system

Abstract

We provide the system. [Solution] Information acquisition methods for obtaining health information, An evaluation method that analyzes acquired health information to assess health status, An advice provision method that provides users with advice for improving their health based on an assessment of their health status, An emergency information provision system that provides users with information on medical institutions when an abnormal situation is detected, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 society, while individual health management is important, due to a busy lifestyle, it is difficult for an individual to appropriately monitor their own health condition and take appropriate measures. Also, obtaining information on appropriate medical institutions quickly in an emergency is essential for protecting individual health and safety, but there is a lack of appropriate support for this. As a result, a system for realizing effective health maintenance and rapid medical response is required.

Means for Solving the Problems

[0005] This invention provides an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and evaluating the user's health status, and an advice provision means for providing advice to the user for health improvement based on the evaluation of their health status. Furthermore, by constructing a system that includes an emergency information provision means for providing the user with information on medical institutions when an abnormal situation is detected, the invention effectively supports individual health management and enables a rapid response in emergencies. In addition, by predicting health risks through long-term analysis of the user's lifestyle habits and continuously optimizing the content of the advice provided based on user feedback, the invention realizes personalized care.

[0006] "Health information" is a general term for data related to an individual's physical condition and lifestyle, such as heart rate, activity level, sleep duration, and dietary records, obtained from smart devices and wearable devices.

[0007] "Information acquisition means" refers to a function or device that acquires data from devices or systems in order to collect health information.

[0008] "Evaluation means" refers to a function or algorithm for analyzing acquired health information and evaluating the individual health status.

[0009] "Advice provision means" refers to a function or system that provides users with specific suggestions regarding health improvement or maintenance based on the results of their health status assessment.

[0010] An "abnormal situation" refers to a state that deviates from normal health conditions and requires emergency response or medical treatment.

[0011] "Emergency information provision means" refers to a function or system that quickly provides users with necessary medical institution and contact information when an abnormal situation is detected. [Brief explanation of the drawing]

[0012] [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]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a 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.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The present invention is a system for supporting the health management of residents, and specific embodiments are shown below.

[0034] This system collects health information from smart devices that users use daily, analyzes the data to understand each individual's health status, and includes heart rate, activity levels, sleep duration, and dietary records. This data is transmitted from the user's device to a server where it is centrally managed and analyzed.

[0035] The server runs an algorithm to assess the user's health status based on the received data. This algorithm includes detecting anomalies and predicting long-term health risks. The results of the health status analysis are generated as customized advice for health improvement, tailored to the user's specific situation. For example, if recent analysis results indicate a lack of exercise, the user will be advised, "We recommend a 45-minute walk this weekend."

[0036] The device serves to notify the user of advice provided by the server. Users can check these notifications in their daily lives and modify their actions as needed. Users can also provide feedback through the device, and this feedback is reflected in the server's algorithms and used to improve the advice provided.

[0037] Furthermore, the system is designed with emergency response in mind; in the event of an emergency, it can quickly provide information on the nearest medical facility based on the user's current location. This enables prompt medical assistance when necessary.

[0038] Thus, the present invention is a system that supports users' health management, enabling daily health maintenance and improvement, as well as rapid response in emergencies. In its specific implementation, consideration is given to adjusting the types of data collected, the analysis algorithms, and the content of the advice, and it is designed to be provided in the most optimal form for the user.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires health information such as heart rate, activity level, sleep duration, and meal records from smart devices. Data is collected in real time or automatically at regular intervals.

[0042] Step 2:

[0043] The device encrypts the acquired health information and transmits it to the server using a secure communication protocol.

[0044] Step 3:

[0045] The server stores the received health information in a database and performs data preprocessing. This preprocessing includes formatting the data and detecting outliers.

[0046] Step 4:

[0047] The server performs analysis to assess health status. This analysis is supported by machine learning algorithms and predicts abnormal health indicators and health risks.

[0048] Step 5:

[0049] The server generates health improvement advice for the user based on the analysis results. This advice is customized to the user's lifestyle.

[0050] Step 6:

[0051] The server sends the generated advice to the terminal and prepares to notify the user.

[0052] Step 7:

[0053] The device notifies the user of advice received from the server. The user checks the notification and uses it to improve their health in their daily life.

[0054] Step 8:

[0055] Users can provide feedback through their devices as needed. This feedback includes information regarding the effectiveness and feasibility of the advice.

[0056] Step 9:

[0057] The server receives feedback from users and incorporates it into machine learning models to improve the accuracy of future advice generation.

[0058] Step 10:

[0059] When an abnormal situation is detected, the device acquires the user's location information and sends it to the server.

[0060] Step 11:

[0061] The server searches for information on the nearest medical facility based on the user's location and sends the results to the terminal.

[0062] Step 12:

[0063] The terminal notifies the user of information about medical institutions received from the server and supports rapid medical response as needed.

[0064] (Example 1)

[0065] 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."

[0066] In recent years, health problems caused by unhealthy lifestyles and stress have been increasing, but appropriate health management is not widespread. Conventional health management systems have shortcomings in providing personalized advice and responding quickly to emergencies, making it difficult to contribute to users' long-term health maintenance.

[0067] 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.

[0068] In this invention, the server includes means for acquiring biometric information using a data collection device, means for transmitting the acquired biometric information to a data storage device using communication technology, means for analyzing the biometric information stored in the data storage device using an analysis device to evaluate the user's health status, means for generating and providing personalized recommendations for health improvement using generation technology, and means for providing the user with information on medical institutions using location information technology when an abnormal condition is detected. This enables the user to improve their daily health management and receive prompt emergency response.

[0069] A "data collection device" is a device that detects and records daily activity data such as a user's biometric information, and plays a role in acquiring information necessary for health management.

[0070] "Biometric information" refers to information that indicates the user's physical and physiological state, such as heart rate, sleep duration, activity level, and dietary records.

[0071] "Communication technology" refers to technologies for transmitting data safely and efficiently, and is used when transmitting information from a user's terminal to a remote server.

[0072] A "data storage device" is a device for temporarily or long-term storage of collected biological information, enabling information management and subsequent processing.

[0073] An "analytical device" is a device used to analyze stored data and evaluate health status, and it has the function of extracting useful insights from the data.

[0074] "Generative technology" refers to a technology that creates personalized health improvement advice based on a user's health data, and is used to provide users with specific guidance.

[0075] "Personalized recommendations" refer to advice for improving health that is generated based on each user's specific health condition and lifestyle.

[0076] "Location-based technology" is a technology that obtains a user's geographical location and provides appropriate information as needed, playing a particularly important role in supporting users during emergencies.

[0077] "Medical institution information" refers to detailed information about medical facilities, such as their location, contact information, and available medical services, to help users receive medical assistance quickly.

[0078] This system is designed to effectively support users' health management. This document details how smart devices are used to collect biometric information from users' daily lives, assess their health status based on that information, and provide appropriate advice.

[0079] Users wear smart devices that collect biometric information based on their daily activities. This includes data such as heart rate, activity level, sleep duration, and meal records. The collected data is transmitted from the device to a server via Bluetooth or Wi-Fi. The server securely stores the received data in a cloud-based data storage system (e.g., Amazon RDS).

[0080] The server analyzes the stored data using analytical software such as Python or R. This process uses machine learning algorithms to identify unusual patterns and long-term health risks. Based on the analysis results, a generative AI model generates specific health improvement advice. This advice is tailored to the user's situation, for example, "You have been getting less than six hours of sleep for three consecutive days and are feeling tired during the day."

[0081] The device notifies the user of the generated advice. This notification is delivered via the smartphone's push notification function or a dedicated app. The user can adjust their lifestyle habits based on the received advice. Furthermore, the user can send feedback back to the server via the device, and this feedback is used to improve the generated AI model on the server.

[0082] Furthermore, this system has the capability to utilize the user's location information service in emergencies to quickly provide information on the nearest medical facilities. This allows users to respond quickly to abnormal situations.

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

[0084] Step 1:

[0085] Users collect biometric information using smart devices. This biometric information includes heart rate, activity level, sleep duration, and meal records. The device acquires this data using sensors, and the data is temporarily stored in local storage.

[0086] Step 2:

[0087] The device transmits the collected data to the server via Wi-Fi or Bluetooth. Data transmission is secure using the HTTPS protocol. Input is biometric data from local storage, and output is data packets sent to the server.

[0088] Step 3:

[0089] The server stores the received data in a cloud-based data storage device. At this stage, the data undergoes format checking and encryption. The input is the transmitted data packet, and the output is a notification that registration to the database is complete.

[0090] Step 4:

[0091] The server analyzes stored data using data analysis algorithms. It uses Python to detect anomalies and predict health risks. The input is biometric information from the database, and the output is the analyzed health assessment results.

[0092] Step 5:

[0093] The server uses a generative AI model to generate health improvement advice based on the analysis results. Using the prompt "You have been getting less than 6 hours of sleep for 3 days and feel tired during the day," it generates personalized recommendations. The input is the health assessment results, and the output is health improvement advice.

[0094] Step 6:

[0095] The device notifies the user of the generated advice. Using the app's push notification function, the advice is displayed on the user's smartphone. The input is the generated advice, and the output is the notification displayed on the user's screen.

[0096] Step 7:

[0097] Users send feedback on the advice to the server via their device. The feedback is received in text format and used to improve the generated AI model. The input is the user's feedback, and the output is a record of the feedback data sent to the server.

[0098] Step 8:

[0099] The server obtains the user's location information in emergencies and provides information on the nearest medical facility. It uses a geographic information system to determine the user's current location and generates appropriate medical information. The input is the user's location information, and the output is the provision of medical facility information.

[0100] (Application Example 1)

[0101] 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."

[0102] In modern society, individuals, including the elderly, are expected to continuously manage and appropriately improve their own health. However, many people find it difficult to choose appropriate health management methods and implement sustainable health behaviors in their daily lives. Furthermore, there is a lack of prompt responses in emergencies and health advice tailored to individual living environments. As a result, it is difficult to prevent potential health risks.

[0103] 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.

[0104] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and evaluating the health status, and a health behavior promotion means for analyzing the user's activity data and promoting specific health behaviors. As a result, users can receive rational behavioral suggestions based on their health status and manage and improve their health status themselves. Furthermore, they can receive appropriate medical information quickly in emergencies, enabling safe health management. In addition, environment-adaptive health advice enables optimal health support tailored to individual living situations.

[0105] "Information acquisition means" refers to devices and methods for acquiring users' health information, such as heart rate, activity level, and sleep duration, which are collected through smart devices.

[0106] An "evaluation tool" is a device or method that analyzes acquired health information and evaluates the health status based on the results. Evaluation includes detecting abnormal values ​​and predicting health risks.

[0107] "Means of providing advice" refers to means of providing users with specific advice for improving their health based on the results of their health assessment. This advice is tailored to each individual's health condition.

[0108] An "emergency information provision means" is a device or method that has the function of quickly providing information on the nearest medical institution when an abnormality in a person's health condition is detected.

[0109] A "health behavior promotion tool" is a tool that analyzes users' activity data and generates advice to promote specific health behaviors.

[0110] An "environmentally adaptive advice provision method" refers to a device or method that uses the user's location data to suggest optimal health behaviors tailored to their individual living environment.

[0111] A "location information provision means" is a means that has the function of obtaining the user's current location and providing route information to the nearest medical facility.

[0112] This invention provides a system that collects health information, analyzes the user's health status, and provides appropriate advice.

[0113] The server collects data from users' smart devices through data acquisition methods for collecting health information. This data includes heart rate, activity level, sleep duration, etc., and is sent to a cloud server. Google Cloud is used as the cloud server, and the collected data is evaluated using an analysis algorithm with Python and scikit-learn. The evaluation method has the function of detecting anomalies in the data and predicting long-term health risks.

[0114] The device notifies the user of specific health improvement advice tailored to their health condition, based on analysis results received from the server. Real-time notifications are sent to the device using Firebase Cloud Messaging. Users can use this advice to improve their own health behaviors. Furthermore, by sending user feedback to the server via the device, the advice delivery method is continuously optimized.

[0115] In emergencies, the server provides information on the nearest medical facility based on the user's location information. The GPS function of smart devices is used to obtain location information, enabling a rapid response.

[0116] For example, if a 60-year-old user is using this system, and an abnormality in their heart rate is detected, they will be provided with advice on appropriate exercise and lifestyle improvements. In an emergency, the route to the nearest hospital will be displayed on their smartphone. As an example, a prompt message to the generating AI model would be, "Detect abnormal values ​​from the user's heart rate history data and generate advice for health improvement."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The terminal acquires health information from the user's smart device. It receives data such as heart rate, activity level, and sleep duration from sensors as input, and pre-formats this data for transmission to the surveyor. The output is the collection of data.

[0120] Step 2:

[0121] The server receives health information sent from the terminal and stores it in the database. The received data is formatted for storage in the database as input and then stored as is. The output is the data being stored in the database.

[0122] Step 3:

[0123] The server analyzes health information. Using Python and scikit-learn, it retrieves information from a database and performs anomaly detection and predicts long-term health risks. The input is data retrieved from the database, and the output is the analysis results based on that data. Specifically, this includes detecting abnormal heart rates and irregular activity patterns.

[0124] Step 4:

[0125] The server generates advice based on the analysis results. It prompts the generating AI model with sentences to create specific advice text. The input is the analysis results, and the output is the generated advice text.

[0126] Step 5:

[0127] The device notifies the user of advice received from the server. Firebase Cloud Messaging is used to send real-time notifications to the user's smart device. The input is the generated advice, and the output is the alert the user receives.

[0128] Step 6:

[0129] The user takes action based on the advice and inputs the results into the terminal. The input is user-provided feedback data, and the output is feedback data used for the next analysis.

[0130] Step 7:

[0131] The server receives user feedback data and optimizes the content of the advice it provides. It analyzes the feedback data and incorporates it into the AI ​​model. The input is the feedback data, and the output is the parameters of the updated generative model.

[0132] 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.

[0133] This invention provides a system that comprehensively supports users' health management and includes the ability to recognize not only the user's health information but also their emotional state. This makes it possible to provide more personalized health improvement advice and emergency response.

[0134] The system's configuration includes a user terminal that acquires health information such as heart rate, activity level, and sleep records, and sends this data to a server. In addition, it incorporates an emotion engine that recognizes the user's emotional state by analyzing their voice and text data. Emotions are categorized, for example, stress, joy, and sadness, and the analysis results are sent to the server.

[0135] The server aggregates health and emotional information and uses it to comprehensively assess the user's health status. The assessment employs machine learning algorithms, considering not only trends in everyday health indicators but also mental health aspects stemming from emotions. For example, if a consistently high level of stress is detected, the server generates suggestions for relaxation methods and advice for improving sleep quality.

[0136] The device receives instructions from the server and notifies the user with advice. For example, if it detects that the user has been experiencing high stress levels for several consecutive days, the device will provide specific advice such as "Try taking a deep breath" or "Refresh yourself in nature next weekend."

[0137] If an emergency is detected, the system also considers emotional data and provides information on appropriate medical facilities. In particular, if the emotional engine detects severe emotional instability, the user will be notified quickly and in detail of access information to specialized medical facilities.

[0138] In this way, the system of the present invention can monitor both the user's physical and emotional state and comprehensively evaluate them, thereby providing care and support tailored to individual needs. This allows the user to aim for the maintenance and improvement of their physical and mental health.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The device collects health information such as heart rate, activity level, and sleep duration from smart devices. In parallel, the emotion engine built into the device recognizes and categorizes the user's emotional state from their voice and text.

[0142] Step 2:

[0143] The device encrypts both health and emotional information and transmits it to the server through a secure communication channel.

[0144] Step 3:

[0145] The server stores the received health and emotional information in a database. The data is formatted and preprocessed, including anomaly detection.

[0146] Step 4:

[0147] The server uses machine learning algorithms to comprehensively analyze health and emotional information. This analysis assesses the user's physical and mental health status.

[0148] Step 5:

[0149] Based on the analysis results, the server generates personalized health improvement advice for the user. This advice may include specific suggestions, such as "try deep breathing exercises to relax" if the user is experiencing high stress levels.

[0150] Step 6:

[0151] The server sends the generated advice to the terminal.

[0152] Step 7:

[0153] The device notifies the user of the advice it has received. The user can then use this notification as a reference and implement it in their daily life.

[0154] Step 8:

[0155] Users can input feedback on the effects of the advice and other details on their device, and this feedback is then sent back to the server.

[0156] Step 9:

[0157] The server updates its machine learning model to continuously optimize the advice it provides based on user feedback.

[0158] Step 10:

[0159] The device notifies the server if the emotion engine detects severe emotional instability.

[0160] Step 11:

[0161] The server uses the user's location and sentiment information to search for the nearest and most suitable medical facility and sends it to the terminal, including detailed directions.

[0162] Step 12:

[0163] The device provides users with information about medical institutions and prompts them to take prompt action as needed.

[0164] (Example 2)

[0165] 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".

[0166] In modern living environments, users face complex health and emotional issues, but conventional systems struggle to comprehensively manage these and provide individually optimized advice. Furthermore, there is a challenge in evaluating the impact of abnormal emotional states on health in real time and taking appropriate action.

[0167] 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.

[0168] In this invention, the server includes information acquisition means for acquiring user health information, evaluation means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and advice provision means for generating and providing personalized health improvement advice to the user based on the evaluation. This enables the user to comprehensively manage both physical and mental health and receive optimal care based on each state.

[0169] "Information acquisition means" refers to means for collecting user health-related data in real time or periodically, and includes sensors and corresponding communication technologies.

[0170] The "evaluation method" is a means of comprehensively evaluating the user's health and emotional state by analyzing acquired health and emotional information, and it utilizes machine learning algorithms.

[0171] An "advice provision method" is a means of generating specific, personalized advice based on evaluations that helps improve the user's health, and informing the user of that advice.

[0172] An "emergency information provision system" is a means of quickly providing users with information on appropriate medical institutions when an abnormal health or emotional state is detected.

[0173] "Lifestyle habits" refer to the patterns of behavior and routines that users engage in on a daily basis, and these include eating habits and exercise habits.

[0174] "Feedback" refers to the opinions and reactions received from users, and the system's advice is improved based on this feedback.

[0175] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal advice for users based on training data.

[0176] A "prompt" refers to pre-configured text data that is input into a generative AI model and is used to generate responses on a specific topic or problem.

[0177] This invention is an advanced system for efficiently supporting users' health management. The system provides appropriate feedback to the user by performing analysis on the server side based on data acquired from the user's terminal.

[0178] The device uses sensors and applications to acquire various health-related information. Specifically, it utilizes wearable devices and smartphone applications to acquire heart rate, step count, activity level, and even emotional data from the user's voice and messages. For emotion recognition, it uses speech recognition APIs and natural language processing libraries to analyze voice and text data and identify emotion categories.

[0179] The server receives health and emotional information sent from the terminal and stores it in a database. The server uses TENSORFLOW® and scikit-learn for data analysis. This allows for a quantitative evaluation of the user's health and emotional state, and the analysis results are used to generate personalized advice. For example, if a persistent high-stress state is detected, the generative AI model generates a prompt message such as, "You are experiencing persistent high stress. What measures would you suggest to help you relax?" Based on this, specific advice on relaxation techniques and lifestyle improvements is created.

[0180] Users receive real-time advice from the server through their devices. For example, they might receive messages such as, "Try deep breathing for 5 minutes right now," or "Take a walk in nature this weekend to refresh yourself." This allows users to gain appropriate knowledge about their own health and improve their health by putting it into practice in their daily lives.

[0181] In this way, the invention aims to comprehensively manage the user's health and emotional state and provide support optimized for individual needs.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The device acquires data related to the user's health. This input data includes heart rate, activity level, steps taken, sleep patterns, and emotional expressions through voice and text. The device collects this data and temporarily stores it for later transmission. This data is continuously acquired using sensors in wearable devices and smartphones.

[0185] Step 2:

[0186] The emotion engine installed in the device analyzes the acquired voice and text data. Utilizing speech recognition and natural language processing technologies, it classifies the data into categories of emotional states (e.g., stress, joy, sadness). The results of this analysis are then formatted as emotion data and prepared for subsequent steps.

[0187] Step 3:

[0188] The device sends prepared health and emotional data to the server. The data is transferred using a secure protocol and prepared for further analysis on the server side. The transmitted data also includes timestamps and user identifiers.

[0189] Step 4:

[0190] The server stores the received health and emotion data in a database. It checks the integrity of the received data and prepares it as input data for analysis. In this step, it detects missing or anomaly data and performs preprocessing as necessary.

[0191] Step 5:

[0192] The server uses the stored data to execute an analysis algorithm. Specifically, it utilizes machine learning models to evaluate health and emotional states. This analysis process uses frameworks such as TensorFlow to identify fluctuations in health and emotional indicators and generate indicators based on them.

[0193] Step 6:

[0194] The server inputs prompt messages into the generating AI model, which then generates advice to provide to the user. This generation process uses specific prompt messages, such as "What to do if a persistent high-stress state is detected," to output optimal health advice.

[0195] Step 7:

[0196] The server sends the generated advice to the terminal. The data output is formatted to be meaningful to the user and converted into a format that can be displayed on the terminal.

[0197] Step 8:

[0198] The device notifies the user of advice received from the server. The user receives advice that encourages activities and lifestyle habits that are effective for improving health. For example, a message such as "Try taking 5 minutes of deep breathing right now" may appear on the device screen. This action allows the user to incorporate specific health improvement measures into their daily life.

[0199] (Application Example 2)

[0200] 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".

[0201] In modern society, there is a growing need to manage users' physical and mental health simultaneously. However, conventional health management systems focus only on physical health information, making it difficult to take into account mental health and emotional changes. Therefore, there is a need for technology that can comprehensively assess a user's overall health status and provide individually optimized advice.

[0202] 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.

[0203] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and audio signals to evaluate the health state and emotional state, and a suggestion means for providing the user with advice for health improvement and suggestions that take mental health into consideration based on the evaluation of the health state and emotional state. This makes it possible to comprehensively manage the user's physical and mental health and provide health improvement advice that meets individual needs.

[0204] A "health information acquisition method" is a device that collects the user's heart rate, activity level, and sleep data in real time and provides it as data.

[0205] The "evaluation method" is a system that analyzes acquired health information and voice signals to comprehensively evaluate the user's physical health and emotional state.

[0206] The "advice provision method" is a function that provides users with personalized advice for health improvement based on information about their health and emotional state obtained through the evaluation method.

[0207] An "emergency information provision device" is a device that quickly notifies the user of information about appropriate medical institutions when an abnormal situation is detected based on the user's health information or emotional state.

[0208] "Emotion recognition means" refers to technology that recognizes emotional states such as stress, joy, and sadness by analyzing the user's voice signal.

[0209] "Suggested methods" refer to functions that provide users with specific relaxation methods and daily behavioral improvement measures that take into account mental health related to emotional states.

[0210] This invention realizes a system for monitoring a user's health and emotional state and providing personalized advice. The system mainly consists of a user terminal, a server, and various sensor devices.

[0211] The user's device can utilize a compact computing platform such as a Raspberry Pi or Jetson Nano. The device collects health information and emotion-related data through various sensors, including heart rate sensors, activity trackers, and microphones. This health information acquisition method aggregates data in real time and transmits it to a server via the internet.

[0212] The server is equipped with a Python-based program and machine learning libraries such as TensorFlow and scikit-learn. The server uses acquired health information and audio signals to assess the user's health and emotional state. This assessment method analyzes the data to comprehensively evaluate the user's mental and physical health.

[0213] The means of providing advice to users is generated using machine learning models based on evaluation results. This means provides users with specific advice on improving their health in a natural, conversational format using speech synthesis. Furthermore, if an abnormal situation is detected, an emergency information provision system is activated, notifying users of the nearest medical facility and access information for necessary medical services.

[0214] For example, if a user experiences high levels of stress on a daily basis, the system might suggest "trying 10 minutes of yoga to relax."

[0215] Examples of prompts to input into a generative AI model:

[0216] "Design an application that analyzes the user's voice patterns to recognize their emotional state and then suggests appropriate home care activities based on that."

[0217] This system makes it easier for users to manage their daily health and maintain a better quality of life.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The device collects user health information and voice data in real time through sensor devices such as heart rate sensors, activity trackers, and microphones. The input is raw sensor data, and the output is data converted to a standard format. This data undergoes data cleansing and normalization before being transmitted to the server via the network.

[0221] Step 2:

[0222] The server analyzes the received standard-format health information and voice data to evaluate the user's physical and emotional state. The input is health information and voice data sent from the terminal, and the output is a numerically expressed health score and emotion analysis results. A machine learning model is used to estimate stress levels and emotions at that time from the data.

[0223] Step 3:

[0224] The server generates personalized advice on health improvement and emotional management based on the evaluation results. Inputs are health status scores and emotional analysis results, while output is specific advice messages. Using a generative AI model, it recommends relaxation methods and activities to incorporate into daily life that are best suited to the user's condition.

[0225] Step 4:

[0226] Users receive advice through their devices. Input is advice messages from the server, and output is feedback presented to the user through speech synthesis or display. Through voice output and visual presentation, users can incorporate the suggested improvements into their daily lives.

[0227] Step 5:

[0228] The server activates an emergency information provision system when an abnormal situation is detected. Inputs are abnormal health conditions or high-stress states detected during the evaluation phase, and outputs are access information for the nearest medical facilities and necessary emergency services. The system supports the user by transmitting route information to the designated medical facility to the user's terminal in real time.

[0229] 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.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] The present invention is a system for supporting the health management of residents, and specific embodiments are shown below.

[0246] This system collects health information from smart devices that users use daily, analyzes the data to understand each individual's health status, and includes heart rate, activity levels, sleep duration, and dietary records. This data is transmitted from the user's device to a server where it is centrally managed and analyzed.

[0247] The server runs an algorithm to assess the user's health status based on the received data. This algorithm includes detecting anomalies and predicting long-term health risks. The results of the health status analysis are generated as customized advice for health improvement, tailored to the user's specific situation. For example, if recent analysis results indicate a lack of exercise, the user will be advised, "We recommend a 45-minute walk this weekend."

[0248] The device serves to notify the user of advice provided by the server. Users can check these notifications in their daily lives and modify their actions as needed. Users can also provide feedback through the device, and this feedback is reflected in the server's algorithms and used to improve the advice provided.

[0249] Furthermore, the system is designed with emergency response in mind; in the event of an emergency, it can quickly provide information on the nearest medical facility based on the user's current location. This enables prompt medical assistance when necessary.

[0250] Thus, the present invention is a system that supports users' health management, enabling daily health maintenance and improvement, as well as rapid response in emergencies. In its specific implementation, consideration is given to adjusting the types of data collected, the analysis algorithms, and the content of the advice, and it is designed to be provided in the most optimal form for the user.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The device acquires health information such as heart rate, activity level, sleep duration, and meal records from smart devices. Data is collected in real time or automatically at regular intervals.

[0254] Step 2:

[0255] The device encrypts the acquired health information and transmits it to the server using a secure communication protocol.

[0256] Step 3:

[0257] The server stores the received health information in a database and performs data preprocessing. This preprocessing includes formatting the data and detecting outliers.

[0258] Step 4:

[0259] The server performs analysis to assess health status. This analysis is supported by machine learning algorithms and predicts abnormal health indicators and health risks.

[0260] Step 5:

[0261] The server generates health improvement advice for the user based on the analysis results. This advice is customized to the user's lifestyle.

[0262] Step 6:

[0263] The server sends the generated advice to the terminal and prepares to notify the user.

[0264] Step 7:

[0265] The device notifies the user of advice received from the server. The user checks the notification and uses it to improve their health in their daily life.

[0266] Step 8:

[0267] Users can provide feedback through their devices as needed. This feedback includes information regarding the effectiveness and feasibility of the advice.

[0268] Step 9:

[0269] The server receives feedback from users and incorporates it into machine learning models to improve the accuracy of future advice generation.

[0270] Step 10:

[0271] When an abnormal situation is detected, the device acquires the user's location information and sends it to the server.

[0272] Step 11:

[0273] The server searches for information on the nearest medical facility based on the user's location and sends the results to the terminal.

[0274] Step 12:

[0275] The terminal notifies the user of information about medical institutions received from the server and supports rapid medical response as needed.

[0276] (Example 1)

[0277] 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."

[0278] In recent years, health problems caused by unhealthy lifestyles and stress have been increasing, but appropriate health management is not widespread. Conventional health management systems have shortcomings in providing personalized advice and responding quickly to emergencies, making it difficult to contribute to users' long-term health maintenance.

[0279] 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.

[0280] In this invention, the server includes means for acquiring biological information using a data collection device, means for transmitting the acquired biological information to a data storage device using communication technology, means for analyzing the biological information stored in the data storage device by an analysis device to evaluate the health status, means for generating and providing individualized advice for health improvement using generation technology, and means for providing information on medical institutions to the user using location information technology when an abnormal state is detected. As a result, the user can improve daily health management and receive prompt emergency responses.

[0281] The "data collection device" is a device that detects and records daily activity data such as the biological information of the user, and plays a role in acquiring information necessary for health management.

[0282] The "biological information" refers to information indicating the physical and physiological state of the user, such as heart rate, sleep time, activity level, and diet records.

[0283] The "communication technology" is a technology for transmitting data safely and efficiently, and is used when transmitting information from the user's terminal to a remote server.

[0284] The "data storage device" is a device for temporarily or permanently holding the collected biological information, and enables the management and subsequent processing of information.

[0285] The "analysis device" is a device for analyzing the stored data to evaluate the health status, and has a function of extracting useful findings from the data.

[0286] The "generation technology" is a technology for creating individualized health improvement advice based on the health data of the user, and is utilized to provide specific guidelines to the user.

[0287] The "individualized advice" refers to advice for health improvement generated based on the specific health status and lifestyle habits of each user.

[0288] "Location-based technology" is a technology that obtains a user's geographical location and provides appropriate information as needed, playing a particularly important role in supporting users during emergencies.

[0289] "Medical institution information" refers to detailed information about medical facilities, such as their location, contact information, and available medical services, to help users receive medical assistance quickly.

[0290] This system is designed to effectively support users' health management. This document details how smart devices are used to collect biometric information from users' daily lives, assess their health status based on that information, and provide appropriate advice.

[0291] Users wear smart devices that collect biometric information based on their daily activities. This includes data such as heart rate, activity level, sleep duration, and meal records. The collected data is transmitted from the device to a server via Bluetooth or Wi-Fi. The server securely stores the received data in a cloud-based data storage system (e.g., Amazon RDS).

[0292] The server analyzes the stored data using analytical software such as Python or R. This process uses machine learning algorithms to identify unusual patterns and long-term health risks. Based on the analysis results, a generative AI model generates specific health improvement advice. This advice is tailored to the user's situation, for example, "You have been getting less than six hours of sleep for three consecutive days and are feeling tired during the day."

[0293] The device notifies the user of the generated advice. This notification is delivered via the smartphone's push notification function or a dedicated app. The user can adjust their lifestyle habits based on the received advice. Furthermore, the user can send feedback back to the server via the device, and this feedback is used to improve the generated AI model on the server.

[0294] Furthermore, this system has the capability to utilize the user's location information service in emergencies to quickly provide information on the nearest medical facilities. This allows users to respond quickly to abnormal situations.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] Users collect biometric information using smart devices. This biometric information includes heart rate, activity level, sleep duration, and meal records. The device acquires this data using sensors, and the data is temporarily stored in local storage.

[0298] Step 2:

[0299] The device transmits the collected data to the server via Wi-Fi or Bluetooth. Data transmission is secure using the HTTPS protocol. Input is biometric data from local storage, and output is data packets sent to the server.

[0300] Step 3:

[0301] The server stores the received data in a cloud-based data storage device. At this stage, the data undergoes format checking and encryption. The input is the transmitted data packet, and the output is a notification that registration to the database is complete.

[0302] Step 4:

[0303] The server analyzes stored data using data analysis algorithms. It uses Python to detect anomalies and predict health risks. The input is biometric information from the database, and the output is the analyzed health assessment results.

[0304] Step 5:

[0305] The server uses the generative AI model to generate health improvement advice based on the analysis results. Using the prompt sentence "You have had less than 6 hours of sleep for 3 days and feel tired during the day", it generates personalized advice. The input is the health assessment result, and the output is the health improvement advice.

[0306] Step 6:

[0307] The terminal notifies the user of the generated advice. Using the push notification function of the app, it displays the advice content on the user's smartphone. The input is the generated advice, and the output is the notification displayed on the user's screen.

[0308] Step 7:

[0309] The user sends feedback on the advice to the server via the terminal. The feedback is received in text format and used to improve the generative AI model. The input is the user's feedback, and the output is the recording of the feedback data sent to the server.

[0310] Step 8:

[0311] The server obtains the user's location information in case of emergency and provides information on the nearest medical institutions. Using a geographic information system, it identifies the user's current location and generates appropriate medical information. The input is the user's location information, and the output is the provision of medical institution information.

[0312] (Application Example 1)

[0313] 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".

[0314] In modern society, individuals, including the elderly, are expected to continuously manage and appropriately improve their own health. However, many people find it difficult to choose appropriate health management methods and implement sustainable health behaviors in their daily lives. Furthermore, there is a lack of prompt responses in emergencies and health advice tailored to individual living environments. As a result, it is difficult to prevent potential health risks.

[0315] 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.

[0316] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and evaluating the health status, and a health behavior promotion means for analyzing the user's activity data and promoting specific health behaviors. As a result, users can receive rational behavioral suggestions based on their health status and manage and improve their health status themselves. Furthermore, they can receive appropriate medical information quickly in emergencies, enabling safe health management. In addition, environment-adaptive health advice enables optimal health support tailored to individual living situations.

[0317] "Information acquisition means" refers to devices and methods for acquiring users' health information, such as heart rate, activity level, and sleep duration, which are collected through smart devices.

[0318] An "evaluation tool" is a device or method that analyzes acquired health information and evaluates the health status based on the results. Evaluation includes detecting abnormal values ​​and predicting health risks.

[0319] "Means of providing advice" refers to means of providing users with specific advice for improving their health based on the results of their health assessment. This advice is tailored to each individual's health condition.

[0320] An "emergency information provision means" is a device or method that has the function of quickly providing information on the nearest medical institution when an abnormality in a person's health condition is detected.

[0321] A "health behavior promotion tool" is a tool that analyzes users' activity data and generates advice to promote specific health behaviors.

[0322] An "environmentally adaptive advice provision method" refers to a device or method that uses the user's location data to suggest optimal health behaviors tailored to their individual living environment.

[0323] A "location information provision means" is a means that has the function of obtaining the user's current location and providing route information to the nearest medical facility.

[0324] This invention provides a system that collects health information, analyzes the user's health status, and provides appropriate advice.

[0325] The server collects data from users' smart devices through data acquisition mechanisms for collecting health information. This data includes heart rate, activity level, sleep duration, etc., and is sent to a cloud server. Google Cloud is used as the cloud server, and the collected data is evaluated using an analysis algorithm with Python and scikit-learn. The evaluation mechanism has the function of detecting anomalies in the data and predicting long-term health risks.

[0326] The device notifies the user of specific health improvement advice tailored to their health condition, based on analysis results received from the server. Real-time notifications are sent to the device using Firebase Cloud Messaging. Users can use this advice to improve their own health behaviors. Furthermore, by sending user feedback to the server via the device, the advice delivery method is continuously optimized.

[0327] In emergencies, the server provides information on the nearest medical facility based on the user's location information. The GPS function of smart devices is used to obtain location information, enabling a rapid response.

[0328] For example, if a 60-year-old user is using this system, and an abnormality in their heart rate is detected, they will be provided with advice on appropriate exercise and lifestyle improvements. In an emergency, the route to the nearest hospital will be displayed on their smartphone. As an example, a prompt message to the generating AI model would be, "Detect abnormal values ​​from the user's heart rate history data and generate advice for health improvement."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The terminal acquires health information from the user's smart device. It receives data such as heart rate, activity level, and sleep duration from sensors as input, and pre-formats this data for transmission to the surveyor. The output is the collection of data.

[0332] Step 2:

[0333] The server receives health information sent from the terminal and stores it in the database. The received data is formatted for storage in the database as input and then stored as is. The output is the data being stored in the database.

[0334] Step 3:

[0335] The server analyzes health information. Using Python and scikit-learn, it retrieves information from a database and performs anomaly detection and predicts long-term health risks. The input is data retrieved from the database, and the output is the analysis results based on that data. Specifically, this includes detecting abnormal heart rates and irregular activity patterns.

[0336] Step 4:

[0337] The server generates advice based on the analysis results. It prompts the generating AI model with sentences to create specific advice text. The input is the analysis results, and the output is the generated advice text.

[0338] Step 5:

[0339] The device notifies the user of advice received from the server. Firebase Cloud Messaging is used to send real-time notifications to the user's smart device. The input is the generated advice, and the output is the alert the user receives.

[0340] Step 6:

[0341] The user takes action based on the advice and inputs the results into the terminal. The input is user-provided feedback data, and the output is feedback data used for the next analysis.

[0342] Step 7:

[0343] The server receives user feedback data and optimizes the content of the advice it provides. It analyzes the feedback data and incorporates it into the AI ​​model. The input is the feedback data, and the output is the parameters of the updated generative model.

[0344] 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.

[0345] This invention provides a system that comprehensively supports users' health management and includes the ability to recognize not only the user's health information but also their emotional state. This makes it possible to provide more personalized health improvement advice and emergency response.

[0346] The system's configuration includes a user terminal that acquires health information such as heart rate, activity level, and sleep records, and sends this data to a server. In addition, it incorporates an emotion engine that recognizes the user's emotional state by analyzing their voice and text data. Emotions are categorized, for example, stress, joy, and sadness, and the analysis results are sent to the server.

[0347] The server aggregates health and emotional information and uses it to comprehensively assess the user's health status. The assessment employs machine learning algorithms, considering not only trends in everyday health indicators but also mental health aspects stemming from emotions. For example, if a consistently high level of stress is detected, the server generates suggestions for relaxation methods and advice for improving sleep quality.

[0348] The device receives instructions from the server and notifies the user with advice. For example, if it detects that the user has been experiencing high stress levels for several consecutive days, the device will provide specific advice such as "Try taking a deep breath" or "Refresh yourself in nature next weekend."

[0349] If an emergency is detected, the system also considers emotional data and provides information on appropriate medical facilities. In particular, if the emotional engine detects severe emotional instability, the user will be notified quickly and in detail of access information to specialized medical facilities.

[0350] In this way, the system of the present invention can monitor both the user's physical and emotional state and comprehensively evaluate them, thereby providing care and support tailored to individual needs. This allows the user to aim for the maintenance and improvement of their physical and mental health.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The device collects health information such as heart rate, activity level, and sleep duration from smart devices. In parallel, the emotion engine built into the device recognizes and categorizes the user's emotional state from their voice and text.

[0354] Step 2:

[0355] The device encrypts both health and emotional information and transmits it to the server through a secure communication channel.

[0356] Step 3:

[0357] The server stores the received health and emotional information in a database. The data is formatted and preprocessed, including anomaly detection.

[0358] Step 4:

[0359] The server uses machine learning algorithms to comprehensively analyze health and emotional information. This analysis assesses the user's physical and mental health status.

[0360] Step 5:

[0361] Based on the analysis results, the server generates personalized health improvement advice for the user. This advice may include specific suggestions, such as "try deep breathing exercises to relax" if the user is experiencing high stress levels.

[0362] Step 6:

[0363] The server sends the generated advice to the terminal.

[0364] Step 7:

[0365] The device notifies the user of the advice it has received. The user can then use this notification as a reference and implement it in their daily life.

[0366] Step 8:

[0367] Users can input feedback on the effects of the advice and other details on their device, and this feedback is then sent back to the server.

[0368] Step 9:

[0369] The server updates its machine learning model to continuously optimize the advice it provides based on user feedback.

[0370] Step 10:

[0371] The device notifies the server if the emotion engine detects severe emotional instability.

[0372] Step 11:

[0373] The server uses the user's location and sentiment information to search for the nearest and most suitable medical facility and sends it to the terminal, including detailed directions.

[0374] Step 12:

[0375] The device provides users with information about medical institutions and prompts them to take prompt action as needed.

[0376] (Example 2)

[0377] 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".

[0378] In modern living environments, users face complex health and emotional issues, but conventional systems struggle to comprehensively manage these and provide individually optimized advice. Furthermore, there is a challenge in evaluating the impact of abnormal emotional states on health in real time and taking appropriate action.

[0379] 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.

[0380] In this invention, the server includes information acquisition means for acquiring user health information, evaluation means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and advice provision means for generating and providing personalized health improvement advice to the user based on the evaluation. This enables the user to comprehensively manage both physical and mental health and receive optimal care based on each state.

[0381] "Information acquisition means" refers to means for collecting user health-related data in real time or periodically, and includes sensors and corresponding communication technologies.

[0382] The "evaluation method" is a means of comprehensively evaluating the user's health and emotional state by analyzing acquired health and emotional information, and it utilizes machine learning algorithms.

[0383] An "advice provision method" is a means of generating specific, personalized advice based on evaluations that helps improve the user's health, and informing the user of that advice.

[0384] An "emergency information provision system" is a means of quickly providing users with information on appropriate medical institutions when an abnormal health or emotional state is detected.

[0385] "Lifestyle habits" refer to the patterns of behavior and routines that users engage in on a daily basis, and these include eating habits and exercise habits.

[0386] "Feedback" refers to the opinions and reactions received from users, and the system's advice is improved based on this feedback.

[0387] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal advice for users based on training data.

[0388] A "prompt" refers to pre-configured text data that is input into a generative AI model and is used to generate responses on a specific topic or problem.

[0389] This invention is an advanced system for efficiently supporting users' health management. The system provides appropriate feedback to the user by performing analysis on the server side based on data acquired from the user's terminal.

[0390] The device uses sensors and applications to acquire various health-related information. Specifically, it utilizes wearable devices and smartphone applications to acquire heart rate, step count, activity level, and even emotional data from the user's voice and messages. For emotion recognition, it uses speech recognition APIs and natural language processing libraries to analyze voice and text data and identify emotion categories.

[0391] The server receives health and emotional information sent from the terminal and stores it in a database. TensorFlow and scikit-learn are used on the server for data analysis. This allows for a quantitative evaluation of the user's health and emotional state, and the analysis results are used to generate personalized advice. For example, if a persistent high-stress state is detected, the generative AI model generates a prompt message such as, "You are experiencing persistent high stress. What measures would you suggest to help you relax?" Based on this, specific advice on relaxation techniques and lifestyle improvements is created.

[0392] Users receive real-time advice from the server through their devices. For example, they might receive messages such as, "Try deep breathing for 5 minutes right now," or "Take a walk in nature this weekend to refresh yourself." This allows users to gain appropriate knowledge about their own health and improve their health by putting it into practice in their daily lives.

[0393] In this way, the invention aims to comprehensively manage the user's health and emotional state and provide support optimized for individual needs.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The device acquires data related to the user's health. This input data includes heart rate, activity level, steps taken, sleep patterns, and emotional expressions through voice and text. The device collects this data and temporarily stores it for later transmission. This data is continuously acquired using sensors in wearable devices and smartphones.

[0397] Step 2:

[0398] The emotion engine installed in the device analyzes the acquired voice and text data. Utilizing speech recognition and natural language processing technologies, it classifies the data into categories of emotional states (e.g., stress, joy, sadness). The results of this analysis are then formatted as emotion data and prepared for subsequent steps.

[0399] Step 3:

[0400] The device sends prepared health and emotional data to the server. The data is transferred using a secure protocol and prepared for further analysis on the server side. The transmitted data also includes timestamps and user identifiers.

[0401] Step 4:

[0402] The server stores the received health and emotion data in a database. It checks the integrity of the received data and prepares it as input data for analysis. In this step, it detects missing or anomaly data and performs preprocessing as necessary.

[0403] Step 5:

[0404] The server uses the stored data to execute an analysis algorithm. Specifically, it utilizes machine learning models to evaluate health and emotional states. This analysis process uses frameworks such as TensorFlow to identify fluctuations in health and emotional indicators and generate indicators based on them.

[0405] Step 6:

[0406] The server inputs prompt messages into the generating AI model, which then generates advice to provide to the user. This generation process uses specific prompt messages, such as "What to do if a persistent high-stress state is detected," to output optimal health advice.

[0407] Step 7:

[0408] The server sends the generated advice to the terminal. The data output is formatted to be meaningful to the user and converted into a format that can be displayed on the terminal.

[0409] Step 8:

[0410] The device notifies the user of advice received from the server. The user receives advice that encourages activities and lifestyle habits that are effective for improving health. For example, a message such as "Try taking 5 minutes of deep breathing right now" may appear on the device screen. This action allows the user to incorporate specific health improvement measures into their daily life.

[0411] (Application Example 2)

[0412] 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."

[0413] In modern society, there is a growing need to manage users' physical and mental health simultaneously. However, conventional health management systems focus only on physical health information, making it difficult to take into account mental health and emotional changes. Therefore, there is a need for technology that can comprehensively assess a user's overall health status and provide individually optimized advice.

[0414] 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.

[0415] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and audio signals to evaluate the health state and emotional state, and a suggestion means for providing the user with advice for health improvement and suggestions that take mental health into consideration based on the evaluation of the health state and emotional state. This makes it possible to comprehensively manage the user's physical and mental health and provide health improvement advice that meets individual needs.

[0416] A "health information acquisition method" is a device that collects the user's heart rate, activity level, and sleep data in real time and provides it as data.

[0417] The "evaluation method" is a system that analyzes acquired health information and voice signals to comprehensively evaluate the user's physical health and emotional state.

[0418] The "advice provision method" is a function that provides users with personalized advice for health improvement based on information about their health and emotional state obtained through the evaluation method.

[0419] An "emergency information provision device" is a device that quickly notifies the user of information about appropriate medical institutions when an abnormal situation is detected based on the user's health information or emotional state.

[0420] "Emotion recognition means" refers to technology that recognizes emotional states such as stress, joy, and sadness by analyzing the user's voice signal.

[0421] "Suggested methods" refer to functions that provide users with specific relaxation methods and daily behavioral improvement measures that take into account mental health related to emotional states.

[0422] This invention realizes a system for monitoring a user's health and emotional state and providing personalized advice. The system mainly consists of a user terminal, a server, and various sensor devices.

[0423] The user's device can utilize a compact computing platform such as a Raspberry Pi or Jetson Nano. The device collects health information and emotion-related data through various sensors, including heart rate sensors, activity trackers, and microphones. This health information acquisition method aggregates data in real time and transmits it to a server via the internet.

[0424] The server is equipped with a Python-based program and machine learning libraries such as TensorFlow and scikit-learn. The server uses acquired health information and audio signals to assess the user's health and emotional state. This assessment method analyzes the data to comprehensively evaluate the user's mental and physical health.

[0425] The means of providing advice to users is generated using machine learning models based on evaluation results. This means provides users with specific advice on improving their health in a natural, conversational format using speech synthesis. Furthermore, if an abnormal situation is detected, an emergency information provision system is activated, notifying users of the nearest medical facility and access information for necessary medical services.

[0426] For example, if a user experiences high levels of stress on a daily basis, the system might suggest "trying 10 minutes of yoga to relax."

[0427] Examples of prompts to input into a generative AI model:

[0428] "Design an application that analyzes the user's voice patterns to recognize their emotional state and then suggests appropriate home care activities based on that."

[0429] This system makes it easier for users to manage their daily health and maintain a better quality of life.

[0430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0431] Step 1:

[0432] The device collects user health information and voice data in real time through sensor devices such as heart rate sensors, activity trackers, and microphones. The input is raw sensor data, and the output is data converted to a standard format. This data undergoes data cleansing and normalization before being transmitted to the server via the network.

[0433] Step 2:

[0434] The server analyzes the received standard-format health information and voice data to evaluate the user's physical and emotional state. The input is health information and voice data sent from the terminal, and the output is a numerically expressed health score and emotion analysis results. A machine learning model is used to estimate stress levels and emotions at that time from the data.

[0435] Step 3:

[0436] The server generates personalized advice on health improvement and emotional management based on the evaluation results. Inputs are health status scores and emotional analysis results, while output is specific advice messages. Using a generative AI model, it recommends relaxation methods and activities to incorporate into daily life that are best suited to the user's condition.

[0437] Step 4:

[0438] Users receive advice through their devices. Input is advice messages from the server, and output is feedback presented to the user through speech synthesis or display. Through voice output and visual presentation, users can incorporate the suggested improvements into their daily lives.

[0439] Step 5:

[0440] The server activates an emergency information provision system when an abnormal situation is detected. Inputs are abnormal health conditions or high-stress states detected during the evaluation phase, and outputs are access information for the nearest medical facilities and necessary emergency services. The system supports the user by transmitting route information to the designated medical facility to the user's terminal in real time.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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".

[0457] The present invention is a system for supporting the health management of residents, and specific embodiments are shown below.

[0458] This system collects health information from smart devices that users use daily, analyzes the data to understand each individual's health status, and includes heart rate, activity levels, sleep duration, and dietary records. This data is transmitted from the user's device to a server where it is centrally managed and analyzed.

[0459] The server runs an algorithm to assess the user's health status based on the received data. This algorithm includes detecting anomalies and predicting long-term health risks. The results of the health status analysis are generated as customized advice for health improvement, tailored to the user's specific situation. For example, if recent analysis results indicate a lack of exercise, the user will be advised, "We recommend a 45-minute walk this weekend."

[0460] The device serves to notify the user of advice provided by the server. Users can check these notifications in their daily lives and modify their actions as needed. Users can also provide feedback through the device, and this feedback is reflected in the server's algorithms and used to improve the advice provided.

[0461] Furthermore, the system is designed with emergency response in mind; in the event of an emergency, it can quickly provide information on the nearest medical facility based on the user's current location. This enables prompt medical assistance when necessary.

[0462] Thus, the present invention is a system that supports users' health management, enabling daily health maintenance and improvement, as well as rapid response in emergencies. In its specific implementation, consideration is given to adjusting the types of data collected, the analysis algorithms, and the content of the advice, and it is designed to be provided in the most optimal form for the user.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The device acquires health information such as heart rate, activity level, sleep duration, and meal records from smart devices. Data is collected in real time or automatically at regular intervals.

[0466] Step 2:

[0467] The device encrypts the acquired health information and transmits it to the server using a secure communication protocol.

[0468] Step 3:

[0469] The server stores the received health information in a database and performs data preprocessing. This preprocessing includes formatting the data and detecting outliers.

[0470] Step 4:

[0471] The server performs analysis to assess health status. This analysis is supported by machine learning algorithms and predicts abnormal health indicators and health risks.

[0472] Step 5:

[0473] The server generates health improvement advice for the user based on the analysis results. This advice is customized to the user's lifestyle.

[0474] Step 6:

[0475] The server sends the generated advice to the terminal and prepares to notify the user.

[0476] Step 7:

[0477] The device notifies the user of advice received from the server. The user checks the notification and uses it to improve their health in their daily life.

[0478] Step 8:

[0479] Users can provide feedback through their devices as needed. This feedback includes information regarding the effectiveness and feasibility of the advice.

[0480] Step 9:

[0481] The server receives feedback from users and incorporates it into machine learning models to improve the accuracy of future advice generation.

[0482] Step 10:

[0483] When an abnormal situation is detected, the device acquires the user's location information and sends it to the server.

[0484] Step 11:

[0485] The server searches for information on the nearest medical facility based on the user's location and sends the results to the terminal.

[0486] Step 12:

[0487] The terminal notifies the user of information about medical institutions received from the server and supports rapid medical response as needed.

[0488] (Example 1)

[0489] 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."

[0490] In recent years, health problems caused by unhealthy lifestyles and stress have been increasing, but appropriate health management is not widespread. Conventional health management systems have shortcomings in providing personalized advice and responding quickly to emergencies, making it difficult to contribute to users' long-term health maintenance.

[0491] 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.

[0492] In this invention, the server includes means for acquiring biometric information using a data collection device, means for transmitting the acquired biometric information to a data storage device using communication technology, means for analyzing the biometric information stored in the data storage device using an analysis device to evaluate the user's health status, means for generating and providing personalized recommendations for health improvement using generation technology, and means for providing the user with information on medical institutions using location information technology when an abnormal condition is detected. This enables the user to improve their daily health management and receive prompt emergency response.

[0493] A "data collection device" is a device that detects and records daily activity data such as a user's biometric information, and plays a role in acquiring information necessary for health management.

[0494] "Biometric information" refers to information that indicates the user's physical and physiological state, such as heart rate, sleep duration, activity level, and dietary records.

[0495] "Communication technology" refers to technologies for transmitting data safely and efficiently, and is used when transmitting information from a user's terminal to a remote server.

[0496] A "data storage device" is a device for temporarily or long-term storage of collected biological information, enabling information management and subsequent processing.

[0497] An "analytical device" is a device used to analyze stored data and evaluate health status, and it has the function of extracting useful insights from the data.

[0498] "Generative technology" refers to a technology that creates personalized health improvement advice based on a user's health data, and is used to provide users with specific guidance.

[0499] "Personalized recommendations" refer to advice for improving health that is generated based on each user's specific health condition and lifestyle.

[0500] "Location-based technology" is a technology that obtains a user's geographical location and provides appropriate information as needed, playing a particularly important role in supporting users during emergencies.

[0501] "Medical institution information" refers to detailed information about medical facilities, such as their location, contact information, and available medical services, to help users receive medical assistance quickly.

[0502] This system is designed to effectively support users' health management. This document details how smart devices are used to collect biometric information from users' daily lives, assess their health status based on that information, and provide appropriate advice.

[0503] Users wear smart devices that collect biometric information based on their daily activities. This includes data such as heart rate, activity level, sleep duration, and meal records. The collected data is transmitted from the device to a server via Bluetooth or Wi-Fi. The server securely stores the received data in a cloud-based data storage system (e.g., Amazon RDS).

[0504] The server analyzes the stored data using analytical software such as Python or R. This process uses machine learning algorithms to identify unusual patterns and long-term health risks. Based on the analysis results, a generative AI model generates specific health improvement advice. This advice is tailored to the user's situation, for example, "You have been getting less than six hours of sleep for three consecutive days and are feeling tired during the day."

[0505] The device notifies the user of the generated advice. This notification is delivered via the smartphone's push notification function or a dedicated app. The user can adjust their lifestyle habits based on the received advice. Furthermore, the user can send feedback back to the server via the device, and this feedback is used to improve the generated AI model on the server.

[0506] Furthermore, this system has the capability to utilize the user's location information service in emergencies to quickly provide information on the nearest medical facilities. This allows users to respond quickly to abnormal situations.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users collect biometric information using smart devices. This biometric information includes heart rate, activity level, sleep duration, and meal records. The device acquires this data using sensors, and the data is temporarily stored in local storage.

[0510] Step 2:

[0511] The device transmits the collected data to the server via Wi-Fi or Bluetooth. Data transmission is secure using the HTTPS protocol. Input is biometric data from local storage, and output is data packets sent to the server.

[0512] Step 3:

[0513] The server stores the received data in a cloud-based data storage device. At this stage, the data undergoes format checking and encryption. The input is the transmitted data packet, and the output is a notification that registration to the database is complete.

[0514] Step 4:

[0515] The server analyzes stored data using data analysis algorithms. It uses Python to detect anomalies and predict health risks. The input is biometric information from the database, and the output is the analyzed health assessment results.

[0516] Step 5:

[0517] The server uses a generative AI model to generate health improvement advice based on the analysis results. Using the prompt "You have been getting less than 6 hours of sleep for 3 days and feel tired during the day," it generates personalized recommendations. The input is the health assessment results, and the output is health improvement advice.

[0518] Step 6:

[0519] The device notifies the user of the generated advice. Using the app's push notification function, the advice is displayed on the user's smartphone. The input is the generated advice, and the output is the notification displayed on the user's screen.

[0520] Step 7:

[0521] Users send feedback on the advice to the server via their device. The feedback is received in text format and used to improve the generated AI model. The input is the user's feedback, and the output is a record of the feedback data sent to the server.

[0522] Step 8:

[0523] The server obtains the user's location information in emergencies and provides information on the nearest medical facility. It uses a geographic information system to determine the user's current location and generates appropriate medical information. The input is the user's location information, and the output is the provision of medical facility information.

[0524] (Application Example 1)

[0525] 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."

[0526] In modern society, individuals, including the elderly, are expected to continuously manage and appropriately improve their own health. However, many people find it difficult to choose appropriate health management methods and implement sustainable health behaviors in their daily lives. Furthermore, there is a lack of prompt responses in emergencies and health advice tailored to individual living environments. As a result, it is difficult to prevent potential health risks.

[0527] 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.

[0528] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and evaluating the health status, and a health behavior promotion means for analyzing the user's activity data and promoting specific health behaviors. As a result, users can receive rational behavioral suggestions based on their health status and manage and improve their health status themselves. Furthermore, they can receive appropriate medical information quickly in emergencies, enabling safe health management. In addition, environment-adaptive health advice enables optimal health support tailored to individual living situations.

[0529] "Information acquisition means" refers to devices and methods for acquiring users' health information, such as heart rate, activity level, and sleep duration, which are collected through smart devices.

[0530] An "evaluation tool" is a device or method that analyzes acquired health information and evaluates the health status based on the results. Evaluation includes detecting abnormal values ​​and predicting health risks.

[0531] "Means of providing advice" refers to means of providing users with specific advice for improving their health based on the results of their health assessment. This advice is tailored to each individual's health condition.

[0532] An "emergency information provision means" is a device or method that has the function of quickly providing information on the nearest medical institution when an abnormality in a person's health condition is detected.

[0533] A "health behavior promotion tool" is a tool that analyzes users' activity data and generates advice to promote specific health behaviors.

[0534] An "environmentally adaptive advice provision method" refers to a device or method that uses the user's location data to suggest optimal health behaviors tailored to their individual living environment.

[0535] A "location information provision means" is a means that has the function of obtaining the user's current location and providing route information to the nearest medical facility.

[0536] This invention provides a system that collects health information, analyzes the user's health status, and provides appropriate advice.

[0537] The server collects data from users' smart devices through data acquisition mechanisms for collecting health information. This data includes heart rate, activity level, sleep duration, etc., and is sent to a cloud server. Google Cloud is used as the cloud server, and the collected data is evaluated using an analysis algorithm with Python and scikit-learn. The evaluation mechanism has the function of detecting anomalies in the data and predicting long-term health risks.

[0538] The device notifies the user of specific health improvement advice tailored to their health condition, based on analysis results received from the server. Real-time notifications are sent to the device using Firebase Cloud Messaging. Users can use this advice to improve their own health behaviors. Furthermore, by sending user feedback to the server via the device, the advice delivery method is continuously optimized.

[0539] In emergencies, the server provides information on the nearest medical facility based on the user's location information. The GPS function of smart devices is used to obtain location information, enabling a rapid response.

[0540] For example, if a 60-year-old user is using this system, and an abnormality in their heart rate is detected, they will be provided with advice on appropriate exercise and lifestyle improvements. In an emergency, the route to the nearest hospital will be displayed on their smartphone. As an example, a prompt message to the generating AI model would be, "Detect abnormal values ​​from the user's heart rate history data and generate advice for health improvement."

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] The terminal acquires health information from the user's smart device. It receives data such as heart rate, activity level, and sleep duration from sensors as input, and pre-formats this data for transmission to the surveyor. The output is the collection of data.

[0544] Step 2:

[0545] The server receives health information sent from the terminal and stores it in the database. The received data is formatted for storage in the database as input and then stored as is. The output is the data being stored in the database.

[0546] Step 3:

[0547] The server analyzes health information. Using Python and scikit-learn, it retrieves information from a database and performs anomaly detection and predicts long-term health risks. The input is data retrieved from the database, and the output is the analysis results based on that data. Specifically, this includes detecting abnormal heart rates and irregular activity patterns.

[0548] Step 4:

[0549] The server generates advice based on the analysis results. It prompts the generating AI model with sentences to create specific advice text. The input is the analysis results, and the output is the generated advice text.

[0550] Step 5:

[0551] The device notifies the user of advice received from the server. Firebase Cloud Messaging is used to send real-time notifications to the user's smart device. The input is the generated advice, and the output is the alert the user receives.

[0552] Step 6:

[0553] The user takes action based on the advice and inputs the results into the terminal. The input is user-provided feedback data, and the output is feedback data used for the next analysis.

[0554] Step 7:

[0555] The server receives user feedback data and optimizes the content of the advice it provides. It analyzes the feedback data and incorporates it into the AI ​​model. The input is the feedback data, and the output is the parameters of the updated generative model.

[0556] 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.

[0557] This invention provides a system that comprehensively supports users' health management and includes the ability to recognize not only the user's health information but also their emotional state. This makes it possible to provide more personalized health improvement advice and emergency response.

[0558] The system's configuration includes a user terminal that acquires health information such as heart rate, activity level, and sleep records, and sends this data to a server. In addition, it incorporates an emotion engine that recognizes the user's emotional state by analyzing their voice and text data. Emotions are categorized, for example, stress, joy, and sadness, and the analysis results are sent to the server.

[0559] The server aggregates health and emotional information and uses it to comprehensively assess the user's health status. The assessment employs machine learning algorithms, considering not only trends in everyday health indicators but also mental health aspects stemming from emotions. For example, if a consistently high level of stress is detected, the server generates suggestions for relaxation methods and advice for improving sleep quality.

[0560] The device receives instructions from the server and notifies the user with advice. For example, if it detects that the user has been experiencing high stress levels for several consecutive days, the device will provide specific advice such as "Try taking a deep breath" or "Refresh yourself in nature next weekend."

[0561] If an emergency is detected, the system also considers emotional data and provides information on appropriate medical facilities. In particular, if the emotional engine detects severe emotional instability, the user will be notified quickly and in detail of access information to specialized medical facilities.

[0562] In this way, the system of the present invention can monitor both the user's physical and emotional state and comprehensively evaluate them, thereby providing care and support tailored to individual needs. This allows the user to aim for the maintenance and improvement of their physical and mental health.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The device collects health information such as heart rate, activity level, and sleep duration from smart devices. In parallel, the emotion engine built into the device recognizes and categorizes the user's emotional state from their voice and text.

[0566] Step 2:

[0567] The device encrypts both health and emotional information and transmits it to the server through a secure communication channel.

[0568] Step 3:

[0569] The server stores the received health and emotional information in a database. The data is formatted and preprocessed, including anomaly detection.

[0570] Step 4:

[0571] The server uses machine learning algorithms to comprehensively analyze health and emotional information. This analysis assesses the user's physical and mental health status.

[0572] Step 5:

[0573] Based on the analysis results, the server generates personalized health improvement advice for the user. This advice may include specific suggestions, such as "try deep breathing exercises to relax" if the user is experiencing high stress levels.

[0574] Step 6:

[0575] The server sends the generated advice to the terminal.

[0576] Step 7:

[0577] The device notifies the user of the advice it has received. The user can then use this notification as a reference and implement it in their daily life.

[0578] Step 8:

[0579] Users can input feedback on the effects of the advice and other details on their device, and this feedback is then sent back to the server.

[0580] Step 9:

[0581] The server updates its machine learning model to continuously optimize the advice it provides based on user feedback.

[0582] Step 10:

[0583] The device notifies the server if the emotion engine detects severe emotional instability.

[0584] Step 11:

[0585] The server uses the user's location and sentiment information to search for the nearest and most suitable medical facility and sends it to the terminal, including detailed directions.

[0586] Step 12:

[0587] The device provides users with information about medical institutions and prompts them to take prompt action as needed.

[0588] (Example 2)

[0589] 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."

[0590] In modern living environments, users face complex health and emotional issues, but conventional systems struggle to comprehensively manage these and provide individually optimized advice. Furthermore, there is a challenge in evaluating the impact of abnormal emotional states on health in real time and taking appropriate action.

[0591] 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.

[0592] In this invention, the server includes information acquisition means for acquiring user health information, evaluation means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and advice provision means for generating and providing personalized health improvement advice to the user based on the evaluation. This enables the user to comprehensively manage both physical and mental health and receive optimal care based on each state.

[0593] "Information acquisition means" refers to means for collecting user health-related data in real time or periodically, and includes sensors and corresponding communication technologies.

[0594] The "evaluation method" is a means of comprehensively evaluating the user's health and emotional state by analyzing acquired health and emotional information, and it utilizes machine learning algorithms.

[0595] An "advice provision method" is a means of generating specific, personalized advice based on evaluations that helps improve the user's health, and informing the user of that advice.

[0596] An "emergency information provision system" is a means of quickly providing users with information on appropriate medical institutions when an abnormal health or emotional state is detected.

[0597] "Lifestyle habits" refer to the patterns of behavior and routines that users engage in on a daily basis, and these include eating habits and exercise habits.

[0598] "Feedback" refers to the opinions and reactions received from users, and the system's advice is improved based on this feedback.

[0599] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal advice for users based on training data.

[0600] A "prompt" refers to pre-configured text data that is input into a generative AI model and is used to generate responses on a specific topic or problem.

[0601] This invention is an advanced system for efficiently supporting users' health management. The system provides appropriate feedback to the user by performing analysis on the server side based on data acquired from the user's terminal.

[0602] The device uses sensors and applications to acquire various health-related information. Specifically, it utilizes wearable devices and smartphone applications to acquire heart rate, step count, activity level, and even emotional data from the user's voice and messages. For emotion recognition, it uses speech recognition APIs and natural language processing libraries to analyze voice and text data and identify emotion categories.

[0603] The server receives health and emotional information sent from the terminal and stores it in a database. TensorFlow and scikit-learn are used on the server for data analysis. This allows for a quantitative evaluation of the user's health and emotional state, and the analysis results are used to generate personalized advice. For example, if a persistent high-stress state is detected, the generative AI model generates a prompt message such as, "You are experiencing persistent high stress. What measures would you suggest to help you relax?" Based on this, specific advice on relaxation techniques and lifestyle improvements is created.

[0604] Users receive real-time advice from the server through their devices. For example, they might receive messages such as, "Try deep breathing for 5 minutes right now," or "Take a walk in nature this weekend to refresh yourself." This allows users to gain appropriate knowledge about their own health and improve their health by putting it into practice in their daily lives.

[0605] In this way, the invention aims to comprehensively manage the user's health and emotional state and provide support optimized for individual needs.

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] The device acquires data related to the user's health. This input data includes heart rate, activity level, steps taken, sleep patterns, and emotional expressions through voice and text. The device collects this data and temporarily stores it for later transmission. This data is continuously acquired using sensors in wearable devices and smartphones.

[0609] Step 2:

[0610] The emotion engine installed in the device analyzes the acquired voice and text data. Utilizing speech recognition and natural language processing technologies, it classifies the data into categories of emotional states (e.g., stress, joy, sadness). The results of this analysis are then formatted as emotion data and prepared for subsequent steps.

[0611] Step 3:

[0612] The device sends prepared health and emotional data to the server. The data is transferred using a secure protocol and prepared for further analysis on the server side. The transmitted data also includes timestamps and user identifiers.

[0613] Step 4:

[0614] The server stores the received health and emotion data in a database. It checks the integrity of the received data and prepares it as input data for analysis. In this step, it detects missing or anomaly data and performs preprocessing as necessary.

[0615] Step 5:

[0616] The server uses the stored data to execute an analysis algorithm. Specifically, it utilizes machine learning models to evaluate health and emotional states. This analysis process uses frameworks such as TensorFlow to identify fluctuations in health and emotional indicators and generate indicators based on them.

[0617] Step 6:

[0618] The server inputs prompt messages into the generating AI model, which then generates advice to provide to the user. This generation process uses specific prompt messages, such as "What to do if a persistent high-stress state is detected," to output optimal health advice.

[0619] Step 7:

[0620] The server sends the generated advice to the terminal. The data output is formatted to be meaningful to the user and converted into a format that can be displayed on the terminal.

[0621] Step 8:

[0622] The device notifies the user of advice received from the server. The user receives advice that encourages activities and lifestyle habits that are effective for improving health. For example, a message such as "Try taking 5 minutes of deep breathing right now" may appear on the device screen. This action allows the user to incorporate specific health improvement measures into their daily life.

[0623] (Application Example 2)

[0624] 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."

[0625] In modern society, there is a growing need to manage users' physical and mental health simultaneously. However, conventional health management systems focus only on physical health information, making it difficult to take into account mental health and emotional changes. Therefore, there is a need for technology that can comprehensively assess a user's overall health status and provide individually optimized advice.

[0626] 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.

[0627] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and audio signals to evaluate the health state and emotional state, and a suggestion means for providing the user with advice for health improvement and suggestions that take mental health into consideration based on the evaluation of the health state and emotional state. This makes it possible to comprehensively manage the user's physical and mental health and provide health improvement advice that meets individual needs.

[0628] A "health information acquisition method" is a device that collects the user's heart rate, activity level, and sleep data in real time and provides it as data.

[0629] The "evaluation method" is a system that analyzes acquired health information and voice signals to comprehensively evaluate the user's physical health and emotional state.

[0630] The "advice provision method" is a function that provides users with personalized advice for health improvement based on information about their health and emotional state obtained through the evaluation method.

[0631] An "emergency information provision device" is a device that quickly notifies the user of information about appropriate medical institutions when an abnormal situation is detected based on the user's health information or emotional state.

[0632] "Emotion recognition means" refers to technology that recognizes emotional states such as stress, joy, and sadness by analyzing the user's voice signal.

[0633] "Suggested methods" refer to functions that provide users with specific relaxation methods and daily behavioral improvement measures that take into account mental health related to emotional states.

[0634] This invention realizes a system for monitoring a user's health and emotional state and providing personalized advice. The system mainly consists of a user terminal, a server, and various sensor devices.

[0635] The user's device can utilize a compact computing platform such as a Raspberry Pi or Jetson Nano. The device collects health information and emotion-related data through various sensors, including heart rate sensors, activity trackers, and microphones. This health information acquisition method aggregates data in real time and transmits it to a server via the internet.

[0636] The server is equipped with a Python-based program and machine learning libraries such as TensorFlow and scikit-learn. The server uses acquired health information and audio signals to assess the user's health and emotional state. This assessment method analyzes the data to comprehensively evaluate the user's mental and physical health.

[0637] The means of providing advice to users is generated using machine learning models based on evaluation results. This means provides users with specific advice on improving their health in a natural, conversational format using speech synthesis. Furthermore, if an abnormal situation is detected, an emergency information provision system is activated, notifying users of the nearest medical facility and access information for necessary medical services.

[0638] For example, if a user experiences high levels of stress on a daily basis, the system might suggest "trying 10 minutes of yoga to relax."

[0639] Examples of prompts to input into a generative AI model:

[0640] "Design an application that analyzes the user's voice patterns to recognize their emotional state and then suggests appropriate home care activities based on that."

[0641] This system makes it easier for users to manage their daily health and maintain a better quality of life.

[0642] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0643] Step 1:

[0644] The device collects user health information and voice data in real time through sensor devices such as heart rate sensors, activity trackers, and microphones. The input is raw sensor data, and the output is data converted to a standard format. This data undergoes data cleansing and normalization before being transmitted to the server via the network.

[0645] Step 2:

[0646] The server analyzes the received standard-format health information and voice data to evaluate the user's physical and emotional state. The input is health information and voice data sent from the terminal, and the output is a numerically expressed health score and emotion analysis results. A machine learning model is used to estimate stress levels and emotions at that time from the data.

[0647] Step 3:

[0648] The server generates personalized advice on health improvement and emotional management based on the evaluation results. Inputs are health status scores and emotional analysis results, while output is specific advice messages. Using a generative AI model, it recommends relaxation methods and activities to incorporate into daily life that are best suited to the user's condition.

[0649] Step 4:

[0650] Users receive advice through their devices. Input is advice messages from the server, and output is feedback presented to the user through speech synthesis or display. Through voice output and visual presentation, users can incorporate the suggested improvements into their daily lives.

[0651] Step 5:

[0652] The server activates an emergency information provision system when an abnormal situation is detected. Inputs are abnormal health conditions or high-stress states detected during the evaluation phase, and outputs are access information for the nearest medical facilities and necessary emergency services. The system supports the user by transmitting route information to the designated medical facility to the user's terminal in real time.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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".

[0670] The present invention is a system for supporting the health management of residents, and specific embodiments are shown below.

[0671] This system collects health information from smart devices that users use daily, analyzes the data to understand each individual's health status, and includes heart rate, activity levels, sleep duration, and dietary records. This data is transmitted from the user's device to a server where it is centrally managed and analyzed.

[0672] The server runs an algorithm to assess the user's health status based on the received data. This algorithm includes detecting anomalies and predicting long-term health risks. The results of the health status analysis are generated as customized advice for health improvement, tailored to the user's specific situation. For example, if recent analysis results indicate a lack of exercise, the user will be advised, "We recommend a 45-minute walk this weekend."

[0673] The device serves to notify the user of advice provided by the server. Users can check these notifications in their daily lives and modify their actions as needed. Users can also provide feedback through the device, and this feedback is reflected in the server's algorithms and used to improve the advice provided.

[0674] Furthermore, the system is designed with emergency response in mind; in the event of an emergency, it can quickly provide information on the nearest medical facility based on the user's current location. This enables prompt medical assistance when necessary.

[0675] Thus, the present invention is a system that supports users' health management, enabling daily health maintenance and improvement, as well as rapid response in emergencies. In its specific implementation, consideration is given to adjusting the types of data collected, the analysis algorithms, and the content of the advice, and it is designed to be provided in the most optimal form for the user.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The device acquires health information such as heart rate, activity level, sleep duration, and meal records from smart devices. Data is collected in real time or automatically at regular intervals.

[0679] Step 2:

[0680] The device encrypts the acquired health information and transmits it to the server using a secure communication protocol.

[0681] Step 3:

[0682] The server stores the received health information in a database and performs data preprocessing. This preprocessing includes formatting the data and detecting outliers.

[0683] Step 4:

[0684] The server performs analysis to assess health status. This analysis is supported by machine learning algorithms and predicts abnormal health indicators and health risks.

[0685] Step 5:

[0686] The server generates health improvement advice for the user based on the analysis results. This advice is customized to the user's lifestyle.

[0687] Step 6:

[0688] The server sends the generated advice to the terminal and prepares to notify the user.

[0689] Step 7:

[0690] The device notifies the user of advice received from the server. The user checks the notification and uses it to improve their health in their daily life.

[0691] Step 8:

[0692] Users can provide feedback through their devices as needed. This feedback includes information regarding the effectiveness and feasibility of the advice.

[0693] Step 9:

[0694] The server receives feedback from users and incorporates it into machine learning models to improve the accuracy of future advice generation.

[0695] Step 10:

[0696] When an abnormal situation is detected, the device acquires the user's location information and sends it to the server.

[0697] Step 11:

[0698] The server searches for information on the nearest medical facility based on the user's location and sends the results to the terminal.

[0699] Step 12:

[0700] The terminal notifies the user of information about medical institutions received from the server and supports rapid medical response as needed.

[0701] (Example 1)

[0702] 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".

[0703] In recent years, health problems caused by unhealthy lifestyles and stress have been increasing, but appropriate health management is not widespread. Conventional health management systems have shortcomings in providing personalized advice and responding quickly to emergencies, making it difficult to contribute to users' long-term health maintenance.

[0704] 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.

[0705] In this invention, the server includes means for acquiring biometric information using a data collection device, means for transmitting the acquired biometric information to a data storage device using communication technology, means for analyzing the biometric information stored in the data storage device using an analysis device to evaluate the user's health status, means for generating and providing personalized recommendations for health improvement using generation technology, and means for providing the user with information on medical institutions using location information technology when an abnormal condition is detected. This enables the user to improve their daily health management and receive prompt emergency response.

[0706] A "data collection device" is a device that detects and records daily activity data such as a user's biometric information, and plays a role in acquiring information necessary for health management.

[0707] "Biometric information" refers to information that indicates the user's physical and physiological state, such as heart rate, sleep duration, activity level, and dietary records.

[0708] "Communication technology" refers to technologies for transmitting data safely and efficiently, and is used when transmitting information from a user's terminal to a remote server.

[0709] A "data storage device" is a device for temporarily or long-term storage of collected biological information, enabling information management and subsequent processing.

[0710] An "analytical device" is a device used to analyze stored data and evaluate health status, and it has the function of extracting useful insights from the data.

[0711] "Generative technology" refers to a technology that creates personalized health improvement advice based on a user's health data, and is used to provide users with specific guidance.

[0712] "Personalized recommendations" refer to advice for improving health that is generated based on each user's specific health condition and lifestyle.

[0713] "Location-based technology" is a technology that obtains a user's geographical location and provides appropriate information as needed, playing a particularly important role in supporting users during emergencies.

[0714] "Medical institution information" refers to detailed information about medical facilities, such as their location, contact information, and available medical services, to help users receive medical assistance quickly.

[0715] This system is designed to effectively support users' health management. This document details how smart devices are used to collect biometric information from users' daily lives, assess their health status based on that information, and provide appropriate advice.

[0716] Users wear smart devices that collect biometric information based on their daily activities. This includes data such as heart rate, activity level, sleep duration, and meal records. The collected data is transmitted from the device to a server via Bluetooth or Wi-Fi. The server securely stores the received data in a cloud-based data storage system (e.g., Amazon RDS).

[0717] The server analyzes the stored data using analytical software such as Python or R. This process uses machine learning algorithms to identify unusual patterns and long-term health risks. Based on the analysis results, a generative AI model generates specific health improvement advice. This advice is tailored to the user's situation, for example, "You have been getting less than six hours of sleep for three consecutive days and are feeling tired during the day."

[0718] The device notifies the user of the generated advice. This notification is delivered via the smartphone's push notification function or a dedicated app. The user can adjust their lifestyle habits based on the received advice. Furthermore, the user can send feedback back to the server via the device, and this feedback is used to improve the generated AI model on the server.

[0719] Furthermore, this system has the capability to utilize the user's location information service in emergencies to quickly provide information on the nearest medical facilities. This allows users to respond quickly to abnormal situations.

[0720] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0721] Step 1:

[0722] Users collect biometric information using smart devices. This biometric information includes heart rate, activity level, sleep duration, and meal records. The device acquires this data using sensors, and the data is temporarily stored in local storage.

[0723] Step 2:

[0724] The device transmits the collected data to the server via Wi-Fi or Bluetooth. Data transmission is secure using the HTTPS protocol. Input is biometric data from local storage, and output is data packets sent to the server.

[0725] Step 3:

[0726] The server stores the received data in a cloud-based data storage device. At this stage, the data undergoes format checking and encryption. The input is the transmitted data packet, and the output is a notification that registration to the database is complete.

[0727] Step 4:

[0728] The server analyzes stored data using data analysis algorithms. It uses Python to detect anomalies and predict health risks. The input is biometric information from the database, and the output is the analyzed health assessment results.

[0729] Step 5:

[0730] The server uses a generative AI model to generate health improvement advice based on the analysis results. Using the prompt "You have been getting less than 6 hours of sleep for 3 days and feel tired during the day," it generates personalized recommendations. The input is the health assessment results, and the output is health improvement advice.

[0731] Step 6:

[0732] The device notifies the user of the generated advice. Using the app's push notification function, the advice is displayed on the user's smartphone. The input is the generated advice, and the output is the notification displayed on the user's screen.

[0733] Step 7:

[0734] Users send feedback on the advice to the server via their device. The feedback is received in text format and used to improve the generated AI model. The input is the user's feedback, and the output is a record of the feedback data sent to the server.

[0735] Step 8:

[0736] The server obtains the user's location information in emergencies and provides information on the nearest medical facility. It uses a geographic information system to determine the user's current location and generates appropriate medical information. The input is the user's location information, and the output is the provision of medical facility information.

[0737] (Application Example 1)

[0738] 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".

[0739] In modern society, individuals, including the elderly, are expected to continuously manage and appropriately improve their own health. However, many people find it difficult to choose appropriate health management methods and implement sustainable health behaviors in their daily lives. Furthermore, there is a lack of prompt responses in emergencies and health advice tailored to individual living environments. As a result, it is difficult to prevent potential health risks.

[0740] 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.

[0741] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and evaluating the health status, and a health behavior promotion means for analyzing the user's activity data and promoting specific health behaviors. As a result, users can receive rational behavioral suggestions based on their health status and manage and improve their health status themselves. Furthermore, they can receive appropriate medical information quickly in emergencies, enabling safe health management. In addition, environment-adaptive health advice enables optimal health support tailored to individual living situations.

[0742] "Information acquisition means" refers to devices and methods for acquiring users' health information, such as heart rate, activity level, and sleep duration, which are collected through smart devices.

[0743] An "evaluation tool" is a device or method that analyzes acquired health information and evaluates the health status based on the results. Evaluation includes detecting abnormal values ​​and predicting health risks.

[0744] "Means of providing advice" refers to means of providing users with specific advice for improving their health based on the results of their health assessment. This advice is tailored to each individual's health condition.

[0745] An "emergency information provision means" is a device or method that has the function of quickly providing information on the nearest medical institution when an abnormality in a person's health condition is detected.

[0746] A "health behavior promotion tool" is a tool that analyzes users' activity data and generates advice to promote specific health behaviors.

[0747] An "environmentally adaptive advice provision method" refers to a device or method that uses the user's location data to suggest optimal health behaviors tailored to their individual living environment.

[0748] A "location information provision means" is a means that has the function of obtaining the user's current location and providing route information to the nearest medical facility.

[0749] This invention provides a system that collects health information, analyzes the user's health status, and provides appropriate advice.

[0750] The server collects data from users' smart devices through data acquisition mechanisms for collecting health information. This data includes heart rate, activity level, sleep duration, etc., and is sent to a cloud server. Google Cloud is used as the cloud server, and the collected data is evaluated using an analysis algorithm with Python and scikit-learn. The evaluation mechanism has the function of detecting anomalies in the data and predicting long-term health risks.

[0751] The device notifies the user of specific health improvement advice tailored to their health condition, based on analysis results received from the server. Real-time notifications are sent to the device using Firebase Cloud Messaging. Users can use this advice to improve their own health behaviors. Furthermore, by sending user feedback to the server via the device, the advice delivery method is continuously optimized.

[0752] In emergencies, the server provides information on the nearest medical facility based on the user's location information. The GPS function of smart devices is used to obtain location information, enabling a rapid response.

[0753] For example, if a 60-year-old user is using this system, and an abnormality in their heart rate is detected, they will be provided with advice on appropriate exercise and lifestyle improvements. In an emergency, the route to the nearest hospital will be displayed on their smartphone. As an example, a prompt message to the generating AI model would be, "Detect abnormal values ​​from the user's heart rate history data and generate advice for health improvement."

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] The terminal acquires health information from the user's smart device. It receives data such as heart rate, activity level, and sleep duration from sensors as input, and pre-formats this data for transmission to the surveyor. The output is the collection of data.

[0757] Step 2:

[0758] The server receives health information sent from the terminal and stores it in the database. The received data is formatted for storage in the database as input and then stored as is. The output is the data being stored in the database.

[0759] Step 3:

[0760] The server analyzes health information. Using Python and scikit-learn, it retrieves information from a database and performs anomaly detection and predicts long-term health risks. The input is data retrieved from the database, and the output is the analysis results based on that data. Specifically, this includes detecting abnormal heart rates and irregular activity patterns.

[0761] Step 4:

[0762] The server generates advice based on the analysis results. It prompts the generating AI model with sentences to create specific advice text. The input is the analysis results, and the output is the generated advice text.

[0763] Step 5:

[0764] The device notifies the user of advice received from the server. Firebase Cloud Messaging is used to send real-time notifications to the user's smart device. The input is the generated advice, and the output is the alert the user receives.

[0765] Step 6:

[0766] The user takes action based on the advice and inputs the results into the terminal. The input is user-provided feedback data, and the output is feedback data used for the next analysis.

[0767] Step 7:

[0768] The server receives user feedback data and optimizes the content of the advice it provides. It analyzes the feedback data and incorporates it into the AI ​​model. The input is the feedback data, and the output is the parameters of the updated generative model.

[0769] 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.

[0770] This invention provides a system that comprehensively supports users' health management and includes the ability to recognize not only the user's health information but also their emotional state. This makes it possible to provide more personalized health improvement advice and emergency response.

[0771] The system's configuration includes a user terminal that acquires health information such as heart rate, activity level, and sleep records, and sends this data to a server. In addition, it incorporates an emotion engine that recognizes the user's emotional state by analyzing their voice and text data. Emotions are categorized, for example, stress, joy, and sadness, and the analysis results are sent to the server.

[0772] The server aggregates health and emotional information and uses it to comprehensively assess the user's health status. The assessment employs machine learning algorithms, considering not only trends in everyday health indicators but also mental health aspects stemming from emotions. For example, if a consistently high level of stress is detected, the server generates suggestions for relaxation methods and advice for improving sleep quality.

[0773] The device receives instructions from the server and notifies the user with advice. For example, if it detects that the user has been experiencing high stress levels for several consecutive days, the device will provide specific advice such as "Try taking a deep breath" or "Refresh yourself in nature next weekend."

[0774] If an emergency is detected, the system also considers emotional data and provides information on appropriate medical facilities. In particular, if the emotional engine detects severe emotional instability, the user will be notified quickly and in detail of access information to specialized medical facilities.

[0775] In this way, the system of the present invention can monitor both the user's physical and emotional state and comprehensively evaluate them, thereby providing care and support tailored to individual needs. This allows the user to aim for the maintenance and improvement of their physical and mental health.

[0776] The following describes the processing flow.

[0777] Step 1:

[0778] The device collects health information such as heart rate, activity level, and sleep duration from smart devices. In parallel, the emotion engine built into the device recognizes and categorizes the user's emotional state from their voice and text.

[0779] Step 2:

[0780] The device encrypts both health and emotional information and transmits it to the server through a secure communication channel.

[0781] Step 3:

[0782] The server stores the received health and emotional information in a database. The data is formatted and preprocessed, including anomaly detection.

[0783] Step 4:

[0784] The server uses machine learning algorithms to comprehensively analyze health and emotional information. This analysis assesses the user's physical and mental health status.

[0785] Step 5:

[0786] Based on the analysis results, the server generates personalized health improvement advice for the user. This advice may include specific suggestions, such as "try deep breathing exercises to relax" if the user is experiencing high stress levels.

[0787] Step 6:

[0788] The server sends the generated advice to the terminal.

[0789] Step 7:

[0790] The device notifies the user of the advice it has received. The user can then use this notification as a reference and implement it in their daily life.

[0791] Step 8:

[0792] Users can input feedback on the effects of the advice and other details on their device, and this feedback is then sent back to the server.

[0793] Step 9:

[0794] The server updates its machine learning model to continuously optimize the advice it provides based on user feedback.

[0795] Step 10:

[0796] The device notifies the server if the emotion engine detects severe emotional instability.

[0797] Step 11:

[0798] The server uses the user's location and sentiment information to search for the nearest and most suitable medical facility and sends it to the terminal, including detailed directions.

[0799] Step 12:

[0800] The device provides users with information about medical institutions and prompts them to take prompt action as needed.

[0801] (Example 2)

[0802] 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".

[0803] In modern living environments, users face complex health and emotional issues, but conventional systems struggle to comprehensively manage these and provide individually optimized advice. Furthermore, there is a challenge in evaluating the impact of abnormal emotional states on health in real time and taking appropriate action.

[0804] 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.

[0805] In this invention, the server includes information acquisition means for acquiring user health information, evaluation means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and advice provision means for generating and providing personalized health improvement advice to the user based on the evaluation. This enables the user to comprehensively manage both physical and mental health and receive optimal care based on each state.

[0806] "Information acquisition means" refers to means for collecting user health-related data in real time or periodically, and includes sensors and corresponding communication technologies.

[0807] The "evaluation method" is a means of comprehensively evaluating the user's health and emotional state by analyzing acquired health and emotional information, and it utilizes machine learning algorithms.

[0808] An "advice provision method" is a means of generating specific, personalized advice based on evaluations that helps improve the user's health, and informing the user of that advice.

[0809] An "emergency information provision system" is a means of quickly providing users with information on appropriate medical institutions when an abnormal health or emotional state is detected.

[0810] "Lifestyle habits" refer to the patterns of behavior and routines that users engage in on a daily basis, and these include eating habits and exercise habits.

[0811] "Feedback" refers to the opinions and reactions received from users, and the system's advice is improved based on this feedback.

[0812] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal advice for users based on training data.

[0813] A "prompt" refers to pre-configured text data that is input into a generative AI model and is used to generate responses on a specific topic or problem.

[0814] This invention is an advanced system for efficiently supporting users' health management. The system provides appropriate feedback to the user by performing analysis on the server side based on data acquired from the user's terminal.

[0815] The device uses sensors and applications to acquire various health-related information. Specifically, it utilizes wearable devices and smartphone applications to acquire heart rate, step count, activity level, and even emotional data from the user's voice and messages. For emotion recognition, it uses speech recognition APIs and natural language processing libraries to analyze voice and text data and identify emotion categories.

[0816] The server receives health and emotional information sent from the terminal and stores it in a database. TensorFlow and scikit-learn are used on the server for data analysis. This allows for a quantitative evaluation of the user's health and emotional state, and the analysis results are used to generate personalized advice. For example, if a persistent high-stress state is detected, the generative AI model generates a prompt message such as, "You are experiencing persistent high stress. What measures would you suggest to help you relax?" Based on this, specific advice on relaxation techniques and lifestyle improvements is created.

[0817] Users receive real-time advice from the server through their devices. For example, they might receive messages such as, "Try deep breathing for 5 minutes right now," or "Take a walk in nature this weekend to refresh yourself." This allows users to gain appropriate knowledge about their own health and improve their health by putting it into practice in their daily lives.

[0818] In this way, the invention aims to comprehensively manage the user's health and emotional state and provide support optimized for individual needs.

[0819] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0820] Step 1:

[0821] The device acquires data related to the user's health. This input data includes heart rate, activity level, steps taken, sleep patterns, and emotional expressions through voice and text. The device collects this data and temporarily stores it for later transmission. This data is continuously acquired using sensors in wearable devices and smartphones.

[0822] Step 2:

[0823] The emotion engine installed in the device analyzes the acquired voice and text data. Utilizing speech recognition and natural language processing technologies, it classifies the data into categories of emotional states (e.g., stress, joy, sadness). The results of this analysis are then formatted as emotion data and prepared for subsequent steps.

[0824] Step 3:

[0825] The device sends prepared health and emotional data to the server. The data is transferred using a secure protocol and prepared for further analysis on the server side. The transmitted data also includes timestamps and user identifiers.

[0826] Step 4:

[0827] The server stores the received health and emotion data in a database. It checks the integrity of the received data and prepares it as input data for analysis. In this step, it detects missing or anomaly data and performs preprocessing as necessary.

[0828] Step 5:

[0829] The server uses the stored data to execute an analysis algorithm. Specifically, it utilizes machine learning models to evaluate health and emotional states. This analysis process uses frameworks such as TensorFlow to identify fluctuations in health and emotional indicators and generate indicators based on them.

[0830] Step 6:

[0831] The server inputs prompt messages into the generating AI model, which then generates advice to provide to the user. This generation process uses specific prompt messages, such as "What to do if a persistent high-stress state is detected," to output optimal health advice.

[0832] Step 7:

[0833] The server sends the generated advice to the terminal. The data output is formatted to be meaningful to the user and converted into a format that can be displayed on the terminal.

[0834] Step 8:

[0835] The device notifies the user of advice received from the server. The user receives advice that encourages activities and lifestyle habits that are effective for improving health. For example, a message such as "Try taking 5 minutes of deep breathing right now" may appear on the device screen. This action allows the user to incorporate specific health improvement measures into their daily life.

[0836] (Application Example 2)

[0837] 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".

[0838] In modern society, there is a growing need to manage users' physical and mental health simultaneously. However, conventional health management systems focus only on physical health information, making it difficult to take into account mental health and emotional changes. Therefore, there is a need for technology that can comprehensively assess a user's overall health status and provide individually optimized advice.

[0839] 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.

[0840] In this invention, the server includes an information acquisition means for acquiring health information, an evaluation means for analyzing the acquired health information and audio signals to evaluate the health state and emotional state, and a suggestion means for providing the user with advice for health improvement and suggestions that take mental health into consideration based on the evaluation of the health state and emotional state. This makes it possible to comprehensively manage the user's physical and mental health and provide health improvement advice that meets individual needs.

[0841] A "health information acquisition method" is a device that collects the user's heart rate, activity level, and sleep data in real time and provides it as data.

[0842] The "evaluation method" is a system that analyzes acquired health information and voice signals to comprehensively evaluate the user's physical health and emotional state.

[0843] The "advice provision method" is a function that provides users with personalized advice for health improvement based on information about their health and emotional state obtained through the evaluation method.

[0844] An "emergency information provision device" is a device that quickly notifies the user of information about appropriate medical institutions when an abnormal situation is detected based on the user's health information or emotional state.

[0845] "Emotion recognition means" refers to technology that recognizes emotional states such as stress, joy, and sadness by analyzing the user's voice signal.

[0846] "Suggested methods" refer to functions that provide users with specific relaxation methods and daily behavioral improvement measures that take into account mental health related to emotional states.

[0847] This invention realizes a system for monitoring a user's health and emotional state and providing personalized advice. The system mainly consists of a user terminal, a server, and various sensor devices.

[0848] The user's device can utilize a compact computing platform such as a Raspberry Pi or Jetson Nano. The device collects health information and emotion-related data through various sensors, including heart rate sensors, activity trackers, and microphones. This health information acquisition method aggregates data in real time and transmits it to a server via the internet.

[0849] The server is equipped with a Python-based program and machine learning libraries such as TensorFlow and scikit-learn. The server uses acquired health information and audio signals to assess the user's health and emotional state. This assessment method analyzes the data to comprehensively evaluate the user's mental and physical health.

[0850] The means of providing advice to users is generated using machine learning models based on evaluation results. This means provides users with specific advice on improving their health in a natural, conversational format using speech synthesis. Furthermore, if an abnormal situation is detected, an emergency information provision system is activated, notifying users of the nearest medical facility and access information for necessary medical services.

[0851] For example, if a user experiences high levels of stress on a daily basis, the system might suggest "trying 10 minutes of yoga to relax."

[0852] Examples of prompts to input into a generative AI model:

[0853] "Design an application that analyzes the user's voice patterns to recognize their emotional state and then suggests appropriate home care activities based on that."

[0854] This system makes it easier for users to manage their daily health and maintain a better quality of life.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The device collects user health information and voice data in real time through sensor devices such as heart rate sensors, activity trackers, and microphones. The input is raw sensor data, and the output is data converted to a standard format. This data undergoes data cleansing and normalization before being transmitted to the server via the network.

[0858] Step 2:

[0859] The server analyzes the received standard-format health information and voice data to evaluate the user's physical and emotional state. The input is health information and voice data sent from the terminal, and the output is a numerically expressed health score and emotion analysis results. A machine learning model is used to estimate stress levels and emotions at that time from the data.

[0860] Step 3:

[0861] The server generates personalized advice on health improvement and emotional management based on the evaluation results. Inputs are health status scores and emotional analysis results, while output is specific advice messages. Using a generative AI model, it recommends relaxation methods and activities to incorporate into daily life that are best suited to the user's condition.

[0862] Step 4:

[0863] Users receive advice through their devices. Input is advice messages from the server, and output is feedback presented to the user through speech synthesis or display. Through voice output and visual presentation, users can incorporate the suggested improvements into their daily lives.

[0864] Step 5:

[0865] The server activates an emergency information provision system when an abnormal situation is detected. Inputs are abnormal health conditions or high-stress states detected during the evaluation phase, and outputs are access information for the nearest medical facilities and necessary emergency services. The system supports the user by transmitting route information to the designated medical facility to the user's terminal in real time.

[0866] 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.

[0867] 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.

[0868] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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."

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] The following is further disclosed regarding the embodiments described above.

[0888] (Claim 1)

[0889] Information acquisition methods for obtaining health information,

[0890] An evaluation method that analyzes acquired health information to assess health status,

[0891] An advice provision method that provides users with advice for improving their health based on an assessment of their health status,

[0892] An emergency information provision system that provides users with information on medical institutions when an abnormal situation is detected,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] A means of analyzing users' lifestyle habits over the long term and using the analysis results to predict health risks,

[0896] A means to continuously optimize the content of the advice provided based on user feedback,

[0897] The system according to claim 1, including the following:

[0898] (Claim 3)

[0899] The system further includes a location information provision means that obtains the user's current location and provides route information to the nearest medical facility.

[0900] The system according to claim 1.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A means for acquiring biological information using a data collection device,

[0904] A means for transmitting acquired biometric information to a data storage device using communication technology,

[0905] A means for analyzing biological information stored in a data storage device using an analysis device to evaluate health status,

[0906] A means for generating and providing personalized recommendations for health improvement using generation technology based on the analyzed results,

[0907] A means of providing users with information on medical institutions using location information technology when an abnormal condition is detected,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] A method for analyzing users' lifestyle patterns over a long period and using the analysis results to predict health risks,

[0911] A means of continuously improving the content of the provided recommendations using generation technology based on user responses,

[0912] The system according to claim 1, including the following:

[0913] (Claim 3)

[0914] The system further includes means for obtaining the user's current location and providing route information to the nearest medical facility.

[0915] The system according to claim 1.

[0916] "Application Example 1"

[0917] (Claim 1)

[0918] Information acquisition methods for obtaining health information,

[0919] An evaluation method that analyzes acquired health information to assess health status,

[0920] An advisory provision method that provides users with advice for health improvement based on an assessment of their health status,

[0921] An emergency information provision system that provides information on medical institutions when an abnormal situation is detected,

[0922] A health behavior promotion tool that analyzes user activity data and generates advice to promote specific health behaviors,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] A means of analyzing users' lifestyle patterns over the long term and using the analysis results to predict health risks,

[0926] A means to continuously optimize the content of the advice provided based on user evaluations,

[0927] An environmentally adaptive advice provision method that uses the user's location data to suggest appropriate health behaviors according to the living environment,

[0928] The system according to claim 1, including the following:

[0929] (Claim 3)

[0930] The system further includes a location information provision method that obtains the user's current location and provides route information to the nearest medical facility.

[0931] The system according to claim 1.

[0932] "Example 2 of combining an emotion engine"

[0933] (Claim 1)

[0934] A means of acquiring information to obtain user health information,

[0935] An evaluation means for analyzing acquired health information and emotional information to evaluate health status and emotional status,

[0936] An advice delivery system that generates and provides personalized health improvement advice to users based on their evaluations,

[0937] An emergency information provision method that provides users with information on medical institutions when an abnormal emotional state is detected,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] A means of analyzing users' lifestyles and emotional states over the long term and using the analysis results to predict health risks and emotional risks,

[0941] A means to continuously optimize the content of the advice provided based on user feedback and usage history,

[0942] A means of aggregating and analyzing user health and emotional data and improving health and emotional states using deep learning,

[0943] The system according to claim 1, including the following:

[0944] (Claim 3)

[0945] The system further includes a location information provision method that obtains the user's current location and provides route information to the nearest specialized medical institution.

[0946] The system according to claim 1.

[0947] "Application example 2 when combining with an emotional engine"

[0948] (Claim 1)

[0949] Information acquisition methods for obtaining health information,

[0950] An evaluation method that analyzes acquired health information to assess health status,

[0951] An advice provision method that provides users with advice for improving their health based on an assessment of their health status,

[0952] An emergency information provision system that provides users with information on medical institutions when an abnormal situation is detected,

[0953] An emotion recognition means that analyzes audio signals to recognize emotional states,

[0954] A proposal method that makes suggestions that take mental health into consideration based on emotional state,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] A means of analyzing users' lifestyle habits over the long term and using the analysis results to predict health risks,

[0958] A means to continuously optimize the content of the advice provided based on user feedback,

[0959] A means of generating more personalized advice by taking into account the user's emotional state,

[0960] The system according to claim 1, including the following:

[0961] (Claim 3)

[0962] The system further includes a location information provision means that obtains the user's current location and provides route information to the nearest medical facility.

[0963] This includes means of selecting the most suitable medical institution based on emotional recognition.

[0964] The system according to claim 1. [Explanation of symbols]

[0965] 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. Information acquisition methods for obtaining health information, An evaluation method that analyzes acquired health information to assess health status, An advice provision method that provides users with advice for improving their health based on an assessment of their health status, An emergency information provision system that provides users with information on medical institutions when an abnormal situation is detected, A system that includes this.

2. A means of analyzing users' lifestyle habits over the long term and using the analysis results to predict health risks, A means to continuously optimize the content of the advice provided based on user feedback, The system according to claim 1, including the following:

3. The system further includes a location information provision means that obtains the user's current location and provides route information to the nearest medical facility. The system according to claim 1.

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