System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting health data from a user; means for transmitting the health data to a server; means at the server for storing the transmitted health data; means at the server for analyzing the transmitted health data with an AI algorithm; means for generating a notification for a user based on the analysis; and means for presenting the notification to the user.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 modern society, health management is an important issue, with early disease prevention and appropriate medical treatment being particularly important. However, many people do not have the means to centrally manage their health data, which results in delayed detection of changes in their physical condition and increased risk of delayed treatment. Furthermore, a lack of analysis of health data and appropriate feedback can hinder daily health management. To address this issue, a system is needed that centrally collects and analyzes individual health information and notifies and suggests changes to users at the appropriate time. [Means for solving the problem]

[0005] The present invention relates to a system that includes a means for collecting health data from a user and transmitting it to a server, a means for storing the transmitted health data on the server, a means for analyzing the health data using an AI algorithm on the server, a means for generating a notification to the user based on the analysis results, and a means for presenting the notification to the user. The collected health data includes body temperature, weight, dietary content, and exercise records, and can be comprehensively evaluated. The system also includes a means for acquiring external data on the server and integrating it with the health data for analysis. This allows users to accurately understand their physical condition and receive necessary medical care early, thereby improving health management.

[0006] "Health data" refers to data related to the user's daily physical condition and lifestyle habits, specifically body temperature, weight, dietary content, exercise records, etc.

[0007] A "server" is a computer system that receives, stores, and analyzes data over a network and provides the results to other devices and users.

[0008] An "AI algorithm" is a method of analyzing data and detecting patterns and anomalies using artificial intelligence technologies such as machine learning and deep learning.

[0009] "Notification" refers to messages or alerts that inform the user of analysis results or suggestions and encourage the user to take action.

[0010] "External data" is additional information beyond that provided directly by the user, such as weather forecasts or air quality data, that can be used to analyze health data. [Brief explanation of the drawings]

[0011] [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

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] 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).

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] The present invention is a system that collects a user's health data, transmits it to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0033] Health data collection

[0034] User: Enters daily body temperature, weight, dietary information, and exercise records into a dedicated device (e.g., smartphone or tablet). This operation is performed through a dedicated application. The entered data is temporarily stored within the application.

[0035] Terminal: Receives health data entered by the user and converts it into an appropriate data format, such as JSON, then transmits the converted data to the server via secure (SSL / TLS) communication.

[0036] Sending and Receiving Data

[0037] Transmission from device to server: Collected data is securely transmitted to the server, where data encryption is applied to ensure user privacy.

[0038] Server: Receives the transmitted data and first validates it. After verifying that the data is in the correct format and complete, it stores it in the database. When stored, it further encrypts the data to protect it from unauthorized access.

[0039] Analyzing the data

[0040] Server: The stored health data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem.

[0041] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health data for analysis, enabling more accurate analysis.

[0042] Notifications and Suggestions

[0043] Server: Generates notifications and suggestions to the user based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several days in a row. We recommend that you see a doctor" is generated.

[0044] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages.

[0045] Specific examples

[0046] The following are specific usage examples.

[0047] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0048] Terminal: Converts this data into JSON format and sends it to the server.

[0049] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0050] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0051] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0052] This system allows users to understand their own physical condition in real time and to seek medical attention early, significantly improving the quality of health management.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] User: Enter health data (body temperature, weight, dietary information, exercise records, etc.) into the device. Specifically, the user opens a dedicated application and enters numbers or text into each data field (e.g., "Body temperature: 36.5°C," "Breakfast: oatmeal and fruit," etc.).

[0056] Step 2:

[0057] Terminal: Receives the input health data and converts it into an appropriate data format, such as JSON. Specifically, a data conversion module in the application serializes the data.

[0058] Step 3:

[0059] Terminal: The converted data is sent to the server using secure communication (SSL / TLS). Specifically, the sending API is called and the data packet is sent to the endpoint.

[0060] Step 4:

[0061] Server: Receives data sent from the device. Specifically, the receiving API takes in the data, performs initial validation, and checks the data format and completeness.

[0062] Step 5:

[0063] Server: Before storing the data in the database, the data is further encrypted by calling the data encryption module and performing AES encryption.

[0064] Step 6:

[0065] Server: Analyzes stored health data using AI algorithms, specifically machine learning and deep learning models, to interpret the data and detect outliers and trends.

[0066] Step 7:

[0067] Server: Obtains external data (such as weather forecasts and air quality data) as needed, integrates it with health data, and analyzes it. Specifically, it calls external APIs to obtain data and merges it with the internal database.

[0068] Step 8:

[0069] Server: Generates notifications and suggestions for users based on the analysis results. Specifically, the notification generation module creates a message and queues the notification for the user's device ID.

[0070] Step 9:

[0071] Terminal: Receives notifications from the server and displays them to the user, either by polling or by using push notifications, and displays the received notifications in the user interface.

[0072] Step 10:

[0073] User: Check the notifications and suggestions displayed on the device and take necessary action. For example, if you receive a notification that you have a high temperature, seek medical attention.

[0074] In this way, the system executes a series of steps, from collecting and analyzing the user's health data to notifying them, allowing the user to manage their health at the appropriate time.

[0075] Example 1

[0076] 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."

[0077] Conventional health management systems have struggled to efficiently collect user-entered health data, perform appropriate analysis, and provide timely notifications and suggestions to users. They also lack the ability to integrate and analyze user health data with external data to more accurately understand and predict health status. Furthermore, processing data securely while ensuring user privacy has also been a challenge.

[0078] 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.

[0079] In this invention, the server includes a means for storing the transmitted health data, a means for analyzing the transmitted health data using an AI algorithm, and a means for generating notifications to the user based on the analysis results. This allows users to efficiently collect, manage, and analyze their own health data and securely receive appropriate notifications. Furthermore, by acquiring external data and integrating and analyzing it with the health data, it becomes possible to grasp and predict health status more accurately.

[0080] "User" refers to an individual who uses the system to input and manage health data.

[0081] "Health Data" refers to information about an individual's health status, such as temperature, weight, diet, and exercise records.

[0082] "Server" refers to the computer system that receives, stores, analyzes, and generates notifications about health data submitted by users.

[0083] "Terminal" refers to the device (e.g., smartphone or tablet) through which the user inputs health data and receives and displays notifications from the server.

[0084] "AI algorithms" refers to data analysis methods, including machine learning and deep learning technologies, used to detect and predict trends and anomalies in health data.

[0085] "Notification" refers to messages or suggestions sent to the user by the server based on the results of the analysis.

[0086] "External data" refers to additional data, such as weather forecasts or air quality data, that the server acquires to integrate with health data for analysis.

[0087] "Secure" refers to ensuring privacy and confidentiality when sending, receiving, or storing data using protocols such as SSL / TLS.

[0088] The present invention provides a system for efficiently collecting and analyzing a user's health data and providing the user with appropriate notifications and suggestions. This system is configured as follows.

[0089] (Collection of health data)

[0090] User: The user uses a dedicated application to input daily temperature, weight, dietary intake, and exercise records. The application runs on a well-known smartphone or tablet.

[0091] Terminal: The terminal takes the data entered by the user and converts it to JSON format, for example:

[0092] json

[0093] {

[0094] "Temperature": 37.0,

[0095] "Diet": "Oatmeal and fruit",

[0096] "Date and Time": "2023-10-06"

[0097] }

[0098] This data is encrypted using the SSL / TLS protocol and sent securely to the server.

[0099] (sending and receiving data)

[0100] Transmission from device to server: The device encrypts the generated JSON data and sends it to the server, ensuring user privacy and data confidentiality in the process.

[0101] Server: The server validates the received JSON data to ensure it is well-formed and complete. After validation, it is stored in the database. The data is also encrypted during storage to protect it from unauthorized access.

[0102] (Data Analysis)

[0103] Server: The server analyzes the health data stored in the database using AI algorithms that utilize machine learning and deep learning techniques, such as random forest and deep learning models to detect abnormal increases in body temperature or changes in dietary patterns.

[0104] Server: If necessary, the server retrieves external data such as weather forecast data and air quality data, and analyzes it in combination with health data, enabling highly accurate predictions that take environmental factors into account.

[0105] (Notifications and Suggestions)

[0106] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if the user's physical condition is abnormal, the server generates a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0107] Device: Receives notifications sent from the server and displays them to the user. Notifications can be displayed as push notifications or in-app pop-up messages.

[0108] Specific examples

[0109] The following are specific usage examples.

[0110] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0111] Terminal: Converts this data into JSON format and sends it to the server.

[0112] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0113] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0114] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0115] Prompt Sentence Examples

[0116] For example, the following prompt sentence is input into the generative AI model: "Detect abnormalities in physical condition based on the health data entered by the user and generate a notification recommending a doctor's visit."

[0117] This system allows users to monitor their own health condition in real time and seek medical attention early, significantly improving the quality of health care.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] User: The user opens the dedicated application and enters their body temperature, weight, dietary information, and exercise record. For example, they enter a body temperature of 37.0°C and a breakfast of "oatmeal and fruit." This data is temporarily stored in the application.

[0121] Input: User's health data (body temperature, weight, dietary details, exercise records)

[0122] Output: Temporarily stored health data

[0123] Step 2:

[0124] Terminal: The terminal receives the health data entered by the user and converts it into JSON format. For example, the following JSON data is generated:

[0125] json

[0126] {

[0127] "Temperature": 37.0,

[0128] "Diet": "Oatmeal and fruit",

[0129] "Date and Time": "2023-10-06"

[0130] }

[0131] This JSON data is then encrypted using the SSL / TLS protocol and sent to the server via secure communication.

[0132] Input: Temporarily stored health data

[0133] Output: JSON formatted data, and encrypted data

[0134] Step 3:

[0135] Server: The server receives the encrypted JSON data and decrypts it. It validates that the data is in the correct format and is complete. This validation involves checking that each field is of the correct type and has all the required information. Once validation is complete, the data is saved to the database. The data is further encrypted at rest to protect it from unauthorized access.

[0136] Input: Encrypted JSON data

[0137] Output: Validated data, data stored in database

[0138] Step 4:

[0139] Server: The server retrieves health data stored in the database and analyzes it using AI algorithms. For example, it uses machine learning models (e.g., random forests and neural networks) to detect whether a person's body temperature has risen for several consecutive days or whether there are any abnormalities in their eating patterns. Furthermore, it also incorporates external data such as weather forecast data and air quality data as needed, and integrates this data with the health data for analysis.

[0140] Input: Health data stored in database, external data

[0141] Output: Analysis results (anomaly detection, trend analysis)

[0142] Step 5:

[0143] Server: Based on the analysis results, generate an appropriate notification for the user. For example, create a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0144] Input: Analysis results

[0145] Output: Notification content

[0146] Step 6:

[0147] Server: Securely encrypts the generated notification and sends it to the device. The SSL / TLS protocol is used to communicate while maintaining data confidentiality.

[0148] Input: Notification content

[0149] Output: Encrypted notification data

[0150] Step 7:

[0151] Device: The device receives the notification sent by the server, decrypts it, and displays it to the user as a pop-up message or push notification, allowing the user to view it and take appropriate action.

[0152] Input: Encrypted notification data

[0153] Output: The notification displayed to the user

[0154] This system allows users to understand their own physical condition in real time and quickly determine whether they need to see a doctor early, thereby realizing efficient health management and prevention.

[0155] (Application example 1)

[0156] 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."

[0157] Employees are required to manage their own health status on a daily basis and to immediately notify managers if any abnormalities are found in their health data. This is important for improving the quality of employee health management. However, current systems lack mechanisms for immediately notifying managers when abnormalities are found, which can result in delays in employee health management. Therefore, it is necessary to provide a system that collects employee health data, promptly notifies managers when abnormalities are detected, and prompts appropriate action.

[0158] 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.

[0159] In this invention, the server includes means for collecting health data from users, means for transmitting the health data to the server, means for storing the transmitted health data in the server, means for analyzing the transmitted health data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for allowing users to record their own health data every day, and for generating and presenting a notification to a manager when an abnormality is detected through analysis of the recorded data. This makes it possible to quickly notify a manager when an abnormality is found in an employee's health data, thereby improving the quality of employee health management.

[0160] "User" refers to the entity that uses the system to input and manage their own health data.

[0161] "Health data" is a general term for information about a user's body, such as their temperature, weight, diet, and exercise records.

[0162] "Server" refers to the part of the computer system that stores health data submitted by users and analyzes it using AI algorithms.

[0163] "AI algorithm" refers to a program that uses artificial intelligence technologies such as machine learning and deep learning to analyze data and perform anomaly detection and prediction.

[0164] A "notification" is a message that conveys the results of an analysis of health data to a user or administrator, and includes important information such as abnormalities.

[0165] "Administrator" refers to the person or organization responsible for monitoring a user's health data and taking action as necessary.

[0166] "Transmission means" is a general term for the functions and protocols used to send health data collected from users to a server.

[0167] "Means for storing" refers to the functionality for the server to record and retain the health data received.

[0168] "Means of analysis" refers to the ability to use AI algorithms to analyze health data and detect anomalies and trends.

[0169] "Presentation means" is a general term for methods and techniques for displaying notifications to users and administrators.

[0170] An "anomaly" is a value or pattern that is different from the normal in health data and indicates an abnormal condition.

[0171] MODE FOR CARRYING OUT THE INVENTION

[0172] Health data collection

[0173] Users enter their health data, such as their daily body temperature, weight, dietary habits, and exercise records, into a dedicated device (e.g., smartphone or tablet). This data entry is done through a dedicated application. The entered data is temporarily stored within the application. The device converts the health data entered by the user into an appropriate data format, such as JSON format. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0174] Sending and Receiving Data

[0175] When transmitting from the device to the server, the collected health data is securely sent to the server. During this process, data encryption is applied to ensure user privacy. The server receives the transmitted data and first verifies it. After confirming that the data format is correct and complete, it stores it in a database. When stored, the data is further encrypted to protect it from unauthorized access.

[0176] Analyzing the data

[0177] The server analyzes the stored health data using AI algorithms. These algorithms include machine learning and deep learning technologies. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem. The server also obtains external data (e.g., weather forecasts, air quality data) as needed and integrates it with the health data for analysis. This enables more accurate analysis.

[0178] Notifications and Suggestions

[0179] The server generates notifications and suggestions for users and administrators based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several consecutive days. We recommend that you see a doctor" is generated. The device receives the notification from the server and displays it to the user and administrator. This notification is displayed as a push notification or a pop-up message within the app.

[0180] Specific examples

[0181] Let's say one day a user enters a body temperature of 37.5°C into the app. This data is converted to JSON format and sent to the server. The server receives the data, stores it in a database, and then analyzes it using an AI algorithm. From the analysis results, it detects that the temperature is continuing to rise. The server generates a notification stating, "Your temperature has been high for several days in a row. We recommend that you see a doctor," and sends a similar notification to the administrator. The device receives the notification and displays a pop-up for the user and administrator. The user and administrator can then review it and take appropriate action.

[0182] Hardware and software used

[0183] Hardware: smartphones, tablets, servers

[0184] Software: Dedicated applications, AI algorithms (machine learning, deep learning), encryption (SSL / TLS)

[0185] Prompt Sentence Examples

[0186] "Collect user health data (e.g., body temperature, weight) and generate notifications if there are any abnormalities. For example, if the body temperature is over 37.5°C, notify the administrator immediately."

[0187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0188] Step 1:

[0189] Users use a dedicated smartphone application to input their daily health data, such as their body temperature, weight, dietary habits, and exercise records, and the input data is temporarily stored within the application.

[0190] Input: User-entered temperature, weight, dietary information, and exercise records

[0191] Output: Health data temporarily stored within the application

[0192] Step 2:

[0193] The device converts the health data entered by the user into an appropriate data format, such as JSON, and then transmits the converted data to the server via secure (SSL / TLS) communication.

[0194] Input: Health data entered by the user

[0195] Output: Health data converted to JSON format, encrypted and sent

[0196] Step 3:

[0197] The server receives the health data and first verifies it to ensure it is in the correct format and complete. It then stores it in a database. It also encrypts the data while it is stored to protect it from unauthorized access.

[0198] Input: Health data in JSON format

[0199] Output: Verified health data stored encrypted in a database

[0200] Step 4:

[0201] The server analyzes the stored health data using AI algorithms, specifically machine learning and deep learning techniques, to detect trends and anomalies in the data and generate predictive models.

[0202] Input: Health data stored in a database

[0203] Output: Trend information and anomaly detection results as analysis results

[0204] Step 5:

[0205] The server generates appropriate notifications to users and administrators based on the analysis results. For example, if a user's body temperature remains high for a certain period of time, the server determines that the user's health is abnormal and generates a notification stating, "We recommend that you see a doctor."

[0206] Input: Analysis results by AI algorithm

[0207] Output: Notification message for users and administrators

[0208] Step 6:

[0209] The device receives the notification sent from the server and displays it to the user and administrator as a push notification or a pop-up message within the app.

[0210] Input: Notification message sent by the server

[0211] Output: Notifications displayed to users and administrators

[0212] 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.

[0213] The present invention is a system that collects a user's health data and emotional data, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0214] Health and emotional data collection

[0215] User: The user inputs health data such as daily body temperature, weight, dietary habits, and exercise records, as well as emotional data including facial expressions and voice, into a dedicated device. This operation is performed using a dedicated application via the device's camera and microphone. The input data is temporarily stored within the application.

[0216] Terminal: Receives health and emotion data entered by the user and converts it into an appropriate data format such as JSON. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0217] Sending and Receiving Data

[0218] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is applied to ensure user privacy.

[0219] Server: Receives the transmitted data and performs an initial validation. After verifying that the data is in the correct format and complete, it stores it in a database. While stored, the data is further encrypted to protect it from unauthorized access.

[0220] Analyzing the data

[0221] Server: The stored health and emotional data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze emotional states, and assess the user's overall health.

[0222] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health and emotion data for analysis, enabling more accurate analysis.

[0223] Notifications and Suggestions

[0224] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if an abnormality in physical condition is detected or a significant change in mood is detected, a notification such as "Your temperature has been high for several days in a row. Furthermore, your recent mood has been unstable. We recommend that you see a doctor" is generated.

[0225] On the device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages, and also provide personalized feedback and suggestions based on the user's emotional state.

[0226] Specific examples

[0227] The following are specific examples:

[0228] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0229] Terminal: Converts this data into JSON format and sends it to the server.

[0230] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0231] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0232] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0233] This system allows users to comprehensively manage their health by taking into account not only their health data but also their emotional data, allowing them to gain a more detailed understanding of their health condition and take appropriate measures as soon as necessary.

[0234] The processing flow will be explained below.

[0235] Step 1:

[0236] User: Opens the dedicated application and inputs health data such as body temperature, weight, dietary habits, and exercise records. The device's camera and microphone also collect facial expressions and voice data, which are then used to input emotional data. For example, if the user inputs a body temperature of 37.2°C, "anxiety" is detected from the facial expression.

[0237] Step 2:

[0238] Terminal: Receives input health and emotion data and converts it into JSON format. Uses a data conversion module to serialize multiple data fields.

[0239] Step 3:

[0240] Terminal: Sends the converted JSON data to the server using secure communication (SSL / TLS). Calls the Send API and sends the encrypted data packet to the server's receiving endpoint.

[0241] Step 4:

[0242] Server: Receives data sent from the device. The receiving API takes in the data and verifies its format and completeness. The verified data is stored in the database.

[0243] Step 5:

[0244] Server: Analyzes stored health and emotion data using AI algorithms, first preprocessing the data and then using machine learning and deep learning models to detect anomalies and trends.

[0245] Step 6:

[0246] Server: Retrieves external data (e.g., weather forecasts and air quality data) as needed, integrates it with health and emotion data for further analysis, and calls external APIs to merge the retrieved data with the internal database.

[0247] Step 7:

[0248] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if a rise in body temperature and the emotion "anxiety" are detected at the same time, a notification will be created saying, "Your body temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0249] Step 8:

[0250] Terminal: Receives notifications from the server. It periodically polls for notifications from the server and displays them as a popup in the user interface after receiving them.

[0251] Step 9:

[0252] User: Checks notifications and suggestions displayed on the device and takes necessary action, such as scheduling a doctor's appointment.

[0253] In this way, the system supports users in comprehensive health management by centrally managing, analyzing, and notifying both health and emotional data, allowing users to take early and appropriate measures.

[0254] Example 2

[0255] 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."

[0256] In modern society, busy daily lives and stress make it difficult for users to properly manage their own health and emotional state. Furthermore, conventional health management systems only target health data, and therefore are unable to provide comprehensive health management that takes into account changes in the user's emotional state. Therefore, there is a need for a system that can collect and analyze more comprehensive data and provide appropriate notifications and suggestions to users.

[0257] 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.

[0258] In this invention, the server includes means for collecting health data and emotional data from a user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for integrating the analyzed data with external data and generating notifications and suggestions based on the analysis results, and means for presenting the generated notifications and suggestions to the user. This enables the user's health data and emotional data to be managed in a unified manner, enabling a comprehensive understanding of the user's health condition and the provision of appropriate notifications and suggestions.

[0259] "Health data" is information that indicates the user's physical condition, such as body temperature, weight, dietary content, and exercise records.

[0260] "Emotion data" is information that indicates the psychological state of the user extracted from facial expressions, voice, and the like.

[0261] A "server" is a central device that receives, stores, analyzes collected data, and notifies users of the results.

[0262] "AI algorithms" are machine learning and deep learning techniques used to analyze collected data and detect trends and anomalies.

[0263] "Notifications" are messages that alert or suggest users based on the analysis results.

[0264] "External data" is external information that may affect a user's health or emotional state, such as weather forecasts or air quality data.

[0265] A "terminal" is a device that allows a user to input data and transmit it to a server, and includes, for example, a smartphone or tablet.

[0266] "Data Format" means the JSON format or other appropriate format used to transmit the collected data to the server.

[0267] "Data encryption" is a cryptographic technique for protecting data in transmission and storage.

[0268] "Push notifications" are a type of notification that appears on a device screen and are a means of instantly conveying important information to users.

[0269] The present invention is a system that collects health and emotional data from users, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user.

[0270] Health and emotional data collection

[0271] User: In addition to health data such as daily body temperature, weight, dietary habits, and exercise records, emotional data including facial expressions and voice are entered into a dedicated device. This is done using a dedicated application via the device's camera and microphone. The entered data is temporarily stored within the application. For example, data can be collected by a user entering their body temperature as "37.2°C" into a smartphone app and simultaneously saying "I feel a little anxious" into the camera.

[0272] Terminal: The terminal receives the health and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is sent to the server via secure (SSL / TLS) communication. Specifically, the data is sent to the server in a format such as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0273] Sending and Receiving Data

[0274] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is performed to ensure user privacy. For example, AES encryption technology is used to protect the data from tampering.

[0275] Server: The server receives the transmitted data and performs an initial verification. After verifying that the data format is correct and complete, it stores it in the database. When stored, the data is further encrypted to protect it from unauthorized access. For example, the data is stored in the format "encrypted({"body_temperature": 37.2, "emotion": "anxiety"})".

[0276] Analyzing the data

[0277] Server: The server analyzes the stored health and emotional data using AI algorithms. These algorithms include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze the emotional state, and assess the user's overall health. For example, based on data showing a body temperature of 37.2°C and an emotion of "anxiety," the anomaly detection model analyzes the temperature trend and emotional changes.

[0278] Server: Optionally, external data (e.g., weather forecasts, air quality data) is also acquired and combined with health and emotion data for analysis. This allows for more accurate analysis. For example, if the weather data indicates that the temperature is high that day, it can determine whether the rise in body temperature is due to environmental factors.

[0279] Generate notifications and suggestions

[0280] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if an abnormality in physical condition is detected or if there is a significant change in emotions, the server generates a notification such as, "Your temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0281] Viewing notifications

[0282] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages. For example, a notification might appear on the smartphone screen saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor."

[0283] Specific examples

[0284] Specific examples are given below.

[0285] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0286] Device: Convert this data into JSON format and send it to the server. Send the data to the server as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0287] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0288] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0289] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0290] Example prompts for generative AI models

[0291] Below are some specific examples of prompt sentences to input into the generative AI model.

[0292] "Generate specific suggestions for when the user's temperature is 37.2°C and emotion data indicates anxiety."

[0293] In response to this prompt, the AI ​​model is expected to generate appropriate suggestions for users who are experiencing elevated body temperature and anxiety.

[0294] In this way, the present invention comprehensively analyzes the user's health data and emotional data and provides appropriate notifications and suggestions to assist the user in managing their health.

[0295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0296] Step 1:

[0297] Input: The user inputs health data such as body temperature, weight, dietary content, and exercise records, as well as facial expressions and voice data, into a dedicated terminal.

[0298] Specific actions: The user opens a dedicated app on their smartphone, enters their body temperature (37.2°C) and exercise record, and says to the camera, "I'm a little anxious."

[0299] Output: This data is temporarily stored within the application.

[0300] Step 2:

[0301] Input: Health and emotional data stored within the application.

[0302] What happens: The device converts the data into JSON format, like this: {"body_temperature": 37.2, "emotion": "anxiety"}.

[0303] Output: The converted data is sent to the server using a secure communication protocol (SSL / TLS).

[0304] Step 3:

[0305] Input: Data sent from the terminal.

[0306] What happens: The server receives the data and verifies the JSON format and completeness of required fields. For example, it verifies that the temperature data is numeric and the emotion data is in the specified format.

[0307] Output: Once the data has been verified, it is further encrypted and stored in the database.

[0308] Step 4:

[0309] Input: Stored health and emotion data.

[0310] How it works: The server inputs the data into the AI ​​algorithm and begins analysis. The AI ​​algorithm performs trend analysis, anomaly detection, and sentiment analysis.

[0311] Output: As a result of the analysis, trends such as "rising body temperature" and "emotional instability" are detected.

[0312] Step 5:

[0313] Input: Analysis results from the AI ​​algorithm and external data where necessary (e.g. weather data).

[0314] What it does: The server also integrates external data to generate more accurate notifications and suggestions. For example, if a series of hot days continues, it will consider whether the weather is affecting your body temperature.

[0315] Output: Generates a notification such as "Your temperature has been elevated for several days in a row. Additionally, you have recently been in an unstable emotional state. We recommend that you see a doctor."

[0316] Step 6:

[0317] Input: The notification sent by the server.

[0318] What happens: The device receives the notification and displays it to the user. This notification can appear as a push notification or a pop-up message within the app. Specifically, the device displays a message on the smartphone screen stating, "Your temperature has been elevated for several consecutive days. We recommend that you see a doctor."

[0319] Output: The user can review the notification and take appropriate action if necessary.

[0320] Through the above steps, it becomes possible to comprehensively manage the user's health data and emotional data and provide appropriate notifications and suggestions.

[0321] (Application example 2)

[0322] 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."

[0323] Conventional health management systems primarily analyze and notify users based on their health data, but do not take emotional data into account. This makes it difficult to comprehensively evaluate a user's overall health status and provide urgent notifications. Furthermore, even when a rapid response is required when an abnormality is detected, delays in response have been a problem.

[0324] 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 from the user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for generating an emergency notification if an abnormality is detected. This makes it possible to evaluate the user's overall health condition from the user's health data and emotional data, and to take prompt action, especially in urgent cases.

[0325] "Health data" refers to physiological data such as the user's body temperature, weight, dietary habits, and exercise records.

[0326] "Emotional data" refers to data about a user's emotional state collected through facial expressions and voice.

[0327] "Server" refers to a central system for receiving, storing, and analyzing data.

[0328] "AI algorithm" refers to an algorithm for analyzing data using artificial intelligence technologies such as machine learning and deep learning.

[0329] "Notification" refers to a message that informs the user of the results of an analysis of their health and emotional data.

[0330] "Abnormality notification" refers to an alert that is generated in particularly urgent cases based on the results of an analysis of a user's health data and emotional data.

[0331] "External data" refers to environmental data that affects the user's health, such as weather and air quality.

[0332] "Generative AI model" refers to an artificial intelligence model that notifies users through generated text messages, etc.

[0333] To implement this invention, the following hardware and software are required: A user collects health and emotional data using a smartphone or head-mounted display and transmits the data to a server, which stores and analyzes the data and sends appropriate notifications to the user.

[0334] Hardware and software used

[0335] Hardware: Smartphone (iOS / Android), head-mounted display (Microsoft HoloLens, Oculus Quest)

[0336] software:

[0337] Data collection and transmission: Application (collects health and emotion data, converts it to JSON format, and transmits it with TLS encryption)

[0338] Data storage and analysis: Database management systems (MySQL, PostgreSQL) running on the server, AI algorithms (using Python, TensorFlow, PyTorch)

[0339] Notification generation and delivery: Generative AI models for notification services

[0340] Data collection and transmission

[0341] Users collect health and emotional data through the camera and microphone on their smartphone or head-mounted display. The data is converted into JSON format by the application and sent to the server using TLS encryption. The server validates the received data and stores it in a database.

[0342] Data storage and analysis

[0343] The server uses the stored health and emotion data to analyze it with an AI algorithm. The algorithm, built using TensorFlow and PyTorch, applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. It also integrates external data such as weather and air quality data for analysis as needed.

[0344] Generate and send notifications

[0345] The server generates a notification for the user based on the analysis results of the AI ​​algorithm. This notification contains a specific text message using the generative AI model. For example, it may contain content such as, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." These notifications are displayed on the smartphone or head-mounted display as push notifications or pop-up messages.

[0346] Specific examples

[0347] One day, a user's heart rate suddenly rises while at work, and their blood pressure is detected to be higher than normal. Furthermore, their facial expression shows signs of strong stress. In this case, the server generates a notification that reads, "Your heart rate and blood pressure have remained high, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." and sends it to the user.

[0348] Prompt Sentence Examples

[0349] "Your heart rate and blood pressure have been elevated above normal, indicating signs of stress. Please stay safe and take some time to rest. Contact your security manager if necessary."

[0350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0351] Step 1:

[0352] Users collect health and emotional data using the camera and microphone on their smartphone or head-mounted display. The collected data includes body temperature, weight, heart rate, blood pressure, facial expression data, and voice data. The input is health and emotional data, which is temporarily stored within the application.

[0353] Step 2:

[0354] The device converts the collected health and emotion data into JSON format and sends it to the server using TLS encryption. The input is the converted data, and the output is the encrypted data sent to the server.

[0355] Step 3:

[0356] The server validates the received data and stores it in a database. The input is the received data and the output is the stored data. The server verifies the integrity of the data format and checks for incomplete data.

[0357] Step 4:

[0358] The server analyzes the stored health and emotion data using an AI algorithm. The algorithm is built using TensorFlow and PyTorch and applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. The input is the stored data, and the output is the analysis result of the AI ​​model.

[0359] Step 5:

[0360] The server acquires external data, such as weather and air quality, as needed, and integrates it with health and emotion data for analysis. The input is the integration of health and emotion data with external data, and the output is the new analysis results.

[0361] Step 6:

[0362] The server generates a notification for the user based on the analysis results of the AI ​​algorithm, which includes a specific text message using a generative AI model. The input is the analysis result, and the output is the generated notification message.

[0363] Step 7:

[0364] The device receives notifications from the server and displays them to the user as push notifications or popup messages. The input is the generated notification message, and the output is the notification displayed on the user's device.

[0365] Step 8:

[0366] The user checks the received notification and takes appropriate action according to the instructions in the notification. The input is the displayed notification, and the output is the user's action. The specific prompt text is, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary."

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Second embodiment]

[0371] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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."

[0383] The present invention is a system that collects a user's health data, transmits it to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0384] Health data collection

[0385] User: Enters daily body temperature, weight, dietary information, and exercise records into a dedicated device (e.g., smartphone or tablet). This operation is performed through a dedicated application. The entered data is temporarily stored within the application.

[0386] Terminal: Receives health data entered by the user and converts it into an appropriate data format, such as JSON, then transmits the converted data to the server via secure (SSL / TLS) communication.

[0387] Sending and Receiving Data

[0388] Transmission from device to server: Collected data is securely transmitted to the server, where data encryption is applied to ensure user privacy.

[0389] Server: Receives the transmitted data and first validates it. After verifying that the data is in the correct format and complete, it stores it in the database. When stored, it further encrypts the data to protect it from unauthorized access.

[0390] Analyzing the data

[0391] Server: The stored health data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem.

[0392] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health data for analysis, enabling more accurate analysis.

[0393] Notifications and Suggestions

[0394] Server: Generates notifications and suggestions to the user based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several days in a row. We recommend that you see a doctor" is generated.

[0395] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages.

[0396] Specific examples

[0397] The following are specific usage examples.

[0398] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0399] Terminal: Converts this data into JSON format and sends it to the server.

[0400] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0401] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0402] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0403] This system allows users to understand their own physical condition in real time and to seek medical attention early, significantly improving the quality of health management.

[0404] The processing flow will be explained below.

[0405] Step 1:

[0406] User: Enter health data (body temperature, weight, dietary information, exercise records, etc.) into the device. Specifically, the user opens a dedicated application and enters numbers or text into each data field (e.g., "Body temperature: 36.5°C," "Breakfast: oatmeal and fruit," etc.).

[0407] Step 2:

[0408] Terminal: Receives the input health data and converts it into an appropriate data format, such as JSON. Specifically, a data conversion module in the application serializes the data.

[0409] Step 3:

[0410] Terminal: The converted data is sent to the server using secure communication (SSL / TLS). Specifically, the sending API is called and the data packet is sent to the endpoint.

[0411] Step 4:

[0412] Server: Receives data sent from the device. Specifically, the receiving API takes in the data, performs initial validation, and checks the data format and completeness.

[0413] Step 5:

[0414] Server: Before storing the data in the database, the data is further encrypted by calling the data encryption module and performing AES encryption.

[0415] Step 6:

[0416] Server: Analyzes stored health data using AI algorithms, specifically machine learning and deep learning models, to interpret the data and detect outliers and trends.

[0417] Step 7:

[0418] Server: Obtains external data (such as weather forecasts and air quality data) as needed, integrates it with health data, and analyzes it. Specifically, it calls external APIs to obtain data and merges it with the internal database.

[0419] Step 8:

[0420] Server: Generates notifications and suggestions for users based on the analysis results. Specifically, the notification generation module creates a message and queues the notification for the user's device ID.

[0421] Step 9:

[0422] Terminal: Receives notifications from the server and displays them to the user, either by polling or by using push notifications, and displays the received notifications in the user interface.

[0423] Step 10:

[0424] User: Check the notifications and suggestions displayed on the device and take necessary action. For example, if you receive a notification that you have a high temperature, seek medical attention.

[0425] In this way, the system executes a series of steps, from collecting and analyzing the user's health data to notifying them, allowing the user to manage their health at the appropriate time.

[0426] Example 1

[0427] 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."

[0428] Conventional health management systems have struggled to efficiently collect user-entered health data, perform appropriate analysis, and provide timely notifications and suggestions to users. They also lack the ability to integrate and analyze user health data with external data to more accurately understand and predict health status. Furthermore, processing data securely while ensuring user privacy has also been a challenge.

[0429] 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.

[0430] In this invention, the server includes a means for storing the transmitted health data, a means for analyzing the transmitted health data using an AI algorithm, and a means for generating notifications to the user based on the analysis results. This allows users to efficiently collect, manage, and analyze their own health data and securely receive appropriate notifications. Furthermore, by acquiring external data and integrating and analyzing it with the health data, it becomes possible to grasp and predict health status more accurately.

[0431] "User" refers to an individual who uses the system to input and manage health data.

[0432] "Health Data" refers to information about an individual's health status, such as temperature, weight, diet, and exercise records.

[0433] "Server" refers to the computer system that receives, stores, analyzes, and generates notifications about health data submitted by users.

[0434] "Terminal" refers to the device (e.g., smartphone or tablet) through which the user inputs health data and receives and displays notifications from the server.

[0435] "AI algorithms" refers to data analysis methods, including machine learning and deep learning technologies, used to detect and predict trends and anomalies in health data.

[0436] "Notification" refers to messages or suggestions sent to the user by the server based on the results of the analysis.

[0437] "External data" refers to additional data, such as weather forecasts or air quality data, that the server acquires to integrate with health data for analysis.

[0438] "Secure" refers to ensuring privacy and confidentiality when sending, receiving, or storing data using protocols such as SSL / TLS.

[0439] The present invention provides a system for efficiently collecting and analyzing a user's health data and providing the user with appropriate notifications and suggestions. This system is configured as follows.

[0440] (Collection of health data)

[0441] User: The user uses a dedicated application to input daily temperature, weight, dietary intake, and exercise records. The application runs on a well-known smartphone or tablet.

[0442] Terminal: The terminal takes the data entered by the user and converts it to JSON format, for example:

[0443] json

[0444] {

[0445] "Temperature": 37.0,

[0446] "Diet": "Oatmeal and fruit",

[0447] "Date and Time": "2023-10-06"

[0448] }

[0449] This data is encrypted using the SSL / TLS protocol and sent securely to the server.

[0450] (sending and receiving data)

[0451] Transmission from device to server: The device encrypts the generated JSON data and sends it to the server, ensuring user privacy and data confidentiality in the process.

[0452] Server: The server validates the received JSON data to ensure it is well-formed and complete. After validation, it is stored in the database. The data is also encrypted during storage to protect it from unauthorized access.

[0453] (Data Analysis)

[0454] Server: The server analyzes the health data stored in the database using AI algorithms that utilize machine learning and deep learning techniques, such as random forest and deep learning models to detect abnormal increases in body temperature or changes in dietary patterns.

[0455] Server: If necessary, the server retrieves external data such as weather forecast data and air quality data, and analyzes it in combination with health data, enabling highly accurate predictions that take environmental factors into account.

[0456] (Notifications and Suggestions)

[0457] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if the user's physical condition is abnormal, the server generates a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0458] Device: Receives notifications sent from the server and displays them to the user. Notifications can be displayed as push notifications or in-app pop-up messages.

[0459] Specific examples

[0460] The following are specific usage examples.

[0461] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0462] Terminal: Converts this data into JSON format and sends it to the server.

[0463] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0464] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0465] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0466] Prompt Sentence Examples

[0467] For example, the following prompt sentence is input into the generative AI model: "Detect abnormalities in physical condition based on the health data entered by the user and generate a notification recommending a doctor's visit."

[0468] This system allows users to monitor their own health condition in real time and seek medical attention early, significantly improving the quality of health care.

[0469] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0470] Step 1:

[0471] User: The user opens the dedicated application and enters their body temperature, weight, dietary information, and exercise record. For example, they enter a body temperature of 37.0°C and a breakfast of "oatmeal and fruit." This data is temporarily stored in the application.

[0472] Input: User's health data (body temperature, weight, dietary details, exercise records)

[0473] Output: Temporarily stored health data

[0474] Step 2:

[0475] Terminal: The terminal receives the health data entered by the user and converts it into JSON format. For example, the following JSON data is generated:

[0476] json

[0477] {

[0478] "Temperature": 37.0,

[0479] "Diet": "Oatmeal and fruit",

[0480] "Date and Time": "2023-10-06"

[0481] }

[0482] This JSON data is then encrypted using the SSL / TLS protocol and sent to the server via secure communication.

[0483] Input: Temporarily stored health data

[0484] Output: JSON formatted data, and encrypted data

[0485] Step 3:

[0486] Server: The server receives the encrypted JSON data and decrypts it. It validates that the data is in the correct format and is complete. This validation involves checking that each field is of the correct type and has all the required information. Once validation is complete, the data is saved to the database. The data is further encrypted at rest to protect it from unauthorized access.

[0487] Input: Encrypted JSON data

[0488] Output: Validated data, data stored in database

[0489] Step 4:

[0490] Server: The server retrieves health data stored in the database and analyzes it using AI algorithms. For example, it uses machine learning models (e.g., random forests and neural networks) to detect whether a person's body temperature has risen for several consecutive days or whether there are any abnormalities in their eating patterns. Furthermore, it also incorporates external data such as weather forecast data and air quality data as needed, and integrates this data with the health data for analysis.

[0491] Input: Health data stored in database, external data

[0492] Output: Analysis results (anomaly detection, trend analysis)

[0493] Step 5:

[0494] Server: Based on the analysis results, generate an appropriate notification for the user. For example, create a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0495] Input: Analysis results

[0496] Output: Notification content

[0497] Step 6:

[0498] Server: Securely encrypts the generated notification and sends it to the device. The SSL / TLS protocol is used to communicate while maintaining data confidentiality.

[0499] Input: Notification content

[0500] Output: Encrypted notification data

[0501] Step 7:

[0502] Device: The device receives the notification sent by the server, decrypts it, and displays it to the user as a pop-up message or push notification, allowing the user to view it and take appropriate action.

[0503] Input: Encrypted notification data

[0504] Output: The notification displayed to the user

[0505] This system allows users to understand their own physical condition in real time and quickly determine whether they need to see a doctor early, thereby realizing efficient health management and prevention.

[0506] (Application example 1)

[0507] 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."

[0508] Employees are required to manage their own health status on a daily basis and to immediately notify managers if any abnormalities are found in their health data. This is important for improving the quality of employee health management. However, current systems lack mechanisms for immediately notifying managers when abnormalities are found, which can result in delays in employee health management. Therefore, it is necessary to provide a system that collects employee health data, promptly notifies managers when abnormalities are detected, and prompts appropriate action.

[0509] 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.

[0510] In this invention, the server includes means for collecting health data from users, means for transmitting the health data to the server, means for storing the transmitted health data in the server, means for analyzing the transmitted health data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for allowing users to record their own health data every day, and for generating and presenting a notification to a manager when an abnormality is detected through analysis of the recorded data. This makes it possible to quickly notify a manager when an abnormality is found in an employee's health data, thereby improving the quality of employee health management.

[0511] "User" refers to the entity that uses the system to input and manage their own health data.

[0512] "Health data" is a general term for information about a user's body, such as their temperature, weight, diet, and exercise records.

[0513] "Server" refers to the part of the computer system that stores health data submitted by users and analyzes it using AI algorithms.

[0514] "AI algorithm" refers to a program that uses artificial intelligence technologies such as machine learning and deep learning to analyze data and perform anomaly detection and prediction.

[0515] A "notification" is a message that conveys the results of an analysis of health data to a user or administrator, and includes important information such as abnormalities.

[0516] "Administrator" refers to the person or organization responsible for monitoring a user's health data and taking action as necessary.

[0517] "Transmission means" is a general term for the functions and protocols used to send health data collected from users to a server.

[0518] "Means for storing" refers to the functionality for the server to record and retain the health data received.

[0519] "Means of analysis" refers to the ability to use AI algorithms to analyze health data and detect anomalies and trends.

[0520] "Presentation means" is a general term for methods and techniques for displaying notifications to users and administrators.

[0521] An "anomaly" is a value or pattern that is different from the normal in health data and indicates an abnormal condition.

[0522] MODE FOR CARRYING OUT THE INVENTION

[0523] Health data collection

[0524] Users enter their health data, such as their daily body temperature, weight, dietary habits, and exercise records, into a dedicated device (e.g., smartphone or tablet). This data entry is done through a dedicated application. The entered data is temporarily stored within the application. The device converts the health data entered by the user into an appropriate data format, such as JSON format. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0525] Sending and Receiving Data

[0526] When transmitting from the device to the server, the collected health data is securely sent to the server. During this process, data encryption is applied to ensure user privacy. The server receives the transmitted data and first verifies it. After confirming that the data format is correct and complete, it stores it in a database. When stored, the data is further encrypted to protect it from unauthorized access.

[0527] Analyzing the data

[0528] The server analyzes the stored health data using AI algorithms. These algorithms include machine learning and deep learning technologies. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem. The server also obtains external data (e.g., weather forecasts, air quality data) as needed and integrates it with the health data for analysis. This enables more accurate analysis.

[0529] Notifications and Suggestions

[0530] The server generates notifications and suggestions for users and administrators based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several consecutive days. We recommend that you see a doctor" is generated. The device receives the notification from the server and displays it to the user and administrator. This notification is displayed as a push notification or a pop-up message within the app.

[0531] Specific examples

[0532] Let's say one day a user enters a body temperature of 37.5°C into the app. This data is converted to JSON format and sent to the server. The server receives the data, stores it in a database, and then analyzes it using an AI algorithm. From the analysis results, it detects that the temperature is continuing to rise. The server generates a notification stating, "Your temperature has been high for several days in a row. We recommend that you see a doctor," and sends a similar notification to the administrator. The device receives the notification and displays a pop-up for the user and administrator. The user and administrator can then review it and take appropriate action.

[0533] Hardware and software used

[0534] Hardware: smartphones, tablets, servers

[0535] Software: Dedicated applications, AI algorithms (machine learning, deep learning), encryption (SSL / TLS)

[0536] Prompt Sentence Examples

[0537] "Collect user health data (e.g., body temperature, weight) and generate notifications if there are any abnormalities. For example, if the body temperature is over 37.5°C, notify the administrator immediately."

[0538] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0539] Step 1:

[0540] Users use a dedicated smartphone application to input their daily health data, such as their body temperature, weight, dietary habits, and exercise records, and the input data is temporarily stored within the application.

[0541] Input: User-entered temperature, weight, dietary information, and exercise records

[0542] Output: Health data temporarily stored within the application

[0543] Step 2:

[0544] The device converts the health data entered by the user into an appropriate data format, such as JSON, and then transmits the converted data to the server via secure (SSL / TLS) communication.

[0545] Input: Health data entered by the user

[0546] Output: Health data converted to JSON format, encrypted and sent

[0547] Step 3:

[0548] The server receives the health data and first verifies it to ensure it is in the correct format and complete. It then stores it in a database. It also encrypts the data while it is stored to protect it from unauthorized access.

[0549] Input: Health data in JSON format

[0550] Output: Verified health data stored encrypted in a database

[0551] Step 4:

[0552] The server analyzes the stored health data using AI algorithms, specifically machine learning and deep learning techniques, to detect trends and anomalies in the data and generate predictive models.

[0553] Input: Health data stored in a database

[0554] Output: Trend information and anomaly detection results as analysis results

[0555] Step 5:

[0556] The server generates appropriate notifications to users and administrators based on the analysis results. For example, if a user's body temperature remains high for a certain period of time, the server determines that the user's health is abnormal and generates a notification stating, "We recommend that you see a doctor."

[0557] Input: Analysis results by AI algorithm

[0558] Output: Notification message for users and administrators

[0559] Step 6:

[0560] The device receives the notification sent from the server and displays it to the user and administrator as a push notification or a pop-up message within the app.

[0561] Input: Notification message sent by the server

[0562] Output: Notifications displayed to users and administrators

[0563] 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.

[0564] The present invention is a system that collects a user's health data and emotional data, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0565] Health and emotional data collection

[0566] User: The user inputs health data such as daily body temperature, weight, dietary habits, and exercise records, as well as emotional data including facial expressions and voice, into a dedicated device. This operation is performed using a dedicated application via the device's camera and microphone. The input data is temporarily stored within the application.

[0567] Terminal: Receives health and emotion data entered by the user and converts it into an appropriate data format such as JSON. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0568] Sending and Receiving Data

[0569] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is applied to ensure user privacy.

[0570] Server: Receives the transmitted data and performs an initial validation. After verifying that the data is in the correct format and complete, it stores it in a database. While stored, the data is further encrypted to protect it from unauthorized access.

[0571] Analyzing the data

[0572] Server: The stored health and emotional data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze emotional states, and assess the user's overall health.

[0573] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health and emotion data for analysis, enabling more accurate analysis.

[0574] Notifications and Suggestions

[0575] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if an abnormality in physical condition is detected or a significant change in mood is detected, a notification such as "Your temperature has been high for several days in a row. Furthermore, your recent mood has been unstable. We recommend that you see a doctor" is generated.

[0576] On the device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages, and also provide personalized feedback and suggestions based on the user's emotional state.

[0577] Specific examples

[0578] The following are specific examples:

[0579] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0580] Terminal: Converts this data into JSON format and sends it to the server.

[0581] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0582] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0583] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0584] This system allows users to comprehensively manage their health by taking into account not only their health data but also their emotional data, allowing them to gain a more detailed understanding of their health condition and take appropriate measures as soon as necessary.

[0585] The processing flow will be explained below.

[0586] Step 1:

[0587] User: Opens the dedicated application and inputs health data such as body temperature, weight, dietary habits, and exercise records. The device's camera and microphone also collect facial expressions and voice data, which are then used to input emotional data. For example, if the user inputs a body temperature of 37.2°C, "anxiety" is detected from the facial expression.

[0588] Step 2:

[0589] Terminal: Receives input health and emotion data and converts it into JSON format. Uses a data conversion module to serialize multiple data fields.

[0590] Step 3:

[0591] Terminal: Sends the converted JSON data to the server using secure communication (SSL / TLS). Calls the Send API and sends the encrypted data packet to the server's receiving endpoint.

[0592] Step 4:

[0593] Server: Receives data sent from the device. The receiving API takes in the data and verifies its format and completeness. The verified data is stored in the database.

[0594] Step 5:

[0595] Server: Analyzes stored health and emotion data using AI algorithms, first preprocessing the data and then using machine learning and deep learning models to detect anomalies and trends.

[0596] Step 6:

[0597] Server: Retrieves external data (e.g., weather forecasts and air quality data) as needed, integrates it with health and emotion data for further analysis, and calls external APIs to merge the retrieved data with the internal database.

[0598] Step 7:

[0599] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if a rise in body temperature and the emotion "anxiety" are detected at the same time, a notification will be created saying, "Your body temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0600] Step 8:

[0601] Terminal: Receives notifications from the server. It periodically polls for notifications from the server and displays them as a popup in the user interface after receiving them.

[0602] Step 9:

[0603] User: Checks notifications and suggestions displayed on the device and takes necessary action, such as scheduling a doctor's appointment.

[0604] In this way, the system supports users in comprehensive health management by centrally managing, analyzing, and notifying both health and emotional data, allowing users to take early and appropriate measures.

[0605] Example 2

[0606] 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."

[0607] In modern society, busy daily lives and stress make it difficult for users to properly manage their own health and emotional state. Furthermore, conventional health management systems only target health data, and therefore are unable to provide comprehensive health management that takes into account changes in the user's emotional state. Therefore, there is a need for a system that can collect and analyze more comprehensive data and provide appropriate notifications and suggestions to users.

[0608] 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.

[0609] In this invention, the server includes means for collecting health data and emotional data from a user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for integrating the analyzed data with external data and generating notifications and suggestions based on the analysis results, and means for presenting the generated notifications and suggestions to the user. This enables the user's health data and emotional data to be managed in a unified manner, enabling a comprehensive understanding of the user's health condition and the provision of appropriate notifications and suggestions.

[0610] "Health data" is information that indicates the user's physical condition, such as body temperature, weight, dietary content, and exercise records.

[0611] "Emotion data" is information that indicates the psychological state of the user extracted from facial expressions, voice, and the like.

[0612] A "server" is a central device that receives, stores, analyzes collected data, and notifies users of the results.

[0613] "AI algorithms" are machine learning and deep learning techniques used to analyze collected data and detect trends and anomalies.

[0614] "Notifications" are messages that alert or suggest users based on the analysis results.

[0615] "External data" is external information that may affect a user's health or emotional state, such as weather forecasts or air quality data.

[0616] A "terminal" is a device that allows a user to input data and transmit it to a server, and includes, for example, a smartphone or tablet.

[0617] "Data Format" means the JSON format or other appropriate format used to transmit the collected data to the server.

[0618] "Data encryption" is a cryptographic technique for protecting data in transmission and storage.

[0619] "Push notifications" are a type of notification that appears on a device screen and are a means of instantly conveying important information to users.

[0620] The present invention is a system that collects health and emotional data from users, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user.

[0621] Health and emotional data collection

[0622] User: In addition to health data such as daily body temperature, weight, dietary habits, and exercise records, emotional data including facial expressions and voice are entered into a dedicated device. This is done using a dedicated application via the device's camera and microphone. The entered data is temporarily stored within the application. For example, data can be collected by a user entering their body temperature as "37.2°C" into a smartphone app and simultaneously saying "I feel a little anxious" into the camera.

[0623] Terminal: The terminal receives the health and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is sent to the server via secure (SSL / TLS) communication. Specifically, the data is sent to the server in a format such as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0624] Sending and Receiving Data

[0625] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is performed to ensure user privacy. For example, AES encryption technology is used to protect the data from tampering.

[0626] Server: The server receives the transmitted data and performs an initial verification. After verifying that the data format is correct and complete, it stores it in the database. When stored, the data is further encrypted to protect it from unauthorized access. For example, the data is stored in the format "encrypted({"body_temperature": 37.2, "emotion": "anxiety"})".

[0627] Analyzing the data

[0628] Server: The server analyzes the stored health and emotional data using AI algorithms. These algorithms include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze the emotional state, and assess the user's overall health. For example, based on data showing a body temperature of 37.2°C and an emotion of "anxiety," the anomaly detection model analyzes the temperature trend and emotional changes.

[0629] Server: Optionally, external data (e.g., weather forecasts, air quality data) is also acquired and combined with health and emotion data for analysis. This allows for more accurate analysis. For example, if the weather data indicates that the temperature is high that day, it can determine whether the rise in body temperature is due to environmental factors.

[0630] Generate notifications and suggestions

[0631] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if an abnormality in physical condition is detected or if there is a significant change in emotions, the server generates a notification such as, "Your temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0632] Viewing notifications

[0633] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages. For example, a notification might appear on the smartphone screen saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor."

[0634] Specific examples

[0635] Specific examples are given below.

[0636] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0637] Device: Convert this data into JSON format and send it to the server. Send the data to the server as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0638] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0639] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0640] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0641] Example prompts for generative AI models

[0642] Below are some specific examples of prompt sentences to input into the generative AI model.

[0643] "Generate specific suggestions for when the user's temperature is 37.2°C and emotion data indicates anxiety."

[0644] In response to this prompt, the AI ​​model is expected to generate appropriate suggestions for users who are experiencing elevated body temperature and anxiety.

[0645] In this way, the present invention comprehensively analyzes the user's health data and emotional data and provides appropriate notifications and suggestions to assist the user in managing their health.

[0646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] Input: The user inputs health data such as body temperature, weight, dietary content, and exercise records, as well as facial expressions and voice data, into a dedicated terminal.

[0649] Specific actions: The user opens a dedicated app on their smartphone, enters their body temperature (37.2°C) and exercise record, and says to the camera, "I'm a little anxious."

[0650] Output: This data is temporarily stored within the application.

[0651] Step 2:

[0652] Input: Health and emotional data stored within the application.

[0653] What happens: The device converts the data into JSON format, like this: {"body_temperature": 37.2, "emotion": "anxiety"}.

[0654] Output: The converted data is sent to the server using a secure communication protocol (SSL / TLS).

[0655] Step 3:

[0656] Input: Data sent from the terminal.

[0657] What happens: The server receives the data and verifies the JSON format and completeness of required fields. For example, it verifies that the temperature data is numeric and the emotion data is in the specified format.

[0658] Output: Once the data has been verified, it is further encrypted and stored in the database.

[0659] Step 4:

[0660] Input: Stored health and emotion data.

[0661] How it works: The server inputs the data into the AI ​​algorithm and begins analysis. The AI ​​algorithm performs trend analysis, anomaly detection, and sentiment analysis.

[0662] Output: As a result of the analysis, trends such as "rising body temperature" and "emotional instability" are detected.

[0663] Step 5:

[0664] Input: Analysis results from the AI ​​algorithm and external data where necessary (e.g. weather data).

[0665] What it does: The server also integrates external data to generate more accurate notifications and suggestions. For example, if a series of hot days continues, it will consider whether the weather is affecting your body temperature.

[0666] Output: Generates a notification such as "Your temperature has been elevated for several days in a row. Additionally, you have recently been in an unstable emotional state. We recommend that you see a doctor."

[0667] Step 6:

[0668] Input: The notification sent by the server.

[0669] What happens: The device receives the notification and displays it to the user. This notification can appear as a push notification or a pop-up message within the app. Specifically, the device displays a message on the smartphone screen stating, "Your temperature has been elevated for several consecutive days. We recommend that you see a doctor."

[0670] Output: The user can review the notification and take appropriate action if necessary.

[0671] Through the above steps, it becomes possible to comprehensively manage the user's health data and emotional data and provide appropriate notifications and suggestions.

[0672] (Application example 2)

[0673] 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."

[0674] Conventional health management systems primarily analyze and notify users based on their health data, but do not take emotional data into account. This makes it difficult to comprehensively evaluate a user's overall health status and provide urgent notifications. Furthermore, even when a rapid response is required when an abnormality is detected, delays in response have been a problem.

[0675] 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 from the user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for generating an emergency notification if an abnormality is detected. This makes it possible to evaluate the user's overall health condition from the user's health data and emotional data, and to take prompt action, especially in urgent cases.

[0676] "Health data" refers to physiological data such as the user's body temperature, weight, dietary habits, and exercise records.

[0677] "Emotional data" refers to data about a user's emotional state collected through facial expressions and voice.

[0678] "Server" refers to a central system for receiving, storing, and analyzing data.

[0679] "AI algorithm" refers to an algorithm for analyzing data using artificial intelligence technologies such as machine learning and deep learning.

[0680] "Notification" refers to a message that informs the user of the results of an analysis of their health and emotional data.

[0681] "Abnormality notification" refers to an alert that is generated in particularly urgent cases based on the results of an analysis of a user's health data and emotional data.

[0682] "External data" refers to environmental data that affects the user's health, such as weather and air quality.

[0683] "Generative AI model" refers to an artificial intelligence model that notifies users through generated text messages, etc.

[0684] To implement this invention, the following hardware and software are required: A user collects health and emotional data using a smartphone or head-mounted display and transmits the data to a server, which stores and analyzes the data and sends appropriate notifications to the user.

[0685] Hardware and software used

[0686] Hardware: Smartphone (iOS / Android), head-mounted display (Microsoft HoloLens, Oculus Quest)

[0687] software:

[0688] Data collection and transmission: Application (collects health and emotion data, converts it to JSON format, and transmits it with TLS encryption)

[0689] Data storage and analysis: Database management systems (MySQL, PostgreSQL) running on the server, AI algorithms (using Python, TensorFlow, PyTorch)

[0690] Notification generation and delivery: Generative AI models for notification services

[0691] Data collection and transmission

[0692] Users collect health and emotional data through the camera and microphone on their smartphone or head-mounted display. The data is converted into JSON format by the application and sent to the server using TLS encryption. The server validates the received data and stores it in a database.

[0693] Data storage and analysis

[0694] The server uses the stored health and emotion data to analyze it with an AI algorithm. The algorithm, built using TensorFlow and PyTorch, applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. It also integrates external data such as weather and air quality data for analysis as needed.

[0695] Generate and send notifications

[0696] The server generates a notification for the user based on the analysis results of the AI ​​algorithm. This notification contains a specific text message using the generative AI model. For example, it may contain content such as, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." These notifications are displayed on the smartphone or head-mounted display as push notifications or pop-up messages.

[0697] Specific examples

[0698] One day, a user's heart rate suddenly rises while at work, and their blood pressure is detected to be higher than normal. Furthermore, their facial expression shows signs of strong stress. In this case, the server generates a notification that reads, "Your heart rate and blood pressure have remained high, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." and sends it to the user.

[0699] Prompt Sentence Examples

[0700] "Your heart rate and blood pressure have been elevated above normal, indicating signs of stress. Please stay safe and take some time to rest. Contact your security manager if necessary."

[0701] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0702] Step 1:

[0703] Users collect health and emotional data using the camera and microphone on their smartphone or head-mounted display. The collected data includes body temperature, weight, heart rate, blood pressure, facial expression data, and voice data. The input is health and emotional data, which is temporarily stored within the application.

[0704] Step 2:

[0705] The device converts the collected health and emotion data into JSON format and sends it to the server using TLS encryption. The input is the converted data, and the output is the encrypted data sent to the server.

[0706] Step 3:

[0707] The server validates the received data and stores it in a database. The input is the received data and the output is the stored data. The server verifies the integrity of the data format and checks for incomplete data.

[0708] Step 4:

[0709] The server analyzes the stored health and emotion data using an AI algorithm. The algorithm is built using TensorFlow and PyTorch and applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. The input is the stored data, and the output is the analysis result of the AI ​​model.

[0710] Step 5:

[0711] The server acquires external data, such as weather and air quality, as needed, and integrates it with health and emotion data for analysis. The input is the integration of health and emotion data with external data, and the output is the new analysis results.

[0712] Step 6:

[0713] The server generates a notification for the user based on the analysis results of the AI ​​algorithm, which includes a specific text message using a generative AI model. The input is the analysis result, and the output is the generated notification message.

[0714] Step 7:

[0715] The device receives notifications from the server and displays them to the user as push notifications or popup messages. The input is the generated notification message, and the output is the notification displayed on the user's device.

[0716] Step 8:

[0717] The user checks the received notification and takes appropriate action according to the instructions in the notification. The input is the displayed notification, and the output is the user's action. The specific prompt text is, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary."

[0718] 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.

[0719] 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.

[0720] 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.

[0721] [Third embodiment]

[0722] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0723] 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.

[0724] 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).

[0725] 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.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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."

[0734] The present invention is a system that collects a user's health data, transmits it to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0735] Health data collection

[0736] User: Enters daily body temperature, weight, dietary information, and exercise records into a dedicated device (e.g., smartphone or tablet). This operation is performed through a dedicated application. The entered data is temporarily stored within the application.

[0737] Terminal: Receives health data entered by the user and converts it into an appropriate data format, such as JSON, then transmits the converted data to the server via secure (SSL / TLS) communication.

[0738] Sending and Receiving Data

[0739] Transmission from device to server: Collected data is securely transmitted to the server, where data encryption is applied to ensure user privacy.

[0740] Server: Receives the transmitted data and first validates it. After verifying that the data is in the correct format and complete, it stores it in the database. When stored, it further encrypts the data to protect it from unauthorized access.

[0741] Analyzing the data

[0742] Server: The stored health data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem.

[0743] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health data for analysis, enabling more accurate analysis.

[0744] Notifications and Suggestions

[0745] Server: Generates notifications and suggestions to the user based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several days in a row. We recommend that you see a doctor" is generated.

[0746] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages.

[0747] Specific examples

[0748] The following are specific usage examples.

[0749] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0750] Terminal: Converts this data into JSON format and sends it to the server.

[0751] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0752] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0753] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0754] This system allows users to understand their own physical condition in real time and to seek medical attention early, significantly improving the quality of health management.

[0755] The processing flow will be explained below.

[0756] Step 1:

[0757] User: Enter health data (body temperature, weight, dietary information, exercise records, etc.) into the device. Specifically, the user opens a dedicated application and enters numbers or text into each data field (e.g., "Body temperature: 36.5°C," "Breakfast: oatmeal and fruit," etc.).

[0758] Step 2:

[0759] Terminal: Receives the input health data and converts it into an appropriate data format, such as JSON. Specifically, a data conversion module in the application serializes the data.

[0760] Step 3:

[0761] Terminal: The converted data is sent to the server using secure communication (SSL / TLS). Specifically, the sending API is called and the data packet is sent to the endpoint.

[0762] Step 4:

[0763] Server: Receives data sent from the device. Specifically, the receiving API takes in the data, performs initial validation, and checks the data format and completeness.

[0764] Step 5:

[0765] Server: Before storing the data in the database, the data is further encrypted by calling the data encryption module and performing AES encryption.

[0766] Step 6:

[0767] Server: Analyzes stored health data using AI algorithms, specifically machine learning and deep learning models, to interpret the data and detect outliers and trends.

[0768] Step 7:

[0769] Server: Obtains external data (such as weather forecasts and air quality data) as needed, integrates it with health data, and analyzes it. Specifically, it calls external APIs to obtain data and merges it with the internal database.

[0770] Step 8:

[0771] Server: Generates notifications and suggestions for users based on the analysis results. Specifically, the notification generation module creates a message and queues the notification for the user's device ID.

[0772] Step 9:

[0773] Terminal: Receives notifications from the server and displays them to the user, either by polling or by using push notifications, and displays the received notifications in the user interface.

[0774] Step 10:

[0775] User: Check the notifications and suggestions displayed on the device and take necessary action. For example, if you receive a notification that you have a high temperature, seek medical attention.

[0776] In this way, the system executes a series of steps, from collecting and analyzing the user's health data to notifying them, allowing the user to manage their health at the appropriate time.

[0777] Example 1

[0778] 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."

[0779] Conventional health management systems have struggled to efficiently collect user-entered health data, perform appropriate analysis, and provide timely notifications and suggestions to users. They also lack the ability to integrate and analyze user health data with external data to more accurately understand and predict health status. Furthermore, processing data securely while ensuring user privacy has also been a challenge.

[0780] 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.

[0781] In this invention, the server includes a means for storing the transmitted health data, a means for analyzing the transmitted health data using an AI algorithm, and a means for generating notifications to the user based on the analysis results. This allows users to efficiently collect, manage, and analyze their own health data and securely receive appropriate notifications. Furthermore, by acquiring external data and integrating and analyzing it with the health data, it becomes possible to grasp and predict health status more accurately.

[0782] "User" refers to an individual who uses the system to input and manage health data.

[0783] "Health Data" refers to information about an individual's health status, such as temperature, weight, diet, and exercise records.

[0784] "Server" refers to the computer system that receives, stores, analyzes, and generates notifications about health data submitted by users.

[0785] "Terminal" refers to the device (e.g., smartphone or tablet) through which the user inputs health data and receives and displays notifications from the server.

[0786] "AI algorithms" refers to data analysis methods, including machine learning and deep learning technologies, used to detect and predict trends and anomalies in health data.

[0787] "Notification" refers to messages or suggestions sent to the user by the server based on the results of the analysis.

[0788] "External data" refers to additional data, such as weather forecasts or air quality data, that the server acquires to integrate with health data for analysis.

[0789] "Secure" refers to ensuring privacy and confidentiality when sending, receiving, or storing data using protocols such as SSL / TLS.

[0790] The present invention provides a system for efficiently collecting and analyzing a user's health data and providing the user with appropriate notifications and suggestions. This system is configured as follows.

[0791] (Collection of health data)

[0792] User: The user uses a dedicated application to input daily temperature, weight, dietary intake, and exercise records. The application runs on a well-known smartphone or tablet.

[0793] Terminal: The terminal takes the data entered by the user and converts it to JSON format, for example:

[0794] json

[0795] {

[0796] "Temperature": 37.0,

[0797] "Diet": "Oatmeal and fruit",

[0798] "Date and Time": "2023-10-06"

[0799] }

[0800] This data is encrypted using the SSL / TLS protocol and sent securely to the server.

[0801] (sending and receiving data)

[0802] Transmission from device to server: The device encrypts the generated JSON data and sends it to the server, ensuring user privacy and data confidentiality in the process.

[0803] Server: The server validates the received JSON data to ensure it is well-formed and complete. After validation, it is stored in the database. The data is also encrypted during storage to protect it from unauthorized access.

[0804] (Data Analysis)

[0805] Server: The server analyzes the health data stored in the database using AI algorithms that utilize machine learning and deep learning techniques, such as random forest and deep learning models to detect abnormal increases in body temperature or changes in dietary patterns.

[0806] Server: If necessary, the server retrieves external data such as weather forecast data and air quality data, and analyzes it in combination with health data, enabling highly accurate predictions that take environmental factors into account.

[0807] (Notifications and Suggestions)

[0808] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if the user's physical condition is abnormal, the server generates a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0809] Device: Receives notifications sent from the server and displays them to the user. Notifications can be displayed as push notifications or in-app pop-up messages.

[0810] Specific examples

[0811] The following are specific usage examples.

[0812] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[0813] Terminal: Converts this data into JSON format and sends it to the server.

[0814] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[0815] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[0816] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[0817] Prompt Sentence Examples

[0818] For example, the following prompt sentence is input into the generative AI model: "Detect abnormalities in physical condition based on the health data entered by the user and generate a notification recommending a doctor's visit."

[0819] This system allows users to monitor their own health condition in real time and seek medical attention early, significantly improving the quality of health care.

[0820] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0821] Step 1:

[0822] User: The user opens the dedicated application and enters their body temperature, weight, dietary information, and exercise record. For example, they enter a body temperature of 37.0°C and a breakfast of "oatmeal and fruit." This data is temporarily stored in the application.

[0823] Input: User's health data (body temperature, weight, dietary details, exercise records)

[0824] Output: Temporarily stored health data

[0825] Step 2:

[0826] Terminal: The terminal receives the health data entered by the user and converts it into JSON format. For example, the following JSON data is generated:

[0827] json

[0828] {

[0829] "Temperature": 37.0,

[0830] "Diet": "Oatmeal and fruit",

[0831] "Date and Time": "2023-10-06"

[0832] }

[0833] This JSON data is then encrypted using the SSL / TLS protocol and sent to the server via secure communication.

[0834] Input: Temporarily stored health data

[0835] Output: JSON formatted data, and encrypted data

[0836] Step 3:

[0837] Server: The server receives the encrypted JSON data and decrypts it. It validates that the data is in the correct format and is complete. This validation involves checking that each field is of the correct type and has all the required information. Once validation is complete, the data is saved to the database. The data is further encrypted at rest to protect it from unauthorized access.

[0838] Input: Encrypted JSON data

[0839] Output: Validated data, data stored in database

[0840] Step 4:

[0841] Server: The server retrieves health data stored in the database and analyzes it using AI algorithms. For example, it uses machine learning models (e.g., random forests and neural networks) to detect whether a person's body temperature has risen for several consecutive days or whether there are any abnormalities in their eating patterns. Furthermore, it also incorporates external data such as weather forecast data and air quality data as needed, and integrates this data with the health data for analysis.

[0842] Input: Health data stored in database, external data

[0843] Output: Analysis results (anomaly detection, trend analysis)

[0844] Step 5:

[0845] Server: Based on the analysis results, generate an appropriate notification for the user. For example, create a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[0846] Input: Analysis results

[0847] Output: Notification content

[0848] Step 6:

[0849] Server: Securely encrypts the generated notification and sends it to the device. The SSL / TLS protocol is used to communicate while maintaining data confidentiality.

[0850] Input: Notification content

[0851] Output: Encrypted notification data

[0852] Step 7:

[0853] Device: The device receives the notification sent by the server, decrypts it, and displays it to the user as a pop-up message or push notification, allowing the user to view it and take appropriate action.

[0854] Input: Encrypted notification data

[0855] Output: The notification displayed to the user

[0856] This system allows users to understand their own physical condition in real time and quickly determine whether they need to see a doctor early, thereby realizing efficient health management and prevention.

[0857] (Application example 1)

[0858] 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."

[0859] Employees are required to manage their own health status on a daily basis and to immediately notify managers if any abnormalities are found in their health data. This is important for improving the quality of employee health management. However, current systems lack mechanisms for immediately notifying managers when abnormalities are found, which can result in delays in employee health management. Therefore, it is necessary to provide a system that collects employee health data, promptly notifies managers when abnormalities are detected, and prompts appropriate action.

[0860] 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.

[0861] In this invention, the server includes means for collecting health data from users, means for transmitting the health data to the server, means for storing the transmitted health data in the server, means for analyzing the transmitted health data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for allowing users to record their own health data every day, and for generating and presenting a notification to a manager when an abnormality is detected through analysis of the recorded data. This makes it possible to quickly notify a manager when an abnormality is found in an employee's health data, thereby improving the quality of employee health management.

[0862] "User" refers to the entity that uses the system to input and manage their own health data.

[0863] "Health data" is a general term for information about a user's body, such as their temperature, weight, diet, and exercise records.

[0864] "Server" refers to the part of the computer system that stores health data submitted by users and analyzes it using AI algorithms.

[0865] "AI algorithm" refers to a program that uses artificial intelligence technologies such as machine learning and deep learning to analyze data and perform anomaly detection and prediction.

[0866] A "notification" is a message that conveys the results of an analysis of health data to a user or administrator, and includes important information such as abnormalities.

[0867] "Administrator" refers to the person or organization responsible for monitoring a user's health data and taking action as necessary.

[0868] "Transmission means" is a general term for the functions and protocols used to send health data collected from users to a server.

[0869] "Means for storing" refers to the functionality for the server to record and retain the health data received.

[0870] "Means of analysis" refers to the ability to use AI algorithms to analyze health data and detect anomalies and trends.

[0871] "Presentation means" is a general term for methods and techniques for displaying notifications to users and administrators.

[0872] An "anomaly" is a value or pattern that is different from the normal in health data and indicates an abnormal condition.

[0873] MODE FOR CARRYING OUT THE INVENTION

[0874] Health data collection

[0875] Users enter their health data, such as their daily body temperature, weight, dietary habits, and exercise records, into a dedicated device (e.g., smartphone or tablet). This data entry is done through a dedicated application. The entered data is temporarily stored within the application. The device converts the health data entered by the user into an appropriate data format, such as JSON format. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0876] Sending and Receiving Data

[0877] When transmitting from the device to the server, the collected health data is securely sent to the server. During this process, data encryption is applied to ensure user privacy. The server receives the transmitted data and first verifies it. After confirming that the data format is correct and complete, it stores it in a database. When stored, the data is further encrypted to protect it from unauthorized access.

[0878] Analyzing the data

[0879] The server analyzes the stored health data using AI algorithms. These algorithms include machine learning and deep learning technologies. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem. The server also obtains external data (e.g., weather forecasts, air quality data) as needed and integrates it with the health data for analysis. This enables more accurate analysis.

[0880] Notifications and Suggestions

[0881] The server generates notifications and suggestions for users and administrators based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several consecutive days. We recommend that you see a doctor" is generated. The device receives the notification from the server and displays it to the user and administrator. This notification is displayed as a push notification or a pop-up message within the app.

[0882] Specific examples

[0883] Let's say one day a user enters a body temperature of 37.5°C into the app. This data is converted to JSON format and sent to the server. The server receives the data, stores it in a database, and then analyzes it using an AI algorithm. From the analysis results, it detects that the temperature is continuing to rise. The server generates a notification stating, "Your temperature has been high for several days in a row. We recommend that you see a doctor," and sends a similar notification to the administrator. The device receives the notification and displays a pop-up for the user and administrator. The user and administrator can then review it and take appropriate action.

[0884] Hardware and software used

[0885] Hardware: smartphones, tablets, servers

[0886] Software: Dedicated applications, AI algorithms (machine learning, deep learning), encryption (SSL / TLS)

[0887] Prompt Sentence Examples

[0888] "Collect user health data (e.g., body temperature, weight) and generate notifications if there are any abnormalities. For example, if the body temperature is over 37.5°C, notify the administrator immediately."

[0889] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0890] Step 1:

[0891] Users use a dedicated smartphone application to input their daily health data, such as their body temperature, weight, dietary habits, and exercise records, and the input data is temporarily stored within the application.

[0892] Input: User-entered temperature, weight, dietary information, and exercise records

[0893] Output: Health data temporarily stored within the application

[0894] Step 2:

[0895] The device converts the health data entered by the user into an appropriate data format, such as JSON, and then transmits the converted data to the server via secure (SSL / TLS) communication.

[0896] Input: Health data entered by the user

[0897] Output: Health data converted to JSON format, encrypted and sent

[0898] Step 3:

[0899] The server receives the health data and first verifies it to ensure it is in the correct format and complete. It then stores it in a database. It also encrypts the data while it is stored to protect it from unauthorized access.

[0900] Input: Health data in JSON format

[0901] Output: Verified health data stored encrypted in a database

[0902] Step 4:

[0903] The server analyzes the stored health data using AI algorithms, specifically machine learning and deep learning techniques, to detect trends and anomalies in the data and generate predictive models.

[0904] Input: Health data stored in a database

[0905] Output: Trend information and anomaly detection results as analysis results

[0906] Step 5:

[0907] The server generates appropriate notifications to users and administrators based on the analysis results. For example, if a user's body temperature remains high for a certain period of time, the server determines that the user's health is abnormal and generates a notification stating, "We recommend that you see a doctor."

[0908] Input: Analysis results by AI algorithm

[0909] Output: Notification message for users and administrators

[0910] Step 6:

[0911] The device receives the notification sent from the server and displays it to the user and administrator as a push notification or a pop-up message within the app.

[0912] Input: Notification message sent by the server

[0913] Output: Notifications displayed to users and administrators

[0914] 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.

[0915] The present invention is a system that collects a user's health data and emotional data, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[0916] Health and emotional data collection

[0917] User: The user inputs health data such as daily body temperature, weight, dietary habits, and exercise records, as well as emotional data including facial expressions and voice, into a dedicated device. This operation is performed using a dedicated application via the device's camera and microphone. The input data is temporarily stored within the application.

[0918] Terminal: Receives health and emotion data entered by the user and converts it into an appropriate data format such as JSON. The converted data is then sent to the server via secure (SSL / TLS) communication.

[0919] Sending and Receiving Data

[0920] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is applied to ensure user privacy.

[0921] Server: Receives the transmitted data and performs an initial validation. After verifying that the data is in the correct format and complete, it stores it in a database. While stored, the data is further encrypted to protect it from unauthorized access.

[0922] Analyzing the data

[0923] Server: The stored health and emotional data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze emotional states, and assess the user's overall health.

[0924] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health and emotion data for analysis, enabling more accurate analysis.

[0925] Notifications and Suggestions

[0926] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if an abnormality in physical condition is detected or a significant change in mood is detected, a notification such as "Your temperature has been high for several days in a row. Furthermore, your recent mood has been unstable. We recommend that you see a doctor" is generated.

[0927] On the device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages, and also provide personalized feedback and suggestions based on the user's emotional state.

[0928] Specific examples

[0929] The following are specific examples:

[0930] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0931] Terminal: Converts this data into JSON format and sends it to the server.

[0932] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0933] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0934] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0935] This system allows users to comprehensively manage their health by taking into account not only their health data but also their emotional data, allowing them to gain a more detailed understanding of their health condition and take appropriate measures as soon as necessary.

[0936] The processing flow will be explained below.

[0937] Step 1:

[0938] User: Opens the dedicated application and inputs health data such as body temperature, weight, dietary habits, and exercise records. The device's camera and microphone also collect facial expressions and voice data, which are then used to input emotional data. For example, if the user inputs a body temperature of 37.2°C, "anxiety" is detected from the facial expression.

[0939] Step 2:

[0940] Terminal: Receives input health and emotion data and converts it into JSON format. Uses a data conversion module to serialize multiple data fields.

[0941] Step 3:

[0942] Terminal: Sends the converted JSON data to the server using secure communication (SSL / TLS). Calls the Send API and sends the encrypted data packet to the server's receiving endpoint.

[0943] Step 4:

[0944] Server: Receives data sent from the device. The receiving API takes in the data and verifies its format and completeness. The verified data is stored in the database.

[0945] Step 5:

[0946] Server: Analyzes stored health and emotion data using AI algorithms, first preprocessing the data and then using machine learning and deep learning models to detect anomalies and trends.

[0947] Step 6:

[0948] Server: Retrieves external data (e.g., weather forecasts and air quality data) as needed, integrates it with health and emotion data for further analysis, and calls external APIs to merge the retrieved data with the internal database.

[0949] Step 7:

[0950] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if a rise in body temperature and the emotion "anxiety" are detected at the same time, a notification will be created saying, "Your body temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0951] Step 8:

[0952] Terminal: Receives notifications from the server. It periodically polls for notifications from the server and displays them as a popup in the user interface after receiving them.

[0953] Step 9:

[0954] User: Checks notifications and suggestions displayed on the device and takes necessary action, such as scheduling a doctor's appointment.

[0955] In this way, the system supports users in comprehensive health management by centrally managing, analyzing, and notifying both health and emotional data, allowing users to take early and appropriate measures.

[0956] Example 2

[0957] 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."

[0958] In modern society, busy daily lives and stress make it difficult for users to properly manage their own health and emotional state. Furthermore, conventional health management systems only target health data, and therefore are unable to provide comprehensive health management that takes into account changes in the user's emotional state. Therefore, there is a need for a system that can collect and analyze more comprehensive data and provide appropriate notifications and suggestions to users.

[0959] 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.

[0960] In this invention, the server includes means for collecting health data and emotional data from a user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for integrating the analyzed data with external data and generating notifications and suggestions based on the analysis results, and means for presenting the generated notifications and suggestions to the user. This enables the user's health data and emotional data to be managed in a unified manner, enabling a comprehensive understanding of the user's health condition and the provision of appropriate notifications and suggestions.

[0961] "Health data" is information that indicates the user's physical condition, such as body temperature, weight, dietary content, and exercise records.

[0962] "Emotion data" is information that indicates the psychological state of the user extracted from facial expressions, voice, and the like.

[0963] A "server" is a central device that receives, stores, analyzes collected data, and notifies users of the results.

[0964] "AI algorithms" are machine learning and deep learning techniques used to analyze collected data and detect trends and anomalies.

[0965] "Notifications" are messages that alert or suggest users based on the analysis results.

[0966] "External data" is external information that may affect a user's health or emotional state, such as weather forecasts or air quality data.

[0967] A "terminal" is a device that allows a user to input data and transmit it to a server, and includes, for example, a smartphone or tablet.

[0968] "Data Format" means the JSON format or other appropriate format used to transmit the collected data to the server.

[0969] "Data encryption" is a cryptographic technique for protecting data in transmission and storage.

[0970] "Push notifications" are a type of notification that appears on a device screen and are a means of instantly conveying important information to users.

[0971] The present invention is a system that collects health and emotional data from users, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user.

[0972] Health and emotional data collection

[0973] User: In addition to health data such as daily body temperature, weight, dietary habits, and exercise records, emotional data including facial expressions and voice are entered into a dedicated device. This is done using a dedicated application via the device's camera and microphone. The entered data is temporarily stored within the application. For example, data can be collected by a user entering their body temperature as "37.2°C" into a smartphone app and simultaneously saying "I feel a little anxious" into the camera.

[0974] Terminal: The terminal receives the health and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is sent to the server via secure (SSL / TLS) communication. Specifically, the data is sent to the server in a format such as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0975] Sending and Receiving Data

[0976] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is performed to ensure user privacy. For example, AES encryption technology is used to protect the data from tampering.

[0977] Server: The server receives the transmitted data and performs an initial verification. After verifying that the data format is correct and complete, it stores it in the database. When stored, the data is further encrypted to protect it from unauthorized access. For example, the data is stored in the format "encrypted({"body_temperature": 37.2, "emotion": "anxiety"})".

[0978] Analyzing the data

[0979] Server: The server analyzes the stored health and emotional data using AI algorithms. These algorithms include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze the emotional state, and assess the user's overall health. For example, based on data showing a body temperature of 37.2°C and an emotion of "anxiety," the anomaly detection model analyzes the temperature trend and emotional changes.

[0980] Server: Optionally, external data (e.g., weather forecasts, air quality data) is also acquired and combined with health and emotion data for analysis. This allows for more accurate analysis. For example, if the weather data indicates that the temperature is high that day, it can determine whether the rise in body temperature is due to environmental factors.

[0981] Generate notifications and suggestions

[0982] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if an abnormality in physical condition is detected or if there is a significant change in emotions, the server generates a notification such as, "Your temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[0983] Viewing notifications

[0984] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages. For example, a notification might appear on the smartphone screen saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor."

[0985] Specific examples

[0986] Specific examples are given below.

[0987] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[0988] Device: Convert this data into JSON format and send it to the server. Send the data to the server as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[0989] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[0990] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[0991] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[0992] Example prompts for generative AI models

[0993] Below are some specific examples of prompt sentences to input into the generative AI model.

[0994] "Generate specific suggestions for when the user's temperature is 37.2°C and emotion data indicates anxiety."

[0995] In response to this prompt, the AI ​​model is expected to generate appropriate suggestions for users who are experiencing elevated body temperature and anxiety.

[0996] In this way, the present invention comprehensively analyzes the user's health data and emotional data and provides appropriate notifications and suggestions to assist the user in managing their health.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] Input: The user inputs health data such as body temperature, weight, dietary content, and exercise records, as well as facial expressions and voice data, into a dedicated terminal.

[1000] Specific actions: The user opens a dedicated app on their smartphone, enters their body temperature (37.2°C) and exercise record, and says to the camera, "I'm a little anxious."

[1001] Output: This data is temporarily stored within the application.

[1002] Step 2:

[1003] Input: Health and emotional data stored within the application.

[1004] What happens: The device converts the data into JSON format, like this: {"body_temperature": 37.2, "emotion": "anxiety"}.

[1005] Output: The converted data is sent to the server using a secure communication protocol (SSL / TLS).

[1006] Step 3:

[1007] Input: Data sent from the terminal.

[1008] What happens: The server receives the data and verifies the JSON format and completeness of required fields. For example, it verifies that the temperature data is numeric and the emotion data is in the specified format.

[1009] Output: Once the data has been verified, it is further encrypted and stored in the database.

[1010] Step 4:

[1011] Input: Stored health and emotion data.

[1012] How it works: The server inputs the data into the AI ​​algorithm and begins analysis. The AI ​​algorithm performs trend analysis, anomaly detection, and sentiment analysis.

[1013] Output: As a result of the analysis, trends such as "rising body temperature" and "emotional instability" are detected.

[1014] Step 5:

[1015] Input: Analysis results from the AI ​​algorithm and external data where necessary (e.g. weather data).

[1016] What it does: The server also integrates external data to generate more accurate notifications and suggestions. For example, if a series of hot days continues, it will consider whether the weather is affecting your body temperature.

[1017] Output: Generates a notification such as "Your temperature has been elevated for several days in a row. Additionally, you have recently been in an unstable emotional state. We recommend that you see a doctor."

[1018] Step 6:

[1019] Input: The notification sent by the server.

[1020] What happens: The device receives the notification and displays it to the user. This notification can appear as a push notification or a pop-up message within the app. Specifically, the device displays a message on the smartphone screen stating, "Your temperature has been elevated for several consecutive days. We recommend that you see a doctor."

[1021] Output: The user can review the notification and take appropriate action if necessary.

[1022] Through the above steps, it becomes possible to comprehensively manage the user's health data and emotional data and provide appropriate notifications and suggestions.

[1023] (Application example 2)

[1024] 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."

[1025] Conventional health management systems primarily analyze and notify users based on their health data, but do not take emotional data into account. This makes it difficult to comprehensively evaluate a user's overall health status and provide urgent notifications. Furthermore, even when a rapid response is required when an abnormality is detected, delays in response have been a problem.

[1026] 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 from the user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for generating an emergency notification if an abnormality is detected. This makes it possible to evaluate the user's overall health condition from the user's health data and emotional data, and to take prompt action, especially in urgent cases.

[1027] "Health data" refers to physiological data such as the user's body temperature, weight, dietary habits, and exercise records.

[1028] "Emotional data" refers to data about a user's emotional state collected through facial expressions and voice.

[1029] "Server" refers to a central system for receiving, storing, and analyzing data.

[1030] "AI algorithm" refers to an algorithm for analyzing data using artificial intelligence technologies such as machine learning and deep learning.

[1031] "Notification" refers to a message that informs the user of the results of an analysis of their health and emotional data.

[1032] "Abnormality notification" refers to an alert that is generated in particularly urgent cases based on the results of an analysis of a user's health data and emotional data.

[1033] "External data" refers to environmental data that affects the user's health, such as weather and air quality.

[1034] "Generative AI model" refers to an artificial intelligence model that notifies users through generated text messages, etc.

[1035] To implement this invention, the following hardware and software are required: A user collects health and emotional data using a smartphone or head-mounted display and transmits the data to a server, which stores and analyzes the data and sends appropriate notifications to the user.

[1036] Hardware and software used

[1037] Hardware: Smartphone (iOS / Android), head-mounted display (Microsoft HoloLens, Oculus Quest)

[1038] software:

[1039] Data collection and transmission: Application (collects health and emotion data, converts it to JSON format, and transmits it with TLS encryption)

[1040] Data storage and analysis: Database management systems (MySQL, PostgreSQL) running on the server, AI algorithms (using Python, TensorFlow, PyTorch)

[1041] Notification generation and delivery: Generative AI models for notification services

[1042] Data collection and transmission

[1043] Users collect health and emotional data through the camera and microphone on their smartphone or head-mounted display. The data is converted into JSON format by the application and sent to the server using TLS encryption. The server validates the received data and stores it in a database.

[1044] Data storage and analysis

[1045] The server uses the stored health and emotion data to analyze it with an AI algorithm. The algorithm, built using TensorFlow and PyTorch, applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. It also integrates external data such as weather and air quality data for analysis as needed.

[1046] Generate and send notifications

[1047] The server generates a notification for the user based on the analysis results of the AI ​​algorithm. This notification contains a specific text message using the generative AI model. For example, it may contain content such as, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." These notifications are displayed on the smartphone or head-mounted display as push notifications or pop-up messages.

[1048] Specific examples

[1049] One day, a user's heart rate suddenly rises while at work, and their blood pressure is detected to be higher than normal. Furthermore, their facial expression shows signs of strong stress. In this case, the server generates a notification that reads, "Your heart rate and blood pressure have remained high, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." and sends it to the user.

[1050] Prompt Sentence Examples

[1051] "Your heart rate and blood pressure have been elevated above normal, indicating signs of stress. Please stay safe and take some time to rest. Contact your security manager if necessary."

[1052] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1053] Step 1:

[1054] Users collect health and emotional data using the camera and microphone on their smartphone or head-mounted display. The collected data includes body temperature, weight, heart rate, blood pressure, facial expression data, and voice data. The input is health and emotional data, which is temporarily stored within the application.

[1055] Step 2:

[1056] The device converts the collected health and emotion data into JSON format and sends it to the server using TLS encryption. The input is the converted data, and the output is the encrypted data sent to the server.

[1057] Step 3:

[1058] The server validates the received data and stores it in a database. The input is the received data and the output is the stored data. The server verifies the integrity of the data format and checks for incomplete data.

[1059] Step 4:

[1060] The server analyzes the stored health and emotion data using an AI algorithm. The algorithm is built using TensorFlow and PyTorch and applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. The input is the stored data, and the output is the analysis result of the AI ​​model.

[1061] Step 5:

[1062] The server acquires external data, such as weather and air quality, as needed, and integrates it with health and emotion data for analysis. The input is the integration of health and emotion data with external data, and the output is the new analysis results.

[1063] Step 6:

[1064] The server generates a notification for the user based on the analysis results of the AI ​​algorithm, which includes a specific text message using a generative AI model. The input is the analysis result, and the output is the generated notification message.

[1065] Step 7:

[1066] The device receives notifications from the server and displays them to the user as push notifications or popup messages. The input is the generated notification message, and the output is the notification displayed on the user's device.

[1067] Step 8:

[1068] The user checks the received notification and takes appropriate action according to the instructions in the notification. The input is the displayed notification, and the output is the user's action. The specific prompt text is, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary."

[1069] 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.

[1070] 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.

[1071] 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.

[1072] [Fourth embodiment]

[1073] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1074] 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.

[1075] 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).

[1076] 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.

[1077] 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.

[1078] 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).

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] 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."

[1086] The present invention is a system that collects a user's health data, transmits it to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[1087] Health data collection

[1088] User: Enters daily body temperature, weight, dietary information, and exercise records into a dedicated device (e.g., smartphone or tablet). This operation is performed through a dedicated application. The entered data is temporarily stored within the application.

[1089] Terminal: Receives health data entered by the user and converts it into an appropriate data format, such as JSON, then transmits the converted data to the server via secure (SSL / TLS) communication.

[1090] Sending and Receiving Data

[1091] Transmission from device to server: Collected data is securely transmitted to the server, where data encryption is applied to ensure user privacy.

[1092] Server: Receives the transmitted data and first validates it. After verifying that the data is in the correct format and complete, it stores it in the database. When stored, it further encrypts the data to protect it from unauthorized access.

[1093] Analyzing the data

[1094] Server: The stored health data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem.

[1095] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health data for analysis, enabling more accurate analysis.

[1096] Notifications and Suggestions

[1097] Server: Generates notifications and suggestions to the user based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several days in a row. We recommend that you see a doctor" is generated.

[1098] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages.

[1099] Specific examples

[1100] The following are specific usage examples.

[1101] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[1102] Terminal: Converts this data into JSON format and sends it to the server.

[1103] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[1104] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[1105] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[1106] This system allows users to understand their own physical condition in real time and to seek medical attention early, significantly improving the quality of health management.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] User: Enter health data (body temperature, weight, dietary information, exercise records, etc.) into the device. Specifically, the user opens a dedicated application and enters numbers or text into each data field (e.g., "Body temperature: 36.5°C," "Breakfast: oatmeal and fruit," etc.).

[1110] Step 2:

[1111] Terminal: Receives the input health data and converts it into an appropriate data format, such as JSON. Specifically, a data conversion module in the application serializes the data.

[1112] Step 3:

[1113] Terminal: The converted data is sent to the server using secure communication (SSL / TLS). Specifically, the sending API is called and the data packet is sent to the endpoint.

[1114] Step 4:

[1115] Server: Receives data sent from the device. Specifically, the receiving API takes in the data, performs initial validation, and checks the data format and completeness.

[1116] Step 5:

[1117] Server: Before storing the data in the database, the data is further encrypted by calling the data encryption module and performing AES encryption.

[1118] Step 6:

[1119] Server: Analyzes stored health data using AI algorithms, specifically machine learning and deep learning models, to interpret the data and detect outliers and trends.

[1120] Step 7:

[1121] Server: Obtains external data (such as weather forecasts and air quality data) as needed, integrates it with health data, and analyzes it. Specifically, it calls external APIs to obtain data and merges it with the internal database.

[1122] Step 8:

[1123] Server: Generates notifications and suggestions for users based on the analysis results. Specifically, the notification generation module creates a message and queues the notification for the user's device ID.

[1124] Step 9:

[1125] Terminal: Receives notifications from the server and displays them to the user, either by polling or by using push notifications, and displays the received notifications in the user interface.

[1126] Step 10:

[1127] User: Check the notifications and suggestions displayed on the device and take necessary action. For example, if you receive a notification that you have a high temperature, seek medical attention.

[1128] In this way, the system executes a series of steps, from collecting and analyzing the user's health data to notifying them, allowing the user to manage their health at the appropriate time.

[1129] Example 1

[1130] 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."

[1131] Conventional health management systems have struggled to efficiently collect user-entered health data, perform appropriate analysis, and provide timely notifications and suggestions to users. They also lack the ability to integrate and analyze user health data with external data to more accurately understand and predict health status. Furthermore, processing data securely while ensuring user privacy has also been a challenge.

[1132] 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.

[1133] In this invention, the server includes a means for storing the transmitted health data, a means for analyzing the transmitted health data using an AI algorithm, and a means for generating notifications to the user based on the analysis results. This allows users to efficiently collect, manage, and analyze their own health data and securely receive appropriate notifications. Furthermore, by acquiring external data and integrating and analyzing it with the health data, it becomes possible to grasp and predict health status more accurately.

[1134] "User" refers to an individual who uses the system to input and manage health data.

[1135] "Health Data" refers to information about an individual's health status, such as temperature, weight, diet, and exercise records.

[1136] "Server" refers to the computer system that receives, stores, analyzes, and generates notifications about health data submitted by users.

[1137] "Terminal" refers to the device (e.g., smartphone or tablet) through which the user inputs health data and receives and displays notifications from the server.

[1138] "AI algorithms" refers to data analysis methods, including machine learning and deep learning technologies, used to detect and predict trends and anomalies in health data.

[1139] "Notification" refers to messages or suggestions sent to the user by the server based on the results of the analysis.

[1140] "External data" refers to additional data, such as weather forecasts or air quality data, that the server acquires to integrate with health data for analysis.

[1141] "Secure" refers to ensuring privacy and confidentiality when sending, receiving, or storing data using protocols such as SSL / TLS.

[1142] The present invention provides a system for efficiently collecting and analyzing a user's health data and providing the user with appropriate notifications and suggestions. This system is configured as follows.

[1143] (Collection of health data)

[1144] User: The user uses a dedicated application to input daily temperature, weight, dietary intake, and exercise records. The application runs on a well-known smartphone or tablet.

[1145] Terminal: The terminal takes the data entered by the user and converts it to JSON format, for example:

[1146] json

[1147] {

[1148] "Temperature": 37.0,

[1149] "Diet": "Oatmeal and fruit",

[1150] "Date and Time": "2023-10-06"

[1151] }

[1152] This data is encrypted using the SSL / TLS protocol and sent securely to the server.

[1153] (sending and receiving data)

[1154] Transmission from device to server: The device encrypts the generated JSON data and sends it to the server, ensuring user privacy and data confidentiality in the process.

[1155] Server: The server validates the received JSON data to ensure it is well-formed and complete. After validation, it is stored in the database. The data is also encrypted during storage to protect it from unauthorized access.

[1156] (Data Analysis)

[1157] Server: The server analyzes the health data stored in the database using AI algorithms that utilize machine learning and deep learning techniques, such as random forest and deep learning models to detect abnormal increases in body temperature or changes in dietary patterns.

[1158] Server: If necessary, the server retrieves external data such as weather forecast data and air quality data, and analyzes it in combination with health data, enabling highly accurate predictions that take environmental factors into account.

[1159] (Notifications and Suggestions)

[1160] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if the user's physical condition is abnormal, the server generates a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[1161] Device: Receives notifications sent from the server and displays them to the user. Notifications can be displayed as push notifications or in-app pop-up messages.

[1162] Specific examples

[1163] The following are specific usage examples.

[1164] User: One day, the user enters their body temperature of 37.0°C into the app. They also record that they had "oatmeal and fruit" for breakfast.

[1165] Terminal: Converts this data into JSON format and sends it to the server.

[1166] Server: Receives the data, stores it in a database, and then analyzes it using an AI algorithm. The analysis results detect that the body temperature is continuing to rise.

[1167] Server: Generates a notification saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor." and sends it to the device.

[1168] Device: Receives a notification and displays a popup to the user, who can then view it and take appropriate action.

[1169] Prompt Sentence Examples

[1170] For example, the following prompt sentence is input into the generative AI model: "Detect abnormalities in physical condition based on the health data entered by the user and generate a notification recommending a doctor's visit."

[1171] This system allows users to monitor their own health condition in real time and seek medical attention early, significantly improving the quality of health care.

[1172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] User: The user opens the dedicated application and enters their body temperature, weight, dietary information, and exercise record. For example, they enter a body temperature of 37.0°C and a breakfast of "oatmeal and fruit." This data is temporarily stored in the application.

[1175] Input: User's health data (body temperature, weight, dietary details, exercise records)

[1176] Output: Temporarily stored health data

[1177] Step 2:

[1178] Terminal: The terminal receives the health data entered by the user and converts it into JSON format. For example, the following JSON data is generated:

[1179] json

[1180] {

[1181] "Temperature": 37.0,

[1182] "Diet": "Oatmeal and fruit",

[1183] "Date and Time": "2023-10-06"

[1184] }

[1185] This JSON data is then encrypted using the SSL / TLS protocol and sent to the server via secure communication.

[1186] Input: Temporarily stored health data

[1187] Output: JSON formatted data, and encrypted data

[1188] Step 3:

[1189] Server: The server receives the encrypted JSON data and decrypts it. It validates that the data is in the correct format and is complete. This validation involves checking that each field is of the correct type and has all the required information. Once validation is complete, the data is saved to the database. The data is further encrypted at rest to protect it from unauthorized access.

[1190] Input: Encrypted JSON data

[1191] Output: Validated data, data stored in database

[1192] Step 4:

[1193] Server: The server retrieves health data stored in the database and analyzes it using AI algorithms. For example, it uses machine learning models (e.g., random forests and neural networks) to detect whether a person's body temperature has risen for several consecutive days or whether there are any abnormalities in their eating patterns. Furthermore, it also incorporates external data such as weather forecast data and air quality data as needed, and integrates this data with the health data for analysis.

[1194] Input: Health data stored in database, external data

[1195] Output: Analysis results (anomaly detection, trend analysis)

[1196] Step 5:

[1197] Server: Based on the analysis results, generate an appropriate notification for the user. For example, create a notification saying, "Your temperature has been high for several days in a row. We recommend that you see a doctor."

[1198] Input: Analysis results

[1199] Output: Notification content

[1200] Step 6:

[1201] Server: Securely encrypts the generated notification and sends it to the device. The SSL / TLS protocol is used to communicate while maintaining data confidentiality.

[1202] Input: Notification content

[1203] Output: Encrypted notification data

[1204] Step 7:

[1205] Device: The device receives the notification sent by the server, decrypts it, and displays it to the user as a pop-up message or push notification, allowing the user to view it and take appropriate action.

[1206] Input: Encrypted notification data

[1207] Output: The notification displayed to the user

[1208] This system allows users to understand their own physical condition in real time and quickly determine whether they need to see a doctor early, thereby realizing efficient health management and prevention.

[1209] (Application example 1)

[1210] 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."

[1211] Employees are required to manage their own health status on a daily basis and to immediately notify managers if any abnormalities are found in their health data. This is important for improving the quality of employee health management. However, current systems lack mechanisms for immediately notifying managers when abnormalities are found, which can result in delays in employee health management. Therefore, it is necessary to provide a system that collects employee health data, promptly notifies managers when abnormalities are detected, and prompts appropriate action.

[1212] 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.

[1213] In this invention, the server includes means for collecting health data from users, means for transmitting the health data to the server, means for storing the transmitted health data in the server, means for analyzing the transmitted health data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for allowing users to record their own health data every day, and for generating and presenting a notification to a manager when an abnormality is detected through analysis of the recorded data. This makes it possible to quickly notify a manager when an abnormality is found in an employee's health data, thereby improving the quality of employee health management.

[1214] "User" refers to the entity that uses the system to input and manage their own health data.

[1215] "Health data" is a general term for information about a user's body, such as their temperature, weight, diet, and exercise records.

[1216] "Server" refers to the part of the computer system that stores health data submitted by users and analyzes it using AI algorithms.

[1217] "AI algorithm" refers to a program that uses artificial intelligence technologies such as machine learning and deep learning to analyze data and perform anomaly detection and prediction.

[1218] A "notification" is a message that conveys the results of an analysis of health data to a user or administrator, and includes important information such as abnormalities.

[1219] "Administrator" refers to the person or organization responsible for monitoring a user's health data and taking action as necessary.

[1220] "Transmission means" is a general term for the functions and protocols used to send health data collected from users to a server.

[1221] "Means for storing" refers to the functionality for the server to record and retain the health data received.

[1222] "Means of analysis" refers to the ability to use AI algorithms to analyze health data and detect anomalies and trends.

[1223] "Presentation means" is a general term for methods and techniques for displaying notifications to users and administrators.

[1224] An "anomaly" is a value or pattern that is different from the normal in health data and indicates an abnormal condition.

[1225] MODE FOR CARRYING OUT THE INVENTION

[1226] Health data collection

[1227] Users enter their health data, such as their daily body temperature, weight, dietary habits, and exercise records, into a dedicated device (e.g., smartphone or tablet). This data entry is done through a dedicated application. The entered data is temporarily stored within the application. The device converts the health data entered by the user into an appropriate data format, such as JSON format. The converted data is then sent to the server via secure (SSL / TLS) communication.

[1228] Sending and Receiving Data

[1229] When transmitting from the device to the server, the collected health data is securely sent to the server. During this process, data encryption is applied to ensure user privacy. The server receives the transmitted data and first verifies it. After confirming that the data format is correct and complete, it stores it in a database. When stored, the data is further encrypted to protect it from unauthorized access.

[1230] Analyzing the data

[1231] The server analyzes the stored health data using AI algorithms. These algorithms include machine learning and deep learning technologies. The AI ​​algorithms detect trends and anomalies in the input health data and generate predictive models. For example, if a user's body temperature continues to rise, this may indicate a potential health problem. The server also obtains external data (e.g., weather forecasts, air quality data) as needed and integrates it with the health data for analysis. This enables more accurate analysis.

[1232] Notifications and Suggestions

[1233] The server generates notifications and suggestions for users and administrators based on the analysis results. For example, if an abnormality in health is detected, a notification such as "Your temperature has been high for several consecutive days. We recommend that you see a doctor" is generated. The device receives the notification from the server and displays it to the user and administrator. This notification is displayed as a push notification or a pop-up message within the app.

[1234] Specific examples

[1235] Let's say one day a user enters a body temperature of 37.5°C into the app. This data is converted to JSON format and sent to the server. The server receives the data, stores it in a database, and then analyzes it using an AI algorithm. From the analysis results, it detects that the temperature is continuing to rise. The server generates a notification stating, "Your temperature has been high for several days in a row. We recommend that you see a doctor," and sends a similar notification to the administrator. The device receives the notification and displays a pop-up for the user and administrator. The user and administrator can then review it and take appropriate action.

[1236] Hardware and software used

[1237] Hardware: smartphones, tablets, servers

[1238] Software: Dedicated applications, AI algorithms (machine learning, deep learning), encryption (SSL / TLS)

[1239] Prompt Sentence Examples

[1240] "Collect user health data (e.g., body temperature, weight) and generate notifications if there are any abnormalities. For example, if the body temperature is over 37.5°C, notify the administrator immediately."

[1241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1242] Step 1:

[1243] Users use a dedicated smartphone application to input their daily health data, such as their body temperature, weight, dietary habits, and exercise records, and the input data is temporarily stored within the application.

[1244] Input: User-entered temperature, weight, dietary information, and exercise records

[1245] Output: Health data temporarily stored within the application

[1246] Step 2:

[1247] The device converts the health data entered by the user into an appropriate data format, such as JSON, and then transmits the converted data to the server via secure (SSL / TLS) communication.

[1248] Input: Health data entered by the user

[1249] Output: Health data converted to JSON format, encrypted and sent

[1250] Step 3:

[1251] The server receives the health data and first verifies it to ensure it is in the correct format and complete. It then stores it in a database. It also encrypts the data while it is stored to protect it from unauthorized access.

[1252] Input: Health data in JSON format

[1253] Output: Verified health data stored encrypted in a database

[1254] Step 4:

[1255] The server analyzes the stored health data using AI algorithms, specifically machine learning and deep learning techniques, to detect trends and anomalies in the data and generate predictive models.

[1256] Input: Health data stored in a database

[1257] Output: Trend information and anomaly detection results as analysis results

[1258] Step 5:

[1259] The server generates appropriate notifications to users and administrators based on the analysis results. For example, if a user's body temperature remains high for a certain period of time, the server determines that the user's health is abnormal and generates a notification stating, "We recommend that you see a doctor."

[1260] Input: Analysis results by AI algorithm

[1261] Output: Notification message for users and administrators

[1262] Step 6:

[1263] The device receives the notification sent from the server and displays it to the user and administrator as a push notification or a pop-up message within the app.

[1264] Input: Notification message sent by the server

[1265] Output: Notifications displayed to users and administrators

[1266] 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.

[1267] The present invention is a system that collects a user's health data and emotional data, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user. This system includes the following processing steps:

[1268] Health and emotional data collection

[1269] User: The user inputs health data such as daily body temperature, weight, dietary habits, and exercise records, as well as emotional data including facial expressions and voice, into a dedicated device. This operation is performed using a dedicated application via the device's camera and microphone. The input data is temporarily stored within the application.

[1270] Terminal: Receives health and emotion data entered by the user and converts it into an appropriate data format such as JSON. The converted data is then sent to the server via secure (SSL / TLS) communication.

[1271] Sending and Receiving Data

[1272] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is applied to ensure user privacy.

[1273] Server: Receives the transmitted data and performs an initial validation. After verifying that the data is in the correct format and complete, it stores it in a database. While stored, the data is further encrypted to protect it from unauthorized access.

[1274] Analyzing the data

[1275] Server: The stored health and emotional data is analyzed using AI algorithms, which include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze emotional states, and assess the user's overall health.

[1276] Server: If necessary, external data (e.g., weather forecasts, air quality data) is also acquired and integrated with health and emotion data for analysis, enabling more accurate analysis.

[1277] Notifications and Suggestions

[1278] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if an abnormality in physical condition is detected or a significant change in mood is detected, a notification such as "Your temperature has been high for several days in a row. Furthermore, your recent mood has been unstable. We recommend that you see a doctor" is generated.

[1279] On the device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages, and also provide personalized feedback and suggestions based on the user's emotional state.

[1280] Specific examples

[1281] The following are specific examples:

[1282] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[1283] Terminal: Converts this data into JSON format and sends it to the server.

[1284] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[1285] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[1286] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[1287] This system allows users to comprehensively manage their health by taking into account not only their health data but also their emotional data, allowing them to gain a more detailed understanding of their health condition and take appropriate measures as soon as necessary.

[1288] The processing flow will be explained below.

[1289] Step 1:

[1290] User: Opens the dedicated application and inputs health data such as body temperature, weight, dietary habits, and exercise records. The device's camera and microphone also collect facial expressions and voice data, which are then used to input emotional data. For example, if the user inputs a body temperature of 37.2°C, "anxiety" is detected from the facial expression.

[1291] Step 2:

[1292] Terminal: Receives input health and emotion data and converts it into JSON format. Uses a data conversion module to serialize multiple data fields.

[1293] Step 3:

[1294] Terminal: Sends the converted JSON data to the server using secure communication (SSL / TLS). Calls the Send API and sends the encrypted data packet to the server's receiving endpoint.

[1295] Step 4:

[1296] Server: Receives data sent from the device. The receiving API takes in the data and verifies its format and completeness. The verified data is stored in the database.

[1297] Step 5:

[1298] Server: Analyzes stored health and emotion data using AI algorithms, first preprocessing the data and then using machine learning and deep learning models to detect anomalies and trends.

[1299] Step 6:

[1300] Server: Retrieves external data (e.g., weather forecasts and air quality data) as needed, integrates it with health and emotion data for further analysis, and calls external APIs to merge the retrieved data with the internal database.

[1301] Step 7:

[1302] Server: Generates notifications and suggestions for the user based on the analysis results. For example, if a rise in body temperature and the emotion "anxiety" are detected at the same time, a notification will be created saying, "Your body temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[1303] Step 8:

[1304] Terminal: Receives notifications from the server. It periodically polls for notifications from the server and displays them as a popup in the user interface after receiving them.

[1305] Step 9:

[1306] User: Checks notifications and suggestions displayed on the device and takes necessary action, such as scheduling a doctor's appointment.

[1307] In this way, the system supports users in comprehensive health management by centrally managing, analyzing, and notifying both health and emotional data, allowing users to take early and appropriate measures.

[1308] Example 2

[1309] 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."

[1310] In modern society, busy daily lives and stress make it difficult for users to properly manage their own health and emotional state. Furthermore, conventional health management systems only target health data, and therefore are unable to provide comprehensive health management that takes into account changes in the user's emotional state. Therefore, there is a need for a system that can collect and analyze more comprehensive data and provide appropriate notifications and suggestions to users.

[1311] 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.

[1312] In this invention, the server includes means for collecting health data and emotional data from a user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for integrating the analyzed data with external data and generating notifications and suggestions based on the analysis results, and means for presenting the generated notifications and suggestions to the user. This enables the user's health data and emotional data to be managed in a unified manner, enabling a comprehensive understanding of the user's health condition and the provision of appropriate notifications and suggestions.

[1313] "Health data" is information that indicates the user's physical condition, such as body temperature, weight, dietary content, and exercise records.

[1314] "Emotion data" is information that indicates the psychological state of the user extracted from facial expressions, voice, and the like.

[1315] A "server" is a central device that receives, stores, analyzes collected data, and notifies users of the results.

[1316] "AI algorithms" are machine learning and deep learning techniques used to analyze collected data and detect trends and anomalies.

[1317] "Notifications" are messages that alert or suggest users based on the analysis results.

[1318] "External data" is external information that may affect a user's health or emotional state, such as weather forecasts or air quality data.

[1319] A "terminal" is a device that allows a user to input data and transmit it to a server, and includes, for example, a smartphone or tablet.

[1320] "Data Format" means the JSON format or other appropriate format used to transmit the collected data to the server.

[1321] "Data encryption" is a cryptographic technique for protecting data in transmission and storage.

[1322] "Push notifications" are a type of notification that appears on a device screen and are a means of instantly conveying important information to users.

[1323] The present invention is a system that collects health and emotional data from users, transmits the data to a server, analyzes the data using an AI algorithm, and provides appropriate notifications and suggestions to the user.

[1324] Health and emotional data collection

[1325] User: In addition to health data such as daily body temperature, weight, dietary habits, and exercise records, emotional data including facial expressions and voice are entered into a dedicated device. This is done using a dedicated application via the device's camera and microphone. The entered data is temporarily stored within the application. For example, data can be collected by a user entering their body temperature as "37.2°C" into a smartphone app and simultaneously saying "I feel a little anxious" into the camera.

[1326] Terminal: The terminal receives the health and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is sent to the server via secure (SSL / TLS) communication. Specifically, the data is sent to the server in a format such as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[1327] Sending and Receiving Data

[1328] Transmission from device to server: The collected health and emotion data is securely transmitted to the server. During this process, data encryption is performed to ensure user privacy. For example, AES encryption technology is used to protect the data from tampering.

[1329] Server: The server receives the transmitted data and performs an initial verification. After verifying that the data format is correct and complete, it stores it in the database. When stored, the data is further encrypted to protect it from unauthorized access. For example, the data is stored in the format "encrypted({"body_temperature": 37.2, "emotion": "anxiety"})".

[1330] Analyzing the data

[1331] Server: The server analyzes the stored health and emotional data using AI algorithms. These algorithms include machine learning and deep learning techniques. The AI ​​algorithms detect trends and anomalies in the input data, analyze the emotional state, and assess the user's overall health. For example, based on data showing a body temperature of 37.2°C and an emotion of "anxiety," the anomaly detection model analyzes the temperature trend and emotional changes.

[1332] Server: Optionally, external data (e.g., weather forecasts, air quality data) is also acquired and combined with health and emotion data for analysis. This allows for more accurate analysis. For example, if the weather data indicates that the temperature is high that day, it can determine whether the rise in body temperature is due to environmental factors.

[1333] Generate notifications and suggestions

[1334] Server: Based on the analysis results, the server generates notifications and suggestions for the user. For example, if an abnormality in physical condition is detected or if there is a significant change in emotions, the server generates a notification such as, "Your temperature has been high for several days in a row. Furthermore, your recent emotional state has been unstable. We recommend that you see a doctor."

[1335] Viewing notifications

[1336] Device: Receives notifications from the server and displays them to the user. These notifications can appear as push notifications or in-app pop-up messages. For example, a notification might appear on the smartphone screen saying, "Your temperature has been elevated for several days in a row. We recommend that you see a doctor."

[1337] Specific examples

[1338] Specific examples are given below.

[1339] User: One day, the user enters their body temperature as 37.2°C into the app. At the same time, emotion data is collected through facial expressions and voice. The emotion engine determines that the user is "anxious" based on their facial expressions and tone of voice.

[1340] Device: Convert this data into JSON format and send it to the server. Send the data to the server as "{"body_temperature": 37.2, "emotion": "anxiety"}".

[1341] Server: After receiving, verifying, and storing the data, it analyzes it using an AI algorithm, which detects when a rise in body temperature and a change in emotion occur simultaneously.

[1342] Server: Generate a notification to the device saying, "Your temperature has been elevated for several days in a row. Additionally, your recent emotional state has been unstable. We recommend that you see a doctor."

[1343] Device: Receives the notification and displays it to the user, who can then view it and take appropriate action.

[1344] Example prompts for generative AI models

[1345] Below are some specific examples of prompt sentences to input into the generative AI model.

[1346] "Generate specific suggestions for when the user's temperature is 37.2°C and emotion data indicates anxiety."

[1347] In response to this prompt, the AI ​​model is expected to generate appropriate suggestions for users who are experiencing elevated body temperature and anxiety.

[1348] In this way, the present invention comprehensively analyzes the user's health data and emotional data and provides appropriate notifications and suggestions to assist the user in managing their health.

[1349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] Input: The user inputs health data such as body temperature, weight, dietary content, and exercise records, as well as facial expressions and voice data, into a dedicated terminal.

[1352] Specific actions: The user opens a dedicated app on their smartphone, enters their body temperature (37.2°C) and exercise record, and says to the camera, "I'm a little anxious."

[1353] Output: This data is temporarily stored within the application.

[1354] Step 2:

[1355] Input: Health and emotional data stored within the application.

[1356] What happens: The device converts the data into JSON format, like this: {"body_temperature": 37.2, "emotion": "anxiety"}.

[1357] Output: The converted data is sent to the server using a secure communication protocol (SSL / TLS).

[1358] Step 3:

[1359] Input: Data sent from the terminal.

[1360] What happens: The server receives the data and verifies the JSON format and completeness of required fields. For example, it verifies that the temperature data is numeric and the emotion data is in the specified format.

[1361] Output: Once the data has been verified, it is further encrypted and stored in the database.

[1362] Step 4:

[1363] Input: Stored health and emotion data.

[1364] How it works: The server inputs the data into the AI ​​algorithm and begins analysis. The AI ​​algorithm performs trend analysis, anomaly detection, and sentiment analysis.

[1365] Output: As a result of the analysis, trends such as "rising body temperature" and "emotional instability" are detected.

[1366] Step 5:

[1367] Input: Analysis results from the AI ​​algorithm and external data where necessary (e.g. weather data).

[1368] What it does: The server also integrates external data to generate more accurate notifications and suggestions. For example, if a series of hot days continues, it will consider whether the weather is affecting your body temperature.

[1369] Output: Generates a notification such as "Your temperature has been elevated for several days in a row. Additionally, you have recently been in an unstable emotional state. We recommend that you see a doctor."

[1370] Step 6:

[1371] Input: The notification sent by the server.

[1372] What happens: The device receives the notification and displays it to the user. This notification can appear as a push notification or a pop-up message within the app. Specifically, the device displays a message on the smartphone screen stating, "Your temperature has been elevated for several consecutive days. We recommend that you see a doctor."

[1373] Output: The user can review the notification and take appropriate action if necessary.

[1374] Through the above steps, it becomes possible to comprehensively manage the user's health data and emotional data and provide appropriate notifications and suggestions.

[1375] (Application example 2)

[1376] 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."

[1377] Conventional health management systems primarily analyze and notify users based on their health data, but do not take emotional data into account. This makes it difficult to comprehensively evaluate a user's overall health status and provide urgent notifications. Furthermore, even when a rapid response is required when an abnormality is detected, delays in response have been a problem.

[1378] 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 from the user, means for transmitting the health data and emotional data to the server, means for storing the transmitted health data and emotional data in the server, means for analyzing the transmitted health data and emotional data in the server using an AI algorithm, means for generating a notification to the user based on the analysis results, means for presenting the notification to the user, and means for generating an emergency notification if an abnormality is detected. This makes it possible to evaluate the user's overall health condition from the user's health data and emotional data, and to take prompt action, especially in urgent cases.

[1379] "Health data" refers to physiological data such as the user's body temperature, weight, dietary habits, and exercise records.

[1380] "Emotional data" refers to data about a user's emotional state collected through facial expressions and voice.

[1381] "Server" refers to a central system for receiving, storing, and analyzing data.

[1382] "AI algorithm" refers to an algorithm for analyzing data using artificial intelligence technologies such as machine learning and deep learning.

[1383] "Notification" refers to a message that informs the user of the results of an analysis of their health and emotional data.

[1384] "Abnormality notification" refers to an alert that is generated in particularly urgent cases based on the results of an analysis of a user's health data and emotional data.

[1385] "External data" refers to environmental data that affects the user's health, such as weather and air quality.

[1386] "Generative AI model" refers to an artificial intelligence model that notifies users through generated text messages, etc.

[1387] To implement this invention, the following hardware and software are required: A user collects health and emotional data using a smartphone or head-mounted display and transmits the data to a server, which stores and analyzes the data and sends appropriate notifications to the user.

[1388] Hardware and software used

[1389] Hardware: Smartphone (iOS / Android), head-mounted display (Microsoft HoloLens, Oculus Quest)

[1390] software:

[1391] Data collection and transmission: Application (collects health and emotion data, converts it to JSON format, and transmits it with TLS encryption)

[1392] Data storage and analysis: Database management systems (MySQL, PostgreSQL) running on the server, AI algorithms (using Python, TensorFlow, PyTorch)

[1393] Notification generation and delivery: Generative AI models for notification services

[1394] Data collection and transmission

[1395] Users collect health and emotional data through the camera and microphone on their smartphone or head-mounted display. The data is converted into JSON format by the application and sent to the server using TLS encryption. The server validates the received data and stores it in a database.

[1396] Data storage and analysis

[1397] The server uses the stored health and emotion data to analyze it with an AI algorithm. The algorithm, built using TensorFlow and PyTorch, applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. It also integrates external data such as weather and air quality data for analysis as needed.

[1398] Generate and send notifications

[1399] The server generates a notification for the user based on the analysis results of the AI ​​algorithm. This notification contains a specific text message using the generative AI model. For example, it may contain content such as, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." These notifications are displayed on the smartphone or head-mounted display as push notifications or pop-up messages.

[1400] Specific examples

[1401] One day, a user's heart rate suddenly rises while at work, and their blood pressure is detected to be higher than normal. Furthermore, their facial expression shows signs of strong stress. In this case, the server generates a notification that reads, "Your heart rate and blood pressure have remained high, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary." and sends it to the user.

[1402] Prompt Sentence Examples

[1403] "Your heart rate and blood pressure have been elevated above normal, indicating signs of stress. Please stay safe and take some time to rest. Contact your security manager if necessary."

[1404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1405] Step 1:

[1406] Users collect health and emotional data using the camera and microphone on their smartphone or head-mounted display. The collected data includes body temperature, weight, heart rate, blood pressure, facial expression data, and voice data. The input is health and emotional data, which is temporarily stored within the application.

[1407] Step 2:

[1408] The device converts the collected health and emotion data into JSON format and sends it to the server using TLS encryption. The input is the converted data, and the output is the encrypted data sent to the server.

[1409] Step 3:

[1410] The server validates the received data and stores it in a database. The input is the received data and the output is the stored data. The server verifies the integrity of the data format and checks for incomplete data.

[1411] Step 4:

[1412] The server analyzes the stored health and emotion data using an AI algorithm. The algorithm is built using TensorFlow and PyTorch and applies machine learning and deep learning techniques to assess the user's health status and detect anomalies and trends. The input is the stored data, and the output is the analysis result of the AI ​​model.

[1413] Step 5:

[1414] The server acquires external data, such as weather and air quality, as needed, and integrates it with health and emotion data for analysis. The input is the integration of health and emotion data with external data, and the output is the new analysis results.

[1415] Step 6:

[1416] The server generates a notification for the user based on the analysis results of the AI ​​algorithm, which includes a specific text message using a generative AI model. The input is the analysis result, and the output is the generated notification message.

[1417] Step 7:

[1418] The device receives notifications from the server and displays them to the user as push notifications or popup messages. The input is the generated notification message, and the output is the notification displayed on the user's device.

[1419] Step 8:

[1420] The user checks the received notification and takes appropriate action according to the instructions in the notification. The input is the displayed notification, and the output is the user's action. The specific prompt text is, "Your heart rate and blood pressure have remained higher than normal, and signs of stress have been detected. Please stay safe and take some rest. Contact your security administrator if necessary."

[1421] 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.

[1422] 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.

[1423] 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 robot 414.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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).

[1428] 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.

[1429] 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."

[1430] 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.

[1431] 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).

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] The following is further disclosed regarding the above embodiment.

[1443] (Claim 1)

[1444] means for collecting health data from a user;

[1445] means for transmitting the health data to a server;

[1446] a means for storing the transmitted health data in the server;

[1447] A means for analyzing the transmitted health data using an AI algorithm in the server;

[1448] means for generating a notification to a user based on the analysis results;

[1449] means for presenting said notification to a user;

[1450] A system including:

[1451] (Claim 2)

[1452] The system according to claim 1, wherein the health data includes body temperature, weight, dietary content, and exercise records, and the system comprises means for collecting these data.

[1453] (Claim 3)

[1454] The system according to claim 1, further comprising means in the server for acquiring external data, integrating the data with the health data, and performing analysis.

[1455] "Example 1"

[1456] (Claim 1)

[1457] means for collecting health data from a user;

[1458] means for transmitting the health data to a server;

[1459] a means for storing the transmitted health data in the server;

[1460] A means for analyzing the transmitted health data using an AI algorithm in the server;

[1461] means for generating a notification to a user based on the analysis results;

[1462] means for securely transmitting said notification to a user terminal;

[1463] means for the terminal to present the notification to a user;

[1464] The health data includes body temperature, weight, dietary content, and exercise records, and a means for collecting these;

[1465] A system including:

[1466] (Claim 2)

[1467] The system according to claim 1, further comprising means in the server for acquiring external data, integrating the data with the health data, and performing analysis.

[1468] (Claim 3)

[1469] 2. The system of claim 1, wherein the notification is displayed on the user's device as a push notification or a pop-up.

[1470] "Application Example 1"

[1471] (Claim 1)

[1472] means for collecting health data from a user;

[1473] means for transmitting the health data to a server;

[1474] a means for storing the transmitted health data in the server;

[1475] A means for analyzing the transmitted health data using an AI algorithm in the server;

[1476] means for generating a notification to a user based on the analysis results;

[1477] means for presenting said notification to a user;

[1478] A means for allowing a user to record his / her own health data every day, and for analyzing the recorded data and generating and presenting a notification to an administrator when an abnormality is detected;

[1479] A system including:

[1480] (Claim 2)

[1481] The system according to claim 1, wherein the health data includes body temperature, weight, dietary content, and exercise records, and the system comprises means for collecting these data.

[1482] (Claim 3)

[1483] The system according to claim 1, further comprising means in the server for acquiring external data, integrating the data with the health data, and performing analysis.

[1484] "Example 2: Combining Emotion Engines"

[1485] (Claim 1)

[1486] means for collecting health and emotional data from a user;

[1487] means for transmitting the health data and emotion data to a server;

[1488] a means for storing the transmitted health data and emotion data in the server;

[1489] a means for analyzing the transmitted health data and emotion data using an AI algorithm in the server;

[1490] A means to integrate external data and generate notifications and recommendations based on the analysis results;

[1491] means for presenting the generated notifications and suggestions to the user;

[1492] A system including:

[1493] (Claim 2)

[1494] The health data includes body temperature, weight, dietary content, and exercise records, and the emotional data includes facial expressions and voice.

[1495] (Claim 3)

[1496] The system of claim 1, wherein the server comprises means for acquiring external data and integrating the data with health data and emotion data for analysis.

[1497] "Application example 2 when combining emotion engines"

[1498] (Claim 1)

[1499] means for collecting health and emotional data from a user;

[1500] means for transmitting the health data and emotion data to a server;

[1501] a means for storing the transmitted health data and emotion data in the server;

[1502] a means for analyzing the transmitted health data and emotion data using an AI algorithm in the server;

[1503] means for generating a notification to a user based on the analysis results;

[1504] means for presenting said notification to a user;

[1505] means for generating an emergency notification when an anomaly is detected;

[1506] A system including:

[1507] (Claim 2)

[1508] The system according to claim 1, wherein the health data includes body temperature, weight, dietary content, and exercise records, and the system comprises means for collecting these data.

[1509] (Claim 3)

[1510] 2. The system according to claim 1, wherein the emotion data includes facial expression data and voice data, and the system further comprises means for collecting these data.

[1511] (Claim 4)

[1512] The system according to claim 1, further comprising means for acquiring external data in the server, integrating the data with the health data and emotion data, and performing analysis.

[1513] (Claim 5)

[1514] 10. The system of claim 1, further comprising means for the notification to include a text message generated using a generative AI model. [Explanation of symbols]

[1515] 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 from a user; means for transmitting the health data to a server; a means for storing the transmitted health data in the server; A means for analyzing the transmitted health data using an AI algorithm in the server; means for generating a notification to a user based on the analysis results; means for presenting said notification to a user; A system including:

2. The system according to claim 1 , wherein the health data includes body temperature, weight, dietary content, and exercise records, and the system comprises means for collecting these data.

3. The system according to claim 1 , further comprising means in the server for acquiring external data, integrating the external data with the health data, and performing analysis.

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