system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining the user's biometric information, Information analysis means for analyzing the aforementioned biological information, A means for generating health guidance based on the analysis results, A notification means for notifying the user of the generated health guidance, A question-and-answer means for receiving and responding to the user's inquiries, A means of providing information to medical support organizations when a user reports an anomaly, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern cities, especially for the elderly and residents with chronic diseases, daily health management and obtaining medical information are complicated. Also, there is a problem that it is easy to forget regular health checks in the busy daily life. Furthermore, the lack of prompt medical response in case of emergency increases the risk to the health of residents. Therefore, there is a need for a system that can effectively manage the health status of users and easily obtain appropriate health information and advice.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a new healthcare system. Specifically, it includes a data acquisition means for acquiring user health information in real time, and a data analysis and advice generation means for analyzing that information and generating optimal health advice for the user. Furthermore, a notification means for notifying the user of the generated advice provides an environment in which users can easily manage their health. In addition, a question-answering means is introduced to enable users to quickly obtain information about their individual health problems. Moreover, by having functions to send reminders for regular health checks and to provide information on nearby medical institutions in emergencies, the system aims to maintain the health of residents and improve their access to medical care.

[0006] "Data acquisition means" refers to technical means for collecting users' health information in real time.

[0007] "Data analysis means" refers to technical means for analyzing collected health information and evaluating the user's health status.

[0008] An "advice generation method" is a technical means for automatically generating health advice to be provided to the user based on the analysis results.

[0009] A "notification method" is a technical means of informing the user's device of the generated health advice.

[0010] A "question answering system" is a technical means for receiving health-related questions from users and providing appropriate answers. [Brief explanation of the drawing]

[0011] [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system according to the present invention is designed to efficiently manage health and provides users with useful information and support when they aim to improve their health. This system consists of a terminal that can connect to the internet via a general communication network and a server located in a cloud environment.

[0033] First, the device connects with the user's health devices (e.g., smartwatch, health app, etc.) to collect health information such as heart rate, activity level, nutrition intake, and sleep patterns. This data is temporarily stored on the device and securely transmitted to the server at predetermined times. The collected data is used only to the extent explicitly permitted by the user and is handled with consideration for privacy.

[0034] The server analyzes the received health information. Advanced machine learning models are used for the analysis, and the user's health status is assessed based on the data. During the assessment process, past health information and publicly collected health databases are also referenced to gain a comprehensive understanding of the user's condition.

[0035] Based on the analysis results, the server generates appropriate health advice for the user. This advice includes dietary suggestions, exercise plans, and stress management methods best suited to the user's condition. The generated advice is sent to the terminal in real time, and the user is notified so they can check it immediately.

[0036] Furthermore, users can input various health-related questions they face daily through the application interface on their device. For example, in response to a question such as "How can I reduce stress?", the server uses natural language processing to understand the question, quickly searches the database for relevant information, generates an appropriate answer, and provides it to the user via the device.

[0037] In addition, the server manages the user's regular health check schedule and sends reminders as needed. This allows users to be aware of their own health status and visit a medical institution at the appropriate time. Furthermore, for a quick response in emergencies, the server also takes the user's location into consideration and promptly provides information on nearby medical institutions in emergency situations.

[0038] For example, if a user's heart rate reading on their watch is abnormal while they are at a gym, the device receives a notification and immediately sends the data to the server. The server analyzes this data and generates a list of nearby medical facilities, which it then informs the user of through the device. This allows the user to quickly select and visit a medical facility.

[0039] Thus, the present invention aims to comprehensively support users' health management and realize a comfortable and safe life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects health information such as heart rate, exercise data, dietary records, and sleep patterns from the user's health device. This data is integrated into the device in real time via communication methods such as Bluetooth and Wi-Fi.

[0043] Step 2:

[0044] The device securely encrypts the collected data and transmits it to the server via the internet. The latest security protocols are applied throughout this process to prevent data leakage.

[0045] Step 3:

[0046] The server saves the received data to the database. During saving, the data is integrated into a standard format and prepared for analysis.

[0047] Step 4:

[0048] The server begins data analysis using a machine learning model. Here, it evaluates the user's current health status by comparing it to past trends and general health indicators based on the user's health data.

[0049] Step 5:

[0050] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements, exercise schedules, and stress management techniques.

[0051] Step 6:

[0052] The server sends the generated health advice to the terminal. The terminal notifies the user and displays the advice on the smartphone screen or other display.

[0053] Step 7:

[0054] Users enter health-related questions via a smartphone app and send them to the server. The questions include specific advice and information for maintaining good health.

[0055] Step 8:

[0056] The server uses natural language processing technology to analyze the user's question and searches for the most suitable answer from a specialized health database.

[0057] Step 9:

[0058] The server sends the generated response to the terminal, which then notifies the user and displays the details on the screen.

[0059] Step 10:

[0060] The server creates a schedule for regular health checks and sends reminders to the user via their device. This notification is sent at the optimal time based on the previous health information.

[0061] Step 11:

[0062] In an emergency, if a user reports an anomaly, the device will immediately send emergency information, including location data, to the server.

[0063] Step 12:

[0064] The server identifies the nearest medical facility from the received emergency data and provides that information to the user via the terminal. This information allows the user to receive prompt medical assistance.

[0065] (Example 1)

[0066] 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."

[0067] In modern society, personal health management is becoming increasingly important, but traditional methods of collecting and analyzing health information are cumbersome, making it difficult for users to obtain appropriate health advice. Furthermore, the lack of readily available information to quickly obtain relevant information when health conditions suddenly change makes it difficult for users to respond effectively.

[0068] 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.

[0069] In this invention, the server includes information acquisition means, information analysis means, and suggestion generation means. This makes it possible to efficiently collect and analyze the user's health information and provide individually customized lifestyle improvement suggestions. Furthermore, in emergencies, it can support the user's rapid response by quickly providing information on nearby medical facilities.

[0070] "Information acquisition means" refers to a system for collecting information about a user's biometric data and lifestyle habits.

[0071] "Information analysis means" refers to a system that analyzes acquired biometric data and evaluates the user's health status.

[0072] A "proposal generation method" is a system for creating lifestyle improvement suggestions tailored to the user based on analysis results.

[0073] An "information notification system" is a mechanism that provides users with generated lifestyle improvement suggestions and health information, and informs them of information they request.

[0074] A "response mechanism" is a system for receiving inquiries from users and providing information and advice in response.

[0075] "Communication means" refers to a mechanism for sending and receiving data between a server and a terminal, enabling the secure transfer of user health information.

[0076] This invention is configured as an information system for efficient health management. This system primarily consists of an internet-connected terminal and a server located in a cloud environment. It can acquire various data from health management devices, analyze it, and provide personalized health advice to users.

[0077] The device uses hardware such as smartwatches or health tracking apps on smartphones to collect biometric data such as heart rate, activity level, nutritional intake, and sleep patterns. The collected data is temporarily stored on the device and then periodically transmitted to a cloud server using communication technologies such as Wi-Fi or Bluetooth.

[0078] The server analyzes the received data using advanced machine learning algorithms. The generative AI model used here assesses the user's health status from the data and generates personalized lifestyle improvement suggestions. This includes referencing the user's past data and public health databases. Specifically, this involves analyzing the user's exercise data for the past week and suggesting an exercise plan for the following week.

[0079] The generated health advice is sent to the device in real time and notified to the user. For example, specific suggestions such as, "Your heart rate is a little high, so we recommend relaxing activities today," may be given.

[0080] Furthermore, users can input health-related questions into the system through their devices. For example, if a user enters a prompt such as, "What should I do if I can't sleep at night?", the server will use natural language processing technology to analyze the prompt, search its database for relevant solutions, and provide an answer.

[0081] This system also has a function that allows the server to manage the schedule of regular health checkups and send reminders to users via their devices. This makes it easy for users to keep track of their health management schedule and receive health checkups at the appropriate time.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device collects biometric data using a smartwatch or health tracking app. This data includes heart rate, activity level, nutrition intake, and sleep patterns, and is temporarily stored within the device. The input is biometric data acquired by sensors, and the output is an organized dataset. Specifically, the smartwatch records daily activity levels and synchronizes the data with the application.

[0085] Step 2:

[0086] The collected data is regularly organized, the device encrypts this information, and sends it to the server using a secure communication protocol. The input is the organized data stored on the device, and the output is a packet of encrypted data. Specifically, the collected data is automatically uploaded to the server via Wi-Fi overnight.

[0087] Step 3:

[0088] The server inputs the received data into a machine learning algorithm for analysis. This algorithm also utilizes a large health database to assess the user's health status. The input is encrypted biometric data packets, and the output is a health assessment score. Specifically, it detects abnormal patterns based on past heart rate data.

[0089] Step 4:

[0090] Based on the analysis results, the server generates personalized health advice. The generating AI model creates the advice and provides suggestions tailored to the user's lifestyle. The input is a health assessment score, and the output is a list of specific suggestions. For example, one example of advice might be, "Your stress levels are high, so we recommend meditating for 10 minutes."

[0091] Step 5:

[0092] Health advice generated by the server is sent to the terminal in real time and notified to the user. The input is the generated list of suggestions, and the output is the notification message to the user. Specifically, the advice is pushed as a notification via the terminal's notification function.

[0093] Step 6:

[0094] The user uses a terminal application to input health-related questions and sends them to the server. These questions are processed as prompts by a generative AI model. The input is the user's question, and the output is an answer containing relevant information. A concrete example of this process is inputting the prompt, "How can I improve my sleep quality?"

[0095] Step 7:

[0096] The server analyzes the received question using natural language processing techniques and searches the database for corresponding information. The input is a prompt, and the output is an answer based on the search results. Specifically, it extracts relevant stress management articles and information and generates a summarized answer.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern society, while there is a demand for more efficient individual health management, analyzing individual health data and providing appropriate health guidance is not easy. Furthermore, prompt action is essential when health abnormalities occur, and delays in information provision by medical support organizations can have life-threatening consequences. Therefore, a comprehensive system for efficient and effective health management is needed.

[0100] 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.

[0101] In this invention, the server includes data acquisition means for acquiring biometric information, information analysis means, and guidance generation means for generating health guidance. This enables the effective collection and analysis of the user's biometric information, the provision of appropriate health guidance in real time, and the rapid provision of information from medical support organizations in emergencies.

[0102] "Biometric information" refers to health-related data such as the user's heart rate, activity level, and sleep patterns.

[0103] "Information analysis means" refers to a device or program for analyzing acquired biological information and evaluating the user's health status.

[0104] A "guidance generation means" is a device or program that generates health guidance for a user based on the analysis results obtained by an information analysis means.

[0105] "Notification means" refers to a device or software that informs the user of generated health guidance or advice.

[0106] A "question and answer system" is a device or program that generates and provides answers in response to questions from users.

[0107] "Information provision means" refers to a device or program for providing information from medical support organizations based on the user's health status.

[0108] In embodiments of the present invention, the cooperation between the user, terminal, and server is crucial. The user wears a health device such as a smartwatch or fitness band. These devices continuously collect the user's biometric information, such as heart rate, activity level, and sleep patterns. The terminal cooperates with these devices and temporarily stores the collected data. Periodically or as needed, the terminal sends the data to a cloud server.

[0109] The server uses machine learning models such as TENSORFLOW® and PyTorch to analyze the received biometric information. This allows it to compare the data with historical data and general health databases to assess the user's health status. Based on the assessment, the server generates health guidance tailored to the user. This guidance is generated via a guidance generation system and notified to the user's terminal in real time.

[0110] Users can submit health-related questions to a server in natural language via a device or robot. These questions are analyzed through a question-and-answer system, and relevant answers are generated using a generative AI model. This process utilizes natural language processing tools such as BERT and spaCy. The generated answers are provided to the user in either voice or text format.

[0111] For example, if a user inputs "I'd like some advice on my recent eating habits," the server generates advice such as "Slightly reduce your carbohydrate intake and increase your vegetable intake" based on past eating data and health information, and communicates it to the user via their device. If the user reports an abnormality, the information provision system generates information on the nearest medical institution, enabling prompt action.

[0112] Specific examples of prompt messages include, "What should I do if I feel my heart rate is high?" and "How can I improve the quality of my sleep today?" This allows users to obtain detailed and immediate information about their health status.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user wears a health device, which collects biometric information. The terminal receives this biometric information (heart rate, activity level, sleep patterns, etc.) as input, extracts the data from the device, and temporarily stores it. As a result, the terminal accumulates the user's latest health data.

[0116] Step 2:

[0117] The device periodically or on command transmits collected biometric information to the server. The input here is biometric data, and the output is data packets transmitted using secure communication methods. The SSL / TLS protocol is used for this transmission, thus ensuring data security.

[0118] Step 3:

[0119] The server inputs the received biometric information into a data analysis module and performs analysis using machine learning models (TensorFlow or PyTorch). Data processing involves converting the biometric information into a format suitable for the model and performing feature engineering. As a result, the user's health status is evaluated, and this becomes the output data.

[0120] Step 4:

[0121] The server generates health guidance based on the analysis results. The input is the evaluation results obtained in step 3, and the server uses the guidance generation mechanism to create individual health guidance for the user. The output is a health guidance message that includes specific advice tailored to the user's condition.

[0122] Step 5:

[0123] The generated health guidance is notified to the device. The device receives the health guidance message as input and outputs it to the user as voice or text. Specifically, it displays the message on the device's screen or plays the guidance as audio through the speaker.

[0124] Step 6:

[0125] Users input health-related questions via a terminal. The terminal sends the entered questions to a server, prompting processing through a question-and-answer system. This allows users to ask their questions to the system.

[0126] Step 7:

[0127] The server inputs user questions into a natural language processing module and analyzes them using generative AI models (such as BERT or spaCy). The input is a question in natural language form, and the system searches for the best answer based on prompts. The output is the answer, providing appropriate information to the user's question.

[0128] Step 8:

[0129] The generated answers are communicated to the user via the device. The device receives the answer text as input and outputs it to the user through text display or audio playback. This allows the user to receive direct feedback on the question.

[0130] 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.

[0131] This invention is a system developed to support users' health management, utilizing both the user's health information and emotional information to provide more personalized advice and responses. The system consists of an internet-connected terminal, a server in the cloud, and an emotional engine.

[0132] First, the device connects with the user's health devices (e.g., smartwatches or health tracking apps) to collect health information such as heart rate, exercise data, nutritional intake, and sleep patterns. In parallel, the device's built-in camera and microphone capture the user's facial expressions and voice tone, and transmit this data to an emotion engine for analysis of the user's emotions. This collected information is temporarily stored on the device, then encrypted and sent to a server.

[0133] The server records received health and emotional information in a database. Using this information, data analysis tools on the server perform analysis using machine learning algorithms. The analysis utilizes historical accumulated data and general health indicators to evaluate the user's current health status and emotional tendencies.

[0134] Based on the analysis results, the server creates personalized health advice for the user. This advice includes suggestions for diet, exercise, and stress management tailored to the user's health condition, and its tone is adjusted according to the detected emotional state. The advice is sent to the device in real time and notified to the user's smartphone.

[0135] In addition, users can input questions about their health and emotions through an application on their device. The server analyzes the content of the questions using natural language processing technology, combines it with emotional information, and generates the most appropriate answer at that time. The answer is provided to the user via the device and displayed in a format that is easy for the user to understand.

[0136] Furthermore, the server manages the schedule for regular checkups and creates reminders that take into account the user's emotional state. These reminders are delivered using language designed to soothe the user's emotions. In emergencies, the server also provides information on the nearest appropriate medical facility, including the user's location and emotional state, to help the user respond with confidence.

[0137] For example, if a user is feeling stressed, the device detects this state using an emotion engine, and the server generates relaxing health advice and sends a notification that includes an encouraging message. In this way, the present invention aims to support users from both a health and emotional perspective and improve their quality of life.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The device continuously collects health information such as heart rate, exercise data, nutritional intake, and sleep patterns from the user's smartwatch or health tracking app. Subsequently, the device's built-in camera and microphone capture the user's facial expressions and voice tone, which are then used as data for analysis by the emotion engine.

[0141] Step 2:

[0142] The emotion engine analyzes voice and facial expression data collected by the device to quantify and classify the user's emotional state. For example, it identifies emotions such as stress, joy, and sadness, and creates a digital profile of the emotional state.

[0143] Step 3:

[0144] The device encrypts the collected health and emotional information and securely transmits this data to a server via the internet.

[0145] Step 4:

[0146] The server stores the received information in a database and performs integrated analysis of health and emotional information using machine learning algorithms. Based on the analysis, the user's health risks and emotional tendencies are evaluated, and an appropriate advice plan is formulated.

[0147] Step 5:

[0148] Based on the analysis results, the server generates personalized health and emotional care advice for the user. This includes suggestions for diet and exercise, as well as emotionally sensitive encouragement and recommendations for relaxation techniques.

[0149] Step 6:

[0150] A device that receives advice from the server will notify the user. The user can view the advice on their smartphone screen and delve deeper into the details as needed.

[0151] Step 7:

[0152] Users input specific questions about their own condition through the terminal's interface, and this information is immediately sent to the server. These questions can cover both health and emotional aspects.

[0153] Step 8:

[0154] The server analyzes the user's question using natural language processing and generates an appropriate answer from the database, taking into account their emotional state. This answer takes into account both health and emotional considerations in line with the intent of the question.

[0155] Step 9:

[0156] The generated responses are sent to the user via the device, and the user can continue to manage their health using the information displayed on the screen.

[0157] Step 10:

[0158] The server generates and sends an emotionally sensitive reminder message to the user's device, taking into account the user's next scheduled check-up date. This ensures that the user does not forget to take necessary medical action.

[0159] Step 11:

[0160] In emergencies, users can use a feature to report any health problems. The device immediately creates emergency data, including the user's location and emotional state, and sends it to the server.

[0161] Step 12:

[0162] The server quickly identifies the most suitable medical facility based on emergency data and provides the user with the necessary information via their terminal. This enables a rapid medical response.

[0163] (Example 2)

[0164] 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".

[0165] In the modern era, providing individually optimized health management by comprehensively utilizing users' biometric and emotional information is a challenging task. Conventional health management systems rely solely on biometric information and do not take into account the user's emotional state, making it difficult to provide comprehensive health advice. Furthermore, the lack of a system in place to respond quickly when a user experiences an abnormality is also a problem.

[0166] 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.

[0167] In this invention, the server includes information acquisition means for acquiring the user's biometric and emotional information, information analysis means for analyzing the said information, and recommendation generation means for generating advice based on the analysis results. This makes it possible to provide users with individually optimized advice and prompt response information.

[0168] "Information acquisition means" is a general term for devices and software used to collect users' biometric and emotional information.

[0169] "Information analysis means" refers to a function that analyzes the user's health status and emotional tendencies based on collected biometric and emotional information.

[0170] The "recommendation generation method" is a system function that automatically generates optimal advice tailored to the user's health and emotions based on the analyzed information.

[0171] A "notification method" is a means of transmitting generated advice and information to the user in real time, and is provided through a terminal.

[0172] A "question answering tool" is a function that analyzes natural language questions entered by users and generates and provides highly relevant answers.

[0173] This invention is a system for providing individually optimized health management by utilizing users' biometric and emotional information. The system primarily consists of an internet-connected terminal, a cloud-based server, and an emotional engine responsible for analysis and recommendation generation.

[0174] The device collects biometric information such as heart rate, activity level, and sleep patterns through biometric detection devices worn by the user (e.g., smartwatches) and health tracking applications. It also uses the device's built-in camera and microphone to capture the user's facial expressions and voice, sending this information to an emotion engine for emotional analysis. The acquired data is securely transmitted to the server using encryption protocols.

[0175] The server stores received biometric and emotional information in a database and uses machine learning algorithms to analyze it. This analysis evaluates the user's health status and emotional tendencies, and automatically generates personalized health advice. The advice includes recommendations regarding diet, exercise, and stress management tailored to the user's situation, and its accuracy is enhanced by inference using a generative AI model.

[0176] The generated advice is delivered to the device in real time using carefully chosen language that takes the user's emotional state into consideration. Users can receive this advice via their smartphone or other devices and use it to improve their quality of life.

[0177] As a concrete example, consider a case related to everyday stress. When a user sends a prompt message using their device saying, "I've been feeling stressed lately. How can I relax?", the server analyzes this input, combines it with the user's current biological and emotional information, and provides advice, including appropriate relaxation methods. In this way, the system of the present invention enables comprehensive and individually optimized health support for the user.

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

[0179] Step 1:

[0180] The device works in conjunction with the user's biometric detection device to acquire biometric information such as heart rate, activity level, and sleep patterns. This data is captured in real time as input and temporarily stored within the device. This prepares the foundational data for monitoring daily health status.

[0181] Step 2:

[0182] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. The emotion engine processes the acquired emotion-related data as input, determining the emotional state (e.g., joy, sadness, anger). The analyzed emotion information is then generated as output.

[0183] Step 3:

[0184] The device encrypts the biometric and emotional information obtained in Step 1 and Step 2 and sends it to a server in the cloud. This establishes a system for centrally managing data while ensuring security.

[0185] Step 4:

[0186] The server stores the received information in a database and performs analysis using machine learning algorithms. It utilizes historical data and general health indicators from the database as input to evaluate the user's health status and emotional tendencies. The output of this process is a detailed analysis of the user's specific state.

[0187] Step 5:

[0188] The server generates optimized health advice using a generated AI model based on the analysis results. This advice includes recommendations for nutrition, exercise, and stress management tailored to the user, as well as words of encouragement according to their emotional state. It then generates specific advice text as output and prepares it for transmission to the terminal.

[0189] Step 6:

[0190] The device notifies the user in real time of advice received from the server. The user checks this advice through a smartphone application and uses it to improve their daily life. This creates a feedback loop that allows the user to adjust their actions based on the information they receive.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In elderly care, emotional care is just as important as managing the user's health. However, there is a lack of means to grasp the user's health status and emotional changes in real time and respond appropriately, resulting in a problem where the quality of care is not always sufficient. This invention aims to solve these problems and provide more personalized support.

[0194] 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.

[0195] In this invention, the server includes data acquisition means for obtaining user health information, data analysis means for analyzing the user's health information and emotional information, and advice generation means for generating health advice and emotional care messages based on the analysis results. This enables caregivers to grasp the user's health and emotional state in real time and provide prompt and appropriate care accordingly.

[0196] "Data acquisition means" refers to a system for collecting user health information from sensors and devices.

[0197] A "data analysis tool" is a system equipped with algorithms for analyzing collected health and emotional information and evaluating the user's state.

[0198] The "advice generation means" is a system for generating personalized health advice and emotional care messages based on analyzed user health status and emotional information.

[0199] A "notification mechanism" is an interface used to deliver generated health advice and emotional care messages to the user.

[0200] A "question answering system" is a system that receives questions from users and generates and provides answers that take emotional information into consideration.

[0201] "Information sharing means" refers to communication methods for sharing information about the user's health and emotional state with care workers in real time.

[0202] A system implementing this invention aims to manage the user's health and emotional well-being, and is comprised of a combination of means for data acquisition, data analysis, advice generation, and notification.

[0203] The server operates primarily on data acquired from internet-connected devices. These devices collect health information (heart rate, exercise data, nutritional intake, sleep patterns, etc.) from smartwatches and health tracking apps worn by the user. They also use the device's camera and microphone to detect emotional information from the user's facial expressions and tone of voice. This data is temporarily stored on the device, then encrypted and sent to the server.

[0204] The server analyzes the received information using data analysis tools. This analysis employs machine learning algorithms to evaluate the user's health status and emotional tendencies by comparing them with past data and general health indicators. Based on the analyzed data, the advice generation tool generates personalized health advice and emotional care messages. For example, if the user has a high heart rate and is feeling stressed, it will provide advice to encourage relaxation.

[0205] The generated advice is transmitted to the device via a notification system. Users can input questions about their health and emotions through the application. The question-answering system analyzes the input questions using natural language processing technology and generates answers that take emotional information into account. In this process, an AI model is used to generate prompts and provide appropriate responses.

[0206] For example, if a user asks, "I have trouble sleeping at night," the system will check the user's sleep patterns and provide advice on possible causes and solutions. Furthermore, prompts such as "What relaxation methods would you recommend considering the user's recent emotional tendencies?" can be input to the generating AI model to obtain the optimal solution. This allows caregivers to quickly provide specific care based on the user's condition.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device uses sensors to collect the user's health information. This includes obtaining data such as heart rate, exercise data, nutritional intake, and sleep patterns from smartwatches and health tracking apps. It also uses a camera and microphone to collect the user's facial expressions and voice tone as emotional information, which is temporarily stored on the device.

[0210] Step 2:

[0211] The device encrypts the collected health and emotional information before sending it to the server. The transmitted data becomes input, and the server receives it and records it in its database.

[0212] Step 3:

[0213] The server analyzes incoming data using data analysis tools. Machine learning algorithms compare the current data with past data and general indicators to evaluate the user's current health status and emotional tendencies. The input consists of health information and emotional information, and the output is the evaluation result.

[0214] Step 4:

[0215] The advice generation system generates personalized health advice and emotional care messages based on the analysis results. Here, the evaluation results obtained from data analysis are used. For example, for users with high stress levels, advice promoting relaxation is generated.

[0216] Step 5:

[0217] The server sends the generated advice and messages to the terminal via a notification system. The input is the advice and messages, and the output is sent to the user's terminal.

[0218] Step 6:

[0219] The user enters questions about their health and emotions into an application on their device. These questions constitute the input.

[0220] Step 7:

[0221] The server uses a question-answering mechanism and natural language processing techniques to analyze the question and generate a prompt. Based on this, it generates an answer that takes sentiment into account. The input is the user's question and sentiment information, and the output is an answer that takes sentiment into account.

[0222] Step 8:

[0223] Finally, the server inputs a prompt message into the generation AI model to generate the optimal answer, and then provides that answer to the user via the terminal.

[0224] 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.

[0225] Data generation model 58 is a type of 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] The system according to the present invention is designed to efficiently manage health and provides users with useful information and support when they aim to improve their health. This system consists of a terminal that can connect to the internet via a general communication network and a server located in a cloud environment.

[0241] First, the device connects with the user's health devices (e.g., smartwatch, health app, etc.) to collect health information such as heart rate, activity level, nutrition intake, and sleep patterns. This data is temporarily stored on the device and securely transmitted to the server at predetermined times. The collected data is used only to the extent explicitly permitted by the user and is handled with consideration for privacy.

[0242] The server analyzes the received health information. Advanced machine learning models are used for the analysis, and the user's health status is assessed based on the data. During the assessment process, past health information and publicly collected health databases are also referenced to gain a comprehensive understanding of the user's condition.

[0243] Based on the analysis results, the server generates appropriate health advice for the user. This advice includes dietary suggestions, exercise plans, and stress management methods best suited to the user's condition. The generated advice is sent to the terminal in real time, and the user is notified so they can check it immediately.

[0244] Furthermore, users can input various health-related questions they face daily through the application interface on their device. For example, in response to a question such as "How can I reduce stress?", the server uses natural language processing to understand the question, quickly searches the database for relevant information, generates an appropriate answer, and provides it to the user via the device.

[0245] In addition, the server manages the user's regular health check schedule and sends reminders as needed. This allows users to be aware of their own health status and visit a medical institution at the appropriate time. Furthermore, for a quick response in emergencies, the server also takes the user's location into consideration and promptly provides information on nearby medical institutions in emergency situations.

[0246] For example, if a user's heart rate reading on their watch is abnormal while they are at a gym, the device receives a notification and immediately sends the data to the server. The server analyzes this data and generates a list of nearby medical facilities, which it then informs the user of through the device. This allows the user to quickly select and visit a medical facility.

[0247] Thus, the present invention aims to comprehensively support users' health management and realize a comfortable and safe life.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The device collects health information such as heart rate, exercise data, dietary records, and sleep patterns from the user's health device. This data is integrated into the device in real time via communication methods such as Bluetooth and Wi-Fi.

[0251] Step 2:

[0252] The device securely encrypts the collected data and transmits it to the server via the internet. The latest security protocols are applied throughout this process to prevent data leakage.

[0253] Step 3:

[0254] The server saves the received data to the database. During saving, the data is integrated into a standard format and prepared for analysis.

[0255] Step 4:

[0256] The server begins data analysis using a machine learning model. Here, it evaluates the user's current health status by comparing it to past trends and general health indicators based on the user's health data.

[0257] Step 5:

[0258] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements, exercise schedules, and stress management techniques.

[0259] Step 6:

[0260] The server sends the generated health advice to the terminal. The terminal notifies the user and displays the advice on the smartphone screen or other display.

[0261] Step 7:

[0262] Users enter health-related questions via a smartphone app and send them to the server. The questions include specific advice and information for maintaining good health.

[0263] Step 8:

[0264] The server uses natural language processing technology to analyze the user's question and searches for the most suitable answer from a specialized health database.

[0265] Step 9:

[0266] The server sends the generated response to the terminal, which then notifies the user and displays the details on the screen.

[0267] Step 10:

[0268] The server creates a schedule for regular health checks and sends reminders to the user via their device. This notification is sent at the optimal time based on the previous health information.

[0269] Step 11:

[0270] In an emergency, if a user reports an anomaly, the device will immediately send emergency information, including location data, to the server.

[0271] Step 12:

[0272] The server identifies the nearest medical facility from the received emergency data and provides that information to the user via the terminal. This information allows the user to receive prompt medical assistance.

[0273] (Example 1)

[0274] 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."

[0275] In modern society, personal health management is becoming increasingly important, but traditional methods of collecting and analyzing health information are cumbersome, making it difficult for users to obtain appropriate health advice. Furthermore, the lack of readily available information to quickly obtain relevant information when health conditions suddenly change makes it difficult for users to respond effectively.

[0276] 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.

[0277] In this invention, the server includes information acquisition means, information analysis means, and suggestion generation means. This makes it possible to efficiently collect and analyze the user's health information and provide individually customized lifestyle improvement suggestions. Furthermore, in emergencies, it can support the user's rapid response by quickly providing information on nearby medical facilities.

[0278] "Information acquisition means" refers to a system for collecting information about a user's biometric data and lifestyle habits.

[0279] "Information analysis means" refers to a system that analyzes acquired biometric data and evaluates the user's health status.

[0280] The "proposal generation means" is a mechanism for creating proposals for improving life suitable for the user based on the analysis results.

[0281] The "information notification means" is a mechanism for providing the user with the generated proposals for improving life and health information, and for notifying the user of the information requested by the user.

[0282] The "response means" is a mechanism for receiving inquiries from the user and providing corresponding information and advice.

[0283] The "communication means" is a mechanism for transmitting and receiving data between the server and the terminal, and enables the safe transfer of the user's health information.

[0284] The present invention is configured as an information system for efficiently performing health management. This system mainly consists of a terminal connectable to the Internet and a server installed in a cloud environment. It can acquire various data from a health management device, analyze it, and provide individual health advice to the user.

[0285] The terminal uses hardware such as a health tracking app installed on a smartwatch or smartphone to collect biometric data such as heart rate, activity level, nutrient intake, and sleep pattern. The collected data is temporarily stored in the terminal and is periodically transmitted to the cloud server using communication technologies such as Wi-Fi and Bluetooth.

[0286] The server analyzes the received data using an advanced machine learning algorithm. The generative AI model used here evaluates the user's health status from the data and generates individually customized proposals for improving life. This also includes referring to the user's past data and public health databases. Specifically, analyzing the user's one-week exercise data and proposing a next-week exercise plan falls under this.

[0287] The generated health advice is sent to the device in real time and notified to the user. For example, specific suggestions such as, "Your heart rate is a little high, so we recommend relaxing activities today," may be given.

[0288] Furthermore, users can input health-related questions into the system through their devices. For example, if a user enters a prompt such as, "What should I do if I can't sleep at night?", the server will use natural language processing technology to analyze the prompt, search its database for relevant solutions, and provide an answer.

[0289] This system also has a function that allows the server to manage the schedule of regular health checkups and send reminders to users via their devices. This makes it easy for users to keep track of their health management schedule and receive health checkups at the appropriate time.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The device collects biometric data using a smartwatch or health tracking app. This data includes heart rate, activity level, nutrition intake, and sleep patterns, and is temporarily stored within the device. The input is biometric data acquired by sensors, and the output is an organized dataset. Specifically, the smartwatch records daily activity levels and synchronizes the data with the application.

[0293] Step 2:

[0294] The collected data is regularly organized, the device encrypts this information, and sends it to the server using a secure communication protocol. The input is the organized data stored on the device, and the output is a packet of encrypted data. Specifically, the collected data is automatically uploaded to the server via Wi-Fi overnight.

[0295] Step 3:

[0296] The server inputs the received data into a machine learning algorithm for analysis. This algorithm also utilizes a large health database to assess the user's health status. The input is encrypted biometric data packets, and the output is a health assessment score. Specifically, it detects abnormal patterns based on past heart rate data.

[0297] Step 4:

[0298] Based on the analysis results, the server generates personalized health advice. The generating AI model creates the advice and provides suggestions tailored to the user's lifestyle. The input is a health assessment score, and the output is a list of specific suggestions. For example, one example of advice might be, "Your stress levels are high, so we recommend meditating for 10 minutes."

[0299] Step 5:

[0300] Health advice generated by the server is sent to the terminal in real time and notified to the user. The input is the generated list of suggestions, and the output is the notification message to the user. Specifically, the advice is pushed as a notification via the terminal's notification function.

[0301] Step 6:

[0302] The user uses a terminal application to input health-related questions and sends them to the server. These questions are processed as prompts by a generative AI model. The input is the user's question, and the output is an answer containing relevant information. A concrete example of this process is inputting the prompt, "How can I improve my sleep quality?"

[0303] Step 7:

[0304] The server analyzes the received questions using natural language processing technology and searches for corresponding information from the database. The input is the prompt text, and the output is the answer based on the search results. The specific operation is to extract relevant stress management articles and information and generate a summarized answer.

[0305] (Application Example 1)

[0306] 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".

[0307] In modern society, while there is a demand for improving the efficiency of individual health management, it is not easy to analyze the health data possessed by individuals and provide appropriate health guidance. Furthermore, when there is an abnormality in the health condition, a prompt response is essential, and if the information provision by the medical support institution is delayed, it may affect individual lives. Therefore, an inclusive system for efficient and effective health management is required.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes a data acquisition means for acquiring biological information, an information analysis means, and a guidance generation means for generating health guidance. Thereby, it becomes possible to effectively collect and analyze the biological information of the user, provide appropriate health guidance in real time, and quickly provide the information of the medical support institution in an emergency.

[0310] "Biological information" refers to data related to health such as the user's heart rate, activity level, sleep pattern, etc.

[0311] "Information analysis means" is a device or program for analyzing the acquired biological information and evaluating the user's health status.

[0312] A "guidance generation means" is a device or program that generates health guidance for a user based on the analysis results obtained by an information analysis means.

[0313] "Notification means" refers to a device or software that informs the user of generated health guidance or advice.

[0314] A "question and answer system" is a device or program that generates and provides answers in response to questions from users.

[0315] "Information provision means" refers to a device or program for providing information from medical support organizations based on the user's health status.

[0316] In embodiments of the present invention, the cooperation between the user, terminal, and server is crucial. The user wears a health device such as a smartwatch or fitness band. These devices continuously collect the user's biometric information, such as heart rate, activity level, and sleep patterns. The terminal cooperates with these devices and temporarily stores the collected data. Periodically or as needed, the terminal sends the data to a cloud server.

[0317] The server uses machine learning models such as TensorFlow and PyTorch to analyze the received biometric information. This allows it to compare the data with historical data and general health databases to assess the user's health status. Based on the assessment, the server generates health guidance tailored to the user. This guidance is generated via a guidance generation system and notified to the user's device in real time.

[0318] Users can submit health-related questions to a server in natural language via a device or robot. These questions are analyzed through a question-and-answer system, and relevant answers are generated using a generative AI model. This process utilizes natural language processing tools such as BERT and spaCy. The generated answers are provided to the user in either voice or text format.

[0319] For example, if a user inputs "I'd like some advice on my recent eating habits," the server generates advice such as "Slightly reduce your carbohydrate intake and increase your vegetable intake" based on past eating data and health information, and communicates it to the user via their device. If the user reports an abnormality, the information provision system generates information on the nearest medical institution, enabling prompt action.

[0320] Specific examples of prompt messages include, "What should I do if I feel my heart rate is high?" and "How can I improve the quality of my sleep today?" This allows users to obtain detailed and immediate information about their health status.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The user wears a health device, which collects biometric information. The terminal receives this biometric information (heart rate, activity level, sleep patterns, etc.) as input, extracts the data from the device, and temporarily stores it. As a result, the terminal accumulates the user's latest health data.

[0324] Step 2:

[0325] The device periodically or on command transmits collected biometric information to the server. The input here is biometric data, and the output is data packets transmitted using secure communication methods. The SSL / TLS protocol is used for this transmission, thus ensuring data security.

[0326] Step 3:

[0327] The server inputs the received biometric information into a data analysis module and performs analysis using machine learning models (TensorFlow or PyTorch). Data processing involves converting the biometric information into a format suitable for the model and performing feature engineering. As a result, the user's health status is evaluated, and this becomes the output data.

[0328] Step 4:

[0329] The server generates health guidance based on the analysis results. The input is the evaluation results obtained in step 3, and the server uses the guidance generation mechanism to create individual health guidance for the user. The output is a health guidance message that includes specific advice tailored to the user's condition.

[0330] Step 5:

[0331] The generated health guidance is notified to the device. The device receives the health guidance message as input and outputs it to the user as voice or text. Specifically, it displays the message on the device's screen or plays the guidance as audio through the speaker.

[0332] Step 6:

[0333] Users input health-related questions via a terminal. The terminal sends the entered questions to a server, prompting processing through a question-and-answer system. This allows users to ask their questions to the system.

[0334] Step 7:

[0335] The server inputs user questions into a natural language processing module and analyzes them using generative AI models (such as BERT or spaCy). The input is a question in natural language form, and the system searches for the best answer based on prompts. The output is the answer, providing appropriate information to the user's question.

[0336] Step 8:

[0337] The generated answers are communicated to the user via the device. The device receives the answer text as input and outputs it to the user through text display or audio playback. This allows the user to receive direct feedback on the question.

[0338] 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.

[0339] This invention is a system developed to support users' health management, utilizing both the user's health information and emotional information to provide more personalized advice and responses. The system consists of an internet-connected terminal, a server in the cloud, and an emotional engine.

[0340] First, the device connects with the user's health devices (e.g., smartwatches or health tracking apps) to collect health information such as heart rate, exercise data, nutritional intake, and sleep patterns. In parallel, the device's built-in camera and microphone capture the user's facial expressions and voice tone, and transmit this data to an emotion engine for analysis of the user's emotions. This collected information is temporarily stored on the device, then encrypted and sent to a server.

[0341] The server records received health and emotional information in a database. Using this information, data analysis tools on the server perform analysis using machine learning algorithms. The analysis utilizes historical accumulated data and general health indicators to evaluate the user's current health status and emotional tendencies.

[0342] Based on the analysis results, the server creates personalized health advice for the user. This advice includes suggestions for diet, exercise, and stress management tailored to the user's health condition, and its tone is adjusted according to the detected emotional state. The advice is sent to the device in real time and notified to the user's smartphone.

[0343] In addition, users can input questions about their health and emotions through an application on their device. The server analyzes the content of the questions using natural language processing technology, combines it with emotional information, and generates the most appropriate answer at that time. The answer is provided to the user via the device and displayed in a format that is easy for the user to understand.

[0344] Furthermore, the server manages the schedule for regular checkups and creates reminders that take into account the user's emotional state. These reminders are delivered using language designed to soothe the user's emotions. In emergencies, the server also provides information on the nearest appropriate medical facility, including the user's location and emotional state, to help the user respond with confidence.

[0345] For example, if a user is feeling stressed, the device detects this state using an emotion engine, and the server generates relaxing health advice and sends a notification that includes an encouraging message. In this way, the present invention aims to support users from both a health and emotional perspective and improve their quality of life.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The device continuously collects health information such as heart rate, exercise data, nutritional intake, and sleep patterns from the user's smartwatch or health tracking app. Subsequently, the device's built-in camera and microphone capture the user's facial expressions and voice tone, which are then used as data for analysis by the emotion engine.

[0349] Step 2:

[0350] The emotion engine analyzes voice and facial expression data collected by the device to quantify and classify the user's emotional state. For example, it identifies emotions such as stress, joy, and sadness, and creates a digital profile of the emotional state.

[0351] Step 3:

[0352] The device encrypts the collected health and emotional information and securely transmits this data to a server via the internet.

[0353] Step 4:

[0354] The server stores the received information in a database and performs integrated analysis of health and emotional information using machine learning algorithms. Based on the analysis, the user's health risks and emotional tendencies are evaluated, and an appropriate advice plan is formulated.

[0355] Step 5:

[0356] Based on the analysis results, the server generates personalized health and emotional care advice for the user. This includes suggestions for diet and exercise, as well as emotionally sensitive encouragement and recommendations for relaxation techniques.

[0357] Step 6:

[0358] A device that receives advice from the server will notify the user. The user can view the advice on their smartphone screen and delve deeper into the details as needed.

[0359] Step 7:

[0360] Users input specific questions about their own condition through the terminal's interface, and this information is immediately sent to the server. These questions can cover both health and emotional aspects.

[0361] Step 8:

[0362] The server analyzes the user's question using natural language processing and generates an appropriate answer from the database, taking into account their emotional state. This answer takes into account both health and emotional considerations in line with the intent of the question.

[0363] Step 9:

[0364] The generated responses are sent to the user via the device, and the user can continue to manage their health using the information displayed on the screen.

[0365] Step 10:

[0366] The server generates and sends an emotionally sensitive reminder message to the user's device, taking into account the user's next scheduled check-up date. This ensures that the user does not forget to take necessary medical action.

[0367] Step 11:

[0368] In emergencies, users can use a feature to report any health problems. The device immediately creates emergency data, including the user's location and emotional state, and sends it to the server.

[0369] Step 12:

[0370] The server quickly identifies the most suitable medical facility based on emergency data and provides the user with the necessary information via their terminal. This enables a rapid medical response.

[0371] (Example 2)

[0372] 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".

[0373] In the modern era, providing individually optimized health management by comprehensively utilizing users' biometric and emotional information is a challenging task. Conventional health management systems rely solely on biometric information and do not take into account the user's emotional state, making it difficult to provide comprehensive health advice. Furthermore, the lack of a system in place to respond quickly when a user experiences an abnormality is also a problem.

[0374] 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.

[0375] In this invention, the server includes information acquisition means for acquiring the user's biometric and emotional information, information analysis means for analyzing the said information, and recommendation generation means for generating advice based on the analysis results. This makes it possible to provide users with individually optimized advice and prompt response information.

[0376] "Information acquisition means" is a general term for devices and software used to collect users' biometric and emotional information.

[0377] "Information analysis means" refers to a function that analyzes the user's health status and emotional tendencies based on collected biometric and emotional information.

[0378] The "recommendation generation method" is a system function that automatically generates optimal advice tailored to the user's health and emotions based on the analyzed information.

[0379] A "notification method" is a means of transmitting generated advice and information to the user in real time, and is provided through a terminal.

[0380] A "question answering tool" is a function that analyzes natural language questions entered by users and generates and provides highly relevant answers.

[0381] This invention is a system for providing individually optimized health management by utilizing users' biometric and emotional information. The system primarily consists of an internet-connected terminal, a cloud-based server, and an emotional engine responsible for analysis and recommendation generation.

[0382] The device collects biometric information such as heart rate, activity level, and sleep patterns through biometric detection devices worn by the user (e.g., smartwatches) and health tracking applications. It also uses the device's built-in camera and microphone to capture the user's facial expressions and voice, sending this information to an emotion engine for emotional analysis. The acquired data is securely transmitted to the server using encryption protocols.

[0383] The server stores received biometric and emotional information in a database and uses machine learning algorithms to analyze it. This analysis evaluates the user's health status and emotional tendencies, and automatically generates personalized health advice. The advice includes recommendations regarding diet, exercise, and stress management tailored to the user's situation, and its accuracy is enhanced by inference using a generative AI model.

[0384] The generated advice is delivered to the device in real time using carefully chosen language that takes the user's emotional state into consideration. Users can receive this advice via their smartphone or other devices and use it to improve their quality of life.

[0385] As a concrete example, consider a case related to everyday stress. When a user sends a prompt message using their device saying, "I've been feeling stressed lately. How can I relax?", the server analyzes this input, combines it with the user's current biological and emotional information, and provides advice, including appropriate relaxation methods. In this way, the system of the present invention enables comprehensive and individually optimized health support for the user.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The device works in conjunction with the user's biometric detection device to acquire biometric information such as heart rate, activity level, and sleep patterns. This data is captured in real time as input and temporarily stored within the device. This prepares the foundational data for monitoring daily health status.

[0389] Step 2:

[0390] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. The emotion engine processes the acquired emotion-related data as input, determining the emotional state (e.g., joy, sadness, anger). The analyzed emotion information is then generated as output.

[0391] Step 3:

[0392] The device encrypts the biometric and emotional information obtained in Step 1 and Step 2 and sends it to a server in the cloud. This establishes a system for centrally managing data while ensuring security.

[0393] Step 4:

[0394] The server stores the received information in a database and performs analysis using machine learning algorithms. It utilizes historical data and general health indicators from the database as input to evaluate the user's health status and emotional tendencies. The output of this process is a detailed analysis of the user's specific state.

[0395] Step 5:

[0396] The server generates optimized health advice using a generated AI model based on the analysis results. This advice includes recommendations for nutrition, exercise, and stress management tailored to the user, as well as words of encouragement according to their emotional state. It then generates specific advice text as output and prepares it for transmission to the terminal.

[0397] Step 6:

[0398] The device notifies the user in real time of advice received from the server. The user checks this advice through a smartphone application and uses it to improve their daily life. This creates a feedback loop that allows the user to adjust their actions based on the information they receive.

[0399] (Application Example 2)

[0400] 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."

[0401] In elderly care, emotional care is just as important as managing the user's health. However, there is a lack of means to grasp the user's health status and emotional changes in real time and respond appropriately, resulting in a problem where the quality of care is not always sufficient. This invention aims to solve these problems and provide more personalized support.

[0402] 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.

[0403] In this invention, the server includes data acquisition means for obtaining user health information, data analysis means for analyzing the user's health information and emotional information, and advice generation means for generating health advice and emotional care messages based on the analysis results. This enables caregivers to grasp the user's health and emotional state in real time and provide prompt and appropriate care accordingly.

[0404] "Data acquisition means" refers to a system for collecting user health information from sensors and devices.

[0405] A "data analysis tool" is a system equipped with algorithms for analyzing collected health and emotional information and evaluating the user's state.

[0406] The "advice generation means" is a system for generating personalized health advice and emotional care messages based on analyzed user health status and emotional information.

[0407] A "notification mechanism" is an interface used to deliver generated health advice and emotional care messages to the user.

[0408] A "question answering system" is a system that receives questions from users and generates and provides answers that take emotional information into consideration.

[0409] "Information sharing means" refers to communication methods for sharing information about the user's health and emotional state with care workers in real time.

[0410] A system implementing this invention aims to manage the user's health and emotional well-being, and is comprised of a combination of means for data acquisition, data analysis, advice generation, and notification.

[0411] The server operates primarily on data acquired from internet-connected devices. These devices collect health information (heart rate, exercise data, nutritional intake, sleep patterns, etc.) from smartwatches and health tracking apps worn by the user. They also use the device's camera and microphone to detect emotional information from the user's facial expressions and tone of voice. This data is temporarily stored on the device, then encrypted and sent to the server.

[0412] The server analyzes the received information using data analysis tools. This analysis employs machine learning algorithms to evaluate the user's health status and emotional tendencies by comparing them with past data and general health indicators. Based on the analyzed data, the advice generation tool generates personalized health advice and emotional care messages. For example, if the user has a high heart rate and is feeling stressed, it will provide advice to encourage relaxation.

[0413] The generated advice is transmitted to the device via a notification system. Users can input questions about their health and emotions through the application. The question-answering system analyzes the input questions using natural language processing technology and generates answers that take emotional information into account. In this process, an AI model is used to generate prompts and provide appropriate responses.

[0414] For example, if a user asks, "I have trouble sleeping at night," the system will check the user's sleep patterns and provide advice on possible causes and solutions. Furthermore, prompts such as "What relaxation methods would you recommend considering the user's recent emotional tendencies?" can be input to the generating AI model to obtain the optimal solution. This allows caregivers to quickly provide specific care based on the user's condition.

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

[0416] Step 1:

[0417] The device uses sensors to collect the user's health information. This includes obtaining data such as heart rate, exercise data, nutritional intake, and sleep patterns from smartwatches and health tracking apps. It also uses a camera and microphone to collect the user's facial expressions and voice tone as emotional information, which is temporarily stored on the device.

[0418] Step 2:

[0419] The device encrypts the collected health and emotional information before sending it to the server. The transmitted data becomes input, and the server receives it and records it in its database.

[0420] Step 3:

[0421] The server analyzes incoming data using data analysis tools. Machine learning algorithms compare the current data with past data and general indicators to evaluate the user's current health status and emotional tendencies. The input consists of health information and emotional information, and the output is the evaluation result.

[0422] Step 4:

[0423] The advice generation system generates personalized health advice and emotional care messages based on the analysis results. Here, the evaluation results obtained from data analysis are used. For example, for users with high stress levels, advice promoting relaxation is generated.

[0424] Step 5:

[0425] The server sends the generated advice and messages to the terminal via a notification system. The input is the advice and messages, and the output is sent to the user's terminal.

[0426] Step 6:

[0427] The user enters questions about their health and emotions into an application on their device. These questions constitute the input.

[0428] Step 7:

[0429] The server uses a question-answering mechanism and natural language processing techniques to analyze the question and generate a prompt. Based on this, it generates an answer that takes sentiment into account. The input is the user's question and sentiment information, and the output is an answer that takes sentiment into account.

[0430] Step 8:

[0431] Finally, the server inputs a prompt message into the generation AI model to generate the optimal answer, and then provides that answer to the user via the terminal.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] The system according to the present invention is designed to efficiently manage health and provides users with useful information and support when they aim to improve their health. This system consists of a terminal that can connect to the internet via a general communication network and a server located in a cloud environment.

[0449] First, the device connects with the user's health devices (e.g., smartwatch, health app, etc.) to collect health information such as heart rate, activity level, nutrition intake, and sleep patterns. This data is temporarily stored on the device and securely transmitted to the server at predetermined times. The collected data is used only to the extent explicitly permitted by the user and is handled with consideration for privacy.

[0450] The server analyzes the received health information. Advanced machine learning models are used for the analysis, and the user's health status is assessed based on the data. During the assessment process, past health information and publicly collected health databases are also referenced to gain a comprehensive understanding of the user's condition.

[0451] Based on the analysis results, the server generates appropriate health advice for the user. This advice includes dietary suggestions, exercise plans, and stress management methods best suited to the user's condition. The generated advice is sent to the terminal in real time, and the user is notified so they can check it immediately.

[0452] Furthermore, users can input various health-related questions they face daily through the application interface on their device. For example, in response to a question such as "How can I reduce stress?", the server uses natural language processing to understand the question, quickly searches the database for relevant information, generates an appropriate answer, and provides it to the user via the device.

[0453] In addition, the server manages the user's regular health check schedule and sends reminders as needed. This allows users to be aware of their own health status and visit a medical institution at the appropriate time. Furthermore, for a quick response in emergencies, the server also takes the user's location into consideration and promptly provides information on nearby medical institutions in emergency situations.

[0454] For example, if a user's heart rate reading on their watch is abnormal while they are at a gym, the device receives a notification and immediately sends the data to the server. The server analyzes this data and generates a list of nearby medical facilities, which it then informs the user of through the device. This allows the user to quickly select and visit a medical facility.

[0455] Thus, the present invention aims to comprehensively support users' health management and realize a comfortable and safe life.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The device collects health information such as heart rate, exercise data, dietary records, and sleep patterns from the user's health device. This data is integrated into the device in real time via communication methods such as Bluetooth and Wi-Fi.

[0459] Step 2:

[0460] The device securely encrypts the collected data and transmits it to the server via the internet. The latest security protocols are applied throughout this process to prevent data leakage.

[0461] Step 3:

[0462] The server saves the received data to the database. During saving, the data is integrated into a standard format and prepared for analysis.

[0463] Step 4:

[0464] The server begins data analysis using a machine learning model. Here, it evaluates the user's current health status by comparing it to past trends and general health indicators based on the user's health data.

[0465] Step 5:

[0466] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements, exercise schedules, and stress management techniques.

[0467] Step 6:

[0468] The server sends the generated health advice to the terminal. The terminal notifies the user and displays the advice on the smartphone screen or other display.

[0469] Step 7:

[0470] Users enter health-related questions via a smartphone app and send them to the server. The questions include specific advice and information for maintaining good health.

[0471] Step 8:

[0472] The server uses natural language processing technology to analyze the user's question and searches for the most suitable answer from a specialized health database.

[0473] Step 9:

[0474] The server sends the generated response to the terminal, which then notifies the user and displays the details on the screen.

[0475] Step 10:

[0476] The server creates a schedule for regular health checks and sends reminders to the user via their device. This notification is sent at the optimal time based on the previous health information.

[0477] Step 11:

[0478] In an emergency, if a user reports an anomaly, the device will immediately send emergency information, including location data, to the server.

[0479] Step 12:

[0480] The server identifies the nearest medical facility from the received emergency data and provides that information to the user via the terminal. This information allows the user to receive prompt medical assistance.

[0481] (Example 1)

[0482] 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."

[0483] In modern society, personal health management is becoming increasingly important, but traditional methods of collecting and analyzing health information are cumbersome, making it difficult for users to obtain appropriate health advice. Furthermore, the lack of readily available information to quickly obtain relevant information when health conditions suddenly change makes it difficult for users to respond effectively.

[0484] 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.

[0485] In this invention, the server includes information acquisition means, information analysis means, and suggestion generation means. This makes it possible to efficiently collect and analyze the user's health information and provide individually customized lifestyle improvement suggestions. Furthermore, in emergencies, it can support the user's rapid response by quickly providing information on nearby medical facilities.

[0486] "Information acquisition means" refers to a system for collecting information about a user's biometric data and lifestyle habits.

[0487] "Information analysis means" refers to a system that analyzes acquired biometric data and evaluates the user's health status.

[0488] A "proposal generation method" is a system for creating lifestyle improvement suggestions tailored to the user based on analysis results.

[0489] An "information notification system" is a mechanism that provides users with generated lifestyle improvement suggestions and health information, and informs them of information they request.

[0490] A "response mechanism" is a system for receiving inquiries from users and providing information and advice in response.

[0491] "Communication means" refers to a mechanism for sending and receiving data between a server and a terminal, enabling the secure transfer of user health information.

[0492] This invention is configured as an information system for efficient health management. This system primarily consists of an internet-connected terminal and a server located in a cloud environment. It can acquire various data from health management devices, analyze it, and provide personalized health advice to users.

[0493] The device uses hardware such as smartwatches or health tracking apps on smartphones to collect biometric data such as heart rate, activity level, nutritional intake, and sleep patterns. The collected data is temporarily stored on the device and then periodically transmitted to a cloud server using communication technologies such as Wi-Fi or Bluetooth.

[0494] The server analyzes the received data using advanced machine learning algorithms. The generative AI model used here assesses the user's health status from the data and generates personalized lifestyle improvement suggestions. This includes referencing the user's past data and public health databases. Specifically, this involves analyzing the user's exercise data for the past week and suggesting an exercise plan for the following week.

[0495] The generated health advice is sent to the device in real time and notified to the user. For example, specific suggestions such as, "Your heart rate is a little high, so we recommend relaxing activities today," may be given.

[0496] Furthermore, users can input health-related questions into the system through their devices. For example, if a user enters a prompt such as, "What should I do if I can't sleep at night?", the server will use natural language processing technology to analyze the prompt, search its database for relevant solutions, and provide an answer.

[0497] This system also has a function that allows the server to manage the schedule of regular health checkups and send reminders to users via their devices. This makes it easy for users to keep track of their health management schedule and receive health checkups at the appropriate time.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] The device collects biometric data using a smartwatch or health tracking app. This data includes heart rate, activity level, nutrition intake, and sleep patterns, and is temporarily stored within the device. The input is biometric data acquired by sensors, and the output is an organized dataset. Specifically, the smartwatch records daily activity levels and synchronizes the data with the application.

[0501] Step 2:

[0502] The collected data is regularly organized, the device encrypts this information, and sends it to the server using a secure communication protocol. The input is the organized data stored on the device, and the output is a packet of encrypted data. Specifically, the collected data is automatically uploaded to the server via Wi-Fi overnight.

[0503] Step 3:

[0504] The server inputs the received data into a machine learning algorithm for analysis. This algorithm also utilizes a large health database to assess the user's health status. The input is encrypted biometric data packets, and the output is a health assessment score. Specifically, it detects abnormal patterns based on past heart rate data.

[0505] Step 4:

[0506] Based on the analysis results, the server generates personalized health advice. The generating AI model creates the advice and provides suggestions tailored to the user's lifestyle. The input is a health assessment score, and the output is a list of specific suggestions. For example, one example of advice might be, "Your stress levels are high, so we recommend meditating for 10 minutes."

[0507] Step 5:

[0508] Health advice generated by the server is sent to the terminal in real time and notified to the user. The input is the generated list of suggestions, and the output is the notification message to the user. Specifically, the advice is pushed as a notification via the terminal's notification function.

[0509] Step 6:

[0510] The user uses a terminal application to input health-related questions and sends them to the server. These questions are processed as prompts by a generative AI model. The input is the user's question, and the output is an answer containing relevant information. A concrete example of this process is inputting the prompt, "How can I improve my sleep quality?"

[0511] Step 7:

[0512] The server analyzes the received question using natural language processing techniques and searches the database for corresponding information. The input is a prompt, and the output is an answer based on the search results. Specifically, it extracts relevant stress management articles and information and generates a summarized answer.

[0513] (Application Example 1)

[0514] 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."

[0515] In modern society, while there is a demand for more efficient individual health management, analyzing individual health data and providing appropriate health guidance is not easy. Furthermore, prompt action is essential when health abnormalities occur, and delays in information provision by medical support organizations can have life-threatening consequences. Therefore, a comprehensive system for efficient and effective health management is needed.

[0516] 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.

[0517] In this invention, the server includes data acquisition means for acquiring biometric information, information analysis means, and guidance generation means for generating health guidance. This enables the effective collection and analysis of the user's biometric information, the provision of appropriate health guidance in real time, and the rapid provision of information from medical support organizations in emergencies.

[0518] "Biometric information" refers to health-related data such as the user's heart rate, activity level, and sleep patterns.

[0519] "Information analysis means" refers to a device or program for analyzing acquired biological information and evaluating the user's health status.

[0520] A "guidance generation means" is a device or program that generates health guidance for a user based on the analysis results obtained by an information analysis means.

[0521] "Notification means" refers to a device or software that informs the user of generated health guidance or advice.

[0522] A "question and answer system" is a device or program that generates and provides answers in response to questions from users.

[0523] "Information provision means" refers to a device or program for providing information from medical support organizations based on the user's health status.

[0524] In embodiments of the present invention, the cooperation between the user, terminal, and server is crucial. The user wears a health device such as a smartwatch or fitness band. These devices continuously collect the user's biometric information, such as heart rate, activity level, and sleep patterns. The terminal cooperates with these devices and temporarily stores the collected data. Periodically or as needed, the terminal sends the data to a cloud server.

[0525] The server uses machine learning models such as TensorFlow and PyTorch to analyze the received biometric information. This allows it to compare the data with historical data and general health databases to assess the user's health status. Based on the assessment, the server generates health guidance tailored to the user. This guidance is generated via a guidance generation system and notified to the user's device in real time.

[0526] Users can submit health-related questions to a server in natural language via a device or robot. These questions are analyzed through a question-and-answer system, and relevant answers are generated using a generative AI model. This process utilizes natural language processing tools such as BERT and spaCy. The generated answers are provided to the user in either voice or text format.

[0527] For example, if a user inputs "I'd like some advice on my recent eating habits," the server generates advice such as "Slightly reduce your carbohydrate intake and increase your vegetable intake" based on past eating data and health information, and communicates it to the user via their device. If the user reports an abnormality, the information provision system generates information on the nearest medical institution, enabling prompt action.

[0528] Specific examples of prompt messages include, "What should I do if I feel my heart rate is high?" and "How can I improve the quality of my sleep today?" This allows users to obtain detailed and immediate information about their health status.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The user wears a health device, which collects biometric information. The terminal receives this biometric information (heart rate, activity level, sleep patterns, etc.) as input, extracts the data from the device, and temporarily stores it. As a result, the terminal accumulates the user's latest health data.

[0532] Step 2:

[0533] The device periodically or on command transmits collected biometric information to the server. The input here is biometric data, and the output is data packets transmitted using secure communication methods. The SSL / TLS protocol is used for this transmission, thus ensuring data security.

[0534] Step 3:

[0535] The server inputs the received biometric information into a data analysis module and performs analysis using machine learning models (TensorFlow or PyTorch). Data processing involves converting the biometric information into a format suitable for the model and performing feature engineering. As a result, the user's health status is evaluated, and this becomes the output data.

[0536] Step 4:

[0537] The server generates health guidance based on the analysis results. The input is the evaluation results obtained in step 3, and the server uses the guidance generation mechanism to create individual health guidance for the user. The output is a health guidance message that includes specific advice tailored to the user's condition.

[0538] Step 5:

[0539] The generated health guidance is notified to the device. The device receives the health guidance message as input and outputs it to the user as voice or text. Specifically, it displays the message on the device's screen or plays the guidance as audio through the speaker.

[0540] Step 6:

[0541] Users input health-related questions via a terminal. The terminal sends the entered questions to a server, prompting processing through a question-and-answer system. This allows users to ask their questions to the system.

[0542] Step 7:

[0543] The server inputs user questions into a natural language processing module and analyzes them using generative AI models (such as BERT or spaCy). The input is a question in natural language form, and the system searches for the best answer based on prompts. The output is the answer, providing appropriate information to the user's question.

[0544] Step 8:

[0545] The generated answers are communicated to the user via the device. The device receives the answer text as input and outputs it to the user through text display or audio playback. This allows the user to receive direct feedback on the question.

[0546] 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.

[0547] This invention is a system developed to support users' health management, utilizing both the user's health information and emotional information to provide more personalized advice and responses. The system consists of an internet-connected terminal, a server in the cloud, and an emotional engine.

[0548] First, the device connects with the user's health devices (e.g., smartwatches or health tracking apps) to collect health information such as heart rate, exercise data, nutritional intake, and sleep patterns. In parallel, the device's built-in camera and microphone capture the user's facial expressions and voice tone, and transmit this data to an emotion engine for analysis of the user's emotions. This collected information is temporarily stored on the device, then encrypted and sent to a server.

[0549] The server records received health and emotional information in a database. Using this information, data analysis tools on the server perform analysis using machine learning algorithms. The analysis utilizes historical accumulated data and general health indicators to evaluate the user's current health status and emotional tendencies.

[0550] Based on the analysis results, the server creates personalized health advice for the user. This advice includes suggestions for diet, exercise, and stress management tailored to the user's health condition, and its tone is adjusted according to the detected emotional state. The advice is sent to the device in real time and notified to the user's smartphone.

[0551] In addition, users can input questions about their health and emotions through an application on their device. The server analyzes the content of the questions using natural language processing technology, combines it with emotional information, and generates the most appropriate answer at that time. The answer is provided to the user via the device and displayed in a format that is easy for the user to understand.

[0552] Furthermore, the server manages the schedule for regular checkups and creates reminders that take into account the user's emotional state. These reminders are delivered using language designed to soothe the user's emotions. In emergencies, the server also provides information on the nearest appropriate medical facility, including the user's location and emotional state, to help the user respond with confidence.

[0553] For example, if a user is feeling stressed, the device detects this state using an emotion engine, and the server generates relaxing health advice and sends a notification that includes an encouraging message. In this way, the present invention aims to support users from both a health and emotional perspective and improve their quality of life.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The device continuously collects health information such as heart rate, exercise data, nutritional intake, and sleep patterns from the user's smartwatch or health tracking app. Subsequently, the device's built-in camera and microphone capture the user's facial expressions and voice tone, which are then used as data for analysis by the emotion engine.

[0557] Step 2:

[0558] The emotion engine analyzes voice and facial expression data collected by the device to quantify and classify the user's emotional state. For example, it identifies emotions such as stress, joy, and sadness, and creates a digital profile of the emotional state.

[0559] Step 3:

[0560] The device encrypts the collected health and emotional information and securely transmits this data to a server via the internet.

[0561] Step 4:

[0562] The server stores the received information in a database and performs integrated analysis of health and emotional information using machine learning algorithms. Based on the analysis, the user's health risks and emotional tendencies are evaluated, and an appropriate advice plan is formulated.

[0563] Step 5:

[0564] Based on the analysis results, the server generates personalized health and emotional care advice for the user. This includes suggestions for diet and exercise, as well as emotionally sensitive encouragement and recommendations for relaxation techniques.

[0565] Step 6:

[0566] A device that receives advice from the server will notify the user. The user can view the advice on their smartphone screen and delve deeper into the details as needed.

[0567] Step 7:

[0568] Users input specific questions about their own condition through the terminal's interface, and this information is immediately sent to the server. These questions can cover both health and emotional aspects.

[0569] Step 8:

[0570] The server analyzes the user's question using natural language processing and generates an appropriate answer from the database, taking into account their emotional state. This answer takes into account both health and emotional considerations in line with the intent of the question.

[0571] Step 9:

[0572] The generated responses are sent to the user via the device, and the user can continue to manage their health using the information displayed on the screen.

[0573] Step 10:

[0574] The server generates and sends an emotionally sensitive reminder message to the user's device, taking into account the user's next scheduled check-up date. This ensures that the user does not forget to take necessary medical action.

[0575] Step 11:

[0576] In emergencies, users can use a feature to report any health problems. The device immediately creates emergency data, including the user's location and emotional state, and sends it to the server.

[0577] Step 12:

[0578] The server quickly identifies the most suitable medical facility based on emergency data and provides the user with the necessary information via their terminal. This enables a rapid medical response.

[0579] (Example 2)

[0580] 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."

[0581] In the modern era, providing individually optimized health management by comprehensively utilizing users' biometric and emotional information is a challenging task. Conventional health management systems rely solely on biometric information and do not take into account the user's emotional state, making it difficult to provide comprehensive health advice. Furthermore, the lack of a system in place to respond quickly when a user experiences an abnormality is also a problem.

[0582] 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.

[0583] In this invention, the server includes information acquisition means for acquiring the user's biometric and emotional information, information analysis means for analyzing the said information, and recommendation generation means for generating advice based on the analysis results. This makes it possible to provide users with individually optimized advice and prompt response information.

[0584] "Information acquisition means" is a general term for devices and software used to collect users' biometric and emotional information.

[0585] "Information analysis means" refers to a function that analyzes the user's health status and emotional tendencies based on collected biometric and emotional information.

[0586] The "recommendation generation method" is a system function that automatically generates optimal advice tailored to the user's health and emotions based on the analyzed information.

[0587] A "notification method" is a means of transmitting generated advice and information to the user in real time, and is provided through a terminal.

[0588] A "question answering tool" is a function that analyzes natural language questions entered by users and generates and provides highly relevant answers.

[0589] This invention is a system for providing individually optimized health management by utilizing users' biometric and emotional information. The system primarily consists of an internet-connected terminal, a cloud-based server, and an emotional engine responsible for analysis and recommendation generation.

[0590] The device collects biometric information such as heart rate, activity level, and sleep patterns through biometric detection devices worn by the user (e.g., smartwatches) and health tracking applications. It also uses the device's built-in camera and microphone to capture the user's facial expressions and voice, sending this information to an emotion engine for emotional analysis. The acquired data is securely transmitted to the server using encryption protocols.

[0591] The server stores received biometric and emotional information in a database and uses machine learning algorithms to analyze it. This analysis evaluates the user's health status and emotional tendencies, and automatically generates personalized health advice. The advice includes recommendations regarding diet, exercise, and stress management tailored to the user's situation, and its accuracy is enhanced by inference using a generative AI model.

[0592] The generated advice is delivered to the device in real time using carefully chosen language that takes the user's emotional state into consideration. Users can receive this advice via their smartphone or other devices and use it to improve their quality of life.

[0593] As a concrete example, consider a case related to everyday stress. When a user sends a prompt message using their device saying, "I've been feeling stressed lately. How can I relax?", the server analyzes this input, combines it with the user's current biological and emotional information, and provides advice, including appropriate relaxation methods. In this way, the system of the present invention enables comprehensive and individually optimized health support for the user.

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] The device works in conjunction with the user's biometric detection device to acquire biometric information such as heart rate, activity level, and sleep patterns. This data is captured in real time as input and temporarily stored within the device. This prepares the foundational data for monitoring daily health status.

[0597] Step 2:

[0598] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. The emotion engine processes the acquired emotion-related data as input, determining the emotional state (e.g., joy, sadness, anger). The analyzed emotion information is then generated as output.

[0599] Step 3:

[0600] The device encrypts the biometric and emotional information obtained in Step 1 and Step 2 and sends it to a server in the cloud. This establishes a system for centrally managing data while ensuring security.

[0601] Step 4:

[0602] The server stores the received information in a database and performs analysis using machine learning algorithms. It utilizes historical data and general health indicators from the database as input to evaluate the user's health status and emotional tendencies. The output of this process is a detailed analysis of the user's specific state.

[0603] Step 5:

[0604] The server generates optimized health advice using a generated AI model based on the analysis results. This advice includes recommendations for nutrition, exercise, and stress management tailored to the user, as well as words of encouragement according to their emotional state. It then generates specific advice text as output and prepares it for transmission to the terminal.

[0605] Step 6:

[0606] The device notifies the user in real time of advice received from the server. The user checks this advice through a smartphone application and uses it to improve their daily life. This creates a feedback loop that allows the user to adjust their actions based on the information they receive.

[0607] (Application Example 2)

[0608] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0609] In elderly care, emotional care is just as important as managing the user's health. However, there is a lack of means to grasp the user's health status and emotional changes in real time and respond appropriately, resulting in a problem where the quality of care is not always sufficient. This invention aims to solve these problems and provide more personalized support.

[0610] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0611] In this invention, the server includes data acquisition means for obtaining user health information, data analysis means for analyzing the user's health information and emotional information, and advice generation means for generating health advice and emotional care messages based on the analysis results. This enables caregivers to grasp the user's health and emotional state in real time and provide prompt and appropriate care accordingly.

[0612] "Data acquisition means" refers to a system for collecting user health information from sensors and devices.

[0613] A "data analysis tool" is a system equipped with algorithms for analyzing collected health and emotional information and evaluating the user's state.

[0614] The "advice generation means" is a system for generating personalized health advice and emotional care messages based on analyzed user health status and emotional information.

[0615] A "notification mechanism" is an interface used to deliver generated health advice and emotional care messages to the user.

[0616] A "question answering system" is a system that receives questions from users and generates and provides answers that take emotional information into consideration.

[0617] "Information sharing means" refers to communication methods for sharing information about the user's health and emotional state with care workers in real time.

[0618] A system implementing this invention aims to manage the user's health and emotional well-being, and is comprised of a combination of means for data acquisition, data analysis, advice generation, and notification.

[0619] The server operates primarily on data acquired from internet-connected devices. These devices collect health information (heart rate, exercise data, nutritional intake, sleep patterns, etc.) from smartwatches and health tracking apps worn by the user. They also use the device's camera and microphone to detect emotional information from the user's facial expressions and tone of voice. This data is temporarily stored on the device, then encrypted and sent to the server.

[0620] The server analyzes the received information using data analysis tools. This analysis employs machine learning algorithms to evaluate the user's health status and emotional tendencies by comparing them with past data and general health indicators. Based on the analyzed data, the advice generation tool generates personalized health advice and emotional care messages. For example, if the user has a high heart rate and is feeling stressed, it will provide advice to encourage relaxation.

[0621] The generated advice is transmitted to the device via a notification system. Users can input questions about their health and emotions through the application. The question-answering system analyzes the input questions using natural language processing technology and generates answers that take emotional information into account. In this process, an AI model is used to generate prompts and provide appropriate responses.

[0622] For example, if a user asks, "I have trouble sleeping at night," the system will check the user's sleep patterns and provide advice on possible causes and solutions. Furthermore, prompts such as "What relaxation methods would you recommend considering the user's recent emotional tendencies?" can be input to the generating AI model to obtain the optimal solution. This allows caregivers to quickly provide specific care based on the user's condition.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The device uses sensors to collect the user's health information. This includes obtaining data such as heart rate, exercise data, nutritional intake, and sleep patterns from smartwatches and health tracking apps. It also uses a camera and microphone to collect the user's facial expressions and voice tone as emotional information, which is temporarily stored on the device.

[0626] Step 2:

[0627] The device encrypts the collected health and emotional information before sending it to the server. The transmitted data becomes input, and the server receives it and records it in its database.

[0628] Step 3:

[0629] The server analyzes incoming data using data analysis tools. Machine learning algorithms compare the current data with past data and general indicators to evaluate the user's current health status and emotional tendencies. The input consists of health information and emotional information, and the output is the evaluation result.

[0630] Step 4:

[0631] The advice generation system generates personalized health advice and emotional care messages based on the analysis results. Here, the evaluation results obtained from data analysis are used. For example, for users with high stress levels, advice promoting relaxation is generated.

[0632] Step 5:

[0633] The server sends the generated advice and messages to the terminal via a notification system. The input is the advice and messages, and the output is sent to the user's terminal.

[0634] Step 6:

[0635] The user enters questions about their health and emotions into an application on their device. These questions constitute the input.

[0636] Step 7:

[0637] The server uses a question-answering mechanism and natural language processing techniques to analyze the question and generate a prompt. Based on this, it generates an answer that takes sentiment into account. The input is the user's question and sentiment information, and the output is an answer that takes sentiment into account.

[0638] Step 8:

[0639] Finally, the server inputs a prompt message into the generation AI model to generate the optimal answer, and then provides that answer to the user via the terminal.

[0640] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0641] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0642] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0643] [Fourth Embodiment]

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

[0645] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0646] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0647] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0648] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0649] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0650] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0651] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0652] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0653] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0654] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0655] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0656] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] The system according to the present invention is designed to efficiently manage health and provides users with useful information and support when they aim to improve their health. This system consists of a terminal that can connect to the internet via a general communication network and a server located in a cloud environment.

[0658] First, the device connects with the user's health devices (e.g., smartwatch, health app, etc.) to collect health information such as heart rate, activity level, nutrition intake, and sleep patterns. This data is temporarily stored on the device and securely transmitted to the server at predetermined times. The collected data is used only to the extent explicitly permitted by the user and is handled with consideration for privacy.

[0659] The server analyzes the received health information. Advanced machine learning models are used for the analysis, and the user's health status is assessed based on the data. During the assessment process, past health information and publicly collected health databases are also referenced to gain a comprehensive understanding of the user's condition.

[0660] Based on the analysis results, the server generates appropriate health advice for the user. This advice includes dietary suggestions, exercise plans, and stress management methods best suited to the user's condition. The generated advice is sent to the terminal in real time, and the user is notified so they can check it immediately.

[0661] Furthermore, users can input various health-related questions they face daily through the application interface on their device. For example, in response to a question such as "How can I reduce stress?", the server uses natural language processing to understand the question, quickly searches the database for relevant information, generates an appropriate answer, and provides it to the user via the device.

[0662] In addition, the server manages the user's regular health check schedule and sends reminders as needed. This allows users to be aware of their own health status and visit a medical institution at the appropriate time. Furthermore, for a quick response in emergencies, the server also takes the user's location into consideration and promptly provides information on nearby medical institutions in emergency situations.

[0663] For example, if a user's heart rate reading on their watch is abnormal while they are at a gym, the device receives a notification and immediately sends the data to the server. The server analyzes this data and generates a list of nearby medical facilities, which it then informs the user of through the device. This allows the user to quickly select and visit a medical facility.

[0664] Thus, the present invention aims to comprehensively support users' health management and realize a comfortable and safe life.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The device collects health information such as heart rate, exercise data, dietary records, and sleep patterns from the user's health device. This data is integrated into the device in real time via communication methods such as Bluetooth and Wi-Fi.

[0668] Step 2:

[0669] The device securely encrypts the collected data and transmits it to the server via the internet. The latest security protocols are applied throughout this process to prevent data leakage.

[0670] Step 3:

[0671] The server saves the received data to the database. During saving, the data is integrated into a standard format and prepared for analysis.

[0672] Step 4:

[0673] The server begins data analysis using a machine learning model. Here, it evaluates the user's current health status by comparing it to past trends and general health indicators based on the user's health data.

[0674] Step 5:

[0675] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements, exercise schedules, and stress management techniques.

[0676] Step 6:

[0677] The server sends the generated health advice to the terminal. The terminal notifies the user and displays the advice on the smartphone screen or other display.

[0678] Step 7:

[0679] Users enter health-related questions via a smartphone app and send them to the server. The questions include specific advice and information for maintaining good health.

[0680] Step 8:

[0681] The server uses natural language processing technology to analyze the user's question and searches for the most suitable answer from a specialized health database.

[0682] Step 9:

[0683] The server sends the generated response to the terminal, which then notifies the user and displays the details on the screen.

[0684] Step 10:

[0685] The server creates a schedule for regular health checks and sends reminders to the user via their device. This notification is sent at the optimal time based on the previous health information.

[0686] Step 11:

[0687] In an emergency, if a user reports an anomaly, the device will immediately send emergency information, including location data, to the server.

[0688] Step 12:

[0689] The server identifies the nearest medical facility from the received emergency data and provides that information to the user via the terminal. This information allows the user to receive prompt medical assistance.

[0690] (Example 1)

[0691] 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".

[0692] In modern society, personal health management is becoming increasingly important, but traditional methods of collecting and analyzing health information are cumbersome, making it difficult for users to obtain appropriate health advice. Furthermore, the lack of readily available information to quickly obtain relevant information when health conditions suddenly change makes it difficult for users to respond effectively.

[0693] 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.

[0694] In this invention, the server includes information acquisition means, information analysis means, and suggestion generation means. This makes it possible to efficiently collect and analyze the user's health information and provide individually customized lifestyle improvement suggestions. Furthermore, in emergencies, it can support the user's rapid response by quickly providing information on nearby medical facilities.

[0695] "Information acquisition means" refers to a system for collecting information about a user's biometric data and lifestyle habits.

[0696] "Information analysis means" refers to a system that analyzes acquired biometric data and evaluates the user's health status.

[0697] A "proposal generation method" is a system for creating lifestyle improvement suggestions tailored to the user based on analysis results.

[0698] An "information notification system" is a mechanism that provides users with generated lifestyle improvement suggestions and health information, and informs them of information they request.

[0699] A "response mechanism" is a system for receiving inquiries from users and providing information and advice in response.

[0700] "Communication means" refers to a mechanism for sending and receiving data between a server and a terminal, enabling the secure transfer of user health information.

[0701] This invention is configured as an information system for efficient health management. This system primarily consists of an internet-connected terminal and a server located in a cloud environment. It can acquire various data from health management devices, analyze it, and provide personalized health advice to users.

[0702] The device uses hardware such as smartwatches or health tracking apps on smartphones to collect biometric data such as heart rate, activity level, nutritional intake, and sleep patterns. The collected data is temporarily stored on the device and then periodically transmitted to a cloud server using communication technologies such as Wi-Fi or Bluetooth.

[0703] The server analyzes the received data using advanced machine learning algorithms. The generative AI model used here assesses the user's health status from the data and generates personalized lifestyle improvement suggestions. This includes referencing the user's past data and public health databases. Specifically, this involves analyzing the user's exercise data for the past week and suggesting an exercise plan for the following week.

[0704] The generated health advice is sent to the device in real time and notified to the user. For example, specific suggestions such as, "Your heart rate is a little high, so we recommend relaxing activities today," may be given.

[0705] Furthermore, users can input health-related questions into the system through their devices. For example, if a user enters a prompt such as, "What should I do if I can't sleep at night?", the server will use natural language processing technology to analyze the prompt, search its database for relevant solutions, and provide an answer.

[0706] This system also has a function that allows the server to manage the schedule of regular health checkups and send reminders to users via their devices. This makes it easy for users to keep track of their health management schedule and receive health checkups at the appropriate time.

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The device collects biometric data using a smartwatch or health tracking app. This data includes heart rate, activity level, nutrition intake, and sleep patterns, and is temporarily stored within the device. The input is biometric data acquired by sensors, and the output is an organized dataset. Specifically, the smartwatch records daily activity levels and synchronizes the data with the application.

[0710] Step 2:

[0711] The collected data is regularly organized, the device encrypts this information, and sends it to the server using a secure communication protocol. The input is the organized data stored on the device, and the output is a packet of encrypted data. Specifically, the collected data is automatically uploaded to the server via Wi-Fi overnight.

[0712] Step 3:

[0713] The server inputs the received data into a machine learning algorithm for analysis. This algorithm also utilizes a large health database to assess the user's health status. The input is encrypted biometric data packets, and the output is a health assessment score. Specifically, it detects abnormal patterns based on past heart rate data.

[0714] Step 4:

[0715] Based on the analysis results, the server generates personalized health advice. The generating AI model creates the advice and provides suggestions tailored to the user's lifestyle. The input is a health assessment score, and the output is a list of specific suggestions. For example, one example of advice might be, "Your stress levels are high, so we recommend meditating for 10 minutes."

[0716] Step 5:

[0717] Health advice generated by the server is sent to the terminal in real time and notified to the user. The input is the generated list of suggestions, and the output is the notification message to the user. Specifically, the advice is pushed as a notification via the terminal's notification function.

[0718] Step 6:

[0719] The user uses a terminal application to input health-related questions and sends them to the server. These questions are processed as prompts by a generative AI model. The input is the user's question, and the output is an answer containing relevant information. A concrete example of this process is inputting the prompt, "How can I improve my sleep quality?"

[0720] Step 7:

[0721] The server analyzes the received question using natural language processing techniques and searches the database for corresponding information. The input is a prompt, and the output is an answer based on the search results. Specifically, it extracts relevant stress management articles and information and generates a summarized answer.

[0722] (Application Example 1)

[0723] 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".

[0724] In modern society, while there is a demand for more efficient individual health management, analyzing individual health data and providing appropriate health guidance is not easy. Furthermore, prompt action is essential when health abnormalities occur, and delays in information provision by medical support organizations can have life-threatening consequences. Therefore, a comprehensive system for efficient and effective health management is needed.

[0725] 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.

[0726] In this invention, the server includes data acquisition means for acquiring biometric information, information analysis means, and guidance generation means for generating health guidance. This enables the effective collection and analysis of the user's biometric information, the provision of appropriate health guidance in real time, and the rapid provision of information from medical support organizations in emergencies.

[0727] "Biometric information" refers to health-related data such as the user's heart rate, activity level, and sleep patterns.

[0728] "Information analysis means" refers to a device or program for analyzing acquired biological information and evaluating the user's health status.

[0729] A "guidance generation means" is a device or program that generates health guidance for a user based on the analysis results obtained by an information analysis means.

[0730] "Notification means" refers to a device or software that informs the user of generated health guidance or advice.

[0731] A "question and answer system" is a device or program that generates and provides answers in response to questions from users.

[0732] "Information provision means" refers to a device or program for providing information from medical support organizations based on the user's health status.

[0733] In embodiments of the present invention, the cooperation between the user, terminal, and server is crucial. The user wears a health device such as a smartwatch or fitness band. These devices continuously collect the user's biometric information, such as heart rate, activity level, and sleep patterns. The terminal cooperates with these devices and temporarily stores the collected data. Periodically or as needed, the terminal sends the data to a cloud server.

[0734] The server uses machine learning models such as TensorFlow and PyTorch to analyze the received biometric information. This allows it to compare the data with historical data and general health databases to assess the user's health status. Based on the assessment, the server generates health guidance tailored to the user. This guidance is generated via a guidance generation system and notified to the user's device in real time.

[0735] Users can submit health-related questions to a server in natural language via a device or robot. These questions are analyzed through a question-and-answer system, and relevant answers are generated using a generative AI model. This process utilizes natural language processing tools such as BERT and spaCy. The generated answers are provided to the user in either voice or text format.

[0736] For example, if a user inputs "I'd like some advice on my recent eating habits," the server generates advice such as "Slightly reduce your carbohydrate intake and increase your vegetable intake" based on past eating data and health information, and communicates it to the user via their device. If the user reports an abnormality, the information provision system generates information on the nearest medical institution, enabling prompt action.

[0737] Specific examples of prompt messages include, "What should I do if I feel my heart rate is high?" and "How can I improve the quality of my sleep today?" This allows users to obtain detailed and immediate information about their health status.

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The user wears a health device, which collects biometric information. The terminal receives this biometric information (heart rate, activity level, sleep patterns, etc.) as input, extracts the data from the device, and temporarily stores it. As a result, the terminal accumulates the user's latest health data.

[0741] Step 2:

[0742] The device periodically or on command transmits collected biometric information to the server. The input here is biometric data, and the output is data packets transmitted using secure communication methods. The SSL / TLS protocol is used for this transmission, thus ensuring data security.

[0743] Step 3:

[0744] The server inputs the received biometric information into a data analysis module and performs analysis using machine learning models (TensorFlow or PyTorch). Data processing involves converting the biometric information into a format suitable for the model and performing feature engineering. As a result, the user's health status is evaluated, and this becomes the output data.

[0745] Step 4:

[0746] The server generates health guidance based on the analysis results. The input is the evaluation results obtained in step 3, and the server uses the guidance generation mechanism to create individual health guidance for the user. The output is a health guidance message that includes specific advice tailored to the user's condition.

[0747] Step 5:

[0748] The generated health guidance is notified to the device. The device receives the health guidance message as input and outputs it to the user as voice or text. Specifically, it displays the message on the device's screen or plays the guidance as audio through the speaker.

[0749] Step 6:

[0750] Users input health-related questions via a terminal. The terminal sends the entered questions to a server, prompting processing through a question-and-answer system. This allows users to ask their questions to the system.

[0751] Step 7:

[0752] The server inputs user questions into a natural language processing module and analyzes them using generative AI models (such as BERT or spaCy). The input is a question in natural language form, and the system searches for the best answer based on prompts. The output is the answer, providing appropriate information to the user's question.

[0753] Step 8:

[0754] The generated answers are communicated to the user via the device. The device receives the answer text as input and outputs it to the user through text display or audio playback. This allows the user to receive direct feedback on the question.

[0755] 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.

[0756] This invention is a system developed to support users' health management, utilizing both the user's health information and emotional information to provide more personalized advice and responses. The system consists of an internet-connected terminal, a server in the cloud, and an emotional engine.

[0757] First, the device connects with the user's health devices (e.g., smartwatches or health tracking apps) to collect health information such as heart rate, exercise data, nutritional intake, and sleep patterns. In parallel, the device's built-in camera and microphone capture the user's facial expressions and voice tone, and transmit this data to an emotion engine for analysis of the user's emotions. This collected information is temporarily stored on the device, then encrypted and sent to a server.

[0758] The server records received health and emotional information in a database. Using this information, data analysis tools on the server perform analysis using machine learning algorithms. The analysis utilizes historical accumulated data and general health indicators to evaluate the user's current health status and emotional tendencies.

[0759] Based on the analysis results, the server creates personalized health advice for the user. This advice includes suggestions for diet, exercise, and stress management tailored to the user's health condition, and its tone is adjusted according to the detected emotional state. The advice is sent to the device in real time and notified to the user's smartphone.

[0760] In addition, users can input questions about their health and emotions through an application on their device. The server analyzes the content of the questions using natural language processing technology, combines it with emotional information, and generates the most appropriate answer at that time. The answer is provided to the user via the device and displayed in a format that is easy for the user to understand.

[0761] Furthermore, the server manages the schedule for regular checkups and creates reminders that take into account the user's emotional state. These reminders are delivered using language designed to soothe the user's emotions. In emergencies, the server also provides information on the nearest appropriate medical facility, including the user's location and emotional state, to help the user respond with confidence.

[0762] For example, if a user is feeling stressed, the device detects this state using an emotion engine, and the server generates relaxing health advice and sends a notification that includes an encouraging message. In this way, the present invention aims to support users from both a health and emotional perspective and improve their quality of life.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The device continuously collects health information such as heart rate, exercise data, nutritional intake, and sleep patterns from the user's smartwatch or health tracking app. Subsequently, the device's built-in camera and microphone capture the user's facial expressions and voice tone, which are then used as data for analysis by the emotion engine.

[0766] Step 2:

[0767] The emotion engine analyzes voice and facial expression data collected by the device to quantify and classify the user's emotional state. For example, it identifies emotions such as stress, joy, and sadness, and creates a digital profile of the emotional state.

[0768] Step 3:

[0769] The device encrypts the collected health and emotional information and securely transmits this data to a server via the internet.

[0770] Step 4:

[0771] The server stores the received information in a database and performs integrated analysis of health and emotional information using machine learning algorithms. Based on the analysis, the user's health risks and emotional tendencies are evaluated, and an appropriate advice plan is formulated.

[0772] Step 5:

[0773] Based on the analysis results, the server generates personalized health and emotional care advice for the user. This includes suggestions for diet and exercise, as well as emotionally sensitive encouragement and recommendations for relaxation techniques.

[0774] Step 6:

[0775] A device that receives advice from the server will notify the user. The user can view the advice on their smartphone screen and delve deeper into the details as needed.

[0776] Step 7:

[0777] Users input specific questions about their own condition through the terminal's interface, and this information is immediately sent to the server. These questions can cover both health and emotional aspects.

[0778] Step 8:

[0779] The server analyzes the user's question using natural language processing and generates an appropriate answer from the database, taking into account their emotional state. This answer takes into account both health and emotional considerations in line with the intent of the question.

[0780] Step 9:

[0781] The generated responses are sent to the user via the device, and the user can continue to manage their health using the information displayed on the screen.

[0782] Step 10:

[0783] The server generates and sends an emotionally sensitive reminder message to the user's device, taking into account the user's next scheduled check-up date. This ensures that the user does not forget to take necessary medical action.

[0784] Step 11:

[0785] In emergencies, users can use a feature to report any health problems. The device immediately creates emergency data, including the user's location and emotional state, and sends it to the server.

[0786] Step 12:

[0787] The server quickly identifies the most suitable medical facility based on emergency data and provides the user with the necessary information via their terminal. This enables a rapid medical response.

[0788] (Example 2)

[0789] 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".

[0790] In the modern era, providing individually optimized health management by comprehensively utilizing users' biometric and emotional information is a challenging task. Conventional health management systems rely solely on biometric information and do not take into account the user's emotional state, making it difficult to provide comprehensive health advice. Furthermore, the lack of a system in place to respond quickly when a user experiences an abnormality is also a problem.

[0791] 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.

[0792] In this invention, the server includes information acquisition means for acquiring the user's biometric and emotional information, information analysis means for analyzing the said information, and recommendation generation means for generating advice based on the analysis results. This makes it possible to provide users with individually optimized advice and prompt response information.

[0793] "Information acquisition means" is a general term for devices and software used to collect users' biometric and emotional information.

[0794] "Information analysis means" refers to a function that analyzes the user's health status and emotional tendencies based on collected biometric and emotional information.

[0795] The "recommendation generation method" is a system function that automatically generates optimal advice tailored to the user's health and emotions based on the analyzed information.

[0796] A "notification method" is a means of transmitting generated advice and information to the user in real time, and is provided through a terminal.

[0797] A "question answering tool" is a function that analyzes natural language questions entered by users and generates and provides highly relevant answers.

[0798] This invention is a system for providing individually optimized health management by utilizing users' biometric and emotional information. The system primarily consists of an internet-connected terminal, a cloud-based server, and an emotional engine responsible for analysis and recommendation generation.

[0799] The device collects biometric information such as heart rate, activity level, and sleep patterns through biometric detection devices worn by the user (e.g., smartwatches) and health tracking applications. It also uses the device's built-in camera and microphone to capture the user's facial expressions and voice, sending this information to an emotion engine for emotional analysis. The acquired data is securely transmitted to the server using encryption protocols.

[0800] The server stores received biometric and emotional information in a database and uses machine learning algorithms to analyze it. This analysis evaluates the user's health status and emotional tendencies, and automatically generates personalized health advice. The advice includes recommendations regarding diet, exercise, and stress management tailored to the user's situation, and its accuracy is enhanced by inference using a generative AI model.

[0801] The generated advice is delivered to the device in real time using carefully chosen language that takes the user's emotional state into consideration. Users can receive this advice via their smartphone or other devices and use it to improve their quality of life.

[0802] As a concrete example, consider a case related to everyday stress. When a user sends a prompt message using their device saying, "I've been feeling stressed lately. How can I relax?", the server analyzes this input, combines it with the user's current biological and emotional information, and provides advice, including appropriate relaxation methods. In this way, the system of the present invention enables comprehensive and individually optimized health support for the user.

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] The device works in conjunction with the user's biometric detection device to acquire biometric information such as heart rate, activity level, and sleep patterns. This data is captured in real time as input and temporarily stored within the device. This prepares the foundational data for monitoring daily health status.

[0806] Step 2:

[0807] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone. The emotion engine processes the acquired emotion-related data as input, determining the emotional state (e.g., joy, sadness, anger). The analyzed emotion information is then generated as output.

[0808] Step 3:

[0809] The device encrypts the biometric and emotional information obtained in Step 1 and Step 2 and sends it to a server in the cloud. This establishes a system for centrally managing data while ensuring security.

[0810] Step 4:

[0811] The server stores the received information in a database and performs analysis using machine learning algorithms. It utilizes historical data and general health indicators from the database as input to evaluate the user's health status and emotional tendencies. The output of this process is a detailed analysis of the user's specific state.

[0812] Step 5:

[0813] The server generates optimized health advice using a generated AI model based on the analysis results. This advice includes recommendations for nutrition, exercise, and stress management tailored to the user, as well as words of encouragement according to their emotional state. It then generates specific advice text as output and prepares it for transmission to the terminal.

[0814] Step 6:

[0815] The device notifies the user in real time of advice received from the server. The user checks this advice through a smartphone application and uses it to improve their daily life. This creates a feedback loop that allows the user to adjust their actions based on the information they receive.

[0816] (Application Example 2)

[0817] 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".

[0818] In elderly care, emotional care is just as important as managing the user's health. However, there is a lack of means to grasp the user's health status and emotional changes in real time and respond appropriately, resulting in a problem where the quality of care is not always sufficient. This invention aims to solve these problems and provide more personalized support.

[0819] 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.

[0820] In this invention, the server includes data acquisition means for obtaining user health information, data analysis means for analyzing the user's health information and emotional information, and advice generation means for generating health advice and emotional care messages based on the analysis results. This enables caregivers to grasp the user's health and emotional state in real time and provide prompt and appropriate care accordingly.

[0821] "Data acquisition means" refers to a system for collecting user health information from sensors and devices.

[0822] A "data analysis tool" is a system equipped with algorithms for analyzing collected health and emotional information and evaluating the user's state.

[0823] The "advice generation means" is a system for generating personalized health advice and emotional care messages based on analyzed user health status and emotional information.

[0824] A "notification mechanism" is an interface used to deliver generated health advice and emotional care messages to the user.

[0825] A "question answering system" is a system that receives questions from users and generates and provides answers that take emotional information into consideration.

[0826] "Information sharing means" refers to communication methods for sharing information about the user's health and emotional state with care workers in real time.

[0827] A system implementing this invention aims to manage the user's health and emotional well-being, and is comprised of a combination of means for data acquisition, data analysis, advice generation, and notification.

[0828] The server operates primarily on data acquired from internet-connected devices. These devices collect health information (heart rate, exercise data, nutritional intake, sleep patterns, etc.) from smartwatches and health tracking apps worn by the user. They also use the device's camera and microphone to detect emotional information from the user's facial expressions and tone of voice. This data is temporarily stored on the device, then encrypted and sent to the server.

[0829] The server analyzes the received information using data analysis tools. This analysis employs machine learning algorithms to evaluate the user's health status and emotional tendencies by comparing them with past data and general health indicators. Based on the analyzed data, the advice generation tool generates personalized health advice and emotional care messages. For example, if the user has a high heart rate and is feeling stressed, it will provide advice to encourage relaxation.

[0830] The generated advice is transmitted to the device via a notification system. Users can input questions about their health and emotions through the application. The question-answering system analyzes the input questions using natural language processing technology and generates answers that take emotional information into account. In this process, an AI model is used to generate prompts and provide appropriate responses.

[0831] For example, if a user asks, "I have trouble sleeping at night," the system will check the user's sleep patterns and provide advice on possible causes and solutions. Furthermore, prompts such as "What relaxation methods would you recommend considering the user's recent emotional tendencies?" can be input to the generating AI model to obtain the optimal solution. This allows caregivers to quickly provide specific care based on the user's condition.

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The device uses sensors to collect the user's health information. This includes obtaining data such as heart rate, exercise data, nutritional intake, and sleep patterns from smartwatches and health tracking apps. It also uses a camera and microphone to collect the user's facial expressions and voice tone as emotional information, which is temporarily stored on the device.

[0835] Step 2:

[0836] The device encrypts the collected health and emotional information before sending it to the server. The transmitted data becomes input, and the server receives it and records it in its database.

[0837] Step 3:

[0838] The server analyzes incoming data using data analysis tools. Machine learning algorithms compare the current data with past data and general indicators to evaluate the user's current health status and emotional tendencies. The input consists of health information and emotional information, and the output is the evaluation result.

[0839] Step 4:

[0840] The advice generation system generates personalized health advice and emotional care messages based on the analysis results. Here, the evaluation results obtained from data analysis are used. For example, for users with high stress levels, advice promoting relaxation is generated.

[0841] Step 5:

[0842] The server sends the generated advice and messages to the terminal via a notification system. The input is the advice and messages, and the output is sent to the user's terminal.

[0843] Step 6:

[0844] The user enters questions about their health and emotions into an application on their device. These questions constitute the input.

[0845] Step 7:

[0846] The server uses a question-answering mechanism and natural language processing techniques to analyze the question and generate a prompt. Based on this, it generates an answer that takes sentiment into account. The input is the user's question and sentiment information, and the output is an answer that takes sentiment into account.

[0847] Step 8:

[0848] Finally, the server inputs a prompt message into the generation AI model to generate the optimal answer, and then provides that answer to the user via the terminal.

[0849] 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.

[0850] 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.

[0851] 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 robot 414.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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."

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A means of acquiring data to obtain user health information,

[0873] A data analysis means for analyzing the user's health information,

[0874] An advice generation means for generating health advice based on the aforementioned analysis results,

[0875] A notification means for notifying the user of the generated health advice,

[0876] A question-answering means for receiving and providing answers to the user's questions,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the notification means has the function of creating a schedule for periodic health checks and notifying the user of reminders.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for providing information on nearby medical facilities when a user reports an abnormality.

[0882] "Example 1"

[0883] (Claim 1)

[0884] Information acquisition means for obtaining the user's biometric data,

[0885] Information analysis means for analyzing the aforementioned biological data,

[0886] A proposal generation means for generating lifestyle improvement proposals based on the aforementioned analysis results,

[0887] Information notification means for notifying the user of the generated lifestyle improvement suggestions,

[0888] A means of responding to user inquiries and providing the necessary information,

[0889] A communication means for collecting and transmitting health-related data using a digital processing device,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, wherein the notification means has the function of managing the schedule of periodic health assessments and providing warnings to the user.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for providing information on nearby medical facilities when a user reports an abnormal health condition.

[0895] "Application Example 1"

[0896] (Claim 1)

[0897] A data acquisition method for obtaining the user's biometric information,

[0898] Information analysis means for analyzing the aforementioned biological information,

[0899] A means for generating health guidance based on the analysis results,

[0900] A notification means for notifying the user of the generated health guidance,

[0901] A question-and-answer means for receiving and responding to the user's inquiries,

[0902] A means of providing information to medical support organizations when a user reports an anomaly,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, wherein the notification means has the function of managing the schedule of periodic health checkups and notifying the user of warnings.

[0906] (Claim 3)

[0907] The system according to claim 1, characterized in that the question-and-answer means understands natural language questions from the user and generates appropriate answers using a generative AI model.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] Information acquisition means for obtaining the user's biometric and emotional information,

[0911] Information analysis means for analyzing the user's biometric and emotional information,

[0912] A recommendation generation means for generating advice on biological and emotional aspects based on the aforementioned analysis results,

[0913] A notification means for notifying the user of the generated advice in an adjusted tone,

[0914] A question-answering means for receiving questions from the user in natural language and providing answers that combine emotional information,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the notification means has the function of creating a schedule for regular health checks that take into account the emotional state and notifying the user of the reminder in a gentle manner.

[0918] (Claim 3)

[0919] The system according to claim 1, further comprising means for providing information on nearby medical facilities, taking into account the user's location and emotional information, when the user reports an anomaly.

[0920] "Application example 2 when combining with an emotional engine"

[0921] (Claim 1)

[0922] A means of acquiring data to obtain user health information,

[0923] A data analysis means for analyzing the user's health information and emotional information,

[0924] An advice generation means for generating health advice and emotional care messages based on the analysis results,

[0925] Notification means for notifying the user of the generated health advice and emotional care messages,

[0926] A question-answering means for receiving the user's question and providing an answer that takes emotional information into consideration,

[0927] A means of sharing information to notify care workers of the user's health and emotional state in real time,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the notification means has the function of creating a schedule for regular health checks and notifying the user and caregivers of reminders that take into account the emotional state.

[0931] (Claim 3)

[0932] The system according to claim 1, further comprising means for providing information on nearby medical institutions and providing notifications, including emotional support, when a user reports an abnormality. [Explanation of Symbols]

[0933] 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 data acquisition method for obtaining the user's biometric information, Information analysis means for analyzing the aforementioned biological information, A means for generating health guidance based on the analysis results, A notification means for notifying the user of the generated health guidance, A question-and-answer means for receiving and responding to the user's inquiries, A means of providing information to medical support organizations when a user reports an anomaly, A system that includes this.

2. The system according to claim 1, wherein the notification means has the function of managing the schedule of periodic health checkups and notifying the user of warnings.

3. The system according to claim 1, characterized in that the question-and-answer means understands natural language questions from the user and generates appropriate answers using a generative AI model.

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