system

Abstract

We provide the system. [Solution] Sensing means for receiving personal health data, An information processing means for analyzing data obtained from the sensing means and generating health-related indicators, An advice generation means for providing individual health management advice based on health indicators generated by the aforementioned information processing means, A communication means for notifying the user terminal of the health management advice generated by the advice generation means, A means for monitoring health status using the aforementioned health indicators and anomaly detection algorithm, and for notifying stakeholders when an anomaly is detected, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, people's health awareness is increasing. However, in many cases, health management depends on regular health check-ups and visits to medical institutions. However, with such management methods, it is difficult to continuously monitor the daily health status, and it is impossible to detect in real time the deterioration of the health status. Therefore, it is difficult to detect chronic diseases at an early stage and maintain daily health, which may lead to an increase in medical costs. The purpose of the present invention is to solve these problems by providing a system that allows users to obtain health data in real time during their daily lives and enables autonomous health management.

Means for Solving the Problems

[0005] This invention proposes a system comprising: information collection means for receiving personal health data; data analysis means for analyzing the received data and generating health indicators; advice generation means for providing individual health management advice based on the generated health indicators; and notification means for notifying the user terminal of the advice. This system allows users to receive personalized advice based on health data collected on a daily basis. This makes it easier to grasp changes in health status in real time, contributing to the prevention of chronic diseases and the reduction of medical expenses.

[0006] "Information gathering means" refers to components used to acquire health data from smart devices and sensors.

[0007] A "data analysis tool" is a component used to analyze acquired health data and generate indicators that show the state of health.

[0008] The "advice generation method" is a component that creates customized health management advice for the user based on the analyzed data.

[0009] A "notification means" is a component for providing the generated health management advice to the user's terminal.

[0010] A "health indicator" is a standard generated through data analysis to quantitatively evaluate an individual's health status.

[0011] A "user terminal" refers to a device, such as a smartphone or tablet, that a user uses to receive digital information. [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes 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), and the like.

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] The system for implementing the present invention consists of a server, a terminal, and a user, all of which interact with each other. The server is responsible for acquiring and analyzing health data and generating personalized health management advice. The terminal aggregates individual health data obtained from wearable devices and smart toilets and transmits it to the server. It also notifies the user of the generated health management advice. The user receives the information provided via the terminal and uses it to manage their daily health.

[0034] A concrete example is a smart toilet that users use daily. This toilet analyzes the components of urine and transmits the results to the user's smartphone via Bluetooth. The smartphone uploads this information to a server in the cloud. This server analyzes the received data using a generative AI model. For example, it can analyze the concentrations of glucose and sodium in the urine to assess the user's health. Based on this analysis, if the server determines that there is a risk of excessive salt intake, it will generate advice recommending that the user reduce their salt intake.

[0035] As a result, the generated advice is notified to the user via their smartphone. Based on this advice, the user can take specific health management actions, such as reviewing their meal plan or adjusting the timing of hydration. In addition, if an abnormality is detected, a notification may be sent encouraging consultation with a specialist or recommending a visit to a medical institution.

[0036] In this way, the system can be naturally integrated into the user's daily life, providing a streamlined and personalized health management process.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server sends data collection requests to smart toilets and wearable devices. These requests specify the types of health data and timestamps required.

[0040] Step 2:

[0041] The terminal receives raw data transmitted from the smart device. This includes items such as heart rate, body temperature, weight, and urine analysis results. The terminal converts this data into an appropriate format and prepares it as data packets.

[0042] Step 3:

[0043] The device transfers the prepared data packets to a server in the cloud via Wi-Fi or Bluetooth. During this process, protocols are used to maintain data integrity and security.

[0044] Step 4:

[0045] The server converts the received data into a format that is easy to analyze. This involves pre-processing such as detecting outliers, imputing missing values, and unit conversion.

[0046] Step 5:

[0047] The server analyzes the data using a generative AI model. Specifically, it generates health indicators and performs trend analysis by comparing them with historical data.

[0048] Step 6:

[0049] The server generates personalized health management advice for each user based on the analysis results. This advice includes suggestions for dietary adjustments, exercise, and fluid intake.

[0050] Step 7:

[0051] The server sends the generated advice to the device, which then notifies the user. The advice is displayed as a push notification on the smartphone, and further details can be viewed within the app.

[0052] Step 8:

[0053] Users make choices based on the advice provided. For example, they might review their diet or adjust their daily activities.

[0054] Step 9:

[0055] The user then provides feedback to the server through their device regarding the results of their actions. The server uses this data to learn and improve the accuracy of its advice, thereby evolving the overall functionality of the system.

[0056] (Example 1)

[0057] 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."

[0058] In modern society, health management is a crucial issue for individuals, but it is not easy to continuously and efficiently monitor one's health status and obtain appropriate health advice in daily life. Furthermore, there is a need to provide more accurate advice based on individual health conditions by utilizing big data and machine learning. Therefore, the development of proactive and personalized health management systems is required.

[0059] 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.

[0060] In this invention, the server includes a device collection means for receiving biometric data, a data analysis means including a machine learning model for analyzing the data obtained from the device collection means and generating health indicators, and an advice construction means for providing individualized health management advice based on the health indicators generated by the data analysis means. This makes it possible to continuously monitor an individual's health status and provide appropriate health management advice quickly and effectively.

[0061] "Biometric data" refers to measurable information obtained from the human body, including, for example, blood glucose levels and sodium concentration.

[0062] "Device data collection means" refers to equipment or devices used to acquire biometric data from users, with specific examples including wearable devices and smart toilets.

[0063] "Data analysis means" refers to methods and systems for processing collected biometric data and generating health-related indicators, and involves analyzing data using machine learning models.

[0064] A "machine learning model" is a computational model that recognizes patterns based on data and makes predictions and classifications about future data. A specific example is a neural network.

[0065] "Advice building means" refers to methods and systems for creating specific health management advice to provide to users based on health indicators generated by data analysis means.

[0066] "Information distribution means" refers to methods or systems for notifying users of generated health management advice, and is usually done using mobile devices or computers.

[0067] This invention is a system for supporting personal health management. A specific embodiment of the invention involves a mechanism in which a server, a terminal, and a user work together. This system is implemented based on the following components.

[0068] The server is built on cloud infrastructure and uses generative AI models to analyze biometric data sent by users. This analysis employs data analysis methods incorporating machine learning libraries such as TENSORFLOW® and PyTorch. Based on the data received from the device collection method, the server calculates health indicators and generates personalized health management advice. Because this involves handling a massive amount of data, hardware with efficient data processing capabilities (e.g., high-performance CPUs and GPUs) is required.

[0069] The terminal operates as a platform for devices that collect biometric data from users' daily lives. Specific examples of such devices include wearable devices (e.g., fitness trackers) and smart toilets in the home. These devices transmit biometric data to smartphones via Bluetooth or Wi-Fi and then upload that data to servers in the cloud.

[0070] Users are the direct beneficiaries of this system, enabling them to understand their own health status based on the advice provided by their devices and proactively engage in daily health management. Users can review personalized health advice notified via their smartphones and modify their behavior accordingly.

[0071] As a concrete example, a smart toilet used daily by users is equipped with a function to measure glucose and sodium concentrations in urine. This data is collected, sent from the device to a server, and analyzed by a generative AI model. As a result, advice regarding the user's health (e.g., "reduce excessive salt intake") is generated and notified to the user via their smartphone.

[0072] Examples of prompt messages that might be entered are as follows:

[0073] "Analysis subjects: Urinary components. Data: Glucose concentration - 105 mg / dL, Sodium concentration - 145 mmol / L. Output: Health advice."

[0074] This invention enables continuous and personalized health management, allowing users to take appropriate actions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device acquires biometric data. Specifically, it collects data such as glucose and sodium concentrations in urine through wearable devices used by the user or smart toilets. The input is numerical data measured by sensors, which is then transmitted to a smartphone via Bluetooth or Wi-Fi. The output is the biometric data transferred to the smartphone.

[0078] Step 2:

[0079] The device sends the collected biometric data to a server in the cloud. The smartphone uploads the data in real time using an internet connection. The input in this process is the biometric data stored on the device, and the output is the biometric data sent to the server. This prepares the data for the next analysis step.

[0080] Step 3:

[0081] The server analyzes the received data. A generative AI model is implemented here, and it is used to perform data analysis. The input is biometric data sent to the server, and the data is applied to the machine learning model to detect patterns and anomalies. Specifically, a model built with TensorFlow or PyTorch analyzes the data and calculates health indicators. The output is the generated health indicators.

[0082] Step 4:

[0083] The server generates personalized health management advice based on the analyzed health indicators. The input is the health indicators obtained in the previous step, and the AI ​​constructs meaningful advice based on the analysis results. The output is specific advice to notify the user (e.g., a recommendation to reduce salt intake).

[0084] Step 5:

[0085] The device receives advice from the server and notifies the user. Specifically, it displays the advice through the smartphone's notification function. The input is health management advice sent from the server, and the output is a visual notification to the user.

[0086] Step 6:

[0087] Based on the advice received from the device, users take daily health management actions. For example, they might review their diet or adjust their exercise plan. In this step, the input is the advice from the device, and the output is the specific health actions the user takes. This allows users to proactively manage their own health.

[0088] (Application Example 1)

[0089] 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."

[0090] With the aging of society and the increase in lifestyle-related diseases, there is a growing need for more detailed individual health management. Furthermore, it is crucial to collect diverse health data in real time, quickly understand individual health conditions, and provide necessary advice. However, current systems often fail to adequately aggregate and analyze health data, leading to delays in early detection of abnormalities and appropriate responses.

[0091] 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.

[0092] In this invention, the server includes sensing means for receiving personal health data, information processing means for analyzing the data obtained from the sensing means and generating health indicators, advice generation means for providing individual health management advice based on the health indicators generated by the information processing means, communication means for notifying the user terminal of the health management advice generated by the advice generation means, and warning means for monitoring the health status using the health indicators and an anomaly detection algorithm, and notifying stakeholders if an anomaly is detected. This enables the provision of prompt and appropriate advice based on collected health data and the early detection of anomalies.

[0093] "Sensing means" refers to devices or components used to collect personal health data.

[0094] An "information processing device" is a technological device that has the function of analyzing data obtained from sensing devices to generate health-related indicators.

[0095] "Advice generation means" refers to a system function for creating individual health management advice based on health indicators generated by information processing means.

[0096] "Communication means" refers to the technical devices and protocols used to notify the user's terminal of the generated health management advice.

[0097] A "pre-warning system" is a system that uses health indicators and anomaly detection algorithms to monitor health conditions and notify stakeholders in the event of an anomaly.

[0098] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server is responsible for the primary function of receiving and analyzing personal health data. Specifically, it collects health data using wearable devices and smart toilets as sensing means and transmits this data to the server.

[0099] The server uses a generative AI model as its information processing tool to analyze the received data. This model analyzes the data using deep learning techniques and other methods to generate important health indicators. It also has an advice generation tool that generates personalized health management advice for each user based on these indicators. This advice is immediately notified to the user's smartphone or tablet via communication means.

[0100] The device functions as a hub where users receive notifications and advice from the server. Through this, users can monitor their health status in real time and take early action in case of abnormalities. To this end, applications on the device take actions based on collected data and analysis results, improving the quality of health management in daily life.

[0101] Furthermore, as a preventative measure, the generating AI model has the function of issuing warnings to the user and designated stakeholders when it detects an anomaly. For example, if the glucose concentration in the urine enters a dangerous range, it can generate a prompt message such as "High urine glucose test result -> Generate exercise / dietary advice" and promptly inform the user.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server receives health data from wearable devices and smart toilets through sensing means. This data includes the user's vital signs and biochemical information. The received data is filtered to remove noise and formatted into a format suitable for analysis.

[0105] Step 2:

[0106] The server, as a means of information processing, inputs pre-processed health data into a generative AI model. This generative AI model performs deep analysis of the data. Specifically, it extracts feature patterns from the input data and generates indicators for evaluating the user's health status. In this process, it also uses past data history to infer new health indicators.

[0107] Step 3:

[0108] Based on the generated health indicators, the server creates personalized health management advice using an advice generation system. This advice includes specific action suggestions to improve the user's health. For example, it may include recommendations for dietary adjustments or exercise. At this time, prompt statements are generated and the advice content is organized.

[0109] Step 4:

[0110] The device receives advice from the server via communication. The content of this advice is then notified to the user as an alert or recommended action. The device converts the received information into a format that is easy for the user to understand and displays it.

[0111] Step 5:

[0112] Users review the advice received on their devices and incorporate it into their daily health management routines. If any abnormalities are detected, it is recommended that they promptly seek expert advice.

[0113] Step 6:

[0114] The server uses proactive warning mechanisms to alert stakeholders if an anomaly is detected in the generated AI model. This includes notifying the user's emergency contacts, registered caregivers, and medical institutions.

[0115] 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.

[0116] The system for implementing the present invention consists of a server, a terminal, a user, and an emotion engine. The server is responsible for acquiring and analyzing health data and emotion data, and for generating personalized health management and emotion-based advice. The terminal aggregates individual data obtained from wearable devices, smart toilets, and devices for recognizing emotions, and transmits it to the server. It also has the role of notifying the user of the generated health management advice. The user receives the information provided via the terminal and uses it for daily health management and emotion management.

[0117] A concrete example is a wearable device that users use daily. This device recognizes emotional states from heart rate, activity levels, and voice, and transmits the results to the user's smartphone via Wi-Fi. The smartphone uploads this information to a server in the cloud. The server analyzes this data and evaluates the user's health and emotional state. If this analysis reveals an increase in stress levels, it generates advice on exercises or meditation to promote relaxation.

[0118] Furthermore, the emotion engine can understand the user's emotional state by evaluating changes in voice tone and facial expressions. Based on this evaluation, it also provides health management advice that reflects the user's emotional changes. For example, if the user is feeling tired or stressed, it will send advice recommending a balanced diet and sufficient rest.

[0119] In this way, the system integrates health data and emotional data to support users in more personalized health and emotional management. As a result, users can not only understand and improve their health status, but also improve their quality of life through emotional management.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The device receives personal health and emotional data from wearable devices, smart toilets, and devices that recognize voice and facial expressions. Specifically, it acquires data such as heart rate, activity level, urine analysis results, voice tone, and changes in facial expressions.

[0123] Step 2:

[0124] The device combines the received health and emotional data into a single data packet and transmits it to the server via Wi-Fi or Bluetooth. Encryption is performed during this process to ensure data integrity and security.

[0125] Step 3:

[0126] The server receives the transmitted data packets and performs preprocessing, including format conversion and detection of anomalies. This preprocessing ensures the accuracy necessary for data analysis.

[0127] Step 4:

[0128] The server analyzes health data using a generation AI model to generate indicators showing the user's current health status. This analysis also includes predictive analytics that take into account past health data history.

[0129] Step 5:

[0130] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. Based on this evaluation, it understands stress levels and changes in emotions.

[0131] Step 6:

[0132] The server generates personalized health and emotional management advice for the user based on assessments of both their physical and emotional state. This includes suggestions for exercise to reduce stress and recommendations for relaxation techniques.

[0133] Step 7:

[0134] The server sends generated advice to the device, which then provides it to the user as a push notification on their smartphone. The user can then view detailed advice within the app.

[0135] Step 8:

[0136] Based on the advice provided, users take specific actions to manage their health and regulate their emotions. For example, they might incorporate recommended exercises or make relaxation a daily routine.

[0137] Step 9:

[0138] Users provide feedback on their actions through their devices, and the server uses this feedback to continuously learn and improve the accuracy of the system's advice.

[0139] (Example 2)

[0140] 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".

[0141] Current health management systems collect users' biometric information and analyze their health status, but they are unable to provide personalized health guidance that takes emotional states into account, or to conduct sufficient analysis to detect abnormalities early. As a result, users have limited opportunities to receive appropriate guidance based on the stress and emotional changes they experience in their daily lives.

[0142] 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.

[0143] In this invention, the server includes information gathering means for receiving an individual's biometric information, data processing means for processing the information and generating health evaluation indicators, and guidance generation means for generating individualized health management guidance based on the evaluation indicators and emotional state. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide appropriate health management guidance.

[0144] "Information gathering means" refers to a device or function for collecting an individual's biometric information and transmitting it to a processing device.

[0145] "Data processing means" refers to a device or software that has the function of analyzing collected biological information and generating health-related evaluation indicators.

[0146] A "guidance generation means" is a device or algorithm that creates specific health management guidance for a user based on evaluation indicators and emotional states obtained by a data processing means.

[0147] "Means of provision" refers to equipment or software for displaying or transmitting the generated health management guidance to the user's terminal.

[0148] "Emotional analysis means" refers to a technology or device for understanding a user's emotional state by analyzing changes in voice and facial expressions.

[0149] "Additional guidance generation means" refers to an algorithm or device that creates additional health management guidance tailored to the user's emotional state based on the results of the emotion analysis means.

[0150] This invention is a system for providing users with appropriate health management guidance by comprehensively analyzing an individual's biological information and emotional state. The system's components and their specific implementation methods are described below.

[0151] The server receives data from information gathering devices and generates health assessment indicators using data processing devices. These indicators are analyzed based on heart rate, activity levels, and voice data collected from wearable devices and information analysis equipment. The server efficiently processes data by using Python libraries (e.g., Pandas, NumPy) for data analysis.

[0152] As a means of emotion analysis, a generative AI model is used to analyze voice data and facial expression data. Existing voice analysis technologies are applied to analyze emotional states from voice signals. Furthermore, facial recognition technology is used to understand changes in facial expressions, and this emotional information is reflected in health management guidance.

[0153] The instruction generation system generates specific instruction based on evaluation metrics and sentiment analysis results. The generated instruction is delivered to the user's device via a delivery system. The device is a smartphone or tablet, and the instruction content is notified to the user through the user interface.

[0154] For example, if the system determines that a user's stress level is high based on fluctuations in their biometric information or emotional state, it will generate and provide advice on exercises or meditation to promote relaxation. The user can receive notifications from their device and manage their health according to the instructions.

[0155] An example of a prompt message for a generative AI model is: "Generate specific health management guidance based on the user's emotional data. For example, suggest actions to take when the user's stress level is high."

[0156] In this way, the system can comprehensively manage the user's health and emotional state and provide health management guidance tailored to their individual circumstances.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The terminal collects biometric information from wearable devices and data analysis equipment. Specifically, the terminal uses Bluetooth and Wi-Fi to acquire heart rate, activity levels, and voice data. This input data is temporarily stored on the terminal in JSON format. As output, this data is prepared for subsequent processing.

[0160] Step 2:

[0161] The device sends the collected biometric information to a server in the cloud. Specifically, the device uses the HTTPS protocol to send previously stored JSON data to the server. The input is the data collected in step 1, and the output is the biometric information stored in the server's database.

[0162] Step 3:

[0163] The server analyzes the received biometric information. Using Python data analysis libraries (e.g., Pandas, NumPy), the server generates health assessment indicators by analyzing the input data. This analysis process calculates fluctuations in heart rate and activity level, converting them into indicators such as stress level and activity level. The generated assessment indicators are then output.

[0164] Step 4:

[0165] The server uses emotion analysis tools to analyze the user's emotional state from the voice data. It utilizes a generative AI model to perform computational processing to identify emotions from the input voice data. This process outputs the user's emotional state, which is then used to generate guidance along with health assessment indicators.

[0166] Step 5:

[0167] The server generates personalized health management guidance based on evaluation indicators and emotional state. The guidance generation mechanism integrates the input health indicators and emotional state data, and uses a generation AI model based on prompt text to generate health management guidance tailored to the user. Specific guidance content is provided as output.

[0168] Step 6:

[0169] The device notifies the user of health management guidance sent from the server. Specifically, the device uses push notifications and in-app messages to display the guidance content received as input to the user. The output is the guidance message displayed on the user's device.

[0170] Step 7:

[0171] The user reviews the health management guidance they receive and acts accordingly. The input here is the guidance message displayed on their smartphone or tablet, and the output is the user's actions. Specifically, the user might perform recommended exercises or try stress-relief methods.

[0172] Through this process, the system can effectively utilize biometric information and emotional states to provide users with personalized health management guidance.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In elderly care settings, it is essential to carefully understand the health and emotional state of those receiving care and provide individualized care. However, the current system prioritizes health data, and adequate advice based on emotional state is not provided. This can lead to overlooking stress and fatigue, making it difficult to provide a comfortable living environment.

[0176] 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.

[0177] In this invention, the server includes data collection means for receiving personal health information, information analysis means for analyzing the information obtained from the data collection means and generating health standards, guidance generation means for providing individual health management guidance based on the health standards generated by the information analysis means, emotion analysis means for evaluating emotional state, and guidance generation means for providing emotional management guidance based on the results obtained from the emotion analysis means. This enables the integrated evaluation of both the health and emotional state of the person receiving care, making it possible to provide more appropriate care.

[0178] "Data collection means" refers to a device or system for receiving an individual's health information.

[0179] "Information analysis means" refers to a device or system for analyzing information obtained from data collection means and generating health-related standards.

[0180] A "guidance generation means" is a device or system for providing individualized health management guidance based on health standards generated by an information analysis means.

[0181] "Transmission means" refers to a device or system that notifies the user terminal of health management guidance generated by the guidance generation means.

[0182] "Emotional analysis means" refers to a device or system for evaluating an emotional state.

[0183] The system of this invention is designed to support health management and emotional management in caregiving settings. It is an integrated system centered around a server, terminals, and users.

[0184] First, the server has a data collection mechanism that receives individual health information from devices such as wearable devices and smart toilets. This information includes heart rate, activity level, and blood pressure. It also includes an emotion analysis mechanism that uses voice recognition and facial recognition technology to evaluate emotional states.

[0185] Next, the server uses information analysis tools to process the collected data and generate health criteria. These health criteria are dynamically adjusted, taking into account the care recipient's past health information history.

[0186] The server generates individualized health and emotional management guidance based on health standards and emotional analysis results using guidance generation tools. For example, if it is determined that a user's stress level is high, it may suggest deep breathing exercises or music therapy sessions.

[0187] The generated instructions are notified to the user's device via a communication method. These user devices include smartphones and tablets, enabling real-time information sharing.

[0188] For example, if recent stress levels are detected in an elderly person's health data, the system suggests deep breathing exercises and sets up a music therapy session. In this way, the management of both health and emotional well-being in care settings is promoted.

[0189] An example of a prompt message would be, "Based on this data, please suggest the next action. If the emotional state is 'high stress,' consider what stress reduction measures would be effective." This would leverage a generative AI model to support the delivery of personalized care.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server receives health information from wearable devices and smart toilets. This information includes data such as heart rate, activity level, and blood pressure. This data is transferred from the device to the server via Wi-Fi and stored in a database.

[0193] Step 2:

[0194] The server receives audio and visual data using emotion analysis tools. Inputs include voice tone and facial expressions. A generative AI model is used to analyze this data and output emotional states (e.g., stress level, happiness level).

[0195] Step 3:

[0196] The server integrates the data obtained in Step 1 and Step 2 using information analysis tools. Health information and emotional state are taken in as input, and individual health criteria and emotional assessments are performed using machine learning algorithms. The output consists of the respective criterion values ​​and assessment results.

[0197] Step 4:

[0198] The server uses a guidance generation mechanism to generate health management guidance and emotional management guidance based on individual health criteria and emotional assessments. For example, it might suggest deep breathing exercises and recommend music therapy for users experiencing high stress levels. This guidance is generated as output.

[0199] Step 5:

[0200] The server notifies the user's device of the instruction generated by the instruction generation method. Smartphones and tablets are used as the device. The notification includes instruction in the form of text messages and push notifications, which the user receives.

[0201] Step 6:

[0202] Users review the health and emotional management guidance they receive on their devices and incorporate it into their daily lives. This allows users to comprehensively manage their health and emotional state and improve their quality of life.

[0203] 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.

[0204] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The system for implementing the present invention consists of a server, a terminal, and a user, all of which interact with each other. The server is responsible for acquiring and analyzing health data and generating personalized health management advice. The terminal aggregates individual health data obtained from wearable devices and smart toilets and transmits it to the server. It also notifies the user of the generated health management advice. The user receives the information provided via the terminal and uses it to manage their daily health.

[0220] A concrete example is a smart toilet that users use daily. This toilet analyzes the components of urine and transmits the results to the user's smartphone via Bluetooth. The smartphone uploads this information to a server in the cloud. This server analyzes the received data using a generative AI model. For example, it can analyze the concentrations of glucose and sodium in the urine to assess the user's health. Based on this analysis, if the server determines that there is a risk of excessive salt intake, it will generate advice recommending that the user reduce their salt intake.

[0221] As a result, the generated advice is notified to the user via their smartphone. Based on this advice, the user can take specific health management actions, such as reviewing their meal plan or adjusting the timing of hydration. In addition, if an abnormality is detected, a notification may be sent encouraging consultation with a specialist or recommending a visit to a medical institution.

[0222] In this way, the system can be naturally integrated into the user's daily life, providing a streamlined and personalized health management process.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server sends data collection requests to smart toilets and wearable devices. These requests specify the types of health data and timestamps required.

[0226] Step 2:

[0227] The terminal receives raw data transmitted from the smart device. This includes items such as heart rate, body temperature, weight, and urine analysis results. The terminal converts this data into an appropriate format and prepares it as data packets.

[0228] Step 3:

[0229] The device transfers the prepared data packets to a server in the cloud via Wi-Fi or Bluetooth. During this process, protocols are used to maintain data integrity and security.

[0230] Step 4:

[0231] The server converts the received data into a format that is easy to analyze. This involves pre-processing such as detecting outliers, imputing missing values, and unit conversion.

[0232] Step 5:

[0233] The server analyzes the data using a generative AI model. Specifically, it generates health indicators and performs trend analysis by comparing them with historical data.

[0234] Step 6:

[0235] The server generates personalized health management advice for each user based on the analysis results. This advice includes suggestions for dietary adjustments, exercise, and fluid intake.

[0236] Step 7:

[0237] The server sends the generated advice to the device, which then notifies the user. The advice is displayed as a push notification on the smartphone, and further details can be viewed within the app.

[0238] Step 8:

[0239] Users make choices based on the advice provided. For example, they might review their diet or adjust their daily activities.

[0240] Step 9:

[0241] The user then provides feedback to the server through their device regarding the results of their actions. The server uses this data to learn and improve the accuracy of its advice, thereby evolving the overall functionality of the system.

[0242] (Example 1)

[0243] 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."

[0244] In modern society, health management is a crucial issue for individuals, but it is not easy to continuously and efficiently monitor one's health status and obtain appropriate health advice in daily life. Furthermore, there is a need to provide more accurate advice based on individual health conditions by utilizing big data and machine learning. Therefore, the development of proactive and personalized health management systems is required.

[0245] 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.

[0246] In this invention, the server includes a device collection means for receiving biometric data, a data analysis means including a machine learning model for analyzing the data obtained from the device collection means and generating health indicators, and an advice construction means for providing individualized health management advice based on the health indicators generated by the data analysis means. This makes it possible to continuously monitor an individual's health status and provide appropriate health management advice quickly and effectively.

[0247] "Biometric data" refers to measurable information obtained from the human body, including, for example, blood glucose levels and sodium concentration.

[0248] "Device data collection means" refers to equipment or devices used to acquire biometric data from users, with specific examples including wearable devices and smart toilets.

[0249] "Data analysis means" refers to methods and systems for processing collected biometric data and generating health-related indicators, and involves analyzing data using machine learning models.

[0250] A "machine learning model" is a computational model that recognizes patterns based on data and makes predictions and classifications about future data. A specific example is a neural network.

[0251] "Advice building method" refers to a method or system for creating specific health management advice to provide to users based on health indicators generated by data analysis methods.

[0252] "Information distribution means" refers to methods or systems for notifying users of generated health management advice, and is usually done using mobile devices or computers.

[0253] This invention is a system for supporting personal health management. A specific embodiment of the invention involves a mechanism in which a server, a terminal, and a user work together. This system is implemented based on the following components:

[0254] The server is built on cloud infrastructure and uses generative AI models to analyze biometric data sent from users. This analysis employs data analysis methods incorporating machine learning libraries such as TensorFlow and PyTorch. Based on the data received from device collection devices, the server calculates health indicators and generates personalized health management advice. Because this involves handling a massive amount of data, hardware with efficient data processing capabilities (e.g., high-performance CPUs and GPUs) is required.

[0255] The terminal operates as a platform for devices that collect biometric data from users' daily lives. Specific examples of such devices include wearable devices (e.g., fitness trackers) and smart toilets in the home. These devices transmit biometric data to smartphones via Bluetooth or Wi-Fi and then upload that data to servers in the cloud.

[0256] Users are the direct beneficiaries of this system, enabling them to understand their own health status based on the advice provided by their devices and proactively engage in daily health management. Users can review personalized health advice notified via their smartphones and modify their behavior accordingly.

[0257] As a concrete example, a smart toilet used daily by users is equipped with a function to measure glucose and sodium concentrations in urine. This data is collected, sent from the device to a server, and analyzed by a generative AI model. As a result, advice regarding the user's health (e.g., "reduce excessive salt intake") is generated and notified to the user via their smartphone.

[0258] Examples of prompt messages that might be entered are as follows:

[0259] "Analysis subjects: Urinary components. Data: Glucose concentration - 105 mg / dL, Sodium concentration - 145 mmol / L. Output: Health advice."

[0260] This invention enables continuous and personalized health management, allowing users to take appropriate actions.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The device acquires biometric data. Specifically, it collects data such as glucose and sodium concentrations in urine through wearable devices used by the user or smart toilets. The input is numerical data measured by sensors, which is then transmitted to a smartphone via Bluetooth or Wi-Fi. The output is the biometric data transferred to the smartphone.

[0264] Step 2:

[0265] The device sends the collected biometric data to a server in the cloud. The smartphone uploads the data in real time using an internet connection. The input in this process is the biometric data stored on the device, and the output is the biometric data sent to the server. This prepares the data for the next analysis step.

[0266] Step 3:

[0267] The server analyzes the received data. A generative AI model is implemented here, and it is used to perform data analysis. The input is biometric data sent to the server, and the data is applied to the machine learning model to detect patterns and anomalies. Specifically, a model built with TensorFlow or PyTorch analyzes the data and calculates health indicators. The output is the generated health indicators.

[0268] Step 4:

[0269] The server generates personalized health management advice based on the analyzed health indicators. The input is the health indicators obtained in the previous step, and the AI ​​constructs meaningful advice based on the analysis results. The output is specific advice to notify the user (e.g., a recommendation to reduce salt intake).

[0270] Step 5:

[0271] The device receives advice from the server and notifies the user. Specifically, it displays the advice through the smartphone's notification function. The input is health management advice sent from the server, and the output is a visual notification to the user.

[0272] Step 6:

[0273] Based on the advice received from the device, users take daily health management actions. For example, they might review their diet or adjust their exercise plan. In this step, the input is the advice from the device, and the output is the specific health actions the user takes. This allows users to proactively manage their own health.

[0274] (Application Example 1)

[0275] 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."

[0276] With the aging of society and the increase in lifestyle-related diseases, there is a growing need for more detailed individual health management. Furthermore, it is crucial to collect diverse health data in real time, quickly understand individual health conditions, and provide necessary advice. However, current systems often fail to adequately aggregate and analyze health data, leading to delays in early detection of abnormalities and appropriate responses.

[0277] 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.

[0278] In this invention, the server includes sensing means for receiving personal health data, information processing means for analyzing the data obtained from the sensing means and generating health-related indicators, advice generation means for providing individual health management advice based on the health indicators generated by the information processing means, communication means for notifying the user terminal of the health management advice generated by the advice generation means, and warning means for monitoring the health status using the health indicators and an anomaly detection algorithm and notifying interested parties when an anomaly is detected. This enables the provision of prompt and appropriate advice based on the collected health data and the early detection of anomalies.

[0279] The "sensing means" refers to devices or components used to collect personal health data.

[0280] The "information processing means" is a technical device having the function of analyzing the data obtained from the sensing means and generating health-related indicators.

[0281] The "advice generation means" is a system function for creating individual health management advice based on the health indicators generated by the information processing means.

[0282] The "communication means" refers to technical devices or protocols for notifying the generated health management advice to the user's terminal.

[0283] The "warning means" is a system having the function of monitoring the health status and notifying interested parties in case of anomalies using health indicators and an anomaly detection algorithm.

[0284] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server undertakes the main function of receiving and analyzing personal health data. Specifically, wearable devices or smart toilets are used as sensing means to collect health data and transmit this data to the server.

[0285] The server uses a generative AI model for analyzing the received data as information processing means. This model analyzes the data by means of deep learning techniques and generates important health indicators. It also has advice generation means for generating individual health management advice for each user based on these indicators. This advice is immediately notified to terminals such as the user's smartphone or tablet using communication means.

[0286] The terminal functions as a hub for the user to receive notifications and advice from the server. Through this, the user can grasp the real-time health status and take early action in case of abnormalities. For this purpose, the application on the terminal takes actions based on the collected data and analysis results, improving the quality of health management in daily life.

[0287] Furthermore, the part as warning means has a function of warning the user and designated stakeholders when the generative AI model detects an abnormality. As a specific example, when the glucose concentration in urine enters the danger range, prompt sentences such as "High urine sugar test result -> generation of exercise / diet advice" can be generated and quickly conveyed to the user.

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server receives health data from wearable devices and smart toilets through sensing means. This data includes the user's vital signs and biochemical information. The received data is filtered to remove noise by initial filtering and formatted into a form suitable for analysis.

[0291] Step 2:

[0292] The server, as a means of information processing, inputs pre-processed health data into a generative AI model. This generative AI model performs deep analysis of the data. Specifically, it extracts feature patterns from the input data and generates indicators for evaluating the user's health status. In this process, it also uses past data history to infer new health indicators.

[0293] Step 3:

[0294] Based on the generated health indicators, the server creates personalized health management advice using an advice generation system. This advice includes specific action suggestions to improve the user's health. For example, it may include recommendations for dietary adjustments or exercise. At this time, prompt statements are generated and the advice content is organized.

[0295] Step 4:

[0296] The device receives advice from the server via communication. The content of this advice is then notified to the user as an alert or recommended action. The device converts the received information into a format that is easy for the user to understand and displays it.

[0297] Step 5:

[0298] Users review the advice received on their devices and incorporate it into their daily health management routines. If any abnormalities are detected, it is recommended that they promptly seek expert advice.

[0299] Step 6:

[0300] The server uses proactive warning mechanisms to alert stakeholders if an anomaly is detected in the generated AI model. This includes notifying the user's emergency contacts, registered caregivers, and medical institutions.

[0301] 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.

[0302] The system for implementing the present invention consists of a server, a terminal, a user, and an emotion engine. The server is responsible for acquiring and analyzing health data and emotion data, and for generating personalized health management and emotion-based advice. The terminal aggregates individual data obtained from wearable devices, smart toilets, and devices for recognizing emotions, and transmits it to the server. It also has the role of notifying the user of the generated health management advice. The user receives the information provided via the terminal and uses it for daily health management and emotion management.

[0303] A concrete example is a wearable device that users use daily. This device recognizes emotional states from heart rate, activity levels, and voice, and transmits the results to the user's smartphone via Wi-Fi. The smartphone uploads this information to a server in the cloud. The server analyzes this data and evaluates the user's health and emotional state. If this analysis reveals an increase in stress levels, it generates advice on exercises or meditation to promote relaxation.

[0304] Furthermore, the emotion engine can understand the user's emotional state by evaluating changes in voice tone and facial expressions. Based on this evaluation, it also provides health management advice that reflects the user's emotional changes. For example, if the user is feeling tired or stressed, it will send advice recommending a balanced diet and sufficient rest.

[0305] In this way, the system integrates health data and emotional data to support users in more personalized health and emotional management. As a result, users can not only understand and improve their health status, but also improve their quality of life through emotional management.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] The terminal receives personal health data and emotional data from wearable devices, smart toilets, and devices that recognize voice and expressions. Specifically, it obtains heart rate, activity level, urine analysis results, voice tone, changes in expressions, etc.

[0309] Step 2:

[0310] The terminal combines the received health data and emotional data into one data packet and transmits it to the server via Wi-Fi or Bluetooth. At this time, encryption is performed to ensure data integrity and security.

[0311] Step 3:

[0312] The server receives the transmitted data packet and performs preprocessing including data format conversion and detection of abnormal values. This preprocessing ensures the accuracy required for data analysis.

[0313] Step 4:

[0314] The server analyzes the health data using a generated AI model and generates indicators indicating the user's current health status. This analysis also includes predictive analysis considering the past health data history.

[0315] Step 5:

[0316] The server analyzes voice and expression data using an emotion engine and evaluates the user's emotional state. Based on this evaluation result, the stress level and changes in emotions are grasped.

[0317] Step 6:

[0318] <00010The server generates personalized health and emotional management advice for the user based on assessments of both their physical and emotional state. This includes suggestions for exercise to reduce stress and recommendations for relaxation techniques.

[0319] Step 7:

[0320] The server sends generated advice to the device, which then provides it to the user as a push notification on their smartphone. The user can then view detailed advice within the app.

[0321] Step 8:

[0322] Based on the advice provided, users take specific actions to manage their health and regulate their emotions. For example, they might incorporate recommended exercises or make relaxation a daily routine.

[0323] Step 9:

[0324] Users provide feedback on their actions through their devices, and the server uses this feedback to continuously learn and improve the accuracy of the system's advice.

[0325] (Example 2)

[0326] 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".

[0327] Current health management systems collect users' biometric information and analyze their health status, but they are unable to provide personalized health guidance that takes emotional states into account, or to conduct sufficient analysis to detect abnormalities early. As a result, users have limited opportunities to receive appropriate guidance based on the stress and emotional changes they experience in their daily lives.

[0328] 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.

[0329] In this invention, the server includes information gathering means for receiving an individual's biometric information, data processing means for processing the information and generating health evaluation indicators, and guidance generation means for generating individualized health management guidance based on the evaluation indicators and emotional state. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide appropriate health management guidance.

[0330] "Information gathering means" refers to a device or function for collecting an individual's biometric information and transmitting it to a processing device.

[0331] "Data processing means" refers to a device or software that has the function of analyzing collected biological information and generating health-related evaluation indicators.

[0332] A "guidance generation means" is a device or algorithm that creates specific health management guidance for a user based on evaluation indicators and emotional states obtained by a data processing means.

[0333] "Means of provision" refers to equipment or software for displaying or transmitting the generated health management guidance to the user's terminal.

[0334] "Emotional analysis means" refers to a technology or device for understanding a user's emotional state by analyzing changes in voice and facial expressions.

[0335] "Additional guidance generation means" refers to an algorithm or device that creates additional health management guidance tailored to the user's emotional state based on the results of the emotion analysis means.

[0336] This invention is a system for providing users with appropriate health management guidance by comprehensively analyzing an individual's biological information and emotional state. The system's components and their specific implementation methods are described below.

[0337] The server receives data from information gathering devices and generates health assessment indicators using data processing devices. These indicators are analyzed based on heart rate, activity levels, and voice data collected from wearable devices and information analysis equipment. The server efficiently processes data by using Python libraries (e.g., Pandas, NumPy) for data analysis.

[0338] As a means of emotion analysis, a generative AI model is used to analyze voice data and facial expression data. Existing voice analysis technologies are applied to analyze emotional states from voice signals. Furthermore, facial recognition technology is used to understand changes in facial expressions, and this emotional information is reflected in health management guidance.

[0339] The instruction generation system generates specific instruction based on evaluation metrics and sentiment analysis results. The generated instruction is delivered to the user's device via a delivery system. The device is a smartphone or tablet, and the instruction content is notified to the user through the user interface.

[0340] For example, if the system determines that a user's stress level is high based on fluctuations in their biometric information or emotional state, it will generate and provide advice on exercises or meditation to promote relaxation. The user can receive notifications from their device and manage their health according to the instructions.

[0341] An example of a prompt message for a generative AI model is: "Generate specific health management guidance based on the user's emotional data. For example, suggest actions to take when the user's stress level is high."

[0342] In this way, the system can comprehensively manage the user's health and emotional state and provide health management guidance tailored to their individual circumstances.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] The terminal collects biometric information from wearable devices and data analysis equipment. Specifically, the terminal uses Bluetooth and Wi-Fi to acquire heart rate, activity levels, and voice data. This input data is temporarily stored on the terminal in JSON format. As output, this data is prepared for subsequent processing.

[0346] Step 2:

[0347] The device sends the collected biometric information to a server in the cloud. Specifically, the device uses the HTTPS protocol to send previously stored JSON data to the server. The input is the data collected in step 1, and the output is the biometric information stored in the server's database.

[0348] Step 3:

[0349] The server analyzes the received biometric information. Using Python data analysis libraries (e.g., Pandas, NumPy), the server generates health assessment indicators by analyzing the input data. This analysis process calculates fluctuations in heart rate and activity level, converting them into indicators such as stress level and activity level. The generated assessment indicators are then output.

[0350] Step 4:

[0351] The server uses emotion analysis tools to analyze the user's emotional state from the voice data. It utilizes a generative AI model to perform computational processing to identify emotions from the input voice data. This process outputs the user's emotional state, which is then used to generate guidance along with health assessment indicators.

[0352] Step 5:

[0353] The server generates personalized health management guidance based on evaluation indicators and emotional state. The guidance generation mechanism integrates the input health indicators and emotional state data, and uses a generation AI model based on prompt text to generate health management guidance tailored to the user. Specific guidance content is provided as output.

[0354] Step 6:

[0355] The device notifies the user of health management guidance sent from the server. Specifically, the device uses push notifications and in-app messages to display the guidance content received as input to the user. The output is the guidance message displayed on the user's device.

[0356] Step 7:

[0357] The user reviews the health management guidance they receive and acts accordingly. The input here is the guidance message displayed on their smartphone or tablet, and the output is the user's actions. Specifically, the user might perform recommended exercises or try stress-relief methods.

[0358] Through this process, the system can effectively utilize biometric information and emotional states to provide users with personalized health management guidance.

[0359] (Application Example 2)

[0360] 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 as the "terminal".

[0361] In elderly care settings, it is essential to carefully understand the health and emotional state of those receiving care and provide individualized care. However, the current system prioritizes health data, and adequate advice based on emotional state is not provided. This can lead to overlooking stress and fatigue, making it difficult to provide a comfortable living environment.

[0362] 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.

[0363] In this invention, the server includes data collection means for receiving personal health information, information analysis means for analyzing the information obtained from the data collection means and generating health standards, guidance generation means for providing individual health management guidance based on the health standards generated by the information analysis means, emotion analysis means for evaluating emotional state, and guidance generation means for providing emotional management guidance based on the results obtained from the emotion analysis means. This enables the integrated evaluation of both the health and emotional state of the person receiving care, making it possible to provide more appropriate care.

[0364] "Data collection means" refers to a device or system for receiving an individual's health information.

[0365] "Information analysis means" refers to a device or system for analyzing information obtained from data collection means and generating health-related standards.

[0366] A "guidance generation means" is a device or system for providing individualized health management guidance based on health standards generated by an information analysis means.

[0367] "Transmission means" refers to a device or system that notifies the user terminal of health management guidance generated by the guidance generation means.

[0368] "Emotional analysis means" refers to a device or system for evaluating an emotional state.

[0369] The system of this invention is designed to support health management and emotional management in caregiving settings. It is an integrated system centered around a server, terminals, and users.

[0370] First, the server has a data collection mechanism that receives individual health information from devices such as wearable devices and smart toilets. This information includes heart rate, activity level, and blood pressure. It also includes an emotion analysis mechanism that uses voice recognition and facial recognition technology to evaluate emotional states.

[0371] Next, the server uses information analysis tools to process the collected data and generate health criteria. These health criteria are dynamically adjusted, taking into account the care recipient's past health information history.

[0372] The server generates individualized health and emotional management guidance based on health standards and emotional analysis results using guidance generation tools. For example, if it is determined that a user's stress level is high, it may suggest deep breathing exercises or music therapy sessions.

[0373] The generated instructions are notified to the user's device via a communication method. These user devices include smartphones and tablets, enabling real-time information sharing.

[0374] For example, if recent stress levels are detected in an elderly person's health data, the system suggests deep breathing exercises and sets up a music therapy session. In this way, the management of both health and emotional well-being in care settings is promoted.

[0375] An example of a prompt message would be, "Based on this data, please suggest the next action. If the emotional state is 'high stress,' consider what stress reduction measures would be effective." This would leverage a generative AI model to support the delivery of personalized care.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The server receives health information from wearable devices and smart toilets. This information includes data such as heart rate, activity level, and blood pressure. This data is transferred from the device to the server via Wi-Fi and stored in a database.

[0379] Step 2:

[0380] The server receives audio and visual data using emotion analysis tools. Inputs include voice tone and facial expressions. A generative AI model is used to analyze this data and output emotional states (e.g., stress level, happiness level).

[0381] Step 3:

[0382] The server integrates the data obtained in Step 1 and Step 2 using information analysis tools. Health information and emotional state are taken in as input, and individual health criteria and emotional assessments are performed using machine learning algorithms. The output consists of the respective criterion values ​​and assessment results.

[0383] Step 4:

[0384] The server uses a guidance generation mechanism to generate health management guidance and emotional management guidance based on individual health criteria and emotional assessments. For example, it might suggest deep breathing exercises and recommend music therapy for users experiencing high stress levels. This guidance is generated as output.

[0385] Step 5:

[0386] The server notifies the user's device of the instruction generated by the instruction generation method. Smartphones and tablets are used as the device. The notification includes instruction in the form of text messages and push notifications, which the user receives.

[0387] Step 6:

[0388] Users review the health and emotional management guidance they receive on their devices and incorporate it into their daily lives. This allows users to comprehensively manage their health and emotional state and improve their quality of life.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] The system for implementing the present invention consists of a server, a terminal, and a user, all of which interact with each other. The server is responsible for acquiring and analyzing health data and generating personalized health management advice. The terminal aggregates individual health data obtained from wearable devices and smart toilets and transmits it to the server. It also notifies the user of the generated health management advice. The user receives the information provided via the terminal and uses it to manage their daily health.

[0406] A concrete example is a smart toilet that users use daily. This toilet analyzes the components of urine and transmits the results to the user's smartphone via Bluetooth. The smartphone uploads this information to a server in the cloud. This server analyzes the received data using a generative AI model. For example, it can analyze the concentrations of glucose and sodium in the urine to assess the user's health. Based on this analysis, if the server determines that there is a risk of excessive salt intake, it will generate advice recommending that the user reduce their salt intake.

[0407] As a result, the generated advice is notified to the user via their smartphone. Based on this advice, the user can take specific health management actions, such as reviewing their meal plan or adjusting the timing of hydration. In addition, if an abnormality is detected, a notification may be sent encouraging consultation with a specialist or recommending a visit to a medical institution.

[0408] In this way, the system can be naturally integrated into the user's daily life, providing a streamlined and personalized health management process.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server sends data collection requests to smart toilets and wearable devices. These requests specify the types of health data and timestamps required.

[0412] Step 2:

[0413] The terminal receives raw data transmitted from the smart device. This includes items such as heart rate, body temperature, weight, and urine analysis results. The terminal converts this data into an appropriate format and prepares it as data packets.

[0414] Step 3:

[0415] The device transfers the prepared data packets to a server in the cloud via Wi-Fi or Bluetooth. During this process, protocols are used to maintain data integrity and security.

[0416] Step 4:

[0417] The server converts the received data into a format that is easy to analyze. This involves pre-processing such as detecting outliers, imputing missing values, and unit conversion.

[0418] Step 5:

[0419] The server analyzes the data using a generative AI model. Specifically, it generates health indicators and performs trend analysis by comparing them with historical data.

[0420] Step 6:

[0421] The server generates personalized health management advice for each user based on the analysis results. This advice includes suggestions for dietary adjustments, exercise, and fluid intake.

[0422] Step 7:

[0423] The server sends the generated advice to the device, which then notifies the user. The advice is displayed as a push notification on the smartphone, and further details can be viewed within the app.

[0424] Step 8:

[0425] Users make choices based on the advice provided. For example, they might review their diet or adjust their daily activities.

[0426] Step 9:

[0427] The user then provides feedback to the server through their device regarding the results of their actions. The server uses this data to learn and improve the accuracy of its advice, thereby evolving the overall functionality of the system.

[0428] (Example 1)

[0429] 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."

[0430] In modern society, health management is a crucial issue for individuals, but it is not easy to continuously and efficiently monitor one's health status and obtain appropriate health advice in daily life. Furthermore, there is a need to provide more accurate advice based on individual health conditions by utilizing big data and machine learning. Therefore, the development of proactive and personalized health management systems is required.

[0431] 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.

[0432] In this invention, the server includes a device collection means for receiving biometric data, a data analysis means including a machine learning model for analyzing the data obtained from the device collection means and generating health indicators, and an advice construction means for providing individualized health management advice based on the health indicators generated by the data analysis means. This makes it possible to continuously monitor an individual's health status and provide appropriate health management advice quickly and effectively.

[0433] "Biometric data" refers to measurable information obtained from the human body, including, for example, blood glucose levels and sodium concentration.

[0434] "Device data collection means" refers to equipment or devices used to acquire biometric data from users, with specific examples including wearable devices and smart toilets.

[0435] "Data analysis means" refers to methods and systems for processing collected biometric data and generating health-related indicators, and involves analyzing data using machine learning models.

[0436] A "machine learning model" is a computational model that recognizes patterns based on data and makes predictions and classifications about future data. A specific example is a neural network.

[0437] "Advice building means" refers to methods and systems for creating specific health management advice to provide to users based on health indicators generated by data analysis means.

[0438] "Information distribution means" refers to methods or systems for notifying users of generated health management advice, and is usually done using mobile devices or computers.

[0439] This invention is a system for supporting personal health management. A specific embodiment of the invention involves a mechanism in which a server, a terminal, and a user work together. This system is implemented based on the following components.

[0440] The server is built on cloud infrastructure and uses generative AI models to analyze biometric data sent from users. This analysis employs data analysis methods incorporating machine learning libraries such as TensorFlow and PyTorch. Based on the data received from device collection devices, the server calculates health indicators and generates personalized health management advice. Because this involves handling a massive amount of data, hardware with efficient data processing capabilities (e.g., high-performance CPUs and GPUs) is required.

[0441] The terminal operates as a platform for devices that collect biometric data from users' daily lives. Specific examples of such devices include wearable devices (e.g., fitness trackers) and smart toilets in the home. These devices transmit biometric data to smartphones via Bluetooth or Wi-Fi and then upload that data to servers in the cloud.

[0442] Users are the direct beneficiaries of this system, enabling them to understand their own health status based on the advice provided by their devices and proactively engage in daily health management. Users can review personalized health advice notified via their smartphones and modify their behavior accordingly.

[0443] As a concrete example, a smart toilet used daily by users is equipped with a function to measure glucose and sodium concentrations in urine. This data is collected, sent from the device to a server, and analyzed by a generative AI model. As a result, advice regarding the user's health (e.g., "reduce excessive salt intake") is generated and notified to the user via their smartphone.

[0444] Examples of prompt messages that might be entered are as follows:

[0445] "Analysis subjects: Urinary components. Data: Glucose concentration - 105 mg / dL, Sodium concentration - 145 mmol / L. Output: Health advice."

[0446] This invention enables continuous and personalized health management, allowing users to take appropriate actions.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The device acquires biometric data. Specifically, it collects data such as glucose and sodium concentrations in urine through wearable devices used by the user or smart toilets. The input is numerical data measured by sensors, which is then transmitted to a smartphone via Bluetooth or Wi-Fi. The output is the biometric data transferred to the smartphone.

[0450] Step 2:

[0451] The device sends the collected biometric data to a server in the cloud. The smartphone uploads the data in real time using an internet connection. The input in this process is the biometric data stored on the device, and the output is the biometric data sent to the server. This prepares the data for the next analysis step.

[0452] Step 3:

[0453] The server analyzes the received data. A generative AI model is implemented here, and it is used to perform data analysis. The input is biometric data sent to the server, and the data is applied to the machine learning model to detect patterns and anomalies. Specifically, a model built with TensorFlow or PyTorch analyzes the data and calculates health indicators. The output is the generated health indicators.

[0454] Step 4:

[0455] The server generates personalized health management advice based on the analyzed health indicators. The input is the health indicators obtained in the previous step, and the AI ​​constructs meaningful advice based on the analysis results. The output is specific advice to notify the user (e.g., a recommendation to reduce salt intake).

[0456] Step 5:

[0457] The device receives advice from the server and notifies the user. Specifically, it displays the advice through the smartphone's notification function. The input is health management advice sent from the server, and the output is a visual notification to the user.

[0458] Step 6:

[0459] Based on the advice received from the device, users take daily health management actions. For example, they might review their diet or adjust their exercise plan. In this step, the input is the advice from the device, and the output is the specific health actions the user takes. This allows users to proactively manage their own health.

[0460] (Application Example 1)

[0461] 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."

[0462] With the aging of society and the increase in lifestyle-related diseases, there is a growing need for more detailed individual health management. Furthermore, it is crucial to collect diverse health data in real time, quickly understand individual health conditions, and provide necessary advice. However, current systems often fail to adequately aggregate and analyze health data, leading to delays in early detection of abnormalities and appropriate responses.

[0463] 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.

[0464] In this invention, the server includes sensing means for receiving personal health data, information processing means for analyzing the data obtained from the sensing means and generating health indicators, advice generation means for providing individual health management advice based on the health indicators generated by the information processing means, communication means for notifying the user terminal of the health management advice generated by the advice generation means, and warning means for monitoring the health status using the health indicators and an anomaly detection algorithm, and notifying stakeholders if an anomaly is detected. This enables the provision of prompt and appropriate advice based on collected health data and the early detection of anomalies.

[0465] "Sensing means" refers to devices or components used to collect personal health data.

[0466] An "information processing device" is a technological device that has the function of analyzing data obtained from sensing devices to generate health-related indicators.

[0467] "Advice generation means" refers to a system function for creating individual health management advice based on health indicators generated by information processing means.

[0468] "Communication means" refers to the technical devices and protocols used to notify the user's terminal of the generated health management advice.

[0469] A "pre-warning system" is a system that uses health indicators and anomaly detection algorithms to monitor health conditions and notify stakeholders in the event of an anomaly.

[0470] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server is responsible for the primary function of receiving and analyzing personal health data. Specifically, it collects health data using wearable devices and smart toilets as sensing means and transmits this data to the server.

[0471] The server uses a generative AI model as its information processing tool to analyze the received data. This model analyzes the data using deep learning techniques and other methods to generate important health indicators. It also has an advice generation tool that generates personalized health management advice for each user based on these indicators. This advice is immediately notified to the user's smartphone or tablet via communication means.

[0472] The device functions as a hub where users receive notifications and advice from the server. Through this, users can monitor their health status in real time and take early action in case of abnormalities. To this end, applications on the device take actions based on collected data and analysis results, improving the quality of health management in daily life.

[0473] Furthermore, as a preventative measure, the generating AI model has the function of issuing warnings to the user and designated stakeholders when it detects an anomaly. For example, if the glucose concentration in the urine enters a dangerous range, it can generate a prompt message such as "High urine glucose test result -> Generate exercise / dietary advice" and promptly inform the user.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The server receives health data from wearable devices and smart toilets through sensing means. This data includes the user's vital signs and biochemical information. The received data is filtered to remove noise and formatted into a format suitable for analysis.

[0477] Step 2:

[0478] The server, as a means of information processing, inputs pre-processed health data into a generative AI model. This generative AI model performs deep analysis of the data. Specifically, it extracts feature patterns from the input data and generates indicators for evaluating the user's health status. In this process, it also uses past data history to infer new health indicators.

[0479] Step 3:

[0480] Based on the generated health indicators, the server creates personalized health management advice using an advice generation system. This advice includes specific action suggestions to improve the user's health. For example, it may include recommendations for dietary adjustments or exercise. At this time, prompt statements are generated and the advice content is organized.

[0481] Step 4:

[0482] The device receives advice from the server via communication. The content of this advice is then notified to the user as an alert or recommended action. The device converts the received information into a format that is easy for the user to understand and displays it.

[0483] Step 5:

[0484] Users review the advice received on their devices and incorporate it into their daily health management routines. If any abnormalities are detected, it is recommended that they promptly seek expert advice.

[0485] Step 6:

[0486] The server uses proactive warning mechanisms to alert stakeholders if an anomaly is detected in the generated AI model. This includes notifying the user's emergency contacts, registered caregivers, and medical institutions.

[0487] 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.

[0488] The system for implementing the present invention consists of a server, a terminal, a user, and an emotion engine. The server is responsible for acquiring and analyzing health data and emotion data, and for generating personalized health management and emotion-based advice. The terminal aggregates individual data obtained from wearable devices, smart toilets, and devices for recognizing emotions, and transmits it to the server. It also has the role of notifying the user of the generated health management advice. The user receives the information provided via the terminal and uses it for daily health management and emotion management.

[0489] A concrete example is a wearable device that users use daily. This device recognizes emotional states from heart rate, activity levels, and voice, and transmits the results to the user's smartphone via Wi-Fi. The smartphone uploads this information to a server in the cloud. The server analyzes this data and evaluates the user's health and emotional state. If this analysis reveals an increase in stress levels, it generates advice on exercises or meditation to promote relaxation.

[0490] Furthermore, the emotion engine can understand the user's emotional state by evaluating changes in voice tone and facial expressions. Based on this evaluation, it also provides health management advice that reflects the user's emotional changes. For example, if the user is feeling tired or stressed, it will send advice recommending a balanced diet and sufficient rest.

[0491] In this way, the system integrates health data and emotional data to support users in more personalized health and emotional management. As a result, users can not only understand and improve their health status, but also improve their quality of life through emotional management.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The device receives personal health and emotional data from wearable devices, smart toilets, and devices that recognize voice and facial expressions. Specifically, it acquires data such as heart rate, activity level, urine analysis results, voice tone, and changes in facial expressions.

[0495] Step 2:

[0496] The device combines the received health and emotional data into a single data packet and transmits it to the server via Wi-Fi or Bluetooth. Encryption is performed during this process to ensure data integrity and security.

[0497] Step 3:

[0498] The server receives the transmitted data packets and performs preprocessing, including format conversion and detection of anomalies. This preprocessing ensures the accuracy necessary for data analysis.

[0499] Step 4:

[0500] The server analyzes health data using a generation AI model to generate indicators showing the user's current health status. This analysis also includes predictive analytics that take into account past health data history.

[0501] Step 5:

[0502] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. Based on this evaluation, it understands stress levels and changes in emotions.

[0503] Step 6:

[0504] The server generates personalized health and emotional management advice for the user based on assessments of both their physical and emotional state. This includes suggestions for exercise to reduce stress and recommendations for relaxation techniques.

[0505] Step 7:

[0506] The server sends generated advice to the device, which then provides it to the user as a push notification on their smartphone. The user can then view detailed advice within the app.

[0507] Step 8:

[0508] Based on the advice provided, users take specific actions to manage their health and regulate their emotions. For example, they might incorporate recommended exercises or make relaxation a daily routine.

[0509] Step 9:

[0510] Users provide feedback on their actions through their devices, and the server uses this feedback to continuously learn and improve the accuracy of the system's advice.

[0511] (Example 2)

[0512] 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."

[0513] Current health management systems collect users' biometric information and analyze their health status, but they are unable to provide personalized health guidance that takes emotional states into account, or to conduct sufficient analysis to detect abnormalities early. As a result, users have limited opportunities to receive appropriate guidance based on the stress and emotional changes they experience in their daily lives.

[0514] 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.

[0515] In this invention, the server includes information gathering means for receiving an individual's biometric information, data processing means for processing the information and generating health evaluation indicators, and guidance generation means for generating individualized health management guidance based on the evaluation indicators and emotional state. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide appropriate health management guidance.

[0516] "Information gathering means" refers to a device or function for collecting an individual's biometric information and transmitting it to a processing device.

[0517] "Data processing means" refers to a device or software that has the function of analyzing collected biological information and generating health-related evaluation indicators.

[0518] A "guidance generation means" is a device or algorithm that creates specific health management guidance for a user based on evaluation indicators and emotional states obtained by a data processing means.

[0519] "Means of provision" refers to equipment or software for displaying or transmitting the generated health management guidance to the user's terminal.

[0520] "Emotional analysis means" refers to a technology or device for understanding a user's emotional state by analyzing changes in voice and facial expressions.

[0521] "Additional guidance generation means" refers to an algorithm or device that creates additional health management guidance tailored to the user's emotional state based on the results of the emotion analysis means.

[0522] This invention is a system for providing users with appropriate health management guidance by comprehensively analyzing an individual's biological information and emotional state. The system's components and their specific implementation methods are described below.

[0523] The server receives data from information gathering devices and generates health assessment indicators using data processing devices. These indicators are analyzed based on heart rate, activity levels, and voice data collected from wearable devices and information analysis equipment. The server efficiently processes data by using Python libraries (e.g., Pandas, NumPy) for data analysis.

[0524] As a means of emotion analysis, a generative AI model is used to analyze voice data and facial expression data. Existing voice analysis technologies are applied to analyze emotional states from voice signals. Furthermore, facial recognition technology is used to understand changes in facial expressions, and this emotional information is reflected in health management guidance.

[0525] The instruction generation system generates specific instruction based on evaluation metrics and sentiment analysis results. The generated instruction is delivered to the user's device via a delivery system. The device is a smartphone or tablet, and the instruction content is notified to the user through the user interface.

[0526] For example, if the system determines that a user's stress level is high based on fluctuations in their biometric information or emotional state, it will generate and provide advice on exercises or meditation to promote relaxation. The user can receive notifications from their device and manage their health according to the instructions.

[0527] An example of a prompt message for a generative AI model is: "Generate specific health management guidance based on the user's emotional data. For example, suggest actions to take when the user's stress level is high."

[0528] In this way, the system can comprehensively manage the user's health and emotional state and provide health management guidance tailored to their individual circumstances.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The terminal collects biometric information from wearable devices and data analysis equipment. Specifically, the terminal uses Bluetooth and Wi-Fi to acquire heart rate, activity levels, and voice data. This input data is temporarily stored on the terminal in JSON format. As output, this data is prepared for subsequent processing.

[0532] Step 2:

[0533] The device sends the collected biometric information to a server in the cloud. Specifically, the device uses the HTTPS protocol to send previously stored JSON data to the server. The input is the data collected in step 1, and the output is the biometric information stored in the server's database.

[0534] Step 3:

[0535] The server analyzes the received biometric information. Using Python data analysis libraries (e.g., Pandas, NumPy), the server generates health assessment indicators by analyzing the input data. This analysis process calculates fluctuations in heart rate and activity level, converting them into indicators such as stress level and activity level. The generated assessment indicators are then output.

[0536] Step 4:

[0537] The server uses emotion analysis tools to analyze the user's emotional state from the voice data. It utilizes a generative AI model to perform computational processing to identify emotions from the input voice data. This process outputs the user's emotional state, which is then used to generate guidance along with health assessment indicators.

[0538] Step 5:

[0539] The server generates personalized health management guidance based on evaluation indicators and emotional state. The guidance generation mechanism integrates the input health indicators and emotional state data, and uses a generation AI model based on prompt text to generate health management guidance tailored to the user. Specific guidance content is provided as output.

[0540] Step 6:

[0541] The device notifies the user of health management guidance sent from the server. Specifically, the device uses push notifications and in-app messages to display the guidance content received as input to the user. The output is the guidance message displayed on the user's device.

[0542] Step 7:

[0543] The user reviews the health management guidance they receive and acts accordingly. The input here is the guidance message displayed on their smartphone or tablet, and the output is the user's actions. Specifically, the user might perform recommended exercises or try stress-relief methods.

[0544] Through this process, the system can effectively utilize biometric information and emotional states to provide users with personalized health management guidance.

[0545] (Application Example 2)

[0546] 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."

[0547] In elderly care settings, it is essential to carefully understand the health and emotional state of those receiving care and provide individualized care. However, the current system prioritizes health data, and adequate advice based on emotional state is not provided. This can lead to overlooking stress and fatigue, making it difficult to provide a comfortable living environment.

[0548] 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.

[0549] In this invention, the server includes data collection means for receiving personal health information, information analysis means for analyzing the information obtained from the data collection means and generating health standards, guidance generation means for providing individual health management guidance based on the health standards generated by the information analysis means, emotion analysis means for evaluating emotional state, and guidance generation means for providing emotional management guidance based on the results obtained from the emotion analysis means. This enables the integrated evaluation of both the health and emotional state of the person receiving care, making it possible to provide more appropriate care.

[0550] "Data collection means" refers to a device or system for receiving an individual's health information.

[0551] "Information analysis means" refers to a device or system for analyzing information obtained from data collection means and generating health-related standards.

[0552] A "guidance generation means" is a device or system for providing individualized health management guidance based on health standards generated by an information analysis means.

[0553] "Transmission means" refers to a device or system that notifies the user terminal of health management guidance generated by the guidance generation means.

[0554] "Emotional analysis means" refers to a device or system for evaluating an emotional state.

[0555] The system of this invention is designed to support health management and emotional management in caregiving settings. It is an integrated system centered around a server, terminals, and users.

[0556] First, the server has a data collection mechanism that receives individual health information from devices such as wearable devices and smart toilets. This information includes heart rate, activity level, and blood pressure. It also includes an emotion analysis mechanism that uses voice recognition and facial recognition technology to evaluate emotional states.

[0557] Next, the server uses information analysis tools to process the collected data and generate health criteria. These health criteria are dynamically adjusted, taking into account the care recipient's past health information history.

[0558] The server generates individualized health and emotional management guidance based on health standards and emotional analysis results using guidance generation tools. For example, if it is determined that a user's stress level is high, it may suggest deep breathing exercises or music therapy sessions.

[0559] The generated instructions are notified to the user's device via a communication method. These user devices include smartphones and tablets, enabling real-time information sharing.

[0560] For example, if recent stress levels are detected in an elderly person's health data, the system suggests deep breathing exercises and sets up a music therapy session. In this way, the management of both health and emotional well-being in care settings is promoted.

[0561] An example of a prompt message would be, "Based on this data, please suggest the next action. If the emotional state is 'high stress,' consider what stress reduction measures would be effective." This would leverage a generative AI model to support the delivery of personalized care.

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] The server receives health information from wearable devices and smart toilets. This information includes data such as heart rate, activity level, and blood pressure. This data is transferred from the device to the server via Wi-Fi and stored in a database.

[0565] Step 2:

[0566] The server receives audio and visual data using emotion analysis tools. Inputs include voice tone and facial expressions. A generative AI model is used to analyze this data and output emotional states (e.g., stress level, happiness level).

[0567] Step 3:

[0568] The server integrates the data obtained in Step 1 and Step 2 using information analysis tools. Health information and emotional state are taken in as input, and individual health criteria and emotional assessments are performed using machine learning algorithms. The output consists of the respective criterion values ​​and assessment results.

[0569] Step 4:

[0570] The server uses a guidance generation mechanism to generate health management guidance and emotional management guidance based on individual health criteria and emotional assessments. For example, it might suggest deep breathing exercises and recommend music therapy for users experiencing high stress levels. This guidance is generated as output.

[0571] Step 5:

[0572] The server notifies the user's device of the instruction generated by the instruction generation method. Smartphones and tablets are used as the device. The notification includes instruction in the form of text messages and push notifications, which the user receives.

[0573] Step 6:

[0574] Users review the health and emotional management guidance they receive on their devices and incorporate it into their daily lives. This allows users to comprehensively manage their health and emotional state and improve their quality of life.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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".

[0592] The system for implementing the present invention consists of a server, a terminal, and a user, all of which interact with each other. The server is responsible for acquiring and analyzing health data and generating personalized health management advice. The terminal aggregates individual health data obtained from wearable devices and smart toilets and transmits it to the server. It also notifies the user of the generated health management advice. The user receives the information provided via the terminal and uses it to manage their daily health.

[0593] A concrete example is a smart toilet that users use daily. This toilet analyzes the components of urine and transmits the results to the user's smartphone via Bluetooth. The smartphone uploads this information to a server in the cloud. This server analyzes the received data using a generative AI model. For example, it can analyze the concentrations of glucose and sodium in the urine to assess the user's health. Based on this analysis, if the server determines that there is a risk of excessive salt intake, it will generate advice recommending that the user reduce their salt intake.

[0594] As a result, the generated advice is notified to the user via their smartphone. Based on this advice, the user can take specific health management actions, such as reviewing their meal plan or adjusting the timing of hydration. In addition, if an abnormality is detected, a notification may be sent encouraging consultation with a specialist or recommending a visit to a medical institution.

[0595] In this way, the system can be naturally integrated into the user's daily life, providing a streamlined and personalized health management process.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The server sends data collection requests to smart toilets and wearable devices. These requests specify the types of health data and timestamps required.

[0599] Step 2:

[0600] The terminal receives raw data transmitted from the smart device. This includes items such as heart rate, body temperature, weight, and urine analysis results. The terminal converts this data into an appropriate format and prepares it as data packets.

[0601] Step 3:

[0602] The device transfers the prepared data packets to a server in the cloud via Wi-Fi or Bluetooth. During this process, protocols are used to maintain data integrity and security.

[0603] Step 4:

[0604] The server converts the received data into a format that is easy to analyze. This involves pre-processing such as detecting outliers, imputing missing values, and unit conversion.

[0605] Step 5:

[0606] The server analyzes the data using a generative AI model. Specifically, it generates health indicators and performs trend analysis by comparing them with historical data.

[0607] Step 6:

[0608] The server generates personalized health management advice for each user based on the analysis results. This advice includes suggestions for dietary adjustments, exercise, and fluid intake.

[0609] Step 7:

[0610] The server sends the generated advice to the device, which then notifies the user. The advice is displayed as a push notification on the smartphone, and further details can be viewed within the app.

[0611] Step 8:

[0612] Users make choices based on the advice provided. For example, they might review their diet or adjust their daily activities.

[0613] Step 9:

[0614] The user then provides feedback to the server through their device regarding the results of their actions. The server uses this data to learn and improve the accuracy of its advice, thereby evolving the overall functionality of the system.

[0615] (Example 1)

[0616] 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".

[0617] In modern society, health management is a crucial issue for individuals, but it is not easy to continuously and efficiently monitor one's health status and obtain appropriate health advice in daily life. Furthermore, there is a need to provide more accurate advice based on individual health conditions by utilizing big data and machine learning. Therefore, the development of proactive and personalized health management systems is required.

[0618] 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.

[0619] In this invention, the server includes a device collection means for receiving biometric data, a data analysis means including a machine learning model for analyzing the data obtained from the device collection means and generating health indicators, and an advice construction means for providing individualized health management advice based on the health indicators generated by the data analysis means. This makes it possible to continuously monitor an individual's health status and provide appropriate health management advice quickly and effectively.

[0620] "Biometric data" refers to measurable information obtained from the human body, including, for example, blood glucose levels and sodium concentration.

[0621] "Device data collection means" refers to equipment or devices used to acquire biometric data from users, with specific examples including wearable devices and smart toilets.

[0622] "Data analysis means" refers to methods and systems for processing collected biometric data and generating health-related indicators, and involves analyzing data using machine learning models.

[0623] A "machine learning model" is a computational model that recognizes patterns based on data and makes predictions and classifications about future data. A specific example is a neural network.

[0624] "Advice building means" refers to methods and systems for creating specific health management advice to provide to users based on health indicators generated by data analysis means.

[0625] "Information distribution means" refers to methods or systems for notifying users of generated health management advice, and is usually done using mobile devices or computers.

[0626] This invention is a system for supporting personal health management. A specific embodiment of the invention involves a mechanism in which a server, a terminal, and a user work together. This system is implemented based on the following components.

[0627] The server is built on cloud infrastructure and uses generative AI models to analyze biometric data sent from users. This analysis employs data analysis methods incorporating machine learning libraries such as TensorFlow and PyTorch. Based on the data received from device collection devices, the server calculates health indicators and generates personalized health management advice. Because this involves handling a massive amount of data, hardware with efficient data processing capabilities (e.g., high-performance CPUs and GPUs) is required.

[0628] The terminal operates as a platform for devices that collect biometric data from users' daily lives. Specific examples of such devices include wearable devices (e.g., fitness trackers) and smart toilets in the home. These devices transmit biometric data to smartphones via Bluetooth or Wi-Fi and then upload that data to servers in the cloud.

[0629] Users are the direct beneficiaries of this system, enabling them to understand their own health status based on the advice provided by their devices and proactively engage in daily health management. Users can review personalized health advice notified via their smartphones and modify their behavior accordingly.

[0630] As a concrete example, a smart toilet used daily by users is equipped with a function to measure glucose and sodium concentrations in urine. This data is collected, sent from the device to a server, and analyzed by a generative AI model. As a result, advice regarding the user's health (e.g., "reduce excessive salt intake") is generated and notified to the user via their smartphone.

[0631] Examples of prompt messages that might be entered are as follows:

[0632] "Analysis subjects: Urinary components. Data: Glucose concentration - 105 mg / dL, Sodium concentration - 145 mmol / L. Output: Health advice."

[0633] This invention enables continuous and personalized health management, allowing users to take appropriate actions.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The device acquires biometric data. Specifically, it collects data such as glucose and sodium concentrations in urine through wearable devices used by the user or smart toilets. The input is numerical data measured by sensors, which is then transmitted to a smartphone via Bluetooth or Wi-Fi. The output is the biometric data transferred to the smartphone.

[0637] Step 2:

[0638] The device sends the collected biometric data to a server in the cloud. The smartphone uploads the data in real time using an internet connection. The input in this process is the biometric data stored on the device, and the output is the biometric data sent to the server. This prepares the data for the next analysis step.

[0639] Step 3:

[0640] The server analyzes the received data. A generative AI model is implemented here, and it is used to perform data analysis. The input is biometric data sent to the server, and the data is applied to the machine learning model to detect patterns and anomalies. Specifically, a model built with TensorFlow or PyTorch analyzes the data and calculates health indicators. The output is the generated health indicators.

[0641] Step 4:

[0642] The server generates personalized health management advice based on the analyzed health indicators. The input is the health indicators obtained in the previous step, and the AI ​​constructs meaningful advice based on the analysis results. The output is specific advice to notify the user (e.g., a recommendation to reduce salt intake).

[0643] Step 5:

[0644] The device receives advice from the server and notifies the user. Specifically, it displays the advice through the smartphone's notification function. The input is health management advice sent from the server, and the output is a visual notification to the user.

[0645] Step 6:

[0646] Based on the advice received from the device, users take daily health management actions. For example, they might review their diet or adjust their exercise plan. In this step, the input is the advice from the device, and the output is the specific health actions the user takes. This allows users to proactively manage their own health.

[0647] (Application Example 1)

[0648] 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".

[0649] With the aging of society and the increase in lifestyle-related diseases, there is a growing need for more detailed individual health management. Furthermore, it is crucial to collect diverse health data in real time, quickly understand individual health conditions, and provide necessary advice. However, current systems often fail to adequately aggregate and analyze health data, leading to delays in early detection of abnormalities and appropriate responses.

[0650] 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.

[0651] In this invention, the server includes sensing means for receiving personal health data, information processing means for analyzing the data obtained from the sensing means and generating health indicators, advice generation means for providing individual health management advice based on the health indicators generated by the information processing means, communication means for notifying the user terminal of the health management advice generated by the advice generation means, and warning means for monitoring the health status using the health indicators and an anomaly detection algorithm, and notifying stakeholders if an anomaly is detected. This enables the provision of prompt and appropriate advice based on collected health data and the early detection of anomalies.

[0652] "Sensing means" refers to devices or components used to collect personal health data.

[0653] An "information processing device" is a technological device that has the function of analyzing data obtained from sensing devices to generate health-related indicators.

[0654] "Advice generation means" refers to a system function for creating individual health management advice based on health indicators generated by information processing means.

[0655] "Communication means" refers to the technical devices and protocols used to notify the user's terminal of the generated health management advice.

[0656] A "pre-warning system" is a system that uses health indicators and anomaly detection algorithms to monitor health conditions and notify stakeholders in the event of an anomaly.

[0657] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server is responsible for the primary function of receiving and analyzing personal health data. Specifically, it collects health data using wearable devices and smart toilets as sensing means and transmits this data to the server.

[0658] The server uses a generative AI model as its information processing tool to analyze the received data. This model analyzes the data using deep learning techniques and other methods to generate important health indicators. It also has an advice generation tool that generates personalized health management advice for each user based on these indicators. This advice is immediately notified to the user's smartphone or tablet via communication means.

[0659] The device functions as a hub where users receive notifications and advice from the server. Through this, users can monitor their health status in real time and take early action in case of abnormalities. To this end, applications on the device take actions based on collected data and analysis results, improving the quality of health management in daily life.

[0660] Furthermore, as a preventative measure, the generating AI model has the function of issuing warnings to the user and designated stakeholders when it detects an anomaly. For example, if the glucose concentration in the urine enters a dangerous range, it can generate a prompt message such as "High urine glucose test result -> Generate exercise / dietary advice" and promptly inform the user.

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] The server receives health data from wearable devices and smart toilets through sensing means. This data includes the user's vital signs and biochemical information. The received data is filtered to remove noise and formatted into a format suitable for analysis.

[0664] Step 2:

[0665] The server, as a means of information processing, inputs pre-processed health data into a generative AI model. This generative AI model performs deep analysis of the data. Specifically, it extracts feature patterns from the input data and generates indicators for evaluating the user's health status. In this process, it also uses past data history to infer new health indicators.

[0666] Step 3:

[0667] Based on the generated health indicators, the server creates personalized health management advice using an advice generation system. This advice includes specific action suggestions to improve the user's health. For example, it may include recommendations for dietary adjustments or exercise. At this time, prompt statements are generated and the advice content is organized.

[0668] Step 4:

[0669] The device receives advice from the server via communication. The content of this advice is then notified to the user as an alert or recommended action. The device converts the received information into a format that is easy for the user to understand and displays it.

[0670] Step 5:

[0671] Users review the advice received on their devices and incorporate it into their daily health management routines. If any abnormalities are detected, it is recommended that they promptly seek expert advice.

[0672] Step 6:

[0673] The server uses proactive warning mechanisms to alert stakeholders if an anomaly is detected in the generated AI model. This includes notifying the user's emergency contacts, registered caregivers, and medical institutions.

[0674] 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.

[0675] The system for implementing the present invention consists of a server, a terminal, a user, and an emotion engine. The server is responsible for acquiring and analyzing health data and emotion data, and for generating personalized health management and emotion-based advice. The terminal aggregates individual data obtained from wearable devices, smart toilets, and devices for recognizing emotions, and transmits it to the server. It also has the role of notifying the user of the generated health management advice. The user receives the information provided via the terminal and uses it for daily health management and emotion management.

[0676] A concrete example is a wearable device that users use daily. This device recognizes emotional states from heart rate, activity levels, and voice, and transmits the results to the user's smartphone via Wi-Fi. The smartphone uploads this information to a server in the cloud. The server analyzes this data and evaluates the user's health and emotional state. If this analysis reveals an increase in stress levels, it generates advice on exercises or meditation to promote relaxation.

[0677] Furthermore, the emotion engine can understand the user's emotional state by evaluating changes in voice tone and facial expressions. Based on this evaluation, it also provides health management advice that reflects the user's emotional changes. For example, if the user is feeling tired or stressed, it will send advice recommending a balanced diet and sufficient rest.

[0678] In this way, the system integrates health data and emotional data to support users in more personalized health and emotional management. As a result, users can not only understand and improve their health status, but also improve their quality of life through emotional management.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The device receives personal health and emotional data from wearable devices, smart toilets, and devices that recognize voice and facial expressions. Specifically, it acquires heart rate, activity levels, urine analysis results, voice tone, and changes in facial expressions.

[0682] Step 2:

[0683] The device combines the received health and emotional data into a single data packet and transmits it to the server via Wi-Fi or Bluetooth. Encryption is performed during this process to ensure data integrity and security.

[0684] Step 3:

[0685] The server receives the transmitted data packets and performs preprocessing, including data format conversion and detection of anomalies. This preprocessing ensures the accuracy necessary for data analysis.

[0686] Step 4:

[0687] The server analyzes health data using a generation AI model to generate indicators showing the user's current health status. This analysis also includes predictive analytics that take into account past health data history.

[0688] Step 5:

[0689] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. Based on this evaluation, it understands stress levels and changes in emotions.

[0690] Step 6:

[0691] The server generates personalized health and emotional management advice for the user based on assessments of both their physical and emotional state. This includes suggestions for exercise to reduce stress and recommendations for relaxation techniques.

[0692] Step 7:

[0693] The server sends generated advice to the device, which then provides it to the user as a push notification on their smartphone. The user can then view detailed advice within the app.

[0694] Step 8:

[0695] Based on the advice provided, users take specific actions to manage their health and regulate their emotions. For example, they might incorporate recommended exercises or make relaxation a daily routine.

[0696] Step 9:

[0697] Users provide feedback on their actions through their devices, and the server uses this feedback to continuously learn and improve the accuracy of the system's advice.

[0698] (Example 2)

[0699] 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".

[0700] Current health management systems collect users' biometric information and analyze their health status, but they are unable to provide personalized health guidance that takes emotional states into account, or to conduct sufficient analysis to detect abnormalities early. As a result, users have limited opportunities to receive appropriate guidance based on the stress and emotional changes they experience in their daily lives.

[0701] 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.

[0702] In this invention, the server includes information gathering means for receiving an individual's biometric information, data processing means for processing the information and generating health evaluation indicators, and guidance generation means for generating individualized health management guidance based on the evaluation indicators and emotional state. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide appropriate health management guidance.

[0703] "Information gathering means" refers to a device or function for collecting an individual's biometric information and transmitting it to a processing device.

[0704] "Data processing means" refers to a device or software that has the function of analyzing collected biological information and generating health-related evaluation indicators.

[0705] A "guidance generation means" is a device or algorithm that creates specific health management guidance for a user based on evaluation indicators and emotional states obtained by a data processing means.

[0706] "Means of provision" refers to equipment or software for displaying or transmitting the generated health management guidance to the user's terminal.

[0707] "Emotional analysis means" refers to a technology or device for understanding a user's emotional state by analyzing changes in voice and facial expressions.

[0708] "Additional guidance generation means" refers to an algorithm or device that creates additional health management guidance tailored to the user's emotional state based on the results of the emotion analysis means.

[0709] This invention is a system for providing users with appropriate health management guidance by comprehensively analyzing an individual's biological information and emotional state. The system's components and their specific implementation methods are described below.

[0710] The server receives data from information gathering devices and generates health assessment indicators using data processing devices. These indicators are analyzed based on heart rate, activity levels, and voice data collected from wearable devices and information analysis equipment. The server efficiently processes data by using Python libraries (e.g., Pandas, NumPy) for data analysis.

[0711] As a means of emotion analysis, a generative AI model is used to analyze voice data and facial expression data. Existing voice analysis technologies are applied to analyze emotional states from voice signals. Furthermore, facial recognition technology is used to understand changes in facial expressions, and this emotional information is reflected in health management guidance.

[0712] The instruction generation system generates specific instruction based on evaluation metrics and sentiment analysis results. The generated instruction is delivered to the user's device via a delivery system. The device is a smartphone or tablet, and the instruction content is notified to the user through the user interface.

[0713] For example, if the system determines that a user's stress level is high based on fluctuations in their biometric information or emotional state, it will generate and provide advice on exercises or meditation to promote relaxation. The user can receive notifications from their device and manage their health according to the instructions.

[0714] An example of a prompt message for a generative AI model is: "Generate specific health management guidance based on the user's emotional data. For example, suggest actions to take when the user's stress level is high."

[0715] In this way, the system can comprehensively manage the user's health and emotional state and provide health management guidance tailored to their individual circumstances.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The terminal collects biometric information from wearable devices and data analysis equipment. Specifically, the terminal uses Bluetooth and Wi-Fi to acquire heart rate, activity levels, and voice data. This input data is temporarily stored on the terminal in JSON format. As output, this data is prepared for subsequent processing.

[0719] Step 2:

[0720] The device sends the collected biometric information to a server in the cloud. Specifically, the device uses the HTTPS protocol to send previously stored JSON data to the server. The input is the data collected in step 1, and the output is the biometric information stored in the server's database.

[0721] Step 3:

[0722] The server analyzes the received biometric information. Using Python data analysis libraries (e.g., Pandas, NumPy), the server generates health assessment indicators by analyzing the input data. This analysis process calculates fluctuations in heart rate and activity level, converting them into indicators such as stress level and activity level. The generated assessment indicators are then output.

[0723] Step 4:

[0724] The server uses emotion analysis tools to analyze the user's emotional state from the voice data. It utilizes a generative AI model to perform computational processing to identify emotions from the input voice data. This process outputs the user's emotional state, which is then used to generate guidance along with health assessment indicators.

[0725] Step 5:

[0726] The server generates personalized health management guidance based on evaluation indicators and emotional state. The guidance generation mechanism integrates the input health indicators and emotional state data, and uses a generation AI model based on prompt text to generate health management guidance tailored to the user. Specific guidance content is provided as output.

[0727] Step 6:

[0728] The device notifies the user of health management guidance sent from the server. Specifically, the device uses push notifications and in-app messages to display the guidance content received as input to the user. The output is the guidance message displayed on the user's device.

[0729] Step 7:

[0730] The user reviews the health management guidance they receive and acts accordingly. The input here is the guidance message displayed on their smartphone or tablet, and the output is the user's actions. Specifically, the user might perform recommended exercises or try stress-relief methods.

[0731] Through this process, the system can effectively utilize biometric information and emotional states to provide users with personalized health management guidance.

[0732] (Application Example 2)

[0733] 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".

[0734] In elderly care settings, it is essential to carefully understand the health and emotional state of those receiving care and provide individualized care. However, the current system prioritizes health data, and adequate advice based on emotional state is not provided. This can lead to overlooking stress and fatigue, making it difficult to provide a comfortable living environment.

[0735] 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.

[0736] In this invention, the server includes data collection means for receiving personal health information, information analysis means for analyzing the information obtained from the data collection means and generating health standards, guidance generation means for providing individual health management guidance based on the health standards generated by the information analysis means, emotion analysis means for evaluating emotional state, and guidance generation means for providing emotional management guidance based on the results obtained from the emotion analysis means. This enables the integrated evaluation of both the health and emotional state of the person receiving care, making it possible to provide more appropriate care.

[0737] "Data collection means" refers to a device or system for receiving an individual's health information.

[0738] "Information analysis means" refers to a device or system for analyzing information obtained from data collection means and generating health-related standards.

[0739] A "guidance generation means" is a device or system for providing individualized health management guidance based on health standards generated by an information analysis means.

[0740] "Transmission means" refers to a device or system that notifies the user terminal of health management guidance generated by the guidance generation means.

[0741] "Emotional analysis means" refers to a device or system for evaluating an emotional state.

[0742] The system of this invention is designed to support health management and emotional management in caregiving settings. It is an integrated system centered around a server, terminals, and users.

[0743] First, the server has a data collection mechanism that receives individual health information from devices such as wearable devices and smart toilets. This information includes heart rate, activity level, and blood pressure. It also includes an emotion analysis mechanism that uses voice recognition and facial recognition technology to evaluate emotional states.

[0744] Next, the server uses information analysis tools to process the collected data and generate health criteria. These health criteria are dynamically adjusted, taking into account the care recipient's past health information history.

[0745] The server generates individualized health and emotional management guidance based on health standards and emotional analysis results using guidance generation tools. For example, if it is determined that a user's stress level is high, it may suggest deep breathing exercises or music therapy sessions.

[0746] The generated instructions are notified to the user's device via a communication method. These user devices include smartphones and tablets, enabling real-time information sharing.

[0747] For example, if recent stress levels are detected in an elderly person's health data, the system suggests deep breathing exercises and sets up a music therapy session. In this way, the management of both health and emotional well-being in care settings is promoted.

[0748] An example of a prompt message would be, "Based on this data, please suggest the next action. If the emotional state is 'high stress,' consider what stress reduction measures would be effective." This would leverage a generative AI model to support the delivery of personalized care.

[0749] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0750] Step 1:

[0751] The server receives health information from wearable devices and smart toilets. This information includes data such as heart rate, activity level, and blood pressure. This data is transferred from the device to the server via Wi-Fi and stored in a database.

[0752] Step 2:

[0753] The server receives audio and visual data using emotion analysis tools. Inputs include voice tone and facial expressions. A generative AI model is used to analyze this data and output emotional states (e.g., stress level, happiness level).

[0754] Step 3:

[0755] The server integrates the data obtained in Step 1 and Step 2 using information analysis tools. Health information and emotional state are taken in as input, and individual health criteria and emotional assessments are performed using machine learning algorithms. The output consists of the respective criterion values ​​and assessment results.

[0756] Step 4:

[0757] The server uses a guidance generation mechanism to generate health management guidance and emotional management guidance based on individual health criteria and emotional assessments. For example, it might suggest deep breathing exercises and recommend music therapy for users experiencing high stress levels. This guidance is generated as output.

[0758] Step 5:

[0759] The server notifies the user's device of the instruction generated by the instruction generation method. Smartphones and tablets are used as the device. The notification includes instruction in the form of text messages and push notifications, which the user receives.

[0760] Step 6:

[0761] Users review the health and emotional management guidance they receive on their devices and incorporate it into their daily lives. This allows users to comprehensively manage their health and emotional state and improve their quality of life.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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."

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] Information collection methods for receiving personal health data,

[0786] A data analysis means for analyzing data obtained from the aforementioned information gathering means and generating health-related indicators,

[0787] An advice generation means for providing individual health management advice based on health indicators generated by the aforementioned data analysis means,

[0788] A notification means for notifying a user terminal of the health management advice generated by the advice generation means,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the data analysis means takes into account past health data history and predicts new health indicators.

[0792] (Claim 3)

[0793] The system according to claim 1, configured to identify data that deviates from health indicators by applying an anomaly detection algorithm.

[0794] "Example 1"

[0795] (Claim 1)

[0796] A device for receiving biometric data and a means for collecting biometric data.

[0797] A data analysis means including a machine learning model for analyzing data obtained from the device collection means and generating health indicators,

[0798] An advice construction means for providing individual health management advice based on health indicators generated by the aforementioned data analysis means,

[0799] Information distribution means for notifying the user device of the health management advice generated by the advice construction means,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the data analysis means takes into account previous biometric data history and predicts new health indicators.

[0803] (Claim 3)

[0804] The system according to claim 1, configured to identify data that deviates from health indicators by applying an anomaly detection algorithm.

[0805] "Application Example 1"

[0806] (Claim 1)

[0807] Sensing means for receiving personal health data,

[0808] An information processing means for analyzing data obtained from the sensing means and generating health-related indicators,

[0809] An advice generation means for providing individual health management advice based on health indicators generated by the aforementioned information processing means,

[0810] A communication means for notifying the user terminal of the health management advice generated by the advice generation means,

[0811] A means for monitoring health status using the aforementioned health indicators and anomaly detection algorithm, and for notifying stakeholders when an anomaly is detected,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the information processing means takes past health information into consideration and estimates new health indicators.

[0815] (Claim 3)

[0816] The system according to claim 1, which aggregates multiple health data collected in real time from information collection devices and uses a generative AI model to support health management.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] Information collection means for receiving personal biometric information,

[0820] A data processing means for processing biological information obtained from the aforementioned information gathering means and generating health evaluation indicators,

[0821] A guidance generation means for generating individual health management guidance based on the evaluation index and emotional state generated by the data processing means,

[0822] A means for providing the instruction generated by the instruction generation means to the user terminal,

[0823] An emotion analysis means for analyzing audio signals or facial expression changes in order to understand emotional states,

[0824] An additional guidance generation means for creating and providing additional health management guidance based on the results of the emotion analysis means,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the data processing means refers to past biometric information history and predicts a new health assessment index.

[0828] (Claim 3)

[0829] The system according to claim 1, configured to identify data that deviates from health assessment indicators using an anomaly detection method.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] Data collection methods for receiving personal health information,

[0833] Information analysis means for analyzing information obtained from the aforementioned data collection means and generating health standards,

[0834] A guidance generation means for providing individual health management guidance based on health standards generated by the aforementioned information analysis means,

[0835] A means for notifying a user terminal of the health management guidance generated by the guidance generation means,

[0836] A means of emotional analysis for evaluating emotional states,

[0837] Based on the results obtained from the emotion analysis means, a guidance generation means for providing emotion management guidance,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the information analysis means takes into account past health information history and predicts new health standards.

[0841] (Claim 3)

[0842] The system according to claim 1, configured to identify information that deviates from health standards by applying an anomaly detection algorithm. [Explanation of Symbols]

[0843] 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. Sensing means for receiving personal health data, An information processing means for analyzing data obtained from the sensing means and generating health-related indicators, An advice generation means for providing individual health management advice based on health indicators generated by the aforementioned information processing means, A communication means for notifying the user terminal of the health management advice generated by the advice generation means, A means for monitoring health status using the aforementioned health indicators and anomaly detection algorithm, and for notifying stakeholders when an anomaly is detected, A system that includes this.

2. The system according to claim 1, wherein the information processing means takes past health information into consideration and estimates new health indicators.

3. The system according to claim 1, which aggregates multiple health data collected in real time from information collection devices and uses a generative AI model to support health management.

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