System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting health data of a user; means for accumulating the collected health data in a database; means for analyzing the accumulated data and detecting a sign of a disease; means for notifying a user terminal of an alert when a sign of a disease is detected; and means for paying a fee to a user who is confirmed to have a disease.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, average life expectancy has increased, but the gap between healthy life expectancy and average life expectancy has widened. Narrowing this gap and extending healthy life expectancy is a social challenge. Furthermore, with rising medical costs and a heavy burden on the social security system, there is a need for effective preventive medicine. Furthermore, it is difficult to approach people with low health awareness, and preventive measures are not being implemented appropriately. A system that can solve these problems and support individual health management is needed. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems with a system that includes a means for collecting user health data, a means for storing the collected health data in a database, a means for analyzing the stored data and detecting signs of illness, a means for sending an alert to a user's device when signs of illness are detected, and a means for paying a condolence payment to a user who has been confirmed to be ill. Specifically, the collected health data includes the user's heart rate, number of steps, sleep time, health check results, and medical history. Furthermore, a machine learning algorithm is used for data analysis to learn data patterns of users who have previously experienced illness, thereby quickly detecting other users who exhibit similar patterns. Furthermore, alerts are notified to the user in real time, and condolence payments are automatically deposited into the user's bank account. This system encourages users to proactively monitor their health status and take preventative measures. Furthermore, by supporting disease prevention and early detection, it is expected to extend healthy lifespans and reduce medical costs.

[0006] "User" refers to an individual who provides health data or uses the system.

[0007] "Health data" refers to information about the user's health condition, such as the user's heart rate, number of steps, sleep time, health check results, and medical history.

[0008] "Means" refers to specific equipment, software, algorithms, processes, etc. that a system uses to achieve a specific function.

[0009] "Collecting" refers to the act of obtaining data from users and inputting it into a system.

[0010] A "database" refers to a collection of information that stores collected data and is used for searching and analysis.

[0011] "Analyzing" refers to the operation of processing accumulated data to understand, diagnose, and predict its contents.

[0012] "Disease precursors" refer to patterns or signs discovered through data analysis that indicate the possibility of a disease occurring in the future.

[0013] "Detect" refers to the act of finding specific patterns or anomalies in analyzed data.

[0014] An "alert" refers to a notification or warning that notifies the user of a risk or abnormality.

[0015] "Notifying" refers to the act of sending an alert from the system to the user and conveying information.

[0016] "Consolation money" refers to monetary compensation paid to users whose illness is confirmed.

[0017] "Paying" refers to the act of transferring the money to the user's bank account, etc.

[0018] "System" refers to the set of devices and software that collect, store, and analyze users' health data, and generate and notify alerts.

[0019] "Heart rate" refers to the number of times the heart beats within a given period of time.

[0020] "Number of steps" refers to the total number of steps taken by the user.

[0021] "Sleep time" refers to the time the user was asleep.

[0022] "Health checkup results" refers to the numerical values ​​and diagnostic contents of a health checkup conducted at a medical institution.

[0023] "Medical history" refers to the user's past illnesses and treatment history.

[0024] A "machine learning algorithm" refers to a computational method for learning patterns from data and making predictions or classifications.

[0025] A "pattern" refers to a distinctive trend or regularity found in data.

[0026] "Real-time" refers to processing occurring almost immediately after the data is generated.

[0027] "Bank account" refers to the user's bank account at a financial institution into which the condolence money is to be transferred.

[0028] "Automatic transfer" refers to the procedure in which the system transfers the condolence money to the user's bank account without human intervention. [Brief explanation of the drawings]

[0029] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0030] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0031] First, the terms used in the following description will be explained.

[0032] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0033] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0034] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0035] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0036] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0037] [First embodiment]

[0038] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0039] 1, a 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.

[0040] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0041] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0042] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0043] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0044] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0045] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0046] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0047] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0048] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0049] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] The present invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server that operates in conjunction with the user's terminal and wearable device.

[0051] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices. This data is automatically sent to a server, where it is stored in a database. When users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0052] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and builds an algorithm for detecting signs of disease. This algorithm makes it possible to detect users at increased risk of disease early on.

[0053] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to the server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0054] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0055] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0056] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0057] The system of the present invention encourages users to proactively manage their own health status, thereby contributing to extending healthy life expectancy overall. This system allows users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0061] Step 2:

[0062] Terminal: Collected data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0063] Step 3:

[0064] Server: Stores the received data in a database for each user. The stored data includes daily health data, health checkup results, medical history, etc.

[0065] Step 4:

[0066] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0067] Step 5:

[0068] Server: Uses machine learning algorithms to learn data patterns from users who have previously experienced illness and build a model to detect signs of illness.

[0069] Step 6:

[0070] Server: Analyzes newly received data in real time and compares it with the trained model.

[0071] Step 7:

[0072] Server: If the analysis detects abnormal patterns or patterns similar to those of users who have previously experienced illness, a risk assessment is made based on that information.

[0073] Step 8:

[0074] Server: Generates an alert if the risk assessment is elevated.

[0075] Step 9:

[0076] Device: An alert is sent to the user's smartphone, with specific content such as "Your risk of heart disease is increasing. Please consult a doctor."

[0077] Step 10:

[0078] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0079] Step 11:

[0080] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0081] Step 12:

[0082] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0083] Step 13:

[0084] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0085] Examples:

[0086] For example, if a user named Mr. A records his heart rate, number of steps, and sleep time every day using a wearable device, this data is sent to the server in steps 1 and 2. The server accumulates and analyzes this data in steps 3 to 5 and compares it with the trained model. If Mr. A's data matches a pattern indicating a risk of heart disease, a risk assessment is made in steps 6 and 7, and an alert is sent to the user in steps 8 and 9.

[0087] After that, Mr. A receives a diagnosis at the hospital and enters the diagnosis results into the application, which executes steps 10 to 12. The server verifies the diagnosis results and starts the payment process for the condolence money, which is eventually automatically transferred to the user's bank account. In step 13, the user is notified of this information.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] In modern society, lifestyle-related diseases and acute illnesses are on the rise, making early detection and prevention of these diseases crucial. However, many people lack the means to properly manage their daily health and may miss signs of illness. Furthermore, financial support after illness is often insufficient. For this reason, there is a need for a system that continuously collects and analyzes users' health data to detect early signs of illness, promptly notify alerts when necessary, and even provide compensation.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for cleansing the stored data, means for analyzing the data using a machine learning algorithm to detect signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to efficiently manage user health data, support early detection and prevention of illness, and provide financial support after diagnosis.

[0093] "Health data" refers to physiological data and lifestyle-related data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health checkup results, and medical history.

[0094] "Collection means" refers to devices or software, such as wearable devices or smartphone applications, used to obtain health data from users.

[0095] The "means of storing in a database" refers to a server or cloud storage system for storing and managing collected health data in digital form.

[0096] A "cleansing tool" is an algorithm or software that performs a process to remove outliers and missing values ​​from collected health data and normalize the data.

[0097] The "means of analysis" refers to a system that uses machine learning algorithms and data mining techniques to analyze accumulated health data and detect signs of disease.

[0098] "Means for detecting signs of disease" refers to algorithms and logic that identify potential disease risks based on analyzed data and provide early warnings to users.

[0099] "Means for notifying alerts" refers to a system for sending warning messages or alerts to users when a health risk is detected via their smartphones or other communication devices.

[0100] The "means for paying condolence money to users whose illnesses have been confirmed" is a system that allows users to input their doctor's diagnosis into the system, which then automatically starts the payment process based on that information and transfers the condolence money to the user's bank account.

[0101] This invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server and operates in conjunction with the user's terminal and wearable device.

[0102] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices (e.g., Fitbit or Apple Watch). This data is automatically sent to a server. Furthermore, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0103] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model (e.g., TensorFlow) using past disease occurrence data to build an algorithm for detecting signs of disease. This algorithm enables early detection of users at increased risk of disease.

[0104] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to a server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0105] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0106] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0107] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0108] An example of a prompt sentence to input to the generative AI model is as follows:

[0109] A user uses a wearable device to collect daily health data such as heart rate, number of steps, and sleep time, and sends it to a server via their smartphone. The server accumulates this data and analyzes it using a machine learning model to detect signs of illness. Design a system that sends an alert to the user's smartphone if the risk of illness increases, and pays a condolence payment based on the doctor's diagnosis.

[0110] As a result, the system of the present invention encourages users to proactively manage their own health status, contributing to the extension of overall healthy life expectancy. This system enables users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] Users wear wearable devices (e.g., Fitbit or Apple Watch) to collect daily health data.

[0114] Input: Data such as user heart rate, steps, and sleep time

[0115] Output: Data transfer to smartphone

[0116] How it works: As the user uses the wearable device and engages in daily activities, the device automatically records heart rate, steps taken, and sleep time, and transmits the data via Bluetooth to a dedicated app on the smartphone.

[0117] Step 2:

[0118] The terminal transmits the collected health data to a server.

[0119] Input: Health data collected on a smartphone

[0120] Output: Send data to the server

[0121] How it works: The smartphone app periodically uploads data to the server, which is then securely transferred to the server via the internet.

[0122] Step 3:

[0123] The server stores the received health data in a database.

[0124] Input: Health data sent from the device

[0125] Output: Health data stored in a database

[0126] What it does: The server stores the received data in a MySQL database, checks the data for proper format and structure, and then merges it with the existing data in the database.

[0127] Step 4:

[0128] The server cleanses the accumulated data.

[0129] Input: Health data stored in a database

[0130] Output: Cleansed data

[0131] What it does: The server removes outliers and normalizes the data, for example, detecting and removing extreme or missing values ​​and applying normalization to ensure data consistency.

[0132] Step 5:

[0133] The server uses machine learning algorithms to analyze the data and detect signs of disease.

[0134] Input: Cleansed health data

[0135] Output: Risk assessment results

[0136] How it works: The server uses TensorFlow to apply machine learning models trained on past health data to assess the user's health risks, detecting abnormalities in heart rate and sleep patterns and identifying signs of illness.

[0137] Step 6:

[0138] The server sends an alert to the user terminal if any signs of illness are detected.

[0139] Input: Risk assessment results

[0140] Output: Alert notification to user terminal

[0141] Specific operation: Based on the risk assessment results, the server sends a push notification to the user's smartphone, including instructions such as "Your risk of heart disease is increasing. Please consult a doctor."

[0142] Step 7:

[0143] The user receives a diagnosis from a doctor and enters the results into the application.

[0144] Input: Doctor's diagnosis

[0145] Output: Send diagnostic results to the server

[0146] Specific operation: The user receives a diagnosis from a doctor, and the diagnosis of "heart disease" is entered into a smartphone application and sent to the server.

[0147] Step 8:

[0148] The server checks the entered diagnosis results.

[0149] Input: Diagnostic results sent by the user

[0150] Output: Check the diagnostic results

[0151] Specific operation: The server executes a process to check the received diagnostic results against the database and confirm their validity.

[0152] Step 9:

[0153] If the diagnosis result is confirmed to be valid, the server starts the procedure for paying the compensation.

[0154] Input: Verified diagnostic results

[0155] Output: Consolation payment

[0156] Specific operation: The server triggers the condolence payment process and automatically transfers the money to the specified bank account.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] In modern society, it is important for users to manage their own health, but many people are so busy with their daily lives that they tend to neglect their health management. Furthermore, for users who lead particularly busy lives, choosing healthy meals can be difficult. Furthermore, there is a concern that there is an increased risk of health risks, as there is no system that simultaneously monitors health data and suggests meals, and takes immediate action if an abnormality is detected.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes a means for collecting user health data, a means for storing the collected health data in a database, and a means for analyzing the stored data and detecting signs of illness, thereby enabling constant monitoring of the user's health condition and early detection of health risks.

[0162] Furthermore, if signs of illness are detected, the system includes a means for sending an alert to the user's terminal, a means for paying a condolence payment to a user who has been confirmed to be ill, and a means for suggesting an optimal meal menu based on the user's health condition. This encourages the user to take appropriate health management measures and also supports healthy meal selection, enabling overall health maintenance.

[0163] "User's health data" refers to various data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0164] "Database" means an electronic system and structure for storing and managing collected health data.

[0165] "Means for detecting signs of illness" refers to algorithms or programs that use accumulated health data to predict the likelihood of a user becoming ill.

[0166] "Means for notifying an alert" is a function for sending a warning or notification to the user's device when a sign of illness is detected.

[0167] The "means for paying condolence money" is a function for providing financial compensation and support to users who have been confirmed to be ill.

[0168] "Means for suggesting optimal meal menus" refers to a system function that provides and recommends appropriate meal contents based on the user's health condition.

[0169] A "machine learning algorithm" is a computational method for learning patterns based on past data and making predictions and classifications.

[0170] A "health-based meal menu" is a meal plan recommended for maintaining health based on the user's current health data.

[0171] The system that realizes this invention collects user health data, stores it in a database, analyzes it, sends alerts, provides condolence payments, and suggests meal plans, among other functions, allowing users to continuously manage their own health status and respond quickly if an abnormality is detected.

[0172] First, smartphones and wearable devices are used to collect users' health data, including information such as heart rate, number of steps taken, and sleep duration, which is automatically sent to a server.

[0173] The server stores this data in a database. The database can use a relational database management system such as MySQL or PostgreSQL. The information stored in the database is organized by user and used for later analysis.

[0174] The server then analyzes the accumulated data using machine learning algorithms (for example, Python's scikit-learn library, which has skills such as RandomForestClassifier and GradientBoostingClassifier). This allows it to detect signs of illness and send appropriate alerts to the user's device if an abnormality is found. These alerts help users recognize health risks early and take measures.

[0175] For example, if a user experiences an abnormal increase in heart rate, the server analyzes the data and sends a notification to the smartphone stating, "An abnormal heart rate has been detected. We recommend that you see a doctor." In addition, if the user is diagnosed with an illness through the condolence payment system, a condolence payment will be transferred to the user's designated bank account along with the diagnosis.

[0176] The system also suggests optimal meal plans based on the user's health status. For example, it analyzes the latest health data collected from wearable devices and smartphones and recommends meals containing specific ingredients and nutrients via a food delivery service. In this case, the application displays specific recommended meals along with an alert such as, "Your heart rate is high. We recommend a low-calorie menu."

[0177] An example of a prompt might be, "Design an API that collects a user's weekend heart rate data and provides health advice based on that data. The API will detect abnormal heart rates and recommend salads and low-calorie meals." This allows developers to clearly and concretely understand the system's functionality and proceed with implementation.

[0178] In this way, the invention is a system that comprehensively supports the user's health management and is highly effective from the perspective of preventive medicine.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The user's device (smartphone or wearable device) collects health data such as heart rate, number of steps, and sleep time. This data is acquired using sensors on each device (heart rate sensor, accelerometer, gyroscope, etc.) and automatically sent to a server via an application. The input data is biometric data and activity data, and the output is data packets sent from the device to the server.

[0182] Step 2:

[0183] The server stores the collected health data in a database. The database uses a relational database management system such as MySQL or PostgreSQL. The input is the health data sent by the user, and the output is the data organized and stored in the database.

[0184] Step 3:

[0185] The server preprocesses the health data stored in the database. This involves cleansing the data (removing outliers, normalizing, etc.). For example, it detects and removes abnormally high heart rates and zero values, and standardizes the data. The input is raw data obtained from the database, and the output is clean data that has been preprocessed.

[0186] Step 4:

[0187] The server analyzes the preprocessed health data. It uses machine learning algorithms (e.g., RandomForestClassifier, GradientBoostingClassifier, and other scikit-learn libraries) to detect signs of disease. The input is the cleansed health data, and the output is abnormal patterns and disease risk assessment results.

[0188] Step 5:

[0189] If a sign of illness is detected, the server sends an alert to the user's device. The alert uses the smartphone's notification function to notify the user of the abnormality. For example, a notification such as "Your heart rate is abnormally high. Please see a doctor" is displayed. The input is the disease risk assessment result, and the output is an alert notification displayed on the user's device.

[0190] Step 6:

[0191] The server also manages the procedure for paying condolence money to users whose illnesses are confirmed. When a user receives a diagnosis at a hospital or other facility and enters the diagnosis results into the application, the data is sent to the server. The server verifies the data and, if valid, automatically initiates the payment of the condolence money. The input is the diagnosis result entered by the user, and the output is a transfer of the condolence money to the user's bank account.

[0192] Step 7:

[0193] The server proposes an optimal meal menu based on the user's health condition. Based on the analysis results, the application makes specific meal suggestions, such as "Your heart rate is high. We recommend a low-calorie menu." The input is the analysis results of the health data, and the output is information on the recommended meal menu. This information becomes a specific meal suggestion that reflects the user's health condition.

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] The present invention relates to a system that detects signs of illness by collecting and analyzing a user's health data, notifying them of alerts as necessary, and even paying condolence money. The system of the present invention has a central server that operates in conjunction with the user's terminal, wearable device, and emotion engine.

[0196] System configuration

[0197] 1. User health data collection:

[0198] Users use smartphones or wearable devices to collect daily health data such as heart rate, number of steps, and sleep time. This data is automatically sent to a server, where it is stored in a database. In addition, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0199] 2. Collecting Emotional Data:

[0200] The user's smartphone or wearable device is equipped with an emotion engine that analyzes voice and facial expression data to recognize the user's emotions (e.g., stress, joy, sadness, etc.). The recognized emotion data is sent to a server along with health data and stored in a database.

[0201] Data preprocessing and analysis

[0202] The server preprocesses the collected health and emotion data, cleansing it by removing outliers and normalizing it, improving the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and emotion data to build an algorithm for detecting signs of illness. This algorithm enables early detection of users at increased risk of illness and performs risk assessment taking emotional states into account.

[0203] Disease sign detection and alert notification

[0204] The server analyzes the collected data in real time. If an abnormal pattern is detected, such as a pattern similar to that of a user who has previously developed an illness, or a specific emotional state (for example, a persistent state of stress), a risk assessment is made and the server generates an alert. This alert is immediately sent to the user via their device. The notification content is specific, such as "Your risk of heart disease is increasing. Please see a doctor."

[0205] Condolence payment processing

[0206] If a user actually goes to a hospital and is diagnosed with heart disease, the user enters the diagnosis into the application. The server checks the diagnosis and verifies that the user is eligible for a condolence payment. After confirmation, the server automatically transfers the payment to the bank account specified by the user.

[0207] Specific examples

[0208] For example, if user A uses a smartphone and a wearable device, these devices record his / her heart rate, number of steps, and sleep time every day. The emotion engine also collects emotional data from A's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks such as abnormal heart rate fluctuations and persistent stress. If A's data matches a pattern indicating a risk of heart disease, an alert will be sent to his / her smartphone stating, "Your risk of heart disease is increasing. Please consult a doctor."

[0209] If Person A actually goes to the hospital and is diagnosed with heart disease, the diagnosis results are entered into the application, which is then confirmed by the server and a condolence payment is automatically transferred to Person A's bank account. Through this series of processes, users are supported in the prevention and early detection of disease, and can also receive financial support in emergencies.

[0210] The system of the present invention encourages users to proactively manage their own health and emotional states, thereby contributing to extending overall healthy lifespan. This system allows users to effectively utilize their individual health and emotional data and maintain a lifestyle based on preventive medicine.

[0211] The processing flow will be explained below.

[0212] Step 1:

[0213] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0214] Step 2:

[0215] Device: Collected health data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0216] Step 3:

[0217] Device: The emotion engine analyzes the user's voice data and facial expression data to recognize the user's emotions (stress, joy, sadness, etc.).

[0218] Step 4:

[0219] Terminal: Sends the recognized emotion data along with health data to the server.

[0220] Step 5:

[0221] Server: The received health and emotional data is stored in a database for each user. The stored data includes heart rate, number of steps, sleep time, health check results, medical history, and emotional data.

[0222] Step 6:

[0223] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0224] Step 7:

[0225] Server: Uses machine learning algorithms to learn data patterns and emotional patterns of users who have previously experienced illness, and builds a model to detect signs of illness.

[0226] Step 8:

[0227] Server: Analyzes newly received data in real time and compares it with the trained model.

[0228] Step 9:

[0229] Server: If the analysis reveals abnormal patterns, patterns similar to those of users who have previously developed illness, or specific emotional states, a risk assessment is made based on that information.

[0230] Step 10:

[0231] Server: Generates an alert if the risk assessment is elevated.

[0232] Step 11:

[0233] Device: An alert is sent to the user's smartphone. The notification content includes specific information such as "Your risk of heart disease is increasing. Please consult a doctor."

[0234] Step 12:

[0235] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0236] Step 13:

[0237] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0238] Step 14:

[0239] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0240] Step 15:

[0241] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0242] Examples:

[0243] For example, if user A uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time in steps 1 and 2 and send them to the server. At the same time, the emotion engine collects emotion data from A's voice and facial expressions and sends it to the server in steps 3 and 4. The server accumulates this data and analyzes it using preprocessing and machine learning models (steps 5 to 7).

[0244] If the analysis shows that Person A's data matches a pattern indicating a risk of heart disease and that the high stress state continues (steps 8 and 9), the server will make a risk assessment and generate an alert. An alert will be sent to the person's smartphone (steps 10 and 11), warning them that "Your risk of heart disease is increasing. Please see a doctor."

[0245] After Mr. A receives a diagnosis at the hospital and enters the results into the application (step 12), the server verifies the diagnosis (step 13) and performs the automatic payment procedure for the condolence money (step 14). Finally, the condolence money is transferred to Mr. A's bank account, and the information is notified (step 15).

[0246] Example 2

[0247] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0248] In modern society, it is extremely important to properly monitor a user's health status and detect early signs of illness. However, conventional systems have faced challenges in that they do not adequately preprocess collected health data or analyze emotional data, making it difficult to accurately detect signs of illness. Furthermore, when an illness is confirmed, the process of paying condolence money is often done manually, making it difficult to respond quickly. The purpose of the present invention is to solve these challenges.

[0249] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0250] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for preprocessing the stored data, means for analyzing the preprocessed data and detecting signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to constantly monitor the user's health condition, detect signs of illness early through highly accurate data analysis, and quickly pay a condolence payment.

[0251] "User" refers to an individual who uses the system and provides health and emotional data.

[0252] "Health data" refers to information about the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0253] "Database" refers to a system or location for storing and managing collected health and emotional data.

[0254] "Preprocessing" refers to processing such as removing outliers, normalizing, and deleting duplicate data before analyzing collected data.

[0255] "Machine learning algorithms" refer to algorithms that use past data to train models and then make predictions or classifications based on new data.

[0256] An "alert" refers to a warning or notification sent to a user when a sign of illness is detected.

[0257] "Consolation money" refers to financial assistance provided to users who have been confirmed ill.

[0258] This invention relates to a system that collects and analyzes users' health and emotional data to detect signs of illness, sends alerts as necessary, and even pays condolence money. Specifically, it is composed of three main elements: a server, a terminal, and the user.

[0259] Health and emotional data collection

[0260] Users collect daily health data using smartphones or wearable devices. Specific devices include Fitbit and Apple Watch, which record heart rate, steps, and sleep time. These devices transfer data to smartphones via Bluetooth or Wi-Fi. In addition, users manually enter their regular health checkup results and medical history into the device. This health data is automatically sent from the smartphone to a server and stored in a database.

[0261] In addition, users' smartphones and wearable devices are equipped with emotion engines that capture emotion data from the user's voice and facial expressions. For example, the stress a user feels while on the phone or changes in facial expression when they are happy are recorded as emotion data. This emotion data is also sent to the server and stored in a database.

[0262] Data preprocessing and analysis

[0263] When the data is sent, the server first performs preprocessing such as removing outliers, normalizing, and deleting duplicate data, thereby improving the quality of the data.

[0264] Once the preprocessing is complete, the data is analyzed using machine learning algorithms. Specifically, models trained with machine learning libraries such as TensorFlow and PyTorch are used. These models are trained using previously collected disease occurrence and emotion data, and then use new data to detect signs of disease.

[0265] Alerting and Notifications

[0266] If an abnormal pattern is detected and a high risk of disease is determined, the server generates an alert. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. The message may contain specific information such as, "Your risk of heart disease is increasing. Please consult a doctor." This alert is instantly pushed to the user's smartphone.

[0267] Payment of condolence money

[0268] If a user actually goes to a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they can take a photo of the medical certificate and upload it via the app. The server checks the diagnosis results and automatically verifies that the user is eligible for a condolence payment. Once confirmation is complete, the server transfers the payment to the bank account specified by the user. This transfer process uses a bank API or electronic payment service.

[0269] Specific examples

[0270] For example, Person A uses a wearable device that records heart rate, number of steps, and sleep time, as well as a smartphone. These devices collect daily health data and periodically send it to a server. An emotion engine also collects emotional data such as stress or joy from Person A's voice and facial expressions, and sends this data to the server. The server analyzes this data and evaluates the risk level. If it determines that Person A's risk of heart disease is increasing, it sends an alert to Person A's smartphone stating, "Your risk of heart disease is increasing. Please see a doctor."

[0271] If Mr. A is diagnosed with heart disease by a doctor, he uploads the medical certificate to the app. The server checks the certificate and transfers 50,000 yen as a condolence payment to Mr. A's bank account.

[0272] Example prompts to input to the generative AI model

[0273] "I want to create a system that collects and analyzes users' emotional data and health information, and detects and notifies them of signs of illness. The system will analyze data obtained from users' smartphones and wearable devices using an emotion engine, and generate an alert if an abnormal pattern is found. I also want to include a function that will provide a condolence payment if a user is diagnosed with an illness."

[0274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0275] Step 1: Collecting user health data

[0276] Users collect daily health data such as heart rate, steps, and sleep time using smartphones or wearable devices. The devices transfer this data to smartphones via Bluetooth or Wi-Fi. The smartphones automatically send this data to a server. The input is the user's daily health data, and the output is the raw data sent to the server.

[0277] Step 2: Collecting emotion data

[0278] Users use smartphones or wearable devices equipped with an emotion engine to obtain emotional data from voice and facial expressions. The emotion engine analyzes the user's speech and facial expressions to recognize emotions such as stress, joy, and sadness. The smartphone then sends this emotional data to a server. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0279] Step 3: Preprocessing the data

[0280] The server receives the collected health and emotion data. It then performs preprocessing such as removing outliers, normalizing the data, and removing duplicate data. For example, it detects and removes abnormal heart rate values ​​and duplicate data. The input is the collected raw data, and the output is the preprocessed clean data.

[0281] Step 4: Data analysis

[0282] The server uses the preprocessed data to train a machine learning model. Specifically, a model using TensorFlow or PyTorch is used. This model executes an algorithm to detect signs of disease based on past disease occurrence data and emotion data. The input is the preprocessed data, and the output is the disease risk assessment result.

[0283] Step 5: Alerting and Notification

[0284] The server immediately generates an alert if an abnormality is detected as a result of data analysis. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. This message is pushed to the device to notify the user. The input is the risk assessment result, and the output is the generated alert and its notification.

[0285] Step 6: Payment of condolence money

[0286] If a user receives a diagnosis at a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they take a photo of the medical certificate and upload it via the app. The server verifies the diagnosis results and confirms that the user is eligible for a condolence payment. Once verification is complete, the payment is transferred to the bank account specified by the user. The input is the user's diagnosis results, and the output is the confirmed diagnosis results and the transferred condolence payment.

[0287] At each step, the specific actions and technologies used are explained in detail, clearly showing how the user and server process the data to achieve the final goal.

[0288] (Application example 2)

[0289] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional health monitoring systems collect user health data, but they are unable to comprehensively manage health risks, including emotional states, or ensure the safety of motor vehicles in real time. This makes it difficult to respond quickly and appropriately when signs of illness or abnormal emotional states are detected. Furthermore, the procedure for paying compensation when illness is confirmed is complicated. The present invention aims to solve these problems.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data of the user, means for storing the collected health data and emotional data in a database, means for preprocessing and analyzing the stored data to detect signs of illness and abnormal emotional states, means for issuing an alert to the user terminal and stopping the vehicle in a safe location when signs of illness or abnormal emotional states are detected, and means for paying a condolence payment to a user whose illness is confirmed. This makes it possible to comprehensively manage the user's health and emotional states, quickly ensure the safety of the vehicle in the event of an abnormality, and automatically pay the condolence payment, providing peace of mind and convenience to the user.

[0292] "User's health data" refers to the user's daily heart rate, number of steps, sleep time, health check results, and past medical history.

[0293] "Emotion data" refers to the user's emotional state, such as stress, joy, or sadness, obtained by analyzing the user's voice data and facial expression data.

[0294] "Database" refers to a system for storing and managing collected health and emotional data.

[0295] "Preprocessing" refers to data cleansing processes that remove outliers and normalize data to improve the accuracy of analysis.

[0296] "Analysis" refers to analyzing the collected data to detect signs of illness or abnormal emotional states.

[0297] An "alert" refers to a warning message that is sent to the user when signs of illness or abnormal emotional state are detected.

[0298] "User devices" refer to smartphones, tablets, wearable devices, etc. that users use on a daily basis.

[0299] "Autonomous vehicle" means a vehicle that has an automated driving function.

[0300] "Stopping in a safe place" refers to the act of stopping the vehicle in a safe place when an abnormality is detected while the autonomous vehicle is in operation.

[0301] "Consolation money" refers to money paid to a user if they are diagnosed with an illness.

[0302] The present invention is a system that collects and analyzes a user's health and emotional data to detect signs of illness or abnormal emotional states, notifies the user of an alert as necessary, and even provides a compensation payment. In particular, the present invention provides a function for autonomous vehicles to monitor the passengers' health status in real time and stop the autonomous vehicle in a safe place if an abnormality is detected.

[0303] System configuration

[0304] 1. Collecting health and emotional data

[0305] The server collects health and emotional data from users' smartphones and wearable devices. This data includes heart rate, steps taken, sleep time, health checkup results, medical history, and emotional state extracted from the user's voice and facial expressions. The devices transmit the data to the server using Bluetooth Low Energy (BLE) or Wi-Fi.

[0306] 2. Data accumulation and preprocessing

[0307] The server stores the collected health and emotion data in a database. As preprocessing, data cleansing is performed, including removing outliers and normalizing the data, to improve the accuracy of the analysis.

[0308] 3. Data Analysis

[0309] The server analyzes the accumulated health and emotional data and uses machine learning algorithms to detect signs of illness and abnormal emotional states. This analysis also refers to past disease occurrence data and emotional data to perform risk assessment.

[0310] 4. Alert notifications and safe stopping of motor vehicles

[0311] If an abnormality is detected, the server generates an alert and immediately notifies the user's device. The notification will include a specific message such as "Your risk of heart disease is increasing. Please consult a doctor." It will also instruct the autonomous vehicle to stop in a safe position.

[0312] 5. Payment of condolence money

[0313] If a user is diagnosed with an illness at a medical institution, the diagnosis can be entered into the application, and the server will confirm it and automatically transfer a condolence payment to the bank account specified by the user.

[0314] Program processing explanation

[0315] The server uses programming languages ​​such as Python and Java to execute a series of processes including data collection, preprocessing, analysis, alert generation, and compensation payments. MySQL and PostgreSQL databases are sometimes used. Libraries such as TensorFlow and Scikit-learn are used for machine learning algorithms. NLP (natural language processing) technology and CV (computer vision) are used for sentiment analysis.

[0316] Specific examples

[0317] For example, if a user uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time every day. A device equipped with an emotion engine collects emotional data from the user's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks. If a warning sign is detected, an alert is issued and the self-driving vehicle stops in a safe location. Furthermore, if an illness is diagnosed, a condolence payment is deposited into the user's bank account.

[0318] Example prompts for generative AI models

[0319] Create a program for a health monitoring system for autonomous vehicles that collects and analyzes the user's health and emotional data to detect risks and issue alerts. Specifically, the system monitors heart rate and emotional state in real time, and if an abnormality is detected, it safely stops the vehicle, notifies emergency contacts, and, if necessary, provides compensation. Please also explain the specific problem detection algorithm, data collection, and alert notification processes.

[0320] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0321] Step 1:

[0322] Health and emotional data collection

[0323] The server collects heart rate, step count, sleep time, voice data, and facial expression data from the user's smartphone and wearable device. The smartphone and wearable device transfer the data via Bluetooth or Wi-Fi. The input data is in raw data format and is received by the destination server. Specifically, the data sent from the device is automatically uploaded to the server.

[0324] Step 2:

[0325] Data Preprocessing

[0326] The server receives the collected health and emotion data and performs outlier removal and normalization, thereby reducing noise in the data and improving the accuracy of the analysis. The input is raw data, and the output is pre-processed, clean data. Specifically, the server runs algorithms to filter out outliers and normalize the data.

[0327] Step 3:

[0328] Data accumulation

[0329] The server stores the preprocessed health data and emotion data in a database. The input is the preprocessed clean data, and the output is the data stored in the database. Specifically, the server inserts the cleaned data into the database using SQL queries.

[0330] Step 4:

[0331] Data analysis

[0332] The server runs machine learning algorithms based on the accumulated data to detect signs of illness and abnormal emotional states. The input is data stored in the database, and the output is the analysis results (risk assessment and anomaly detection results). Specifically, the server runs the analysis algorithm using TensorFlow and Scikit-learn.

[0333] Step 5:

[0334] Alert Notifications

[0335] If the server detects an abnormality through data analysis, it generates an alert and notifies the user's device. The input is the analysis result, and the output is an alert message. Specifically, the server generates a message and sends it to the smartphone app via push notification. A specific message such as "Your risk of heart disease is increasing. Please consult a doctor" is displayed.

[0336] Step 6:

[0337] Safe stopping of motor vehicles

[0338] If an abnormality is detected, the server instructs the autonomous vehicle to stop in a safe location. The input is the abnormality detection result, and the output is an instruction to stop the vehicle. Specifically, the server sends an API request to the vehicle's control system and stops the vehicle in the specified location.

[0339] Step 7:

[0340] Payment of condolence money

[0341] When a user receives a diagnosis at a medical institution and enters the results into the application, the server verifies this information and transfers the condolence money to the user's bank account. The input is the diagnosis result, and the output is a notification that the condolence money has been transferred. Specifically, the server verifies the user's input and automatically transfers the money to the specified bank account.

[0342] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0343] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0344] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0345] [Second embodiment]

[0346] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0347] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0348] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0349] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0350] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0351] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0352] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0353] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0354] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0355] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0356] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0357] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0358] The present invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server that operates in conjunction with the user's terminal and wearable device.

[0359] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices. This data is automatically sent to a server, where it is stored in a database. When users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0360] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and builds an algorithm for detecting signs of disease. This algorithm makes it possible to detect users at increased risk of disease early on.

[0361] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to the server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0362] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0363] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0364] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0365] The system of the present invention encourages users to proactively manage their own health status, thereby contributing to extending healthy life expectancy overall. This system allows users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0366] The processing flow will be explained below.

[0367] Step 1:

[0368] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0369] Step 2:

[0370] Terminal: Collected data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0371] Step 3:

[0372] Server: Stores the received data in a database for each user. The stored data includes daily health data, health checkup results, medical history, etc.

[0373] Step 4:

[0374] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0375] Step 5:

[0376] Server: Uses machine learning algorithms to learn data patterns from users who have previously experienced illness and build a model to detect signs of illness.

[0377] Step 6:

[0378] Server: Analyzes newly received data in real time and compares it with the trained model.

[0379] Step 7:

[0380] Server: If the analysis detects abnormal patterns or patterns similar to those of users who have previously experienced illness, a risk assessment is made based on that information.

[0381] Step 8:

[0382] Server: Generates an alert if the risk assessment is elevated.

[0383] Step 9:

[0384] Device: An alert is sent to the user's smartphone, with specific content such as "Your risk of heart disease is increasing. Please consult a doctor."

[0385] Step 10:

[0386] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0387] Step 11:

[0388] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0389] Step 12:

[0390] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0391] Step 13:

[0392] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0393] Examples:

[0394] For example, if a user named Mr. A records his heart rate, number of steps, and sleep time every day using a wearable device, this data is sent to the server in steps 1 and 2. The server accumulates and analyzes this data in steps 3 to 5 and compares it with the trained model. If Mr. A's data matches a pattern indicating a risk of heart disease, a risk assessment is made in steps 6 and 7, and an alert is sent to the user in steps 8 and 9.

[0395] After that, Mr. A receives a diagnosis at the hospital and enters the diagnosis results into the application, which executes steps 10 to 12. The server verifies the diagnosis results and starts the payment process for the condolence money, which is eventually automatically transferred to the user's bank account. In step 13, the user is notified of this information.

[0396] Example 1

[0397] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0398] In modern society, lifestyle-related diseases and acute illnesses are on the rise, making early detection and prevention of these diseases crucial. However, many people lack the means to properly manage their daily health and may miss signs of illness. Furthermore, financial support after illness is often insufficient. For this reason, there is a need for a system that continuously collects and analyzes users' health data to detect early signs of illness, promptly notify alerts when necessary, and even provide compensation.

[0399] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0400] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for cleansing the stored data, means for analyzing the data using a machine learning algorithm to detect signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to efficiently manage user health data, support early detection and prevention of illness, and provide financial support after diagnosis.

[0401] "Health data" refers to physiological data and lifestyle-related data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health checkup results, and medical history.

[0402] "Collection means" refers to devices or software, such as wearable devices or smartphone applications, used to obtain health data from users.

[0403] The "means of storing in a database" refers to a server or cloud storage system for storing and managing collected health data in digital form.

[0404] A "cleansing tool" is an algorithm or software that performs a process to remove outliers and missing values ​​from collected health data and normalize the data.

[0405] The "means of analysis" refers to a system that uses machine learning algorithms and data mining techniques to analyze accumulated health data and detect signs of disease.

[0406] "Means for detecting signs of disease" refers to algorithms and logic that identify potential disease risks based on analyzed data and provide early warnings to users.

[0407] "Means for notifying alerts" refers to a system for sending warning messages or alerts to users when a health risk is detected via their smartphones or other communication devices.

[0408] The "means for paying condolence money to users whose illnesses have been confirmed" is a system that allows users to input their doctor's diagnosis into the system, which then automatically starts the payment process based on that information and transfers the condolence money to the user's bank account.

[0409] This invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server and operates in conjunction with the user's terminal and wearable device.

[0410] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices (e.g., Fitbit or Apple Watch). This data is automatically sent to a server. Furthermore, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0411] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model (e.g., TensorFlow) using past disease occurrence data to build an algorithm for detecting signs of disease. This algorithm enables early detection of users at increased risk of disease.

[0412] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to a server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0413] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0414] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0415] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0416] An example of a prompt sentence to input to the generative AI model is as follows:

[0417] A user uses a wearable device to collect daily health data such as heart rate, number of steps, and sleep time, and sends it to a server via their smartphone. The server accumulates this data and analyzes it using a machine learning model to detect signs of illness. Design a system that sends an alert to the user's smartphone if the risk of illness increases, and pays a condolence payment based on the doctor's diagnosis.

[0418] As a result, the system of the present invention encourages users to proactively manage their own health status, contributing to the extension of overall healthy life expectancy. This system enables users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] Users wear wearable devices (e.g., Fitbit or Apple Watch) to collect daily health data.

[0422] Input: Data such as user heart rate, steps, and sleep time

[0423] Output: Data transfer to smartphone

[0424] How it works: As the user uses the wearable device and engages in daily activities, the device automatically records heart rate, steps taken, and sleep time, and transmits the data via Bluetooth to a dedicated app on the smartphone.

[0425] Step 2:

[0426] The terminal transmits the collected health data to a server.

[0427] Input: Health data collected on a smartphone

[0428] Output: Send data to the server

[0429] How it works: The smartphone app periodically uploads data to the server, which is then securely transferred to the server via the internet.

[0430] Step 3:

[0431] The server stores the received health data in a database.

[0432] Input: Health data sent from the device

[0433] Output: Health data stored in a database

[0434] What it does: The server stores the received data in a MySQL database, checks the data for proper format and structure, and then merges it with the existing data in the database.

[0435] Step 4:

[0436] The server cleanses the accumulated data.

[0437] Input: Health data stored in a database

[0438] Output: Cleansed data

[0439] What it does: The server removes outliers and normalizes the data, for example, detecting and removing extreme or missing values ​​and applying normalization to ensure data consistency.

[0440] Step 5:

[0441] The server uses machine learning algorithms to analyze the data and detect signs of disease.

[0442] Input: Cleansed health data

[0443] Output: Risk assessment results

[0444] How it works: The server uses TensorFlow to apply machine learning models trained on past health data to assess the user's health risks, detecting abnormalities in heart rate and sleep patterns and identifying signs of illness.

[0445] Step 6:

[0446] The server sends an alert to the user terminal if any signs of illness are detected.

[0447] Input: Risk assessment results

[0448] Output: Alert notification to user terminal

[0449] Specific operation: Based on the risk assessment results, the server sends a push notification to the user's smartphone, including instructions such as "Your risk of heart disease is increasing. Please consult a doctor."

[0450] Step 7:

[0451] The user receives a diagnosis from a doctor and enters the results into the application.

[0452] Input: Doctor's diagnosis

[0453] Output: Send diagnostic results to the server

[0454] Specific operation: The user receives a diagnosis from a doctor, and the diagnosis of "heart disease" is entered into a smartphone application and sent to the server.

[0455] Step 8:

[0456] The server checks the entered diagnosis results.

[0457] Input: Diagnostic results sent by the user

[0458] Output: Check the diagnostic results

[0459] Specific operation: The server executes a process to check the received diagnostic results against the database and confirm their validity.

[0460] Step 9:

[0461] If the diagnosis result is confirmed to be valid, the server starts the procedure for paying the compensation.

[0462] Input: Verified diagnostic results

[0463] Output: Consolation payment

[0464] Specific operation: The server triggers the condolence payment process and automatically transfers the money to the specified bank account.

[0465] (Application example 1)

[0466] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0467] In modern society, it is important for users to manage their own health, but many people are so busy with their daily lives that they tend to neglect their health management. Furthermore, for users who lead particularly busy lives, choosing healthy meals can be difficult. Furthermore, there is a concern that there is an increased risk of health risks, as there is no system that simultaneously monitors health data and suggests meals, and takes immediate action if an abnormality is detected.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0469] In this invention, the server includes a means for collecting user health data, a means for storing the collected health data in a database, and a means for analyzing the stored data and detecting signs of illness, thereby enabling constant monitoring of the user's health condition and early detection of health risks.

[0470] Furthermore, if signs of illness are detected, the system includes a means for sending an alert to the user's terminal, a means for paying a condolence payment to a user who has been confirmed to be ill, and a means for suggesting an optimal meal menu based on the user's health condition. This encourages the user to take appropriate health management measures and also supports healthy meal selection, enabling overall health maintenance.

[0471] "User's health data" refers to various data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0472] "Database" means an electronic system and structure for storing and managing collected health data.

[0473] "Means for detecting signs of illness" refers to algorithms or programs that use accumulated health data to predict the likelihood of a user becoming ill.

[0474] "Means for notifying an alert" is a function for sending a warning or notification to the user's device when a sign of illness is detected.

[0475] The "means for paying condolence money" is a function for providing financial compensation and support to users who have been confirmed to be ill.

[0476] "Means for suggesting optimal meal menus" refers to a system function that provides and recommends appropriate meal contents based on the user's health condition.

[0477] A "machine learning algorithm" is a computational method for learning patterns based on past data and making predictions and classifications.

[0478] A "health-based meal menu" is a meal plan recommended for maintaining health based on the user's current health data.

[0479] The system that realizes this invention collects user health data, stores it in a database, analyzes it, sends alerts, provides condolence payments, and suggests meal plans, among other functions, allowing users to continuously manage their own health status and respond quickly if an abnormality is detected.

[0480] First, smartphones and wearable devices are used to collect users' health data, including information such as heart rate, number of steps taken, and sleep duration, which is automatically sent to a server.

[0481] The server stores this data in a database. The database can use a relational database management system such as MySQL or PostgreSQL. The information stored in the database is organized by user and used for later analysis.

[0482] The server then analyzes the accumulated data using machine learning algorithms (for example, Python's scikit-learn library, which has skills such as RandomForestClassifier and GradientBoostingClassifier). This allows it to detect signs of illness and send appropriate alerts to the user's device if an abnormality is found. These alerts help users recognize health risks early and take measures.

[0483] For example, if a user experiences an abnormal increase in heart rate, the server analyzes the data and sends a notification to the smartphone stating, "An abnormal heart rate has been detected. We recommend that you see a doctor." In addition, if the user is diagnosed with an illness through the condolence payment system, a condolence payment will be transferred to the user's designated bank account along with the diagnosis.

[0484] The system also suggests optimal meal plans based on the user's health status. For example, it analyzes the latest health data collected from wearable devices and smartphones and recommends meals containing specific ingredients and nutrients via a food delivery service. In this case, the application displays specific recommended meals along with an alert such as, "Your heart rate is high. We recommend a low-calorie menu."

[0485] An example of a prompt might be, "Design an API that collects a user's weekend heart rate data and provides health advice based on that data. The API will detect abnormal heart rates and recommend salads and low-calorie meals." This allows developers to clearly and concretely understand the system's functionality and proceed with implementation.

[0486] In this way, the invention is a system that comprehensively supports the user's health management and is highly effective from the perspective of preventive medicine.

[0487] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0488] Step 1:

[0489] The user's device (smartphone or wearable device) collects health data such as heart rate, number of steps, and sleep time. This data is acquired using sensors on each device (heart rate sensor, accelerometer, gyroscope, etc.) and automatically sent to a server via an application. The input data is biometric data and activity data, and the output is data packets sent from the device to the server.

[0490] Step 2:

[0491] The server stores the collected health data in a database. The database uses a relational database management system such as MySQL or PostgreSQL. The input is the health data sent by the user, and the output is the data organized and stored in the database.

[0492] Step 3:

[0493] The server preprocesses the health data stored in the database. This involves cleansing the data (removing outliers, normalizing, etc.). For example, it detects and removes abnormally high heart rates and zero values, and standardizes the data. The input is raw data obtained from the database, and the output is clean data that has been preprocessed.

[0494] Step 4:

[0495] The server analyzes the preprocessed health data. It uses machine learning algorithms (e.g., RandomForestClassifier, GradientBoostingClassifier, and other scikit-learn libraries) to detect signs of disease. The input is the cleansed health data, and the output is abnormal patterns and disease risk assessment results.

[0496] Step 5:

[0497] If a sign of illness is detected, the server sends an alert to the user's device. The alert uses the smartphone's notification function to notify the user of the abnormality. For example, a notification such as "Your heart rate is abnormally high. Please see a doctor" is displayed. The input is the disease risk assessment result, and the output is an alert notification displayed on the user's device.

[0498] Step 6:

[0499] The server also manages the procedure for paying condolence money to users whose illnesses are confirmed. When a user receives a diagnosis at a hospital or other facility and enters the diagnosis results into the application, the data is sent to the server. The server verifies the data and, if valid, automatically initiates the payment of the condolence money. The input is the diagnosis result entered by the user, and the output is a transfer of the condolence money to the user's bank account.

[0500] Step 7:

[0501] The server proposes an optimal meal menu based on the user's health condition. Based on the analysis results, the application makes specific meal suggestions, such as "Your heart rate is high. We recommend a low-calorie menu." The input is the analysis results of the health data, and the output is information on the recommended meal menu. This information becomes a specific meal suggestion that reflects the user's health condition.

[0502] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0503] The present invention relates to a system that detects signs of illness by collecting and analyzing a user's health data, notifying them of alerts as necessary, and even paying condolence money. The system of the present invention has a central server that operates in conjunction with the user's terminal, wearable device, and emotion engine.

[0504] System configuration

[0505] 1. User health data collection:

[0506] Users use smartphones or wearable devices to collect daily health data such as heart rate, number of steps, and sleep time. This data is automatically sent to a server, where it is stored in a database. In addition, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0507] 2. Collecting Emotional Data:

[0508] The user's smartphone or wearable device is equipped with an emotion engine that analyzes voice and facial expression data to recognize the user's emotions (e.g., stress, joy, sadness, etc.). The recognized emotion data is sent to a server along with health data and stored in a database.

[0509] Data preprocessing and analysis

[0510] The server preprocesses the collected health and emotion data, cleansing it by removing outliers and normalizing it, improving the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and emotion data to build an algorithm for detecting signs of illness. This algorithm enables early detection of users at increased risk of illness and performs risk assessment taking emotional states into account.

[0511] Disease sign detection and alert notification

[0512] The server analyzes the collected data in real time. If an abnormal pattern is detected, such as a pattern similar to that of a user who has previously developed an illness, or a specific emotional state (for example, a persistent state of stress), a risk assessment is made and the server generates an alert. This alert is immediately sent to the user via their device. The notification content is specific, such as "Your risk of heart disease is increasing. Please see a doctor."

[0513] Condolence payment processing

[0514] If a user actually goes to a hospital and is diagnosed with heart disease, the user enters the diagnosis into the application. The server checks the diagnosis and verifies that the user is eligible for a condolence payment. After confirmation, the server automatically transfers the payment to the bank account specified by the user.

[0515] Specific examples

[0516] For example, if user A uses a smartphone and a wearable device, these devices record his / her heart rate, number of steps, and sleep time every day. The emotion engine also collects emotional data from A's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks such as abnormal heart rate fluctuations and persistent stress. If A's data matches a pattern indicating a risk of heart disease, an alert will be sent to his / her smartphone stating, "Your risk of heart disease is increasing. Please consult a doctor."

[0517] If Person A actually goes to the hospital and is diagnosed with heart disease, the diagnosis results are entered into the application, which is then confirmed by the server and a condolence payment is automatically transferred to Person A's bank account. Through this series of processes, users are supported in the prevention and early detection of disease, and can also receive financial support in emergencies.

[0518] The system of the present invention encourages users to proactively manage their own health and emotional states, thereby contributing to extending overall healthy lifespan. This system allows users to effectively utilize their individual health and emotional data and maintain a lifestyle based on preventive medicine.

[0519] The processing flow will be explained below.

[0520] Step 1:

[0521] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0522] Step 2:

[0523] Device: Collected health data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0524] Step 3:

[0525] Device: The emotion engine analyzes the user's voice data and facial expression data to recognize the user's emotions (stress, joy, sadness, etc.).

[0526] Step 4:

[0527] Terminal: Sends the recognized emotion data along with health data to the server.

[0528] Step 5:

[0529] Server: The received health and emotional data is stored in a database for each user. The stored data includes heart rate, number of steps, sleep time, health check results, medical history, and emotional data.

[0530] Step 6:

[0531] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0532] Step 7:

[0533] Server: Uses machine learning algorithms to learn data patterns and emotional patterns of users who have previously experienced illness, and builds a model to detect signs of illness.

[0534] Step 8:

[0535] Server: Analyzes newly received data in real time and compares it with the trained model.

[0536] Step 9:

[0537] Server: If the analysis reveals abnormal patterns, patterns similar to those of users who have previously developed illness, or specific emotional states, a risk assessment is made based on that information.

[0538] Step 10:

[0539] Server: Generates an alert if the risk assessment is elevated.

[0540] Step 11:

[0541] Device: An alert is sent to the user's smartphone. The notification content includes specific information such as "Your risk of heart disease is increasing. Please consult a doctor."

[0542] Step 12:

[0543] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0544] Step 13:

[0545] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0546] Step 14:

[0547] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0548] Step 15:

[0549] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0550] Examples:

[0551] For example, if user A uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time in steps 1 and 2 and send them to the server. At the same time, the emotion engine collects emotion data from A's voice and facial expressions and sends it to the server in steps 3 and 4. The server accumulates this data and analyzes it using preprocessing and machine learning models (steps 5 to 7).

[0552] If the analysis shows that Person A's data matches a pattern indicating a risk of heart disease and that the high stress state continues (steps 8 and 9), the server will make a risk assessment and generate an alert. An alert will be sent to the person's smartphone (steps 10 and 11), warning them that "Your risk of heart disease is increasing. Please see a doctor."

[0553] After Mr. A receives a diagnosis at the hospital and enters the results into the application (step 12), the server verifies the diagnosis (step 13) and performs the automatic payment procedure for the condolence money (step 14). Finally, the condolence money is transferred to Mr. A's bank account, and the information is notified (step 15).

[0554] Example 2

[0555] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0556] In modern society, it is extremely important to properly monitor a user's health status and detect early signs of illness. However, conventional systems have faced challenges in that they do not adequately preprocess collected health data or analyze emotional data, making it difficult to accurately detect signs of illness. Furthermore, when an illness is confirmed, the process of paying condolence money is often done manually, making it difficult to respond quickly. The purpose of the present invention is to solve these challenges.

[0557] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0558] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for preprocessing the stored data, means for analyzing the preprocessed data and detecting signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to constantly monitor the user's health condition, detect signs of illness early through highly accurate data analysis, and quickly pay a condolence payment.

[0559] "User" refers to an individual who uses the system and provides health and emotional data.

[0560] "Health data" refers to information about the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0561] "Database" refers to a system or location for storing and managing collected health and emotional data.

[0562] "Preprocessing" refers to processing such as removing outliers, normalizing, and deleting duplicate data before analyzing collected data.

[0563] "Machine learning algorithms" refer to algorithms that use past data to train models and then make predictions or classifications based on new data.

[0564] An "alert" refers to a warning or notification sent to a user when a sign of illness is detected.

[0565] "Consolation money" refers to financial assistance provided to users who have been confirmed ill.

[0566] This invention relates to a system that collects and analyzes users' health and emotional data to detect signs of illness, sends alerts as necessary, and even pays condolence money. Specifically, it is composed of three main elements: a server, a terminal, and the user.

[0567] Health and emotional data collection

[0568] Users collect daily health data using smartphones or wearable devices. Specific devices include Fitbit and Apple Watch, which record heart rate, steps, and sleep time. These devices transfer data to smartphones via Bluetooth or Wi-Fi. In addition, users manually enter their regular health checkup results and medical history into the device. This health data is automatically sent from the smartphone to a server and stored in a database.

[0569] In addition, users' smartphones and wearable devices are equipped with emotion engines that capture emotion data from the user's voice and facial expressions. For example, the stress a user feels while on the phone or changes in facial expression when they are happy are recorded as emotion data. This emotion data is also sent to the server and stored in a database.

[0570] Data preprocessing and analysis

[0571] When the data is sent, the server first performs preprocessing such as removing outliers, normalizing, and deleting duplicate data, thereby improving the quality of the data.

[0572] Once the preprocessing is complete, the data is analyzed using machine learning algorithms. Specifically, models trained with machine learning libraries such as TensorFlow and PyTorch are used. These models are trained using previously collected disease occurrence and emotion data, and then use new data to detect signs of disease.

[0573] Alerting and Notifications

[0574] If an abnormal pattern is detected and a high risk of disease is determined, the server generates an alert. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. The message may contain specific information such as, "Your risk of heart disease is increasing. Please consult a doctor." This alert is instantly pushed to the user's smartphone.

[0575] Payment of condolence money

[0576] If a user actually goes to a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they can take a photo of the medical certificate and upload it via the app. The server checks the diagnosis results and automatically verifies that the user is eligible for a condolence payment. Once confirmation is complete, the server transfers the payment to the bank account specified by the user. This transfer process uses a bank API or electronic payment service.

[0577] Specific examples

[0578] For example, Person A uses a wearable device that records heart rate, number of steps, and sleep time, as well as a smartphone. These devices collect daily health data and periodically send it to a server. An emotion engine also collects emotional data such as stress or joy from Person A's voice and facial expressions, and sends this data to the server. The server analyzes this data and evaluates the risk level. If it determines that Person A's risk of heart disease is increasing, it sends an alert to Person A's smartphone stating, "Your risk of heart disease is increasing. Please see a doctor."

[0579] If Mr. A is diagnosed with heart disease by a doctor, he uploads the medical certificate to the app. The server checks the certificate and transfers 50,000 yen as a condolence payment to Mr. A's bank account.

[0580] Example prompts to input to the generative AI model

[0581] "I want to create a system that collects and analyzes users' emotional data and health information, and detects and notifies them of signs of illness. The system will analyze data obtained from users' smartphones and wearable devices using an emotion engine, and generate an alert if an abnormal pattern is found. I also want to include a function that will provide a condolence payment if a user is diagnosed with an illness."

[0582] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0583] Step 1: Collecting user health data

[0584] Users collect daily health data such as heart rate, steps, and sleep time using smartphones or wearable devices. The devices transfer this data to smartphones via Bluetooth or Wi-Fi. The smartphones automatically send this data to a server. The input is the user's daily health data, and the output is the raw data sent to the server.

[0585] Step 2: Collecting emotion data

[0586] Users use smartphones or wearable devices equipped with an emotion engine to obtain emotional data from voice and facial expressions. The emotion engine analyzes the user's speech and facial expressions to recognize emotions such as stress, joy, and sadness. The smartphone then sends this emotional data to a server. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0587] Step 3: Preprocessing the data

[0588] The server receives the collected health and emotion data. It then performs preprocessing such as removing outliers, normalizing the data, and removing duplicate data. For example, it detects and removes abnormal heart rate values ​​and duplicate data. The input is the collected raw data, and the output is the preprocessed clean data.

[0589] Step 4: Data analysis

[0590] The server uses the preprocessed data to train a machine learning model. Specifically, a model using TensorFlow or PyTorch is used. This model executes an algorithm to detect signs of disease based on past disease occurrence data and emotion data. The input is the preprocessed data, and the output is the disease risk assessment result.

[0591] Step 5: Alerting and Notification

[0592] The server immediately generates an alert if an abnormality is detected as a result of data analysis. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. This message is pushed to the device to notify the user. The input is the risk assessment result, and the output is the generated alert and its notification.

[0593] Step 6: Payment of condolence money

[0594] If a user receives a diagnosis at a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they take a photo of the medical certificate and upload it via the app. The server verifies the diagnosis results and confirms that the user is eligible for a condolence payment. Once verification is complete, the payment is transferred to the bank account specified by the user. The input is the user's diagnosis results, and the output is the confirmed diagnosis results and the transferred condolence payment.

[0595] At each step, the specific actions and technologies used are explained in detail, clearly showing how the user and server process the data to achieve the final goal.

[0596] (Application example 2)

[0597] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Conventional health monitoring systems collect user health data, but they are unable to comprehensively manage health risks, including emotional states, or ensure the safety of motor vehicles in real time. This makes it difficult to respond quickly and appropriately when signs of illness or abnormal emotional states are detected. Furthermore, the procedure for paying compensation when illness is confirmed is complicated. The present invention aims to solve these problems.

[0599] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data of the user, means for storing the collected health data and emotional data in a database, means for preprocessing and analyzing the stored data to detect signs of illness and abnormal emotional states, means for issuing an alert to the user terminal and stopping the vehicle in a safe location when signs of illness or abnormal emotional states are detected, and means for paying a condolence payment to a user whose illness is confirmed. This makes it possible to comprehensively manage the user's health and emotional states, quickly ensure the safety of the vehicle in the event of an abnormality, and automatically pay the condolence payment, providing peace of mind and convenience to the user.

[0600] "User's health data" refers to the user's daily heart rate, number of steps, sleep time, health check results, and past medical history.

[0601] "Emotion data" refers to the user's emotional state, such as stress, joy, or sadness, obtained by analyzing the user's voice data and facial expression data.

[0602] "Database" refers to a system for storing and managing collected health and emotional data.

[0603] "Preprocessing" refers to data cleansing processes that remove outliers and normalize data to improve the accuracy of analysis.

[0604] "Analysis" refers to analyzing the collected data to detect signs of illness or abnormal emotional states.

[0605] An "alert" refers to a warning message that is sent to the user when signs of illness or abnormal emotional state are detected.

[0606] "User devices" refer to smartphones, tablets, wearable devices, etc. that users use on a daily basis.

[0607] "Autonomous vehicle" means a vehicle that has an automated driving function.

[0608] "Stopping in a safe place" refers to the act of stopping the vehicle in a safe place when an abnormality is detected while the autonomous vehicle is in operation.

[0609] "Consolation money" refers to money paid to a user if they are diagnosed with an illness.

[0610] The present invention is a system that collects and analyzes a user's health and emotional data to detect signs of illness or abnormal emotional states, notifies the user of an alert as necessary, and even provides a compensation payment. In particular, the present invention provides a function for autonomous vehicles to monitor the passengers' health status in real time and stop the autonomous vehicle in a safe place if an abnormality is detected.

[0611] System configuration

[0612] 1. Collecting health and emotional data

[0613] The server collects health and emotional data from users' smartphones and wearable devices. This data includes heart rate, steps taken, sleep time, health checkup results, medical history, and emotional state extracted from the user's voice and facial expressions. The devices transmit the data to the server using Bluetooth Low Energy (BLE) or Wi-Fi.

[0614] 2. Data accumulation and preprocessing

[0615] The server stores the collected health and emotion data in a database. As preprocessing, data cleansing is performed, including removing outliers and normalizing the data, to improve the accuracy of the analysis.

[0616] 3. Data Analysis

[0617] The server analyzes the accumulated health and emotional data and uses machine learning algorithms to detect signs of illness and abnormal emotional states. This analysis also refers to past disease occurrence data and emotional data to perform risk assessment.

[0618] 4. Alert notifications and safe stopping of motor vehicles

[0619] If an abnormality is detected, the server generates an alert and immediately notifies the user's device. The notification will include a specific message such as "Your risk of heart disease is increasing. Please consult a doctor." It will also instruct the autonomous vehicle to stop in a safe position.

[0620] 5. Payment of condolence money

[0621] If a user is diagnosed with an illness at a medical institution, the diagnosis can be entered into the application, and the server will confirm it and automatically transfer a condolence payment to the bank account specified by the user.

[0622] Program processing explanation

[0623] The server uses programming languages ​​such as Python and Java to execute a series of processes including data collection, preprocessing, analysis, alert generation, and compensation payments. MySQL and PostgreSQL databases are sometimes used. Libraries such as TensorFlow and Scikit-learn are used for machine learning algorithms. NLP (natural language processing) technology and CV (computer vision) are used for sentiment analysis.

[0624] Specific examples

[0625] For example, if a user uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time every day. A device equipped with an emotion engine collects emotional data from the user's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks. If a warning sign is detected, an alert is issued and the self-driving vehicle stops in a safe location. Furthermore, if an illness is diagnosed, a condolence payment is deposited into the user's bank account.

[0626] Example prompts for generative AI models

[0627] Create a program for a health monitoring system for autonomous vehicles that collects and analyzes the user's health and emotional data to detect risks and issue alerts. Specifically, the system monitors heart rate and emotional state in real time, and if an abnormality is detected, it safely stops the vehicle, notifies emergency contacts, and, if necessary, provides compensation. Please also explain the specific problem detection algorithm, data collection, and alert notification processes.

[0628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0629] Step 1:

[0630] Health and emotional data collection

[0631] The server collects heart rate, step count, sleep time, voice data, and facial expression data from the user's smartphone and wearable device. The smartphone and wearable device transfer the data via Bluetooth or Wi-Fi. The input data is in raw data format and is received by the destination server. Specifically, the data sent from the device is automatically uploaded to the server.

[0632] Step 2:

[0633] Data Preprocessing

[0634] The server receives the collected health and emotion data and performs outlier removal and normalization, thereby reducing noise in the data and improving the accuracy of the analysis. The input is raw data, and the output is pre-processed, clean data. Specifically, the server runs algorithms to filter out outliers and normalize the data.

[0635] Step 3:

[0636] Data accumulation

[0637] The server stores the preprocessed health data and emotion data in a database. The input is the preprocessed clean data, and the output is the data stored in the database. Specifically, the server inserts the cleaned data into the database using SQL queries.

[0638] Step 4:

[0639] Data analysis

[0640] The server runs machine learning algorithms based on the accumulated data to detect signs of illness and abnormal emotional states. The input is data stored in the database, and the output is the analysis results (risk assessment and anomaly detection results). Specifically, the server runs the analysis algorithm using TensorFlow and Scikit-learn.

[0641] Step 5:

[0642] Alert Notifications

[0643] If the server detects an abnormality through data analysis, it generates an alert and notifies the user's device. The input is the analysis result, and the output is an alert message. Specifically, the server generates a message and sends it to the smartphone app via push notification. A specific message such as "Your risk of heart disease is increasing. Please consult a doctor" is displayed.

[0644] Step 6:

[0645] Safe stopping of motor vehicles

[0646] If an abnormality is detected, the server instructs the autonomous vehicle to stop in a safe location. The input is the abnormality detection result, and the output is an instruction to stop the vehicle. Specifically, the server sends an API request to the vehicle's control system and stops the vehicle in the specified location.

[0647] Step 7:

[0648] Payment of condolence money

[0649] When a user receives a diagnosis at a medical institution and enters the results into the application, the server verifies this information and transfers the condolence money to the user's bank account. The input is the diagnosis result, and the output is a notification that the condolence money has been transferred. Specifically, the server verifies the user's input and automatically transfers the money to the specified bank account.

[0650] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0651] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0652] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0653] [Third embodiment]

[0654] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0655] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0656] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0657] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0658] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0659] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0660] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0661] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0662] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0663] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0665] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0666] The present invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server that operates in conjunction with the user's terminal and wearable device.

[0667] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices. This data is automatically sent to a server, where it is stored in a database. When users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0668] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and builds an algorithm for detecting signs of disease. This algorithm makes it possible to detect users at increased risk of disease early on.

[0669] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to the server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0670] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0671] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0672] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0673] The system of the present invention encourages users to proactively manage their own health status, thereby contributing to extending healthy life expectancy overall. This system allows users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0677] Step 2:

[0678] Terminal: Collected data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0679] Step 3:

[0680] Server: Stores the received data in a database for each user. The stored data includes daily health data, health checkup results, medical history, etc.

[0681] Step 4:

[0682] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0683] Step 5:

[0684] Server: Uses machine learning algorithms to learn data patterns from users who have previously experienced illness and build a model to detect signs of illness.

[0685] Step 6:

[0686] Server: Analyzes newly received data in real time and compares it with the trained model.

[0687] Step 7:

[0688] Server: If the analysis detects abnormal patterns or patterns similar to those of users who have previously experienced illness, a risk assessment is made based on that information.

[0689] Step 8:

[0690] Server: Generates an alert if the risk assessment is elevated.

[0691] Step 9:

[0692] Device: An alert is sent to the user's smartphone, with specific content such as "Your risk of heart disease is increasing. Please consult a doctor."

[0693] Step 10:

[0694] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0695] Step 11:

[0696] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0697] Step 12:

[0698] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0699] Step 13:

[0700] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0701] Examples:

[0702] For example, if a user named Mr. A records his heart rate, number of steps, and sleep time every day using a wearable device, this data is sent to the server in steps 1 and 2. The server accumulates and analyzes this data in steps 3 to 5 and compares it with the trained model. If Mr. A's data matches a pattern indicating a risk of heart disease, a risk assessment is made in steps 6 and 7, and an alert is sent to the user in steps 8 and 9.

[0703] After that, Mr. A receives a diagnosis at the hospital and enters the diagnosis results into the application, which executes steps 10 to 12. The server verifies the diagnosis results and starts the payment process for the condolence money, which is eventually automatically transferred to the user's bank account. In step 13, the user is notified of this information.

[0704] Example 1

[0705] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0706] In modern society, lifestyle-related diseases and acute illnesses are on the rise, making early detection and prevention of these diseases crucial. However, many people lack the means to properly manage their daily health and may miss signs of illness. Furthermore, financial support after illness is often insufficient. For this reason, there is a need for a system that continuously collects and analyzes users' health data to detect early signs of illness, promptly notify alerts when necessary, and even provide compensation.

[0707] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0708] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for cleansing the stored data, means for analyzing the data using a machine learning algorithm to detect signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to efficiently manage user health data, support early detection and prevention of illness, and provide financial support after diagnosis.

[0709] "Health data" refers to physiological data and lifestyle-related data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health checkup results, and medical history.

[0710] "Collection means" refers to devices or software, such as wearable devices or smartphone applications, used to obtain health data from users.

[0711] The "means of storing in a database" refers to a server or cloud storage system for storing and managing collected health data in digital form.

[0712] A "cleansing tool" is an algorithm or software that performs a process to remove outliers and missing values ​​from collected health data and normalize the data.

[0713] The "means of analysis" refers to a system that uses machine learning algorithms and data mining techniques to analyze accumulated health data and detect signs of disease.

[0714] "Means for detecting signs of disease" refers to algorithms and logic that identify potential disease risks based on analyzed data and provide early warnings to users.

[0715] "Means for notifying alerts" refers to a system for sending warning messages or alerts to users when a health risk is detected via their smartphones or other communication devices.

[0716] The "means for paying condolence money to users whose illnesses have been confirmed" is a system that allows users to input their doctor's diagnosis into the system, which then automatically starts the payment process based on that information and transfers the condolence money to the user's bank account.

[0717] This invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server and operates in conjunction with the user's terminal and wearable device.

[0718] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices (e.g., Fitbit or Apple Watch). This data is automatically sent to a server. Furthermore, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0719] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model (e.g., TensorFlow) using past disease occurrence data to build an algorithm for detecting signs of disease. This algorithm enables early detection of users at increased risk of disease.

[0720] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to a server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0721] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0722] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0723] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0724] An example of a prompt sentence to input to the generative AI model is as follows:

[0725] A user uses a wearable device to collect daily health data such as heart rate, number of steps, and sleep time, and sends it to a server via their smartphone. The server accumulates this data and analyzes it using a machine learning model to detect signs of illness. Design a system that sends an alert to the user's smartphone if the risk of illness increases, and pays a condolence payment based on the doctor's diagnosis.

[0726] As a result, the system of the present invention encourages users to proactively manage their own health status, contributing to the extension of overall healthy life expectancy. This system enables users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] Step 1:

[0729] Users wear wearable devices (e.g., Fitbit or Apple Watch) to collect daily health data.

[0730] Input: Data such as user heart rate, steps, and sleep time

[0731] Output: Data transfer to smartphone

[0732] How it works: As the user uses the wearable device and engages in daily activities, the device automatically records heart rate, steps taken, and sleep time, and transmits the data via Bluetooth to a dedicated app on the smartphone.

[0733] Step 2:

[0734] The terminal transmits the collected health data to a server.

[0735] Input: Health data collected on a smartphone

[0736] Output: Send data to the server

[0737] How it works: The smartphone app periodically uploads data to the server, which is then securely transferred to the server via the internet.

[0738] Step 3:

[0739] The server stores the received health data in a database.

[0740] Input: Health data sent from the device

[0741] Output: Health data stored in a database

[0742] What it does: The server stores the received data in a MySQL database, checks the data for proper format and structure, and then merges it with the existing data in the database.

[0743] Step 4:

[0744] The server cleanses the accumulated data.

[0745] Input: Health data stored in a database

[0746] Output: Cleansed data

[0747] What it does: The server removes outliers and normalizes the data, for example, detecting and removing extreme or missing values ​​and applying normalization to ensure data consistency.

[0748] Step 5:

[0749] The server uses machine learning algorithms to analyze the data and detect signs of disease.

[0750] Input: Cleansed health data

[0751] Output: Risk assessment results

[0752] How it works: The server uses TensorFlow to apply machine learning models trained on past health data to assess the user's health risks, detecting abnormalities in heart rate and sleep patterns and identifying signs of illness.

[0753] Step 6:

[0754] The server sends an alert to the user terminal if any signs of illness are detected.

[0755] Input: Risk assessment results

[0756] Output: Alert notification to user terminal

[0757] Specific operation: Based on the risk assessment results, the server sends a push notification to the user's smartphone, including instructions such as "Your risk of heart disease is increasing. Please consult a doctor."

[0758] Step 7:

[0759] The user receives a diagnosis from a doctor and enters the results into the application.

[0760] Input: Doctor's diagnosis

[0761] Output: Send diagnostic results to the server

[0762] Specific operation: The user receives a diagnosis from a doctor, and the diagnosis of "heart disease" is entered into a smartphone application and sent to the server.

[0763] Step 8:

[0764] The server checks the entered diagnosis results.

[0765] Input: Diagnostic results sent by the user

[0766] Output: Check the diagnostic results

[0767] Specific operation: The server executes a process to check the received diagnostic results against the database and confirm their validity.

[0768] Step 9:

[0769] If the diagnosis result is confirmed to be valid, the server starts the procedure for paying the compensation.

[0770] Input: Verified diagnostic results

[0771] Output: Consolation payment

[0772] Specific operation: The server triggers the condolence payment process and automatically transfers the money to the specified bank account.

[0773] (Application example 1)

[0774] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0775] In modern society, it is important for users to manage their own health, but many people are so busy with their daily lives that they tend to neglect their health management. Furthermore, for users who lead particularly busy lives, choosing healthy meals can be difficult. Furthermore, there is a concern that there is an increased risk of health risks, as there is no system that simultaneously monitors health data and suggests meals, and takes immediate action if an abnormality is detected.

[0776] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0777] In this invention, the server includes a means for collecting user health data, a means for storing the collected health data in a database, and a means for analyzing the stored data and detecting signs of illness, thereby enabling constant monitoring of the user's health condition and early detection of health risks.

[0778] Furthermore, if signs of illness are detected, the system includes a means for sending an alert to the user's terminal, a means for paying a condolence payment to a user who has been confirmed to be ill, and a means for suggesting an optimal meal menu based on the user's health condition. This encourages the user to take appropriate health management measures and also supports healthy meal selection, enabling overall health maintenance.

[0779] "User's health data" refers to various data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0780] "Database" means an electronic system and structure for storing and managing collected health data.

[0781] "Means for detecting signs of illness" refers to algorithms or programs that use accumulated health data to predict the likelihood of a user becoming ill.

[0782] "Means for notifying an alert" is a function for sending a warning or notification to the user's device when a sign of illness is detected.

[0783] The "means for paying condolence money" is a function for providing financial compensation and support to users who have been confirmed to be ill.

[0784] "Means for suggesting optimal meal menus" refers to a system function that provides and recommends appropriate meal contents based on the user's health condition.

[0785] A "machine learning algorithm" is a computational method for learning patterns based on past data and making predictions and classifications.

[0786] A "health-based meal menu" is a meal plan recommended for maintaining health based on the user's current health data.

[0787] The system that realizes this invention collects user health data, stores it in a database, analyzes it, sends alerts, provides condolence payments, and suggests meal plans, among other functions, allowing users to continuously manage their own health status and respond quickly if an abnormality is detected.

[0788] First, smartphones and wearable devices are used to collect users' health data, including information such as heart rate, number of steps taken, and sleep duration, which is automatically sent to a server.

[0789] The server stores this data in a database. The database can use a relational database management system such as MySQL or PostgreSQL. The information stored in the database is organized by user and used for later analysis.

[0790] The server then analyzes the accumulated data using machine learning algorithms (for example, Python's scikit-learn library, which has skills such as RandomForestClassifier and GradientBoostingClassifier). This allows it to detect signs of illness and send appropriate alerts to the user's device if an abnormality is found. These alerts help users recognize health risks early and take measures.

[0791] For example, if a user experiences an abnormal increase in heart rate, the server analyzes the data and sends a notification to the smartphone stating, "An abnormal heart rate has been detected. We recommend that you see a doctor." In addition, if the user is diagnosed with an illness through the condolence payment system, a condolence payment will be transferred to the user's designated bank account along with the diagnosis.

[0792] The system also suggests optimal meal plans based on the user's health status. For example, it analyzes the latest health data collected from wearable devices and smartphones and recommends meals containing specific ingredients and nutrients via a food delivery service. In this case, the application displays specific recommended meals along with an alert such as, "Your heart rate is high. We recommend a low-calorie menu."

[0793] An example of a prompt might be, "Design an API that collects a user's weekend heart rate data and provides health advice based on that data. The API will detect abnormal heart rates and recommend salads and low-calorie meals." This allows developers to clearly and concretely understand the system's functionality and proceed with implementation.

[0794] In this way, the invention is a system that comprehensively supports the user's health management and is highly effective from the perspective of preventive medicine.

[0795] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0796] Step 1:

[0797] The user's device (smartphone or wearable device) collects health data such as heart rate, number of steps, and sleep time. This data is acquired using sensors on each device (heart rate sensor, accelerometer, gyroscope, etc.) and automatically sent to a server via an application. The input data is biometric data and activity data, and the output is data packets sent from the device to the server.

[0798] Step 2:

[0799] The server stores the collected health data in a database. The database uses a relational database management system such as MySQL or PostgreSQL. The input is the health data sent by the user, and the output is the data organized and stored in the database.

[0800] Step 3:

[0801] The server preprocesses the health data stored in the database. This involves cleansing the data (removing outliers, normalizing, etc.). For example, it detects and removes abnormally high heart rates and zero values, and standardizes the data. The input is raw data obtained from the database, and the output is clean data that has been preprocessed.

[0802] Step 4:

[0803] The server analyzes the preprocessed health data. It uses machine learning algorithms (e.g., RandomForestClassifier, GradientBoostingClassifier, and other scikit-learn libraries) to detect signs of disease. The input is the cleansed health data, and the output is abnormal patterns and disease risk assessment results.

[0804] Step 5:

[0805] If a sign of illness is detected, the server sends an alert to the user's device. The alert uses the smartphone's notification function to notify the user of the abnormality. For example, a notification such as "Your heart rate is abnormally high. Please see a doctor" is displayed. The input is the disease risk assessment result, and the output is an alert notification displayed on the user's device.

[0806] Step 6:

[0807] The server also manages the procedure for paying condolence money to users whose illnesses are confirmed. When a user receives a diagnosis at a hospital or other facility and enters the diagnosis results into the application, the data is sent to the server. The server verifies the data and, if valid, automatically initiates the payment of the condolence money. The input is the diagnosis result entered by the user, and the output is a transfer of the condolence money to the user's bank account.

[0808] Step 7:

[0809] The server proposes an optimal meal menu based on the user's health condition. Based on the analysis results, the application makes specific meal suggestions, such as "Your heart rate is high. We recommend a low-calorie menu." The input is the analysis results of the health data, and the output is information on the recommended meal menu. This information becomes a specific meal suggestion that reflects the user's health condition.

[0810] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0811] The present invention relates to a system that detects signs of illness by collecting and analyzing a user's health data, notifying them of alerts as necessary, and even paying condolence money. The system of the present invention has a central server that operates in conjunction with the user's terminal, wearable device, and emotion engine.

[0812] System configuration

[0813] 1. User health data collection:

[0814] Users use smartphones or wearable devices to collect daily health data such as heart rate, number of steps, and sleep time. This data is automatically sent to a server, where it is stored in a database. In addition, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0815] 2. Collecting Emotional Data:

[0816] The user's smartphone or wearable device is equipped with an emotion engine that analyzes voice and facial expression data to recognize the user's emotions (e.g., stress, joy, sadness, etc.). The recognized emotion data is sent to a server along with health data and stored in a database.

[0817] Data preprocessing and analysis

[0818] The server preprocesses the collected health and emotion data, cleansing it by removing outliers and normalizing it, improving the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and emotion data to build an algorithm for detecting signs of illness. This algorithm enables early detection of users at increased risk of illness and performs risk assessment taking emotional states into account.

[0819] Disease sign detection and alert notification

[0820] The server analyzes the collected data in real time. If an abnormal pattern is detected, such as a pattern similar to that of a user who has previously developed an illness, or a specific emotional state (for example, a persistent state of stress), a risk assessment is made and the server generates an alert. This alert is immediately sent to the user via their device. The notification content is specific, such as "Your risk of heart disease is increasing. Please see a doctor."

[0821] Condolence payment processing

[0822] If a user actually goes to a hospital and is diagnosed with heart disease, the user enters the diagnosis into the application. The server checks the diagnosis and verifies that the user is eligible for a condolence payment. After confirmation, the server automatically transfers the payment to the bank account specified by the user.

[0823] Specific examples

[0824] For example, if user A uses a smartphone and a wearable device, these devices record his / her heart rate, number of steps, and sleep time every day. The emotion engine also collects emotional data from A's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks such as abnormal heart rate fluctuations and persistent stress. If A's data matches a pattern indicating a risk of heart disease, an alert will be sent to his / her smartphone stating, "Your risk of heart disease is increasing. Please consult a doctor."

[0825] If Person A actually goes to the hospital and is diagnosed with heart disease, the diagnosis results are entered into the application, which is then confirmed by the server and a condolence payment is automatically transferred to Person A's bank account. Through this series of processes, users are supported in the prevention and early detection of disease, and can also receive financial support in emergencies.

[0826] The system of the present invention encourages users to proactively manage their own health and emotional states, thereby contributing to extending overall healthy lifespan. This system allows users to effectively utilize their individual health and emotional data and maintain a lifestyle based on preventive medicine.

[0827] The processing flow will be explained below.

[0828] Step 1:

[0829] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0830] Step 2:

[0831] Device: Collected health data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0832] Step 3:

[0833] Device: The emotion engine analyzes the user's voice data and facial expression data to recognize the user's emotions (stress, joy, sadness, etc.).

[0834] Step 4:

[0835] Terminal: Sends the recognized emotion data along with health data to the server.

[0836] Step 5:

[0837] Server: The received health and emotional data is stored in a database for each user. The stored data includes heart rate, number of steps, sleep time, health check results, medical history, and emotional data.

[0838] Step 6:

[0839] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0840] Step 7:

[0841] Server: Uses machine learning algorithms to learn data patterns and emotional patterns of users who have previously experienced illness, and builds a model to detect signs of illness.

[0842] Step 8:

[0843] Server: Analyzes newly received data in real time and compares it with the trained model.

[0844] Step 9:

[0845] Server: If the analysis reveals abnormal patterns, patterns similar to those of users who have previously developed illness, or specific emotional states, a risk assessment is made based on that information.

[0846] Step 10:

[0847] Server: Generates an alert if the risk assessment is elevated.

[0848] Step 11:

[0849] Device: An alert is sent to the user's smartphone. The notification content includes specific information such as "Your risk of heart disease is increasing. Please consult a doctor."

[0850] Step 12:

[0851] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[0852] Step 13:

[0853] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[0854] Step 14:

[0855] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[0856] Step 15:

[0857] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[0858] Examples:

[0859] For example, if user A uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time in steps 1 and 2 and send them to the server. At the same time, the emotion engine collects emotion data from A's voice and facial expressions and sends it to the server in steps 3 and 4. The server accumulates this data and analyzes it using preprocessing and machine learning models (steps 5 to 7).

[0860] If the analysis shows that Person A's data matches a pattern indicating a risk of heart disease and that the high stress state continues (steps 8 and 9), the server will make a risk assessment and generate an alert. An alert will be sent to the person's smartphone (steps 10 and 11), warning them that "Your risk of heart disease is increasing. Please see a doctor."

[0861] After Mr. A receives a diagnosis at the hospital and enters the results into the application (step 12), the server verifies the diagnosis (step 13) and performs the automatic payment procedure for the condolence money (step 14). Finally, the condolence money is transferred to Mr. A's bank account, and the information is notified (step 15).

[0862] Example 2

[0863] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0864] In modern society, it is extremely important to properly monitor a user's health status and detect early signs of illness. However, conventional systems have faced challenges in that they do not adequately preprocess collected health data or analyze emotional data, making it difficult to accurately detect signs of illness. Furthermore, when an illness is confirmed, the process of paying condolence money is often done manually, making it difficult to respond quickly. The purpose of the present invention is to solve these challenges.

[0865] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0866] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for preprocessing the stored data, means for analyzing the preprocessed data and detecting signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to constantly monitor the user's health condition, detect signs of illness early through highly accurate data analysis, and quickly pay a condolence payment.

[0867] "User" refers to an individual who uses the system and provides health and emotional data.

[0868] "Health data" refers to information about the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[0869] "Database" refers to a system or location for storing and managing collected health and emotional data.

[0870] "Preprocessing" refers to processing such as removing outliers, normalizing, and deleting duplicate data before analyzing collected data.

[0871] "Machine learning algorithms" refer to algorithms that use past data to train models and then make predictions or classifications based on new data.

[0872] An "alert" refers to a warning or notification sent to a user when a sign of illness is detected.

[0873] "Consolation money" refers to financial assistance provided to users who have been confirmed ill.

[0874] This invention relates to a system that collects and analyzes users' health and emotional data to detect signs of illness, sends alerts as necessary, and even pays condolence money. Specifically, it is composed of three main elements: a server, a terminal, and the user.

[0875] Health and emotional data collection

[0876] Users collect daily health data using smartphones or wearable devices. Specific devices include Fitbit and Apple Watch, which record heart rate, steps, and sleep time. These devices transfer data to smartphones via Bluetooth or Wi-Fi. In addition, users manually enter their regular health checkup results and medical history into the device. This health data is automatically sent from the smartphone to a server and stored in a database.

[0877] In addition, users' smartphones and wearable devices are equipped with emotion engines that capture emotion data from the user's voice and facial expressions. For example, the stress a user feels while on the phone or changes in facial expression when they are happy are recorded as emotion data. This emotion data is also sent to the server and stored in a database.

[0878] Data preprocessing and analysis

[0879] When the data is sent, the server first performs preprocessing such as removing outliers, normalizing, and deleting duplicate data, thereby improving the quality of the data.

[0880] Once the preprocessing is complete, the data is analyzed using machine learning algorithms. Specifically, models trained with machine learning libraries such as TensorFlow and PyTorch are used. These models are trained using previously collected disease occurrence and emotion data, and then use new data to detect signs of disease.

[0881] Alerting and Notifications

[0882] If an abnormal pattern is detected and a high risk of disease is determined, the server generates an alert. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. The message may contain specific information such as, "Your risk of heart disease is increasing. Please consult a doctor." This alert is instantly pushed to the user's smartphone.

[0883] Payment of condolence money

[0884] If a user actually goes to a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they can take a photo of the medical certificate and upload it via the app. The server checks the diagnosis results and automatically verifies that the user is eligible for a condolence payment. Once confirmation is complete, the server transfers the payment to the bank account specified by the user. This transfer process uses a bank API or electronic payment service.

[0885] Specific examples

[0886] For example, Person A uses a wearable device that records heart rate, number of steps, and sleep time, as well as a smartphone. These devices collect daily health data and periodically send it to a server. An emotion engine also collects emotional data such as stress or joy from Person A's voice and facial expressions, and sends this data to the server. The server analyzes this data and evaluates the risk level. If it determines that Person A's risk of heart disease is increasing, it sends an alert to Person A's smartphone stating, "Your risk of heart disease is increasing. Please see a doctor."

[0887] If Mr. A is diagnosed with heart disease by a doctor, he uploads the medical certificate to the app. The server checks the certificate and transfers 50,000 yen as a condolence payment to Mr. A's bank account.

[0888] Example prompts to input to the generative AI model

[0889] "I want to create a system that collects and analyzes users' emotional data and health information, and detects and notifies them of signs of illness. The system will analyze data obtained from users' smartphones and wearable devices using an emotion engine, and generate an alert if an abnormal pattern is found. I also want to include a function that will provide a condolence payment if a user is diagnosed with an illness."

[0890] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0891] Step 1: Collecting user health data

[0892] Users collect daily health data such as heart rate, steps, and sleep time using smartphones or wearable devices. The devices transfer this data to smartphones via Bluetooth or Wi-Fi. The smartphones automatically send this data to a server. The input is the user's daily health data, and the output is the raw data sent to the server.

[0893] Step 2: Collecting emotion data

[0894] Users use smartphones or wearable devices equipped with an emotion engine to obtain emotional data from voice and facial expressions. The emotion engine analyzes the user's speech and facial expressions to recognize emotions such as stress, joy, and sadness. The smartphone then sends this emotional data to a server. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0895] Step 3: Preprocessing the data

[0896] The server receives the collected health and emotion data. It then performs preprocessing such as removing outliers, normalizing the data, and removing duplicate data. For example, it detects and removes abnormal heart rate values ​​and duplicate data. The input is the collected raw data, and the output is the preprocessed clean data.

[0897] Step 4: Data analysis

[0898] The server uses the preprocessed data to train a machine learning model. Specifically, a model using TensorFlow or PyTorch is used. This model executes an algorithm to detect signs of disease based on past disease occurrence data and emotion data. The input is the preprocessed data, and the output is the disease risk assessment result.

[0899] Step 5: Alerting and Notification

[0900] The server immediately generates an alert if an abnormality is detected as a result of data analysis. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. This message is pushed to the device to notify the user. The input is the risk assessment result, and the output is the generated alert and its notification.

[0901] Step 6: Payment of condolence money

[0902] If a user receives a diagnosis at a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they take a photo of the medical certificate and upload it via the app. The server verifies the diagnosis results and confirms that the user is eligible for a condolence payment. Once verification is complete, the payment is transferred to the bank account specified by the user. The input is the user's diagnosis results, and the output is the confirmed diagnosis results and the transferred condolence payment.

[0903] At each step, the specific actions and technologies used are explained in detail, clearly showing how the user and server process the data to achieve the final goal.

[0904] (Application example 2)

[0905] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0906] Conventional health monitoring systems collect user health data, but they are unable to comprehensively manage health risks, including emotional states, or ensure the safety of motor vehicles in real time. This makes it difficult to respond quickly and appropriately when signs of illness or abnormal emotional states are detected. Furthermore, the procedure for paying compensation when illness is confirmed is complicated. The present invention aims to solve these problems.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data of the user, means for storing the collected health data and emotional data in a database, means for preprocessing and analyzing the stored data to detect signs of illness and abnormal emotional states, means for issuing an alert to the user terminal and stopping the vehicle in a safe location when signs of illness or abnormal emotional states are detected, and means for paying a condolence payment to a user whose illness is confirmed. This makes it possible to comprehensively manage the user's health and emotional states, quickly ensure the safety of the vehicle in the event of an abnormality, and automatically pay the condolence payment, providing peace of mind and convenience to the user.

[0908] "User's health data" refers to the user's daily heart rate, number of steps, sleep time, health check results, and past medical history.

[0909] "Emotion data" refers to the user's emotional state, such as stress, joy, or sadness, obtained by analyzing the user's voice data and facial expression data.

[0910] "Database" refers to a system for storing and managing collected health and emotional data.

[0911] "Preprocessing" refers to data cleansing processes that remove outliers and normalize data to improve the accuracy of analysis.

[0912] "Analysis" refers to analyzing the collected data to detect signs of illness or abnormal emotional states.

[0913] An "alert" refers to a warning message that is sent to the user when signs of illness or abnormal emotional state are detected.

[0914] "User devices" refer to smartphones, tablets, wearable devices, etc. that users use on a daily basis.

[0915] "Autonomous vehicle" means a vehicle that has an automated driving function.

[0916] "Stopping in a safe place" refers to the act of stopping the vehicle in a safe place when an abnormality is detected while the autonomous vehicle is in operation.

[0917] "Consolation money" refers to money paid to a user if they are diagnosed with an illness.

[0918] The present invention is a system that collects and analyzes a user's health and emotional data to detect signs of illness or abnormal emotional states, notifies the user of an alert as necessary, and even provides a compensation payment. In particular, the present invention provides a function for autonomous vehicles to monitor the passengers' health status in real time and stop the autonomous vehicle in a safe place if an abnormality is detected.

[0919] System configuration

[0920] 1. Collecting health and emotional data

[0921] The server collects health and emotional data from users' smartphones and wearable devices. This data includes heart rate, steps taken, sleep time, health checkup results, medical history, and emotional state extracted from the user's voice and facial expressions. The devices transmit the data to the server using Bluetooth Low Energy (BLE) or Wi-Fi.

[0922] 2. Data accumulation and preprocessing

[0923] The server stores the collected health and emotion data in a database. As preprocessing, data cleansing is performed, including removing outliers and normalizing the data, to improve the accuracy of the analysis.

[0924] 3. Data Analysis

[0925] The server analyzes the accumulated health and emotional data and uses machine learning algorithms to detect signs of illness and abnormal emotional states. This analysis also refers to past disease occurrence data and emotional data to perform risk assessment.

[0926] 4. Alert notifications and safe stopping of motor vehicles

[0927] If an abnormality is detected, the server generates an alert and immediately notifies the user's device. The notification will include a specific message such as "Your risk of heart disease is increasing. Please consult a doctor." It will also instruct the autonomous vehicle to stop in a safe position.

[0928] 5. Payment of condolence money

[0929] If a user is diagnosed with an illness at a medical institution, the diagnosis can be entered into the application, and the server will confirm it and automatically transfer a condolence payment to the bank account specified by the user.

[0930] Program processing explanation

[0931] The server uses programming languages ​​such as Python and Java to execute a series of processes including data collection, preprocessing, analysis, alert generation, and compensation payments. MySQL and PostgreSQL databases are sometimes used. Libraries such as TensorFlow and Scikit-learn are used for machine learning algorithms. NLP (natural language processing) technology and CV (computer vision) are used for sentiment analysis.

[0932] Specific examples

[0933] For example, if a user uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time every day. A device equipped with an emotion engine collects emotional data from the user's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks. If a warning sign is detected, an alert is issued and the self-driving vehicle stops in a safe location. Furthermore, if an illness is diagnosed, a condolence payment is deposited into the user's bank account.

[0934] Example prompts for generative AI models

[0935] Create a program for a health monitoring system for autonomous vehicles that collects and analyzes the user's health and emotional data to detect risks and issue alerts. Specifically, the system monitors heart rate and emotional state in real time, and if an abnormality is detected, it safely stops the vehicle, notifies emergency contacts, and, if necessary, provides compensation. Please also explain the specific problem detection algorithm, data collection, and alert notification processes.

[0936] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0937] Step 1:

[0938] Health and emotional data collection

[0939] The server collects heart rate, step count, sleep time, voice data, and facial expression data from the user's smartphone and wearable device. The smartphone and wearable device transfer the data via Bluetooth or Wi-Fi. The input data is in raw data format and is received by the destination server. Specifically, the data sent from the device is automatically uploaded to the server.

[0940] Step 2:

[0941] Data Preprocessing

[0942] The server receives the collected health and emotion data and performs outlier removal and normalization, thereby reducing noise in the data and improving the accuracy of the analysis. The input is raw data, and the output is pre-processed, clean data. Specifically, the server runs algorithms to filter out outliers and normalize the data.

[0943] Step 3:

[0944] Data accumulation

[0945] The server stores the preprocessed health data and emotion data in a database. The input is the preprocessed clean data, and the output is the data stored in the database. Specifically, the server inserts the cleaned data into the database using SQL queries.

[0946] Step 4:

[0947] Data analysis

[0948] The server runs machine learning algorithms based on the accumulated data to detect signs of illness and abnormal emotional states. The input is data stored in the database, and the output is the analysis results (risk assessment and anomaly detection results). Specifically, the server runs the analysis algorithm using TensorFlow and Scikit-learn.

[0949] Step 5:

[0950] Alert Notifications

[0951] If the server detects an abnormality through data analysis, it generates an alert and notifies the user's device. The input is the analysis result, and the output is an alert message. Specifically, the server generates a message and sends it to the smartphone app via push notification. A specific message such as "Your risk of heart disease is increasing. Please consult a doctor" is displayed.

[0952] Step 6:

[0953] Safe stopping of motor vehicles

[0954] If an abnormality is detected, the server instructs the autonomous vehicle to stop in a safe location. The input is the abnormality detection result, and the output is an instruction to stop the vehicle. Specifically, the server sends an API request to the vehicle's control system and stops the vehicle in the specified location.

[0955] Step 7:

[0956] Payment of condolence money

[0957] When a user receives a diagnosis at a medical institution and enters the results into the application, the server verifies this information and transfers the condolence money to the user's bank account. The input is the diagnosis result, and the output is a notification that the condolence money has been transferred. Specifically, the server verifies the user's input and automatically transfers the money to the specified bank account.

[0958] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0959] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0960] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0961] [Fourth embodiment]

[0962] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0963] 7, a 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.

[0964] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0965] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0966] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0967] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0968] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0969] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0970] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0971] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0972] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0973] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0974] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0975] The present invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server that operates in conjunction with the user's terminal and wearable device.

[0976] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices. This data is automatically sent to a server, where it is stored in a database. When users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[0977] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and builds an algorithm for detecting signs of disease. This algorithm makes it possible to detect users at increased risk of disease early on.

[0978] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to the server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[0979] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[0980] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[0981] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[0982] The system of the present invention encourages users to proactively manage their own health status, thereby contributing to extending healthy life expectancy overall. This system allows users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[0986] Step 2:

[0987] Terminal: Collected data is automatically sent to the server at regular intervals (for example, every hour or every day).

[0988] Step 3:

[0989] Server: Stores the received data in a database for each user. The stored data includes daily health data, health checkup results, medical history, etc.

[0990] Step 4:

[0991] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[0992] Step 5:

[0993] Server: Uses machine learning algorithms to learn data patterns from users who have previously experienced illness and build a model to detect signs of illness.

[0994] Step 6:

[0995] Server: Analyzes newly received data in real time and compares it with the trained model.

[0996] Step 7:

[0997] Server: If the analysis detects abnormal patterns or patterns similar to those of users who have previously experienced illness, a risk assessment is made based on that information.

[0998] Step 8:

[0999] Server: Generates an alert if the risk assessment is elevated.

[1000] Step 9:

[1001] Device: An alert is sent to the user's smartphone, with specific content such as "Your risk of heart disease is increasing. Please consult a doctor."

[1002] Step 10:

[1003] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[1004] Step 11:

[1005] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[1006] Step 12:

[1007] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[1008] Step 13:

[1009] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[1010] Examples:

[1011] For example, if a user named Mr. A records his heart rate, number of steps, and sleep time every day using a wearable device, this data is sent to the server in steps 1 and 2. The server accumulates and analyzes this data in steps 3 to 5 and compares it with the trained model. If Mr. A's data matches a pattern indicating a risk of heart disease, a risk assessment is made in steps 6 and 7, and an alert is sent to the user in steps 8 and 9.

[1012] After that, Mr. A receives a diagnosis at the hospital and enters the diagnosis results into the application, which executes steps 10 to 12. The server verifies the diagnosis results and starts the payment process for the condolence money, which is eventually automatically transferred to the user's bank account. In step 13, the user is notified of this information.

[1013] Example 1

[1014] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1015] In modern society, lifestyle-related diseases and acute illnesses are on the rise, making early detection and prevention of these diseases crucial. However, many people lack the means to properly manage their daily health and may miss signs of illness. Furthermore, financial support after illness is often insufficient. For this reason, there is a need for a system that continuously collects and analyzes users' health data to detect early signs of illness, promptly notify alerts when necessary, and even provide compensation.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1017] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for cleansing the stored data, means for analyzing the data using a machine learning algorithm to detect signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to efficiently manage user health data, support early detection and prevention of illness, and provide financial support after diagnosis.

[1018] "Health data" refers to physiological data and lifestyle-related data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health checkup results, and medical history.

[1019] "Collection means" refers to devices or software, such as wearable devices or smartphone applications, used to obtain health data from users.

[1020] The "means of storing in a database" refers to a server or cloud storage system for storing and managing collected health data in digital form.

[1021] A "cleansing tool" is an algorithm or software that performs a process to remove outliers and missing values ​​from collected health data and normalize the data.

[1022] The "means of analysis" refers to a system that uses machine learning algorithms and data mining techniques to analyze accumulated health data and detect signs of disease.

[1023] "Means for detecting signs of disease" refers to algorithms and logic that identify potential disease risks based on analyzed data and provide early warnings to users.

[1024] "Means for notifying alerts" refers to a system for sending warning messages or alerts to users when a health risk is detected via their smartphones or other communication devices.

[1025] The "means for paying condolence money to users whose illnesses have been confirmed" is a system that allows users to input their doctor's diagnosis into the system, which then automatically starts the payment process based on that information and transfers the condolence money to the user's bank account.

[1026] This invention relates to a system that collects and analyzes a user's health data to detect signs of illness, notifies the user of an alert as necessary, and even pays a condolence payment. The system of the present invention has a central server and operates in conjunction with the user's terminal and wearable device.

[1027] Users collect daily health data (heart rate, number of steps, sleep time, etc.) using smartphones or wearable devices (e.g., Fitbit or Apple Watch). This data is automatically sent to a server. Furthermore, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[1028] The server preprocesses the collected health data before analyzing it. Specifically, it cleanses the data by removing outliers and normalizing it to improve the accuracy of the analysis. The server then trains a machine learning model (e.g., TensorFlow) using past disease occurrence data to build an algorithm for detecting signs of disease. This algorithm enables early detection of users at increased risk of disease.

[1029] For example, if a user named Mr. A uses a smartphone and a wearable device, these devices record his heart rate, number of steps, and sleep time every day. This data is periodically sent to a server. The results of his health checkups and medical history are also sent to the server and stored in a database.

[1030] The server analyzes this data and detects abnormal heart rate fluctuations and sleep patterns. If these fluctuations show patterns similar to those of past heart disease patients, the server will assess the risk and send an alert to Mr. A's smartphone saying, "Your risk of heart disease is increasing. Please see a doctor."

[1031] If the user actually goes to the hospital and is diagnosed with heart disease, they enter the diagnosis into the application. The server checks the diagnosis and initiates the payment procedure for the condolence money. After confirmation, the server automatically transfers the money to the bank account specified by the user.

[1032] As a concrete example, when Person B receives a diagnosis as described above and enters the diagnosis results into the system, the server verifies the diagnosis results, and if they are confirmed to be legitimate, transfers a condolence payment to Person B's bank account. Through this series of processes, users are supported in the prevention and early detection of illness, and can also receive financial support in emergencies.

[1033] An example of a prompt sentence to input to the generative AI model is as follows:

[1034] A user uses a wearable device to collect daily health data such as heart rate, number of steps, and sleep time, and sends it to a server via their smartphone. The server accumulates this data and analyzes it using a machine learning model to detect signs of illness. Design a system that sends an alert to the user's smartphone if the risk of illness increases, and pays a condolence payment based on the doctor's diagnosis.

[1035] As a result, the system of the present invention encourages users to proactively manage their own health status, contributing to the extension of overall healthy life expectancy. This system enables users to effectively utilize their individual health data and maintain lifestyle habits based on preventive medicine.

[1036] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1037] Step 1:

[1038] Users wear wearable devices (e.g., Fitbit or Apple Watch) to collect daily health data.

[1039] Input: Data such as user heart rate, steps, and sleep time

[1040] Output: Data transfer to smartphone

[1041] How it works: As the user uses the wearable device and engages in daily activities, the device automatically records heart rate, steps taken, and sleep time, and transmits the data via Bluetooth to a dedicated app on the smartphone.

[1042] Step 2:

[1043] The terminal transmits the collected health data to a server.

[1044] Input: Health data collected on a smartphone

[1045] Output: Send data to the server

[1046] How it works: The smartphone app periodically uploads data to the server, which is then securely transferred to the server via the internet.

[1047] Step 3:

[1048] The server stores the received health data in a database.

[1049] Input: Health data sent from the device

[1050] Output: Health data stored in a database

[1051] What it does: The server stores the received data in a MySQL database, checks the data for proper format and structure, and then merges it with the existing data in the database.

[1052] Step 4:

[1053] The server cleanses the accumulated data.

[1054] Input: Health data stored in a database

[1055] Output: Cleansed data

[1056] What it does: The server removes outliers and normalizes the data, for example, detecting and removing extreme or missing values ​​and applying normalization to ensure data consistency.

[1057] Step 5:

[1058] The server uses machine learning algorithms to analyze the data and detect signs of disease.

[1059] Input: Cleansed health data

[1060] Output: Risk assessment results

[1061] How it works: The server uses TensorFlow to apply machine learning models trained on past health data to assess the user's health risks, detecting abnormalities in heart rate and sleep patterns and identifying signs of illness.

[1062] Step 6:

[1063] The server sends an alert to the user terminal if any signs of illness are detected.

[1064] Input: Risk assessment results

[1065] Output: Alert notification to user terminal

[1066] Specific operation: Based on the risk assessment results, the server sends a push notification to the user's smartphone, including instructions such as "Your risk of heart disease is increasing. Please consult a doctor."

[1067] Step 7:

[1068] The user receives a diagnosis from a doctor and enters the results into the application.

[1069] Input: Doctor's diagnosis

[1070] Output: Send diagnostic results to the server

[1071] Specific operation: The user receives a diagnosis from a doctor, and the diagnosis of "heart disease" is entered into a smartphone application and sent to the server.

[1072] Step 8:

[1073] The server checks the entered diagnosis results.

[1074] Input: Diagnostic results sent by the user

[1075] Output: Check the diagnostic results

[1076] Specific operation: The server executes a process to check the received diagnostic results against the database and confirm their validity.

[1077] Step 9:

[1078] If the diagnosis result is confirmed to be valid, the server starts the procedure for paying the compensation.

[1079] Input: Verified diagnostic results

[1080] Output: Consolation payment

[1081] Specific operation: The server triggers the condolence payment process and automatically transfers the money to the specified bank account.

[1082] (Application example 1)

[1083] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1084] In modern society, it is important for users to manage their own health, but many people are so busy with their daily lives that they tend to neglect their health management. Furthermore, for users who lead particularly busy lives, choosing healthy meals can be difficult. Furthermore, there is a concern that there is an increased risk of health risks, as there is no system that simultaneously monitors health data and suggests meals, and takes immediate action if an abnormality is detected.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1086] In this invention, the server includes a means for collecting user health data, a means for storing the collected health data in a database, and a means for analyzing the stored data and detecting signs of illness, thereby enabling constant monitoring of the user's health condition and early detection of health risks.

[1087] Furthermore, if signs of illness are detected, the system includes a means for sending an alert to the user's terminal, a means for paying a condolence payment to a user who has been confirmed to be ill, and a means for suggesting an optimal meal menu based on the user's health condition. This encourages the user to take appropriate health management measures and also supports healthy meal selection, enabling overall health maintenance.

[1088] "User's health data" refers to various data that indicate the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[1089] "Database" means an electronic system and structure for storing and managing collected health data.

[1090] "Means for detecting signs of illness" refers to algorithms or programs that use accumulated health data to predict the likelihood of a user becoming ill.

[1091] "Means for notifying an alert" is a function for sending a warning or notification to the user's device when a sign of illness is detected.

[1092] The "means for paying condolence money" is a function for providing financial compensation and support to users who have been confirmed to be ill.

[1093] "Means for suggesting optimal meal menus" refers to a system function that provides and recommends appropriate meal contents based on the user's health condition.

[1094] A "machine learning algorithm" is a computational method for learning patterns based on past data and making predictions and classifications.

[1095] A "health-based meal menu" is a meal plan recommended for maintaining health based on the user's current health data.

[1096] The system that realizes this invention collects user health data, stores it in a database, analyzes it, sends alerts, provides condolence payments, and suggests meal plans, among other functions, allowing users to continuously manage their own health status and respond quickly if an abnormality is detected.

[1097] First, smartphones and wearable devices are used to collect users' health data, including information such as heart rate, number of steps taken, and sleep duration, which is automatically sent to a server.

[1098] The server stores this data in a database. The database can use a relational database management system such as MySQL or PostgreSQL. The information stored in the database is organized by user and used for later analysis.

[1099] The server then analyzes the accumulated data using machine learning algorithms (for example, Python's scikit-learn library, which has skills such as RandomForestClassifier and GradientBoostingClassifier). This allows it to detect signs of illness and send appropriate alerts to the user's device if an abnormality is found. These alerts help users recognize health risks early and take measures.

[1100] For example, if a user experiences an abnormal increase in heart rate, the server analyzes the data and sends a notification to the smartphone stating, "An abnormal heart rate has been detected. We recommend that you see a doctor." In addition, if the user is diagnosed with an illness through the condolence payment system, a condolence payment will be transferred to the user's designated bank account along with the diagnosis.

[1101] The system also suggests optimal meal plans based on the user's health status. For example, it analyzes the latest health data collected from wearable devices and smartphones and recommends meals containing specific ingredients and nutrients via a food delivery service. In this case, the application displays specific recommended meals along with an alert such as, "Your heart rate is high. We recommend a low-calorie menu."

[1102] An example of a prompt might be, "Design an API that collects a user's weekend heart rate data and provides health advice based on that data. The API will detect abnormal heart rates and recommend salads and low-calorie meals." This allows developers to clearly and concretely understand the system's functionality and proceed with implementation.

[1103] In this way, the invention is a system that comprehensively supports the user's health management and is highly effective from the perspective of preventive medicine.

[1104] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1105] Step 1:

[1106] The user's device (smartphone or wearable device) collects health data such as heart rate, number of steps, and sleep time. This data is acquired using sensors on each device (heart rate sensor, accelerometer, gyroscope, etc.) and automatically sent to a server via an application. The input data is biometric data and activity data, and the output is data packets sent from the device to the server.

[1107] Step 2:

[1108] The server stores the collected health data in a database. The database uses a relational database management system such as MySQL or PostgreSQL. The input is the health data sent by the user, and the output is the data organized and stored in the database.

[1109] Step 3:

[1110] The server preprocesses the health data stored in the database. This involves cleansing the data (removing outliers, normalizing, etc.). For example, it detects and removes abnormally high heart rates and zero values, and standardizes the data. The input is raw data obtained from the database, and the output is clean data that has been preprocessed.

[1111] Step 4:

[1112] The server analyzes the preprocessed health data. It uses machine learning algorithms (e.g., RandomForestClassifier, GradientBoostingClassifier, and other scikit-learn libraries) to detect signs of disease. The input is the cleansed health data, and the output is abnormal patterns and disease risk assessment results.

[1113] Step 5:

[1114] If a sign of illness is detected, the server sends an alert to the user's device. The alert uses the smartphone's notification function to notify the user of the abnormality. For example, a notification such as "Your heart rate is abnormally high. Please see a doctor" is displayed. The input is the disease risk assessment result, and the output is an alert notification displayed on the user's device.

[1115] Step 6:

[1116] The server also manages the procedure for paying condolence money to users whose illnesses are confirmed. When a user receives a diagnosis at a hospital or other facility and enters the diagnosis results into the application, the data is sent to the server. The server verifies the data and, if valid, automatically initiates the payment of the condolence money. The input is the diagnosis result entered by the user, and the output is a transfer of the condolence money to the user's bank account.

[1117] Step 7:

[1118] The server proposes an optimal meal menu based on the user's health condition. Based on the analysis results, the application makes specific meal suggestions, such as "Your heart rate is high. We recommend a low-calorie menu." The input is the analysis results of the health data, and the output is information on the recommended meal menu. This information becomes a specific meal suggestion that reflects the user's health condition.

[1119] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1120] The present invention relates to a system that detects signs of illness by collecting and analyzing a user's health data, notifying them of alerts as necessary, and even paying condolence money. The system of the present invention has a central server that operates in conjunction with the user's terminal, wearable device, and emotion engine.

[1121] System configuration

[1122] 1. User health data collection:

[1123] Users use smartphones or wearable devices to collect daily health data such as heart rate, number of steps, and sleep time. This data is automatically sent to a server, where it is stored in a database. In addition, when users undergo regular health checkups and enter the results of their checkups and medical history information into their devices, this information is also sent to the server.

[1124] 2. Collecting Emotional Data:

[1125] The user's smartphone or wearable device is equipped with an emotion engine that analyzes voice and facial expression data to recognize the user's emotions (e.g., stress, joy, sadness, etc.). The recognized emotion data is sent to a server along with health data and stored in a database.

[1126] Data preprocessing and analysis

[1127] The server preprocesses the collected health and emotion data, cleansing it by removing outliers and normalizing it, improving the accuracy of the analysis. The server then trains a machine learning model using past disease occurrence data and emotion data to build an algorithm for detecting signs of illness. This algorithm enables early detection of users at increased risk of illness and performs risk assessment taking emotional states into account.

[1128] Disease sign detection and alert notification

[1129] The server analyzes the collected data in real time. If an abnormal pattern is detected, such as a pattern similar to that of a user who has previously developed an illness, or a specific emotional state (for example, a persistent state of stress), a risk assessment is made and the server generates an alert. This alert is immediately sent to the user via their device. The notification content is specific, such as "Your risk of heart disease is increasing. Please see a doctor."

[1130] Condolence payment processing

[1131] If a user actually goes to a hospital and is diagnosed with heart disease, the user enters the diagnosis into the application. The server checks the diagnosis and verifies that the user is eligible for a condolence payment. After confirmation, the server automatically transfers the payment to the bank account specified by the user.

[1132] Specific examples

[1133] For example, if user A uses a smartphone and a wearable device, these devices record his / her heart rate, number of steps, and sleep time every day. The emotion engine also collects emotional data from A's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks such as abnormal heart rate fluctuations and persistent stress. If A's data matches a pattern indicating a risk of heart disease, an alert will be sent to his / her smartphone stating, "Your risk of heart disease is increasing. Please consult a doctor."

[1134] If Person A actually goes to the hospital and is diagnosed with heart disease, the diagnosis results are entered into the application, which is then confirmed by the server and a condolence payment is automatically transferred to Person A's bank account. Through this series of processes, users are supported in the prevention and early detection of disease, and can also receive financial support in emergencies.

[1135] The system of the present invention encourages users to proactively manage their own health and emotional states, thereby contributing to extending overall healthy lifespan. This system allows users to effectively utilize their individual health and emotional data and maintain a lifestyle based on preventive medicine.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] Devices: Users' smartphones and wearable devices collect health data such as heart rate, steps taken, and sleep duration.

[1139] Step 2:

[1140] Device: Collected health data is automatically sent to the server at regular intervals (for example, every hour or every day).

[1141] Step 3:

[1142] Device: The emotion engine analyzes the user's voice data and facial expression data to recognize the user's emotions (stress, joy, sadness, etc.).

[1143] Step 4:

[1144] Terminal: Sends the recognized emotion data along with health data to the server.

[1145] Step 5:

[1146] Server: The received health and emotional data is stored in a database for each user. The stored data includes heart rate, number of steps, sleep time, health check results, medical history, and emotional data.

[1147] Step 6:

[1148] Server: Preprocesses the accumulated raw data, removes outliers, and normalizes the data, improving the accuracy of the analysis.

[1149] Step 7:

[1150] Server: Uses machine learning algorithms to learn data patterns and emotional patterns of users who have previously experienced illness, and builds a model to detect signs of illness.

[1151] Step 8:

[1152] Server: Analyzes newly received data in real time and compares it with the trained model.

[1153] Step 9:

[1154] Server: If the analysis reveals abnormal patterns, patterns similar to those of users who have previously developed illness, or specific emotional states, a risk assessment is made based on that information.

[1155] Step 10:

[1156] Server: Generates an alert if the risk assessment is elevated.

[1157] Step 11:

[1158] Device: An alert is sent to the user's smartphone. The notification content includes specific information such as "Your risk of heart disease is increasing. Please consult a doctor."

[1159] Step 12:

[1160] User: Receives a diagnosis at the hospital and enters the diagnosis results into the application.

[1161] Step 13:

[1162] Server: Validates the entered diagnosis results and determines whether the user is eligible for compensation.

[1163] Step 14:

[1164] Server: If it is confirmed that the user is eligible for the condolence payment, the server will begin the process of automatically transferring the money to the bank account that the user registered in advance.

[1165] Step 15:

[1166] Server: Confirms the completion of the automatic transfer and notifies the user of this information.

[1167] Examples:

[1168] For example, if user A uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time in steps 1 and 2 and send them to the server. At the same time, the emotion engine collects emotion data from A's voice and facial expressions and sends it to the server in steps 3 and 4. The server accumulates this data and analyzes it using preprocessing and machine learning models (steps 5 to 7).

[1169] If the analysis shows that Person A's data matches a pattern indicating a risk of heart disease and that the high stress state continues (steps 8 and 9), the server will make a risk assessment and generate an alert. An alert will be sent to the person's smartphone (steps 10 and 11), warning them that "Your risk of heart disease is increasing. Please see a doctor."

[1170] After Mr. A receives a diagnosis at the hospital and enters the results into the application (step 12), the server verifies the diagnosis (step 13) and performs the automatic payment procedure for the condolence money (step 14). Finally, the condolence money is transferred to Mr. A's bank account, and the information is notified (step 15).

[1171] Example 2

[1172] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1173] In modern society, it is extremely important to properly monitor a user's health status and detect early signs of illness. However, conventional systems have faced challenges in that they do not adequately preprocess collected health data or analyze emotional data, making it difficult to accurately detect signs of illness. Furthermore, when an illness is confirmed, the process of paying condolence money is often done manually, making it difficult to respond quickly. The purpose of the present invention is to solve these challenges.

[1174] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1175] In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for preprocessing the stored data, means for analyzing the preprocessed data and detecting signs of illness, means for sending an alert to a user terminal when a sign of illness is detected, and means for paying a condolence payment to a user who is confirmed to be ill. This makes it possible to constantly monitor the user's health condition, detect signs of illness early through highly accurate data analysis, and quickly pay a condolence payment.

[1176] "User" refers to an individual who uses the system and provides health and emotional data.

[1177] "Health data" refers to information about the user's health condition, such as heart rate, number of steps, sleep time, health check results, and medical history.

[1178] "Database" refers to a system or location for storing and managing collected health and emotional data.

[1179] "Preprocessing" refers to processing such as removing outliers, normalizing, and deleting duplicate data before analyzing collected data.

[1180] "Machine learning algorithms" refer to algorithms that use past data to train models and then make predictions or classifications based on new data.

[1181] An "alert" refers to a warning or notification sent to a user when a sign of illness is detected.

[1182] "Consolation money" refers to financial assistance provided to users who have been confirmed ill.

[1183] This invention relates to a system that collects and analyzes users' health and emotional data to detect signs of illness, sends alerts as necessary, and even pays condolence money. Specifically, it is composed of three main elements: a server, a terminal, and the user.

[1184] Health and emotional data collection

[1185] Users collect daily health data using smartphones or wearable devices. Specific devices include Fitbit and Apple Watch, which record heart rate, steps, and sleep time. These devices transfer data to smartphones via Bluetooth or Wi-Fi. In addition, users manually enter their regular health checkup results and medical history into the device. This health data is automatically sent from the smartphone to a server and stored in a database.

[1186] In addition, users' smartphones and wearable devices are equipped with emotion engines that capture emotion data from the user's voice and facial expressions. For example, the stress a user feels while on the phone or changes in facial expression when they are happy are recorded as emotion data. This emotion data is also sent to the server and stored in a database.

[1187] Data preprocessing and analysis

[1188] When the data is sent, the server first performs preprocessing such as removing outliers, normalizing, and deleting duplicate data, thereby improving the quality of the data.

[1189] Once the preprocessing is complete, the data is analyzed using machine learning algorithms. Specifically, models trained with machine learning libraries such as TensorFlow and PyTorch are used. These models are trained using previously collected disease occurrence and emotion data, and then use new data to detect signs of disease.

[1190] Alerting and Notifications

[1191] If an abnormal pattern is detected and a high risk of disease is determined, the server generates an alert. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. The message may contain specific information such as, "Your risk of heart disease is increasing. Please consult a doctor." This alert is instantly pushed to the user's smartphone.

[1192] Payment of condolence money

[1193] If a user actually goes to a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they can take a photo of the medical certificate and upload it via the app. The server checks the diagnosis results and automatically verifies that the user is eligible for a condolence payment. Once confirmation is complete, the server transfers the payment to the bank account specified by the user. This transfer process uses a bank API or electronic payment service.

[1194] Specific examples

[1195] For example, Person A uses a wearable device that records heart rate, number of steps, and sleep time, as well as a smartphone. These devices collect daily health data and periodically send it to a server. An emotion engine also collects emotional data such as stress or joy from Person A's voice and facial expressions, and sends this data to the server. The server analyzes this data and evaluates the risk level. If it determines that Person A's risk of heart disease is increasing, it sends an alert to Person A's smartphone stating, "Your risk of heart disease is increasing. Please see a doctor."

[1196] If Mr. A is diagnosed with heart disease by a doctor, he uploads the medical certificate to the app. The server checks the certificate and transfers 50,000 yen as a condolence payment to Mr. A's bank account.

[1197] Example prompts to input to the generative AI model

[1198] "I want to create a system that collects and analyzes users' emotional data and health information, and detects and notifies them of signs of illness. The system will analyze data obtained from users' smartphones and wearable devices using an emotion engine, and generate an alert if an abnormal pattern is found. I also want to include a function that will provide a condolence payment if a user is diagnosed with an illness."

[1199] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1200] Step 1: Collecting user health data

[1201] Users collect daily health data such as heart rate, steps, and sleep time using smartphones or wearable devices. The devices transfer this data to smartphones via Bluetooth or Wi-Fi. The smartphones automatically send this data to a server. The input is the user's daily health data, and the output is the raw data sent to the server.

[1202] Step 2: Collecting emotion data

[1203] Users use smartphones or wearable devices equipped with an emotion engine to obtain emotional data from voice and facial expressions. The emotion engine analyzes the user's speech and facial expressions to recognize emotions such as stress, joy, and sadness. The smartphone then sends this emotional data to a server. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[1204] Step 3: Preprocessing the data

[1205] The server receives the collected health and emotion data. It then performs preprocessing such as removing outliers, normalizing the data, and removing duplicate data. For example, it detects and removes abnormal heart rate values ​​and duplicate data. The input is the collected raw data, and the output is the preprocessed clean data.

[1206] Step 4: Data analysis

[1207] The server uses the preprocessed data to train a machine learning model. Specifically, a model using TensorFlow or PyTorch is used. This model executes an algorithm to detect signs of disease based on past disease occurrence data and emotion data. The input is the preprocessed data, and the output is the disease risk assessment result.

[1208] Step 5: Alerting and Notification

[1209] The server immediately generates an alert if an abnormality is detected as a result of data analysis. For example, if an abnormal increase in heart rate or a persistent state of stress is detected, an alert message is generated. This message is pushed to the device to notify the user. The input is the risk assessment result, and the output is the generated alert and its notification.

[1210] Step 6: Payment of condolence money

[1211] If a user receives a diagnosis at a hospital and is diagnosed with an illness such as heart disease, they enter the diagnosis results into the application. Specifically, they take a photo of the medical certificate and upload it via the app. The server verifies the diagnosis results and confirms that the user is eligible for a condolence payment. Once verification is complete, the payment is transferred to the bank account specified by the user. The input is the user's diagnosis results, and the output is the confirmed diagnosis results and the transferred condolence payment.

[1212] At each step, the specific actions and technologies used are explained in detail, clearly showing how the user and server process the data to achieve the final goal.

[1213] (Application example 2)

[1214] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1215] Conventional health monitoring systems collect user health data, but they are unable to comprehensively manage health risks, including emotional states, or ensure the safety of motor vehicles in real time. This makes it difficult to respond quickly and appropriately when signs of illness or abnormal emotional states are detected. Furthermore, the procedure for paying compensation when illness is confirmed is complicated. The present invention aims to solve these problems.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data of the user, means for storing the collected health data and emotional data in a database, means for preprocessing and analyzing the stored data to detect signs of illness and abnormal emotional states, means for issuing an alert to the user terminal and stopping the vehicle in a safe location when signs of illness or abnormal emotional states are detected, and means for paying a condolence payment to a user whose illness is confirmed. This makes it possible to comprehensively manage the user's health and emotional states, quickly ensure the safety of the vehicle in the event of an abnormality, and automatically pay the condolence payment, providing peace of mind and convenience to the user.

[1217] "User's health data" refers to the user's daily heart rate, number of steps, sleep time, health check results, and past medical history.

[1218] "Emotion data" refers to the user's emotional state, such as stress, joy, or sadness, obtained by analyzing the user's voice data and facial expression data.

[1219] "Database" refers to a system for storing and managing collected health and emotional data.

[1220] "Preprocessing" refers to data cleansing processes that remove outliers and normalize data to improve the accuracy of analysis.

[1221] "Analysis" refers to analyzing the collected data to detect signs of illness or abnormal emotional states.

[1222] An "alert" refers to a warning message that is sent to the user when signs of illness or abnormal emotional state are detected.

[1223] "User devices" refer to smartphones, tablets, wearable devices, etc. that users use on a daily basis.

[1224] "Autonomous vehicle" means a vehicle that has an automated driving function.

[1225] "Stopping in a safe place" refers to the act of stopping the vehicle in a safe place when an abnormality is detected while the autonomous vehicle is in operation.

[1226] "Consolation money" refers to money paid to a user if they are diagnosed with an illness.

[1227] The present invention is a system that collects and analyzes a user's health and emotional data to detect signs of illness or abnormal emotional states, notifies the user of an alert as necessary, and even provides a compensation payment. In particular, the present invention provides a function for autonomous vehicles to monitor the passengers' health status in real time and stop the autonomous vehicle in a safe place if an abnormality is detected.

[1228] System configuration

[1229] 1. Collecting health and emotional data

[1230] The server collects health and emotional data from users' smartphones and wearable devices. This data includes heart rate, steps taken, sleep time, health checkup results, medical history, and emotional state extracted from the user's voice and facial expressions. The devices transmit the data to the server using Bluetooth Low Energy (BLE) or Wi-Fi.

[1231] 2. Data accumulation and preprocessing

[1232] The server stores the collected health and emotion data in a database. As preprocessing, data cleansing is performed, including removing outliers and normalizing the data, to improve the accuracy of the analysis.

[1233] 3. Data Analysis

[1234] The server analyzes the accumulated health and emotional data and uses machine learning algorithms to detect signs of illness and abnormal emotional states. This analysis also refers to past disease occurrence data and emotional data to perform risk assessment.

[1235] 4. Alert notifications and safe stopping of motor vehicles

[1236] If an abnormality is detected, the server generates an alert and immediately notifies the user's device. The notification will include a specific message such as "Your risk of heart disease is increasing. Please consult a doctor." It will also instruct the autonomous vehicle to stop in a safe position.

[1237] 5. Payment of condolence money

[1238] If a user is diagnosed with an illness at a medical institution, the diagnosis can be entered into the application, and the server will confirm it and automatically transfer a condolence payment to the bank account specified by the user.

[1239] Program processing explanation

[1240] The server uses programming languages ​​such as Python and Java to execute a series of processes including data collection, preprocessing, analysis, alert generation, and compensation payments. MySQL and PostgreSQL databases are sometimes used. Libraries such as TensorFlow and Scikit-learn are used for machine learning algorithms. NLP (natural language processing) technology and CV (computer vision) are used for sentiment analysis.

[1241] Specific examples

[1242] For example, if a user uses a smartphone and a wearable device, these devices record their heart rate, number of steps, and sleep time every day. A device equipped with an emotion engine collects emotional data from the user's voice and facial expressions. This data is periodically sent to a server and stored in a database. The server analyzes this data and determines risks. If a warning sign is detected, an alert is issued and the self-driving vehicle stops in a safe location. Furthermore, if an illness is diagnosed, a condolence payment is deposited into the user's bank account.

[1243] Example prompts for generative AI models

[1244] Create a program for a health monitoring system for autonomous vehicles that collects and analyzes the user's health and emotional data to detect risks and issue alerts. Specifically, the system monitors heart rate and emotional state in real time, and if an abnormality is detected, it safely stops the vehicle, notifies emergency contacts, and, if necessary, provides compensation. Please also explain the specific problem detection algorithm, data collection, and alert notification processes.

[1245] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1246] Step 1:

[1247] Health and emotional data collection

[1248] The server collects heart rate, step count, sleep time, voice data, and facial expression data from the user's smartphone and wearable device. The smartphone and wearable device transfer the data via Bluetooth or Wi-Fi. The input data is in raw data format and is received by the destination server. Specifically, the data sent from the device is automatically uploaded to the server.

[1249] Step 2:

[1250] Data Preprocessing

[1251] The server receives the collected health and emotion data and performs outlier removal and normalization, thereby reducing noise in the data and improving the accuracy of the analysis. The input is raw data, and the output is pre-processed, clean data. Specifically, the server runs algorithms to filter out outliers and normalize the data.

[1252] Step 3:

[1253] Data accumulation

[1254] The server stores the preprocessed health data and emotion data in a database. The input is the preprocessed clean data, and the output is the data stored in the database. Specifically, the server inserts the cleaned data into the database using SQL queries.

[1255] Step 4:

[1256] Data analysis

[1257] The server runs machine learning algorithms based on the accumulated data to detect signs of illness and abnormal emotional states. The input is data stored in the database, and the output is the analysis results (risk assessment and anomaly detection results). Specifically, the server runs the analysis algorithm using TensorFlow and Scikit-learn.

[1258] Step 5:

[1259] Alert Notifications

[1260] If the server detects an abnormality through data analysis, it generates an alert and notifies the user's device. The input is the analysis result, and the output is an alert message. Specifically, the server generates a message and sends it to the smartphone app via push notification. A specific message such as "Your risk of heart disease is increasing. Please consult a doctor" is displayed.

[1261] Step 6:

[1262] Safe stopping of motor vehicles

[1263] If an abnormality is detected, the server instructs the autonomous vehicle to stop in a safe location. The input is the abnormality detection result, and the output is an instruction to stop the vehicle. Specifically, the server sends an API request to the vehicle's control system and stops the vehicle in the specified location.

[1264] Step 7:

[1265] Payment of condolence money

[1266] When a user receives a diagnosis at a medical institution and enters the results into the application, the server verifies this information and transfers the condolence money to the user's bank account. The input is the diagnosis result, and the output is a notification that the condolence money has been transferred. Specifically, the server verifies the user's input and automatically transfers the money to the specified bank account.

[1267] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1268] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1269] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1270] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1271] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1272] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1273] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1274] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1275] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1276] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1277] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1278] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1279] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1280] 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.

[1281] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1282] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1283] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1284] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1285] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1286] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1287] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1288] The following is further disclosed regarding the above embodiment.

[1289] (Claim 1)

[1290] means for collecting health data of a user;

[1291] a means for storing the collected health data in a database;

[1292] A means of analyzing the accumulated data and detecting signs of illness,

[1293] a means for notifying an alert to a user terminal when a sign of illness is detected;

[1294] a means for paying a solatium to a user who has been confirmed to have the disease;

[1295] A system including:

[1296] (Claim 2)

[1297] 10. The system of claim 1, wherein the collected health data includes the user's heart rate, number of steps, sleep time, health check results, and medical history.

[1298] (Claim 3)

[1299] 10. The system of claim 1, wherein the means for analyzing the data uses a machine learning algorithm.

[1300] (Claim 4)

[1301] 2. The system according to claim 1, wherein the means for paying the condolence money is to automatically transfer the money to the user's bank account.

[1302] (Claim 5)

[1303] 2. The system according to claim 1, wherein the means for notifying the alert monitors the user's health condition in real time and immediately notifies the user if an abnormality is detected.

[1304] (Claim 6)

[1305] 2. The system according to claim 1, wherein the analysis means learns data patterns of users who have previously suffered from illness and detects other users who exhibit similar patterns.

[1306] (Claim 7)

[1307] The system of claim 1 , wherein the data collection means collects data from a user's smartphone and wearable device.

[1308] "Example 1"

[1309] (Claim 1)

[1310] means for collecting health data of a user;

[1311] a means for storing the collected health data in a database;

[1312] a means for cleansing the stored data;

[1313] A means of analyzing data using machine learning algorithms to detect signs of disease;

[1314] a means for notifying an alert to a user terminal when a sign of illness is detected;

[1315] a means for paying a solatium to a user who has been confirmed to have the disease;

[1316] A system including:

[1317] (Claim 2)

[1318] 10. The system of claim 1, wherein the collected health data includes the user's vital data, daily activity data, medical checkup results, and medical history.

[1319] (Claim 3)

[1320] 2. The system of claim 1, wherein the data cleansing means includes outlier removal and data normalization.

[1321] "Application Example 1"

[1322] (Claim 1)

[1323] means for collecting health data of a user;

[1324] a means for storing the collected health data in a database;

[1325] A means of analyzing the accumulated data and detecting signs of illness,

[1326] a means for notifying an alert to a user terminal when a sign of illness is detected;

[1327] a means for paying a solatium to a user who has been confirmed to have the disease;

[1328] A means for suggesting an optimal meal menu based on the user's health condition;

[1329] A system including:

[1330] (Claim 2)

[1331] 10. The system of claim 1, wherein the collected health data includes the user's heart rate, number of steps, sleep time, health check results, and medical history.

[1332] (Claim 3)

[1333] 10. The system of claim 1, wherein the means for analyzing the data uses a machine learning algorithm.

[1334] (Claim 4)

[1335] 2. The system according to claim 1, wherein the meal menu suggested based on the user's health condition recommends an appropriate meal when an abnormality is detected in the health data.

[1336] "Example 2: Combining Emotion Engines"

[1337] (Claim 1)

[1338] means for collecting health data of a user;

[1339] a means for storing the collected health data in a database;

[1340] means for preprocessing the accumulated data;

[1341] A means for analyzing the preprocessed data and detecting signs of disease;

[1342] a means for notifying an alert to a user terminal when a sign of illness is detected;

[1343] a means for paying a solatium to a user who has been confirmed to have the disease;

[1344] A system including:

[1345] (Claim 2)

[1346] 10. The system of claim 1, wherein the collected health data includes the user's vital signs, activity levels, sleep patterns, medical checkup results, and medical history.

[1347] (Claim 3)

[1348] 10. The system of claim 1, wherein the means for preprocessing the data includes removing outliers, normalizing, and removing duplicate data.

[1349] (Claim 4)

[1350] 10. The system of claim 1, wherein the means for analyzing the data uses a machine learning algorithm.

[1351] "Application example 2 when combining emotion engines"

[1352] (Claim 1)

[1353] means for collecting health and emotional data of a user;

[1354] means for storing the collected health data and emotion data in a database;

[1355] A means for preprocessing and analyzing the accumulated data to detect signs of illness and abnormal emotional states;

[1356] A means for notifying a user terminal of an alert and stopping the automated vehicle in a safe place when a sign of illness or an abnormal emotional state is detected;

[1357] a means for paying a solatium to a user who has been confirmed to have the disease;

[1358] A system including:

[1359] (Claim 2)

[1360] 10. The system of claim 1, wherein the collected health and emotional data includes the user's heart rate, number of steps, sleep time, emotional state, medical checkup results, and medical history.

[1361] (Claim 3)

[1362] 10. The system of claim 1, wherein the means for preprocessing and analyzing the data uses machine learning algorithms. [Explanation of symbols]

[1363] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting health data of a user; a means for storing the collected health data in a database; A means of analyzing the accumulated data and detecting signs of illness, a means for notifying an alert to a user terminal when a sign of illness is detected; a means for paying a solatium to a user who has been confirmed to have the disease; A system including:

2. The system of claim 1 , wherein the collected health data includes the user's heart rate, number of steps, sleep time, medical checkup results, and medical history.

3. The system of claim 1 , wherein the means for analyzing the data uses a machine learning algorithm.

4. 2. The system according to claim 1, wherein the means for paying the condolence money is to automatically transfer the money to the user's bank account.

5. 2. The system according to claim 1, wherein the means for notifying the alert monitors the user's health condition in real time and immediately notifies the user if an abnormality is detected.

6. 2. The system according to claim 1, wherein the analysis means learns data patterns of users who have previously suffered from illnesses and detects other users who exhibit similar patterns.

7. The system of claim 1 , wherein the data collection means collects data from a user's smartphone and a wearable device.

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