that take into account the user's emotional state.

[0187] "Health information" refers to data about the user's physical condition, including data such as weight, blood pressure, and heart rate.

[0188] "Lifestyle data" refers to data about users' daily activities such as diet, exercise, and sleep.

[0189] An "artificial intelligence algorithm" is a mathematical method used to analyze data, recognize specific patterns, and provide optimal advice to users.

[0190] "Emotional state" refers to the user's psychological condition and includes emotions such as stress, relaxation, and happiness.

[0191] "Goods and services" refers to specific products and activities provided to users, and includes goods, information, entertainment, etc.

[0192] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0193] To realize this invention, the system consists mainly of three main components: a server, a terminal, and an emotion engine. The server is responsible for receiving and recording the user's health information, lifestyle data, and emotional state. This data is first collected by the terminal through sensors and user input, and then transmitted to the server.

[0194] The server analyzes stored data using AI algorithms to generate personalized diet and exercise advice for users, and also evaluates users' health status by utilizing patterns derived from vast datasets. The artificial intelligence technology used here is implemented in languages ​​such as Python, and frameworks such as TensorFlow and PyTorch are often used.

[0195] The emotion engine analyzes the user's emotional state and sends the results to the server. Based on this information, it recommends products and services to the device that are appropriate for the user's emotional state. In particular, considering past purchase history and the current emotional state enables more personalized recommendations.

[0196] For example, if the device detects that the user is experiencing stress, it will suggest items and services that promote relaxation. For instance, it might display something like, "Today we are offering relaxing aromatherapy candles at a special price."

[0197] When using a generative AI model, the server generates a new dataset and creates prompts. A concrete example of a prompt might be, "If the user's emotional state is stressed, suggest products that help them relax, and exclude products they already own based on their purchase history." This allows for more accurate data generation and suggestions based on the user's emotional state and past behavioral history.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The device collects health information, lifestyle data, and emotional states through sensors and user input. This data is necessary to understand the user's current state. Inputs include heart rate, activity level, and subjective emotional state, and this data is formatted for transmission to the server.

[0201] Step 2:

[0202] The server receives data sent from the terminal and stores it in a database. This stored data is used as basic information for analyzing the user's health status. The input is health data sent from the terminal, and the output is the process of saving it to the server.

[0203] Step 3:

[0204] The server inputs stored data into an artificial intelligence algorithm for analysis. This input consists of user health information and lifestyle data, and data mining techniques are used to identify specific patterns. The output is the analysis results, which are then provided to the user as personalized health advice.

[0205] Step 4:

[0206] The server generates specific advice regarding diet and exercise for the user based on the analysis results. Specifically, the advice generated based on the user's current health and emotional state might take the form of, for example, "Let's try some light exercise today." The input is the analysis results from the AI, and the output is advice in text format.

[0207] Step 5:

[0208] The server matches the user's emotional state with their past purchase history to recommend appropriate products and services. The input is the user's emotional state and purchase history, and the output is a list of products tailored to their emotional state. The generated data is displayed on the terminal.

[0209] Step 6:

[0210] The terminal receives advice and product recommendations from the server and presents them visually to the user. Specifically, it displays health advice and product information on the terminal's screen. The input consists of advice and product lists sent from the server, and the output is a visual presentation of information to the user.

[0211] Step 7:

[0212] Users input feedback into their device based on the advice and product recommendations provided, and send it to the server. This feedback helps improve the accuracy of the service. The input is the user's text feedback, which is then sent to the server as output data.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention effectively utilizes health information and lifestyle data provided by individual users to offer personalized health management advice. The system primarily consists of three roles: server, terminal, and user.

[0230] The server receives health information and lifestyle data transmitted from the user's device and securely stores it in a database. The data is updated regularly and reflected in real time. Next, the received data is analyzed using artificial intelligence algorithms executed within the server. This analysis allows for an assessment of health status and prediction of disease risk. Based on the analysis results, the server generates personalized diet and exercise advice for each user and issues warnings for abnormalities as needed. For example, if the server detects an abnormal fluctuation in heart rate, it can automatically notify pre-registered medical institutions.

[0231] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps taken. This data is often obtained from sensor devices closely integrated into the user's daily life (e.g., smartwatches). The device sends the information entered by the user to a server and immediately notifies the user of the received advice. The notification is made through an application, and the user can view the advice on the screen. For example, specific suggestions such as "Let's walk 10,000 steps today" or "Let's try to eat a low-salt diet" may be presented.

[0232] Users manage their health by inputting their health information and daily habits into the device and incorporating the received advice into their daily lives. Furthermore, users can contribute to the system by providing feedback on the suggested advice. This feedback is sent to the server and used to improve the accuracy of the system and enhance the service.

[0233] Thus, the present invention provides users with personalized health management support and enables prompt medical response.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users input their health information and lifestyle data using their devices. Heart rate and step count are automatically collected from sensor devices such as smartwatches.

[0237] Step 2:

[0238] The device temporarily stores collected health information and lifestyle data, and sends it to the server when ready. The transmission is performed automatically and periodically, designed to minimize user burden.

[0239] Step 3:

[0240] The server receives data sent from the terminal and stores it in the database. Data is received in real time, allowing for information updates.

[0241] Step 4:

[0242] An artificial intelligence algorithm within the server operates and analyzes the stored data. The analysis includes assessing health status and predicting potential disease risks, and then generates analysis results.

[0243] Step 5:

[0244] Based on the analysis results, the server generates personalized health management advice for the user. This advice includes specific guidance on diet and exercise.

[0245] Step 6:

[0246] If the server detects an anomaly during analysis, it will immediately issue a warning. It will also automatically contact pre-registered medical institutions as needed.

[0247] Step 7:

[0248] Analysis results and advice sent from the server are received by the device. The device then displays the results to the user through notifications and an in-app dashboard.

[0249] Step 8:

[0250] Users incorporate the advice they receive via their devices into their daily lives as a guide for health management. They also send feedback to the server via their devices regarding the process and results of implementing the advice.

[0251] Step 9:

[0252] The server receives feedback from users and uses it for future analysis. This allows for continuous improvement of the accuracy of advice and the overall system.

[0253] (Example 1)

[0254] 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."

[0255] In modern society, individuals have diverse lifestyles, and the importance of health management is increasing. However, many people rely only on general health information, and there is a lack of advice optimized for individual lifestyles and health conditions. As a result, it is difficult for individuals to effectively manage their health. Therefore, there is a need for a system that provides personalized health advice to individual users based on their specific health conditions and lifestyles.

[0256] 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.

[0257] In this invention, the server includes means for receiving and recording individual users' health status information and lifestyle information, means for using a machine learning model to analyze the recorded health status information and lifestyle information, and means for providing users with personalized nutrition and physical activity guidelines based on the analysis results. This makes it possible to provide health management advice tailored to the characteristics of each user quickly and effectively.

[0258] "Health status information" refers to data related to the user's physical health, and specifically includes numerical information such as heart rate, blood pressure, weight, body temperature, and sleep patterns.

[0259] "Lifestyle information" refers to data about the user's daily activities and habits, and specifically includes information such as the content of their meals, the frequency and type of exercise, and their smoking and drinking habits.

[0260] A "machine learning model" refers to an algorithm used to analyze data. It learns data patterns using statistical methods and is used to make predictions and classifications based on future data.

[0261] "Guidelines on nutrition and physical activity" refer to advice provided to improve or maintain the health of users, and specifically include suggestions for improving dietary content and recommendations for increasing or decreasing exercise levels.

[0262] "Device" refers to a digital device used to receive, display, and provide analysis results of a user's health information, and specifically includes smartphones, tablets, smartwatches, and the like.

[0263] This invention effectively utilizes health status and lifestyle information provided by individual users to offer personalized health management advice. The system mainly consists of three elements: a server, a terminal, and a user.

[0264] The server receives health status and lifestyle information transmitted from the user's device and securely records it in a database. The recorded data is analyzed using machine learning frameworks such as TensorFlow and PyTorch. This allows for the assessment of the user's health status and prediction of disease risk, as well as the generation of personalized nutrition and physical activity guidelines based on the analysis results. For example, the server can analyze the user's heart rate data and provide specific advice such as, "Your heart rate is a little high today. Try taking deep breaths to relax."

[0265] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps via various sensors such as smartwatches and fitness trackers. The device sends this data to a server and immediately notifies the user of the generated guidance. The notification is made through a dedicated application, and the user can check the advice on the app screen. For example, a notification such as "Please do 10 minutes of stretching exercises today" might appear on the device.

[0266] Users input their health information and daily habits into the device and manage their health by incorporating the received advice into their daily lives. Furthermore, users can send their responses and feedback to the suggested advice from the device to the server. This feedback is used to improve the accuracy of the system and enhance the service.

[0267] An example of a prompt message might be, "Male in his 30s, average daily steps 5,000, please provide advice for maintaining good health." This allows the system to generate specific health management guidelines tailored to the user's characteristics.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] User data entry

[0271] Users collect biometric information using devices such as smartwatches and fitness trackers. Through these devices, users input data such as heart rate, steps taken, and sleep patterns into their devices. This input data reflects the user's daily lifestyle habits.

[0272] Step 2:

[0273] Data transmission by terminal

[0274] The terminal organizes health status and lifestyle information entered by the user and sends it to the server. The data sent is in real time, allowing the server to perform immediate analysis based on this information. Encryption is applied during data transmission to ensure the security of the communication.

[0275] Step 3:

[0276] Data recording by server

[0277] The server records health status information received from the terminal into a database. The stored data is identified by individual user IDs, making it easily accessible and manageable. This recording process is regularly backed up to maintain data consistency and reliability.

[0278] Step 4:

[0279] Server-based data analysis

[0280] The server analyzes recorded data using a machine learning model. The input data includes user health information, and the server uses this to derive results such as health assessments and risk predictions. The generative AI model learns data patterns using frameworks such as TensorFlow and PyTorch. The output obtained from this analysis is personalized health management advice for each user.

[0281] Step 5:

[0282] Advice generation by the server

[0283] The server generates optimized nutrition and physical activity guidelines for each user based on the analysis results. As a specific operation, it creates advice such as "Take a 30-minute walk today and reduce your salt intake." These guidelines are adjusted according to the user's specific health condition using prompt sentences.

[0284] Step 6:

[0285] Notification and reception by the terminal

[0286] The terminal receives the advice generated by the server and immediately notifies the user. The notification is performed by the application, and the user can view the guidelines on the screen. In this process, the interface is designed to make it easy for the user to take action.

[0287] Step 7:

[0288] Feedback transmission by the user

[0289] The user incorporates the provided advice into daily life and transmits the results and reactions as feedback to the server through the terminal. This feedback is recorded by the server and utilized to improve the analysis accuracy of the system and the service. The user provides information about specific physical changes and the effectiveness of the advice.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server," and the smart glasses 214 are referred to as the "terminal."

[0292] In modern times, individual users are required not only to manage their own health status but also to efficiently select appropriate products based on that status and to purchase them quickly and safely. However, existing health management systems have been insufficient in providing personalized product recommendations and electronic transaction functions based on users' health data, thus lacking convenience. Therefore, the present invention aims to provide a system that solves these problems.

[0293] 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.

[0294] In this invention, the server includes a device for receiving and storing individual users' health information and lifestyle data, a device for using a machine learning model to analyze the stored health information and lifestyle data, and a device having an electronic transaction function that suggests optimal health-related products to the user based on the analysis data and allows for direct purchase. This enables appropriate product suggestions based on the user's health condition and their rapid purchase.

[0295] "Health information" refers to data on the health status of individual users, specifically biometric data such as heart rate, blood pressure, and body temperature.

[0296] "Lifestyle data" refers to information about users' daily behavioral patterns, diet, and exercise habits.

[0297] A "machine learning model" refers to an algorithm that uses a computer to analyze data and recognize or predict patterns.

[0298] "Personalized dietary and exercise advice" means providing guidance on diet and exercise methods optimized for each user, based on their health information and lifestyle data.

[0299] "Health-related products" are products intended to maintain and improve the health of users, and include supplements and health foods.

[0300] "Electronic transaction functionality" refers to a system that supports a series of operations necessary when purchasing goods or services via the internet.

[0301] "Communication equipment" refers to devices used by users as a means of sending and receiving data, and includes smartphones, tablets, and other similar devices.

[0302] "Feedback" refers to opinions and evaluations provided by users based on their information and experiences, for the purpose of improving services and products.

[0303] The system for implementing this invention consists of three roles: server, terminal, and user.

[0304] The server receives health information and lifestyle data provided by users. This data includes biometric data obtained from wearable devices such as smartwatches, as well as manually entered lifestyle information. The server securely stores this data in a database and performs analysis using machine learning models. As a result of the analysis, personalized health status assessments and suggestions for appropriate health-related products are generated. These suggestions aim to present products that match the user's existing health status.

[0305] The terminal functions as a device that facilitates user input and displays analysis results and suggestions received from the server to the user. The terminal is an application implemented on communication devices such as smartphones and tablets, visualizing the received personalized advice and product suggestions on the screen. Furthermore, the terminal includes electronic transaction functionality for quickly purchasing suggested products, allowing users to easily select items.

[0306] The user inputs their health information via the terminal and checks the received advice and product recommendations. For example, if the user is diagnosed with high blood pressure, a recommendation for a "low-salt diet package" will be displayed on the terminal. And it is possible to purchase it with a single click. At this time, a prompt sentence using a generative AI model such as "Based on your high blood pressure data, we recommend the following low-salt food products. If you want to purchase this product, please click on the following link." is presented to present options to the user in an easy-to-understand form. Also, feedback from the user is sent to the server and utilized to improve the accuracy of analysis and service improvement.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server receives health information and lifestyle data from the user's terminal. The data received as input is data such as heart rate, number of steps, and diet content. These data are safely stored in the database on the server.

[0310] Step 2:

[0311] The server inputs the stored data into a machine learning model for analysis. The data processing here includes normalization of numerical data and integration along the time axis. As output, an evaluation of the health status and a prediction result of the disease risk are obtained.

[0312] Step 3:

[0313] The server generates personalized health advice and product recommendations based on the analysis results. Using a generative AI model, a personalized prompt sentence is created for each user, and for example, a sentence such as "Based on your health status, we recommend this low-salt food product." is presented.

[0314] Step 4:

[0315] The terminal receives advice and suggestions sent from the server and displays them visually to the user. The input is notification data from the server, and the output is a display that the user can review.

[0316] Step 5:

[0317] The user reviews the suggested health-related products on their device and, if they wish to purchase them, selects the items using the provided electronic transaction function and completes the purchase process. Specifically, this involves clicking the "Purchase" button on the screen. As a result, transaction confirmation information is sent to both the device and the server.

[0318] Step 6:

[0319] Users input feedback about the service from their devices and send it to the server. The server receives this input, providing data to improve the accuracy of analysis and enhance the service.

[0320] 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.

[0321] This invention provides personalized health management advice that takes into account the user's emotional state, in addition to their health information and lifestyle data, by utilizing an emotion engine. The system mainly consists of three main components: a server, a terminal, and an emotion engine.

[0322] The server receives health information, lifestyle data, and emotional information transmitted from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms are used to analyze the data, and based on the results obtained during the analysis, the server provides personalized advice to each user. The server comprehensively analyzes the user's data to assess their health status and generates appropriate diet and exercise advice based on that assessment. Furthermore, if an abnormality is detected during the analysis, it can automatically contact registered medical institutions. For example, if the user is experiencing stress, the advice can be adjusted to promote relaxation.

[0323] The terminal functions as an interface with the user and is responsible for collecting health and emotional data. Users input daily health information and subjective emotional states into the terminal. The terminal sends this information to the server and displays the analysis results and advice received from the server. The displayed advice takes into account the emotional data analyzed by the emotion engine, and specific suggestions such as "Let's take a walk today to refresh yourself" are presented to the user.

[0324] The emotion engine recognizes and analyzes the user's emotional state based on user input data and sensor information. This ensures that the user's psychological state is appropriately considered within the system, enabling advice that is more tailored to individual needs.

[0325] The present invention aims to contribute to maintaining the health and preventing illness of users by providing personalized advice that comprehensively takes into account each user's individual health and psychological state.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user uses the device to input health information (e.g., heart rate, weight, diet) and subjective emotional state (e.g., feeling good, feeling stressed). The device temporarily stores this data.

[0329] Step 2:

[0330] The device periodically sends health and emotional information to the server. This transmission is automated and designed to ensure data integrity and efficient delivery to the server.

[0331] Step 3:

[0332] The server stores the data received from the terminal in a database. The stored data is prepared for analysis, and the latest health and emotional information is used.

[0333] Step 4:

[0334] The artificial intelligence algorithms on the server analyze incoming data and assess the user's health and psychological state. This assessment includes detecting health risks and emotional fluctuations based on comparisons with past data.

[0335] Step 5:

[0336] The server generates personalized health management advice based on the analysis results. The results of the emotion engine analysis are also taken into consideration; for example, if mental refreshment is needed, it will suggest relaxing activities.

[0337] Step 6:

[0338] The server sends the generated advice to the terminal. The advice is displayed on the user's device and presented in a format that is easy for the user to understand.

[0339] Step 7:

[0340] Users follow the advice they receive from their device to manage their health and adjust their lifestyle. The advice is designed to be easily incorporated into their daily routines.

[0341] Step 8:

[0342] Users provide feedback via their devices regarding the implementation status of the advice and their impressions. This feedback is used to analyze the user's actions in the future.

[0343] Step 9:

[0344] The server receives feedback and uses it to improve the accuracy of its analysis algorithms and enhance its services. This ensures that the health management support provided to users is constantly optimized.

[0345] (Example 2)

[0346] 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".

[0347] Traditional health management systems have been limited to providing advice based on users' health information and lifestyle data, and have a problem of not fully meeting individual needs because they do not adequately consider users' emotional states. Furthermore, while there is a need for a rapid response when an abnormality is detected, the lack of an automated system for contacting medical institutions meant that there was a possibility of delays in emergency action.

[0348] 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.

[0349] In this invention, the server includes means for receiving and storing individual users' health information, lifestyle data, and emotional information; means for using artificial intelligence algorithms and generative AI models to analyze the stored information; and an emotion engine for analyzing the user's emotional state. This enables the provision of personalized advice that takes into account the user's emotional state, and allows for rapid response in emergencies by automatically contacting medical institutions quickly when an anomaly is detected.

[0350] "Health information" refers to data about the user's physical condition, specifically including weight, blood pressure, heart rate, etc.

[0351] "Lifestyle data" refers to data about the user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0352] "Emotional information" refers to data about the user's subjective emotional state, including psychological elements such as stress levels and mood.

[0353] An "artificial intelligence algorithm" is a computational method that analyzes data and processes it based on a specific purpose. In this invention, it is used to analyze health information and lifestyle data.

[0354] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new information from data, and is used to create personalized advice.

[0355] An "emotion engine" is a device or program that analyzes emotional information obtained from users and evaluates their psychological state based on that analysis.

[0356] "Personalized advice" refers to suggestions and guidance tailored to the user's specific health and emotional state, including specific instructions regarding diet, exercise, and stress management.

[0357] "Anomaly detection" is the process of identifying unusual states or values ​​based on collected data and determining safety and health risks.

[0358] "Automatic contact with medical institutions" is a function that, when an abnormality is detected, automatically contacts pre-registered medical facilities to encourage a prompt medical response.

[0359] This invention is a system that provides health management advice to individual users and mainly consists of a server, a terminal, and an emotion engine.

[0360] The server receives health information, lifestyle data, and emotional information from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms and generative AI models are used for data analysis, which generates personalized health management advice for the user. During the server's analysis process, the emotion engine evaluates the user's psychological state. This information is used to provide personalized advice on diet, exercise, and stress management. For example, if the data analysis determines that the user is experiencing high stress levels, the server might generate advice such as, "Try taking a walk today to refresh yourself."

[0361] The terminal receives input data from the user and simultaneously displays the analysis results. Users input daily health information and subjective emotional states into this terminal. After the data is sent to the server, the analysis results returned from the server are displayed on the terminal, providing the user with specific actionable guidelines.

[0362] The emotion engine analyzes the user's psychological state based on the emotional information they input. This allows the system to provide personalized support that takes into account not only the user's physical health but also their emotional state.

[0363] For example, if a user inputs a prompt such as "a time when I tend to feel stressed" into the system, the AI ​​model will generate advice based on that prompt. Because this prompt reflects the user's psychological tendencies, the generated advice will be more accurate and effective.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users input daily health information and emotional states into the device. Specific data entered includes weight, diet, exercise levels, and stress levels. This information is used as input data for the next processing step.

[0367] Step 2:

[0368] The terminal collects health and emotional information entered by the user and bundles it into data packets. These bundled data packets are then sent to the server via a secure communication protocol. The server receives the data as output.

[0369] Step 3:

[0370] The server stores the received data in its internal database. The stored data undergoes format conversion and is processed into a structured dataset for analysis. The server's next action is to prepare the data for input into the generating AI model.

[0371] Step 4:

[0372] The server inputs the stored dataset into the generating AI model and artificial intelligence algorithm. Prompt statements are also input, and data analysis is performed based on the user's emotional state and lifestyle patterns. Here, an analysis prompt such as "Generate appropriate advice considering the user's stress level and diet" is used.

[0373] Step 5:

[0374] The generative AI model analyzes input data and generates optimal advice for the user. For example, it might produce a specific output such as, "Light walking is recommended to relieve stress."

[0375] Step 6:

[0376] The server sends the generated advice to the device. The device analyzes the received advice and displays it on the screen. At this point, the user can receive personalized health management advice via the device.

[0377] (Application Example 2)

[0378] 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."

[0379] Existing health management systems do not adequately recommend personalized products and services that take into account the emotional state of users, thereby limiting improvements in users' health and quality of life. Therefore, it is necessary to provide more appropriate products and services based on users' emotional states to achieve more effective health management and improved quality of life.

[0380] 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.

[0381] In this invention, the server includes means for receiving and storing individual users' health information and lifestyle data; means for using artificial intelligence algorithms to analyze the stored health information and lifestyle data; means for providing users with personalized diet and exercise advice based on the analysis results; means for recommending products and services to users based on their emotional state; and means for considering the user's past purchase history when recommending products and services. This enables more personalized recommendations of products and services that take into account the user's emotional state.

[0382] "Health information" refers to data about the user's physical body, including data such as weight, blood pressure, and heart rate.

[0383] "Lifestyle data" refers to data about users' daily activities such as diet, exercise, and sleep.

[0384] An "artificial intelligence algorithm" is a mathematical method used to analyze data, recognize specific patterns, and provide optimal advice to users.

[0385] "Emotional state" refers to the user's psychological condition and includes emotions such as stress, relaxation, and happiness.

[0386] "Goods and services" refers to specific products and activities provided to users, and includes goods, information, entertainment, etc.

[0387] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0388] To realize this invention, the system consists mainly of three main components: a server, a terminal, and an emotion engine. The server is responsible for receiving and recording the user's health information, lifestyle data, and emotional state. This data is first collected by the terminal through sensors and user input, and then transmitted to the server.

[0389] The server analyzes stored data using AI algorithms to generate personalized diet and exercise advice for users, and also evaluates users' health status by utilizing patterns derived from vast datasets. The artificial intelligence technology used here is implemented in languages ​​such as Python, and frameworks such as TensorFlow and PyTorch are often used.

[0390] The emotion engine analyzes the user's emotional state and sends the results to the server. Based on this information, it recommends products and services to the device that are appropriate for the user's emotional state. In particular, considering past purchase history and the current emotional state enables more personalized recommendations.

[0391] For example, if the device detects that the user is experiencing stress, it will suggest items and services that promote relaxation. For instance, it might display something like, "Today we are offering relaxing aromatherapy candles at a special price."

[0392] When using a generative AI model, the server generates a new dataset and creates prompts. A concrete example of a prompt might be, "If the user's emotional state is stressed, suggest products that help them relax, and exclude products they already own based on their purchase history." This allows for more accurate data generation and suggestions based on the user's emotional state and past behavioral history.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The device collects health information, lifestyle data, and emotional states through sensors and user input. This data is necessary to understand the user's current state. Inputs include heart rate, activity level, and subjective emotional state, and this data is formatted for transmission to the server.

[0396] Step 2:

[0397] The server receives data sent from the terminal and stores it in a database. This stored data is used as basic information for analyzing the user's health status. The input is health data sent from the terminal, and the output is the process of saving it to the server.

[0398] Step 3:

[0399] The server inputs stored data into an artificial intelligence algorithm for analysis. This input consists of user health information and lifestyle data, and data mining techniques are used to identify specific patterns. The output is the analysis results, which are then provided to the user as personalized health advice.

[0400] Step 4:

[0401] The server generates specific advice regarding diet and exercise for the user based on the analysis results. Specifically, the advice generated based on the user's current health and emotional state might take the form of, for example, "Let's try some light exercise today." The input is the analysis results from the AI, and the output is advice in text format.

[0402] Step 5:

[0403] The server matches the user's emotional state with their past purchase history to recommend appropriate products and services. The input is the user's emotional state and purchase history, and the output is a list of products tailored to their emotional state. The generated data is displayed on the terminal.

[0404] Step 6:

[0405] The terminal receives advice and product recommendations from the server and presents them visually to the user. Specifically, it displays health advice and product information on the terminal's screen. The input consists of advice and product lists sent from the server, and the output is a visual presentation of information to the user.

[0406] Step 7:

[0407] Users input feedback into their device based on the advice and product recommendations provided, and send it to the server. This feedback helps improve the accuracy of the service. The input is the user's text feedback, which is then sent to the server as output data.

[0408] 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.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention effectively utilizes health information and lifestyle data provided by individual users to offer personalized health management advice. The system primarily consists of three roles: server, terminal, and user.

[0425] The server receives health information and lifestyle data transmitted from the user's device and securely stores it in a database. The data is updated regularly and reflected in real time. Next, the received data is analyzed using artificial intelligence algorithms executed within the server. This analysis allows for an assessment of health status and prediction of disease risk. Based on the analysis results, the server generates personalized diet and exercise advice for each user and issues warnings for abnormalities as needed. For example, if the server detects an abnormal fluctuation in heart rate, it can automatically notify pre-registered medical institutions.

[0426] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps taken. This data is often obtained from sensor devices closely integrated into the user's daily life (e.g., smartwatches). The device sends the information entered by the user to a server and immediately notifies the user of the received advice. The notification is made through an application, and the user can view the advice on the screen. For example, specific suggestions such as "Let's walk 10,000 steps today" or "Let's try to eat a low-salt diet" may be presented.

[0427] Users manage their health by inputting their health information and daily habits into the device and incorporating the received advice into their daily lives. Furthermore, users can contribute to the system by providing feedback on the suggested advice. This feedback is sent to the server and used to improve the accuracy of the system and enhance the service.

[0428] Thus, the present invention provides users with personalized health management support and enables prompt medical response.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] Users input their health information and lifestyle data using their devices. Heart rate and step count are automatically collected from sensor devices such as smartwatches.

[0432] Step 2:

[0433] The device temporarily stores collected health information and lifestyle data, and sends it to the server when ready. The transmission is performed automatically and periodically, designed to minimize user burden.

[0434] Step 3:

[0435] The server receives data sent from the terminal and stores it in the database. Data is received in real time, allowing for information updates.

[0436] Step 4:

[0437] An artificial intelligence algorithm within the server operates and analyzes the stored data. The analysis includes assessing health status and predicting potential disease risks, and then generates analysis results.

[0438] Step 5:

[0439] Based on the analysis results, the server generates personalized health management advice for the user. This advice includes specific guidance on diet and exercise.

[0440] Step 6:

[0441] If the server detects an anomaly during analysis, it will immediately issue a warning. It will also automatically contact pre-registered medical institutions as needed.

[0442] Step 7:

[0443] Analysis results and advice sent from the server are received by the device. The device then displays the results to the user through notifications and an in-app dashboard.

[0444] Step 8:

[0445] Users incorporate the advice they receive via their devices into their daily lives as a guide for health management. They also send feedback to the server via their devices regarding the process and results of implementing the advice.

[0446] Step 9:

[0447] The server receives feedback from users and uses it for future analysis. This allows for continuous improvement of the accuracy of advice and the overall system.

[0448] (Example 1)

[0449] 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."

[0450] In modern society, individuals have diverse lifestyles, and the importance of health management is increasing. However, many people rely only on general health information, and there is a lack of advice optimized for individual lifestyles and health conditions. As a result, it is difficult for individuals to effectively manage their health. Therefore, there is a need for a system that provides personalized health advice to individual users based on their specific health conditions and lifestyles.

[0451] 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.

[0452] In this invention, the server includes means for receiving and recording individual users' health status information and lifestyle information, means for using a machine learning model to analyze the recorded health status information and lifestyle information, and means for providing users with personalized nutrition and physical activity guidelines based on the analysis results. This makes it possible to provide health management advice tailored to the characteristics of each user quickly and effectively.

[0453] "Health status information" refers to data related to the user's physical health, and specifically includes numerical information such as heart rate, blood pressure, weight, body temperature, and sleep patterns.

[0454] "Lifestyle information" refers to data about the user's daily activities and habits, and specifically includes information such as the content of their meals, the frequency and type of exercise, and their smoking and drinking habits.

[0455] A "machine learning model" refers to an algorithm used to analyze data. It learns data patterns using statistical methods and is used to make predictions and classifications based on future data.

[0456] "Guidelines on nutrition and physical activity" refer to advice provided to improve or maintain the health of users, and specifically include suggestions for improving dietary content and recommendations for increasing or decreasing exercise levels.

[0457] "Device" refers to a digital device used to receive, display, and provide analysis results of a user's health information, and specifically includes smartphones, tablets, smartwatches, and the like.

[0458] This invention effectively utilizes health status and lifestyle information provided by individual users to offer personalized health management advice. The system mainly consists of three elements: a server, a terminal, and a user.

[0459] The server receives health status and lifestyle information transmitted from the user's device and securely records it in a database. The recorded data is analyzed using machine learning frameworks such as TensorFlow and PyTorch. This allows for the assessment of the user's health status and prediction of disease risk, as well as the generation of personalized nutrition and physical activity guidelines based on the analysis results. For example, the server can analyze the user's heart rate data and provide specific advice such as, "Your heart rate is a little high today. Try taking deep breaths to relax."

[0460] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps via various sensors such as smartwatches and fitness trackers. The device sends this data to a server and immediately notifies the user of the generated guidance. The notification is made through a dedicated application, and the user can check the advice on the app screen. For example, a notification such as "Please do 10 minutes of stretching exercises today" might appear on the device.

[0461] Users input their health information and daily habits into the device and manage their health by incorporating the received advice into their daily lives. Furthermore, users can send their responses and feedback to the suggested advice from the device to the server. This feedback is used to improve the accuracy of the system and enhance the service.

[0462] An example of a prompt message might be, "Male in his 30s, average daily steps 5,000, please provide advice for maintaining good health." This allows the system to generate specific health management guidelines tailored to the user's characteristics.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] User data entry

[0466] Users collect biometric information using devices such as smartwatches and fitness trackers. Through these devices, users input data such as heart rate, steps taken, and sleep patterns into their devices. This input data reflects the user's daily lifestyle habits.

[0467] Step 2:

[0468] Data transmission by terminal

[0469] The terminal organizes health status and lifestyle information entered by the user and sends it to the server. The data sent is in real time, allowing the server to perform immediate analysis based on this information. Encryption is applied during data transmission to ensure the security of the communication.

[0470] Step 3:

[0471] Data recording by server

[0472] The server records health status information received from the terminal into a database. The stored data is identified by individual user IDs, making it easily accessible and manageable. This recording process is regularly backed up to maintain data consistency and reliability.

[0473] Step 4:

[0474] Server-based data analysis

[0475] The server analyzes recorded data using a machine learning model. The input data includes user health information, and the server uses this to derive results such as health assessments and risk predictions. The generative AI model learns data patterns using frameworks such as TensorFlow and PyTorch. The output obtained from this analysis is personalized health management advice for each user.

[0476] Step 5:

[0477] Server-based advice generation

[0478] The server generates personalized nutrition and physical activity guidelines based on the analysis results. For example, it might create advice such as, "Today, take a 30-minute walk and reduce your salt intake." These guidelines are then adjusted to the user's specific health condition using prompts.

[0479] Step 6:

[0480] Notifications and reception via device

[0481] The device receives advice generated from the server and immediately notifies the user. The notification is delivered by an application, allowing the user to view the guidance on the screen. The interface in this process is designed to make it easy for the user to take action.

[0482] Step 7:

[0483] User Feedback Submission

[0484] Users incorporate the provided advice into their daily lives and send feedback to the server via their device, detailing the results and their reactions. This feedback is recorded by the server and used to improve the accuracy of system analysis and enhance the service. Users provide information about specific changes in their physical condition and the effectiveness of the advice.

[0485] (Application Example 1)

[0486] 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."

[0487] In modern times, individual users are required not only to manage their own health status but also to efficiently select appropriate products based on that status and to purchase them quickly and safely. However, existing health management systems have been insufficient in providing personalized product recommendations and electronic transaction functions based on users' health data, thus lacking convenience. Therefore, the present invention aims to provide a system that solves these problems.

[0488] 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.

[0489] In this invention, the server includes a device for receiving and storing individual users' health information and lifestyle data, a device for using a machine learning model to analyze the stored health information and lifestyle data, and a device having an electronic transaction function that suggests optimal health-related products to the user based on the analysis data and allows for direct purchase. This enables appropriate product suggestions based on the user's health condition and their rapid purchase.

[0490] "Health information" refers to data on the health status of individual users, specifically biometric data such as heart rate, blood pressure, and body temperature.

[0491] "Lifestyle data" refers to information about users' daily behavioral patterns, diet, and exercise habits.

[0492] A "machine learning model" refers to an algorithm that uses a computer to analyze data and recognize or predict patterns.

[0493] "Personalized dietary and exercise advice" means providing guidance on diet and exercise methods optimized for each user, based on their health information and lifestyle data.

[0494] "Health-related products" are products intended to maintain and improve the health of users, and include supplements and health foods.

[0495] "Electronic transaction functionality" refers to a system that supports a series of operations necessary when purchasing goods or services via the internet.

[0496] "Communication equipment" refers to devices used by users as a means of sending and receiving data, and includes smartphones, tablets, and other similar devices.

[0497] "Feedback" refers to opinions and evaluations provided by users based on their information and experiences, for the purpose of improving services and products.

[0498] The system for implementing this invention consists of three roles: server, terminal, and user.

[0499] The server receives health information and lifestyle data provided by users. This data includes biometric data obtained from wearable devices such as smartwatches, as well as manually entered lifestyle information. The server securely stores this data in a database and performs analysis using machine learning models. As a result of the analysis, personalized health status assessments and suggestions for appropriate health-related products are generated. These suggestions aim to present products that match the user's existing health status.

[0500] The terminal functions as a device that facilitates user input and displays analysis results and suggestions received from the server to the user. The terminal is an application implemented on communication devices such as smartphones and tablets, visualizing the received personalized advice and product suggestions on the screen. Furthermore, the terminal includes electronic transaction functionality for quickly purchasing suggested products, allowing users to easily select items.

[0501] Users input their health information via their device and review the advice and product suggestions they receive. For example, if a user is diagnosed with high blood pressure, a "low-sodium diet package" suggestion will appear on the device. They can then purchase it with a single click. At this time, a prompt message generated by a generative AI model is presented, such as, "Based on your high blood pressure data, we recommend the following low-sodium diet product. If you wish to purchase this product, please click the link below," presenting the options in an easy-to-understand manner for the user. User feedback is also sent to the server and used to improve the accuracy of the analysis and enhance the service.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server receives health information and lifestyle data from the user's device. The data received as input includes heart rate, steps taken, and dietary information. This data is securely stored in a database on the server.

[0505] Step 2:

[0506] The server inputs the stored data into a machine learning model for analysis. Data processing here includes normalization of numerical data and integration across time axes. The output provides health status assessments and disease risk predictions.

[0507] Step 3:

[0508] The server generates personalized health advice and product recommendations based on the analysis results. Using a generative AI model, it creates personalized prompt messages for each user, such as "Based on your health condition, we recommend this low-sodium food product."

[0509] Step 4:

[0510] The terminal receives advice and suggestions sent from the server and displays them visually to the user. The input is notification data from the server, and the output is a display that the user can review.

[0511] Step 5:

[0512] The user reviews the suggested health-related products on their device and, if they wish to purchase them, selects the items using the provided electronic transaction function and completes the purchase process. Specifically, this involves clicking the "Purchase" button on the screen. As a result, transaction confirmation information is sent to both the device and the server.

[0513] Step 6:

[0514] Users input feedback about the service from their devices and send it to the server. The server receives this input, providing data to improve the accuracy of analysis and enhance the service.

[0515] 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.

[0516] This invention provides personalized health management advice that takes into account the user's emotional state, in addition to their health information and lifestyle data, by utilizing an emotion engine. The system mainly consists of three main components: a server, a terminal, and an emotion engine.

[0517] The server receives health information, lifestyle data, and emotional information transmitted from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms are used to analyze the data, and based on the results obtained during the analysis, the server provides personalized advice to each user. The server comprehensively analyzes the user's data to assess their health status and generates appropriate diet and exercise advice based on that assessment. Furthermore, if an abnormality is detected during the analysis, it can automatically contact registered medical institutions. For example, if the user is experiencing stress, the advice can be adjusted to promote relaxation.

[0518] The terminal functions as an interface with the user and is responsible for collecting health and emotional data. Users input daily health information and subjective emotional states into the terminal. The terminal sends this information to the server and displays the analysis results and advice received from the server. The displayed advice takes into account the emotional data analyzed by the emotion engine, and specific suggestions such as "Let's take a walk today to refresh yourself" are presented to the user.

[0519] The emotion engine recognizes and analyzes the user's emotional state based on user input data and sensor information. This ensures that the user's psychological state is appropriately considered within the system, enabling advice that is more tailored to individual needs.

[0520] The present invention aims to contribute to maintaining the health and preventing illness of users by providing personalized advice that comprehensively takes into account each user's individual health and psychological state.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The user uses the device to input health information (e.g., heart rate, weight, diet) and subjective emotional state (e.g., feeling good, feeling stressed). The device temporarily stores this data.

[0524] Step 2:

[0525] The device periodically sends health and emotional information to the server. This transmission is automated and designed to ensure data integrity and efficient delivery to the server.

[0526] Step 3:

[0527] The server stores the data received from the terminal in a database. The stored data is prepared for analysis, and the latest health and emotional information is used.

[0528] Step 4:

[0529] The artificial intelligence algorithms on the server analyze incoming data and assess the user's health and psychological state. This assessment includes detecting health risks and emotional fluctuations based on comparisons with past data.

[0530] Step 5:

[0531] The server generates personalized health management advice based on the analysis results. The results of the emotion engine analysis are also taken into consideration; for example, if mental refreshment is needed, it will suggest relaxing activities.

[0532] Step 6:

[0533] The server sends the generated advice to the terminal. The advice is displayed on the user's device and presented in a format that is easy for the user to understand.

[0534] Step 7:

[0535] Users follow the advice they receive from their device to manage their health and adjust their lifestyle. The advice is designed to be easily incorporated into their daily routines.

[0536] Step 8:

[0537] Users provide feedback via their devices regarding the implementation status of the advice and their impressions. This feedback is used to analyze the user's actions in the future.

[0538] Step 9:

[0539] The server receives feedback and uses it to improve the accuracy of its analysis algorithms and enhance its services. This ensures that the health management support provided to users is constantly optimized.

[0540] (Example 2)

[0541] 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."

[0542] Traditional health management systems have been limited to providing advice based on users' health information and lifestyle data, and have a problem of not fully meeting individual needs because they do not adequately consider users' emotional states. Furthermore, while there is a need for a rapid response when an abnormality is detected, the lack of an automated system for contacting medical institutions meant that there was a possibility of delays in emergency action.

[0543] 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.

[0544] In this invention, the server includes means for receiving and storing individual users' health information, lifestyle data, and emotional information; means for using artificial intelligence algorithms and generative AI models to analyze the stored information; and an emotion engine for analyzing the user's emotional state. This enables the provision of personalized advice that takes into account the user's emotional state, and allows for rapid response in emergencies by automatically contacting medical institutions quickly when an anomaly is detected.

[0545] "Health information" refers to data about the user's physical condition, specifically including weight, blood pressure, heart rate, etc.

[0546] "Lifestyle data" refers to data about the user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0547] "Emotional information" refers to data about the user's subjective emotional state, including psychological elements such as stress levels and mood.

[0548] An "artificial intelligence algorithm" is a computational method that analyzes data and processes it based on a specific purpose. In this invention, it is used to analyze health information and lifestyle data.

[0549] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new information from data, and is used to create personalized advice.

[0550] An "emotion engine" is a device or program that analyzes emotional information obtained from users and evaluates their psychological state based on that analysis.

[0551] "Personalized advice" refers to suggestions and guidance tailored to the user's specific health and emotional state, including specific instructions regarding diet, exercise, and stress management.

[0552] "Anomaly detection" is the process of identifying unusual states or values ​​based on collected data and determining safety and health risks.

[0553] "Automatic contact with medical institutions" is a function that, when an abnormality is detected, automatically contacts pre-registered medical facilities to encourage a prompt medical response.

[0554] This invention is a system that provides health management advice to individual users and mainly consists of a server, a terminal, and an emotion engine.

[0555] The server receives health information, lifestyle data, and emotional information from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms and generative AI models are used for data analysis, which generates personalized health management advice for the user. During the server's analysis process, the emotion engine evaluates the user's psychological state. This information is used to provide personalized advice on diet, exercise, and stress management. For example, if the data analysis determines that the user is experiencing high stress levels, the server might generate advice such as, "Try taking a walk today to refresh yourself."

[0556] The terminal receives input data from the user and simultaneously displays the analysis results. Users input daily health information and subjective emotional states into this terminal. After the data is sent to the server, the analysis results returned from the server are displayed on the terminal, providing the user with specific actionable guidelines.

[0557] The emotion engine analyzes the user's psychological state based on the emotional information they input. This allows the system to provide personalized support that takes into account not only the user's physical health but also their emotional state.

[0558] For example, if a user inputs a prompt such as "a time when I tend to feel stressed" into the system, the AI ​​model will generate advice based on that prompt. Because this prompt reflects the user's psychological tendencies, the generated advice will be more accurate and effective.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] Users input daily health information and emotional states into the device. Specific data entered includes weight, diet, exercise levels, and stress levels. This information is used as input data for the next processing step.

[0562] Step 2:

[0563] The terminal collects health and emotional information entered by the user and bundles it into data packets. These bundled data packets are then sent to the server via a secure communication protocol. The server receives the data as output.

[0564] Step 3:

[0565] The server stores the received data in its internal database. The stored data undergoes format conversion and is processed into a structured dataset for analysis. The server's next action is to prepare the data for input into the generating AI model.

[0566] Step 4:

[0567] The server inputs the stored dataset into the generating AI model and artificial intelligence algorithm. Prompt statements are also input, and data analysis is performed based on the user's emotional state and lifestyle patterns. Here, an analysis prompt such as "Generate appropriate advice considering the user's stress level and diet" is used.

[0568] Step 5:

[0569] The generative AI model analyzes input data and generates optimal advice for the user. For example, it might produce a specific output such as, "Light walking is recommended to relieve stress."

[0570] Step 6:

[0571] The server sends the generated advice to the device. The device analyzes the received advice and displays it on the screen. At this point, the user can receive personalized health management advice via the device.

[0572] (Application Example 2)

[0573] 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."

[0574] Existing health management systems do not adequately recommend personalized products and services that take into account the emotional state of users, thereby limiting improvements in users' health and quality of life. Therefore, it is necessary to provide more appropriate products and services based on users' emotional states to achieve more effective health management and improved quality of life.

[0575] 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.

[0576] In this invention, the server includes means for receiving and storing individual users' health information and lifestyle data; means for using artificial intelligence algorithms to analyze the stored health information and lifestyle data; means for providing users with personalized diet and exercise advice based on the analysis results; means for recommending products and services to users based on their emotional state; and means for considering the user's past purchase history when recommending products and services. This enables more personalized recommendations of products and services that take into account the user's emotional state.

[0577] "Health information" refers to data about the user's physical condition, including data such as weight, blood pressure, and heart rate.

[0578] "Lifestyle data" refers to data about users' daily activities such as diet, exercise, and sleep.

[0579] An "artificial intelligence algorithm" is a mathematical method used to analyze data, recognize specific patterns, and provide optimal advice to users.

[0580] "Emotional state" refers to the user's psychological condition and includes emotions such as stress, relaxation, and happiness.

[0581] "Goods and services" refers to specific products and activities provided to users, and includes items, information, entertainment, etc.

[0582] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0583] To realize this invention, the system mainly consists of three main components: a server, a terminal, and an emotion engine. The server is responsible for receiving and recording the user's health information, lifestyle data, and emotional state. This data is first collected by the terminal through sensors and user input, and then transmitted to the server.

[0584] The server analyzes stored data using AI algorithms to generate personalized diet and exercise advice for users, and also evaluates users' health status by utilizing patterns derived from vast datasets. The artificial intelligence technology used here is implemented in languages ​​such as Python, and frameworks such as TensorFlow and PyTorch are often used.

[0585] The emotion engine analyzes the user's emotional state and sends the results to the server. Based on this information, it recommends products and services to the device that are appropriate for the user's emotional state. In particular, considering past purchase history and the current emotional state enables more personalized recommendations.

[0586] For example, if the device detects that the user is experiencing stress, it will suggest items and services that promote relaxation. For instance, it might display something like, "Today we are offering relaxing aromatherapy candles at a special price."

[0587] When using a generative AI model, the server generates a new dataset and creates prompts. A concrete example of a prompt might be, "If the user's emotional state is stressed, suggest products that help them relax, and exclude products they already own based on their purchase history." This allows for more accurate data generation and suggestions based on the user's emotional state and past behavioral history.

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The device collects health information, lifestyle data, and emotional states through sensors and user input. This data is necessary to understand the user's current state. Inputs include heart rate, activity level, and subjective emotional state, and this data is formatted for transmission to the server.

[0591] Step 2:

[0592] The server receives data sent from the terminal and stores it in a database. This stored data is used as basic information for analyzing the user's health status. The input is health data sent from the terminal, and the output is the process of saving it to the server.

[0593] Step 3:

[0594] The server inputs stored data into an artificial intelligence algorithm for analysis. This input consists of user health information and lifestyle data, and data mining techniques are used to identify specific patterns. The output is the analysis results, which are then provided to the user as personalized health advice.

[0595] Step 4:

[0596] The server generates specific advice regarding diet and exercise for the user based on the analysis results. Specifically, the advice generated based on the user's current health and emotional state might take the form of, for example, "Let's try some light exercise today." The input is the analysis results from the AI, and the output is advice in text format.

[0597] Step 5:

[0598] The server matches the user's emotional state with their past purchase history to recommend appropriate products and services. The input is the user's emotional state and purchase history, and the output is a list of products tailored to their emotional state. The generated data is displayed on the terminal.

[0599] Step 6:

[0600] The terminal receives advice and product recommendations from the server and presents them visually to the user. Specifically, it displays health advice and product information on the terminal's screen. The input consists of advice and product lists sent from the server, and the output is a visual presentation of information to the user.

[0601] Step 7:

[0602] Users input feedback into their device based on the advice and product recommendations provided, and send it to the server. This feedback helps improve the accuracy of the service. The input is the user's text feedback, which is then sent to the server as output data.

[0603] 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.

[0604] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention effectively utilizes health information and lifestyle data provided by individual users to offer personalized health management advice. The system primarily consists of three roles: server, terminal, and user.

[0621] The server receives health information and lifestyle data transmitted from the user's device and securely stores it in a database. The data is updated regularly and reflected in real time. Next, the received data is analyzed using artificial intelligence algorithms executed within the server. This analysis allows for an assessment of health status and prediction of disease risk. Based on the analysis results, the server generates personalized diet and exercise advice for each user and issues warnings for abnormalities as needed. For example, if the server detects an abnormal fluctuation in heart rate, it can automatically notify pre-registered medical institutions.

[0622] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps taken. This data is often obtained from sensor devices closely integrated into the user's daily life (e.g., smartwatches). The device sends the information entered by the user to a server and immediately notifies the user of the received advice. The notification is made through an application, and the user can view the advice on the screen. For example, specific suggestions such as "Let's walk 10,000 steps today" or "Let's try to eat a low-salt diet" may be presented.

[0623] Users manage their health by inputting their health information and daily habits into the device and incorporating the received advice into their daily lives. Furthermore, users can contribute to the system by providing feedback on the suggested advice. This feedback is sent to the server and used to improve the accuracy of the system and enhance the service.

[0624] Thus, the present invention provides users with personalized health management support and enables prompt medical response.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] Users input their health information and lifestyle data using their devices. Heart rate and step count are automatically collected from sensor devices such as smartwatches.

[0628] Step 2:

[0629] The device temporarily stores collected health information and lifestyle data, and sends it to the server when ready. The transmission is performed automatically and periodically, designed to minimize user burden.

[0630] Step 3:

[0631] The server receives data sent from the terminal and stores it in the database. Data is received in real time, allowing for information updates.

[0632] Step 4:

[0633] An artificial intelligence algorithm within the server operates and analyzes the stored data. The analysis includes assessing health status and predicting potential disease risks, and then generates analysis results.

[0634] Step 5:

[0635] Based on the analysis results, the server generates personalized health management advice for the user. This advice includes specific guidance on diet and exercise.

[0636] Step 6:

[0637] If the server detects an anomaly during analysis, it will immediately issue a warning. It will also automatically contact pre-registered medical institutions as needed.

[0638] Step 7:

[0639] Analysis results and advice sent from the server are received by the device. The device then displays the results to the user through notifications and an in-app dashboard.

[0640] Step 8:

[0641] Users incorporate the advice they receive via their devices into their daily lives as a guide for health management. They also send feedback to the server via their devices regarding the process and results of implementing the advice.

[0642] Step 9:

[0643] The server receives feedback from users and uses it for future analysis. This allows for continuous improvement of the accuracy of advice and the overall system.

[0644] (Example 1)

[0645] 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".

[0646] In modern society, individuals have diverse lifestyles, and the importance of health management is increasing. However, many people rely only on general health information, and there is a lack of advice optimized for individual lifestyles and health conditions. As a result, it is difficult for individuals to effectively manage their health. Therefore, there is a need for a system that provides personalized health advice to individual users based on their specific health conditions and lifestyles.

[0647] 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.

[0648] In this invention, the server includes means for receiving and recording individual users' health status information and lifestyle information, means for using a machine learning model to analyze the recorded health status information and lifestyle information, and means for providing users with personalized nutrition and physical activity guidelines based on the analysis results. This makes it possible to provide health management advice tailored to the characteristics of each user quickly and effectively.

[0649] "Health status information" refers to data related to the user's physical health, and specifically includes numerical information such as heart rate, blood pressure, weight, body temperature, and sleep patterns.

[0650] "Lifestyle information" refers to data about the user's daily activities and habits, and specifically includes information such as the content of their meals, the frequency and type of exercise, and their smoking and drinking habits.

[0651] A "machine learning model" refers to an algorithm used to analyze data. It learns data patterns using statistical methods and is used to make predictions and classifications based on future data.

[0652] "Guidelines on nutrition and physical activity" refer to advice provided to improve or maintain the health of users, and specifically include suggestions for improving dietary content and recommendations for increasing or decreasing exercise levels.

[0653] "Device" refers to a digital device used to receive, display, and provide analysis results of a user's health information, and specifically includes smartphones, tablets, smartwatches, and the like.

[0654] This invention effectively utilizes health status and lifestyle information provided by individual users to offer personalized health management advice. The system mainly consists of three elements: a server, a terminal, and a user.

[0655] The server receives health status and lifestyle information transmitted from the user's device and securely records it in a database. The recorded data is analyzed using machine learning frameworks such as TensorFlow and PyTorch. This allows for the assessment of the user's health status and prediction of disease risk, as well as the generation of personalized nutrition and physical activity guidelines based on the analysis results. For example, the server can analyze the user's heart rate data and provide specific advice such as, "Your heart rate is a little high today. Try taking deep breaths to relax."

[0656] The device functions as a medium for data input from the user, collecting biometric data such as heart rate and steps via various sensors such as smartwatches and fitness trackers. The device sends this data to a server and immediately notifies the user of the generated guidance. The notification is made through a dedicated application, and the user can check the advice on the app screen. For example, a notification such as "Please do 10 minutes of stretching exercises today" might appear on the device.

[0657] Users input their health information and daily habits into the device and manage their health by incorporating the received advice into their daily lives. Furthermore, users can send their responses and feedback to the suggested advice from the device to the server. This feedback is used to improve the accuracy of the system and enhance the service.

[0658] An example of a prompt message might be, "Male in his 30s, average daily steps 5,000, please provide advice for maintaining good health." This allows the system to generate specific health management guidelines tailored to the user's characteristics.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] User data entry

[0662] Users collect biometric information using devices such as smartwatches and fitness trackers. Through these devices, users input data such as heart rate, steps taken, and sleep patterns into their devices. This input data reflects the user's daily lifestyle habits.

[0663] Step 2:

[0664] Data transmission by terminal

[0665] The terminal organizes health status and lifestyle information entered by the user and sends it to the server. The data sent is in real time, allowing the server to perform immediate analysis based on this information. Encryption is applied during data transmission to ensure the security of the communication.

[0666] Step 3:

[0667] Data recording by server

[0668] The server records health status information received from the terminal into a database. The stored data is identified by individual user IDs, making it easily accessible and manageable. This recording process is regularly backed up to maintain data consistency and reliability.

[0669] Step 4:

[0670] Server-based data analysis

[0671] The server analyzes recorded data using a machine learning model. The input data includes user health information, and the server uses this to derive results such as health assessments and risk predictions. The generative AI model learns data patterns using frameworks such as TensorFlow and PyTorch. The output obtained from this analysis is personalized health management advice for each user.

[0672] Step 5:

[0673] Server-based advice generation

[0674] The server generates personalized nutrition and physical activity guidelines based on the analysis results. For example, it might create advice such as, "Today, take a 30-minute walk and reduce your salt intake." These guidelines are then adjusted to the user's specific health condition using prompts.

[0675] Step 6:

[0676] Notifications and reception via device

[0677] The device receives advice generated from the server and immediately notifies the user. The notification is delivered by an application, allowing the user to view the guidance on the screen. The interface in this process is designed to make it easy for the user to take action.

[0678] Step 7:

[0679] User Feedback Submission

[0680] Users incorporate the provided advice into their daily lives and send feedback to the server via their device, detailing the results and their reactions. This feedback is recorded by the server and used to improve the accuracy of system analysis and enhance the service. Users provide information about specific changes in their physical condition and the effectiveness of the advice.

[0681] (Application Example 1)

[0682] 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".

[0683] In modern times, individual users are required not only to manage their own health status but also to efficiently select appropriate products based on that status and to purchase them quickly and safely. However, existing health management systems have been insufficient in providing personalized product recommendations and electronic transaction functions based on users' health data, thus lacking convenience. Therefore, the present invention aims to provide a system that solves these problems.

[0684] 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.

[0685] In this invention, the server includes a device for receiving and storing individual users' health information and lifestyle data, a device for using a machine learning model to analyze the stored health information and lifestyle data, and a device having an electronic transaction function that suggests optimal health-related products to the user based on the analysis data and allows for direct purchase. This enables appropriate product suggestions based on the user's health condition and their rapid purchase.

[0686] "Health information" refers to data on the health status of individual users, specifically biometric data such as heart rate, blood pressure, and body temperature.

[0687] "Lifestyle data" refers to information about users' daily behavioral patterns, diet, and exercise habits.

[0688] A "machine learning model" refers to an algorithm that uses a computer to analyze data and recognize or predict patterns.

[0689] "Personalized dietary and exercise advice" means providing guidance on diet and exercise methods optimized for each user, based on their health information and lifestyle data.

[0690] "Health-related products" are products intended to maintain and improve the health of users, and include supplements and health foods.

[0691] "Electronic transaction functionality" refers to a system that supports a series of operations necessary when purchasing goods or services via the internet.

[0692] "Communication equipment" refers to devices used by users as a means of sending and receiving data, and includes smartphones, tablets, and other similar devices.

[0693] "Feedback" refers to opinions and evaluations provided by users based on their information and experiences, for the purpose of improving services and products.

[0694] The system for implementing this invention consists of three roles: server, terminal, and user.

[0695] The server receives health information and lifestyle data provided by users. This data includes biometric data obtained from wearable devices such as smartwatches, as well as manually entered lifestyle information. The server securely stores this data in a database and performs analysis using machine learning models. As a result of the analysis, personalized health status assessments and suggestions for appropriate health-related products are generated. These suggestions aim to present products that match the user's existing health status.

[0696] The terminal functions as a device that facilitates user input and displays analysis results and suggestions received from the server to the user. The terminal is an application implemented on communication devices such as smartphones and tablets, visualizing the received personalized advice and product suggestions on the screen. Furthermore, the terminal includes electronic transaction functionality for quickly purchasing suggested products, allowing users to easily select items.

[0697] Users input their health information via their device and review the advice and product suggestions they receive. For example, if a user is diagnosed with high blood pressure, a "low-sodium diet package" suggestion will appear on the device. They can then purchase it with a single click. At this time, a prompt message generated by a generative AI model is presented, such as, "Based on your high blood pressure data, we recommend the following low-sodium diet product. If you wish to purchase this product, please click the link below," presenting the options in an easy-to-understand manner for the user. User feedback is also sent to the server and used to improve the accuracy of the analysis and enhance the service.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] The server receives health information and lifestyle data from the user's device. The data received as input includes heart rate, steps taken, and dietary information. This data is securely stored in a database on the server.

[0701] Step 2:

[0702] The server inputs the stored data into a machine learning model for analysis. Data processing here includes normalization of numerical data and integration across time axes. The output provides health status assessments and disease risk predictions.

[0703] Step 3:

[0704] The server generates personalized health advice and product recommendations based on the analysis results. Using a generative AI model, it creates personalized prompt messages for each user, such as "Based on your health condition, we recommend this low-sodium food product."

[0705] Step 4:

[0706] The terminal receives advice and suggestions sent from the server and displays them visually to the user. The input is notification data from the server, and the output is a display that the user can review.

[0707] Step 5:

[0708] The user reviews the suggested health-related products on their device and, if they wish to purchase them, selects the items using the provided electronic transaction function and completes the purchase process. Specifically, this involves clicking the "Purchase" button on the screen. As a result, transaction confirmation information is sent to both the device and the server.

[0709] Step 6:

[0710] Users input feedback about the service from their devices and send it to the server. The server receives this input, providing data to improve the accuracy of analysis and enhance the service.

[0711] 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.

[0712] This invention provides personalized health management advice that takes into account the user's emotional state, in addition to their health information and lifestyle data, by utilizing an emotion engine. The system mainly consists of three main components: a server, a terminal, and an emotion engine.

[0713] The server receives health information, lifestyle data, and emotional information transmitted from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms are used to analyze the data, and based on the results obtained during the analysis, the server provides personalized advice to each user. The server comprehensively analyzes the user's data to assess their health status and generates appropriate diet and exercise advice based on that assessment. Furthermore, if an abnormality is detected during the analysis, it can automatically contact registered medical institutions. For example, if the user is experiencing stress, the advice can be adjusted to promote relaxation.

[0714] The terminal functions as an interface with the user and is responsible for collecting health and emotional data. Users input daily health information and subjective emotional states into the terminal. The terminal sends this information to the server and displays the analysis results and advice received from the server. The displayed advice takes into account the emotional data analyzed by the emotion engine, and specific suggestions such as "Let's take a walk today to refresh yourself" are presented to the user.

[0715] The emotion engine recognizes and analyzes the user's emotional state based on user input data and sensor information. This ensures that the user's psychological state is appropriately considered within the system, enabling advice that is more tailored to individual needs.

[0716] The present invention aims to contribute to maintaining the health and preventing illness of users by providing personalized advice that comprehensively takes into account each user's individual health and psychological state.

[0717] The following describes the processing flow.

[0718] Step 1:

[0719] The user uses the device to input health information (e.g., heart rate, weight, diet) and subjective emotional state (e.g., feeling good, feeling stressed). The device temporarily stores this data.

[0720] Step 2:

[0721] The device periodically sends health and emotional information to the server. This transmission is automated and designed to ensure data integrity and efficient delivery to the server.

[0722] Step 3:

[0723] The server stores the data received from the terminal in a database. The stored data is prepared for analysis, and the latest health and emotional information is used.

[0724] Step 4:

[0725] The artificial intelligence algorithms on the server analyze incoming data and assess the user's health and psychological state. This assessment includes detecting health risks and emotional fluctuations based on comparisons with past data.

[0726] Step 5:

[0727] The server generates personalized health management advice based on the analysis results. The results of the emotion engine analysis are also taken into consideration; for example, if mental refreshment is needed, it will suggest relaxing activities.

[0728] Step 6:

[0729] The server sends the generated advice to the terminal. The advice is displayed on the user's device and presented in a format that is easy for the user to understand.

[0730] Step 7:

[0731] Users follow the advice they receive from their device to manage their health and adjust their lifestyle. The advice is designed to be easily incorporated into their daily routines.

[0732] Step 8:

[0733] Users provide feedback via their devices regarding the implementation status of the advice and their impressions. This feedback is used to analyze the user's actions in the future.

[0734] Step 9:

[0735] The server receives feedback and uses it to improve the accuracy of its analysis algorithms and enhance its services. This ensures that the health management support provided to users is constantly optimized.

[0736] (Example 2)

[0737] 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".

[0738] Traditional health management systems have been limited to providing advice based on users' health information and lifestyle data, and have a problem of not fully meeting individual needs because they do not adequately consider users' emotional states. Furthermore, while there is a need for a rapid response when an abnormality is detected, the lack of an automated system for contacting medical institutions meant that there was a possibility of delays in emergency action.

[0739] 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.

[0740] In this invention, the server includes means for receiving and storing individual users' health information, lifestyle data, and emotional information; means for using artificial intelligence algorithms and generative AI models to analyze the stored information; and an emotion engine for analyzing the user's emotional state. This enables the provision of personalized advice that takes into account the user's emotional state, and allows for rapid response in emergencies by automatically contacting medical institutions quickly when an anomaly is detected.

[0741] "Health information" refers to data about the user's physical condition, specifically including weight, blood pressure, heart rate, etc.

[0742] "Lifestyle data" refers to data about the user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0743] "Emotional information" refers to data about the user's subjective emotional state, including psychological elements such as stress levels and mood.

[0744] An "artificial intelligence algorithm" is a computational method that analyzes data and processes it based on a specific purpose. In this invention, it is used to analyze health information and lifestyle data.

[0745] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new information from data, and is used to create personalized advice.

[0746] An "emotion engine" is a device or program that analyzes emotional information obtained from users and evaluates their psychological state based on that analysis.

[0747] "Personalized advice" refers to suggestions and guidance tailored to the user's specific health and emotional state, including specific instructions regarding diet, exercise, and stress management.

[0748] "Anomaly detection" is the process of identifying unusual states or values ​​based on collected data and determining safety and health risks.

[0749] "Automatic contact with medical institutions" is a function that, when an abnormality is detected, automatically contacts pre-registered medical facilities to encourage a prompt medical response.

[0750] This invention is a system that provides health management advice to individual users and mainly consists of a server, a terminal, and an emotion engine.

[0751] The server receives health information, lifestyle data, and emotional information from the user's device. This data is stored in a database on the server. Artificial intelligence algorithms and generative AI models are used for data analysis, which generates personalized health management advice for the user. During the server's analysis process, the emotion engine evaluates the user's psychological state. This information is used to provide personalized advice on diet, exercise, and stress management. For example, if the data analysis determines that the user is experiencing high stress levels, the server might generate advice such as, "Try taking a walk today to refresh yourself."

[0752] The terminal receives input data from the user and simultaneously displays the analysis results. Users input daily health information and subjective emotional states into this terminal. After the data is sent to the server, the analysis results returned from the server are displayed on the terminal, providing the user with specific actionable guidelines.

[0753] The emotion engine analyzes the user's psychological state based on the emotional information they input. This allows the system to provide personalized support that takes into account not only the user's physical health but also their emotional state.

[0754] For example, if a user inputs a prompt such as "a time when I tend to feel stressed" into the system, the AI ​​model will generate advice based on that prompt. Because this prompt reflects the user's psychological tendencies, the generated advice will be more accurate and effective.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] Users input daily health information and emotional states into the device. Specific data entered includes weight, diet, exercise levels, and stress levels. This information is used as input data for the next processing step.

[0758] Step 2:

[0759] The terminal collects health and emotional information entered by the user and bundles it into data packets. These bundled data packets are then sent to the server via a secure communication protocol. The server receives the data as output.

[0760] Step 3:

[0761] The server stores the received data in its internal database. The stored data undergoes format conversion and is processed into a structured dataset for analysis. The server's next action is to prepare the data for input into the generating AI model.

[0762] Step 4:

[0763] The server inputs the stored dataset into the generating AI model and artificial intelligence algorithm. Prompt statements are also input, and data analysis is performed based on the user's emotional state and lifestyle patterns. Here, an analysis prompt such as "Generate appropriate advice considering the user's stress level and diet" is used.

[0764] Step 5:

[0765] The generative AI model analyzes input data and generates optimal advice for the user. For example, it might produce a specific output such as, "Light walking is recommended to relieve stress."

[0766] Step 6:

[0767] The server sends the generated advice to the device. The device analyzes the received advice and displays it on the screen. At this point, the user can receive personalized health management advice via the device.

[0768] (Application Example 2)

[0769] 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".

[0770] Existing health management systems do not adequately recommend personalized products and services that take into account the emotional state of users, thereby limiting improvements in users' health and quality of life. Therefore, it is necessary to provide more appropriate products and services based on users' emotional states to achieve more effective health management and improved quality of life.

[0771] 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.

[0772] In this invention, the server includes means for receiving and storing individual users' health information and lifestyle data; means for using artificial intelligence algorithms to analyze the stored health information and lifestyle data; means for providing users with personalized diet and exercise advice based on the analysis results; means for recommending products and services to users based on their emotional state; and means for considering the user's past purchase history when recommending products and services. This enables more personalized recommendations of products and services that take into account the user's emotional state.

[0773] "Health information" refers to data about the user's physical body, including data such as weight, blood pressure, and heart rate.

[0774] "Lifestyle data" refers to data about users' daily activities such as diet, exercise, and sleep.

[0775] An "artificial intelligence algorithm" is a mathematical method used to analyze data, recognize specific patterns, and provide optimal advice to users.

[0776] "Emotional state" refers to the user's psychological condition and includes emotions such as stress, relaxation, and happiness.

[0777] "Goods and services" refers to specific products and activities provided to users, and includes goods, information, entertainment, etc.

[0778] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0779] To realize this invention, the system consists mainly of three main components: a server, a terminal, and an emotion engine. The server is responsible for receiving and recording the user's health information, lifestyle data, and emotional state. This data is first collected by the terminal through sensors and user input, and then transmitted to the server.

[0780] The server analyzes stored data using AI algorithms to generate personalized diet and exercise advice for users, and also evaluates users' health status by utilizing patterns derived from vast datasets. The artificial intelligence technology used here is implemented in languages ​​such as Python, and frameworks such as TensorFlow and PyTorch are often used.

[0781] The emotion engine analyzes the user's emotional state and sends the results to the server. Based on this information, it recommends products and services to the device that are appropriate for the user's emotional state. In particular, considering past purchase history and the current emotional state enables more personalized recommendations.

[0782] For example, if the device detects that the user is experiencing stress, it will suggest items and services that promote relaxation. For instance, it might display something like, "Today we are offering relaxing aromatherapy candles at a special price."

[0783] When using a generative AI model, the server generates a new dataset and creates prompts. A concrete example of a prompt might be, "If the user's emotional state is stressed, suggest products that help them relax, and exclude products they already own based on their purchase history." This allows for more accurate data generation and suggestions based on the user's emotional state and past behavioral history.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] The device collects health information, lifestyle data, and emotional states through sensors and user input. This data is necessary to understand the user's current state. Inputs include heart rate, activity level, and subjective emotional state, and this data is formatted for transmission to the server.

[0787] Step 2:

[0788] The server receives data sent from the terminal and stores it in a database. This stored data is used as basic information for analyzing the user's health status. The input is health data sent from the terminal, and the output is the process of saving it to the server.

[0789] Step 3:

[0790] The server inputs stored data into an artificial intelligence algorithm for analysis. This input consists of user health information and lifestyle data, and data mining techniques are used to identify specific patterns. The output is the analysis results, which are then provided to the user as personalized health advice.

[0791] Step 4:

[0792] The server generates specific advice regarding diet and exercise for the user based on the analysis results. Specifically, the advice generated based on the user's current health and emotional state might take the form of, for example, "Let's try some light exercise today." The input is the analysis results from the AI, and the output is advice in text format.

[0793] Step 5:

[0794] The server matches the user's emotional state with their past purchase history to recommend appropriate products and services. The input is the user's emotional state and purchase history, and the output is a list of products tailored to their emotional state. The generated data is displayed on the terminal.

[0795] Step 6:

[0796] The terminal receives advice and product recommendations from the server and presents them visually to the user. Specifically, it displays health advice and product information on the terminal's screen. The input consists of advice and product lists sent from the server, and the output is a visual presentation of information to the user.

[0797] Step 7:

[0798] Users input feedback into their device based on the advice and product recommendations provided, and send it to the server. This feedback helps improve the accuracy of the service. The input is the user's text feedback, which is then sent to the server as output data.

[0799] 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.

[0800] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] A means of receiving and storing individual users' health information and lifestyle data,

[0823] A means of using artificial intelligence algorithms to analyze stored health information and lifestyle data,

[0824] A means of providing users with personalized diet and exercise advice based on the analysis results,

[0825] A means of issuing a warning if an anomaly is detected during analysis and contacting a pre-registered medical institution,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for displaying analysis results and advice on the user's device.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for receiving feedback from users and using it to improve the accuracy of analysis and improve services.

[0831] "Example 1"

[0832] (Claim 1)

[0833] A means for receiving and recording individual users' health status information and lifestyle information,

[0834] A means of using machine learning models to analyze recorded health status information and lifestyle information,

[0835] A means of providing users with personalized nutrition and physical activity guidelines based on the analysis results,

[0836] A means of issuing a warning if an anomaly is detected during analysis and notifying a pre-configured medical organization,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, further comprising means for displaying analysis results and guidelines on the user's device.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for receiving responses from users and using them to improve the accuracy of analysis and enhance activities.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A device that receives and stores individual users' health information and lifestyle data,

[0845] A device that uses machine learning models to analyze stored health information and lifestyle data,

[0846] A device that provides users with personalized advice on food and physical exercise based on analysis results,

[0847] A device that issues a warning if an anomaly is detected during analysis and contacts a pre-registered health management facility,

[0848] A device that proposes the most suitable health-related products to users based on analytical data and has an electronic transaction function that allows them to purchase them directly,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for displaying analysis results, advice, and suggested health-related products on the user's communication device.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising a device for receiving user feedback and using it to improve the accuracy of analysis and improve services.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A means of receiving and storing individual users' health information, lifestyle data, and emotional information,

[0857] Means for using artificial intelligence algorithms and generative AI models to analyze stored health information, lifestyle data, and emotional information,

[0858] A means of providing users with personalized advice on diet, exercise, and stress management that takes emotional information into account, based on the analysis results.

[0859] A means to issue a warning if an anomaly is detected during analysis and to automatically contact pre-registered medical institutions,

[0860] A means including an emotion engine for analyzing the user's emotional state,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for displaying analysis results and advice on the user's device and prompting the user to provide specific action suggestions.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for receiving feedback from users and using it to tune prompt statements in order to improve the accuracy of analysis in the generated AI model and to improve the service.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A means of receiving and storing individual users' health information and lifestyle data,

[0869] A means of using artificial intelligence algorithms to analyze stored health information and lifestyle data,

[0870] A means of providing users with personalized diet and exercise advice based on the analysis results,

[0871] A means of issuing a warning if an anomaly is detected during analysis and contacting a pre-registered medical institution,

[0872] A means of recommending products and services to users based on their emotional state,

[0873] In recommending products and services, means of considering the user's past purchase history,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for displaying analysis results and advice on the user's device.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for receiving feedback from users and using it to improve the accuracy of analysis and improve services. [Explanation of Symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving and storing individual users' health information and lifestyle data, A means of using artificial intelligence algorithms to analyze stored health information and lifestyle data, A means of providing users with personalized diet and exercise advice based on the analysis results, A means of issuing a warning if an anomaly is detected during analysis and contacting a pre-registered medical institution, A system that includes this.

2. The system according to claim 1, further comprising means for displaying analysis results and advice on the user's device.

3. The system according to claim 1, further comprising means for receiving feedback from users and using it to improve the accuracy of analysis and improve services.

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