system

Abstract

Provide a system. 【Solution means】 Information collection means for processing information collected from users and evaluating health status, Analysis means for analyzing the collected information and evaluating the user's health status, Plan generation means for generating an individually optimized health management plan based on the analysis results, Plan providing means for providing the generated health management plan to the user, Monitoring means for continuously monitoring the user's activities and health status and detecting abnormalities, Notification means for notifying the user when an abnormality is detected and prompting appropriate responses, Generative AI model analysis means for analyzing the collected information using a generative AI model and predicting the health status, Prompt sentence generation means for supporting the user's health risk assessment using prompt sentences, A system including the above.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to early detect a decline in the health condition of the elderly and middle-aged adults and provide appropriate care without hassle. In particular, in conventional health management systems, it is difficult to provide a health management plan suitable for each individual, and it is required to efficiently execute individual responses according to medical conditions and lifestyle habits. Also, medical institutions and care service providers have a large workload and need to be made more efficient. Furthermore, ensuring the privacy and security of the collected data is also an important issue in these processes.

Means for Solving the Problems

[0005] This invention includes data collection and analysis means for processing data collected from users and evaluating the user's health status. Furthermore, it includes plan generation and plan provision means for generating and providing individually optimized health management plans to users based on the analysis results. It also uses monitoring means to detect abnormalities by continuously monitoring the user's activities and health status, and promptly notifies the user via notification means when an abnormality is detected, prompting appropriate action. This system makes it possible to automatically and efficiently provide appropriate care according to each individual's health status, supporting the maintenance of the user's health while improving the efficiency of operations in medical settings and ensuring the security of data.

[0006] "Data collection methods" refer to means of obtaining health-related data from users, and involve collecting data using sensor devices such as smartphones and wearable devices.

[0007] "Analysis means" refers to methods for processing collected data and evaluating the user's health status, and involves analyzing diverse data using generative AI models.

[0008] The "plan generation means" is a means of individually creating a health management plan suitable for the user based on the analysis results obtained by the analysis means.

[0009] "Plan delivery method" refers to a means of providing the generated health management plan to the user, by presenting the plan details to the user via a terminal.

[0010] "Monitoring means" are means of continuously monitoring a user's activities and health status and detecting changes.

[0011] A "notification method" is a means of informing the user of warnings or corrective actions when an anomaly is detected by the monitoring method. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This system aims to continuously monitor users' health in their daily lives and provide personalized health management plans. Users can collect health-related data such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. This data is transmitted to the server via the device.

[0034] The server receives the accumulated data and performs data preprocessing. Then, it analyzes the data using a generative AI model to assess the user's frailty risk. Based on the analysis results, the server generates an optimal health management plan for the user. This plan consists of detailed guidelines, including areas for improvement in exercise and diet, and is provided to the user via their device.

[0035] Users can view the health management plan provided on their device and incorporate it into their daily lives. Activity data is monitored in real time, and this information is sent back to the server. The server continuously analyzes the feedback and fine-tunes the plan as needed.

[0036] As a concrete example, suppose an elderly user walks every day and records their activity with a wearable device. The data recorded by the device is sent to a server at regular intervals, and the server analyzes it and determines that there is a concern about insufficient exercise. As a result, the server generates a health management plan suggesting that the user increase the frequency of their walks and sends this to the user's device. The user then uses this plan as a reference to adjust their daily activities and improve their health.

[0037] In this way, the system supports users' health and provides plans tailored to individual needs, helping older adults and middle-aged adults maintain healthier lifestyles.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device collects health-related data from the user. This data includes sensor data such as steps taken and heart rate, as well as photos of meals and voice memos. Users input and record this data using the device in their daily lives.

[0041] Step 2:

[0042] The device sends collected data to the server at regular intervals. The transmitted data uses a secure communication protocol and is kept private.

[0043] Step 3:

[0044] The server preprocesses the received data. It standardizes the data format and extracts necessary information to prepare it for easier analysis.

[0045] Step 4:

[0046] The server analyzes pre-processed data using a generative AI model to assess the user's health status. It calculates a frailty risk score and understands the user's health trends.

[0047] Step 5:

[0048] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. This plan includes recommendations for adjusting exercise levels and dietary choices.

[0049] Step 6:

[0050] The server sends the generated health management plan to the user's device. The user reviews the plan on their device and applies it to their daily life.

[0051] Step 7:

[0052] The device monitors the user's activity in real time and sends feedback to the server. Based on this feedback, the server readjusts and continuously optimizes the health management plan.

[0053] Step 8:

[0054] Based on monitoring results, the server immediately notifies the user if an anomaly is detected. It provides information to encourage the user to visit a medical institution if necessary, thereby mitigating the user's health risks.

[0055] (Example 1)

[0056] 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."

[0057] Conventional health management systems have struggled to accurately monitor users' health status in real time and provide optimized health management plans tailored to individual health conditions. Furthermore, they sometimes failed to respond quickly when abnormalities occurred, resulting in insufficient support for maintaining users' health. Therefore, there was a need for efficient and effective health management.

[0058] 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.

[0059] In this invention, the server includes information gathering means for processing biometric data collected from the user and evaluating their health status, analysis means for preprocessing and analyzing the collected biometric data, and determination means for evaluating the user's health status and determining the risk of frailty based on the data analyzed using generative AI technology. This enables the provision of individually optimized health management plans based on the user's health status and rapid response through real-time anomaly detection.

[0060] "Biometric data" refers to information related to a user's health status, including steps taken, heart rate, and dietary records.

[0061] "Information gathering means" refers to a system for acquiring and storing biometric data from users.

[0062] "Analysis means" refers to the function of organizing collected biological data and analyzing data patterns using machine learning algorithms, etc.

[0063] "Generative AI technology" refers to methods that use artificial intelligence technology to generate new information and suggestions from data.

[0064] "Determination method" refers to a system that determines the user's health status and frailty risk based on the results obtained through analysis.

[0065] A "health management plan" refers to a plan that includes guidelines and suggestions regarding exercise, diet, rest, etc., with the aim of maintaining and improving the user's health.

[0066] "Means of delivery" refers to the function of presenting the generated health management plan to the user and communicating it in a usable format.

[0067] "Monitoring measures" refer to systems that continuously check users' activities and health status and detect abnormalities.

[0068] "Warning measures" refer to notification functions that inform the user when an anomaly is detected and prompt them to take appropriate action.

[0069] "Feedback mechanism" refers to a function that dynamically adjusts the health management plan based on user feedback and the actual progress of the plan's implementation.

[0070] A "multifunctional device" refers to an electronic device that has various sensor functions and is used to acquire the user's biometric data.

[0071] A "central computer" refers to a computer that centrally processes data sent by users and manages the entire system.

[0072] This invention provides a system that monitors a user's health status in real time and provides an individually optimized health management plan.

[0073] First, users use multifunctional devices such as smartphones or wearable devices to collect biometric data such as heart rate, steps taken, and photos of meals. This data is temporarily stored on the user's device. The device periodically transmits this data to a central computer via the internet. Highly secure protocols are used for data transmission to ensure data confidentiality and security.

[0074] The server functions as a central computer, receiving collected biometric data. The server is equipped with software responsible for data preprocessing, including cleaning and formatting the received data. The processed data is then analyzed in detail using a generative AI model. The AI ​​model learns patterns in the biometric data and assesses the user's health status and frailty risk. Based on this assessment, the server generates a personalized health management plan for each user.

[0075] The generated health management plan includes specific exercise instructions and dietary improvement suggestions, and this information is sent back to the user's device. The user can review the plan on their device and incorporate it into their daily life to maintain or improve their health.

[0076] As a concrete example, suppose a user walks every day. Through this system, the user's steps and heart rate are monitored, and this data is analyzed by a server. The server analyzes the user's exercise pattern, generates a plan recommending an increase in walking frequency, and notifies the user.

[0077] An example of a prompt might be, "Use a generative AI model to analyze the user's heart rate data, assess their stress level, and suggest relaxation techniques." In this way, the system supports the user in maintaining their health.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users collect biometric data through smartphones or wearable devices. These devices use sensors to record things like heart rate, steps taken, and photos of meals. The input is biometric data obtained from the user's daily activities, and this data is temporarily stored on the device. The output is the biometric data retained within the device.

[0081] Step 2:

[0082] The device transmits collected biometric data to a server via the internet. Specifically, it periodically uploads data using a data communication module. The data processing performed during this process involves encryption and compression, ensuring secure and efficient transmission. The input is the biometric data within the device, and the output is the data received by the server.

[0083] Step 3:

[0084] The server receives the transmitted biometric data and performs data preprocessing. Specifically, this involves data cleaning (e.g., imputation of missing values, removal of outliers) and normalization. The input for this preprocessing is the biometric data received by the server, and the output is clean data suitable for analysis.

[0085] Step 4:

[0086] The server analyzes pre-processed data using a generative AI model. The AI ​​model utilizes machine learning algorithms to assess the user's health status and frailty risk from the data. Specifically, it performs pattern recognition and predictive analytics. The input is clean data, and the output is health status assessment data.

[0087] Step 5:

[0088] The server generates a health management plan optimized for the user based on evaluation data. Using prompts, it creates a plan that includes specific exercise and dietary guidance. The input is health evaluation data, and the output is the health management plan.

[0089] Step 6:

[0090] The device presents the user with a health management plan received from the server. Specifically, it displays the plan details through a mobile application, providing a means for incorporating it into daily life. The input is the health management plan from the server, and the output is the information provided to the user.

[0091] Step 7:

[0092] The user performs activities based on the proposed health management plan and records the activity data again on the device. This data is continuously collected, and the process returns to step 1. Here, the input is the user's daily activities, and the output is newly acquired biometric data.

[0093] (Application Example 1)

[0094] 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."

[0095] For the elderly and those requiring care, accurately understanding their daily health status, assessing individual health risks, and providing an optimal health management plan are crucial challenges. However, conventional systems often fail to adequately generate detailed plans tailored to each user's condition and provide continuous feedback. This makes it difficult for users to perform appropriate health management in real time, and this problem needs to be addressed.

[0096] 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.

[0097] In this invention, the server includes a generative AI model analysis means that analyzes information collected using a generative AI model and predicts the user's health status, a prompt message generation means that supports the user's health risk assessment using prompt messages, and a notification means that notifies the user when an abnormality is detected and prompts them to take appropriate action. This enables the provision of a detailed health management plan tailored to each individual user, and allows for real-time monitoring and improvement of the user's health status.

[0098] "Information gathering means" refers to devices or methods that acquire health-related information from users, such as step count, heart rate, and dietary information.

[0099] "Analysis means" refers to devices or methods for processing collected information and evaluating the user's health status.

[0100] A "plan generation means" refers to a device or method for creating an individually optimized health management plan based on analysis results.

[0101] "Plan delivery means" refers to devices or methods for communicating the generated health management plan to the user.

[0102] "Monitoring means" refers to devices or methods for continuously observing a user's activities and health status and detecting abnormalities.

[0103] A "notification means" is a device or method that sends a warning or alert to the user when an anomaly is detected, prompting them to take appropriate action.

[0104] A "generative AI model analysis means" is a device or method that uses a generative AI model to analyze collected information and predict health conditions.

[0105] A "prompt message generation means" is a device or method for generating prompt messages to support the user's health risk assessment.

[0106] The system for implementing this invention includes a function to monitor the user's health status in their daily life and provide an individually optimized health management plan.

[0107] First, users collect health-related information such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. The information collection system efficiently acquires this data and transmits it to a server. The server receives the collected information and analyzes it using a generative AI model analysis system. This analysis makes it possible to predict the user's health status and frailty risk with high accuracy.

[0108] Based on the analysis results, the plan generation system creates a health management plan tailored to the user's needs. This plan includes suggestions for improvements in exercise and diet, and is presented to the user via the plan delivery system. If any abnormalities are detected in the user's activity, the monitoring and notification systems immediately issue a warning and prompt the user to take appropriate action.

[0109] Furthermore, the prompt message generation means generates prompt messages to support the user's health risk assessment. These prompt messages provide specific instructions for more effectively utilizing the generated AI model and support user behavioral change.

[0110] As a concrete example, suppose an elderly user records their daily steps using a wearable device. The data collected by the device is sent to a server, where a generating AI model analyzes it and determines the risk of lack of exercise. Based on this, the server provides the user with a specific health management plan, such as "Aim for 5,000 steps of walking every day." This plan is expected to improve the user's health.

[0111] An example of a prompt to the generating AI model would be, "Based on the user's latest heart rate and walking data, please suggest an appropriate exercise plan." This allows the system to provide specific and personalized advice tailored to the user's health condition.

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

[0113] Step 1:

[0114] The device acquires health-related data from the user. Inputs such as steps taken, heart rate, photos of meals, and voice memos are collected by the device. This data is efficiently processed through data collection methods and transmitted to a server.

[0115] Step 2:

[0116] The server analyzes the received data using a generative AI model analysis method. Based on the input data, the generative AI model predicts the user's health status and frailty risk. The data processing performed here includes data normalization and feature extraction.

[0117] Step 3:

[0118] The server creates a health management plan based on the analysis results. The plan generation system formulates points for improving exercise and diet tailored to the user's current situation. The output is a specific health management plan, which is provided to the user's terminal.

[0119] Step 4:

[0120] The server continuously monitors user activity using monitoring tools. It periodically checks whether the health management plan is being implemented and checks the data based on criteria for detecting anomalies.

[0121] Step 5:

[0122] If the server detects an anomaly, it will immediately send a warning to the user using a notification system. The notification will include the nature of the detected anomaly and recommended countermeasures. The output will be a warning message displayed on the user's terminal.

[0123] Step 6:

[0124] The terminal sends user feedback to the server. The feedback processing means receives the feedback and dynamically optimizes the health management plan. At this time, the prompt message generation means generates prompt messages to support user behavior change.

[0125] Through the above processing steps, the system can continuously manage the user's health status and provide individually optimized advice.

[0126] 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.

[0127] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. This system collects physical data and text and voice data representing emotions through the user's smartphone or wearable device.

[0128] The device sends data collected from the user to a server for integration with the emotion engine. The emotion engine has the ability to analyze text and audio data to identify the user's emotional state. Based on this, it generates analytical data to evaluate the impact of the user's emotions on their physical health.

[0129] The server integrates accumulated data and emotional data, and analyzes it using generative AI. This analysis makes it possible to assess the user's frailty risk and generate a detailed health management plan that also takes their emotional state into account.

[0130] For example, if health data detects a mild lack of exercise, but the emotion engine determines the user's stress level is high, the server will suggest an exercise plan that prioritizes reducing psychological stress. This plan may include activities such as yoga or meditation. The server sends this information to the device, where the user can review the plan and apply it to their daily life.

[0131] Users adjust their daily activities according to a health management plan provided using their device. Monitored activity data and emotional changes are sent to the server in real time, and the server receives feedback to further adjust the plan.

[0132] In this way, the system comprehensively manages the user's physical and emotional health, helping older adults and middle-aged adults maintain a healthy and balanced lifestyle.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device collects the user's physical data (such as steps taken and heart rate) as well as text and voice data representing their emotions. Users input or record this data on the device as needed during their daily lives.

[0136] Step 2:

[0137] The device sends the collected data to the server. This data is encrypted to protect privacy.

[0138] Step 3:

[0139] The server stores the received data in a database and performs preprocessing. Preprocessing includes standardizing data formats and removing noise.

[0140] Step 4:

[0141] The server uses an emotion engine to analyze text and audio data to recognize the user's emotional state. This analysis is then combined with health data to prepare for a comprehensive analysis.

[0142] Step 5:

[0143] The server uses a generative AI model to analyze the integrated data and assess the user's frailty risk and emotional health.

[0144] Step 6:

[0145] Based on the analysis results, the server generates a personalized health management plan that takes into account the user's physical and emotional well-being. This plan includes exercise, diet, and stress management methods.

[0146] Step 7:

[0147] The server sends the generated health management plan to the user's device. The user then reviews this plan on the device and incorporates it into their daily life.

[0148] Step 8:

[0149] The device monitors the user's activity status and emotional changes in real time and feeds that data back to the server.

[0150] Step 9:

[0151] The server dynamically adjusts the health management plan based on feedback, continuously providing support optimized for the user. Where possible, it also provides additional care advice tailored to the user's emotional state.

[0152] (Example 2)

[0153] 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".

[0154] In modern life, many people are finding it difficult to maintain both physical and emotional health. Traditional health management systems focus solely on physical health and do not adequately consider emotional aspects, making it difficult to provide appropriate support tailored to individual health conditions. Furthermore, there is a lack of dynamic health management plans that respond to users' real-time changes in their condition.

[0155] 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.

[0156] In this invention, the server includes data collection means for processing biometric and emotional data collected from users, analysis means for analyzing the collected biometric and emotional data, and AI analysis means for analyzing the integrated data using a generative AI model. This makes it possible to comprehensively evaluate the user's physical and emotional health and provide an individually optimized health management plan in real time.

[0157] "Biometric data" refers to information related to a user's physical condition, such as heart rate, steps taken, and sleep patterns.

[0158] "Emotional data" refers to information indicating a user's emotional state, obtained through their statements and text input.

[0159] "Data collection means" refers to devices and technologies used to acquire biometric and emotional data from users.

[0160] "Analysis means" refers to devices and technologies used to analyze collected biometric and emotional data and evaluate the user's health status.

[0161] A "generative AI model" is an artificial intelligence technology that analyzes a user's health status based on their data and generates an appropriate health management plan.

[0162] "AI analysis means" refers to devices and technologies for analyzing integrated data using a generative AI model.

[0163] A "health management plan" is an action plan created based on analysis results, aimed at maintaining and improving the user's health.

[0164] "Plan delivery means" refers to devices or technologies used to notify users of the generated health management plan.

[0165] "Monitoring means" refers to devices and technologies used to continuously observe a user's activities and health status and detect abnormalities.

[0166] A "notification method" refers to a device or technology that issues an alert to the user and prompts them to take appropriate action when an anomaly is detected.

[0167] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. The terminal collects biometric data and emotional data through the user's smartphone or wearable device. Specifically, this includes biometric data such as heart rate, steps taken, and sleep patterns, as well as emotional data acquired through text and voice.

[0168] The device sends the collected data to the server. The server analyzes this data using an emotion engine. The emotion engine utilizes natural language processing technology to identify the user's emotional state from text and voice data. The analyzed emotional data and biometric data are integrated, and a generative AI model is used for deeper analysis. This AI model interprets the data using prompt sentences and evaluates the user's health and emotional state.

[0169] For example, if health data indicates a mild lack of exercise and the emotional engine detects high stress levels, the server will suggest an exercise plan to the user aimed at reducing stress. This plan may include activities such as yoga or meditation.

[0170] As a concrete example, the generative AI model receives the following prompt as input: "Generate a health plan based on this user's current heart rate and emotional stress level."

[0171] The server delivers the generated health management plan to the user's device. The user then uses this information to modify their daily activities and incorporate the health management plan into their real life. Activity data and emotional changes are fed back in real time, and the server uses this information to dynamically optimize the health management plan. This allows the system to effectively manage the user's physical and emotional health, supporting a healthy and balanced lifestyle.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The device collects biometric and emotional data from the user. Specifically, it uses smartphone sensors and wearable devices to acquire physical data such as heart rate, steps, and sleep patterns, and collects emotional data from text messages and voice recordings. The input is data from sensor devices, and the output is the collected biometric and emotional data.

[0175] Step 2:

[0176] The device transmits the collected biometric and emotional data to the server. A secure data transfer protocol is used to ensure accurate and safe delivery of the data. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0177] Step 3:

[0178] The server passes the received data to the emotion engine for processing. The emotion engine uses natural language processing algorithms to analyze text and audio data and identify the user's emotional state. The input is the text and audio data sent to the server, and the output is the analyzed emotion data.

[0179] Step 4:

[0180] The server inputs the analyzed emotional and biometric data into a generating AI model. Based on this data, the generating AI model uses prompts to comprehensively evaluate the user's health and emotional state and determine the risk of frailty. At this stage, the inputs are emotional and biometric data, and the outputs are the analysis results and health risk assessment.

[0181] Step 5:

[0182] The server generates an individually optimized health management plan based on the analysis results from the generative AI model. For example, if the analysis results indicate a lack of exercise and high stress levels, the server will propose a plan that includes yoga and meditation aimed at stress reduction. The input is the analysis results from the generative AI model, and the output is the health management plan.

[0183] Step 6:

[0184] The server delivers the generated health management plan to the user's device. The plan is displayed clearly and presented in a format that is easy for the user to apply to their daily life. The input is the generated health plan, and the output is the delivery of the plan to the user's device.

[0185] Step 7:

[0186] Users adjust their activities according to a health management plan received via their device. Activity data and emotional changes are recorded on the device and fed back to the server in real time. Input is the user's daily activity data, and output is the feedback data sent to the server.

[0187] Through these steps, the system can comprehensively manage the user's health and provide personalized support.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] For older adults and middle-aged adults to maintain a healthy and balanced life, not only physical health but also emotional well-being is crucial. However, conventional health management systems have been insufficient in considering emotions, and have been unable to effectively provide individually optimized health management plans. Therefore, there is a need to comprehensively evaluate physical health and emotional state and provide individually optimized health management plans based on these evaluations, thereby realizing holistic healthcare that also takes emotions into account.

[0191] 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.

[0192] In this invention, the server includes information gathering means for processing biometric and emotional information collected from the user and evaluating the user's health and emotional state; analysis means for analyzing the collected information and evaluating the user's health and emotional state; and plan generation means for generating an individually optimized health management plan that takes the emotional state into account based on the analysis results. This enables the dynamic provision of a health management plan that takes the emotional state into account, allowing elderly people to live their daily lives with peace of mind.

[0193] "Biometric information" refers to data that indicates the user's physical state, such as heart rate, steps taken, and sleep patterns.

[0194] "Emotional information" refers to data that represents a user's emotional state, obtained through text and audio data.

[0195] "Information gathering means" refers to devices or methods for acquiring biometric and emotional information from users, including smartphones and wearable devices.

[0196] "Analysis means" refers to a processing device or method for integrating collected biological information and emotional information to evaluate the user's health and emotional state.

[0197] "Plan generation means" refers to a device or method that creates an individually optimized health management plan, including the user's emotional state, based on the analysis results.

[0198] "Plan delivery means" refers to a device or method for displaying a generated health management plan to the user and encouraging them to incorporate it into their daily life.

[0199] "Monitoring means" refers to a device or method for continuously monitoring a user's activities and health status and detecting any abnormalities that occur.

[0200] "Notification means" refers to a device or method that, when an abnormality is detected by monitoring means, notifies the user of that fact and prompts them to take appropriate action.

[0201] "Feedback processing means" refers to a device or method for receiving user feedback and dynamically optimizing a health management plan.

[0202] The system implementing this invention consists of a smartphone, a wearable device, and a cloud server. Biometric and emotional information collected from the user is acquired by the smartphone or wearable device and transmitted to the cloud server in real time.

[0203] The cloud server receives data from sensor devices used as information gathering tools and then performs emotion analysis and integrated health status analysis. In particular, it extracts emotional information from text and audio using natural language processing (NLP) and emotion analysis APIs. It also integrates biometric information such as the user's heart rate and sleep patterns to comprehensively evaluate the user's health status.

[0204] The analysis method uses an AI model to generate personalized health management plans that take emotional states into account. These plans may include relaxation activities and exercise plans, and are dynamically updated according to the user's individual needs.

[0205] The generated plan is sent to a device such as a smartphone via a plan delivery system, allowing the user to review it and incorporate it into their daily health management. Furthermore, the user's activity is continuously monitored by a monitoring system, and if it deviates from the set criteria, an alert is sent via a notification system to prompt appropriate action.

[0206] For example, if the system detects that an elderly person has recently been experiencing poor sleep, the cloud server may use AI to suggest a special relaxation plan. An example of this prompt might be: "Please provide the optimal care plan based on the elderly person's recent health status (heart rate and sleep duration) and their recent stress level (emotional analysis)."

[0207] In this way, comprehensive health management that takes emotional states into account can be achieved, improving the quality of life for users.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The device collects the user's biometric information (heart rate, steps, sleep patterns, etc.) and emotional information through text and voice from wearable devices and smartphones. Input data is acquired from sensors and converted into a data format to be sent to a cloud server as output.

[0211] Step 2:

[0212] The server receives biometric and emotional information transmitted from the terminal as input and passes this data to the analysis system. For data processing, natural language processing (NLP) is used to extract emotional information from text and audio and integrate it as numerical data. The output is an evaluation of the user's health and emotional state.

[0213] Step 3:

[0214] The server creates an optimal health management plan using a generated AI model based on the analysis results. The input is the evaluation results, and the output is a individually optimized health management plan. Machine learning algorithms are used for data calculation, and the generated plan includes relaxation activities and exercise plans that take into account the user's current emotional state.

[0215] Step 4:

[0216] The server transmits the generated health management plan to the terminal via a plan delivery device. The terminal visually presents this plan to the user, who then adjusts their daily health activities based on it. The input is the health management plan, and the output is the plan information displayed on the terminal.

[0217] Step 5:

[0218] The server receives continuous feedback from the terminal and monitors user activity data using monitoring means. If an anomaly is detected, the server notifies the user using notification means. The input is user activity data, and the output is feedback, which is used to adjust the plan.

[0219] Step 6:

[0220] Based on user feedback, the server uses the plan generation mechanism again and updates the health management plan as needed. The input is feedback data, and the output is the adjusted health management plan. This ensures that a plan is always provided that is up-to-date.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This system aims to continuously monitor users' health in their daily lives and provide personalized health management plans. Users can collect health-related data such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. This data is transmitted to the server via the device.

[0238] The server receives the accumulated data and performs data preprocessing. Then, it analyzes the data using a generative AI model to assess the user's frailty risk. Based on the analysis results, the server generates an optimal health management plan for the user. This plan consists of detailed guidelines, including areas for improvement in exercise and diet, and is provided to the user via their device.

[0239] Users can view the health management plan provided on their device and incorporate it into their daily lives. Activity data is monitored in real time, and this information is sent back to the server. The server continuously analyzes the feedback and fine-tunes the plan as needed.

[0240] As a concrete example, suppose an elderly user walks every day and records their activity with a wearable device. The data recorded by the device is sent to a server at regular intervals, and the server analyzes it and determines that there is a concern about insufficient exercise. As a result, the server generates a health management plan suggesting that the user increase the frequency of their walks and sends this to the user's device. The user then uses this plan as a reference to adjust their daily activities and improve their health.

[0241] In this way, the system supports users' health and provides plans tailored to individual needs, helping older adults and middle-aged adults maintain healthier lifestyles.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device collects health-related data from the user. This data includes sensor data such as steps taken and heart rate, as well as photos of meals and voice memos. Users input and record this data using the device in their daily lives.

[0245] Step 2:

[0246] The device sends collected data to the server at regular intervals. The transmitted data uses a secure communication protocol and is kept private.

[0247] Step 3:

[0248] The server preprocesses the received data. It standardizes the data format and extracts necessary information to prepare it for easier analysis.

[0249] Step 4:

[0250] The server analyzes pre-processed data using a generative AI model to assess the user's health status. It calculates a frailty risk score and understands the user's health trends.

[0251] Step 5:

[0252] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. This plan includes recommendations for adjusting exercise levels and dietary choices.

[0253] Step 6:

[0254] The server sends the generated health management plan to the user's device. The user reviews the plan on their device and applies it to their daily life.

[0255] Step 7:

[0256] The device monitors the user's activity in real time and sends feedback to the server. Based on this feedback, the server readjusts and continuously optimizes the health management plan.

[0257] Step 8:

[0258] Based on monitoring results, the server immediately notifies the user if an anomaly is detected. It provides information to encourage the user to visit a medical institution if necessary, thereby mitigating the user's health risks.

[0259] (Example 1)

[0260] 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."

[0261] Conventional health management systems have struggled to accurately monitor users' health status in real time and provide optimized health management plans tailored to individual health conditions. Furthermore, they sometimes failed to respond quickly when abnormalities occurred, resulting in insufficient support for maintaining users' health. Therefore, there was a need for efficient and effective health management.

[0262] 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.

[0263] In this invention, the server includes information gathering means for processing biometric data collected from the user and evaluating their health status, analysis means for preprocessing and analyzing the collected biometric data, and determination means for evaluating the user's health status and determining the risk of frailty based on the data analyzed using generative AI technology. This enables the provision of individually optimized health management plans based on the user's health status and rapid response through real-time anomaly detection.

[0264] "Biometric data" refers to information related to a user's health status, including steps taken, heart rate, and dietary records.

[0265] "Information gathering means" refers to a system for acquiring and storing biometric data from users.

[0266] "Analysis means" refers to the function of organizing collected biological data and analyzing data patterns using machine learning algorithms, etc.

[0267] "Generative AI technology" refers to methods that use artificial intelligence technology to generate new information and suggestions from data.

[0268] "Determination method" refers to a system that determines the user's health status and frailty risk based on the results obtained through analysis.

[0269] A "health management plan" refers to a plan that includes guidelines and suggestions regarding exercise, diet, rest, etc., aimed at maintaining and improving the user's health.

[0270] "Means of delivery" refers to the function of presenting the generated health management plan to the user and communicating it in a usable format.

[0271] "Monitoring measures" refer to systems that continuously check users' activities and health status and detect abnormalities.

[0272] "Warning measures" refer to notification functions that inform the user when an anomaly is detected and prompt them to take appropriate action.

[0273] "Feedback mechanism" refers to a function that dynamically adjusts the health management plan based on user feedback and the actual progress of the plan's implementation.

[0274] A "multifunctional device" refers to an electronic device that has various sensor functions and is used to acquire the user's biometric data.

[0275] A "central computer" refers to a computer that centrally processes data sent by users and manages the entire system.

[0276] This invention provides a system that monitors a user's health status in real time and provides an individually optimized health management plan.

[0277] First, users use multifunctional devices such as smartphones or wearable devices to collect biometric data such as heart rate, steps taken, and photos of meals. This data is temporarily stored on the user's device. The device periodically transmits this data to a central computer via the internet. Highly secure protocols are used for data transmission to ensure data confidentiality and security.

[0278] The server functions as a central computer, receiving collected biometric data. The server is equipped with software responsible for data preprocessing, including cleaning and formatting the received data. The processed data is then analyzed in detail using a generative AI model. The AI ​​model learns patterns in the biometric data and assesses the user's health status and frailty risk. Based on this assessment, the server generates a personalized health management plan for each user.

[0279] The generated health management plan includes specific exercise instructions and dietary improvement suggestions, and this information is sent back to the user's device. The user can review the plan on their device and incorporate it into their daily life to maintain or improve their health.

[0280] As a concrete example, suppose a user walks every day. Through this system, the user's steps and heart rate are monitored, and this data is analyzed by a server. The server analyzes the user's exercise pattern, generates a plan recommending an increase in walking frequency, and notifies the user.

[0281] An example of a prompt might be, "Use a generative AI model to analyze the user's heart rate data, assess their stress level, and suggest relaxation techniques." In this way, the system supports the user in maintaining their health.

[0282] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0283] Step 1:

[0284] The user collects biometric data through a smartphone or a wearable device. These devices use sensors to record heart rate, number of steps, photos of meals, etc. The input is biometric data obtained from the user's daily activities, and this data is temporarily stored in the terminal. The output is the biometric data held in the device.

[0285] Step 2:

[0286] The terminal transmits the collected biometric data to the server via the Internet. Specifically, it regularly uploads the data using a data communication module. The data processing performed at this time is data encryption and compression, which realizes safe and efficient transmission. The input is the biometric data in the terminal, and the output is the data received by the server.

[0287] Step 3:

[0288] The server receives the transmitted biometric data and performs preprocessing on the data. The specific operations are data cleaning (e.g., filling in missing values, excluding outliers) and normalization. The input to this preprocessing is the biometric data received by the server, and the output is clean data suitable for analysis.

[0289] Step 4:

[0290] The server analyzes the preprocessed data using a generative AI model. The AI model utilizes machine learning algorithms to evaluate the user's health status and frailty risk from the data. As specific operations, pattern recognition and predictive analysis are performed. The input is clean data, and the output is evaluation data regarding the health status.

[0291] Step 5:

[0292] The server generates a health management plan optimized for the user based on evaluation data. Using prompts, it creates a plan that includes specific exercise and dietary guidance. The input is health evaluation data, and the output is the health management plan.

[0293] Step 6:

[0294] The device presents the user with a health management plan received from the server. Specifically, it displays the plan details through a mobile application, providing a means for incorporating it into daily life. The input is the health management plan from the server, and the output is the information provided to the user.

[0295] Step 7:

[0296] The user performs activities based on the proposed health management plan and records the activity data again on the device. This data is continuously collected, and the process returns to step 1. Here, the input is the user's daily activities, and the output is newly acquired biometric data.

[0297] (Application Example 1)

[0298] 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."

[0299] For the elderly and those requiring care, accurately understanding their daily health status, assessing individual health risks, and providing an optimal health management plan are crucial challenges. However, conventional systems often fail to adequately generate detailed plans tailored to each user's condition and provide continuous feedback. This makes it difficult for users to perform appropriate health management in real time, and this problem needs to be addressed.

[0300] 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.

[0301] In this invention, the server includes a generative AI model analysis means for analyzing information collected using a generative AI model and predicting the health status, a prompt sentence generation means for supporting the user's health risk assessment using prompt sentences, and a notification means for notifying the user and prompting appropriate actions when an abnormality is detected. Thereby, a detailed health management plan suitable for individual users can be provided, enabling real-time understanding and improvement of the health status.

[0302] The "information collection means" is a device or method for obtaining health-related information such as the number of steps, heart rate, and dietary content from the user.

[0303] The "analysis means" is a device or method for processing the collected information and evaluating the user's health status.

[0304] The "plan generation means" is a device or method for creating an individually optimized health management plan based on the analysis results.

[0305] The "plan providing means" is a device or method for transmitting the generated health management plan to the user.

[0306] The "monitoring means" is a device or method for continuously observing the user's activities and health status and detecting abnormalities.

[0307] The "notification means" is a device or method for sending an alarm or alert to the user and prompting appropriate actions when an abnormality is detected.

[0308] The "generative AI model analysis means" is a device or method for analyzing information collected using a generative AI model and predicting the health status.

[0309] The "prompt sentence generation means" is a device or method for generating prompt sentences to support the user's health risk assessment.

[0310] The system for implementing this invention includes a function to monitor the user's health status in their daily life and provide an individually optimized health management plan.

[0311] First, users collect health-related information such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. The information collection system efficiently acquires this data and transmits it to a server. The server receives the collected information and analyzes it using a generative AI model analysis system. This analysis makes it possible to predict the user's health status and frailty risk with high accuracy.

[0312] Based on the analysis results, the plan generation system creates a health management plan tailored to the user's needs. This plan includes suggestions for improvements in exercise and diet, and is presented to the user via the plan delivery system. If any abnormalities are detected in the user's activity, the monitoring and notification systems immediately issue a warning and prompt the user to take appropriate action.

[0313] Furthermore, the prompt message generation means generates prompt messages to support the user's health risk assessment. These prompt messages provide specific instructions for more effectively utilizing the generated AI model and support user behavioral change.

[0314] As a concrete example, suppose an elderly user records their daily steps using a wearable device. The data collected by the device is sent to a server, where a generating AI model analyzes it and determines the risk of lack of exercise. Based on this, the server provides the user with a specific health management plan, such as "Aim for 5,000 steps of walking every day." This plan is expected to improve the user's health.

[0315] An example of a prompt to the generating AI model would be, "Based on the user's latest heart rate and walking data, please suggest an appropriate exercise plan." This allows the system to provide specific and personalized advice tailored to the user's health condition.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The device acquires health-related data from the user. Inputs such as steps taken, heart rate, photos of meals, and voice memos are collected by the device. This data is efficiently processed through data collection methods and transmitted to a server.

[0319] Step 2:

[0320] The server analyzes the received data using a generative AI model analysis method. Based on the input data, the generative AI model predicts the user's health status and frailty risk. The data processing performed here includes data normalization and feature extraction.

[0321] Step 3:

[0322] The server creates a health management plan based on the analysis results. The plan generation system formulates points for improving exercise and diet tailored to the user's current situation. The output is a specific health management plan, which is provided to the user's terminal.

[0323] Step 4:

[0324] The server continuously monitors user activity using monitoring tools. It periodically checks whether the health management plan is being implemented and checks the data based on criteria for detecting anomalies.

[0325] Step 5:

[0326] If the server detects an anomaly, it will immediately send a warning to the user using a notification system. The notification will include the nature of the detected anomaly and recommended countermeasures. The output will be a warning message displayed on the user's terminal.

[0327] Step 6:

[0328] The terminal sends user feedback to the server. The feedback processing means receives the feedback and dynamically optimizes the health management plan. At this time, the prompt message generation means generates prompt messages to support user behavior change.

[0329] Through the above processing steps, the system can continuously manage the user's health status and provide individually optimized advice.

[0330] 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.

[0331] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. This system collects physical data and text and voice data representing emotions through the user's smartphone or wearable device.

[0332] The device sends data collected from the user to a server for integration with the emotion engine. The emotion engine has the ability to analyze text and audio data to identify the user's emotional state. Based on this, it generates analytical data to evaluate the impact of the user's emotions on their physical health.

[0333] The server integrates accumulated data and emotional data, and analyzes it using generative AI. This analysis makes it possible to assess the user's frailty risk and generate a detailed health management plan that also takes their emotional state into account.

[0334] For example, if health data detects a mild lack of exercise, but the emotion engine determines the user's stress level is high, the server will suggest an exercise plan that prioritizes reducing psychological stress. This plan may include activities such as yoga or meditation. The server sends this information to the device, where the user can review the plan and apply it to their daily life.

[0335] Users adjust their daily activities according to a health management plan provided using their device. Monitored activity data and emotional changes are sent to the server in real time, and the server receives feedback to further adjust the plan.

[0336] In this way, the system comprehensively manages the user's physical and emotional health, helping older adults and middle-aged adults maintain a healthy and balanced lifestyle.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The device collects the user's physical data (such as steps taken and heart rate) as well as text and voice data representing their emotions. Users input or record this data on the device as needed during their daily lives.

[0340] Step 2:

[0341] The device sends the collected data to the server. This data is encrypted to protect privacy.

[0342] Step 3:

[0343] The server stores the received data in a database and performs preprocessing. Preprocessing includes standardizing data formats and removing noise.

[0344] Step 4:

[0345] The server uses an emotion engine to analyze text and audio data to recognize the user's emotional state. This analysis is then combined with health data to prepare for a comprehensive analysis.

[0346] Step 5:

[0347] The server uses a generative AI model to analyze the integrated data and assess the user's frailty risk and emotional health.

[0348] Step 6:

[0349] Based on the analysis results, the server generates a personalized health management plan that takes into account the user's physical and emotional well-being. This plan includes exercise, diet, and stress management methods.

[0350] Step 7:

[0351] The server sends the generated health management plan to the user's device. The user then reviews this plan on the device and incorporates it into their daily life.

[0352] Step 8:

[0353] The device monitors the user's activity status and emotional changes in real time and feeds that data back to the server.

[0354] Step 9:

[0355] The server dynamically adjusts the health management plan based on feedback, continuously providing support optimized for the user. Where possible, it also provides additional care advice tailored to the user's emotional state.

[0356] (Example 2)

[0357] 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".

[0358] In modern life, many people are finding it difficult to maintain both physical and emotional health. Traditional health management systems focus solely on physical health and do not adequately consider emotional aspects, making it difficult to provide appropriate support tailored to individual health conditions. Furthermore, there is a lack of dynamic health management plans that respond to users' real-time changes in their condition.

[0359] 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.

[0360] In this invention, the server includes data collection means for processing biometric and emotional data collected from users, analysis means for analyzing the collected biometric and emotional data, and AI analysis means for analyzing the integrated data using a generative AI model. This makes it possible to comprehensively evaluate the user's physical and emotional health and provide an individually optimized health management plan in real time.

[0361] "Biometric data" refers to information related to a user's physical condition, such as heart rate, steps taken, and sleep patterns.

[0362] "Emotional data" refers to information indicating a user's emotional state, obtained through their statements and text input.

[0363] "Data collection means" refers to devices and technologies used to acquire biometric and emotional data from users.

[0364] "Analysis means" refers to devices and technologies used to analyze collected biometric and emotional data and evaluate the user's health status.

[0365] A "generative AI model" is an artificial intelligence technology that analyzes a user's health status based on their data and generates an appropriate health management plan.

[0366] "AI analysis means" refers to devices and technologies for analyzing integrated data using a generative AI model.

[0367] A "health management plan" is an action plan created based on analysis results, aimed at maintaining and improving the user's health.

[0368] "Plan delivery means" refers to devices or technologies used to notify users of the generated health management plan.

[0369] "Monitoring means" refers to devices and technologies used to continuously observe a user's activities and health status and detect abnormalities.

[0370] A "notification method" refers to a device or technology that issues an alert to the user and prompts them to take appropriate action when an anomaly is detected.

[0371] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. The terminal collects biometric data and emotional data through the user's smartphone or wearable device. Specifically, this includes biometric data such as heart rate, steps taken, and sleep patterns, as well as emotional data acquired through text and voice.

[0372] The device sends the collected data to the server. The server analyzes this data using an emotion engine. The emotion engine utilizes natural language processing technology to identify the user's emotional state from text and voice data. The analyzed emotional data and biometric data are integrated, and a generative AI model is used for deeper analysis. This AI model interprets the data using prompt sentences and evaluates the user's health and emotional state.

[0373] For example, if health data indicates a mild lack of exercise and the emotional engine detects high stress levels, the server will suggest an exercise plan to the user aimed at reducing stress. This plan may include activities such as yoga or meditation.

[0374] As a concrete example, the generative AI model receives the following prompt as input: "Generate a health plan based on this user's current heart rate and emotional stress level."

[0375] The server delivers the generated health management plan to the user's device. The user then uses this information to modify their daily activities and incorporate the health management plan into their real life. Activity data and emotional changes are fed back in real time, and the server uses this information to dynamically optimize the health management plan. This allows the system to effectively manage the user's physical and emotional health, supporting a healthy and balanced lifestyle.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] The device collects biometric and emotional data from the user. Specifically, it uses smartphone sensors and wearable devices to acquire physical data such as heart rate, steps, and sleep patterns, and collects emotional data from text messages and voice recordings. The input is data from sensor devices, and the output is the collected biometric and emotional data.

[0379] Step 2:

[0380] The device transmits the collected biometric and emotional data to the server. A secure data transfer protocol is used to ensure accurate and safe delivery of the data. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0381] Step 3:

[0382] The server passes the received data to the emotion engine for processing. The emotion engine uses natural language processing algorithms to analyze text and audio data and identify the user's emotional state. The input is the text and audio data sent to the server, and the output is the analyzed emotion data.

[0383] Step 4:

[0384] The server inputs the analyzed emotional and biometric data into a generating AI model. Based on this data, the generating AI model uses prompts to comprehensively evaluate the user's health and emotional state and determine the risk of frailty. At this stage, the inputs are emotional and biometric data, and the outputs are the analysis results and health risk assessment.

[0385] Step 5:

[0386] The server generates an individually optimized health management plan based on the analysis results from the generative AI model. For example, if the analysis results indicate a lack of exercise and high stress levels, the server will propose a plan that includes yoga and meditation aimed at stress reduction. The input is the analysis results from the generative AI model, and the output is the health management plan.

[0387] Step 6:

[0388] The server delivers the generated health management plan to the user's device. The plan is displayed clearly and presented in a format that is easy for the user to apply to their daily life. The input is the generated health plan, and the output is the delivery of the plan to the user's device.

[0389] Step 7:

[0390] Users adjust their activities according to a health management plan received via their device. Activity data and emotional changes are recorded on the device and fed back to the server in real time. Input is the user's daily activity data, and output is the feedback data sent to the server.

[0391] Through these steps, the system can comprehensively manage the user's health and provide personalized support.

[0392] (Application Example 2)

[0393] 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."

[0394] For older adults and middle-aged adults to maintain a healthy and balanced life, not only physical health but also emotional well-being is crucial. However, conventional health management systems have been insufficient in considering emotions, and have been unable to effectively provide individually optimized health management plans. Therefore, there is a need to comprehensively evaluate physical health and emotional state and provide individually optimized health management plans based on these evaluations, thereby realizing holistic healthcare that also takes emotions into account.

[0395] 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.

[0396] In this invention, the server includes information gathering means for processing biometric and emotional information collected from the user and evaluating the user's health and emotional state; analysis means for analyzing the collected information and evaluating the user's health and emotional state; and plan generation means for generating an individually optimized health management plan that takes the emotional state into account based on the analysis results. This enables the dynamic provision of a health management plan that takes the emotional state into account, allowing elderly people to live their daily lives with peace of mind.

[0397] "Biometric information" refers to data that indicates the user's physical state, such as heart rate, steps taken, and sleep patterns.

[0398] "Emotional information" refers to data that represents a user's emotional state, obtained through text and audio data.

[0399] "Information gathering means" refers to devices or methods for acquiring biometric and emotional information from users, including smartphones and wearable devices.

[0400] "Analysis means" refers to a processing device or method for integrating collected biological information and emotional information to evaluate the user's health and emotional state.

[0401] "Plan generation means" refers to a device or method that creates an individually optimized health management plan, including the user's emotional state, based on the analysis results.

[0402] "Plan delivery means" refers to a device or method for displaying a generated health management plan to the user and encouraging them to incorporate it into their daily life.

[0403] "Monitoring means" refers to a device or method for continuously monitoring a user's activities and health status and detecting any abnormalities that occur.

[0404] "Notification means" refers to a device or method that, when an abnormality is detected by a monitoring means, notifies the user of that fact and prompts them to take appropriate action.

[0405] "Feedback processing means" refers to a device or method for receiving user feedback and dynamically optimizing a health management plan.

[0406] The system implementing this invention consists of a smartphone, a wearable device, and a cloud server. Biometric and emotional information collected from the user is acquired by the smartphone or wearable device and transmitted to the cloud server in real time.

[0407] The cloud server receives data from sensor devices used as information gathering tools and then performs emotion analysis and integrated health status analysis. In particular, it extracts emotional information from text and audio using natural language processing (NLP) and emotion analysis APIs. It also integrates biometric information such as the user's heart rate and sleep patterns to comprehensively evaluate the user's health status.

[0408] The analysis method uses an AI model to generate personalized health management plans that take emotional states into account. These plans may include relaxation activities and exercise plans, and are dynamically updated according to the user's individual needs.

[0409] The generated plan is sent to a device such as a smartphone via a plan delivery system, allowing the user to review it and incorporate it into their daily health management. Furthermore, the user's activity is continuously monitored by a monitoring system, and if it deviates from the set criteria, an alert is sent via a notification system to prompt appropriate action.

[0410] For example, if the system detects that an elderly person has recently been experiencing poor sleep, the cloud server may use AI to suggest a special relaxation plan. An example of this prompt might be: "Please provide the optimal care plan based on the elderly person's recent health status (heart rate and sleep duration) and their recent stress level (emotional analysis)."

[0411] In this way, comprehensive health management that takes emotional states into account can be achieved, improving the quality of life for users.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The device collects the user's biometric information (heart rate, steps, sleep patterns, etc.) and emotional information through text and voice from wearable devices and smartphones. Input data is acquired from sensors and converted into a data format to be sent to a cloud server as output.

[0415] Step 2:

[0416] The server receives biometric and emotional information transmitted from the terminal as input and passes this data to the analysis system. For data processing, natural language processing (NLP) is used to extract emotional information from text and audio and integrate it as numerical data. The output is an evaluation of the user's health and emotional state.

[0417] Step 3:

[0418] The server creates an optimal health management plan using a generated AI model based on the analysis results. The input is the evaluation results, and the output is a individually optimized health management plan. Machine learning algorithms are used for data calculation, and the generated plan includes relaxation activities and exercise plans that take into account the user's current emotional state.

[0419] Step 4:

[0420] The server transmits the generated health management plan to the terminal via a plan delivery device. The terminal visually presents this plan to the user, who then adjusts their daily health activities based on it. The input is the health management plan, and the output is the plan information displayed on the terminal.

[0421] Step 5:

[0422] The server receives continuous feedback from the terminal and monitors user activity data using monitoring means. If an anomaly is detected, the server notifies the user using notification means. The input is user activity data, and the output is feedback, which is used to adjust the plan.

[0423] Step 6:

[0424] Based on user feedback, the server uses the plan generation mechanism again and updates the health management plan as needed. The input is feedback data, and the output is the adjusted health management plan. This ensures that a plan is always provided that is up-to-date.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This system aims to continuously monitor users' health in their daily lives and provide personalized health management plans. Users can collect health-related data such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. This data is transmitted to the server via the device.

[0442] The server receives the accumulated data and performs data preprocessing. Then, it analyzes the data using a generative AI model to assess the user's frailty risk. Based on the analysis results, the server generates an optimal health management plan for the user. This plan consists of detailed guidelines, including areas for improvement in exercise and diet, and is provided to the user via their device.

[0443] Users can view the health management plan provided on their device and incorporate it into their daily lives. Activity data is monitored in real time, and this information is sent back to the server. The server continuously analyzes the feedback and fine-tunes the plan as needed.

[0444] As a concrete example, suppose an elderly user walks every day and records their activity with a wearable device. The data recorded by the device is sent to a server at regular intervals, and the server analyzes it and determines that there is a concern about insufficient exercise. As a result, the server generates a health management plan suggesting that the user increase the frequency of their walks and sends this to the user's device. The user then uses this plan as a reference to adjust their daily activities and improve their health.

[0445] In this way, the system supports users' health and provides plans tailored to individual needs, helping older adults and middle-aged adults maintain healthier lifestyles.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] The device collects health-related data from the user. This data includes sensor data such as steps taken and heart rate, as well as photos of meals and voice memos. Users input and record this data using the device in their daily lives.

[0449] Step 2:

[0450] The device sends collected data to the server at regular intervals. The transmitted data uses a secure communication protocol and is kept private.

[0451] Step 3:

[0452] The server preprocesses the received data. It standardizes the data format and extracts necessary information to prepare it for easier analysis.

[0453] Step 4:

[0454] The server analyzes pre-processed data using a generative AI model to assess the user's health status. It calculates a frailty risk score and understands the user's health trends.

[0455] Step 5:

[0456] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. This plan includes recommendations for adjusting exercise levels and dietary choices.

[0457] Step 6:

[0458] The server sends the generated health management plan to the user's device. The user reviews the plan on their device and applies it to their daily life.

[0459] Step 7:

[0460] The device monitors the user's activity in real time and sends feedback to the server. Based on this feedback, the server readjusts and continuously optimizes the health management plan.

[0461] Step 8:

[0462] Based on monitoring results, the server immediately notifies the user if an anomaly is detected. It provides information to encourage the user to visit a medical institution if necessary, thereby mitigating the user's health risks.

[0463] (Example 1)

[0464] 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."

[0465] Conventional health management systems have struggled to accurately monitor users' health status in real time and provide optimized health management plans tailored to individual health conditions. Furthermore, they sometimes failed to respond quickly when abnormalities occurred, resulting in insufficient support for maintaining users' health. Therefore, there was a need for efficient and effective health management.

[0466] 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.

[0467] In this invention, the server includes information gathering means for processing biometric data collected from the user and evaluating their health status, analysis means for preprocessing and analyzing the collected biometric data, and determination means for evaluating the user's health status and determining the risk of frailty based on the data analyzed using generative AI technology. This enables the provision of individually optimized health management plans based on the user's health status and rapid response through real-time anomaly detection.

[0468] "Biometric data" refers to information related to a user's health status, including steps taken, heart rate, and dietary records.

[0469] "Information gathering means" refers to a system for acquiring and storing biometric data from users.

[0470] "Analysis means" refers to the function of organizing collected biological data and analyzing data patterns using machine learning algorithms, etc.

[0471] "Generative AI technology" refers to methods that use artificial intelligence technology to generate new information and suggestions from data.

[0472] "Determination method" refers to a system that determines the user's health status and frailty risk based on the results obtained through analysis.

[0473] A "health management plan" refers to a plan that includes guidelines and suggestions regarding exercise, diet, rest, etc., aimed at maintaining and improving the user's health.

[0474] "Means of delivery" refers to the function of presenting the generated health management plan to the user and communicating it in a usable format.

[0475] "Monitoring measures" refer to systems that continuously check users' activities and health status and detect abnormalities.

[0476] "Warning measures" refer to notification functions that inform the user when an anomaly is detected and prompt them to take appropriate action.

[0477] "Feedback mechanism" refers to a function that dynamically adjusts the health management plan based on user feedback and the actual progress of the plan's implementation.

[0478] A "multifunctional device" refers to an electronic device that has various sensor functions and is used to acquire the user's biometric data.

[0479] A "central computer" refers to a computer that centrally processes data sent by users and manages the entire system.

[0480] This invention provides a system that monitors a user's health status in real time and provides an individually optimized health management plan.

[0481] First, users use multifunctional devices such as smartphones or wearable devices to collect biometric data such as heart rate, steps taken, and photos of meals. This data is temporarily stored on the user's device. The device periodically transmits this data to a central computer via the internet. Highly secure protocols are used for data transmission to ensure data confidentiality and security.

[0482] The server functions as a central computer, receiving collected biometric data. The server is equipped with software responsible for data preprocessing, including cleaning and formatting the received data. The processed data is then analyzed in detail using a generative AI model. The AI ​​model learns patterns in the biometric data and assesses the user's health status and frailty risk. Based on this assessment, the server generates a personalized health management plan for each user.

[0483] The generated health management plan includes specific exercise instructions and dietary improvement suggestions, and this information is sent back to the user's device. The user can review the plan on their device and incorporate it into their daily life to maintain or improve their health.

[0484] As a concrete example, suppose a user walks every day. Through this system, the user's steps and heart rate are monitored, and this data is analyzed by a server. The server analyzes the user's exercise pattern, generates a plan recommending an increase in walking frequency, and notifies the user.

[0485] An example of a prompt might be, "Use a generative AI model to analyze the user's heart rate data, assess their stress level, and suggest relaxation techniques." In this way, the system supports the user in maintaining their health.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] Users collect biometric data through smartphones or wearable devices. These devices use sensors to record things like heart rate, steps taken, and photos of meals. The input is biometric data obtained from the user's daily activities, and this data is temporarily stored on the device. The output is the biometric data retained within the device.

[0489] Step 2:

[0490] The device transmits collected biometric data to a server via the internet. Specifically, it periodically uploads data using a data communication module. The data processing performed during this process involves encryption and compression, ensuring secure and efficient transmission. The input is the biometric data within the device, and the output is the data received by the server.

[0491] Step 3:

[0492] The server receives the transmitted biometric data and performs data preprocessing. Specifically, this involves data cleaning (e.g., imputation of missing values, removal of outliers) and normalization. The input for this preprocessing is the biometric data received by the server, and the output is clean data suitable for analysis.

[0493] Step 4:

[0494] The server analyzes pre-processed data using a generative AI model. The AI ​​model utilizes machine learning algorithms to assess the user's health status and frailty risk from the data. Specifically, it performs pattern recognition and predictive analytics. The input is clean data, and the output is health status assessment data.

[0495] Step 5:

[0496] The server generates a health management plan optimized for the user based on evaluation data. Using prompts, it creates a plan that includes specific exercise and dietary guidance. The input is health evaluation data, and the output is the health management plan.

[0497] Step 6:

[0498] The device presents the user with a health management plan received from the server. Specifically, it displays the plan details through a mobile application, providing a means for incorporating it into daily life. The input is the health management plan from the server, and the output is the information provided to the user.

[0499] Step 7:

[0500] The user performs activities based on the proposed health management plan and records the activity data again on the device. This data is continuously collected, and the process returns to step 1. Here, the input is the user's daily activities, and the output is newly acquired biometric data.

[0501] (Application Example 1)

[0502] 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."

[0503] For the elderly and those requiring care, accurately understanding their daily health status, assessing individual health risks, and providing an optimal health management plan are crucial challenges. However, conventional systems often fail to adequately generate detailed plans tailored to each user's condition and provide continuous feedback. This makes it difficult for users to perform appropriate health management in real time, and this problem needs to be addressed.

[0504] 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.

[0505] In this invention, the server includes a generative AI model analysis means that analyzes information collected using a generative AI model and predicts the user's health status, a prompt message generation means that supports the user's health risk assessment using prompt messages, and a notification means that notifies the user when an abnormality is detected and prompts them to take appropriate action. This enables the provision of a detailed health management plan tailored to each individual user, and allows for real-time monitoring and improvement of the user's health status.

[0506] "Information gathering means" refers to devices or methods that acquire health-related information from users, such as step count, heart rate, and dietary information.

[0507] "Analysis means" refers to devices or methods for processing collected information and evaluating the user's health status.

[0508] A "plan generation means" refers to a device or method for creating an individually optimized health management plan based on analysis results.

[0509] "Plan delivery means" refers to devices or methods for communicating the generated health management plan to the user.

[0510] "Monitoring means" refers to devices or methods for continuously observing a user's activities and health status and detecting abnormalities.

[0511] A "notification means" is a device or method that sends a warning or alert to the user when an anomaly is detected, prompting them to take appropriate action.

[0512] A "generative AI model analysis means" is a device or method that uses a generative AI model to analyze collected information and predict health conditions.

[0513] A "prompt message generation means" is a device or method for generating prompt messages to support the user's health risk assessment.

[0514] The system for implementing this invention includes a function to monitor the user's health status in their daily life and provide an individually optimized health management plan.

[0515] First, users collect health-related information such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. The information collection system efficiently acquires this data and transmits it to a server. The server receives the collected information and analyzes it using a generative AI model analysis system. This analysis makes it possible to predict the user's health status and frailty risk with high accuracy.

[0516] Based on the analysis results, the plan generation system creates a health management plan tailored to the user's needs. This plan includes suggestions for improvements in exercise and diet, and is presented to the user via the plan delivery system. If any abnormalities are detected in the user's activity, the monitoring and notification systems immediately issue a warning and prompt the user to take appropriate action.

[0517] Furthermore, the prompt message generation means generates prompt messages to support the user's health risk assessment. These prompt messages provide specific instructions for more effectively utilizing the generated AI model and support user behavioral change.

[0518] As a concrete example, suppose an elderly user records their daily steps using a wearable device. The data collected by the device is sent to a server, where a generating AI model analyzes it and determines the risk of lack of exercise. Based on this, the server provides the user with a specific health management plan, such as "Aim for 5,000 steps of walking every day." This plan is expected to improve the user's health.

[0519] An example of a prompt to the generating AI model would be, "Based on the user's latest heart rate and walking data, please suggest an appropriate exercise plan." This allows the system to provide specific and personalized advice tailored to the user's health condition.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The device acquires health-related data from the user. Inputs such as steps taken, heart rate, photos of meals, and voice memos are collected by the device. This data is efficiently processed through data collection methods and transmitted to a server.

[0523] Step 2:

[0524] The server analyzes the received data using a generative AI model analysis method. Based on the input data, the generative AI model predicts the user's health status and frailty risk. The data processing performed here includes data normalization and feature extraction.

[0525] Step 3:

[0526] The server creates a health management plan based on the analysis results. The plan generation system formulates points for improving exercise and diet tailored to the user's current situation. The output is a specific health management plan, which is provided to the user's terminal.

[0527] Step 4:

[0528] The server continuously monitors user activity using monitoring tools. It periodically checks whether the health management plan is being implemented and checks the data based on criteria for detecting anomalies.

[0529] Step 5:

[0530] If the server detects an anomaly, it will immediately send a warning to the user using a notification system. The notification will include the nature of the detected anomaly and recommended countermeasures. The output will be a warning message displayed on the user's terminal.

[0531] Step 6:

[0532] The terminal sends user feedback to the server. The feedback processing means receives the feedback and dynamically optimizes the health management plan. At this time, the prompt message generation means generates prompt messages to support user behavior change.

[0533] Through the above processing steps, the system can continuously manage the user's health status and provide individually optimized advice.

[0534] 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.

[0535] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. This system collects physical data and text and voice data representing emotions through the user's smartphone or wearable device.

[0536] The device sends data collected from the user to a server for integration with the emotion engine. The emotion engine has the ability to analyze text and audio data to identify the user's emotional state. Based on this, it generates analytical data to evaluate the impact of the user's emotions on their physical health.

[0537] The server integrates accumulated data and emotional data, and analyzes it using generative AI. This analysis makes it possible to assess the user's frailty risk and generate a detailed health management plan that also takes their emotional state into account.

[0538] For example, if health data detects a mild lack of exercise, but the emotion engine determines the user's stress level is high, the server will suggest an exercise plan that prioritizes reducing psychological stress. This plan may include activities such as yoga or meditation. The server sends this information to the device, where the user can review the plan and apply it to their daily life.

[0539] Users adjust their daily activities according to a health management plan provided using their device. Monitored activity data and emotional changes are sent to the server in real time, and the server receives feedback to further adjust the plan.

[0540] In this way, the system comprehensively manages the user's physical and emotional health, helping older adults and middle-aged adults maintain a healthy and balanced lifestyle.

[0541] The following describes the processing flow.

[0542] Step 1:

[0543] The device collects the user's physical data (such as steps taken and heart rate) as well as text and voice data representing their emotions. Users input or record this data on the device as needed during their daily lives.

[0544] Step 2:

[0545] The device sends the collected data to the server. This data is encrypted to protect privacy.

[0546] Step 3:

[0547] The server stores the received data in a database and performs preprocessing. Preprocessing includes standardizing data formats and removing noise.

[0548] Step 4:

[0549] The server uses an emotion engine to analyze text and audio data to recognize the user's emotional state. This analysis is then combined with health data to prepare for a comprehensive analysis.

[0550] Step 5:

[0551] The server uses a generative AI model to analyze the integrated data and assess the user's frailty risk and emotional health.

[0552] Step 6:

[0553] Based on the analysis results, the server generates a personalized health management plan that takes into account the user's physical and emotional well-being. This plan includes exercise, diet, and stress management methods.

[0554] Step 7:

[0555] The server sends the generated health management plan to the user's device. The user then reviews this plan on the device and incorporates it into their daily life.

[0556] Step 8:

[0557] The device monitors the user's activity status and emotional changes in real time and feeds that data back to the server.

[0558] Step 9:

[0559] The server dynamically adjusts the health management plan based on feedback, continuously providing support optimized for the user. Where possible, it also provides additional care advice tailored to the user's emotional state.

[0560] (Example 2)

[0561] 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."

[0562] In modern life, many people are finding it difficult to maintain both physical and emotional health. Traditional health management systems focus solely on physical health and do not adequately consider emotional aspects, making it difficult to provide appropriate support tailored to individual health conditions. Furthermore, there is a lack of dynamic health management plans that respond to users' real-time changes in their condition.

[0563] 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.

[0564] In this invention, the server includes data collection means for processing biometric and emotional data collected from users, analysis means for analyzing the collected biometric and emotional data, and AI analysis means for analyzing the integrated data using a generative AI model. This makes it possible to comprehensively evaluate the user's physical and emotional health and provide an individually optimized health management plan in real time.

[0565] "Biometric data" refers to information related to a user's physical condition, such as heart rate, steps taken, and sleep patterns.

[0566] "Emotional data" refers to information indicating a user's emotional state, obtained through their statements and text input.

[0567] "Data collection means" refers to devices and technologies used to acquire biometric and emotional data from users.

[0568] "Analysis means" refers to devices and technologies used to analyze collected biometric and emotional data and evaluate the user's health status.

[0569] A "generative AI model" is an artificial intelligence technology that analyzes a user's health status based on their data and generates an appropriate health management plan.

[0570] "AI analysis means" refers to devices and technologies for analyzing integrated data using a generative AI model.

[0571] A "health management plan" is an action plan created based on analysis results, aimed at maintaining and improving the user's health.

[0572] "Plan delivery means" refers to devices or technologies used to notify users of the generated health management plan.

[0573] "Monitoring means" refers to devices and technologies used to continuously observe a user's activities and health status and detect abnormalities.

[0574] A "notification method" refers to a device or technology that issues an alert to the user and prompts them to take appropriate action when an anomaly is detected.

[0575] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. The terminal collects biometric data and emotional data through the user's smartphone or wearable device. Specifically, this includes biometric data such as heart rate, steps taken, and sleep patterns, as well as emotional data acquired through text and voice.

[0576] The device sends the collected data to the server. The server analyzes this data using an emotion engine. The emotion engine utilizes natural language processing technology to identify the user's emotional state from text and voice data. The analyzed emotional data and biometric data are integrated, and a generative AI model is used for deeper analysis. This AI model interprets the data using prompt sentences and evaluates the user's health and emotional state.

[0577] For example, if health data indicates a mild lack of exercise and the emotional engine detects high stress levels, the server will suggest an exercise plan to the user aimed at reducing stress. This plan may include activities such as yoga or meditation.

[0578] As a concrete example, the generative AI model receives the following prompt as input: "Generate a health plan based on this user's current heart rate and emotional stress level."

[0579] The server delivers the generated health management plan to the user's device. The user then uses this information to modify their daily activities and incorporate the health management plan into their real life. Activity data and emotional changes are fed back in real time, and the server uses this information to dynamically optimize the health management plan. This allows the system to effectively manage the user's physical and emotional health, supporting a healthy and balanced lifestyle.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] The device collects biometric and emotional data from the user. Specifically, it uses smartphone sensors and wearable devices to acquire physical data such as heart rate, steps, and sleep patterns, and collects emotional data from text messages and voice recordings. The input is data from sensor devices, and the output is the collected biometric and emotional data.

[0583] Step 2:

[0584] The device transmits the collected biometric and emotional data to the server. A secure data transfer protocol is used to ensure accurate and safe delivery of the data. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0585] Step 3:

[0586] The server passes the received data to the emotion engine for processing. The emotion engine uses natural language processing algorithms to analyze text and audio data and identify the user's emotional state. The input is the text and audio data sent to the server, and the output is the analyzed emotion data.

[0587] Step 4:

[0588] The server inputs the analyzed emotional and biometric data into a generating AI model. Based on this data, the generating AI model uses prompts to comprehensively evaluate the user's health and emotional state and determine the risk of frailty. At this stage, the inputs are emotional and biometric data, and the outputs are the analysis results and health risk assessment.

[0589] Step 5:

[0590] The server generates an individually optimized health management plan based on the analysis results from the generative AI model. For example, if the analysis results indicate a lack of exercise and high stress levels, the server will propose a plan that includes yoga and meditation aimed at stress reduction. The input is the analysis results from the generative AI model, and the output is the health management plan.

[0591] Step 6:

[0592] The server delivers the generated health management plan to the user's device. The plan is displayed clearly and presented in a format that is easy for the user to apply to their daily life. The input is the generated health plan, and the output is the delivery of the plan to the user's device.

[0593] Step 7:

[0594] Users adjust their activities according to a health management plan received via their device. Activity data and emotional changes are recorded on the device and fed back to the server in real time. Input is the user's daily activity data, and output is the feedback data sent to the server.

[0595] Through these steps, the system can comprehensively manage the user's health and provide personalized support.

[0596] (Application Example 2)

[0597] 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."

[0598] For older adults and middle-aged adults to maintain a healthy and balanced life, not only physical health but also emotional well-being is crucial. However, conventional health management systems have been insufficient in considering emotions, and have been unable to effectively provide individually optimized health management plans. Therefore, there is a need to comprehensively evaluate physical health and emotional state and provide individually optimized health management plans based on these evaluations, thereby realizing holistic healthcare that also takes emotions into account.

[0599] 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.

[0600] In this invention, the server includes information gathering means for processing biometric and emotional information collected from the user and evaluating the user's health and emotional state; analysis means for analyzing the collected information and evaluating the user's health and emotional state; and plan generation means for generating an individually optimized health management plan that takes the emotional state into account based on the analysis results. This enables the dynamic provision of a health management plan that takes the emotional state into account, allowing elderly people to live their daily lives with peace of mind.

[0601] "Biometric information" refers to data that indicates the user's physical state, such as heart rate, steps taken, and sleep patterns.

[0602] "Emotional information" refers to data that represents a user's emotional state, obtained through text and audio data.

[0603] "Information gathering means" refers to devices or methods for acquiring biometric and emotional information from users, including smartphones and wearable devices.

[0604] "Analysis means" refers to a processing device or method for integrating collected biological information and emotional information to evaluate the user's health and emotional state.

[0605] "Plan generation means" refers to a device or method that creates an individually optimized health management plan, including the user's emotional state, based on the analysis results.

[0606] "Plan delivery means" refers to a device or method for displaying a generated health management plan to the user and encouraging them to incorporate it into their daily life.

[0607] "Monitoring means" refers to a device or method for continuously monitoring a user's activities and health status and detecting any abnormalities that occur.

[0608] "Notification means" refers to a device or method that, when an abnormality is detected by a monitoring means, notifies the user of that fact and prompts them to take appropriate action.

[0609] "Feedback processing means" refers to a device or method for receiving user feedback and dynamically optimizing a health management plan.

[0610] The system implementing this invention consists of a smartphone, a wearable device, and a cloud server. Biometric and emotional information collected from the user is acquired by the smartphone or wearable device and transmitted to the cloud server in real time.

[0611] The cloud server receives data from sensor devices used as information gathering tools and then performs emotion analysis and integrated health status analysis. In particular, it extracts emotional information from text and audio using natural language processing (NLP) and emotion analysis APIs. It also integrates biometric information such as the user's heart rate and sleep patterns to comprehensively evaluate the user's health status.

[0612] The analysis method uses an AI model to generate personalized health management plans that take emotional states into account. These plans may include relaxation activities and exercise plans, and are dynamically updated according to the user's individual needs.

[0613] The generated plan is sent to a device such as a smartphone via a plan delivery system, allowing the user to review it and incorporate it into their daily health management. Furthermore, the user's activity is continuously monitored by a monitoring system, and if it deviates from the set criteria, an alert is sent via a notification system to prompt appropriate action.

[0614] For example, if the system detects that an elderly person has recently been experiencing poor sleep, the cloud server may use AI to suggest a special relaxation plan. An example of this prompt might be: "Please provide the optimal care plan based on the elderly person's recent health status (heart rate and sleep duration) and their recent stress level (emotional analysis)."

[0615] In this way, comprehensive health management that takes emotional states into account can be achieved, improving the quality of life for users.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The device collects the user's biometric information (heart rate, steps, sleep patterns, etc.) and emotional information through text and voice from wearable devices and smartphones. Input data is acquired from sensors and converted into a data format to be sent to a cloud server as output.

[0619] Step 2:

[0620] The server receives biometric and emotional information transmitted from the terminal as input and passes this data to the analysis system. For data processing, natural language processing (NLP) is used to extract emotional information from text and audio and integrate it as numerical data. The output is an evaluation of the user's health and emotional state.

[0621] Step 3:

[0622] The server creates an optimal health management plan using a generated AI model based on the analysis results. The input is the evaluation results, and the output is a individually optimized health management plan. Machine learning algorithms are used for data calculation, and the generated plan includes relaxation activities and exercise plans that take into account the user's current emotional state.

[0623] Step 4:

[0624] The server transmits the generated health management plan to the terminal via a plan delivery device. The terminal visually presents this plan to the user, who then adjusts their daily health activities based on it. The input is the health management plan, and the output is the plan information displayed on the terminal.

[0625] Step 5:

[0626] The server receives continuous feedback from the terminal and monitors user activity data using monitoring means. If an anomaly is detected, the server notifies the user using notification means. The input is user activity data, and the output is feedback, which is used to adjust the plan.

[0627] Step 6:

[0628] Based on user feedback, the server uses the plan generation mechanism again and updates the health management plan as needed. The input is feedback data, and the output is the adjusted health management plan. This ensures that a plan is always provided that is up-to-date.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This system aims to continuously monitor users' health in their daily lives and provide personalized health management plans. Users can collect health-related data such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. This data is transmitted to the server via the device.

[0647] The server receives the accumulated data and performs data preprocessing. Then, it analyzes the data using a generative AI model to assess the user's frailty risk. Based on the analysis results, the server generates an optimal health management plan for the user. This plan consists of detailed guidelines, including areas for improvement in exercise and diet, and is provided to the user via their device.

[0648] Users can view the health management plan provided on their device and incorporate it into their daily lives. Activity data is monitored in real time, and this information is sent back to the server. The server continuously analyzes the feedback and fine-tunes the plan as needed.

[0649] As a concrete example, suppose an elderly user walks every day and records their activity with a wearable device. The data recorded by the device is sent to a server at regular intervals, and the server analyzes it and determines that there is a concern about insufficient exercise. As a result, the server generates a health management plan suggesting that the user increase the frequency of their walks and sends this to the user's device. The user then uses this plan as a reference to adjust their daily activities and improve their health.

[0650] In this way, the system supports users' health and provides plans tailored to individual needs, helping older adults and middle-aged adults maintain healthier lifestyles.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The device collects health-related data from the user. This data includes sensor data such as steps taken and heart rate, as well as photos of meals and voice memos. Users input and record this data using the device in their daily lives.

[0654] Step 2:

[0655] The device sends collected data to the server at regular intervals. The transmitted data uses a secure communication protocol and is kept private.

[0656] Step 3:

[0657] The server preprocesses the received data. It standardizes the data format and extracts necessary information to prepare it for easier analysis.

[0658] Step 4:

[0659] The server analyzes pre-processed data using a generative AI model to assess the user's health status. It calculates a frailty risk score and understands the user's health trends.

[0660] Step 5:

[0661] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. This plan includes recommendations for adjusting exercise levels and dietary choices.

[0662] Step 6:

[0663] The server sends the generated health management plan to the user's device. The user reviews the plan on their device and applies it to their daily life.

[0664] Step 7:

[0665] The device monitors the user's activity in real time and sends feedback to the server. Based on this feedback, the server readjusts and continuously optimizes the health management plan.

[0666] Step 8:

[0667] Based on monitoring results, the server immediately notifies the user if an anomaly is detected. It provides information to encourage the user to visit a medical institution if necessary, thereby mitigating the user's health risks.

[0668] (Example 1)

[0669] 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".

[0670] Conventional health management systems have struggled to accurately monitor users' health status in real time and provide optimized health management plans tailored to individual health conditions. Furthermore, they sometimes failed to respond quickly when abnormalities occurred, resulting in insufficient support for maintaining users' health. Therefore, there was a need for efficient and effective health management.

[0671] 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.

[0672] In this invention, the server includes information gathering means for processing biometric data collected from the user and evaluating their health status, analysis means for preprocessing and analyzing the collected biometric data, and determination means for evaluating the user's health status and determining the risk of frailty based on the data analyzed using generative AI technology. This enables the provision of individually optimized health management plans based on the user's health status and rapid response through real-time anomaly detection.

[0673] "Biometric data" refers to information related to a user's health status, including steps taken, heart rate, and dietary records.

[0674] "Information gathering means" refers to a system for acquiring and storing biometric data from users.

[0675] "Analysis means" refers to the function of organizing collected biological data and analyzing data patterns using machine learning algorithms, etc.

[0676] "Generative AI technology" refers to methods that use artificial intelligence technology to generate new information and suggestions from data.

[0677] "Determination method" refers to a system that determines the user's health status and frailty risk based on the results obtained through analysis.

[0678] A "health management plan" refers to a plan that includes guidelines and suggestions regarding exercise, diet, rest, etc., aimed at maintaining and improving the user's health.

[0679] "Means of delivery" refers to the function of presenting the generated health management plan to the user and communicating it in a usable format.

[0680] "Monitoring measures" refer to systems that continuously check users' activities and health status and detect abnormalities.

[0681] "Warning measures" refer to notification functions that inform the user when an anomaly is detected and prompt them to take appropriate action.

[0682] "Feedback mechanism" refers to a function that dynamically adjusts the health management plan based on user feedback and the actual progress of the plan's implementation.

[0683] A "multifunctional device" refers to an electronic device that has various sensor functions and is used to acquire the user's biometric data.

[0684] A "central computer" refers to a computer that centrally processes data sent by users and manages the entire system.

[0685] This invention provides a system that monitors a user's health status in real time and provides an individually optimized health management plan.

[0686] First, users use multifunctional devices such as smartphones or wearable devices to collect biometric data such as heart rate, steps taken, and photos of meals. This data is temporarily stored on the user's device. The device periodically transmits this data to a central computer via the internet. Highly secure protocols are used for data transmission to ensure data confidentiality and security.

[0687] The server functions as a central computer, receiving collected biometric data. The server is equipped with software responsible for data preprocessing, including cleaning and formatting the received data. The processed data is then analyzed in detail using a generative AI model. The AI ​​model learns patterns in the biometric data and assesses the user's health status and frailty risk. Based on this assessment, the server generates a personalized health management plan for each user.

[0688] The generated health management plan includes specific exercise instructions and dietary improvement suggestions, and this information is sent back to the user's device. The user can review the plan on their device and incorporate it into their daily life to maintain or improve their health.

[0689] As a concrete example, suppose a user walks every day. Through this system, the user's steps and heart rate are monitored, and this data is analyzed by a server. The server analyzes the user's exercise pattern, generates a plan recommending an increase in walking frequency, and notifies the user.

[0690] An example of a prompt might be, "Use a generative AI model to analyze the user's heart rate data, assess their stress level, and suggest relaxation techniques." In this way, the system supports the user in maintaining their health.

[0691] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0692] Step 1:

[0693] Users collect biometric data through smartphones or wearable devices. These devices use sensors to record things like heart rate, steps taken, and photos of meals. The input is biometric data obtained from the user's daily activities, and this data is temporarily stored on the device. The output is the biometric data retained within the device.

[0694] Step 2:

[0695] The device transmits collected biometric data to a server via the internet. Specifically, it periodically uploads data using a data communication module. The data processing performed during this process involves encryption and compression, ensuring secure and efficient transmission. The input is the biometric data within the device, and the output is the data received by the server.

[0696] Step 3:

[0697] The server receives the transmitted biometric data and performs data preprocessing. Specifically, this involves data cleaning (e.g., imputation of missing values, removal of outliers) and normalization. The input for this preprocessing is the biometric data received by the server, and the output is clean data suitable for analysis.

[0698] Step 4:

[0699] The server analyzes pre-processed data using a generative AI model. The AI ​​model utilizes machine learning algorithms to assess the user's health status and frailty risk from the data. Specifically, it performs pattern recognition and predictive analytics. The input is clean data, and the output is health status assessment data.

[0700] Step 5:

[0701] The server generates a health management plan optimized for the user based on evaluation data. Using prompts, it creates a plan that includes specific exercise and dietary guidance. The input is health evaluation data, and the output is the health management plan.

[0702] Step 6:

[0703] The device presents the user with a health management plan received from the server. Specifically, it displays the plan details through a mobile application, providing a means for incorporating it into daily life. The input is the health management plan from the server, and the output is the information provided to the user.

[0704] Step 7:

[0705] The user performs activities based on the proposed health management plan and records the activity data again on the device. This data is continuously collected, and the process returns to step 1. Here, the input is the user's daily activities, and the output is newly acquired biometric data.

[0706] (Application Example 1)

[0707] 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".

[0708] For the elderly and those requiring care, accurately understanding their daily health status, assessing individual health risks, and providing an optimal health management plan are crucial challenges. However, conventional systems often fail to adequately generate detailed plans tailored to each user's condition and provide continuous feedback. This makes it difficult for users to perform appropriate health management in real time, and this problem needs to be addressed.

[0709] 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.

[0710] In this invention, the server includes a generative AI model analysis means that analyzes information collected using a generative AI model and predicts the user's health status, a prompt message generation means that supports the user's health risk assessment using prompt messages, and a notification means that notifies the user when an abnormality is detected and prompts them to take appropriate action. This enables the provision of a detailed health management plan tailored to each individual user, and allows for real-time monitoring and improvement of the user's health status.

[0711] "Information gathering means" refers to devices or methods that acquire health-related information from users, such as step count, heart rate, and dietary information.

[0712] "Analysis means" refers to devices or methods for processing collected information and evaluating the user's health status.

[0713] A "plan generation means" refers to a device or method for creating an individually optimized health management plan based on analysis results.

[0714] "Plan delivery means" refers to devices or methods for communicating the generated health management plan to the user.

[0715] "Monitoring means" refers to devices or methods for continuously observing a user's activities and health status and detecting abnormalities.

[0716] A "notification means" is a device or method that sends a warning or alert to the user when an anomaly is detected, prompting them to take appropriate action.

[0717] A "generative AI model analysis means" is a device or method that uses a generative AI model to analyze collected information and predict health conditions.

[0718] A "prompt message generation means" is a device or method for generating prompt messages to support the user's health risk assessment.

[0719] The system for implementing this invention includes a function to monitor the user's health status in their daily life and provide an individually optimized health management plan.

[0720] First, users collect health-related information such as steps taken, heart rate, photos of meals, and voice memos through their smartphones or wearable devices. The information collection system efficiently acquires this data and transmits it to a server. The server receives the collected information and analyzes it using a generative AI model analysis system. This analysis makes it possible to predict the user's health status and frailty risk with high accuracy.

[0721] Based on the analysis results, the plan generation system creates a health management plan tailored to the user's needs. This plan includes suggestions for improvements in exercise and diet, and is presented to the user via the plan delivery system. If any abnormalities are detected in the user's activity, the monitoring and notification systems immediately issue a warning and prompt the user to take appropriate action.

[0722] Furthermore, the prompt message generation means generates prompt messages to support the user's health risk assessment. These prompt messages provide specific instructions for more effectively utilizing the generated AI model and support user behavioral change.

[0723] As a concrete example, suppose an elderly user records their daily steps using a wearable device. The data collected by the device is sent to a server, where a generating AI model analyzes it and determines the risk of lack of exercise. Based on this, the server provides the user with a specific health management plan, such as "Aim for 5,000 steps of walking every day." This plan is expected to improve the user's health.

[0724] An example of a prompt to the generating AI model would be, "Based on the user's latest heart rate and walking data, please suggest an appropriate exercise plan." This allows the system to provide specific and personalized advice tailored to the user's health condition.

[0725] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0726] Step 1:

[0727] The device acquires health-related data from the user. Inputs such as steps taken, heart rate, photos of meals, and voice memos are collected by the device. This data is efficiently processed through data collection methods and transmitted to a server.

[0728] Step 2:

[0729] The server analyzes the received data using a generative AI model analysis method. Based on the input data, the generative AI model predicts the user's health status and frailty risk. The data processing performed here includes data normalization and feature extraction.

[0730] Step 3:

[0731] The server creates a health management plan based on the analysis results. The plan generation system formulates points for improving exercise and diet tailored to the user's current situation. The output is a specific health management plan, which is provided to the user's terminal.

[0732] Step 4:

[0733] The server continuously monitors user activity using monitoring tools. It periodically checks whether the health management plan is being implemented and checks the data based on criteria for detecting anomalies.

[0734] Step 5:

[0735] If the server detects an anomaly, it will immediately send a warning to the user using a notification system. The notification will include the nature of the detected anomaly and recommended countermeasures. The output will be a warning message displayed on the user's terminal.

[0736] Step 6:

[0737] The terminal sends user feedback to the server. The feedback processing means receives the feedback and dynamically optimizes the health management plan. At this time, the prompt message generation means generates prompt messages to support user behavior change.

[0738] Through the above processing steps, the system can continuously manage the user's health status and provide individually optimized advice.

[0739] 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.

[0740] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. This system collects physical data and text and voice data representing emotions through the user's smartphone or wearable device.

[0741] The device sends data collected from the user to a server for integration with the emotion engine. The emotion engine has the ability to analyze text and audio data to identify the user's emotional state. Based on this, it generates analytical data to evaluate the impact of the user's emotions on their physical health.

[0742] The server integrates accumulated data and emotional data, and analyzes it using generative AI. This analysis makes it possible to assess the user's frailty risk and generate a detailed health management plan that also takes their emotional state into account.

[0743] For example, if health data detects a mild lack of exercise, but the emotion engine determines the user's stress level is high, the server will suggest an exercise plan that prioritizes reducing psychological stress. This plan may include activities such as yoga or meditation. The server sends this information to the device, where the user can review the plan and apply it to their daily life.

[0744] Users adjust their daily activities according to a health management plan provided using their device. Monitored activity data and emotional changes are sent to the server in real time, and the server receives feedback to further adjust the plan.

[0745] In this way, the system comprehensively manages the user's physical and emotional health, helping older adults and middle-aged adults maintain a healthy and balanced lifestyle.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] The device collects the user's physical data (such as steps taken and heart rate) as well as text and voice data representing their emotions. Users input or record this data on the device as needed during their daily lives.

[0749] Step 2:

[0750] The device sends the collected data to the server. This data is encrypted to protect privacy.

[0751] Step 3:

[0752] The server stores the received data in a database and performs preprocessing. Preprocessing includes standardizing data formats and removing noise.

[0753] Step 4:

[0754] The server uses an emotion engine to analyze text and audio data to recognize the user's emotional state. This analysis is then combined with health data to prepare for a comprehensive analysis.

[0755] Step 5:

[0756] The server uses a generative AI model to analyze the integrated data and assess the user's frailty risk and emotional health.

[0757] Step 6:

[0758] Based on the analysis results, the server generates a personalized health management plan that takes into account the user's physical and emotional well-being. This plan includes exercise, diet, and stress management methods.

[0759] Step 7:

[0760] The server sends the generated health management plan to the user's device. The user then reviews this plan on the device and incorporates it into their daily life.

[0761] Step 8:

[0762] The device monitors the user's activity status and emotional changes in real time and feeds that data back to the server.

[0763] Step 9:

[0764] The server dynamically adjusts the health management plan based on feedback, continuously providing support optimized for the user. Where possible, it also provides additional care advice tailored to the user's emotional state.

[0765] (Example 2)

[0766] 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".

[0767] In modern life, many people are finding it difficult to maintain both physical and emotional health. Traditional health management systems focus solely on physical health and do not adequately consider emotional aspects, making it difficult to provide appropriate support tailored to individual health conditions. Furthermore, there is a lack of dynamic health management plans that respond to users' real-time changes in their condition.

[0768] 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.

[0769] In this invention, the server includes data collection means for processing biometric and emotional data collected from users, analysis means for analyzing the collected biometric and emotional data, and AI analysis means for analyzing the integrated data using a generative AI model. This makes it possible to comprehensively evaluate the user's physical and emotional health and provide an individually optimized health management plan in real time.

[0770] "Biometric data" refers to information related to a user's physical condition, such as heart rate, steps taken, and sleep patterns.

[0771] "Emotional data" refers to information indicating a user's emotional state, obtained through their statements and text input.

[0772] "Data collection means" refers to devices and technologies used to acquire biometric and emotional data from users.

[0773] "Analysis means" refers to devices and technologies used to analyze collected biometric and emotional data and evaluate the user's health status.

[0774] A "generative AI model" is an artificial intelligence technology that analyzes a user's health status based on their data and generates an appropriate health management plan.

[0775] "AI analysis means" refers to devices and technologies for analyzing integrated data using a generative AI model.

[0776] A "health management plan" is an action plan created based on analysis results, aimed at maintaining and improving the user's health.

[0777] "Plan delivery means" refers to devices or technologies used to notify users of the generated health management plan.

[0778] "Monitoring means" refers to devices and technologies used to continuously observe a user's activities and health status and detect abnormalities.

[0779] A "notification method" refers to a device or technology that issues an alert to the user and prompts them to take appropriate action when an anomaly is detected.

[0780] This invention is a system that comprehensively evaluates a user's health and emotional state in their daily life and provides an individually optimized health management plan. The terminal collects biometric data and emotional data through the user's smartphone or wearable device. Specifically, this includes biometric data such as heart rate, steps taken, and sleep patterns, as well as emotional data acquired through text and voice.

[0781] The device sends the collected data to the server. The server analyzes this data using an emotion engine. The emotion engine utilizes natural language processing technology to identify the user's emotional state from text and voice data. The analyzed emotional data and biometric data are integrated, and a generative AI model is used for deeper analysis. This AI model interprets the data using prompt sentences and evaluates the user's health and emotional state.

[0782] For example, if health data indicates a mild lack of exercise and the emotional engine detects high stress levels, the server will suggest an exercise plan to the user aimed at reducing stress. This plan may include activities such as yoga or meditation.

[0783] As a concrete example, the generative AI model receives the following prompt as input: "Generate a health plan based on this user's current heart rate and emotional stress level."

[0784] The server delivers the generated health management plan to the user's device. The user then uses this information to modify their daily activities and incorporate the health management plan into their real life. Activity data and emotional changes are fed back in real time, and the server uses this information to dynamically optimize the health management plan. This allows the system to effectively manage the user's physical and emotional health, supporting a healthy and balanced lifestyle.

[0785] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0786] Step 1:

[0787] The device collects biometric and emotional data from the user. Specifically, it uses smartphone sensors and wearable devices to acquire physical data such as heart rate, steps, and sleep patterns, and collects emotional data from text messages and voice recordings. The input is data from sensor devices, and the output is the collected biometric and emotional data.

[0788] Step 2:

[0789] The device transmits the collected biometric and emotional data to the server. A secure data transfer protocol is used to ensure accurate and safe delivery of the data. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0790] Step 3:

[0791] The server passes the received data to the emotion engine for processing. The emotion engine uses natural language processing algorithms to analyze text and audio data and identify the user's emotional state. The input is the text and audio data sent to the server, and the output is the analyzed emotion data.

[0792] Step 4:

[0793] The server inputs the analyzed emotional and biometric data into a generating AI model. Based on this data, the generating AI model uses prompts to comprehensively evaluate the user's health and emotional state and determine the risk of frailty. At this stage, the inputs are emotional and biometric data, and the outputs are the analysis results and health risk assessment.

[0794] Step 5:

[0795] The server generates an individually optimized health management plan based on the analysis results from the generative AI model. For example, if the analysis results indicate a lack of exercise and high stress levels, the server will propose a plan that includes yoga and meditation aimed at stress reduction. The input is the analysis results from the generative AI model, and the output is the health management plan.

[0796] Step 6:

[0797] The server delivers the generated health management plan to the user's device. The plan is displayed clearly and presented in a format that is easy for the user to apply to their daily life. The input is the generated health plan, and the output is the delivery of the plan to the user's device.

[0798] Step 7:

[0799] Users adjust their activities according to a health management plan received via their device. Activity data and emotional changes are recorded on the device and fed back to the server in real time. Input is the user's daily activity data, and output is the feedback data sent to the server.

[0800] Through these steps, the system can comprehensively manage the user's health and provide personalized support.

[0801] (Application Example 2)

[0802] 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".

[0803] For older adults and middle-aged adults to maintain a healthy and balanced life, not only physical health but also emotional well-being is crucial. However, conventional health management systems have been insufficient in considering emotions, and have been unable to effectively provide individually optimized health management plans. Therefore, there is a need to comprehensively evaluate physical health and emotional state and provide individually optimized health management plans based on these evaluations, thereby realizing holistic healthcare that also takes emotions into account.

[0804] 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.

[0805] In this invention, the server includes information gathering means for processing biometric and emotional information collected from the user and evaluating the user's health and emotional state; analysis means for analyzing the collected information and evaluating the user's health and emotional state; and plan generation means for generating an individually optimized health management plan that takes the emotional state into account based on the analysis results. This enables the dynamic provision of a health management plan that takes the emotional state into account, allowing elderly people to live their daily lives with peace of mind.

[0806] "Biometric information" refers to data that indicates the user's physical state, such as heart rate, steps taken, and sleep patterns.

[0807] "Emotional information" refers to data that represents a user's emotional state, obtained through text and audio data.

[0808] "Information gathering means" refers to devices or methods for acquiring biometric and emotional information from users, including smartphones and wearable devices.

[0809] "Analysis means" refers to a processing device or method for integrating collected biological information and emotional information to evaluate the user's health and emotional state.

[0810] "Plan generation means" refers to a device or method that creates an individually optimized health management plan, including the user's emotional state, based on the analysis results.

[0811] "Plan delivery means" refers to a device or method for displaying a generated health management plan to the user and encouraging them to incorporate it into their daily life.

[0812] "Monitoring means" refers to a device or method for continuously monitoring a user's activities and health status and detecting any abnormalities that occur.

[0813] "Notification means" refers to a device or method that, when an abnormality is detected by a monitoring means, notifies the user of that fact and prompts them to take appropriate action.

[0814] "Feedback processing means" refers to a device or method for receiving user feedback and dynamically optimizing a health management plan.

[0815] The system implementing this invention consists of a smartphone, a wearable device, and a cloud server. Biometric and emotional information collected from the user is acquired by the smartphone or wearable device and transmitted to the cloud server in real time.

[0816] The cloud server receives data from sensor devices used as information gathering tools and then performs emotion analysis and integrated health status analysis. In particular, it extracts emotional information from text and audio using natural language processing (NLP) and emotion analysis APIs. It also integrates biometric information such as the user's heart rate and sleep patterns to comprehensively evaluate the user's health status.

[0817] The analysis method uses an AI model to generate personalized health management plans that take emotional states into account. These plans may include relaxation activities and exercise plans, and are dynamically updated according to the user's individual needs.

[0818] The generated plan is sent to a device such as a smartphone via a plan delivery system, allowing the user to review it and incorporate it into their daily health management. Furthermore, the user's activity is continuously monitored by a monitoring system, and if it deviates from the set criteria, an alert is sent via a notification system to prompt appropriate action.

[0819] For example, if the system detects that an elderly person has recently been experiencing poor sleep, the cloud server may use AI to suggest a special relaxation plan. An example of this prompt might be: "Please provide the optimal care plan based on the elderly person's recent health status (heart rate and sleep duration) and their recent stress level (emotional analysis)."

[0820] In this way, comprehensive health management that takes emotional states into account can be achieved, improving the quality of life for users.

[0821] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0822] Step 1:

[0823] The device collects the user's biometric information (heart rate, steps, sleep patterns, etc.) and emotional information through text and voice from wearable devices and smartphones. Input data is acquired from sensors and converted into a data format to be sent to a cloud server as output.

[0824] Step 2:

[0825] The server receives biometric and emotional information transmitted from the terminal as input and passes this data to the analysis system. For data processing, natural language processing (NLP) is used to extract emotional information from text and audio and integrate it as numerical data. The output is an evaluation of the user's health and emotional state.

[0826] Step 3:

[0827] The server creates an optimal health management plan using a generated AI model based on the analysis results. The input is the evaluation results, and the output is a individually optimized health management plan. Machine learning algorithms are used for data calculation, and the generated plan includes relaxation activities and exercise plans that take into account the user's current emotional state.

[0828] Step 4:

[0829] The server transmits the generated health management plan to the terminal via a plan delivery device. The terminal visually presents this plan to the user, who then adjusts their daily health activities based on it. The input is the health management plan, and the output is the plan information displayed on the terminal.

[0830] Step 5:

[0831] The server receives continuous feedback from the terminal and monitors user activity data using monitoring means. If an anomaly is detected, the server notifies the user using notification means. The input is user activity data, and the output is feedback, which is used to adjust the plan.

[0832] Step 6:

[0833] Based on user feedback, the server uses the plan generation mechanism again and updates the health management plan as needed. The input is feedback data, and the output is the adjusted health management plan. This ensures that a plan is always provided that is up-to-date.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] A data collection method for processing data collected from users and evaluating their health status,

[0858] An analytical means for analyzing collected data and evaluating the user's health status,

[0859] A plan generation means that generates an individually optimized health management plan based on the analysis results,

[0860] A plan provision method that provides the generated health management plan to the user,

[0861] A monitoring system that monitors user activity and health status and detects abnormalities,

[0862] A notification system that notifies the user when an anomaly is detected and prompts them to take appropriate action,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising means for acquiring user data from a sensor device and transmitting it to a server.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising a feedback processing means for receiving user feedback and dynamically optimizing a health management plan.

[0868] "Example 1"

[0869] (Claim 1)

[0870] A means of collecting information for processing biometric data collected from users and evaluating their health status,

[0871] An analytical means for preprocessing and analyzing collected biological data,

[0872] A determination method that evaluates the user's health status and determines the risk of frailty based on data analyzed using generative AI technology,

[0873] A plan generation means that generates an individually optimized health management plan based on the judgment results,

[0874] A means of providing users with a generated health management plan and providing health guidance based on that plan,

[0875] A monitoring system that continuously monitors the user's daily activities and health status and detects abnormal patterns,

[0876] A warning system that alerts the user when an anomaly is detected and prompts them to take appropriate action,

[0877] A feedback mechanism that allows you to update your health management plan based on real-time feedback,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for acquiring a user's biometric data from a multifunctional device and transmitting it to a central computer.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising a feedback processing means for optimizing a health management plan by utilizing AI technology that dynamically generates prompt statements.

[0883] "Application Example 1"

[0884] (Claim 1)

[0885] Information gathering means for processing information collected from users and evaluating their health status,

[0886] An analytical means for analyzing collected information and evaluating the user's health status,

[0887] A plan generation means that generates an individually optimized health management plan based on the analysis results,

[0888] A plan provisioning means that provides the generated health management plan to the user,

[0889] A monitoring system that continuously monitors user activity and health status and detects abnormalities,

[0890] A notification system that notifies the user when an anomaly is detected and prompts them to take appropriate action,

[0891] A generative AI model analysis means that uses a generative AI model to analyze collected information and predict health status,

[0892] A prompt statement generation means that supports the assessment of the user's health risk using prompt statements,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, further comprising means for acquiring user information from a detection device and transmitting it to a data processing device.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising a feedback processing means for receiving user feedback and dynamically optimizing a health management plan.

[0898] "Example 2 of combining an emotion engine"

[0899] (Claim 1)

[0900] A data collection means for processing biometric and emotional data collected from users and for evaluating health and emotional status,

[0901] An analytical means that analyzes collected biometric and emotional data to evaluate the user's health and emotional state,

[0902] A plan generation means that generates an individually optimized health management plan that takes into account the user's health and emotional state based on the analysis results,

[0903] A plan provisioning means that provides the generated health management plan to the user terminal,

[0904] A monitoring system that monitors user activity and health status and detects abnormalities,

[0905] A notification system that provides feedback to the user and prompts appropriate action when an abnormality or change in emotional state is detected,

[0906] An AI analysis method that analyzes integrated data using a generative AI model,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising means for acquiring user biometric and emotional data from a sensor device and transmitting it to a server.

[0910] (Claim 3)

[0911] The system according to claim 1, further comprising a feedback processing means for receiving user feedback data and dynamically optimizing a health management plan in real time.

[0912] "Application example 2 when combining with an emotional engine"

[0913] (Claim 1)

[0914] A means for collecting information to process biometric and emotional information collected from users and to evaluate their health and emotional state,

[0915] An analytical means for analyzing collected information and evaluating the user's health and emotional state,

[0916] A plan generation means that generates an individually optimized health management plan that takes emotional state into account based on the analysis results,

[0917] A means of providing users with a generated health management plan and encouraging them to adjust their activities and emotional state,

[0918] A monitoring system that monitors the user's activities and health status and detects abnormalities,

[0919] A notification system that notifies users when an anomaly is detected and prompts them to take appropriate action, including appropriate countermeasures for their emotional state,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, further comprising means for acquiring a user's biometric information from a sensor device and transmitting it to a cloud server.

[0923] (Claim 3)

[0924] The system according to claim 1, further comprising a feedback processing means for receiving user feedback and dynamically optimizing a health management plan based on emotional state. [Explanation of Symbols]

[0925] 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 gathering means for processing information collected from users and evaluating their health status, An analytical means for analyzing collected information and evaluating the user's health status, A plan generation means that generates an individually optimized health management plan based on the analysis results, A plan provisioning means that provides the generated health management plan to the user, A monitoring system that continuously monitors user activity and health status and detects abnormalities, A notification system that notifies the user when an anomaly is detected and prompts them to take appropriate action, A generative AI model analysis means that uses a generative AI model to analyze collected information and predict health status, A prompt statement generation means that supports the assessment of the user's health risk using prompt statements, A system that includes this.

2. The system according to claim 1, further comprising means for acquiring user information from a detection device and transmitting it to a data processing device.

3. The system according to claim 1, further comprising a feedback processing means for receiving user feedback and dynamically optimizing a health management plan.

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