system

Abstract

We provide the system. [Solution] Information acquisition means for collecting user biometric information, Information analysis means for integrating and analyzing biological information collected by the aforementioned information acquisition means, A prediction means that predicts future health risks based on the analysis results obtained by the information analysis means, A formulation means for generating specific preventive measures against the health risks predicted by the prediction means, A notification means for notifying users of the preventive measures generated by the aforementioned formulation means, A system including a feedback processing means that receives feedback from users and improves the prediction means and the formulation means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the onset of chronic diseases and lifestyle-related diseases has become a social problem, and the importance of early detection of health risks and preventive measures has increased. However, conventional health management methods are based on individual data, making it difficult to accurately predict long-term health risks and provide appropriate preventive measures. In addition, there is a lack of a system that can present personalized preventive measures while reducing the burden on users, which is an issue.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that collects users' biometric information using mobile devices or wearable devices and analyzes it in an integrated manner. By using generative artificial intelligence and statistical models, it predicts future health risks and generates and notifies users of specific, actionable preventive measures based on the results. Furthermore, it continuously improves the predictive model and preventive measures based on user feedback, thereby building a system that enables personalized health management. This enables the provision of efficient and highly accurate preventive measures.

[0006] "Information acquisition means" refers to methods for collecting users' biometric information using mobile devices or wearable devices.

[0007] "Information analysis means" refers to means for integrating and analyzing biological information collected by information acquisition means.

[0008] A "predictive means" is a means that has the function of predicting future health risks based on the analysis results obtained by an information analysis means.

[0009] "Formulation means" refers to means of generating specific preventive measures in response to health risks predicted by prediction means.

[0010] A "notification means" is a means that has the function of notifying users of the preventive measures generated by the formulation means.

[0011] A "feedback processing means" is a means for receiving feedback from users and improving the prediction and formulation means.

[0012] A "mobile device" refers to a portable electronic device with communication capabilities.

[0013] A "wearable device" refers to an electronic device that can be worn on the body and used for various purposes.

[0014] "Generative artificial intelligence" is a technology that utilizes learning algorithms for information analysis and prediction.

[0015] "Statistical model" is a mathematical model that describes the structure and relationships of data and is used for analysis and prediction.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system designed to support users in maintaining their health. It acquires biometric information using mobile devices and wearable devices, analyzes this information to predict future health risks, and provides specific preventative measures. The following describes the program processing of this system in natural language.

[0038] First, the device collects the user's daily biometric information through mobile devices or wearable devices. This information includes heart rate, steps taken, sleep patterns, and weight. The collected data is transmitted to a server via the internet. In addition, the server connects with external medical databases and fitness applications to obtain the user's health-related history and diagnostic data.

[0039] Next, the server integrates the collected data and performs analysis using generative artificial intelligence and statistical models. This analysis allows for an understanding of each user's health trends and predictions of future health risks. For example, it is possible to assess a user's risk of developing heart disease based on data such as steps taken and heart rate.

[0040] Subsequently, the server develops individualized preventative measures for health risks. These measures take into account the user's lifestyle and health condition and are provided as specific action plans. For example, if the server determines that the user is at high risk of heart disease, it may recommend light exercise five times a week.

[0041] The developed preventative measures are communicated to users via their devices. These notifications may include visually clear graphs and animations, making it easier for users to understand the proposed preventative measures.

[0042] Finally, the user implements the suggested preventative measures and sends the results and feedback to the server via their device. Based on the received feedback, the server improves the accuracy of its predictive models and suggestions, functioning as a system to enable more effective health management for the user.

[0043] In this way, the present invention enables the early detection of health risks in users' daily lives and the provision of appropriate preventive measures, thereby strongly supporting users in maintaining their health.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device acquires the user's biometric information from mobile devices and wearable devices. Data such as heart rate, steps, sleep patterns, and weight are collected in real time via sensors. The collected data is temporarily stored in the device and prepared for data transfer.

[0047] Step 2:

[0048] With the user's consent, the device transmits collected biometric information to a server via the internet. The transmitted data is encrypted to protect privacy. In addition, the device periodically collects additional health data from external medical databases and fitness apps and transmits it to the server.

[0049] Step 3:

[0050] The server integrates received biometric and external information to form a unique dataset for each user. This dataset is organized and stored while maintaining information integrity. The stored data is then prepared for analysis.

[0051] Step 4:

[0052] The server applies generative artificial intelligence and statistical models using integrated data. The AI ​​algorithms learn from past trends and predict future health risks. This identifies potential health problems that users may face in the future and allows for risk assessment.

[0053] Step 5:

[0054] The server develops specific preventative measures based on predicted health risks. These measures include action plans that reflect the user's individual health status and lifestyle. For example, instructions such as recommendations for specific exercise habits may be created.

[0055] Step 6:

[0056] The device notifies the user of preventative measures received from the server. The notification is provided in a visually easy-to-understand graphical interface. Users can refer to this information in their daily activities and take appropriate action.

[0057] Step 7:

[0058] Users implement the suggested preventative measures and send feedback about their effectiveness and implementation status to the server via their device. This feedback may include areas for improvement or points of confusion.

[0059] Step 8:

[0060] The server receives feedback from users and uses it to improve the accuracy of predictive models and refine preventative measures. Through this continuous improvement process, the system evolves to support personalized and effective health management for each user.

[0061] (Example 1)

[0062] 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."

[0063] In modern society, the importance of monitoring individual health conditions in real time and taking appropriate preventive measures is increasing. However, conventional health management systems have limitations in data collection and analysis, making it difficult to predict health risks accurately and provide specific preventive measures. There is a need to improve this situation and realize personalized and optimized health maintenance for each user.

[0064] 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.

[0065] In this invention, the server includes data integration means, data analysis means, and risk prediction means. This enables comprehensive data analysis and precise prediction of health risks.

[0066] "Data acquisition means" refers to a device or method that has the function of collecting a user's biometric data.

[0067] "Data integration means" refers to a device or method that has the function of integrating acquired biometric data with external information sources to generate a single comprehensive dataset.

[0068] "Data analysis means" refers to a device or method that has the function of analyzing integrated data and evaluating health trends using generative artificial intelligence or statistical methods.

[0069] A "risk prediction tool" is a device or method that has the function of predicting future health risks based on results obtained through data analysis.

[0070] A "preventive measure generation device" is a device or method that has the function of formulating and generating specific preventive measures in response to predicted health risks.

[0071] "Notification means" refers to a device or method that has the function of notifying users of the precautions that have been generated, and includes visual display technology.

[0072] A "model improvement tool" is a device or method that has the function of receiving feedback from users and improving the accuracy of predictive models and suggestions based on that feedback.

[0073] In order to implement this invention, the following system is required.

[0074] First, the device uses a mobile device or wearable device (e.g., a smartwatch) to collect the user's daily biometric data. This data includes heart rate, steps taken, sleep patterns, and weight. The data collected by the device is encrypted and transmitted to the server via the internet in a privacy-protected manner.

[0075] Next, the server receives the data sent from the terminal and integrates it with external medical databases and fitness applications. This process generates a comprehensive dataset that includes the user's health-related history and diagnostic information.

[0076] The integrated data is analyzed on the server using generative artificial intelligence and statistical models. Python and similar programming languages ​​are used as analysis tools to evaluate users' health trends and risks. Specifically, Python libraries are used to model data trends and quantify health risks.

[0077] Based on the analysis results, the server generates individual preventative measures for health risks. These preventative measures are optimized by a generating AI model to suit the user's lifestyle and health condition. For example, for a user at risk of high blood pressure, specific guidelines recommending regular aerobic exercise are created.

[0078] The generated preventative measures are communicated to the user via their device. The notifications use graphs and animations to make them visually easy to understand, allowing the user to visually comprehend the guidelines presented.

[0079] Finally, the user sends the results and feedback of the preventative measures they have taken from their device to the server. Based on this feedback, the server functions as a system that continuously improves, enhancing the accuracy of its predictive models and recommendations.

[0080] As a concrete example, a prompt message such as "Evaluate the health risks of a male in his 30s who exercises three times a week, and suggest appropriate preventive measures" can be input into an AI model for analysis and generation of preventive measures. In this way, the system can continuously provide personalized health management to the user.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The device collects biometric data through the user's mobile or wearable device. The collected data includes heart rate, steps taken, sleep patterns, and weight. The input is the user's real-time biometric information, and the output is a digital record of this information.

[0084] Step 2:

[0085] The device transmits the collected biometric data to a server via the internet. During this process, the data is encrypted to ensure security during transmission. The input is the biometric information recorded on the device, and the output is the transmission of encrypted data to the server.

[0086] Step 3:

[0087] The server receives biometric data transmitted from the terminal and integrates with external medical databases and fitness applications to acquire additional health-related data. Through the data integration process, the biometric information received as input is output as a comprehensive dataset.

[0088] Step 4:

[0089] The server analyzes the integrated dataset using AI and statistical models. The input is the integrated dataset, and the output is the analysis results regarding the user's health trends and risks. Programming tools such as Python are used for data modeling.

[0090] Step 5:

[0091] The server uses a generated AI model based on the analysis results to formulate specific preventive measures. For example, it may recommend specific exercise regimens or dietary adjustments. The input is the analysis results of health risks, and the output is a customized preventive measure for the user.

[0092] Step 6:

[0093] The terminal notifies the user of preventative measures sent from the server. These notifications are presented as visual dashboards and animations, designed for easy user understanding. The input is preventative measure data, and the output is visual information conveyed to the user.

[0094] Step 7:

[0095] Users implement the suggested preventative measures and send the results and feedback to the server via their device. The server then uses this information to improve its predictive model and suggestions. The input is feedback data, and the output is a continuously improved predictive model.

[0096] (Application Example 1)

[0097] 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."

[0098] In modern society, maintaining personal health is becoming an increasingly important issue. However, many people find it difficult to properly understand their own health status and consistently implement appropriate preventive measures due to the busyness of their daily lives. Furthermore, general health management systems do not adequately provide personalized preventive measures for individual health risks, and there is a lack of ways to convey health information in a way that is easy for users to understand. Therefore, there is a need to provide specific and practical health management tailored to the user's situation and to offer means to support the improvement of their health status in accordance with their lifestyle.

[0099] 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.

[0100] In this invention, the server includes an information acquisition means for collecting the user's biometric information, an information analysis means for integrating and analyzing the biometric information collected by the information acquisition means, and a prediction means for predicting future health risks based on the analysis results obtained by the information analysis means. This makes it possible to generate specific preventive measures tailored to individual health risks and provide them to the user via a consumer robot. Furthermore, by presenting the preventive measures to the user visually and audibly via the consumer robot, the user can more easily intuitively understand and implement the proposed measures.

[0101] "Information acquisition means for collecting user biometric information" refers to a function that acquires biometric information such as heart rate, steps taken, and sleep patterns from users as they go about their daily lives through mobile devices or wearable devices.

[0102] "Information analysis means for integrating and analyzing biological information collected by information acquisition means" refers to a function for combining collected biological information into a single dataset and analyzing patterns and trends.

[0103] A "predictive tool for predicting future health risks" is a function that predicts potential health problems that users may face in the future, based on analyzed data.

[0104] A "formulation tool for generating specific preventive measures" is a function that creates specific actionable guidelines and advice for users in response to predicted health risks.

[0105] "Notification means for informing users" refers to methods for informing users of the generated preventive measures, and includes functions such as visual displays and audio guidance.

[0106] "Feedback processing means for receiving feedback and improving the prediction means and formulation means" refers to a function for receiving responses and results of actions from users and improving the accuracy of predictions and suggestions accordingly.

[0107] "Means of providing preventive measures to users through consumer robots using information analysis tools" refers to a function that appropriately provides preventive measures to users through household robots based on analyzed data.

[0108] "Means of presenting preventive measures visually and audibly using consumer robots" refers to functions that enable household robots to present preventive measures to users in an easy-to-understand manner using displays and voice functions.

[0109] The system based on this invention utilizes mobile devices and wearable devices to collect and analyze biometric information in order to support the user's health management. An example of this system is described below.

[0110] First, users measure and collect biometric information in real time using mobile devices and wearable devices. Specifically, this includes heart rate, steps taken, and sleep patterns. This allows for an understanding of the user's activity level and health status in their daily life.

[0111] Next, the collected data is transmitted to a server via the network. The server integrates this biometric data and analyzes it in detail using generative artificial intelligence and statistical models. The purpose of the analysis is to predict the user's future health risks. For example, it can identify specific patterns in the data and assess the user's risk of developing heart disease.

[0112] Based on predicted health risks, specific preventative measures are developed. These measures are generated by a server and tailored to each user's health condition and lifestyle. The developed preventative measures are transmitted to a consumer robot, which presents this information to the user visually and audibly. This makes it easier for the user to intuitively understand and implement the preventative measures.

[0113] For example, if the robot determines that the user is not getting enough exercise, it will suggest via voice, "It looks like you haven't been getting enough exercise today. How about doing some 5 minutes of stretching?" and will explain how to stretch on the robot's display.

[0114] The user implements the suggested preventative measures and sends the results as feedback to the server via the end device. The server analyzes the user's feedback to improve future prediction accuracy and refine the suggestions.

[0115] This process utilizes a generative AI model and aims to improve user convenience and the effectiveness of health management. Below are examples of prompts used by the generative AI model:

[0116] "Please create audio and visual animations for a health management robot that points out the user's lack of exercise and suggests light exercises."

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

[0118] Step 1:

[0119] The terminal acquires biometric information from the wearable device.

[0120] Specifically, the system acquires data such as heart rate, steps taken, and sleep patterns, and transmits this data to a server via the network. The input is biometric data acquired by the device, and the output is integrated data sent to the server. This data is then formatted for later analysis.

[0121] Step 2:

[0122] The server aggregates the received biometric information and performs data analysis using generating AI models and statistical analysis tools.

[0123] The input is integrated data received from the terminal, and the output is the health status trends and risk assessment results for each user. The server organizes the data chronologically and detects abnormal patterns using statistical methods.

[0124] Step 3:

[0125] The server predicts the user's future health risks based on the analysis results.

[0126] Specifically, the system derives the probability of developing a particular health problem from the analyzed data. The input is the result of the data analysis, and the output is predictive data on specific health risks.

[0127] Step 4:

[0128] The server generates individual preventative measures based on predictive data.

[0129] This system utilizes generative AI models to develop behavioral guidelines tailored to the user's lifestyle and health condition. The input is predictive health risk data, and the output is specific preventative measures for the user.

[0130] Step 5:

[0131] The server transmits preventative measures it has formulated to consumer robots via a terminal.

[0132] The robot prepares to visually and audibly suggest preventative measures to the user. The input is preventative measure data from the server, and the output is the action instructions provided by the robot.

[0133] Step 6:

[0134] The user follows the robot's instructions and takes the suggested preventative measures.

[0135] For example, the user performs stretches according to the robot's instructions. The user's behavioral data is sent to the server as feedback via the terminal.

[0136] Step 7:

[0137] The server receives user feedback and uses it to improve the accuracy of the predictive model and suggestions.

[0138] The input is user feedback data, and the output is a refined prediction and proposal model. The feedback is analyzed as new data and used to train the AI ​​model.

[0139] 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.

[0140] This invention is a health management system that takes into account not only the user's biometric information but also their emotional state. By using an emotion engine, it becomes possible to incorporate psychological elements into preventative measures, resulting in more accurate health management.

[0141] First, the device collects the user's biometric information using mobile or wearable devices. This includes not only standard biometric information such as heart rate, steps taken, and body temperature, but also the acquisition of emotional data from the user via an emotion engine using sensors such as cameras and microphones. Emotional data is obtained from facial expressions, voice tone, breathing patterns, etc. This data is periodically transmitted to a server.

[0142] Next, the server integrates the transmitted biometric and emotional data and performs analysis using information analysis tools. Generative artificial intelligence and statistical models are utilized to advance the analysis process. By considering both biometric and emotional data, it becomes possible to capture new health risk trends that were previously undetectable. Based on these results, predicting future health risks enables a more comprehensive understanding of health status.

[0143] The server then develops specific preventative measures based on the prediction results. Because emotional data is available, advice related to stress reduction and mental health is also incorporated into the development process. For example, if the emotional engine detects a high stress level, suggestions for breaks and information on relaxation techniques will be added.

[0144] The developed preventative measures will be communicated to users via their devices. These notifications will include elements such as diagrams and graphs to convey information in a way that is easy for users to understand. This will enable users to manage their health in a way that also considers their emotional well-being.

[0145] Finally, users implement the notified preventative measures and send feedback on their effectiveness and implementation from their device to the server. The server uses the received feedback to continuously improve the accuracy of its predictive models and preventative measures. This optimizes the system for each individual user, providing strong support for future health management.

[0146] Thus, this invention provides a novel approach that integrates biometric information and emotional data to enable the management and provision of preventive measures for health risks, thereby promoting the comprehensive health maintenance of users.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The device acquires the user's biometric and emotional information through mobile or wearable devices. It uses sensors to collect biometric information such as heart rate, steps, and body temperature, while simultaneously recognizing emotions from facial expressions and voice using cameras and microphones. This data is stored locally for each session.

[0150] Step 2:

[0151] The device transmits collected biometric and emotional information to a server via the internet, based on the user's permission. The data is encrypted to protect privacy. The collected data is organized in a way that allows for session identification and stored in a dedicated database.

[0152] Step 3:

[0153] The server integrates the transmitted biometric and emotional information to build a dataset. The integrated data is preprocessed to remove noise and unnecessary information. As a result, clean data suitable for analysis is formed.

[0154] Step 4:

[0155] The server analyzes integrated data using generative artificial intelligence and statistical models. The AI ​​learns patterns from past data and makes inferences to predict future health risks. By analyzing the impact of changes in emotional state on biometric information, a more accurate understanding of overall health status becomes possible.

[0156] Step 5:

[0157] The server develops preventative measures based on predicted health risks. It utilizes data from the emotion engine to include stress management and mental health-conscious advice. For example, it might recommend mindfulness practices if stress levels are high.

[0158] Step 6:

[0159] The device notifies the user of preventative measures received from the server in a visually easy-to-understand format. The interface is designed to enhance the user experience by displaying detailed information using graphs and animations.

[0160] Step 7:

[0161] Users implement the notified preventative measures and send the results and feedback from their device back to the server. This feedback includes information about the effectiveness of the preventative measures and any changes in their status.

[0162] Step 8:

[0163] The server analyzes user feedback and improves its health risk prediction models and preventive measures. Through continuous improvement, the system can provide optimal health management support to each user.

[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] Conventional health management systems only consider biometric information, making it difficult to predict health risks or develop preventive measures that adequately reflect the user's emotions and psychological state. As a result, there is a possibility of overlooking risks related to stress and mental health, and comprehensive health management has not been achieved.

[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 data acquisition means for collecting the user's biometric information and emotional data; information analysis means for integrating and analyzing the biometric information and emotional data collected by the data acquisition means; and formulation means for predicting future health risks based on the analysis results obtained by the information analysis means and generating specific preventive measures, including psychological factors. This enables a comprehensive evaluation of the user's physical and emotional state, and allows for more accurate and personalized health management.

[0169] "Data acquisition means" refers to methods and devices for collecting users' biometric and emotional data, and includes portable and wearable devices.

[0170] "Information analysis means" refers to methods and devices for integrating and analyzing collected biometric information and emotional data, utilizing generative AI models and statistical models.

[0171] "Formulation methods" refer to methods and devices for predicting future health risks based on analysis results and generating specific preventive measures, including psychological factors.

[0172] "Notification means" refers to methods and devices for visualizing formulated preventive measures and communicating that information to users.

[0173] "Learning processing means" refers to methods and devices that receive feedback from users and use that information to improve information analysis means and formulation means using a generating AI model.

[0174] This invention is a system that realizes comprehensive health management using the user's biometric information and emotional data. The elements constituting the system are data acquisition means, information analysis means, formulation means, notification means, and learning processing means. Each process proceeds as follows.

[0175] The device uses portable devices (e.g., smartphones) and wearable devices (e.g., smartwatches) to collect biometric information such as the user's heart rate, steps taken, and body temperature using sensors. It also uses cameras and microphones to capture the user's facial expressions and voice tone, collecting this as emotional data. This data is periodically transmitted to a server via Wi-Fi or Bluetooth.

[0176] The server integrates the received biometric and emotional data and stores it in a database. Next, it uses generative AI models and statistical models to analyze the biometric and emotional data in detail. The generative AI model identifies new health risk trends by comparing past cases with real-time data.

[0177] Based on the analysis results, the server formulates specific preventative measures. During this process, prompts such as "Suggest effective relaxation methods during high-stress situations" are input into the AI ​​model, which then provides appropriate advice, including psychological factors. This makes it possible to create preventative measures that contribute to stress reduction and improved mental health.

[0178] The developed preventative measures will be communicated to users via their devices. The notifications will use diagrams and graphs to present the information visually and clearly. For example, "Because your stress levels are high, we recommend listening to relaxing music for 10 minutes every day this week."

[0179] Users implement the notified preventative measures and input feedback, which is then sent to the server. This feedback is used as training data for the generating AI model, improving the accuracy of information analysis and formulation methods. An example of a prompt is, "Learn how to provide better health advice based on the user's implementation results." This allows the system to optimize itself to each user's individual circumstances and continuously improve the quality of the service.

[0180] Thus, this invention supports users' health management through a novel approach that integrates biometric information and emotional data.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The device uses portable or wearable equipment to collect biometric information such as the user's heart rate, steps taken, and body temperature, as well as emotional data such as facial expressions and voice tone. This data is acquired by sensors, cameras, and microphones. The input is the user's real-time physical information and audio / video data, and the output generates specific numerical data read by the sensors and digital data in a format suitable for analysis.

[0184] Step 2:

[0185] The device transmits collected biometric and emotional data to a server via Wi-Fi or Bluetooth. This communication is performed periodically to ensure the data remains fresh. Input consists of numerical and digital data organized by the device, while output is an integrated data packet transferred to the server.

[0186] Step 3:

[0187] The server stores the received biometric and emotional data in a database and performs integrated processing. Generative AI models and statistical models are used to analyze the data and identify trends in health risks. The input is a dataset sent from the terminal, and the output is the analyzed data and specific information regarding predicted health risks.

[0188] Step 4:

[0189] The server generates specific preventative measures based on the analyzed data. Here, a generative AI model is used to formulate preventative measures using the prompt "Suggest effective relaxation methods during high stress." The input is the analysis results from step 3, and the output is a specific preventative measure suggestion for the user.

[0190] Step 5:

[0191] The device receives preventative measures transmitted from the server and notifies the user as visual information. The notification is displayed on the smartphone screen with diagrams and graphs, and is designed to be easily understood by the user. The input is the formulated preventative measures, and the output is the visually displayed information.

[0192] Step 6:

[0193] Users implement the notified preventative measures and input the effects and feedback obtained into their device. This feedback is recorded in the form of a questionnaire or free-form text. The input consists of the user's actual experience, impressions, and results, while the output is feedback data sent to the server.

[0194] Step 7:

[0195] The server receives feedback data from users and uses a generated AI model to improve information analysis and formulation methods. This enables the provision of more accurate health management in the future. The input is feedback data, and the output is an updated and improved analysis model and preventive measures.

[0196] (Application Example 2)

[0197] 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".

[0198] Traditional health management systems are limited to biometric information and do not take psychological states into account, making it difficult to predict comprehensive health risks. Furthermore, they lack mechanisms to effectively utilize individual user feedback and improve preventive measures in a timely manner, resulting in a failure to achieve personalized health management that meets real needs.

[0199] 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.

[0200] In this invention, the server includes information gathering means, data analysis means, risk prediction means, countermeasure generation means, transmission means, and feedback processing means. This enables comprehensive health risk prediction based on data integrating biometric information and psychological state, and the provision of personalized preventive measures.

[0201] "Information gathering means" refers to devices and methods for acquiring a user's biometric information and psychological state.

[0202] A "data analysis method" is a system for integrating and analyzing collected biological information and psychological states.

[0203] A "risk prediction tool" is a function that estimates future health risks based on data analysis results.

[0204] "Measures generation methods" refer to the process of designing specific preventive measures in response to predicted health risks.

[0205] "Means of communication" refers to methods for conveying the generated preventive measures to users.

[0206] A "feedback processing mechanism" is the part of the system that receives feedback from users and uses it to improve predictions and countermeasures.

[0207] The objective of this invention is a system that provides comprehensive health management considering the user's biometric information and psychological state. This system collects biometric information and psychological state through mobile devices or wearable devices carried by the user. The obtained data is transmitted to a server in the cloud, where it is processed by data analysis means. The server uses generative artificial intelligence and statistical analysis models to perform an integrated analysis of biometric information and psychological state. Based on the results of this analysis, it predicts the user's future health risks and generates individually tailored preventive measures. The generated preventive measures are notified to the user's terminal and presented in an easy-to-understand manner using diagrams and graphs.

[0208] For example, if a user is detected to be experiencing high stress due to long working hours, the notification system from the server will suggest relaxation techniques to calm their emotions or encourage participation in health guidance events. Furthermore, user feedback is sent to the server and used by the feedback processing system to continuously improve the accuracy of predictive models and preventative measures.

[0209] Specifically, an application installed on a mobile device monitors the user's heart rate, body temperature, facial expressions, voice tone, etc., using sensors, and this data is transmitted in real time to a server in the cloud. On the server, a generative AI model analyzes the data and proposes appropriate preventive measures based on a prompt message that reads, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The generated preventive measures are then notified to the user through the smartphone app. This allows the user to comprehensively understand their health status and take appropriate measures.

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

[0211] Step 1:

[0212] The device collects the user's biometric and psychological state data. Specifically, it uses sensors to acquire heart rate, body temperature, facial expressions, and voice tone. The input is raw data from the sensors, and the output is integrated biometric and psychological state data.

[0213] Step 2:

[0214] The device transmits the collected data to a server in the cloud in real time. This operation includes data packaging and encryption. The input is integrated biometric and psychological state data, and the output is secure data transmission to the server.

[0215] Step 3:

[0216] The server analyzes received data using generative artificial intelligence and statistical analysis models. Specifically, it performs data cleansing and normalization before inputting the data into the analysis algorithm. The input is data sent from the terminal, and the output is a health risk assessment based on the analysis results.

[0217] Step 4:

[0218] The server generates preventative measures based on the analysis results. Here, it uses a generative AI model to execute the prompt message, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The input is the analysis results, and the output is specific and personalized preventative measures.

[0219] Step 5:

[0220] The server transmits the generated preventative measures to the user's device. This transmission includes conversion to a user-friendly interface using push notifications. The input is the preventative measures, and the output is the notification to the user.

[0221] Step 6:

[0222] Users implement the preventative measures they receive and send feedback to the server via their device. This feedback includes details such as which preventative measures were effective and their impressions of the implementation process. The input is user feedback, and the output is used to improve the system.

[0223] Step 7:

[0224] The server improves the accuracy of its predictive models and preventive action generation based on the feedback it receives. This process involves analyzing the feedback data and retraining the model. The input is user feedback, and the output is the improved predictive model and preventive action.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system designed to support users in maintaining their health. It acquires biometric information using mobile devices and wearable devices, analyzes this information to predict future health risks, and provides specific preventative measures. The following describes the program processing of this system in natural language.

[0242] First, the device collects the user's daily biometric information through mobile devices or wearable devices. This information includes heart rate, steps taken, sleep patterns, and weight. The collected data is transmitted to a server via the internet. In addition, the server connects with external medical databases and fitness applications to obtain the user's health-related history and diagnostic data.

[0243] Next, the server integrates the collected data and performs analysis using generative artificial intelligence and statistical models. This analysis allows for an understanding of each user's health trends and predictions of future health risks. For example, it is possible to assess a user's risk of developing heart disease based on data such as steps taken and heart rate.

[0244] Subsequently, the server develops individualized preventative measures for health risks. These measures take into account the user's lifestyle and health condition and are provided as specific action plans. For example, if the server determines that the user is at high risk of heart disease, it may recommend light exercise five times a week.

[0245] The developed preventative measures are communicated to users via their devices. These notifications may include visually clear graphs and animations, making it easier for users to understand the proposed preventative measures.

[0246] Finally, the user implements the suggested preventative measures and sends the results and feedback to the server via their device. Based on the received feedback, the server improves the accuracy of its predictive models and suggestions, functioning as a system to enable more effective health management for the user.

[0247] In this way, the present invention enables the early detection of health risks in users' daily lives and the provision of appropriate preventive measures, thereby strongly supporting users in maintaining their health.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The device acquires the user's biometric information from mobile devices and wearable devices. Data such as heart rate, steps, sleep patterns, and weight are collected in real time via sensors. The collected data is temporarily stored in the device and prepared for data transfer.

[0251] Step 2:

[0252] With the user's consent, the device transmits collected biometric information to a server via the internet. The transmitted data is encrypted to protect privacy. In addition, the device periodically collects additional health data from external medical databases and fitness apps and transmits it to the server.

[0253] Step 3:

[0254] The server integrates received biometric and external information to form a unique dataset for each user. This dataset is organized and stored while maintaining information integrity. The stored data is then prepared for analysis.

[0255] Step 4:

[0256] The server applies generative artificial intelligence and statistical models using integrated data. The AI ​​algorithms learn from past trends and predict future health risks. This identifies potential health problems that users may face in the future and allows for risk assessment.

[0257] Step 5:

[0258] The server develops specific preventative measures based on predicted health risks. These measures include action plans that reflect the user's individual health status and lifestyle. For example, instructions such as recommendations for specific exercise habits may be created.

[0259] Step 6:

[0260] The device notifies the user of preventative measures received from the server. The notification is provided in a visually easy-to-understand graphical interface. Users can refer to this information in their daily activities and take appropriate action.

[0261] Step 7:

[0262] Users implement the suggested preventative measures and send feedback about their effectiveness and implementation status to the server via their device. This feedback may include areas for improvement or points of confusion.

[0263] Step 8:

[0264] The server receives feedback from users and uses it to improve the accuracy of predictive models and refine preventative measures. Through this continuous improvement process, the system evolves to support personalized and effective health management for each user.

[0265] (Example 1)

[0266] 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."

[0267] In modern society, the importance of monitoring individual health conditions in real time and taking appropriate preventive measures is increasing. However, conventional health management systems have limitations in data collection and analysis, making it difficult to predict health risks accurately and provide specific preventive measures. There is a need to improve this situation and realize personalized and optimized health maintenance for each user.

[0268] 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.

[0269] In this invention, the server includes data integration means, data analysis means, and risk prediction means. This enables comprehensive data analysis and precise prediction of health risks.

[0270] "Data acquisition means" refers to a device or method that has the function of collecting a user's biometric data.

[0271] "Data integration means" refers to a device or method that has the function of integrating acquired biometric data with external information sources to generate a single comprehensive dataset.

[0272] "Data analysis means" refers to a device or method that has the function of analyzing integrated data and evaluating health trends using generative artificial intelligence or statistical methods.

[0273] A "risk prediction tool" is a device or method that has the function of predicting future health risks based on results obtained through data analysis.

[0274] A "preventive measure generation device" is a device or method that has the function of formulating and generating specific preventive measures in response to predicted health risks.

[0275] "Notification means" refers to a device or method that has the function of notifying users of the precautions that have been generated, and includes visual display technology.

[0276] A "model improvement tool" is a device or method that has the function of receiving feedback from users and improving the accuracy of predictive models and suggestions based on that feedback.

[0277] In order to implement this invention, the following system is required.

[0278] First, the device uses a mobile device or wearable device (e.g., a smartwatch) to collect the user's daily biometric data. This data includes heart rate, steps taken, sleep patterns, and weight. The data collected by the device is encrypted and transmitted to the server via the internet in a privacy-protected manner.

[0279] Next, the server receives the data sent from the terminal and integrates it with external medical databases and fitness applications. This process generates a comprehensive dataset that includes the user's health-related history and diagnostic information.

[0280] The integrated data is analyzed on the server using generative artificial intelligence and statistical models. Python and similar programming languages ​​are used as analysis tools to evaluate users' health trends and risks. Specifically, Python libraries are used to model data trends and quantify health risks.

[0281] Based on the analysis results, the server generates individual preventative measures for health risks. These preventative measures are optimized by a generating AI model to suit the user's lifestyle and health condition. For example, for a user at risk of high blood pressure, specific guidelines recommending regular aerobic exercise are created.

[0282] The generated preventative measures are communicated to the user via their device. The notifications use graphs and animations to make them visually easy to understand, allowing the user to visually comprehend the guidelines presented.

[0283] Finally, the user sends the results and feedback of the preventative measures they have taken from their device to the server. Based on this feedback, the server functions as a system that continuously improves, enhancing the accuracy of its predictive models and recommendations.

[0284] As a specific example, it can be mentioned that a prompt sentence such as "Evaluate the health risks of users in their 30s with a weekly exercise habit of three times and propose appropriate preventive measures" is input into the generative AI model for analysis and generation of preventive measures. In this way, the system can continuously provide health management specialized for users.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The terminal collects biometric data through the user's mobile device or wearable device. The data collected includes heart rate, number of steps, sleep pattern, and weight. The input is the user's real-time biometric information, and the output is to record this information in digital format.

[0288] Step 2:

[0289] The terminal transmits the collected biometric data to the server via the Internet. In this process, the data is encrypted to ensure security during transmission. The input is the biometric information recorded on the terminal, and the output is the transmission of the encrypted data to the server.

[0290] Step 3:

[0291] The server receives the biometric data transmitted from the terminal and cooperates with external medical databases or fitness applications to obtain additional health-related data. Through the data integration process, the biometric information received as input is output as a comprehensive dataset.

[0292] Step 4:

[0293] The server analyzes the integrated dataset using AI and statistical models. The input is the integrated dataset, and the output is the analysis results regarding the user's health trends and risks. Programming tools such as Python are used for data modeling.

[0294] Step 5:

[0295] The server uses a generated AI model based on the analysis results to formulate specific preventive measures. For example, it may recommend specific exercise regimens or dietary adjustments. The input is the analysis results of health risks, and the output is a customized preventive measure for the user.

[0296] Step 6:

[0297] The terminal notifies the user of preventative measures sent from the server. These notifications are presented as visual dashboards and animations, designed for easy user understanding. The input is preventative measure data, and the output is visual information conveyed to the user.

[0298] Step 7:

[0299] Users implement the suggested preventative measures and send the results and feedback to the server via their device. The server then uses this information to improve its predictive model and suggestions. The input is feedback data, and the output is a continuously improved predictive model.

[0300] (Application Example 1)

[0301] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] In modern society, maintaining personal health has become an increasingly important issue. However, due to the busyness of daily life, it is difficult for many people to appropriately grasp their own health status and continuously practice appropriate preventive measures. In addition, general health management systems do not provide sufficient personalized preventive measures for individual health risks and lack a way to convey health information in an easy-to-understand form for users. Therefore, there is a need to provide specific and practical health management according to the user's situation and to provide means to support the improvement of the health status according to the lifestyle.

[0303] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0304] In this invention, the server includes an information acquisition means for collecting the user's biological information, an information analysis means for integrating and analyzing the biological information collected by the information acquisition means, and a prediction means for predicting future health risks based on the analysis results obtained by the information analysis means. As a result, it becomes possible to generate specific preventive measures according to individual health risks and provide them to the user via a consumer robot. Furthermore, by visually and audibly presenting the preventive measures to the user by the consumer robot, the user can more intuitively understand and easily practice the proposed content.

[0305] The "information acquisition means for collecting the user's biological information" is a function for obtaining biological information such as heart rate, number of steps, sleep pattern, etc. through a mobile device or a wearable device while the user is living their daily life.

[0306] The "information analysis means for integrating and analyzing the biological information collected by the information acquisition means" is a function for combining the collected biological information as one data set and analyzing patterns and trends.

[0307] The "prediction means for predicting future health risks" is a function for predicting possible health problems that the user may have in the future based on the analyzed data.

[0308] A "formulation tool for generating specific preventive measures" is a function that creates specific actionable guidelines and advice for users in response to predicted health risks.

[0309] "Notification means for informing users" refers to methods for informing users of the generated preventive measures, and includes functions such as visual displays and audio guidance.

[0310] "Feedback processing means for receiving feedback and improving the prediction means and formulation means" refers to a function for receiving responses and results of actions from users and improving the accuracy of predictions and suggestions accordingly.

[0311] "Means of providing preventive measures to users through consumer robots using information analysis tools" refers to a function that appropriately provides preventive measures to users through household robots based on analyzed data.

[0312] "Means of presenting preventive measures visually and audibly using consumer robots" refers to functions that enable household robots to present preventive measures to users in an easy-to-understand manner using displays and voice functions.

[0313] The system based on this invention utilizes mobile devices and wearable devices to collect and analyze biometric information in order to support the user's health management. An example of this system is described below.

[0314] First, users measure and collect biometric information in real time using mobile devices and wearable devices. Specifically, this includes heart rate, steps taken, and sleep patterns. This allows for an understanding of the user's activity level and health status in their daily life.

[0315] Next, the collected data is transmitted to a server via the network. The server integrates this biometric data and analyzes it in detail using generative artificial intelligence and statistical models. The purpose of the analysis is to predict the user's future health risks. For example, it can identify specific patterns in the data and assess the user's risk of developing heart disease.

[0316] Based on predicted health risks, specific preventative measures are developed. These measures are generated by a server and tailored to each user's health condition and lifestyle. The developed preventative measures are transmitted to a consumer robot, which presents this information to the user visually and audibly. This makes it easier for the user to intuitively understand and implement the preventative measures.

[0317] For example, if the robot determines that the user is not getting enough exercise, it will suggest via voice, "It looks like you haven't been getting enough exercise today. How about doing some 5 minutes of stretching?" and will explain how to stretch on the robot's display.

[0318] The user implements the suggested preventative measures and sends the results as feedback to the server via the end device. The server analyzes the user's feedback to improve future prediction accuracy and refine the suggestions.

[0319] This process utilizes a generative AI model and aims to improve user convenience and the effectiveness of health management. Below are examples of prompts used by the generative AI model:

[0320] "Please create audio and visual animations for a health management robot that points out the user's lack of exercise and suggests light exercises."

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

[0322] Step 1:

[0323] The terminal acquires biometric information from the wearable device.

[0324] Specifically, the system acquires data such as heart rate, steps taken, and sleep patterns, and transmits this data to a server via the network. The input is biometric data acquired by the device, and the output is integrated data sent to the server. This data is then formatted for later analysis.

[0325] Step 2:

[0326] The server aggregates the received biometric information and performs data analysis using generating AI models and statistical analysis tools.

[0327] The input is integrated data received from the terminal, and the output is the health status trends and risk assessment results for each user. The server organizes the data chronologically and detects abnormal patterns using statistical methods.

[0328] Step 3:

[0329] The server predicts the user's future health risks based on the analysis results.

[0330] Specifically, the system derives the probability of developing a particular health problem from the analyzed data. The input is the result of the data analysis, and the output is predictive data on specific health risks.

[0331] Step 4:

[0332] The server generates individual preventative measures based on predictive data.

[0333] This system utilizes generative AI models to develop behavioral guidelines tailored to the user's lifestyle and health condition. The input is predictive health risk data, and the output is specific preventative measures for the user.

[0334] Step 5:

[0335] The server transmits preventative measures it has formulated to consumer robots via a terminal.

[0336] The robot prepares to visually and audibly suggest preventative measures to the user. The input is preventative measure data from the server, and the output is the action instructions provided by the robot.

[0337] Step 6:

[0338] The user follows the robot's instructions and takes the suggested preventative measures.

[0339] For example, the user performs stretches according to the robot's instructions. The user's behavioral data is sent to the server as feedback via the terminal.

[0340] Step 7:

[0341] The server receives user feedback and uses it to improve the accuracy of the predictive model and suggestions.

[0342] The input is user feedback data, and the output is a refined prediction and proposal model. The feedback is analyzed as new data and used to train the AI ​​model.

[0343] 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.

[0344] This invention is a health management system that takes into account not only the user's biometric information but also their emotional state. By using an emotion engine, it becomes possible to incorporate psychological elements into preventative measures, resulting in more accurate health management.

[0345] First, the device collects the user's biometric information using mobile or wearable devices. This includes not only standard biometric information such as heart rate, steps taken, and body temperature, but also the acquisition of emotional data from the user via an emotion engine using sensors such as cameras and microphones. Emotional data is obtained from facial expressions, voice tone, breathing patterns, etc. This data is periodically transmitted to a server.

[0346] Next, the server integrates the transmitted biometric and emotional data and performs analysis using information analysis tools. Generative artificial intelligence and statistical models are utilized to advance the analysis process. By considering both biometric and emotional data, it becomes possible to capture new health risk trends that were previously undetectable. Based on these results, predicting future health risks enables a more comprehensive understanding of health status.

[0347] The server then develops specific preventative measures based on the prediction results. Because emotional data is available, advice related to stress reduction and mental health is also incorporated into the development process. For example, if the emotional engine detects a high stress level, suggestions for breaks and information on relaxation techniques will be added.

[0348] The developed preventative measures will be communicated to users via their devices. These notifications will include elements such as diagrams and graphs to convey information in a way that is easy for users to understand. This will enable users to manage their health in a way that also considers their emotional well-being.

[0349] Finally, users implement the notified preventative measures and send feedback on their effectiveness and implementation from their device to the server. The server uses the received feedback to continuously improve the accuracy of its predictive models and preventative measures. This optimizes the system for each individual user, providing strong support for future health management.

[0350] Thus, this invention provides a novel approach that integrates biometric information and emotional data to enable the management and provision of preventive measures for health risks, thereby promoting the comprehensive health maintenance of users.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The device acquires the user's biometric and emotional information through mobile or wearable devices. It uses sensors to collect biometric information such as heart rate, steps, and body temperature, while simultaneously recognizing emotions from facial expressions and voice using cameras and microphones. This data is stored locally for each session.

[0354] Step 2:

[0355] The device transmits collected biometric and emotional information to a server via the internet, based on the user's permission. The data is encrypted to protect privacy. The collected data is organized in a way that allows for session identification and stored in a dedicated database.

[0356] Step 3:

[0357] The server integrates the transmitted biometric and emotional information to build a dataset. The integrated data is preprocessed to remove noise and unnecessary information. As a result, clean data suitable for analysis is formed.

[0358] Step 4:

[0359] The server analyzes integrated data using generative artificial intelligence and statistical models. The AI ​​learns patterns from past data and makes inferences to predict future health risks. By analyzing the impact of changes in emotional state on biometric information, a more accurate understanding of overall health status becomes possible.

[0360] Step 5:

[0361] The server develops preventative measures based on predicted health risks. It utilizes data from the emotion engine to include stress management and mental health-conscious advice. For example, it might recommend mindfulness practices if stress levels are high.

[0362] Step 6:

[0363] The device notifies the user of preventative measures received from the server in a visually easy-to-understand format. The interface is designed to enhance the user experience by displaying detailed information using graphs and animations.

[0364] Step 7:

[0365] Users implement the notified preventative measures and send the results and feedback from their device back to the server. This feedback includes information about the effectiveness of the preventative measures and any changes in their status.

[0366] Step 8:

[0367] The server analyzes user feedback and improves its health risk prediction models and preventive measures. Through continuous improvement, the system can provide optimal health management support to each user.

[0368] (Example 2)

[0369] 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".

[0370] Conventional health management systems only consider biometric information, making it difficult to predict health risks or develop preventive measures that adequately reflect the user's emotions and psychological state. As a result, there is a possibility of overlooking risks related to stress and mental health, and comprehensive health management has not been achieved.

[0371] 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.

[0372] In this invention, the server includes data acquisition means for collecting the user's biometric information and emotional data; information analysis means for integrating and analyzing the biometric information and emotional data collected by the data acquisition means; and formulation means for predicting future health risks based on the analysis results obtained by the information analysis means and generating specific preventive measures, including psychological factors. This enables a comprehensive evaluation of the user's physical and emotional state, and allows for more accurate and personalized health management.

[0373] "Data acquisition means" refers to methods and devices for collecting users' biometric and emotional data, and includes portable and wearable devices.

[0374] "Information analysis means" refers to methods and devices for integrating and analyzing collected biometric information and emotional data, utilizing generative AI models and statistical models.

[0375] "Formulation methods" refer to methods and devices for predicting future health risks based on analysis results and generating specific preventive measures, including psychological factors.

[0376] "Notification means" refers to methods and devices for visualizing formulated preventive measures and communicating that information to users.

[0377] "Learning processing means" refers to methods and devices that receive feedback from users and use that information to improve information analysis means and formulation means using a generating AI model.

[0378] This invention is a system that realizes comprehensive health management using the user's biometric information and emotional data. The elements constituting the system are data acquisition means, information analysis means, formulation means, notification means, and learning processing means. Each process proceeds as follows.

[0379] The device uses portable devices (e.g., smartphones) and wearable devices (e.g., smartwatches) to collect biometric information such as the user's heart rate, steps taken, and body temperature using sensors. It also uses cameras and microphones to capture the user's facial expressions and voice tone, collecting this as emotional data. This data is periodically transmitted to a server via Wi-Fi or Bluetooth.

[0380] The server integrates the received biometric and emotional data and stores it in a database. Next, it uses generative AI models and statistical models to analyze the biometric and emotional data in detail. The generative AI model identifies new health risk trends by comparing past cases with real-time data.

[0381] Based on the analysis results, the server formulates specific preventative measures. During this process, prompts such as "Suggest effective relaxation methods during high-stress situations" are input into the AI ​​model, which then provides appropriate advice, including psychological factors. This makes it possible to create preventative measures that contribute to stress reduction and improved mental health.

[0382] The developed preventative measures will be communicated to users via their devices. The notifications will use diagrams and graphs to present the information visually and clearly. For example, "Because your stress levels are high, we recommend listening to relaxing music for 10 minutes every day this week."

[0383] Users implement the notified preventative measures and input feedback, which is then sent to the server. This feedback is used as training data for the generating AI model, improving the accuracy of information analysis and formulation methods. An example of a prompt is, "Learn how to provide better health advice based on the user's implementation results." This allows the system to optimize itself to each user's individual circumstances and continuously improve the quality of the service.

[0384] Thus, this invention supports users' health management through a novel approach that integrates biometric information and emotional data.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The device uses portable or wearable equipment to collect biometric information such as the user's heart rate, steps taken, and body temperature, as well as emotional data such as facial expressions and voice tone. This data is acquired by sensors, cameras, and microphones. The input is the user's real-time physical information and audio / video data, and the output generates specific numerical data read by the sensors and digital data in a format suitable for analysis.

[0388] Step 2:

[0389] The device transmits collected biometric and emotional data to a server via Wi-Fi or Bluetooth. This communication is performed periodically to ensure the data remains fresh. Input consists of numerical and digital data organized by the device, while output is an integrated data packet transferred to the server.

[0390] Step 3:

[0391] The server stores the received biometric and emotional data in a database and performs integrated processing. Generative AI models and statistical models are used to analyze the data and identify trends in health risks. The input is a dataset sent from the terminal, and the output is the analyzed data and specific information regarding predicted health risks.

[0392] Step 4:

[0393] The server generates specific preventative measures based on the analyzed data. Here, a generative AI model is used to formulate preventative measures using the prompt "Suggest effective relaxation methods during high stress." The input is the analysis results from step 3, and the output is a specific preventative measure suggestion for the user.

[0394] Step 5:

[0395] The device receives preventative measures transmitted from the server and notifies the user as visual information. The notification is displayed on the smartphone screen with diagrams and graphs, and is designed to be easily understood by the user. The input is the formulated preventative measures, and the output is the visually displayed information.

[0396] Step 6:

[0397] Users implement the notified preventative measures and input the effects and feedback obtained into their device. This feedback is recorded in the form of a questionnaire or free-form text. The input consists of the user's actual experience, impressions, and results, while the output is feedback data sent to the server.

[0398] Step 7:

[0399] The server receives feedback data from users and uses a generated AI model to improve information analysis and formulation methods. This enables the provision of more accurate health management in the future. The input is feedback data, and the output is an updated and improved analysis model and preventive measures.

[0400] (Application Example 2)

[0401] 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."

[0402] Traditional health management systems are limited to biometric information and do not take psychological states into account, making it difficult to predict comprehensive health risks. Furthermore, they lack mechanisms to effectively utilize individual user feedback and improve preventive measures in a timely manner, resulting in a failure to achieve personalized health management that meets real needs.

[0403] 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.

[0404] In this invention, the server includes information gathering means, data analysis means, risk prediction means, countermeasure generation means, transmission means, and feedback processing means. This enables comprehensive health risk prediction based on data integrating biometric information and psychological state, and the provision of personalized preventive measures.

[0405] "Information gathering means" refers to devices and methods for acquiring a user's biometric information and psychological state.

[0406] A "data analysis method" is a system for integrating and analyzing collected biological information and psychological states.

[0407] A "risk prediction tool" is a function that estimates future health risks based on data analysis results.

[0408] "Measures generation methods" refer to the process of designing specific preventive measures in response to predicted health risks.

[0409] "Means of communication" refers to methods for conveying the generated preventive measures to users.

[0410] A "feedback processing mechanism" is the part of the system that receives feedback from users and uses it to improve predictions and countermeasures.

[0411] The objective of this invention is a system that provides comprehensive health management considering the user's biometric information and psychological state. This system collects biometric information and psychological state through mobile devices or wearable devices carried by the user. The obtained data is transmitted to a server in the cloud, where it is processed by data analysis means. The server uses generative artificial intelligence and statistical analysis models to perform an integrated analysis of biometric information and psychological state. Based on the results of this analysis, it predicts the user's future health risks and generates individually tailored preventive measures. The generated preventive measures are notified to the user's terminal and presented in an easy-to-understand manner using diagrams and graphs.

[0412] For example, if a user is detected to be experiencing high stress due to long working hours, the notification system from the server will suggest relaxation techniques to calm their emotions or encourage participation in health guidance events. Furthermore, user feedback is sent to the server and used by the feedback processing system to continuously improve the accuracy of predictive models and preventative measures.

[0413] Specifically, an application installed on a mobile device monitors the user's heart rate, body temperature, facial expressions, voice tone, etc., using sensors, and this data is transmitted in real time to a server in the cloud. On the server, a generative AI model analyzes the data and proposes appropriate preventive measures based on a prompt message that reads, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The generated preventive measures are then notified to the user through the smartphone app. This allows the user to comprehensively understand their health status and take appropriate measures.

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The device collects the user's biometric and psychological state data. Specifically, it uses sensors to acquire heart rate, body temperature, facial expressions, and voice tone. The input is raw data from the sensors, and the output is integrated biometric and psychological state data.

[0417] Step 2:

[0418] The device transmits the collected data to a server in the cloud in real time. This operation includes data packaging and encryption. The input is integrated biometric and psychological state data, and the output is secure data transmission to the server.

[0419] Step 3:

[0420] The server analyzes received data using generative artificial intelligence and statistical analysis models. Specifically, it performs data cleansing and normalization before inputting the data into the analysis algorithm. The input is data sent from the terminal, and the output is a health risk assessment based on the analysis results.

[0421] Step 4:

[0422] The server generates preventative measures based on the analysis results. Here, it uses a generative AI model to execute the prompt message, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The input is the analysis results, and the output is specific and personalized preventative measures.

[0423] Step 5:

[0424] The server transmits the generated preventative measures to the user's device. This transmission includes conversion to a user-friendly interface using push notifications. The input is the preventative measures, and the output is the notification to the user.

[0425] Step 6:

[0426] Users implement the preventative measures they receive and send feedback to the server via their device. This feedback includes details such as which preventative measures were effective and their impressions of the implementation process. The input is user feedback, and the output is used to improve the system.

[0427] Step 7:

[0428] The server improves the accuracy of its predictive models and preventive action generation based on the feedback it receives. This process involves analyzing the feedback data and retraining the model. The input is user feedback, and the output is the improved predictive model and preventive action.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention is a system designed to support users in maintaining their health. It acquires biometric information using mobile devices and wearable devices, analyzes this information to predict future health risks, and provides specific preventative measures. The following describes the program processing of this system in natural language.

[0446] First, the device collects the user's daily biometric information through mobile devices or wearable devices. This information includes heart rate, steps taken, sleep patterns, and weight. The collected data is transmitted to a server via the internet. In addition, the server connects with external medical databases and fitness applications to obtain the user's health-related history and diagnostic data.

[0447] Next, the server integrates the collected data and performs analysis using generative artificial intelligence and statistical models. This analysis allows for an understanding of each user's health trends and predictions of future health risks. For example, it is possible to assess a user's risk of developing heart disease based on data such as steps taken and heart rate.

[0448] Subsequently, the server develops individualized preventative measures for health risks. These measures take into account the user's lifestyle and health condition and are provided as specific action plans. For example, if the server determines that the user is at high risk of heart disease, it may recommend light exercise five times a week.

[0449] The developed preventative measures are communicated to users via their devices. These notifications may include visually clear graphs and animations, making it easier for users to understand the proposed preventative measures.

[0450] Finally, the user implements the suggested preventative measures and sends the results and feedback to the server via their device. Based on the received feedback, the server improves the accuracy of its predictive models and suggestions, functioning as a system to enable more effective health management for the user.

[0451] In this way, the present invention enables the early detection of health risks in users' daily lives and the provision of appropriate preventive measures, thereby strongly supporting users in maintaining their health.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The device acquires the user's biometric information from mobile devices and wearable devices. Data such as heart rate, steps, sleep patterns, and weight are collected in real time via sensors. The collected data is temporarily stored in the device and prepared for data transfer.

[0455] Step 2:

[0456] With the user's consent, the device transmits collected biometric information to a server via the internet. The transmitted data is encrypted to protect privacy. In addition, the device periodically collects additional health data from external medical databases and fitness apps and transmits it to the server.

[0457] Step 3:

[0458] The server integrates received biometric and external information to form a unique dataset for each user. This dataset is organized and stored while maintaining information integrity. The stored data is then prepared for analysis.

[0459] Step 4:

[0460] The server applies generative artificial intelligence and statistical models using integrated data. The AI ​​algorithms learn from past trends and predict future health risks. This identifies potential health problems that users may face in the future and allows for risk assessment.

[0461] Step 5:

[0462] The server develops specific preventative measures based on predicted health risks. These measures include action plans that reflect the user's individual health status and lifestyle. For example, instructions such as recommendations for specific exercise habits may be created.

[0463] Step 6:

[0464] The device notifies the user of preventative measures received from the server. The notification is provided in a visually easy-to-understand graphical interface. Users can refer to this information in their daily activities and take appropriate action.

[0465] Step 7:

[0466] Users implement the suggested preventative measures and send feedback about their effectiveness and implementation status to the server via their device. This feedback may include areas for improvement or points of confusion.

[0467] Step 8:

[0468] The server receives feedback from users and uses it to improve the accuracy of predictive models and refine preventative measures. Through this continuous improvement process, the system evolves to support personalized and effective health management for each user.

[0469] (Example 1)

[0470] 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."

[0471] In modern society, the importance of monitoring individual health conditions in real time and taking appropriate preventive measures is increasing. However, conventional health management systems have limitations in data collection and analysis, making it difficult to predict health risks accurately and provide specific preventive measures. There is a need to improve this situation and realize personalized and optimized health maintenance for each user.

[0472] 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.

[0473] In this invention, the server includes data integration means, data analysis means, and risk prediction means. This enables comprehensive data analysis and precise prediction of health risks.

[0474] "Data acquisition means" refers to a device or method that has the function of collecting a user's biometric data.

[0475] "Data integration means" refers to a device or method that has the function of integrating acquired biometric data with external information sources to generate a single comprehensive dataset.

[0476] "Data analysis means" refers to a device or method that has the function of analyzing integrated data and evaluating health trends using generative artificial intelligence or statistical methods.

[0477] A "risk prediction tool" is a device or method that has the function of predicting future health risks based on results obtained through data analysis.

[0478] A "preventive measure generation device" is a device or method that has the function of formulating and generating specific preventive measures in response to predicted health risks.

[0479] "Notification means" refers to a device or method that has the function of notifying users of the precautions that have been generated, and includes visual display technology.

[0480] A "model improvement tool" is a device or method that has the function of receiving feedback from users and improving the accuracy of predictive models and suggestions based on that feedback.

[0481] In order to implement this invention, the following system is required.

[0482] First, the device uses a mobile device or wearable device (e.g., a smartwatch) to collect the user's daily biometric data. This data includes heart rate, steps taken, sleep patterns, and weight. The data collected by the device is encrypted and transmitted to the server via the internet in a privacy-protected manner.

[0483] Next, the server receives the data sent from the terminal and integrates it with external medical databases and fitness applications. This process generates a comprehensive dataset that includes the user's health-related history and diagnostic information.

[0484] The integrated data is analyzed on the server using generative artificial intelligence and statistical models. Python and similar programming languages ​​are used as analysis tools to evaluate users' health trends and risks. Specifically, Python libraries are used to model data trends and quantify health risks.

[0485] Based on the analysis results, the server generates individual preventative measures for health risks. These preventative measures are optimized by a generating AI model to suit the user's lifestyle and health condition. For example, for a user at risk of high blood pressure, specific guidelines recommending regular aerobic exercise are created.

[0486] The generated preventative measures are communicated to the user via their device. The notifications use graphs and animations to make them visually easy to understand, allowing the user to visually comprehend the guidelines presented.

[0487] Finally, the user sends the results and feedback of the preventative measures they have taken from their device to the server. Based on this feedback, the server functions as a system that continuously improves, enhancing the accuracy of its predictive models and recommendations.

[0488] As a concrete example, a prompt message such as "Evaluate the health risks of a male in his 30s who exercises three times a week, and suggest appropriate preventive measures" can be input into an AI model for analysis and generation of preventive measures. In this way, the system can continuously provide personalized health management to the user.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The device collects biometric data through the user's mobile or wearable device. The collected data includes heart rate, steps taken, sleep patterns, and weight. The input is the user's real-time biometric information, and the output is a digital record of this information.

[0492] Step 2:

[0493] The device transmits the collected biometric data to a server via the internet. During this process, the data is encrypted to ensure security during transmission. The input is the biometric information recorded on the device, and the output is the transmission of encrypted data to the server.

[0494] Step 3:

[0495] The server receives biometric data transmitted from the terminal and integrates with external medical databases and fitness applications to acquire additional health-related data. Through the data integration process, the biometric information received as input is output as a comprehensive dataset.

[0496] Step 4:

[0497] The server analyzes the integrated dataset using AI and statistical models. The input is the integrated dataset, and the output is the analysis results regarding the user's health trends and risks. Programming tools such as Python are used for data modeling.

[0498] Step 5:

[0499] The server uses a generated AI model based on the analysis results to formulate specific preventive measures. For example, it may recommend specific exercise regimens or dietary adjustments. The input is the analysis results of health risks, and the output is a customized preventive measure for the user.

[0500] Step 6:

[0501] The terminal notifies the user of preventative measures sent from the server. These notifications are presented as visual dashboards and animations, designed for easy user understanding. The input is preventative measure data, and the output is visual information conveyed to the user.

[0502] Step 7:

[0503] Users implement the suggested preventative measures and send the results and feedback to the server via their device. The server then uses this information to improve its predictive model and suggestions. The input is feedback data, and the output is a continuously improved predictive model.

[0504] (Application Example 1)

[0505] 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."

[0506] In modern society, maintaining personal health is becoming an increasingly important issue. However, many people find it difficult to properly understand their own health status and consistently implement appropriate preventive measures due to the busyness of their daily lives. Furthermore, general health management systems do not adequately provide personalized preventive measures for individual health risks, and there is a lack of ways to convey health information in a way that is easy for users to understand. Therefore, there is a need to provide specific and practical health management tailored to the user's situation and to offer means to support the improvement of their health status in accordance with their lifestyle.

[0507] 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.

[0508] In this invention, the server includes an information acquisition means for collecting the user's biometric information, an information analysis means for integrating and analyzing the biometric information collected by the information acquisition means, and a prediction means for predicting future health risks based on the analysis results obtained by the information analysis means. This makes it possible to generate specific preventive measures tailored to individual health risks and provide them to the user via a consumer robot. Furthermore, by presenting the preventive measures to the user visually and audibly via the consumer robot, the user can more easily intuitively understand and implement the proposed measures.

[0509] "Information acquisition means for collecting user biometric information" refers to a function that acquires biometric information such as heart rate, steps taken, and sleep patterns from users as they go about their daily lives through mobile devices or wearable devices.

[0510] "Information analysis means for integrating and analyzing biological information collected by information acquisition means" refers to a function for combining collected biological information into a single dataset and analyzing patterns and trends.

[0511] A "predictive tool for predicting future health risks" is a function that predicts potential health problems that users may face in the future, based on analyzed data.

[0512] A "formulation tool for generating specific preventive measures" is a function that creates specific actionable guidelines and advice for users in response to predicted health risks.

[0513] "Notification means for informing users" refers to methods for informing users of the generated preventive measures, and includes functions such as visual displays and audio guidance.

[0514] "Feedback processing means for receiving feedback and improving the prediction means and formulation means" refers to a function for receiving responses and results of actions from users and improving the accuracy of predictions and suggestions accordingly.

[0515] "Means of providing preventive measures to users through consumer robots using information analysis tools" refers to a function that appropriately provides preventive measures to users through household robots based on analyzed data.

[0516] "Means of presenting preventive measures visually and audibly using consumer robots" refers to functions that enable household robots to present preventive measures to users in an easy-to-understand manner using displays and voice functions.

[0517] The system based on this invention utilizes mobile devices and wearable devices to collect and analyze biometric information in order to support the user's health management. An example of this system is described below.

[0518] First, users measure and collect biometric information in real time using mobile devices and wearable devices. Specifically, this includes heart rate, steps taken, and sleep patterns. This allows for an understanding of the user's activity level and health status in their daily life.

[0519] Next, the collected data is transmitted to a server via the network. The server integrates this biometric data and analyzes it in detail using generative artificial intelligence and statistical models. The purpose of the analysis is to predict the user's future health risks. For example, it can identify specific patterns in the data and assess the user's risk of developing heart disease.

[0520] Based on predicted health risks, specific preventative measures are developed. These measures are generated by a server and tailored to each user's health condition and lifestyle. The developed preventative measures are transmitted to a consumer robot, which presents this information to the user visually and audibly. This makes it easier for the user to intuitively understand and implement the preventative measures.

[0521] For example, if the robot determines that the user is not getting enough exercise, it will suggest via voice, "It looks like you haven't been getting enough exercise today. How about doing some 5 minutes of stretching?" and will explain how to stretch on the robot's display.

[0522] The user implements the suggested preventative measures and sends the results as feedback to the server via the end device. The server analyzes the user's feedback to improve future prediction accuracy and refine the suggestions.

[0523] This process utilizes a generative AI model and aims to improve user convenience and the effectiveness of health management. Below are examples of prompts used by the generative AI model:

[0524] "Please create audio and visual animations for a health management robot that points out the user's lack of exercise and suggests light exercises."

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The terminal acquires biometric information from the wearable device.

[0528] Specifically, the system acquires data such as heart rate, steps taken, and sleep patterns, and transmits this data to a server via the network. The input is biometric data acquired by the device, and the output is integrated data sent to the server. This data is then formatted for later analysis.

[0529] Step 2:

[0530] The server aggregates the received biometric information and performs data analysis using generating AI models and statistical analysis tools.

[0531] The input is integrated data received from the terminal, and the output is the health status trends and risk assessment results for each user. The server organizes the data chronologically and detects abnormal patterns using statistical methods.

[0532] Step 3:

[0533] The server predicts the user's future health risks based on the analysis results.

[0534] Specifically, the system derives the probability of developing a particular health problem from the analyzed data. The input is the result of the data analysis, and the output is predictive data on specific health risks.

[0535] Step 4:

[0536] The server generates individual preventative measures based on predictive data.

[0537] This system utilizes generative AI models to develop behavioral guidelines tailored to the user's lifestyle and health condition. The input is predictive health risk data, and the output is specific preventative measures for the user.

[0538] Step 5:

[0539] The server transmits preventative measures it has formulated to consumer robots via a terminal.

[0540] The robot prepares to visually and audibly suggest preventative measures to the user. The input is preventative measure data from the server, and the output is the action instructions provided by the robot.

[0541] Step 6:

[0542] The user follows the robot's instructions and takes the suggested preventative measures.

[0543] For example, the user performs stretches according to the robot's instructions. The user's behavioral data is sent to the server as feedback via the terminal.

[0544] Step 7:

[0545] The server receives user feedback and uses it to improve the accuracy of the predictive model and suggestions.

[0546] The input is user feedback data, and the output is a refined prediction and proposal model. The feedback is analyzed as new data and used to train the AI ​​model.

[0547] 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.

[0548] This invention is a health management system that takes into account not only the user's biometric information but also their emotional state. By using an emotion engine, it becomes possible to incorporate psychological elements into preventative measures, resulting in more accurate health management.

[0549] First, the device collects the user's biometric information using mobile or wearable devices. This includes not only standard biometric information such as heart rate, steps taken, and body temperature, but also the acquisition of emotional data from the user via an emotion engine using sensors such as cameras and microphones. Emotional data is obtained from facial expressions, voice tone, breathing patterns, etc. This data is periodically transmitted to a server.

[0550] Next, the server integrates the transmitted biometric and emotional data and performs analysis using information analysis tools. Generative artificial intelligence and statistical models are utilized to advance the analysis process. By considering both biometric and emotional data, it becomes possible to capture new health risk trends that were previously undetectable. Based on these results, predicting future health risks enables a more comprehensive understanding of health status.

[0551] The server then develops specific preventative measures based on the prediction results. Because emotional data is available, advice related to stress reduction and mental health is also incorporated into the development process. For example, if the emotional engine detects a high stress level, suggestions for breaks and information on relaxation techniques will be added.

[0552] The developed preventative measures will be communicated to users via their devices. These notifications will include elements such as diagrams and graphs to convey information in a way that is easy for users to understand. This will enable users to manage their health in a way that also considers their emotional well-being.

[0553] Finally, users implement the notified preventative measures and send feedback on their effectiveness and implementation from their device to the server. The server uses the received feedback to continuously improve the accuracy of its predictive models and preventative measures. This optimizes the system for each individual user, providing strong support for future health management.

[0554] Thus, this invention provides a novel approach that integrates biometric information and emotional data to enable the management and provision of preventive measures for health risks, thereby promoting the comprehensive health maintenance of users.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The device acquires the user's biometric and emotional information through mobile or wearable devices. It uses sensors to collect biometric information such as heart rate, steps, and body temperature, while simultaneously recognizing emotions from facial expressions and voice using cameras and microphones. This data is stored locally for each session.

[0558] Step 2:

[0559] The device transmits collected biometric and emotional information to a server via the internet, based on the user's permission. The data is encrypted to protect privacy. The collected data is organized in a way that allows for session identification and stored in a dedicated database.

[0560] Step 3:

[0561] The server integrates the transmitted biometric and emotional information to build a dataset. The integrated data is preprocessed to remove noise and unnecessary information. As a result, clean data suitable for analysis is formed.

[0562] Step 4:

[0563] The server analyzes integrated data using generative artificial intelligence and statistical models. The AI ​​learns patterns from past data and makes inferences to predict future health risks. By analyzing the impact of changes in emotional state on biometric information, a more accurate understanding of overall health status becomes possible.

[0564] Step 5:

[0565] The server develops preventative measures based on predicted health risks. It utilizes data from the emotion engine to include stress management and mental health-conscious advice. For example, it might recommend mindfulness practices if stress levels are high.

[0566] Step 6:

[0567] The device notifies the user of preventative measures received from the server in a visually easy-to-understand format. The interface is designed to enhance the user experience by displaying detailed information using graphs and animations.

[0568] Step 7:

[0569] Users implement the notified preventative measures and send the results and feedback from their device back to the server. This feedback includes information about the effectiveness of the preventative measures and any changes in their status.

[0570] Step 8:

[0571] The server analyzes user feedback and improves its health risk prediction models and preventive measures. Through continuous improvement, the system can provide optimal health management support to each user.

[0572] (Example 2)

[0573] 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."

[0574] Conventional health management systems only consider biometric information, making it difficult to predict health risks or develop preventive measures that adequately reflect the user's emotions and psychological state. As a result, there is a possibility of overlooking risks related to stress and mental health, and comprehensive health management has not been achieved.

[0575] 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.

[0576] In this invention, the server includes data acquisition means for collecting the user's biometric information and emotional data; information analysis means for integrating and analyzing the biometric information and emotional data collected by the data acquisition means; and formulation means for predicting future health risks based on the analysis results obtained by the information analysis means and generating specific preventive measures, including psychological factors. This enables a comprehensive evaluation of the user's physical and emotional state, and allows for more accurate and personalized health management.

[0577] "Data acquisition means" refers to methods and devices for collecting users' biometric and emotional data, and includes portable and wearable devices.

[0578] "Information analysis means" refers to methods and devices for integrating and analyzing collected biometric information and emotional data, utilizing generative AI models and statistical models.

[0579] "Formulation methods" refer to methods and devices for predicting future health risks based on analysis results and generating specific preventive measures, including psychological factors.

[0580] "Notification means" refers to methods and devices for visualizing formulated preventive measures and communicating that information to users.

[0581] "Learning processing means" refers to methods and devices that receive feedback from users and use that information to improve information analysis means and formulation means using a generating AI model.

[0582] This invention is a system that realizes comprehensive health management using the user's biometric information and emotional data. The elements constituting the system are data acquisition means, information analysis means, formulation means, notification means, and learning processing means. Each process proceeds as follows.

[0583] The device uses portable devices (e.g., smartphones) and wearable devices (e.g., smartwatches) to collect biometric information such as the user's heart rate, steps taken, and body temperature using sensors. It also uses cameras and microphones to capture the user's facial expressions and voice tone, collecting this as emotional data. This data is periodically transmitted to a server via Wi-Fi or Bluetooth.

[0584] The server integrates the received biometric and emotional data and stores it in a database. Next, it uses generative AI models and statistical models to analyze the biometric and emotional data in detail. The generative AI model identifies new health risk trends by comparing past cases with real-time data.

[0585] Based on the analysis results, the server formulates specific preventative measures. During this process, prompts such as "Suggest effective relaxation methods during high-stress situations" are input into the AI ​​model, which then provides appropriate advice, including psychological factors. This makes it possible to create preventative measures that contribute to stress reduction and improved mental health.

[0586] The developed preventative measures will be communicated to users via their devices. The notifications will use diagrams and graphs to present the information visually and clearly. For example, "Because your stress levels are high, we recommend listening to relaxing music for 10 minutes every day this week."

[0587] Users implement the notified preventative measures and input feedback, which is then sent to the server. This feedback is used as training data for the generating AI model, improving the accuracy of information analysis and formulation methods. An example of a prompt is, "Learn how to provide better health advice based on the user's implementation results." This allows the system to optimize itself to each user's individual circumstances and continuously improve the quality of the service.

[0588] Thus, this invention supports users' health management through a novel approach that integrates biometric information and emotional data.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The device uses portable or wearable equipment to collect biometric information such as the user's heart rate, steps taken, and body temperature, as well as emotional data such as facial expressions and voice tone. This data is acquired by sensors, cameras, and microphones. The input is the user's real-time physical information and audio / video data, and the output generates specific numerical data read by the sensors and digital data in a format suitable for analysis.

[0592] Step 2:

[0593] The device transmits collected biometric and emotional data to a server via Wi-Fi or Bluetooth. This communication is performed periodically to ensure the data remains fresh. Input consists of numerical and digital data organized by the device, while output is an integrated data packet transferred to the server.

[0594] Step 3:

[0595] The server stores the received biometric and emotional data in a database and performs integrated processing. Generative AI models and statistical models are used to analyze the data and identify trends in health risks. The input is a dataset sent from the terminal, and the output is the analyzed data and specific information regarding predicted health risks.

[0596] Step 4:

[0597] The server generates specific preventative measures based on the analyzed data. Here, a generative AI model is used to formulate preventative measures using the prompt "Suggest effective relaxation methods during high stress." The input is the analysis results from step 3, and the output is a specific preventative measure suggestion for the user.

[0598] Step 5:

[0599] The device receives preventative measures transmitted from the server and notifies the user as visual information. The notification is displayed on the smartphone screen with diagrams and graphs, and is designed to be easily understood by the user. The input is the formulated preventative measures, and the output is the visually displayed information.

[0600] Step 6:

[0601] Users implement the notified preventative measures and input the effects and feedback obtained into their device. This feedback is recorded in the form of a questionnaire or free-form text. The input consists of the user's actual experience, impressions, and results, while the output is feedback data sent to the server.

[0602] Step 7:

[0603] The server receives feedback data from users and uses a generated AI model to improve information analysis and formulation methods. This enables the provision of more accurate health management in the future. The input is feedback data, and the output is an updated and improved analysis model and preventive measures.

[0604] (Application Example 2)

[0605] 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."

[0606] Traditional health management systems are limited to biometric information and do not take psychological states into account, making it difficult to predict comprehensive health risks. Furthermore, they lack mechanisms to effectively utilize individual user feedback and improve preventive measures in a timely manner, resulting in a failure to achieve personalized health management that meets real needs.

[0607] 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.

[0608] In this invention, the server includes information gathering means, data analysis means, risk prediction means, countermeasure generation means, transmission means, and feedback processing means. This enables comprehensive health risk prediction based on data integrating biometric information and psychological state, and the provision of personalized preventive measures.

[0609] "Information gathering means" refers to devices and methods for acquiring a user's biometric information and psychological state.

[0610] A "data analysis method" is a system for integrating and analyzing collected biological information and psychological states.

[0611] A "risk prediction tool" is a function that estimates future health risks based on data analysis results.

[0612] "Measures generation methods" refer to the process of designing specific preventive measures in response to predicted health risks.

[0613] "Means of communication" refers to methods for conveying the generated preventive measures to users.

[0614] A "feedback processing mechanism" is the part of the system that receives feedback from users and uses it to improve predictions and countermeasures.

[0615] The objective of this invention is a system that provides comprehensive health management considering the user's biometric information and psychological state. This system collects biometric information and psychological state through mobile devices or wearable devices carried by the user. The obtained data is transmitted to a server in the cloud, where it is processed by data analysis means. The server uses generative artificial intelligence and statistical analysis models to perform an integrated analysis of biometric information and psychological state. Based on the results of this analysis, it predicts the user's future health risks and generates individually tailored preventive measures. The generated preventive measures are notified to the user's terminal and presented in an easy-to-understand manner using diagrams and graphs.

[0616] For example, if a user is detected to be experiencing high stress due to long working hours, the notification system from the server will suggest relaxation techniques to calm their emotions or encourage participation in health guidance events. Furthermore, user feedback is sent to the server and used by the feedback processing system to continuously improve the accuracy of predictive models and preventative measures.

[0617] Specifically, an application installed on a mobile device monitors the user's heart rate, body temperature, facial expressions, voice tone, etc., using sensors, and this data is transmitted in real time to a server in the cloud. On the server, a generative AI model analyzes the data and proposes appropriate preventive measures based on a prompt message that reads, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The generated preventive measures are then notified to the user through the smartphone app. This allows the user to comprehensively understand their health status and take appropriate measures.

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The device collects the user's biometric and psychological state data. Specifically, it uses sensors to acquire heart rate, body temperature, facial expressions, and voice tone. The input is raw data from the sensors, and the output is integrated biometric and psychological state data.

[0621] Step 2:

[0622] The device transmits the collected data to a server in the cloud in real time. This operation includes data packaging and encryption. The input is integrated biometric and psychological state data, and the output is secure data transmission to the server.

[0623] Step 3:

[0624] The server analyzes received data using generative artificial intelligence and statistical analysis models. Specifically, it performs data cleansing and normalization before inputting the data into the analysis algorithm. The input is data sent from the terminal, and the output is a health risk assessment based on the analysis results.

[0625] Step 4:

[0626] The server generates preventative measures based on the analysis results. Here, it uses a generative AI model to execute the prompt message, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The input is the analysis results, and the output is specific and personalized preventative measures.

[0627] Step 5:

[0628] The server transmits the generated preventative measures to the user's device. This transmission includes conversion to a user-friendly interface using push notifications. The input is the preventative measures, and the output is the notification to the user.

[0629] Step 6:

[0630] Users implement the preventative measures they receive and send feedback to the server via their device. This feedback includes details such as which preventative measures were effective and their impressions of the implementation process. The input is user feedback, and the output is used to improve the system.

[0631] Step 7:

[0632] The server improves the accuracy of its predictive models and preventive action generation based on the feedback it receives. This process involves analyzing the feedback data and retraining the model. The input is user feedback, and the output is the improved predictive model and preventive action.

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

[0634] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). 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.

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

[0636] [Fourth Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

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

[0650] This invention is a system designed to support users in maintaining their health. It acquires biometric information using mobile devices and wearable devices, analyzes this information to predict future health risks, and provides specific preventative measures. The following describes the program processing of this system in natural language.

[0651] First, the device collects the user's daily biometric information through mobile devices or wearable devices. This information includes heart rate, steps taken, sleep patterns, and weight. The collected data is transmitted to a server via the internet. In addition, the server connects with external medical databases and fitness applications to obtain the user's health-related history and diagnostic data.

[0652] Next, the server integrates the collected data and performs analysis using generative artificial intelligence and statistical models. This analysis allows for an understanding of each user's health trends and predictions of future health risks. For example, it is possible to assess a user's risk of developing heart disease based on data such as steps taken and heart rate.

[0653] Subsequently, the server develops individualized preventative measures for health risks. These measures take into account the user's lifestyle and health condition and are provided as specific action plans. For example, if the server determines that the user is at high risk of heart disease, it may recommend light exercise five times a week.

[0654] The developed preventative measures are communicated to users via their devices. These notifications may include visually clear graphs and animations, making it easier for users to understand the proposed preventative measures.

[0655] Finally, the user implements the suggested preventative measures and sends the results and feedback to the server via their device. Based on the received feedback, the server improves the accuracy of its predictive models and suggestions, functioning as a system to enable more effective health management for the user.

[0656] In this way, the present invention enables the early detection of health risks in users' daily lives and the provision of appropriate preventive measures, thereby strongly supporting users in maintaining their health.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The device acquires the user's biometric information from mobile devices and wearable devices. Data such as heart rate, steps, sleep patterns, and weight are collected in real time via sensors. The collected data is temporarily stored in the device and prepared for data transfer.

[0660] Step 2:

[0661] With the user's consent, the device transmits collected biometric information to a server via the internet. The transmitted data is encrypted to protect privacy. In addition, the device periodically collects additional health data from external medical databases and fitness apps and transmits it to the server.

[0662] Step 3:

[0663] The server integrates received biometric and external information to form a unique dataset for each user. This dataset is organized and stored while maintaining information integrity. The stored data is then prepared for analysis.

[0664] Step 4:

[0665] The server applies generative artificial intelligence and statistical models using integrated data. The AI ​​algorithms learn from past trends and predict future health risks. This identifies potential health problems that users may face in the future and allows for risk assessment.

[0666] Step 5:

[0667] The server develops specific preventative measures based on predicted health risks. These measures include action plans that reflect the user's individual health status and lifestyle. For example, instructions such as recommendations for specific exercise habits may be created.

[0668] Step 6:

[0669] The device notifies the user of preventative measures received from the server. The notification is provided in a visually easy-to-understand graphical interface. Users can refer to this information in their daily activities and take appropriate action.

[0670] Step 7:

[0671] Users implement the suggested preventative measures and send feedback about their effectiveness and implementation status to the server via their device. This feedback may include areas for improvement or points of confusion.

[0672] Step 8:

[0673] The server receives feedback from users and uses it to improve the accuracy of predictive models and refine preventative measures. Through this continuous improvement process, the system evolves to support personalized and effective health management for each user.

[0674] (Example 1)

[0675] 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".

[0676] In modern society, the importance of monitoring individual health conditions in real time and taking appropriate preventive measures is increasing. However, conventional health management systems have limitations in data collection and analysis, making it difficult to predict health risks accurately and provide specific preventive measures. There is a need to improve this situation and realize personalized and optimized health maintenance for each user.

[0677] 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.

[0678] In this invention, the server includes data integration means, data analysis means, and risk prediction means. This enables comprehensive data analysis and precise prediction of health risks.

[0679] "Data acquisition means" refers to a device or method that has the function of collecting a user's biometric data.

[0680] "Data integration means" refers to a device or method that has the function of integrating acquired biometric data with external information sources to generate a single comprehensive dataset.

[0681] "Data analysis means" refers to a device or method that has the function of analyzing integrated data and evaluating health trends using generative artificial intelligence or statistical methods.

[0682] A "risk prediction tool" is a device or method that has the function of predicting future health risks based on results obtained through data analysis.

[0683] A "preventive measure generation device" is a device or method that has the function of formulating and generating specific preventive measures in response to predicted health risks.

[0684] "Notification means" refers to a device or method that has the function of notifying users of the precautions that have been generated, and includes visual display technology.

[0685] A "model improvement tool" is a device or method that has the function of receiving feedback from users and improving the accuracy of predictive models and suggestions based on that feedback.

[0686] In order to implement this invention, the following system is required.

[0687] First, the device uses a mobile device or wearable device (e.g., a smartwatch) to collect the user's daily biometric data. This data includes heart rate, steps taken, sleep patterns, and weight. The data collected by the device is encrypted and transmitted to the server via the internet in a privacy-protected manner.

[0688] Next, the server receives the data sent from the terminal and integrates it with external medical databases and fitness applications. This process generates a comprehensive dataset that includes the user's health-related history and diagnostic information.

[0689] The integrated data is analyzed on the server using generative artificial intelligence and statistical models. Python and similar programming languages ​​are used as analysis tools to evaluate users' health trends and risks. Specifically, Python libraries are used to model data trends and quantify health risks.

[0690] Based on the analysis results, the server generates individual preventative measures for health risks. These preventative measures are optimized by a generating AI model to suit the user's lifestyle and health condition. For example, for a user at risk of high blood pressure, specific guidelines recommending regular aerobic exercise are created.

[0691] The generated preventative measures are communicated to the user via their device. The notifications use graphs and animations to make them visually easy to understand, allowing the user to visually comprehend the guidelines presented.

[0692] Finally, the user sends the results and feedback of the preventative measures they have taken from their device to the server. Based on this feedback, the server functions as a system that continuously improves, enhancing the accuracy of its predictive models and recommendations.

[0693] As a concrete example, a prompt message such as "Evaluate the health risks of a male in his 30s who exercises three times a week, and suggest appropriate preventive measures" can be input into an AI model for analysis and generation of preventive measures. In this way, the system can continuously provide personalized health management to the user.

[0694] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0695] Step 1:

[0696] The device collects biometric data through the user's mobile or wearable device. The collected data includes heart rate, steps taken, sleep patterns, and weight. The input is the user's real-time biometric information, and the output is a digital record of this information.

[0697] Step 2:

[0698] The device transmits the collected biometric data to a server via the internet. During this process, the data is encrypted to ensure security during transmission. The input is the biometric information recorded on the device, and the output is the transmission of encrypted data to the server.

[0699] Step 3:

[0700] The server receives biometric data transmitted from the terminal and integrates with external medical databases and fitness applications to acquire additional health-related data. Through the data integration process, the biometric information received as input is output as a comprehensive dataset.

[0701] Step 4:

[0702] The server analyzes the integrated dataset using AI and statistical models. The input is the integrated dataset, and the output is the analysis results regarding the user's health trends and risks. Programming tools such as Python are used for data modeling.

[0703] Step 5:

[0704] The server uses a generated AI model based on the analysis results to formulate specific preventive measures. For example, it may recommend specific exercise regimens or dietary adjustments. The input is the analysis results of health risks, and the output is a customized preventive measure for the user.

[0705] Step 6:

[0706] The terminal notifies the user of preventative measures sent from the server. These notifications are presented as visual dashboards and animations, designed for easy user understanding. The input is preventative measure data, and the output is visual information conveyed to the user.

[0707] Step 7:

[0708] Users implement the suggested preventative measures and send the results and feedback to the server via their device. The server then uses this information to improve its predictive model and suggestions. The input is feedback data, and the output is a continuously improved predictive model.

[0709] (Application Example 1)

[0710] 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".

[0711] In modern society, maintaining personal health is becoming an increasingly important issue. However, many people find it difficult to properly understand their own health status and consistently implement appropriate preventive measures due to the busyness of their daily lives. Furthermore, general health management systems do not adequately provide personalized preventive measures for individual health risks, and there is a lack of ways to convey health information in a way that is easy for users to understand. Therefore, there is a need to provide specific and practical health management tailored to the user's situation and to offer means to support the improvement of their health status in accordance with their lifestyle.

[0712] 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.

[0713] In this invention, the server includes an information acquisition means for collecting the user's biometric information, an information analysis means for integrating and analyzing the biometric information collected by the information acquisition means, and a prediction means for predicting future health risks based on the analysis results obtained by the information analysis means. This makes it possible to generate specific preventive measures tailored to individual health risks and provide them to the user via a consumer robot. Furthermore, by presenting the preventive measures to the user visually and audibly via the consumer robot, the user can more easily intuitively understand and implement the proposed measures.

[0714] "Information acquisition means for collecting user biometric information" refers to a function that acquires biometric information such as heart rate, steps taken, and sleep patterns from users as they go about their daily lives through mobile devices or wearable devices.

[0715] "Information analysis means for integrating and analyzing biological information collected by information acquisition means" refers to a function for combining collected biological information into a single dataset and analyzing patterns and trends.

[0716] A "predictive tool for predicting future health risks" is a function that predicts potential health problems that users may face in the future, based on analyzed data.

[0717] A "formulation tool for generating specific preventive measures" is a function that creates specific actionable guidelines and advice for users in response to predicted health risks.

[0718] "Notification means for informing users" refers to methods for informing users of the generated preventive measures, and includes functions such as visual displays and audio guidance.

[0719] "Feedback processing means for receiving feedback and improving the prediction means and formulation means" refers to a function for receiving responses and results of actions from users and improving the accuracy of predictions and suggestions accordingly.

[0720] "Means of providing preventive measures to users through consumer robots using information analysis tools" refers to a function that appropriately provides preventive measures to users through household robots based on analyzed data.

[0721] "Means of presenting preventive measures visually and audibly using consumer robots" refers to functions that enable household robots to present preventive measures to users in an easy-to-understand manner using displays and voice functions.

[0722] The system based on this invention utilizes mobile devices and wearable devices to collect and analyze biometric information in order to support the user's health management. An example of this system is described below.

[0723] First, users measure and collect biometric information in real time using mobile devices and wearable devices. Specifically, this includes heart rate, steps taken, and sleep patterns. This allows for an understanding of the user's activity level and health status in their daily life.

[0724] Next, the collected data is transmitted to a server via the network. The server integrates this biometric data and analyzes it in detail using generative artificial intelligence and statistical models. The purpose of the analysis is to predict the user's future health risks. For example, it can identify specific patterns in the data and assess the user's risk of developing heart disease.

[0725] Based on predicted health risks, specific preventative measures are developed. These measures are generated by a server and tailored to each user's health condition and lifestyle. The developed preventative measures are transmitted to a consumer robot, which presents this information to the user visually and audibly. This makes it easier for the user to intuitively understand and implement the preventative measures.

[0726] For example, if the robot determines that the user is not getting enough exercise, it will suggest via voice, "It looks like you haven't been getting enough exercise today. How about doing some 5 minutes of stretching?" and will explain how to stretch on the robot's display.

[0727] The user implements the suggested preventative measures and sends the results as feedback to the server via the end device. The server analyzes the user's feedback to improve future prediction accuracy and refine the suggestions.

[0728] This process utilizes a generative AI model and aims to improve user convenience and the effectiveness of health management. Below are examples of prompts used by the generative AI model:

[0729] "Please create audio and visual animations for a health management robot that points out the user's lack of exercise and suggests light exercises."

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] The terminal acquires biometric information from the wearable device.

[0733] Specifically, the system acquires data such as heart rate, steps taken, and sleep patterns, and transmits this data to a server via the network. The input is biometric data acquired by the device, and the output is integrated data sent to the server. This data is then formatted for later analysis.

[0734] Step 2:

[0735] The server aggregates the received biometric information and performs data analysis using generating AI models and statistical analysis tools.

[0736] The input is integrated data received from the terminal, and the output is the health status trends and risk assessment results for each user. The server organizes the data chronologically and detects abnormal patterns using statistical methods.

[0737] Step 3:

[0738] The server predicts the user's future health risks based on the analysis results.

[0739] Specifically, the system derives the probability of developing a particular health problem from the analyzed data. The input is the result of the data analysis, and the output is predictive data on specific health risks.

[0740] Step 4:

[0741] The server generates individual preventative measures based on predictive data.

[0742] This system utilizes generative AI models to develop behavioral guidelines tailored to the user's lifestyle and health condition. The input is predictive health risk data, and the output is specific preventative measures for the user.

[0743] Step 5:

[0744] The server transmits preventative measures it has formulated to consumer robots via a terminal.

[0745] The robot prepares to visually and audibly suggest preventative measures to the user. The input is preventative measure data from the server, and the output is the action instructions provided by the robot.

[0746] Step 6:

[0747] The user follows the robot's instructions and takes the suggested preventative measures.

[0748] For example, the user performs stretches according to the robot's instructions. The user's behavioral data is sent to the server as feedback via the terminal.

[0749] Step 7:

[0750] The server receives user feedback and uses it to improve the accuracy of the predictive model and suggestions.

[0751] The input is user feedback data, and the output is a refined prediction and proposal model. The feedback is analyzed as new data and used to train the AI ​​model.

[0752] 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.

[0753] This invention is a health management system that takes into account not only the user's biometric information but also their emotional state. By using an emotion engine, it becomes possible to incorporate psychological elements into preventative measures, resulting in more accurate health management.

[0754] First, the device collects the user's biometric information using mobile or wearable devices. This includes not only standard biometric information such as heart rate, steps taken, and body temperature, but also the acquisition of emotional data from the user via an emotion engine using sensors such as cameras and microphones. Emotional data is obtained from facial expressions, voice tone, breathing patterns, etc. This data is periodically transmitted to a server.

[0755] Next, the server integrates the transmitted biometric and emotional data and performs analysis using information analysis tools. Generative artificial intelligence and statistical models are utilized to advance the analysis process. By considering both biometric and emotional data, it becomes possible to capture new health risk trends that were previously undetectable. Based on these results, predicting future health risks enables a more comprehensive understanding of health status.

[0756] The server then develops specific preventative measures based on the prediction results. Because emotional data is available, advice related to stress reduction and mental health is also incorporated into the development process. For example, if the emotional engine detects a high stress level, suggestions for breaks and information on relaxation techniques will be added.

[0757] The developed preventative measures will be communicated to users via their devices. These notifications will include elements such as diagrams and graphs to convey information in a way that is easy for users to understand. This will enable users to manage their health in a way that also considers their emotional well-being.

[0758] Finally, users implement the notified preventative measures and send feedback on their effectiveness and implementation from their device to the server. The server uses the received feedback to continuously improve the accuracy of its predictive models and preventative measures. This optimizes the system for each individual user, providing strong support for future health management.

[0759] Thus, this invention provides a novel approach that integrates biometric information and emotional data to enable the management and provision of preventive measures for health risks, thereby promoting the comprehensive health maintenance of users.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The device acquires the user's biometric and emotional information through mobile or wearable devices. It uses sensors to collect biometric information such as heart rate, steps, and body temperature, while simultaneously recognizing emotions from facial expressions and voice using cameras and microphones. This data is stored locally for each session.

[0763] Step 2:

[0764] The device transmits collected biometric and emotional information to a server via the internet, based on the user's permission. The data is encrypted to protect privacy. The collected data is organized in a way that allows for session identification and stored in a dedicated database.

[0765] Step 3:

[0766] The server integrates the transmitted biometric and emotional information to build a dataset. The integrated data is preprocessed to remove noise and unnecessary information. As a result, clean data suitable for analysis is formed.

[0767] Step 4:

[0768] The server analyzes integrated data using generative artificial intelligence and statistical models. The AI ​​learns patterns from past data and makes inferences to predict future health risks. By analyzing the impact of changes in emotional state on biometric information, a more accurate understanding of overall health status becomes possible.

[0769] Step 5:

[0770] The server develops preventative measures based on predicted health risks. It utilizes data from the emotion engine to include stress management and mental health-conscious advice. For example, it might recommend mindfulness practices if stress levels are high.

[0771] Step 6:

[0772] The device notifies the user of preventative measures received from the server in a visually easy-to-understand format. The interface is designed to enhance the user experience by displaying detailed information using graphs and animations.

[0773] Step 7:

[0774] Users implement the notified preventative measures and send the results and feedback from their device back to the server. This feedback includes information about the effectiveness of the preventative measures and any changes in their status.

[0775] Step 8:

[0776] The server analyzes user feedback and improves its health risk prediction models and preventive measures. Through continuous improvement, the system can provide optimal health management support to each user.

[0777] (Example 2)

[0778] 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".

[0779] Conventional health management systems only consider biometric information, making it difficult to predict health risks or develop preventive measures that adequately reflect the user's emotions and psychological state. As a result, there is a possibility of overlooking risks related to stress and mental health, and comprehensive health management has not been achieved.

[0780] 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.

[0781] In this invention, the server includes data acquisition means for collecting the user's biometric information and emotional data; information analysis means for integrating and analyzing the biometric information and emotional data collected by the data acquisition means; and formulation means for predicting future health risks based on the analysis results obtained by the information analysis means and generating specific preventive measures, including psychological factors. This enables a comprehensive evaluation of the user's physical and emotional state, and allows for more accurate and personalized health management.

[0782] "Data acquisition means" refers to methods and devices for collecting users' biometric and emotional data, and includes portable and wearable devices.

[0783] "Information analysis means" refers to methods and devices for integrating and analyzing collected biometric information and emotional data, utilizing generative AI models and statistical models.

[0784] "Formulation methods" refer to methods and devices for predicting future health risks based on analysis results and generating specific preventive measures, including psychological factors.

[0785] "Notification means" refers to methods and devices for visualizing formulated preventive measures and communicating that information to users.

[0786] "Learning processing means" refers to methods and devices that receive feedback from users and use that information to improve information analysis means and formulation means using a generating AI model.

[0787] This invention is a system that realizes comprehensive health management using the user's biometric information and emotional data. The elements constituting the system are data acquisition means, information analysis means, formulation means, notification means, and learning processing means. Each process proceeds as follows.

[0788] The device uses portable devices (e.g., smartphones) and wearable devices (e.g., smartwatches) to collect biometric information such as the user's heart rate, steps taken, and body temperature using sensors. It also uses cameras and microphones to capture the user's facial expressions and voice tone, collecting this as emotional data. This data is periodically transmitted to a server via Wi-Fi or Bluetooth.

[0789] The server integrates the received biometric and emotional data and stores it in a database. Next, it uses generative AI models and statistical models to analyze the biometric and emotional data in detail. The generative AI model identifies new health risk trends by comparing past cases with real-time data.

[0790] Based on the analysis results, the server formulates specific preventative measures. During this process, prompts such as "Suggest effective relaxation methods during high-stress situations" are input into the AI ​​model, which then provides appropriate advice, including psychological factors. This makes it possible to create preventative measures that contribute to stress reduction and improved mental health.

[0791] The developed preventative measures will be communicated to users via their devices. The notifications will use diagrams and graphs to present the information visually and clearly. For example, "Because your stress levels are high, we recommend listening to relaxing music for 10 minutes every day this week."

[0792] Users implement the notified preventative measures and input feedback, which is then sent to the server. This feedback is used as training data for the generating AI model, improving the accuracy of information analysis and formulation methods. An example of a prompt is, "Learn how to provide better health advice based on the user's implementation results." This allows the system to optimize itself to each user's individual circumstances and continuously improve the quality of the service.

[0793] Thus, this invention supports users' health management through a novel approach that integrates biometric information and emotional data.

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

[0795] Step 1:

[0796] The device uses portable or wearable equipment to collect biometric information such as the user's heart rate, steps taken, and body temperature, as well as emotional data such as facial expressions and voice tone. This data is acquired by sensors, cameras, and microphones. The input is the user's real-time physical information and audio / video data, and the output generates specific numerical data read by the sensors and digital data in a format suitable for analysis.

[0797] Step 2:

[0798] The device transmits collected biometric and emotional data to a server via Wi-Fi or Bluetooth. This communication is performed periodically to ensure the data remains fresh. Input consists of numerical and digital data organized by the device, while output is an integrated data packet transferred to the server.

[0799] Step 3:

[0800] The server stores the received biometric and emotional data in a database and performs integrated processing. Generative AI models and statistical models are used to analyze the data and identify trends in health risks. The input is a dataset sent from the terminal, and the output is the analyzed data and specific information regarding predicted health risks.

[0801] Step 4:

[0802] The server generates specific preventative measures based on the analyzed data. Here, a generative AI model is used to formulate preventative measures using the prompt "Suggest effective relaxation methods during high stress." The input is the analysis results from step 3, and the output is a specific preventative measure suggestion for the user.

[0803] Step 5:

[0804] The device receives preventative measures transmitted from the server and notifies the user as visual information. The notification is displayed on the smartphone screen with diagrams and graphs, and is designed to be easily understood by the user. The input is the formulated preventative measures, and the output is the visually displayed information.

[0805] Step 6:

[0806] Users implement the notified preventative measures and input the effects and feedback obtained into their device. This feedback is recorded in the form of a questionnaire or free-form text. The input consists of the user's actual experience, impressions, and results, while the output is feedback data sent to the server.

[0807] Step 7:

[0808] The server receives feedback data from users and uses a generated AI model to improve information analysis and formulation methods. This enables the provision of more accurate health management in the future. The input is feedback data, and the output is an updated and improved analysis model and preventive measures.

[0809] (Application Example 2)

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

[0811] Traditional health management systems are limited to biometric information and do not take psychological states into account, making it difficult to predict comprehensive health risks. Furthermore, they lack mechanisms to effectively utilize individual user feedback and improve preventive measures in a timely manner, resulting in a failure to achieve personalized health management that meets real needs.

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

[0813] In this invention, the server includes information gathering means, data analysis means, risk prediction means, countermeasure generation means, transmission means, and feedback processing means. This enables comprehensive health risk prediction based on data integrating biometric information and psychological state, and the provision of personalized preventive measures.

[0814] "Information gathering means" refers to devices and methods for acquiring a user's biometric information and psychological state.

[0815] A "data analysis method" is a system for integrating and analyzing collected biological information and psychological states.

[0816] A "risk prediction tool" is a function that estimates future health risks based on data analysis results.

[0817] "Measures generation methods" refer to the process of designing specific preventive measures in response to predicted health risks.

[0818] "Means of communication" refers to methods for conveying the generated preventive measures to users.

[0819] A "feedback processing mechanism" is the part of the system that receives feedback from users and uses it to improve predictions and countermeasures.

[0820] The objective of this invention is a system that provides comprehensive health management considering the user's biometric information and psychological state. This system collects biometric information and psychological state through mobile devices or wearable devices carried by the user. The obtained data is transmitted to a server in the cloud, where it is processed by data analysis means. The server uses generative artificial intelligence and statistical analysis models to perform an integrated analysis of biometric information and psychological state. Based on the results of this analysis, it predicts the user's future health risks and generates individually tailored preventive measures. The generated preventive measures are notified to the user's terminal and presented in an easy-to-understand manner using diagrams and graphs.

[0821] For example, if a user is detected to be experiencing high stress due to long working hours, the notification system from the server will suggest relaxation techniques to calm their emotions or encourage participation in health guidance events. Furthermore, user feedback is sent to the server and used by the feedback processing system to continuously improve the accuracy of predictive models and preventative measures.

[0822] Specifically, an application installed on a mobile device monitors the user's heart rate, body temperature, facial expressions, voice tone, etc., using sensors, and this data is transmitted in real time to a server in the cloud. On the server, a generative AI model analyzes the data and proposes appropriate preventive measures based on a prompt message that reads, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The generated preventive measures are then notified to the user through the smartphone app. This allows the user to comprehensively understand their health status and take appropriate measures.

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The device collects the user's biometric and psychological state data. Specifically, it uses sensors to acquire heart rate, body temperature, facial expressions, and voice tone. The input is raw data from the sensors, and the output is integrated biometric and psychological state data.

[0826] Step 2:

[0827] The device transmits the collected data to a server in the cloud in real time. This operation includes data packaging and encryption. The input is integrated biometric and psychological state data, and the output is secure data transmission to the server.

[0828] Step 3:

[0829] The server analyzes received data using generative artificial intelligence and statistical analysis models. Specifically, it performs data cleansing and normalization before inputting the data into the analysis algorithm. The input is data sent from the terminal, and the output is a health risk assessment based on the analysis results.

[0830] Step 4:

[0831] The server generates preventative measures based on the analysis results. Here, it uses a generative AI model to execute the prompt message, "Analyze this user's biometric and emotional data and generate the most appropriate health advice." The input is the analysis results, and the output is specific and personalized preventative measures.

[0832] Step 5:

[0833] The server transmits the generated preventative measures to the user's device. This transmission includes conversion to a user-friendly interface using push notifications. The input is the preventative measures, and the output is the notification to the user.

[0834] Step 6:

[0835] Users implement the preventative measures they receive and send feedback to the server via their device. This feedback includes details such as which preventative measures were effective and their impressions of the implementation process. The input is user feedback, and the output is used to improve the system.

[0836] Step 7:

[0837] The server improves the accuracy of its predictive models and preventive action generation based on the feedback it receives. This process involves analyzing the feedback data and retraining the model. The input is user feedback, and the output is the improved predictive model and preventive action.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] Information acquisition means for collecting user biometric information,

[0862] Information analysis means for integrating and analyzing biological information collected by the aforementioned information acquisition means,

[0863] A prediction means that predicts future health risks based on the analysis results obtained by the information analysis means,

[0864] A formulation means for generating specific preventive measures against the health risks predicted by the prediction means,

[0865] A notification means for notifying users of the preventive measures generated by the aforementioned formulation means,

[0866] A system including a feedback processing means that receives feedback from users and improves the prediction means and the formulation means.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the information acquisition means collects biological information using a mobile device and a wearable device.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the analysis means analyzes biological information using generative artificial intelligence and statistical models.

[0871] "Example 1"

[0872] (Claim 1)

[0873] A data acquisition method for obtaining the user's biometric data,

[0874] A data integration means that integrates the biometric data acquired by the aforementioned data acquisition means and acquires additional health-related data in cooperation with an external information source,

[0875] A data analysis means that analyzes the data obtained by the data integration means using generative artificial intelligence and statistical models to evaluate health trends,

[0876] A risk prediction means that predicts future health risks based on the analysis results obtained by the data analysis means,

[0877] A preventive measure generation means that generates specific preventive measures for health risks predicted by the risk prediction means,

[0878] A notification means for visually notifying the user of the preventive measures generated by the preventive measures generation means,

[0879] A system that includes model improvement means for receiving feedback from users and improving the risk prediction means and the preventive measure generation means.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the data acquisition means acquires biological data using a portable device and a wearable device.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the data analysis means analyzes biological data using generative artificial intelligence and statistical methods.

[0884] "Application Example 1"

[0885] (Claim 1)

[0886] Information acquisition means for collecting user biometric information,

[0887] Information analysis means for integrating and analyzing biological information collected by the aforementioned information acquisition means,

[0888] A prediction means that predicts future health risks based on the analysis results obtained by the information analysis means,

[0889] A formulation means for generating specific preventive measures against the health risks predicted by the prediction means,

[0890] A notification means for notifying users of the preventive measures generated by the aforementioned formulation means,

[0891] A feedback processing means that receives feedback from users and improves the prediction means and formulation means,

[0892] A means of providing preventive measures to users via consumer robots using information analysis tools,

[0893] Means for presenting preventive measures visually and audibly using the aforementioned consumer robot,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the information acquisition means collects biological information using a portable device and a wearable device.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the analysis means analyzes biological information using generative artificial intelligence and statistical models.

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

[0900] (Claim 1)

[0901] A data acquisition method for collecting users' biometric information and emotional data,

[0902] Information analysis means for integrating and analyzing biometric information and emotional data collected by the aforementioned data acquisition means,

[0903] A formulation means for predicting future health risks based on the analysis results obtained by the aforementioned information analysis means and generating specific preventive measures including psychological factors,

[0904] A notification means for visualizing the preventive measures generated by the aforementioned formulation means and notifying the user,

[0905] A system including a learning processing means that receives feedback from users and improves the information analysis means and formulation means using a generating AI model.

[0906] (Claim 2)

[0907] The system according to claim 1, wherein the data acquisition means collects biometric information and emotional data using a portable device and a wearable device.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the information analysis means analyzes biometric information and emotional data using a generative AI model and a statistical model.

[0910] "Application example 2 when combining with an emotional engine"

[0911] (Claim 1)

[0912] Information collection means for collecting users' biometric information and psychological state,

[0913] A data analysis means for integrating and analyzing biological information and psychological state collected by the aforementioned information collection means,

[0914] A risk prediction means that predicts future health risks based on the analysis results obtained from the data analysis means,

[0915] A countermeasure generation means for forming specific preventive measures against health risks predicted by the risk prediction means,

[0916] A communication means for communicating preventive measures formed by the countermeasure generation means to the user,

[0917] A system including a feedback processing means that receives responses from users and improves the risk prediction means and the countermeasure generation means.

[0918] (Claim 2)

[0919] The system according to claim 1, wherein the information gathering means collects biometric information using portable devices and wearable devices.

[0920] (Claim 3)

[0921] The system according to claim 1, wherein the data analysis means analyzes biological information and psychological state using generative artificial intelligence and statistical analysis models. [Explanation of Symbols]

[0922] 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 means for collecting user biometric information, Information analysis means for integrating and analyzing biological information collected by the aforementioned information acquisition means, A prediction means that predicts future health risks based on the analysis results obtained by the information analysis means, A formulation means for generating specific preventive measures against the health risks predicted by the prediction means, A notification means for notifying users of the preventive measures generated by the aforementioned formulation means, A system including a feedback processing means that receives feedback from users and improves the prediction means and the formulation means.

2. The system according to claim 1, wherein the information acquisition means collects biological information using a mobile device and a wearable device.

3. The system according to claim 1, wherein the analysis means analyzes biological information using generative artificial intelligence and statistical models.

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