system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining the user's biometric data, An analytical means for analyzing the acquired biological data and detecting abnormalities, A notification means for notifying the user of an anomaly detected by the analysis means, Information sharing means for transmitting the aforementioned abnormal information to medical institutions, A notification system for detecting abnormalities in the care environment and sending alerts to care workers, 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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Many modern people are not easily aware of their own health conditions in daily life, and it is difficult to cooperate with medical institutions for appropriate diagnosis and treatment even when they feel something abnormal with their health. As a result, it has become a problem that they miss the opportunity to visit a doctor or receive treatment at an appropriate time and their health conditions deteriorate. Therefore, there is a need for a system that allows users to grasp their own health conditions daily, detect abnormalities at any time, and facilitate cooperation with appropriate medical institutions.

Means for Solving the Problems

[0005] To solve this problem, the present invention includes a data collection means capable of acquiring a user's biometric information and an analysis means capable of analyzing the acquired biometric information and detecting abnormalities. Furthermore, it includes a notification means for notifying the user of any abnormalities detected by the analysis means and an information sharing means for transmitting the detected abnormality information to an approved medical institution. As a result, users can not only quickly recognize abnormalities in their health condition but also smoothly share information with medical institutions when necessary, making it easier for them to receive appropriate medical support.

[0006] "Data collection means" refers to a device or system that has the function of acquiring a user's biometric information and transmitting that information to a server.

[0007] "Analysis means" refers to a device or system equipped with the function of analyzing acquired biological information and detecting abnormalities.

[0008] "Notification means" refers to a device or system that has the function of informing the user of an anomaly detected by the analysis means.

[0009] "Information sharing means" refers to a device or system that has the function of transmitting detected abnormal information to an approved medical institution.

[0010] A "generative model" is a model that uses artificial intelligence technology to analyze data and is used to evaluate changes in health status.

[0011] "Biometric information" refers to various physiological data acquired to indicate a user's health status, such as heart rate, sleep, activity level, and calorie consumption. [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 terms 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 including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), 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 of the present invention acquires a user's biometric information, detects abnormalities based on that information, provides necessary notifications, and enables information sharing. An embodiment thereof is shown below.

[0034] Users obtain daily biometric information using devices such as smartwatches and smartphones. These devices use sensors to collect physiological data such as heart rate, sleep, activity level, and calorie consumption in real time. The devices are configured to automatically send this information to a server at specific time intervals.

[0035] The server stores the received biometric information in a database and analyzes it using a generative model. If this analysis detects unusual patterns or anomalies, the server sets an appropriate flag. When this flag is set, the server sends an alert to the user through a notification system. The user's device will display specific advice, such as, "Your heart rate remains higher than normal. We recommend you rest."

[0036] Furthermore, this system can share detected anomaly information with healthcare institutions only if the user requests it. Users are given the option to select a specific healthcare institution during the initial setup. After confirming the user's approval, the server securely sends a report summarizing past health data and details of the anomalies to that healthcare institution. This information sharing enables healthcare institutions to make faster and more accurate diagnoses and provides users with appropriate support.

[0037] As a concrete example, consider a scenario where a user's activity level suddenly decreases, and their heart rate is detected to be significantly higher than their resting average. The server recognizes this as an anomaly and sends a notification to the user recommending rest. Simultaneously, if the anomaly is serious, with the user's consent, the system notifies a selected medical institution to ensure smooth subsequent diagnosis and treatment.

[0038] Thus, the present invention is a system that facilitates daily health management and provides an environment that enables prompt and appropriate medical response in the event of an abnormality.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption through sensors. This data is updated at regular intervals and stored on the device.

[0042] Step 2:

[0043] The user's device transmits collected biometric information to a server at specified intervals. This communication is typically conducted via Bluetooth or Wi-Fi.

[0044] Step 3:

[0045] The server stores the received biometric information in a database. Next, the data is preprocessed to remove noise and impute missing values.

[0046] Step 4:

[0047] The server inputs pre-processed data into a generative model. The generative model is used to detect anomalies by comparing them to normal healthy patterns.

[0048] Step 5:

[0049] When the generative model detects an anomaly, the server determines the type and severity of the anomaly. If the anomaly is deemed critical, a flag is set.

[0050] Step 6:

[0051] The server uses a notification system to send an alert message to the user's terminal in order to notify the user that an anomaly has been detected.

[0052] Step 7:

[0053] The user's device will notify them of received alerts through screen displays and audio notifications. The alerts include advice on specific actions to take.

[0054] Step 8:

[0055] Users will check the approval settings for abnormal transmissions via their device and, if necessary, consent to sharing information with healthcare institutions.

[0056] Step 9:

[0057] If user approval is obtained, the server securely transmits abnormal information and historical biometric data to the identified medical institution. This allows the medical institution to perform a rapid and accurate diagnosis.

[0058] (Example 1)

[0059] 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."

[0060] In modern society, personal health management is becoming increasingly important, but traditional methods have the challenge of not being able to respond quickly to sudden changes in health conditions. In particular, there is a need for smooth information sharing to detect abnormalities early and obtain appropriate medical treatment.

[0061] 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.

[0062] In this invention, the server includes information gathering means for acquiring biometric data in a time series, information processing means for storing and analyzing the acquired biometric data, and communication means for notifying abnormalities detected by the information processing means. This makes it possible to monitor an individual's health status in real time and to detect and respond to abnormalities at an early stage.

[0063] "Information gathering means" refers to devices and technologies for acquiring biological data, and which have the function of acquiring data in a time series.

[0064] "Information processing means" refers to methods and devices for storing and analyzing collected biological data, specifically using databases and generative AI models.

[0065] "Communication means" refers to technologies or devices that notify users of anomalies detected by information processing means, such as having a function to send alert notifications to terminals.

[0066] "Information transmission means" refers to devices or methods for securely transmitting abnormal data to external organizations, and has the function of transmitting information only to the relevant organization with the user's approval.

[0067] "Generative artificial intelligence models" refer to algorithms and technological systems for analyzing collected data and evaluating fluctuations in health information, and include predictive models using machine learning.

[0068] In this invention, the user acquires biometric data using a device such as a smartwatch or smartphone. The device has built-in sensors to collect heart rate, sleep, activity level, calorie consumption, etc., in real time, utilizing optical sensor and accelerometer technologies. This collected biometric data is designed to be periodically transmitted to a server using communication technologies such as Bluetooth or Wi-Fi.

[0069] The server stores the received biometric data in a database and performs analysis using a generative AI model. The generative AI model compares the current data with past data to evaluate outliers and significant changes in health status. For example, it can detect abnormally high heart rates or sudden changes in activity levels. If an abnormality is detected, the server sends a notification to the user's terminal containing specific advice. The notification may include information such as, "Your heart rate remains higher than normal. We recommend that you rest."

[0070] Furthermore, with the user's consent, the server can securely share abnormal data with external healthcare institutions. This feature is selectable by the user during initial setup, and detailed health data is sent only to approved healthcare institutions. The aim of this information sharing is to enable healthcare institutions to make quick and accurate diagnoses and provide users with the necessary support.

[0071] For example, if a user's activity level suddenly decreases and their heart rate significantly exceeds their resting average, the system recognizes this as an anomaly. The server notifies the user of this situation and, if necessary, sends a warning to a designated medical institution, providing support for their assessment and treatment procedures.

[0072] In this way, by using a system that includes a generative AI model, users can manage their health status on a daily basis and have an environment where they can receive a quick response when an abnormality occurs.

[0073] An example of a prompt message is, "Generate a detailed explanation regarding the detection of anomalies in the user's heart rate and activity level." This allows the generating AI model to provide analysis results of the biometric data and a specific evaluation of the anomalies.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device uses built-in sensors to acquire biometric data such as the user's heart rate, sleep, activity level, and calorie consumption in real time. In this process, the sensors utilize optical sensors and accelerometers to accurately capture the user's physical activity and state. The input is the user's physical activity and state, and the output is the collected biometric data.

[0077] Step 2:

[0078] The device is configured to periodically send the collected biometric data to a server via Bluetooth or Wi-Fi. This transmission is automated and performed efficiently without user intervention. The input is the biometric data acquired in step 1, and the output is the data sent to the server.

[0079] Step 3:

[0080] The server stores the received biometric data in a database. This database is designed to enable data storage, organization, and rapid access. The input is biometric data sent from the terminal, and the output is organized data stored in the database.

[0081] Step 4:

[0082] The server analyzes biometric data using a generating AI model. This analysis includes comparing the data to normal patterns and detecting changes in health status, identifying anomalies and unique patterns. The input is biometric data stored in a database, and the output is anomaly detection information as a result of the analysis.

[0083] Step 5:

[0084] If the server detects an anomaly based on the analysis results, it will notify the user. This notification will send an alert to the user's device, displaying a message on the screen such as, "Your heart rate remains higher than normal. We recommend you rest." The input is the anomaly detection information from step 4, and the output is the notification message displayed on the device.

[0085] Step 6:

[0086] The server, with the user's consent, will send anomaly information to an authorized healthcare provider. This involves a process of verifying user approval and sharing data through a secure channel. The input is the anomaly detection information and the user's consent, and the output is the anomaly report sent to the healthcare provider.

[0087] (Application Example 1)

[0088] 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."

[0089] There is a need to detect abnormalities in elderly users and those with unstable health conditions early and to share that information promptly with caregivers and medical facilities to enable a rapid response. Furthermore, in care settings, prompt and appropriate health management is crucial, and a challenge is ensuring that information about abnormalities is notified to the appropriate parties and that a swift response is taken.

[0090] 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.

[0091] In this invention, the server includes data acquisition means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, and notification means for notifying the user of the abnormalities detected by the analysis means. This enables rapid detection of abnormalities in users in a care environment, appropriate information sharing with caregivers and medical institutions, and immediate response.

[0092] "Data acquisition means" refers to devices and methods for collecting a user's biometric data, which is collected via devices such as smartwatches and smartphones.

[0093] "Analysis means" refers to processes and methods for analyzing acquired biological data to detect unusual patterns or abnormalities.

[0094] "Notification means" refers to a system or device for notifying users or caregivers based on the analyzed results.

[0095] "Information sharing means" refers to a function that transmits detected abnormal information to approved medical facilities and care workers, enabling a swift response.

[0096] "Notification methods in the care environment" refers to a system used in care facilities and home care settings to send alerts to care workers and communicate information when an abnormality is detected.

[0097] The system for implementing this invention consists of a user terminal, a server, and a notification function for caregivers. The user terminal can be a smartwatch or a smartphone. These terminals acquire biometric data such as heart rate and activity levels in real time via Bluetooth and transmit it to the smartphone. The smartphone is responsible for periodically uploading this data to the server.

[0098] The server stores the received biometric data in a database and performs analysis using Python. The analysis utilizes generative AI models based on TENSORFLOW® and PyTorch to detect anomalies. When an anomaly is detected, the server notifies caregivers and, if necessary, medical facilities via Firebase Cloud Messaging and the Twilio API, enabling a rapid response.

[0099] For example, if a significantly elevated heart rate is detected in a nursing home, the server immediately sends a notification such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition," and the information is transmitted to the caregiver's mobile device. This allows the caregiver to take necessary actions quickly.

[0100] As a concrete example of a prompt message generated using the AI ​​model, the AI ​​model is input with content such as "Heart rate increase detection prompt: What should be alerted if the heart rate exceeds normal after a rest period?" and generates appropriate alert content. This enables quick and accurate notifications in situations requiring complex judgments.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The device measures biometric data such as the user's heart rate and activity level in real time via a smartwatch and transfers that data to a smartphone via Bluetooth. At this stage, the input is biometric data, and the output is the biometric data transferred to the smartphone.

[0104] Step 2:

[0105] The smartphone uploads the received biometric data to the server at regular intervals. Specifically, it securely transmits the data to the server using the HTTPS protocol. In this process, the input is biometric data from the smartwatch, and the output is the data uploaded to the server.

[0106] Step 3:

[0107] The server stores uploaded biometric data in a database. MySQL® or PostgreSQL is used as the database to ensure data reliability and consistency. Input is data sent from a smartphone, and output is data stored in the database.

[0108] Step 4:

[0109] The server performs data analysis using a generative AI model. Specifically, it uses TensorFlow to analyze biometric data and detect anomalies. In this process, the input is biometric data stored in a database, and the output is flag information indicating when an anomaly is detected.

[0110] Step 5:

[0111] If an anomaly is detected, the server uses Firebase Cloud Messaging to notify caregivers. The notification content is generated based on prompts created by an AI model. For example, if there is an abnormality in heart rate, it will send a message such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition." The input is flag information indicating the detection of an anomaly, and the output is the message notification.

[0112] Step 6:

[0113] Caregivers receive notifications and take action as needed. This process is expected to involve implementing measures based on the received information. The input before action is taken at the caregiving site is the notified abnormal information, and the output is the implementation of caregiving action.

[0114] 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.

[0115] This invention is a system that acquires a user's biometric information, analyzes abnormalities in their health and emotional state based on that information, and provides appropriate notifications to the user. Furthermore, this system has a function to share abnormal information with medical institutions. An embodiment of this system is shown below.

[0116] Users collect daily biometric information using devices such as smartwatches and smartphones. These devices use biometric sensors to acquire various physiological data in real time, including heart rate, sleep, activity level, and calorie consumption. The collected data is periodically transmitted to a server.

[0117] The server stores the received biometric information in a database and analyzes it using a generative model. If an anomaly is detected as a result of the analysis, the server generates a notification for the user. In this process, the emotion engine recognizes the user's emotional state based on the user's input and biometric information, and personalizes the content of the notification according to that emotion.

[0118] As a concrete example, consider a scenario where a high level of stress is detected from the user's biometric information, and the emotion engine further recognizes feelings of anxiety. In such a case, the server would notify the user with personalized advice such as, "Your stress level is high, and you are experiencing persistent anxiety. Please create a relaxing environment and make sure to get enough rest."

[0119] In addition, this system allows users to share details of abnormal information and emotional states with healthcare providers they have approved, but only when necessary. First, users review the information sharing options with healthcare providers and grant permission. Based on user approval, the server compiles the abnormal information and the results of the associated emotional analysis and securely transmits it to the healthcare provider. This information sharing allows healthcare providers to better understand the user and provide appropriate treatment and advice.

[0120] In this way, the present invention is a system that comprehensively supports the user's health management and facilitates smooth collaboration with medical institutions as needed, thereby enabling the user to receive optimal medical services.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The device continuously collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption using sensors. This data is temporarily stored in the device's memory.

[0124] Step 2:

[0125] The device transmits the collected biometric information to the server at regular intervals. This transmission is carried out via the internet using secure encrypted communication.

[0126] Step 3:

[0127] The server stores the received biometric information in a database. The stored information is prepared for input into the generative model in real time.

[0128] Step 4:

[0129] The server uses a generative model to analyze biometric data and detect unusual patterns or anomalies. When an anomaly is found, a flag is set according to the type of anomaly.

[0130] Step 5:

[0131] The server uses an emotion engine to analyze the user's emotional state. This analysis is based on subjective emotional evaluations and biometric information provided by the user through the application.

[0132] Step 6:

[0133] The server generates customized advice messages for the user based on the health and emotional states that it perceives as abnormal.

[0134] Step 7:

[0135] A notification is sent from the server to the user's device. The device displays this notification on the screen and alerts the user with an audible alert or vibration.

[0136] Step 8:

[0137] Users can view notifications on their devices and take further action as needed. They can also provide emotional feedback through the app.

[0138] Step 9:

[0139] If a user authorizes information sharing with a healthcare provider, the server uses a secure protocol to compose the relevant data and send it to the healthcare provider specified by the user. This allows the healthcare provider to comprehensively evaluate the user's health monitoring results and emotional state.

[0140] (Example 2)

[0141] 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".

[0142] In recent years, the importance of health management based on users' biometric information has increased. However, conventional systems only detect abnormalities in biometric information and have shortcomings in providing individualized support that fully considers the user's emotional state and inefficient information sharing with medical institutions.

[0143] 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.

[0144] In this invention, the server includes data collection means for acquiring the user's biometric information, analysis means for analyzing the acquired biometric information and detecting abnormalities, and estimation means for evaluating the user's emotional state. This enables an integrated evaluation of the user's health and emotional state, allowing for individual notifications and information sharing with external organizations.

[0145] "Data collection means" refers to devices and software used to acquire a user's biometric information, including devices that measure heart rate, activity level, sleep patterns, etc.

[0146] "Analysis means" refers to technologies that have the function of analyzing acquired biological information and detecting abnormalities, and this includes machine learning algorithms that use generative models.

[0147] "Estimation means" refers to the functions and methods necessary to evaluate a user's emotional state, and includes technologies that infer emotions based on biometric information and user input data.

[0148] "Notification means" refers to a mechanism for generating and sending individual notifications to users based on analysis results and emotional states, and this includes message delivery and real-time alerts.

[0149] "Information sharing means" refers to the function of transmitting acquired and analyzed anomaly information and emotional state information to an external organization approved by the user via a secure communication channel.

[0150] A "generative model" refers to a model that uses biological information to analyze and predict health and emotional states using algorithms.

[0151] A "prompt statement" refers to a document used as input for a generative AI model, containing information that serves as a basis for analysis and evaluation.

[0152] This invention relates to a system that acquires a user's biometric information, analyzes it to understand their health and emotional state, and shares abnormal information with external organizations as needed. This system has the function of collecting biometric information through sensors installed in devices such as smartwatches and smartphones, which the user uses. For example, information such as heart rate, activity level, and sleep patterns can be acquired by the sensors.

[0153] The device then sends the acquired data to the server at regular intervals. Since the HTTPS protocol is used for transmission, data privacy and security are ensured.

[0154] The server stores the received data in a database and then performs analysis. This analysis uses a generative AI model, employing common machine learning libraries such as TensorFlow and PyTorch. By using a generative AI model, it is possible to comprehensively evaluate changes in emotional state along with health status.

[0155] Once the analysis is complete, the server generates a notification for the user based on the results. This notification is personalized by the emotion engine and includes specific advice tailored to the user's situation. For example, if high stress levels and anxiety are detected, the server will send a notification saying, "Your stress levels are high, and you are experiencing persistent anxiety. Try to create a relaxing environment and get enough rest."

[0156] Furthermore, if a user wishes to share information, that abnormal information and emotional state information will be securely transmitted to an external organization approved by the user. Information transmission will be based on the options selected by the user.

[0157] A concrete example of a prompt would be: "The user's biometric data has detected an elevated heart rate and anxiety. Based on this, please suggest what advice to provide to the user." This prompt functions as an instruction to the generative AI model to provide appropriate feedback to the user.

[0158] In this way, this system supports users in their daily health management and helps them take optimal measures by providing timely and accurate notifications and information sharing functions.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The device collects the user's biometric information. Specifically, devices such as smartwatches use built-in sensors to acquire heart rate, activity levels, sleep patterns, etc., in real time. The input is data from biometric sensors, and the output is biometric data stored in the device's local memory.

[0162] Step 2:

[0163] The device transmits the collected biometric information to the server. Since the HTTPS protocol is used for communication, data security is high. The input is biometric data stored locally, and the output is data transmitted to the server. Specifically, the device automatically starts the transmission process to the server at regular intervals.

[0164] Step 3:

[0165] The server stores the received biometric information in a database. A relational database is often used, and the data is properly categorized and stored. The input is the biometric data sent to the server, and the output is the data entries stored in the database.

[0166] Step 4:

[0167] The server analyzes data using a generative AI model. TensorFlow and PyTorch are used for data processing and computation. The input is biometric information from a database, and the output is the analysis result, i.e., an evaluation of health and emotional state. Specifically, machine learning algorithms predict the presence or absence of abnormalities and emotional tendencies.

[0168] Step 5:

[0169] The server generates a notification for the user based on the analysis results. The emotion engine considers the emotional state and customizes the notification message. The input is the analysis results, and the output is the notification message sent to the user. The server generates a message that includes advice tailored to the individual state and sends it to the user's terminal.

[0170] Step 6:

[0171] Users authorize sharing information with external organizations through the settings. The input is the user's authorization information, and the output is the setting for sharing options with healthcare institutions. This allows the server to send analysis results and emotional state information to healthcare institutions authorized by the user. The specific operation involves the user selecting an option from the settings screen and confirming their authorization.

[0172] (Application Example 2)

[0173] 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".

[0174] In modern society, with the increasing number of elderly people and individuals facing health challenges, there is a growing need to efficiently and appropriately manage their health status. However, current health management systems are insufficient in real-time anomaly detection and personalized notifications based on emotional states. Furthermore, there is a lack of mechanisms for securely sharing abnormal data with healthcare institutions in accordance with the user's intentions. Against this backdrop, there is a need to provide a system that enables more personalized notifications based on an individual's health and emotional state, and efficiently shares information with healthcare institutions as needed.

[0175] 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.

[0176] In this invention, the server includes information gathering means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, communication means for notifying the user based on the abnormalities detected by the analysis means, emotion analysis means characterized in that the notification is personalized according to the user's emotional state, and information exchange means for transmitting the abnormal data to a medical institution authorized by the user. This enables real-time analysis of the health and emotional state of individual users and smooth information sharing with medical institutions as needed.

[0177] "User" refers to an individual who provides biometric data using this system.

[0178] "Biometric data" refers to data that includes the user's physiological information, such as heart rate, activity level, and sleep patterns.

[0179] "Information gathering means" refers to devices and sensors used to acquire biometric data from users.

[0180] "Analysis means" refers to methods and processes for evaluating acquired biological data and detecting abnormalities.

[0181] "Communication method" refers to a method for notifying the user of information based on the analysis results.

[0182] "Emotional analysis means" refers to a method for estimating a user's emotional state from acquired data and personalizing notifications based on that.

[0183] "Information exchange means" refers to a method for transmitting abnormal data to a designated medical institution with the user's permission.

[0184] A "medical institution" refers to an organization or facility that provides health management and diagnosis services.

[0185] The system for implementing this invention is designed to efficiently manage the user's health and emotional state. The system collects biometric data using a device such as a smartwatch or smartphone worn or carried by the user. The device is equipped with sensors that measure physiological information such as heart rate, activity level, and sleep patterns.

[0186] The server receives biometric data transmitted from these devices via Bluetooth, Wi-Fi, etc., and stores it in a database. This data is analyzed based on a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis process to detect anomalies and evaluate emotional states.

[0187] Based on the analysis results, the server generates a notification informing the user of any abnormalities. The notification is personalized by considering the user's emotional state through emotion analysis. For example, if the stress level is determined to be high, a notification will be sent that includes specific content such as suggesting music to promote relaxation.

[0188] If the user authorizes information sharing with a healthcare provider as needed, the server will send abnormal data to the authorized healthcare provider using a secure protocol (e.g., SSL / TLS). This helps the healthcare provider understand the user's condition and provide appropriate medical care.

[0189] As a concrete example, suppose a 70-year-old user regularly uses this system for health management. One afternoon, the device detects a sudden increase in heart rate and sends data to the server. Analysis reveals that this is due to stress. The server creates a notification stating, "Your heart rate is elevated. We recommend taking deep breaths and engaging in relaxing activities," and sends it to the user. By following this advice, the user can reduce their stress.

[0190] Examples of prompts to input into a generative AI model:

[0191] "Please create a notification that analyzes the user's heart rate data and suggests relaxation methods if a high-stress state is detected."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The device acquires biometric data such as heart rate, activity level, and sleep patterns in real time. This data is measured using biometric sensors. Inputs include the voltage output of the sensors, and the output is in a structured digital data format.

[0195] Step 2:

[0196] The device transmits acquired biometric data to the server via Bluetooth or Wi-Fi. A secure protocol is used for this communication to maintain data reliability. The input is biometric data from the device, and the output is data stored in a database on the server.

[0197] Step 3:

[0198] The server stores the received biometric data in a database. The data is saved in a time-series database and prepared for analysis. The input is the newly received biometric data, and the output is the state of the data stored in the database.

[0199] Step 4:

[0200] The server analyzes the accumulated data using a generative AI model. Using frameworks such as TensorFlow and PyTorch, it detects anomalies in the data and evaluates the user's health and emotional state. The input is biometric data from the database, and the output is an evaluation result indicating anomalies.

[0201] Step 5:

[0202] The server generates a message to notify the user based on the analysis results. It uses sentiment analysis to personalize the content according to the user's emotional state. The input is the evaluation result of the generating AI model, and the output is the personalized notification message sent to the user.

[0203] Step 6:

[0204] Users receive notifications from the server and obtain information that helps improve and maintain their health. This includes specific advice regarding the user's physical condition and emotions. The input is the notifications from the server, and the output is the actions taken by the user.

[0205] Step 7:

[0206] If a user requests to share information with a healthcare provider, the server will send the abnormal data to the authorized healthcare facility using the SSL / TLS protocol. The input is the user's sharing permission and the abnormal data, and the output is the status of successful transmission to the healthcare provider.

[0207] 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.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The system of the present invention acquires a user's biometric information, detects abnormalities based on that information, provides necessary notifications, and enables information sharing. An embodiment thereof is shown below.

[0224] Users obtain daily biometric information using devices such as smartwatches and smartphones. These devices use sensors to collect physiological data such as heart rate, sleep, activity level, and calorie consumption in real time. The devices are configured to automatically send this information to a server at specific time intervals.

[0225] The server stores the received biometric information in a database and analyzes it using a generative model. If this analysis detects unusual patterns or anomalies, the server sets an appropriate flag. When this flag is set, the server sends an alert to the user through a notification system. The user's device will display specific advice, such as, "Your heart rate remains higher than normal. We recommend you rest."

[0226] Furthermore, this system can share detected anomaly information with healthcare institutions only if the user requests it. Users are given the option to select a specific healthcare institution during the initial setup. After confirming the user's approval, the server securely sends a report summarizing past health data and details of the anomalies to that healthcare institution. This information sharing enables healthcare institutions to make faster and more accurate diagnoses and provides users with appropriate support.

[0227] As a concrete example, consider a scenario where a user's activity level suddenly decreases, and their heart rate is detected to be significantly higher than their resting average. The server recognizes this as an anomaly and sends a notification to the user recommending rest. Simultaneously, if the anomaly is serious, with the user's consent, the system notifies a selected medical institution to ensure smooth subsequent diagnosis and treatment.

[0228] Thus, the present invention is a system that facilitates daily health management and provides an environment that enables prompt and appropriate medical response in the event of an abnormality.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The device collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption through sensors. This data is updated at regular intervals and stored on the device.

[0232] Step 2:

[0233] The user's device transmits collected biometric information to a server at specified intervals. This communication is typically conducted via Bluetooth or Wi-Fi.

[0234] Step 3:

[0235] The server stores the received biometric information in a database. Next, the data is preprocessed to remove noise and impute missing values.

[0236] Step 4:

[0237] The server inputs pre-processed data into a generative model. The generative model is used to detect anomalies by comparing them to normal healthy patterns.

[0238] Step 5:

[0239] When the generative model detects an anomaly, the server determines the type and severity of the anomaly. If the anomaly is deemed critical, a flag is set.

[0240] Step 6:

[0241] The server uses a notification system to send an alert message to the user's terminal in order to notify the user that an anomaly has been detected.

[0242] Step 7:

[0243] The user's device will notify them of received alerts through screen displays and audio notifications. The alerts include advice on specific actions to take.

[0244] Step 8:

[0245] Users will check the approval settings for abnormal transmissions via their device and, if necessary, consent to sharing information with healthcare institutions.

[0246] Step 9:

[0247] If user approval is obtained, the server securely transmits abnormal information and historical biometric data to the identified medical institution. This allows the medical institution to perform a rapid and accurate diagnosis.

[0248] (Example 1)

[0249] 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."

[0250] In modern society, personal health management is becoming increasingly important, but traditional methods have the challenge of not being able to respond quickly to sudden changes in health conditions. In particular, there is a need for smooth information sharing to detect abnormalities early and obtain appropriate medical treatment.

[0251] 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.

[0252] In this invention, the server includes information gathering means for acquiring biometric data in a time series, information processing means for storing and analyzing the acquired biometric data, and communication means for notifying abnormalities detected by the information processing means. This makes it possible to monitor an individual's health status in real time and to detect and respond to abnormalities at an early stage.

[0253] "Information gathering means" refers to devices and technologies for acquiring biological data, and which have the function of acquiring data in a time series.

[0254] "Information processing means" refers to methods and devices for storing and analyzing collected biological data, specifically using databases and generative AI models.

[0255] "Communication means" refers to technologies or devices that notify users of anomalies detected by information processing means, such as having a function to send alert notifications to terminals.

[0256] "Information transmission means" refers to devices or methods for securely transmitting abnormal data to external organizations, and has the function of transmitting information only to the relevant organization with the user's approval.

[0257] "Generative artificial intelligence models" refer to algorithms and technological systems for analyzing collected data and evaluating fluctuations in health information, and include predictive models using machine learning.

[0258] In this invention, the user acquires biometric data using a device such as a smartwatch or smartphone. The device has built-in sensors to collect heart rate, sleep, activity level, calorie consumption, etc., in real time, utilizing optical sensor and accelerometer technologies. This collected biometric data is designed to be periodically transmitted to a server using communication technologies such as Bluetooth or Wi-Fi.

[0259] The server stores the received biometric data in a database and performs analysis using a generative AI model. The generative AI model compares the current data with past data to evaluate outliers and significant changes in health status. For example, it can detect abnormally high heart rates or sudden changes in activity levels. If an abnormality is detected, the server sends a notification to the user's terminal containing specific advice. The notification may include information such as, "Your heart rate remains higher than normal. We recommend that you rest."

[0260] Furthermore, with the user's consent, the server can securely share abnormal data with external healthcare institutions. This feature is selectable by the user during initial setup, and detailed health data is sent only to approved healthcare institutions. The aim of this information sharing is to enable healthcare institutions to make quick and accurate diagnoses and provide users with the necessary support.

[0261] For example, if a user's activity level suddenly decreases and their heart rate significantly exceeds their resting average, the system recognizes this as an anomaly. The server notifies the user of this situation and, if necessary, sends a warning to a designated medical institution, providing support for their assessment and treatment procedures.

[0262] In this way, by using a system that includes a generative AI model, users can manage their health status on a daily basis and have an environment where they can receive a quick response when an abnormality occurs.

[0263] An example of a prompt message is, "Generate a detailed explanation regarding the detection of anomalies in the user's heart rate and activity level." This allows the generating AI model to provide analysis results of the biometric data and a specific evaluation of the anomalies.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The device uses built-in sensors to acquire biometric data such as the user's heart rate, sleep, activity level, and calorie consumption in real time. In this process, the sensors utilize optical sensors and accelerometers to accurately capture the user's physical activity and state. The input is the user's physical activity and state, and the output is the collected biometric data.

[0267] Step 2:

[0268] The device is configured to periodically send the collected biometric data to a server via Bluetooth or Wi-Fi. This transmission is automated and performed efficiently without user intervention. The input is the biometric data acquired in step 1, and the output is the data sent to the server.

[0269] Step 3:

[0270] The server stores the received biometric data in a database. This database is designed to enable data storage, organization, and rapid access. The input is biometric data sent from the terminal, and the output is organized data stored in the database.

[0271] Step 4:

[0272] The server analyzes biometric data using a generating AI model. This analysis includes comparing the data to normal patterns and detecting changes in health status, identifying anomalies and unique patterns. The input is biometric data stored in a database, and the output is anomaly detection information as a result of the analysis.

[0273] Step 5:

[0274] If the server detects an anomaly based on the analysis results, it will notify the user. This notification will send an alert to the user's device, displaying a message on the screen such as, "Your heart rate remains higher than normal. We recommend you rest." The input is the anomaly detection information from step 4, and the output is the notification message displayed on the device.

[0275] Step 6:

[0276] The server, with the user's consent, will send anomaly information to an authorized healthcare provider. This involves a process of verifying user approval and sharing data through a secure channel. The input is the anomaly detection information and the user's consent, and the output is the anomaly report sent to the healthcare provider.

[0277] (Application Example 1)

[0278] 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."

[0279] It is required to enable prompt response by detecting abnormalities of the elderly and users with unstable health conditions at an early stage and sharing information with caregivers and medical facilities promptly. Also, in the caregiving field, prompt and appropriate health management is important, and when an abnormality occurs, it is an issue that the information is notified to appropriate related parties and the response is carried out promptly.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0281] In this invention, the server includes data acquisition means for acquiring the user's biological data, analysis means for analyzing the acquired biological data to detect abnormalities, and notification means for notifying the user of the abnormalities detected by the analysis means. Thereby, it becomes possible to quickly detect abnormalities of users in the caregiving environment, and to enable appropriate information sharing with caregivers and medical institutions and an immediate response.

[0282] The "data acquisition means" refers to a device or method for collecting the user's biological data, and is collected via terminals such as smartwatches and smartphones.

[0283] The "analysis means" refers to a process or method for analyzing the acquired biological data to detect patterns or abnormalities different from normal ones.

[0284] The "notification means" refers to a mechanism or device for notifying the user or caregiver based on the analyzed result.

[0285] The "information sharing means" refers to a function for transmitting the detected abnormality information to approved medical facilities and caregivers so that a prompt response can be taken.

[0286] The "notification means in the caregiving environment" refers to a mechanism for transmitting an alert to and communicating information to caregivers when an abnormality is detected at a care facility or at the site of home care.

[0287] The system for implementing this invention has a configuration that includes a user's terminal, a server, and a notification function for caregivers. As the user's terminal, a smartwatch or a smartphone is used. These terminals acquire biometric data such as heart rate and activity level in real time via Bluetooth and transmit it to the smartphone. The smartphone plays the role of periodically uploading these data to the server.

[0288] The server accumulates the received biometric data in a database and performs analysis using Python. In the analysis, a generative AI model by TensorFlow or PyTorch is used to detect abnormalities. When an abnormality is detected, the server sends notifications to caregivers and, if necessary, medical facilities through Firebase Cloud Messaging or the Twilio API. This enables a prompt response.

[0289] For example, when a significantly high heart rate is detected in a care facility, the server immediately sends a notification such as "Mr. / Ms. XX's heart rate is higher than normal. Please check the physical condition." and the information is transmitted to the caregiver's mobile terminal. As a result, the caregiver can quickly take appropriate action as needed.

[0290] As a specific example of the prompt text using the generative AI model, input content such as "Heart rate increase detection prompt: What should be alerted if the heart rate exceeds normal after a rest period?" into the AI model to generate appropriate alert content. This enables a prompt and accurate notification in situations where complex judgments are required.

[0291] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0292] Step 1:

[0293] The device measures biometric data such as the user's heart rate and activity level in real time via a smartwatch and transfers that data to a smartphone via Bluetooth. At this stage, the input is biometric data, and the output is the biometric data transferred to the smartphone.

[0294] Step 2:

[0295] The smartphone uploads the received biometric data to the server at regular intervals. Specifically, it securely transmits the data to the server using the HTTPS protocol. In this process, the input is biometric data from the smartwatch, and the output is the data uploaded to the server.

[0296] Step 3:

[0297] The server stores the uploaded biometric data in a database. MySQL or PostgreSQL is used as the database to ensure data reliability and consistency. Input is data sent from a smartphone, and output is data stored in the database.

[0298] Step 4:

[0299] The server performs data analysis using a generative AI model. Specifically, it uses TensorFlow to analyze biometric data and detect anomalies. In this process, the input is biometric data stored in a database, and the output is flag information indicating when an anomaly is detected.

[0300] Step 5:

[0301] If an anomaly is detected, the server uses Firebase Cloud Messaging to notify caregivers. The notification content is generated based on prompts created by an AI model. For example, if there is an abnormality in heart rate, it will send a message such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition." The input is flag information indicating the detection of an anomaly, and the output is the message notification.

[0302] Step 6:

[0303] Caregivers receive notifications and take action as needed. This process is expected to involve implementing measures based on the received information. The input before action is taken at the caregiving site is the notified abnormal information, and the output is the implementation of caregiving action.

[0304] 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.

[0305] This invention is a system that acquires a user's biometric information, analyzes abnormalities in their health and emotional state based on that information, and provides appropriate notifications to the user. Furthermore, this system has a function to share abnormal information with medical institutions. An embodiment of this system is shown below.

[0306] Users collect daily biometric information using devices such as smartwatches and smartphones. These devices use biometric sensors to acquire various physiological data in real time, including heart rate, sleep, activity level, and calorie consumption. The collected data is periodically transmitted to a server.

[0307] The server accumulates the received biometric information in a database and analyzes it using a generation model. If an abnormality is detected as a result of the analysis, the server generates a notification to the user. In this process, the emotion engine recognizes the user's emotional state based on the user's input and biometric information, and personalizes the content of the notification according to the emotion.

[0308] As a specific example, consider the case where a high-stress state is detected from the user's biometric information and a feeling of anxiety is recognized by the emotion engine. In such a case, the server notifies the user with an individual piece of advice such as "Your stress level is high and you are in a state of anxiety. Try to create a relaxing environment and get enough rest."

[0309] In addition, this system can share the details of the abnormal information and the emotional state with the medical institutions approved by the user only when the user needs it. First, the user checks the information sharing option with the medical institution and makes a setting to give permission. The server summarizes the abnormal information and the results of the related emotion analysis based on the user's approval and securely transmits it to the medical institution. Through this information sharing, the medical institution can understand the user more deeply and provide appropriate medical treatment and advice.

[0310] In this way, the present invention is a system that comprehensively supports the health management of the user and smoothly coordinates with medical institutions as needed, so that the user can receive optimal medical services.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The terminal continuously collects biometric information such as the user's heart rate, sleep pattern, activity level, calorie consumption, etc. by sensors. These data are temporarily stored in the terminal memory.

[0314] Step 2:

[0315] The device transmits the collected biometric information to the server at regular intervals. This transmission is carried out via the internet using secure encrypted communication.

[0316] Step 3:

[0317] The server stores the received biometric information in a database. The stored information is prepared for input into the generative model in real time.

[0318] Step 4:

[0319] The server uses a generative model to analyze biometric data and detect unusual patterns or anomalies. When an anomaly is found, a flag is set according to the type of anomaly.

[0320] Step 5:

[0321] The server uses an emotion engine to analyze the user's emotional state. This analysis is based on subjective emotional evaluations and biometric information provided by the user through the application.

[0322] Step 6:

[0323] The server generates customized advice messages for the user based on the health and emotional states that it perceives as abnormal.

[0324] Step 7:

[0325] A notification is sent from the server to the user's device. The device displays this notification on the screen and alerts the user with an audible alert or vibration.

[0326] Step 8:

[0327] Users can view notifications on their devices and take further action as needed. They can also provide emotional feedback through the app.

[0328] Step 9:

[0329] If a user authorizes information sharing with a healthcare provider, the server uses a secure protocol to compose the relevant data and send it to the healthcare provider specified by the user. This allows the healthcare provider to comprehensively evaluate the user's health monitoring results and emotional state.

[0330] (Example 2)

[0331] 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".

[0332] In recent years, the importance of health management based on users' biometric information has increased. However, conventional systems only detect abnormalities in biometric information and have shortcomings in providing individualized support that fully considers the user's emotional state and inefficient information sharing with medical institutions.

[0333] 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.

[0334] In this invention, the server includes data collection means for acquiring the user's biometric information, analysis means for analyzing the acquired biometric information and detecting abnormalities, and estimation means for evaluating the user's emotional state. This enables an integrated evaluation of the user's health and emotional state, allowing for individual notifications and information sharing with external organizations.

[0335] "Data collection means" refers to devices and software used to acquire a user's biometric information, including devices that measure heart rate, activity level, sleep patterns, etc.

[0336] "Analysis means" refers to technologies that have the function of analyzing acquired biological information and detecting abnormalities, and this includes machine learning algorithms that use generative models.

[0337] "Estimation means" refers to the functions and methods necessary to evaluate a user's emotional state, and includes technologies that infer emotions based on biometric information and user input data.

[0338] "Notification means" refers to a mechanism for generating and sending individual notifications to users based on analysis results and emotional states, and this includes message delivery and real-time alerts.

[0339] "Information sharing means" refers to the function of transmitting acquired and analyzed anomaly information and emotional state information to an external organization approved by the user via a secure communication channel.

[0340] A "generative model" refers to a model that uses biological information to analyze and predict health and emotional states using algorithms.

[0341] A "prompt statement" refers to a document used as input for a generative AI model, containing information that serves as a basis for analysis and evaluation.

[0342] This invention relates to a system that acquires a user's biometric information, analyzes it to understand their health and emotional state, and shares abnormal information with external organizations as needed. This system has the function of collecting biometric information through sensors installed in devices such as smartwatches and smartphones, which the user uses. For example, information such as heart rate, activity level, and sleep patterns can be acquired by the sensors.

[0343] The device then sends the acquired data to the server at regular intervals. Since the HTTPS protocol is used for transmission, data privacy and security are ensured.

[0344] The server stores the received data in a database and then performs analysis. This analysis uses a generative AI model, employing common machine learning libraries such as TensorFlow and PyTorch. By using a generative AI model, it is possible to comprehensively evaluate changes in emotional state along with health status.

[0345] Once the analysis is complete, the server generates a notification for the user based on the results. This notification is personalized by the emotion engine and includes specific advice tailored to the user's situation. For example, if high stress levels and anxiety are detected, the server will send a notification saying, "Your stress levels are high, and you are experiencing persistent anxiety. Try to create a relaxing environment and get enough rest."

[0346] Furthermore, if a user wishes to share information, that abnormal information and emotional state information will be securely transmitted to an external organization approved by the user. Information transmission will be based on the options selected by the user.

[0347] A concrete example of a prompt would be: "The user's biometric data has detected an elevated heart rate and anxiety. Based on this, please suggest what advice to provide to the user." This prompt functions as an instruction to the generative AI model to provide appropriate feedback to the user.

[0348] In this way, this system supports users in their daily health management and helps them take optimal measures by providing timely and accurate notifications and information sharing functions.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The device collects the user's biometric information. Specifically, devices such as smartwatches use built-in sensors to acquire heart rate, activity levels, sleep patterns, etc., in real time. The input is data from biometric sensors, and the output is biometric data stored in the device's local memory.

[0352] Step 2:

[0353] The device transmits the collected biometric information to the server. Since the HTTPS protocol is used for communication, data security is high. The input is biometric data stored locally, and the output is data transmitted to the server. Specifically, the device automatically starts the transmission process to the server at regular intervals.

[0354] Step 3:

[0355] The server stores the received biometric information in a database. A relational database is often used, and the data is properly categorized and stored. The input is the biometric data sent to the server, and the output is the data entries stored in the database.

[0356] Step 4:

[0357] The server analyzes data using a generative AI model. TensorFlow and PyTorch are used for data processing and computation. The input is biometric information from a database, and the output is the analysis result, i.e., an evaluation of health and emotional state. Specifically, machine learning algorithms predict the presence or absence of abnormalities and emotional tendencies.

[0358] Step 5:

[0359] The server generates a notification for the user based on the analysis results. The emotion engine considers the emotional state and customizes the notification message. The input is the analysis results, and the output is the notification message sent to the user. The server generates a message that includes advice tailored to the individual state and sends it to the user's terminal.

[0360] Step 6:

[0361] Users authorize sharing information with external organizations through the settings. The input is the user's authorization information, and the output is the setting for sharing options with healthcare institutions. This allows the server to send analysis results and emotional state information to healthcare institutions authorized by the user. The specific operation involves the user selecting an option from the settings screen and confirming their authorization.

[0362] (Application Example 2)

[0363] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0364] In modern society, with the increasing number of elderly people and individuals facing health challenges, there is a growing need to efficiently and appropriately manage their health status. However, current health management systems are insufficient in real-time anomaly detection and personalized notifications based on emotional states. Furthermore, there is a lack of mechanisms for securely sharing abnormal data with healthcare institutions in accordance with the user's intentions. Against this backdrop, there is a need to provide a system that enables more personalized notifications based on an individual's health and emotional state, and efficiently shares information with healthcare institutions as needed.

[0365] 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.

[0366] In this invention, the server includes information gathering means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, communication means for notifying the user based on the abnormalities detected by the analysis means, emotion analysis means characterized in that the notification is personalized according to the user's emotional state, and information exchange means for transmitting the abnormal data to a medical institution authorized by the user. This enables real-time analysis of the health and emotional state of individual users and smooth information sharing with medical institutions as needed.

[0367] "User" refers to an individual who provides biometric data using this system.

[0368] "Biometric data" refers to data that includes the user's physiological information, such as heart rate, activity level, and sleep patterns.

[0369] "Information gathering means" refers to devices and sensors used to acquire biometric data from users.

[0370] "Analysis means" refers to methods and processes for evaluating acquired biological data and detecting abnormalities.

[0371] "Communication method" refers to a method for notifying the user of information based on the analysis results.

[0372] "Emotional analysis means" refers to a method for estimating a user's emotional state from acquired data and personalizing notifications based on that.

[0373] "Information exchange means" refers to a method for transmitting abnormal data to a designated medical institution with the user's permission.

[0374] A "medical institution" refers to an organization or facility that provides health management and diagnosis services.

[0375] The system for implementing this invention is designed to efficiently manage the user's health and emotional state. The system collects biometric data using a device such as a smartwatch or smartphone worn or carried by the user. The device is equipped with sensors that measure physiological information such as heart rate, activity level, and sleep patterns.

[0376] The server receives biometric data transmitted from these devices via Bluetooth, Wi-Fi, etc., and stores it in a database. This data is analyzed based on a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis process to detect anomalies and evaluate emotional states.

[0377] Based on the analysis results, the server generates a notification informing the user of any abnormalities. The notification is personalized by considering the user's emotional state through emotion analysis. For example, if the stress level is determined to be high, a notification will be sent that includes specific content such as suggesting music to promote relaxation.

[0378] If the user authorizes information sharing with a healthcare provider as needed, the server will send abnormal data to the authorized healthcare provider using a secure protocol (e.g., SSL / TLS). This helps the healthcare provider understand the user's condition and provide appropriate medical care.

[0379] As a concrete example, suppose a 70-year-old user regularly uses this system for health management. One afternoon, the device detects a sudden increase in heart rate and sends data to the server. Analysis reveals that this is due to stress. The server creates a notification stating, "Your heart rate is elevated. We recommend taking deep breaths and engaging in relaxing activities," and sends it to the user. By following this advice, the user can reduce their stress.

[0380] Examples of prompts to input into a generative AI model:

[0381] "Please create a notification that analyzes the user's heart rate data and suggests relaxation methods if a high-stress state is detected."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The device acquires biometric data such as heart rate, activity level, and sleep patterns in real time. This data is measured using biometric sensors. Inputs include the voltage output of the sensors, and the output is in a structured digital data format.

[0385] Step 2:

[0386] The device transmits acquired biometric data to the server via Bluetooth or Wi-Fi. A secure protocol is used for this communication to maintain data reliability. The input is biometric data from the device, and the output is data stored in a database on the server.

[0387] Step 3:

[0388] The server stores the received biometric data in a database. The data is saved in a time-series database and prepared for analysis. The input is the newly received biometric data, and the output is the state of the data stored in the database.

[0389] Step 4:

[0390] The server analyzes the accumulated data using a generative AI model. Using frameworks such as TensorFlow and PyTorch, it detects anomalies in the data and evaluates the user's health and emotional state. The input is biometric data from the database, and the output is an evaluation result indicating anomalies.

[0391] Step 5:

[0392] The server generates a message to notify the user based on the analysis results. It uses sentiment analysis to personalize the content according to the user's emotional state. The input is the evaluation result of the generating AI model, and the output is the personalized notification message sent to the user.

[0393] Step 6:

[0394] Users receive notifications from the server and obtain information that helps improve and maintain their health. This includes specific advice regarding the user's physical condition and emotions. The input is the notifications from the server, and the output is the actions taken by the user.

[0395] Step 7:

[0396] If a user requests to share information with a healthcare provider, the server will send the abnormal data to the authorized healthcare facility using the SSL / TLS protocol. The input is the user's sharing permission and the abnormal data, and the output is the status of successful transmission to the healthcare provider.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] The system of the present invention acquires a user's biometric information, detects abnormalities based on that information, provides necessary notifications, and enables information sharing. An embodiment thereof is shown below.

[0414] Users obtain daily biometric information using devices such as smartwatches and smartphones. These devices use sensors to collect physiological data such as heart rate, sleep, activity level, and calorie consumption in real time. The devices are configured to automatically send this information to a server at specific time intervals.

[0415] The server stores the received biometric information in a database and analyzes it using a generative model. If this analysis detects unusual patterns or anomalies, the server sets an appropriate flag. When this flag is set, the server sends an alert to the user through a notification system. The user's device will display specific advice, such as, "Your heart rate remains higher than normal. We recommend you rest."

[0416] Furthermore, this system can share detected anomaly information with healthcare institutions only if the user requests it. Users are given the option to select a specific healthcare institution during the initial setup. After confirming the user's approval, the server securely sends a report summarizing past health data and details of the anomalies to that healthcare institution. This information sharing enables healthcare institutions to make faster and more accurate diagnoses and provides users with appropriate support.

[0417] As a concrete example, consider a scenario where a user's activity level suddenly decreases, and their heart rate is detected to be significantly higher than their resting average. The server recognizes this as an anomaly and sends a notification to the user recommending rest. Simultaneously, if the anomaly is serious, with the user's consent, the system notifies a selected medical institution to ensure smooth subsequent diagnosis and treatment.

[0418] Thus, the present invention is a system that facilitates daily health management and provides an environment that enables prompt and appropriate medical response in the event of an abnormality.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The device collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption through sensors. This data is updated at regular intervals and stored on the device.

[0422] Step 2:

[0423] The user's device transmits collected biometric information to a server at specified intervals. This communication is typically conducted via Bluetooth or Wi-Fi.

[0424] Step 3:

[0425] The server stores the received biometric information in a database. Next, the data is preprocessed to remove noise and impute missing values.

[0426] Step 4:

[0427] The server inputs pre-processed data into a generative model. The generative model is used to detect anomalies by comparing them to normal healthy patterns.

[0428] Step 5:

[0429] When the generative model detects an anomaly, the server determines the type and severity of the anomaly. If the anomaly is deemed critical, a flag is set.

[0430] Step 6:

[0431] The server uses a notification system to send an alert message to the user's terminal in order to notify the user that an anomaly has been detected.

[0432] Step 7:

[0433] The user's device will notify them of received alerts through screen displays and audio notifications. The alerts include advice on specific actions to take.

[0434] Step 8:

[0435] Users will check the approval settings for abnormal transmissions via their device and, if necessary, consent to sharing information with healthcare institutions.

[0436] Step 9:

[0437] If user approval is obtained, the server securely transmits abnormal information and historical biometric data to the identified medical institution. This allows the medical institution to perform a rapid and accurate diagnosis.

[0438] (Example 1)

[0439] 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."

[0440] In modern society, personal health management is becoming increasingly important, but traditional methods have the challenge of not being able to respond quickly to sudden changes in health conditions. In particular, there is a need for smooth information sharing to detect abnormalities early and obtain appropriate medical treatment.

[0441] 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.

[0442] In this invention, the server includes information gathering means for acquiring biometric data in a time series, information processing means for storing and analyzing the acquired biometric data, and communication means for notifying abnormalities detected by the information processing means. This makes it possible to monitor an individual's health status in real time and to detect and respond to abnormalities at an early stage.

[0443] "Information gathering means" refers to devices and technologies for acquiring biological data, and which have the function of acquiring data in a time series.

[0444] "Information processing means" refers to methods and devices for storing and analyzing collected biological data, specifically using databases and generative AI models.

[0445] "Communication means" refers to technologies or devices that notify users of anomalies detected by information processing means, such as having a function to send alert notifications to terminals.

[0446] "Information transmission means" refers to devices or methods for securely transmitting abnormal data to external organizations, and has the function of transmitting information only to the relevant organization with the user's approval.

[0447] "Generative artificial intelligence models" refer to algorithms and technological systems for analyzing collected data and evaluating fluctuations in health information, and include predictive models using machine learning.

[0448] In this invention, the user acquires biometric data using a device such as a smartwatch or smartphone. The device has built-in sensors to collect heart rate, sleep, activity level, calorie consumption, etc., in real time, utilizing optical sensor and accelerometer technologies. This collected biometric data is designed to be periodically transmitted to a server using communication technologies such as Bluetooth or Wi-Fi.

[0449] The server stores the received biometric data in a database and performs analysis using a generative AI model. The generative AI model compares the current data with past data to evaluate outliers and significant changes in health status. For example, it can detect abnormally high heart rates or sudden changes in activity levels. If an abnormality is detected, the server sends a notification to the user's terminal containing specific advice. The notification may include information such as, "Your heart rate remains higher than normal. We recommend that you rest."

[0450] Furthermore, with the user's consent, the server can securely share abnormal data with external healthcare institutions. This feature is selectable by the user during initial setup, and detailed health data is sent only to approved healthcare institutions. The aim of this information sharing is to enable healthcare institutions to make quick and accurate diagnoses and provide users with the necessary support.

[0451] For example, if a user's activity level suddenly decreases and their heart rate significantly exceeds their resting average, the system recognizes this as an anomaly. The server notifies the user of this situation and, if necessary, sends a warning to a designated medical institution, providing support for their assessment and treatment procedures.

[0452] In this way, by using a system that includes a generative AI model, users can manage their health status on a daily basis and have an environment where they can receive a quick response when an abnormality occurs.

[0453] An example of a prompt message is, "Generate a detailed explanation regarding the detection of anomalies in the user's heart rate and activity level." This allows the generating AI model to provide analysis results of the biometric data and a specific evaluation of the anomalies.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The device uses built-in sensors to acquire biometric data such as the user's heart rate, sleep, activity level, and calorie consumption in real time. In this process, the sensors utilize optical sensors and accelerometers to accurately capture the user's physical activity and state. The input is the user's physical activity and state, and the output is the collected biometric data.

[0457] Step 2:

[0458] The device is configured to periodically send the collected biometric data to a server via Bluetooth or Wi-Fi. This transmission is automated and performed efficiently without user intervention. The input is the biometric data acquired in step 1, and the output is the data sent to the server.

[0459] Step 3:

[0460] The server stores the received biometric data in a database. This database is designed to enable data storage, organization, and rapid access. The input is biometric data sent from the terminal, and the output is organized data stored in the database.

[0461] Step 4:

[0462] The server analyzes biometric data using a generating AI model. This analysis includes comparing the data to normal patterns and detecting changes in health status, identifying anomalies and unique patterns. The input is biometric data stored in a database, and the output is anomaly detection information as a result of the analysis.

[0463] Step 5:

[0464] If the server detects an anomaly based on the analysis results, it will notify the user. This notification will send an alert to the user's device, displaying a message on the screen such as, "Your heart rate remains higher than normal. We recommend you rest." The input is the anomaly detection information from step 4, and the output is the notification message displayed on the device.

[0465] Step 6:

[0466] The server, with the user's consent, will send anomaly information to an authorized healthcare provider. This involves a process of verifying user approval and sharing data through a secure channel. The input is the anomaly detection information and the user's consent, and the output is the anomaly report sent to the healthcare provider.

[0467] (Application Example 1)

[0468] 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."

[0469] There is a need to detect abnormalities in elderly users and those with unstable health conditions early and to share that information promptly with caregivers and medical facilities to enable a rapid response. Furthermore, in care settings, prompt and appropriate health management is crucial, and a challenge is ensuring that information about abnormalities is notified to the appropriate parties and that a swift response is taken.

[0470] 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.

[0471] In this invention, the server includes data acquisition means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, and notification means for notifying the user of the abnormalities detected by the analysis means. This enables rapid detection of abnormalities in users in a care environment, appropriate information sharing with caregivers and medical institutions, and immediate response.

[0472] "Data acquisition means" refers to devices and methods for collecting a user's biometric data, which is collected via devices such as smartwatches and smartphones.

[0473] "Analysis means" refers to processes and methods for analyzing acquired biological data to detect unusual patterns or abnormalities.

[0474] "Notification means" refers to a system or device for notifying users or caregivers based on the analyzed results.

[0475] "Information sharing means" refers to a function that transmits detected abnormal information to approved medical facilities and care workers, enabling a swift response.

[0476] "Notification methods in the care environment" refers to a system used in care facilities and home care settings to send alerts to care workers and communicate information when an abnormality is detected.

[0477] The system for implementing this invention consists of a user terminal, a server, and a notification function for caregivers. The user terminal can be a smartwatch or a smartphone. These terminals acquire biometric data such as heart rate and activity levels in real time via Bluetooth and transmit it to the smartphone. The smartphone is responsible for periodically uploading this data to the server.

[0478] The server stores the received biometric data in a database and performs analysis using Python. The analysis utilizes generative AI models based on TensorFlow and PyTorch to detect anomalies. When an anomaly is detected, the server notifies caregivers and, if necessary, medical facilities via Firebase Cloud Messaging and the Twilio API, enabling a rapid response.

[0479] For example, if a significantly elevated heart rate is detected in a nursing home, the server immediately sends a notification such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition," and the information is transmitted to the caregiver's mobile device. This allows the caregiver to take necessary actions quickly.

[0480] As a concrete example of a prompt message generated using the AI ​​model, the AI ​​model is input with content such as "Heart rate increase detection prompt: What should be alerted if the heart rate exceeds normal after a rest period?" and generates appropriate alert content. This enables quick and accurate notifications in situations requiring complex judgments.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The device measures biometric data such as the user's heart rate and activity level in real time via a smartwatch and transfers that data to a smartphone via Bluetooth. At this stage, the input is biometric data, and the output is the biometric data transferred to the smartphone.

[0484] Step 2:

[0485] The smartphone uploads the received biometric data to the server at regular intervals. Specifically, it securely transmits the data to the server using the HTTPS protocol. In this process, the input is biometric data from the smartwatch, and the output is the data uploaded to the server.

[0486] Step 3:

[0487] The server stores the uploaded biometric data in a database. MySQL or PostgreSQL is used as the database to ensure data reliability and consistency. Input is data sent from a smartphone, and output is data stored in the database.

[0488] Step 4:

[0489] The server performs data analysis using a generative AI model. Specifically, it uses TensorFlow to analyze biometric data and detect anomalies. In this process, the input is biometric data stored in a database, and the output is flag information indicating when an anomaly is detected.

[0490] Step 5:

[0491] If an anomaly is detected, the server uses Firebase Cloud Messaging to notify caregivers. The notification content is generated based on prompts created by an AI model. For example, if there is an abnormality in heart rate, it will send a message such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition." The input is flag information indicating the detection of an anomaly, and the output is the message notification.

[0492] Step 6:

[0493] Caregivers receive notifications and take action as needed. This process is expected to involve implementing measures based on the received information. The input before action is taken at the caregiving site is the notified abnormal information, and the output is the implementation of caregiving action.

[0494] 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.

[0495] This invention is a system that acquires a user's biometric information, analyzes abnormalities in their health and emotional state based on that information, and provides appropriate notifications to the user. Furthermore, this system has a function to share abnormal information with medical institutions. An embodiment of this system is shown below.

[0496] Users collect daily biometric information using devices such as smartwatches and smartphones. These devices use biometric sensors to acquire various physiological data in real time, including heart rate, sleep, activity level, and calorie consumption. The collected data is periodically transmitted to a server.

[0497] The server stores the received biometric information in a database and analyzes it using a generative model. If an anomaly is detected as a result of the analysis, the server generates a notification for the user. In this process, the emotion engine recognizes the user's emotional state based on the user's input and biometric information, and personalizes the content of the notification according to that emotion.

[0498] As a concrete example, consider a scenario where a high level of stress is detected from the user's biometric information, and the emotion engine further recognizes feelings of anxiety. In such a case, the server would notify the user with personalized advice such as, "Your stress level is high, and you are experiencing persistent anxiety. Please create a relaxing environment and make sure to get enough rest."

[0499] In addition, this system allows users to share details of abnormal information and emotional states with healthcare providers they have approved, but only when necessary. First, users review the information sharing options with healthcare providers and grant permission. Based on user approval, the server compiles the abnormal information and the results of the associated emotional analysis and securely transmits it to the healthcare provider. This information sharing allows healthcare providers to better understand the user and provide appropriate treatment and advice.

[0500] In this way, the present invention is a system that comprehensively supports the user's health management and facilitates smooth collaboration with medical institutions as needed, thereby enabling the user to receive optimal medical services.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The device continuously collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption using sensors. This data is temporarily stored in the device's memory.

[0504] Step 2:

[0505] The device transmits the collected biometric information to the server at regular intervals. This transmission is carried out via the internet using secure encrypted communication.

[0506] Step 3:

[0507] The server stores the received biometric information in a database. The stored information is prepared for input into the generative model in real time.

[0508] Step 4:

[0509] The server uses a generative model to analyze biometric data and detect unusual patterns or anomalies. When an anomaly is found, a flag is set according to the type of anomaly.

[0510] Step 5:

[0511] The server uses an emotion engine to analyze the user's emotional state. This analysis is based on subjective emotional evaluations and biometric information provided by the user through the application.

[0512] Step 6:

[0513] The server generates customized advice messages for the user based on the health and emotional states that it perceives as abnormal.

[0514] Step 7:

[0515] A notification is sent from the server to the user's device. The device displays this notification on the screen and alerts the user with an audible alert or vibration.

[0516] Step 8:

[0517] Users can view notifications on their devices and take further action as needed. They can also provide emotional feedback through the app.

[0518] Step 9:

[0519] If a user authorizes information sharing with a healthcare provider, the server uses a secure protocol to compose the relevant data and send it to the healthcare provider specified by the user. This allows the healthcare provider to comprehensively evaluate the user's health monitoring results and emotional state.

[0520] (Example 2)

[0521] 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."

[0522] In recent years, the importance of health management based on users' biometric information has increased. However, conventional systems only detect abnormalities in biometric information and have shortcomings in providing individualized support that fully considers the user's emotional state and inefficient information sharing with medical institutions.

[0523] 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.

[0524] In this invention, the server includes data collection means for acquiring the user's biometric information, analysis means for analyzing the acquired biometric information and detecting abnormalities, and estimation means for evaluating the user's emotional state. This enables an integrated evaluation of the user's health and emotional state, allowing for individual notifications and information sharing with external organizations.

[0525] "Data collection means" refers to devices and software used to acquire a user's biometric information, including devices that measure heart rate, activity level, sleep patterns, etc.

[0526] "Analysis means" refers to technologies that have the function of analyzing acquired biological information and detecting abnormalities, and this includes machine learning algorithms that use generative models.

[0527] "Estimation means" refers to the functions and methods necessary to evaluate a user's emotional state, and includes technologies that infer emotions based on biometric information and user input data.

[0528] "Notification means" refers to a mechanism for generating and sending individual notifications to users based on analysis results and emotional states, and this includes message delivery and real-time alerts.

[0529] "Information sharing means" refers to the function of transmitting acquired and analyzed anomaly information and emotional state information to an external organization approved by the user via a secure communication channel.

[0530] A "generative model" refers to a model that uses biological information to analyze and predict health and emotional states using algorithms.

[0531] A "prompt statement" refers to a document used as input for a generative AI model, containing information that serves as a basis for analysis and evaluation.

[0532] This invention relates to a system that acquires a user's biometric information, analyzes it to understand their health and emotional state, and shares abnormal information with external organizations as needed. This system has the function of collecting biometric information through sensors installed in devices such as smartwatches and smartphones, which the user uses. For example, information such as heart rate, activity level, and sleep patterns can be acquired by the sensors.

[0533] The device then sends the acquired data to the server at regular intervals. Since the HTTPS protocol is used for transmission, data privacy and security are ensured.

[0534] The server stores the received data in a database and then performs analysis. This analysis uses a generative AI model, employing common machine learning libraries such as TensorFlow and PyTorch. By using a generative AI model, it is possible to comprehensively evaluate changes in emotional state along with health status.

[0535] Once the analysis is complete, the server generates a notification for the user based on the results. This notification is personalized by the emotion engine and includes specific advice tailored to the user's situation. For example, if high stress levels and anxiety are detected, the server will send a notification saying, "Your stress levels are high, and you are experiencing persistent anxiety. Try to create a relaxing environment and get enough rest."

[0536] Furthermore, if a user wishes to share information, that abnormal information and emotional state information will be securely transmitted to an external organization approved by the user. Information transmission will be based on the options selected by the user.

[0537] A concrete example of a prompt would be: "The user's biometric data has detected an elevated heart rate and anxiety. Based on this, please suggest what advice to provide to the user." This prompt functions as an instruction to the generative AI model to provide appropriate feedback to the user.

[0538] In this way, this system supports users in their daily health management and helps them take optimal measures by providing timely and accurate notifications and information sharing functions.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The device collects the user's biometric information. Specifically, devices such as smartwatches use built-in sensors to acquire heart rate, activity levels, sleep patterns, etc., in real time. The input is data from biometric sensors, and the output is biometric data stored in the device's local memory.

[0542] Step 2:

[0543] The device transmits the collected biometric information to the server. Since the HTTPS protocol is used for communication, data security is high. The input is biometric data stored locally, and the output is data transmitted to the server. Specifically, the device automatically starts the transmission process to the server at regular intervals.

[0544] Step 3:

[0545] The server stores the received biometric information in a database. A relational database is often used, and the data is properly categorized and stored. The input is the biometric data sent to the server, and the output is the data entries stored in the database.

[0546] Step 4:

[0547] The server analyzes data using a generative AI model. TensorFlow and PyTorch are used for data processing and computation. The input is biometric information from a database, and the output is the analysis result, i.e., an evaluation of health and emotional state. Specifically, machine learning algorithms predict the presence or absence of abnormalities and emotional tendencies.

[0548] Step 5:

[0549] The server generates a notification for the user based on the analysis results. The emotion engine considers the emotional state and customizes the notification message. The input is the analysis results, and the output is the notification message sent to the user. The server generates a message that includes advice tailored to the individual state and sends it to the user's terminal.

[0550] Step 6:

[0551] Users authorize sharing information with external organizations through the settings. The input is the user's authorization information, and the output is the setting for sharing options with healthcare institutions. This allows the server to send analysis results and emotional state information to healthcare institutions authorized by the user. The specific operation involves the user selecting an option from the settings screen and confirming their authorization.

[0552] (Application Example 2)

[0553] 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."

[0554] In modern society, with the increasing number of elderly people and individuals facing health challenges, there is a growing need to efficiently and appropriately manage their health status. However, current health management systems are insufficient in real-time anomaly detection and personalized notifications based on emotional states. Furthermore, there is a lack of mechanisms for securely sharing abnormal data with healthcare institutions in accordance with the user's intentions. Against this backdrop, there is a need to provide a system that enables more personalized notifications based on an individual's health and emotional state, and efficiently shares information with healthcare institutions as needed.

[0555] 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.

[0556] In this invention, the server includes information gathering means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, communication means for notifying the user based on the abnormalities detected by the analysis means, emotion analysis means characterized in that the notification is personalized according to the user's emotional state, and information exchange means for transmitting the abnormal data to a medical institution authorized by the user. This enables real-time analysis of the health and emotional state of individual users and smooth information sharing with medical institutions as needed.

[0557] "User" refers to an individual who provides biometric data using this system.

[0558] "Biometric data" refers to data that includes the user's physiological information, such as heart rate, activity level, and sleep patterns.

[0559] "Information gathering means" refers to devices and sensors used to acquire biometric data from users.

[0560] "Analysis means" refers to methods and processes for evaluating acquired biological data and detecting abnormalities.

[0561] "Communication method" refers to a method for notifying the user of information based on the analysis results.

[0562] "Emotional analysis means" refers to a method for estimating a user's emotional state from acquired data and personalizing notifications based on that.

[0563] "Information exchange means" refers to a method for transmitting abnormal data to a designated medical institution with the user's permission.

[0564] A "medical institution" refers to an organization or facility that provides health management and diagnosis services.

[0565] The system for implementing this invention is designed to efficiently manage the user's health and emotional state. The system collects biometric data using a device such as a smartwatch or smartphone worn or carried by the user. The device is equipped with sensors that measure physiological information such as heart rate, activity level, and sleep patterns.

[0566] The server receives biometric data transmitted from these devices via Bluetooth, Wi-Fi, etc., and stores it in a database. This data is analyzed based on a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis process to detect anomalies and evaluate emotional states.

[0567] Based on the analysis results, the server generates a notification informing the user of any abnormalities. The notification is personalized by considering the user's emotional state through emotion analysis. For example, if the stress level is determined to be high, a notification will be sent that includes specific content such as suggesting music to promote relaxation.

[0568] If the user authorizes information sharing with a healthcare provider as needed, the server will send abnormal data to the authorized healthcare provider using a secure protocol (e.g., SSL / TLS). This helps the healthcare provider understand the user's condition and provide appropriate medical care.

[0569] As a concrete example, suppose a 70-year-old user regularly uses this system for health management. One afternoon, the device detects a sudden increase in heart rate and sends data to the server. Analysis reveals that this is due to stress. The server creates a notification stating, "Your heart rate is elevated. We recommend taking deep breaths and engaging in relaxing activities," and sends it to the user. By following this advice, the user can reduce their stress.

[0570] Examples of prompts to input into a generative AI model:

[0571] "Please create a notification that analyzes the user's heart rate data and suggests relaxation methods if a high-stress state is detected."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The device acquires biometric data such as heart rate, activity level, and sleep patterns in real time. This data is measured using biometric sensors. Inputs include the voltage output of the sensors, and the output is in a structured digital data format.

[0575] Step 2:

[0576] The device transmits acquired biometric data to the server via Bluetooth or Wi-Fi. A secure protocol is used for this communication to maintain data reliability. The input is biometric data from the device, and the output is data stored in a database on the server.

[0577] Step 3:

[0578] The server stores the received biometric data in a database. The data is saved in a time-series database and prepared for analysis. The input is the newly received biometric data, and the output is the state of the data stored in the database.

[0579] Step 4:

[0580] The server analyzes the accumulated data using a generative AI model. Using frameworks such as TensorFlow and PyTorch, it detects anomalies in the data and evaluates the user's health and emotional state. The input is biometric data from the database, and the output is an evaluation result indicating anomalies.

[0581] Step 5:

[0582] The server generates a message to notify the user based on the analysis results. It uses sentiment analysis to personalize the content according to the user's emotional state. The input is the evaluation result of the generating AI model, and the output is the personalized notification message sent to the user.

[0583] Step 6:

[0584] Users receive notifications from the server and obtain information that helps improve and maintain their health. This includes specific advice regarding the user's physical condition and emotions. The input is the notifications from the server, and the output is the actions taken by the user.

[0585] Step 7:

[0586] If a user requests to share information with a healthcare provider, the server will send the abnormal data to the authorized healthcare facility using the SSL / TLS protocol. The input is the user's sharing permission and the abnormal data, and the output is the status of successful transmission to the healthcare provider.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] The system of the present invention acquires a user's biometric information, detects abnormalities based on that information, provides necessary notifications, and enables information sharing. An embodiment thereof is shown below.

[0605] Users obtain daily biometric information using devices such as smartwatches and smartphones. These devices use sensors to collect physiological data such as heart rate, sleep, activity level, and calorie consumption in real time. The devices are configured to automatically send this information to a server at specific time intervals.

[0606] The server stores the received biometric information in a database and analyzes it using a generative model. If this analysis detects unusual patterns or anomalies, the server sets an appropriate flag. When this flag is set, the server sends an alert to the user through a notification system. The user's device will display specific advice, such as, "Your heart rate remains higher than normal. We recommend you rest."

[0607] Furthermore, this system can share detected anomaly information with healthcare institutions only if the user requests it. Users are given the option to select a specific healthcare institution during the initial setup. After confirming the user's approval, the server securely sends a report summarizing past health data and details of the anomalies to that healthcare institution. This information sharing enables healthcare institutions to make faster and more accurate diagnoses and provides users with appropriate support.

[0608] As a concrete example, consider a scenario where a user's activity level suddenly decreases, and their heart rate is detected to be significantly higher than their resting average. The server recognizes this as an anomaly and sends a notification to the user recommending rest. Simultaneously, if the anomaly is serious, with the user's consent, the system notifies a selected medical institution to ensure smooth subsequent diagnosis and treatment.

[0609] Thus, the present invention is a system that facilitates daily health management and provides an environment that enables prompt and appropriate medical response in the event of an abnormality.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The device collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption through sensors. This data is updated at regular intervals and stored on the device.

[0613] Step 2:

[0614] The user's device transmits collected biometric information to a server at specified intervals. This communication is typically conducted via Bluetooth or Wi-Fi.

[0615] Step 3:

[0616] The server stores the received biometric information in a database. Next, the data is preprocessed to remove noise and impute missing values.

[0617] Step 4:

[0618] The server inputs pre-processed data into a generative model. The generative model is used to detect anomalies by comparing them to normal healthy patterns.

[0619] Step 5:

[0620] When the generative model detects an anomaly, the server determines the type and severity of the anomaly. If the anomaly is deemed critical, a flag is set.

[0621] Step 6:

[0622] The server uses a notification system to send an alert message to the user's terminal in order to notify the user that an anomaly has been detected.

[0623] Step 7:

[0624] The user's device will notify them of received alerts through screen displays and audio notifications. The alerts include advice on specific actions to take.

[0625] Step 8:

[0626] Users will check the approval settings for abnormal transmissions via their device and, if necessary, consent to sharing information with healthcare institutions.

[0627] Step 9:

[0628] If user approval is obtained, the server securely transmits abnormal information and historical biometric data to the identified medical institution. This allows the medical institution to perform a rapid and accurate diagnosis.

[0629] (Example 1)

[0630] 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".

[0631] In modern society, personal health management is becoming increasingly important, but traditional methods have the challenge of not being able to respond quickly to sudden changes in health conditions. In particular, there is a need for smooth information sharing to detect abnormalities early and obtain appropriate medical treatment.

[0632] 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.

[0633] In this invention, the server includes information gathering means for acquiring biometric data in a time series, information processing means for storing and analyzing the acquired biometric data, and communication means for notifying abnormalities detected by the information processing means. This makes it possible to monitor an individual's health status in real time and to detect and respond to abnormalities at an early stage.

[0634] "Information gathering means" refers to devices and technologies for acquiring biological data, and which have the function of acquiring data in a time series.

[0635] "Information processing means" refers to methods and devices for storing and analyzing collected biological data, specifically using databases and generative AI models.

[0636] "Communication means" refers to technologies or devices that notify users of anomalies detected by information processing means, such as having a function to send alert notifications to terminals.

[0637] "Information transmission means" refers to devices or methods for securely transmitting abnormal data to external organizations, and has the function of transmitting information only to the relevant organization with the user's approval.

[0638] "Generative artificial intelligence models" refer to algorithms and technological systems for analyzing collected data and evaluating fluctuations in health information, and include predictive models using machine learning.

[0639] In this invention, the user acquires biometric data using a device such as a smartwatch or smartphone. The device has built-in sensors to collect heart rate, sleep, activity level, calorie consumption, etc., in real time, utilizing optical sensor and accelerometer technologies. This collected biometric data is designed to be periodically transmitted to a server using communication technologies such as Bluetooth or Wi-Fi.

[0640] The server stores the received biometric data in a database and performs analysis using a generative AI model. The generative AI model compares the current data with past data to evaluate outliers and significant changes in health status. For example, it can detect abnormally high heart rates or sudden changes in activity levels. If an abnormality is detected, the server sends a notification to the user's terminal containing specific advice. The notification may include information such as, "Your heart rate remains higher than normal. We recommend that you rest."

[0641] Furthermore, with the user's consent, the server can securely share abnormal data with external healthcare institutions. This feature is selectable by the user during initial setup, and detailed health data is sent only to approved healthcare institutions. The aim of this information sharing is to enable healthcare institutions to make quick and accurate diagnoses and provide users with the necessary support.

[0642] For example, if a user's activity level suddenly decreases and their heart rate significantly exceeds their resting average, the system recognizes this as an anomaly. The server notifies the user of this situation and, if necessary, sends a warning to a designated medical institution, providing support for their assessment and treatment procedures.

[0643] In this way, by using a system that includes a generative AI model, users can manage their health status on a daily basis and have an environment where they can receive a quick response when an abnormality occurs.

[0644] An example of a prompt message is, "Generate a detailed explanation regarding the detection of anomalies in the user's heart rate and activity level." This allows the generating AI model to provide analysis results of the biometric data and a specific evaluation of the anomalies.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The device uses built-in sensors to acquire biometric data such as the user's heart rate, sleep, activity level, and calorie consumption in real time. In this process, the sensors utilize optical sensors and accelerometers to accurately capture the user's physical activity and state. The input is the user's physical activity and state, and the output is the collected biometric data.

[0648] Step 2:

[0649] The device is configured to periodically send the collected biometric data to a server via Bluetooth or Wi-Fi. This transmission is automated and performed efficiently without user intervention. The input is the biometric data acquired in step 1, and the output is the data sent to the server.

[0650] Step 3:

[0651] The server stores the received biometric data in a database. This database is designed to enable data storage, organization, and rapid access. The input is biometric data sent from the terminal, and the output is organized data stored in the database.

[0652] Step 4:

[0653] The server analyzes biometric data using a generating AI model. This analysis includes comparing the data to normal patterns and detecting changes in health status, identifying anomalies and unique patterns. The input is biometric data stored in a database, and the output is anomaly detection information as a result of the analysis.

[0654] Step 5:

[0655] If the server detects an anomaly based on the analysis results, it will notify the user. This notification will send an alert to the user's device, displaying a message on the screen such as, "Your heart rate remains higher than normal. We recommend you rest." The input is the anomaly detection information from step 4, and the output is the notification message displayed on the device.

[0656] Step 6:

[0657] The server, with the user's consent, will send anomaly information to an authorized healthcare provider. This involves a process of verifying user approval and sharing data through a secure channel. The input is the anomaly detection information and the user's consent, and the output is the anomaly report sent to the healthcare provider.

[0658] (Application Example 1)

[0659] 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".

[0660] There is a need to detect abnormalities in elderly users and those with unstable health conditions early and to share that information promptly with caregivers and medical facilities to enable a rapid response. Furthermore, in care settings, prompt and appropriate health management is crucial, and a challenge is ensuring that information about abnormalities is notified to the appropriate parties and that a swift response is taken.

[0661] 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.

[0662] In this invention, the server includes data acquisition means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, and notification means for notifying the user of the abnormalities detected by the analysis means. This enables rapid detection of abnormalities in users in a care environment, appropriate information sharing with caregivers and medical institutions, and immediate response.

[0663] "Data acquisition means" refers to devices and methods for collecting a user's biometric data, which is collected via devices such as smartwatches and smartphones.

[0664] "Analysis means" refers to processes and methods for analyzing acquired biological data to detect unusual patterns or abnormalities.

[0665] "Notification means" refers to a system or device for notifying users or caregivers based on the analyzed results.

[0666] "Information sharing means" refers to a function that transmits detected abnormal information to approved medical facilities and care workers, enabling a swift response.

[0667] "Notification methods in the care environment" refers to a system used in care facilities and home care settings to send alerts to care workers and communicate information when an abnormality is detected.

[0668] The system for implementing this invention consists of a user terminal, a server, and a notification function for caregivers. The user terminal can be a smartwatch or a smartphone. These terminals acquire biometric data such as heart rate and activity levels in real time via Bluetooth and transmit it to the smartphone. The smartphone is responsible for periodically uploading this data to the server.

[0669] The server stores the received biometric data in a database and performs analysis using Python. The analysis utilizes generative AI models based on TensorFlow and PyTorch to detect anomalies. When an anomaly is detected, the server notifies caregivers and, if necessary, medical facilities via Firebase Cloud Messaging and the Twilio API, enabling a rapid response.

[0670] For example, if a significantly elevated heart rate is detected in a nursing home, the server immediately sends a notification such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition," and the information is transmitted to the caregiver's mobile device. This allows the caregiver to take necessary actions quickly.

[0671] As a concrete example of a prompt message generated using the AI ​​model, the AI ​​model is input with content such as "Heart rate increase detection prompt: What should be alerted if the heart rate exceeds normal after a rest period?" and generates appropriate alert content. This enables quick and accurate notifications in situations requiring complex judgments.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1:

[0674] The device measures biometric data such as the user's heart rate and activity level in real time via a smartwatch and transfers that data to a smartphone via Bluetooth. At this stage, the input is biometric data, and the output is the biometric data transferred to the smartphone.

[0675] Step 2:

[0676] The smartphone uploads the received biometric data to the server at regular intervals. Specifically, it securely transmits the data to the server using the HTTPS protocol. In this process, the input is biometric data from the smartwatch, and the output is the data uploaded to the server.

[0677] Step 3:

[0678] The server stores the uploaded biometric data in a database. MySQL or PostgreSQL is used as the database to ensure data reliability and consistency. Input is data sent from a smartphone, and output is data stored in the database.

[0679] Step 4:

[0680] The server performs data analysis using a generative AI model. Specifically, it uses TensorFlow to analyze biometric data and detect anomalies. In this process, the input is biometric data stored in a database, and the output is flag information indicating when an anomaly is detected.

[0681] Step 5:

[0682] If an anomaly is detected, the server uses Firebase Cloud Messaging to notify caregivers. The notification content is generated based on prompts created by an AI model. For example, if there is an abnormality in heart rate, it will send a message such as, "Mr. / Ms. XX's heart rate is higher than normal. Please check on their condition." The input is flag information indicating the detection of an anomaly, and the output is the message notification.

[0683] Step 6:

[0684] Caregivers receive notifications and take action as needed. This process is expected to involve implementing measures based on the received information. The input before action is taken at the caregiving site is the notified abnormal information, and the output is the implementation of caregiving action.

[0685] 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.

[0686] This invention is a system that acquires a user's biometric information, analyzes abnormalities in their health and emotional state based on that information, and provides appropriate notifications to the user. Furthermore, this system has a function to share abnormal information with medical institutions. An embodiment of this system is shown below.

[0687] Users collect daily biometric information using devices such as smartwatches and smartphones. These devices use biometric sensors to acquire various physiological data in real time, including heart rate, sleep, activity level, and calorie consumption. The collected data is periodically transmitted to a server.

[0688] The server stores the received biometric information in a database and analyzes it using a generative model. If an anomaly is detected as a result of the analysis, the server generates a notification for the user. In this process, the emotion engine recognizes the user's emotional state based on the user's input and biometric information, and personalizes the content of the notification according to that emotion.

[0689] As a concrete example, consider a scenario where a high level of stress is detected from the user's biometric information, and the emotion engine further recognizes feelings of anxiety. In such a case, the server would notify the user with personalized advice such as, "Your stress level is high, and you are experiencing persistent anxiety. Please create a relaxing environment and make sure to get enough rest."

[0690] In addition, this system allows users to share details of abnormal information and emotional states with healthcare providers they have approved, but only when necessary. First, users review the information sharing options with healthcare providers and grant permission. Based on user approval, the server compiles the abnormal information and the results of the associated emotional analysis and securely transmits it to the healthcare provider. This information sharing allows healthcare providers to better understand the user and provide appropriate treatment and advice.

[0691] In this way, the present invention is a system that comprehensively supports the user's health management and facilitates smooth collaboration with medical institutions as needed, thereby enabling the user to receive optimal medical services.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The device continuously collects biometric information such as the user's heart rate, sleep patterns, activity level, and calorie consumption using sensors. This data is temporarily stored in the device's memory.

[0695] Step 2:

[0696] The device transmits the collected biometric information to the server at regular intervals. This transmission is carried out via the internet using secure encrypted communication.

[0697] Step 3:

[0698] The server stores the received biometric information in a database. The stored information is prepared for input into the generative model in real time.

[0699] Step 4:

[0700] The server uses a generative model to analyze biometric data and detect unusual patterns or anomalies. When an anomaly is found, a flag is set according to the type of anomaly.

[0701] Step 5:

[0702] The server uses an emotion engine to analyze the user's emotional state. This analysis is based on subjective emotional evaluations and biometric information provided by the user through the application.

[0703] Step 6:

[0704] The server generates customized advice messages for the user based on the health and emotional states that it perceives as abnormal.

[0705] Step 7:

[0706] A notification is sent from the server to the user's device. The device displays this notification on the screen and alerts the user with an audible alert or vibration.

[0707] Step 8:

[0708] Users can view notifications on their devices and take further action as needed. They can also provide emotional feedback through the app.

[0709] Step 9:

[0710] If a user authorizes information sharing with a healthcare provider, the server uses a secure protocol to compose the relevant data and send it to the healthcare provider specified by the user. This allows the healthcare provider to comprehensively evaluate the user's health monitoring results and emotional state.

[0711] (Example 2)

[0712] 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".

[0713] In recent years, the importance of health management based on users' biometric information has increased. However, conventional systems only detect abnormalities in biometric information and have shortcomings in providing individualized support that fully considers the user's emotional state and inefficient information sharing with medical institutions.

[0714] 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.

[0715] In this invention, the server includes data collection means for acquiring the user's biometric information, analysis means for analyzing the acquired biometric information and detecting abnormalities, and estimation means for evaluating the user's emotional state. This enables an integrated evaluation of the user's health and emotional state, allowing for individual notifications and information sharing with external organizations.

[0716] "Data collection means" refers to devices and software used to acquire a user's biometric information, including devices that measure heart rate, activity level, sleep patterns, etc.

[0717] "Analysis means" refers to technologies that have the function of analyzing acquired biological information and detecting abnormalities, and this includes machine learning algorithms that use generative models.

[0718] "Estimation means" refers to the functions and methods necessary to evaluate a user's emotional state, and includes technologies that infer emotions based on biometric information and user input data.

[0719] "Notification means" refers to a mechanism for generating and sending individual notifications to users based on analysis results and emotional states, and this includes message delivery and real-time alerts.

[0720] "Information sharing means" refers to the function of transmitting acquired and analyzed anomaly information and emotional state information to an external organization approved by the user via a secure communication channel.

[0721] A "generative model" refers to a model that uses biological information to analyze and predict health and emotional states using algorithms.

[0722] A "prompt statement" refers to a document used as input for a generative AI model, containing information that serves as a basis for analysis and evaluation.

[0723] This invention relates to a system that acquires a user's biometric information, analyzes it to understand their health and emotional state, and shares abnormal information with external organizations as needed. This system has the function of collecting biometric information through sensors installed in devices such as smartwatches and smartphones, which the user uses. For example, information such as heart rate, activity level, and sleep patterns can be acquired by the sensors.

[0724] The device then sends the acquired data to the server at regular intervals. Since the HTTPS protocol is used for transmission, data privacy and security are ensured.

[0725] The server stores the received data in a database and then performs analysis. This analysis uses a generative AI model, employing common machine learning libraries such as TensorFlow and PyTorch. By using a generative AI model, it is possible to comprehensively evaluate changes in emotional state along with health status.

[0726] Once the analysis is complete, the server generates a notification for the user based on the results. This notification is personalized by the emotion engine and includes specific advice tailored to the user's situation. For example, if high stress levels and anxiety are detected, the server will send a notification saying, "Your stress levels are high, and you are experiencing persistent anxiety. Try to create a relaxing environment and get enough rest."

[0727] Furthermore, if a user wishes to share information, that abnormal information and emotional state information will be securely transmitted to an external organization approved by the user. Information transmission will be based on the options selected by the user.

[0728] A concrete example of a prompt would be: "The user's biometric data has detected an elevated heart rate and anxiety. Based on this, please suggest what advice to provide to the user." This prompt functions as an instruction to the generative AI model to provide appropriate feedback to the user.

[0729] In this way, this system supports users in their daily health management and helps them take optimal measures by providing timely and accurate notifications and information sharing functions.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The device collects the user's biometric information. Specifically, devices such as smartwatches use built-in sensors to acquire heart rate, activity levels, sleep patterns, etc., in real time. The input is data from biometric sensors, and the output is biometric data stored in the device's local memory.

[0733] Step 2:

[0734] The device transmits the collected biometric information to the server. Since the HTTPS protocol is used for communication, data security is high. The input is biometric data stored locally, and the output is data transmitted to the server. Specifically, the device automatically starts the transmission process to the server at regular intervals.

[0735] Step 3:

[0736] The server stores the received biometric information in a database. A relational database is often used, and the data is properly categorized and stored. The input is the biometric data sent to the server, and the output is the data entries stored in the database.

[0737] Step 4:

[0738] The server analyzes data using a generative AI model. TensorFlow and PyTorch are used for data processing and computation. The input is biometric information from a database, and the output is the analysis result, i.e., an evaluation of health and emotional state. Specifically, machine learning algorithms predict the presence or absence of abnormalities and emotional tendencies.

[0739] Step 5:

[0740] The server generates a notification for the user based on the analysis results. The emotion engine considers the emotional state and customizes the notification message. The input is the analysis results, and the output is the notification message sent to the user. The server generates a message that includes advice tailored to the individual state and sends it to the user's terminal.

[0741] Step 6:

[0742] Users authorize sharing information with external organizations through the settings. The input is the user's authorization information, and the output is the setting for sharing options with healthcare institutions. This allows the server to send analysis results and emotional state information to healthcare institutions authorized by the user. The specific operation involves the user selecting an option from the settings screen and confirming their authorization.

[0743] (Application Example 2)

[0744] 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".

[0745] In modern society, with the increasing number of elderly people and individuals facing health challenges, there is a growing need to efficiently and appropriately manage their health status. However, current health management systems are insufficient in real-time anomaly detection and personalized notifications based on emotional states. Furthermore, there is a lack of mechanisms for securely sharing abnormal data with healthcare institutions in accordance with the user's intentions. Against this backdrop, there is a need to provide a system that enables more personalized notifications based on an individual's health and emotional state, and efficiently shares information with healthcare institutions as needed.

[0746] 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.

[0747] In this invention, the server includes information gathering means for acquiring the user's biometric data, analysis means for analyzing the acquired biometric data and detecting abnormalities, communication means for notifying the user based on the abnormalities detected by the analysis means, emotion analysis means characterized in that the notification is personalized according to the user's emotional state, and information exchange means for transmitting the abnormal data to a medical institution authorized by the user. This enables real-time analysis of the health and emotional state of individual users and smooth information sharing with medical institutions as needed.

[0748] "User" refers to an individual who provides biometric data using this system.

[0749] "Biometric data" refers to data that includes the user's physiological information, such as heart rate, activity level, and sleep patterns.

[0750] "Information gathering means" refers to devices and sensors used to acquire biometric data from users.

[0751] "Analysis means" refers to methods and processes for evaluating acquired biological data and detecting abnormalities.

[0752] "Communication method" refers to a method for notifying the user of information based on the analysis results.

[0753] "Emotional analysis means" refers to a method for estimating a user's emotional state from acquired data and personalizing notifications based on that.

[0754] "Information exchange means" refers to a method for transmitting abnormal data to a designated medical institution with the user's permission.

[0755] A "medical institution" refers to an organization or facility that provides health management and diagnosis services.

[0756] The system for implementing this invention is designed to efficiently manage the user's health and emotional state. The system collects biometric data using a device such as a smartwatch or smartphone worn or carried by the user. The device is equipped with sensors that measure physiological information such as heart rate, activity level, and sleep patterns.

[0757] The server receives biometric data transmitted from these devices via Bluetooth, Wi-Fi, etc., and stores it in a database. This data is analyzed based on a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis process to detect anomalies and evaluate emotional states.

[0758] Based on the analysis results, the server generates a notification informing the user of any abnormalities. The notification is personalized by considering the user's emotional state through emotion analysis. For example, if the stress level is determined to be high, a notification will be sent that includes specific content such as suggesting music to promote relaxation.

[0759] If the user authorizes information sharing with a healthcare provider as needed, the server will send abnormal data to the authorized healthcare provider using a secure protocol (e.g., SSL / TLS). This helps the healthcare provider understand the user's condition and provide appropriate medical care.

[0760] As a concrete example, suppose a 70-year-old user regularly uses this system for health management. One afternoon, the device detects a sudden increase in heart rate and sends data to the server. Analysis reveals that this is due to stress. The server creates a notification stating, "Your heart rate is elevated. We recommend taking deep breaths and engaging in relaxing activities," and sends it to the user. By following this advice, the user can reduce their stress.

[0761] Examples of prompts to input into a generative AI model:

[0762] "Please create a notification that analyzes the user's heart rate data and suggests relaxation methods if a high-stress state is detected."

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The device acquires biometric data such as heart rate, activity level, and sleep patterns in real time. This data is measured using biometric sensors. Inputs include the voltage output of the sensors, and the output is in a structured digital data format.

[0766] Step 2:

[0767] The device transmits acquired biometric data to the server via Bluetooth or Wi-Fi. A secure protocol is used for this communication to maintain data reliability. The input is biometric data from the device, and the output is data stored in a database on the server.

[0768] Step 3:

[0769] The server stores the received biometric data in a database. The data is saved in a time-series database and prepared for analysis. The input is the newly received biometric data, and the output is the state of the data stored in the database.

[0770] Step 4:

[0771] The server analyzes the accumulated data using a generative AI model. Using frameworks such as TensorFlow and PyTorch, it detects anomalies in the data and evaluates the user's health and emotional state. The input is biometric data from the database, and the output is an evaluation result indicating anomalies.

[0772] Step 5:

[0773] The server generates a message to notify the user based on the analysis results. It uses sentiment analysis to personalize the content according to the user's emotional state. The input is the evaluation result of the generating AI model, and the output is the personalized notification message sent to the user.

[0774] Step 6:

[0775] Users receive notifications from the server and obtain information that helps improve and maintain their health. This includes specific advice regarding the user's physical condition and emotions. The input is the notifications from the server, and the output is the actions taken by the user.

[0776] Step 7:

[0777] If a user requests to share information with a healthcare provider, the server will send the abnormal data to the authorized healthcare facility using the SSL / TLS protocol. The input is the user's sharing permission and the abnormal data, and the output is the status of successful transmission to the healthcare provider.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A data collection method for obtaining the user's biometric information,

[0802] An analysis means for analyzing the acquired biological information and detecting abnormalities,

[0803] A notification means for notifying the user of an anomaly detected by the analysis means,

[0804] Information sharing means for transmitting the aforementioned abnormal information to medical institutions,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, characterized in that the analysis means evaluates changes in health status using a generative model.

[0808] (Claim 3)

[0809] The system according to claim 1, characterized in that the information sharing means transmits abnormal information only to medical institutions approved by the user.

[0810] "Example 1"

[0811] (Claim 1)

[0812] Information collection methods for acquiring biometric data in a time series,

[0813] Information processing means for storing and analyzing the acquired biological data,

[0814] A communication means for notifying an anomaly detected by the aforementioned information processing means,

[0815] Information transmission means for transmitting the aforementioned abnormal data to an external organization,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, characterized in that it uses a generative artificial intelligence model to evaluate fluctuations in health information.

[0819] (Claim 3)

[0820] The system according to claim 1, characterized in that the information transmission means transmits abnormal data only to external organizations approved by the user.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A data acquisition method for obtaining the user's biometric data,

[0824] An analytical means for analyzing the acquired biological data and detecting abnormalities,

[0825] A notification means for notifying the user of an anomaly detected by the analysis means,

[0826] Information sharing means for transmitting the aforementioned abnormal information to medical institutions,

[0827] A notification system for detecting abnormalities in the care environment and sending alerts to care workers,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, characterized in that the analysis means has a function to evaluate changes in health status using the generated mathematical model and to warn care workers in the event of an abnormality.

[0831] (Claim 3)

[0832] The system according to claim 1, characterized in that the information sharing means transmits abnormal information only to medical facilities approved by the user, and in addition, the information is also shared with care workers.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A data collection method for obtaining the user's biometric information,

[0836] An analysis means for analyzing the acquired biological information and detecting abnormalities,

[0837] An estimation method for evaluating the user's emotional state,

[0838] Based on the results obtained by the analysis means and estimation means, a notification means for generating and sending individual notifications to the user,

[0839] Information sharing means for securely transmitting the aforementioned abnormal information and emotional state information to an external organization approved by the user,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, characterized by using a generative model to comprehensively evaluate changes in health status and changes in emotional status.

[0843] (Claim 3)

[0844] The system according to claim 1, characterized in that the information sharing means transmits abnormal information and emotional state information only to external organizations approved by the user.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] A means of collecting information to obtain the user's biometric data,

[0848] An analytical means for analyzing the acquired biological data and detecting abnormalities,

[0849] A communication means for notifying the user based on the anomaly detected by the analysis means,

[0850] The notification is characterized by being personalized according to the user's emotional state, and includes an emotion analysis means.

[0851] An information exchange means for transmitting the aforementioned abnormal data to a medical institution authorized by the user,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the analysis means evaluates changes in health and mental state using a generation algorithm.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the information exchange means transmits abnormal data only to medical facilities authorized by the user. [Explanation of Symbols]

[0857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data acquisition method for obtaining the user's biometric data, An analytical means for analyzing the acquired biological data and detecting abnormalities, A notification means for notifying the user of an anomaly detected by the analysis means, Information sharing means for transmitting the aforementioned abnormal information to medical institutions, A notification system for detecting abnormalities in the care environment and sending alerts to care workers, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means has a function to evaluate changes in health status using the generated mathematical model and to issue a warning to care workers in the event of an abnormality.

3. The system according to claim 1, characterized in that the information sharing means transmits abnormal information only to medical facilities approved by the user, and in addition, the information is also shared with care workers.

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