system

Abstract

We provide the system. [Solution] A system for centrally managing personal information, A means of receiving information entered by the user, A means of saving the received information to a database, A means of periodically retrieving stored information, A means of performing analysis based on the acquired information, A system that includes means for notifying the user of the analysis results.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern individuals are facing situations where they have to manage a wide variety of information. The management of individual information such as schedule management, health management, household management, life plans, tax and asset management is complicated, and many people are not able to utilize this information efficiently. Therefore, there is an increasing need for a system that can provide unified management of information and appropriate advice based on it. The purpose of this invention is to enrich people's lives by providing unified management of such complex information and efficient information provision.

Means for Solving the Problems

[0005] This invention provides a system for centrally managing personal information. Specifically, it provides means for receiving information entered by users. It includes means for storing the received information in a database and means for periodically retrieving the stored information. Furthermore, it includes means for performing analysis based on the retrieved information and means for providing appropriate advice and notifications to the user based on the analysis results. As a result, users can manage different types of information in one place and receive useful advice based on integrated information. In addition, it is possible to continuously improve the accuracy and usefulness of the system by receiving feedback from users and adjusting the AI ​​model.

[0006] A "user" refers to an individual who uses a system and inputs their own information.

[0007] "Information" refers to all data that the user intends to manage, specifically including schedules, health data, household financial information, life plans, and data related to taxes and asset management.

[0008] A "database" refers to a structured collection of data used to store received information and retrieve it as needed.

[0009] "Analysis" refers to the process of detecting specific patterns, trends, and anomalies based on stored information, and generating helpful advice and notifications for users.

[0010] "Notifications" refer to information and advice provided to the user based on analysis results, and are usually displayed via the device.

[0011] "Feedback" refers to the evaluations and comments that users make regarding the information and advice they receive.

[0012] An "AI model" refers to artificial intelligence algorithms and methods used to analyze user information and generate appropriate advice and notifications.

[0013] "System" refers to a general term for programs and devices that combine all of the above-mentioned means and provide a unified management service that functions as a single unit. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system according to the present invention centrally manages personal information, performs various analyses, and provides useful advice and notifications. Specific embodiments for carrying out the present invention are described below.

[0036] Data Collection Unit

[0037] Users input their personal information using a dedicated application. This information includes schedules, health data (body temperature, blood pressure, exercise level, etc.), household finances (income, expenses, etc.), life plans, and information about taxes and assets. The information entered in this way is temporarily stored on the device.

[0038] Data transmission unit

[0039] The terminal periodically sends user-entered information to the server. This information is sent to the server in JSON format using, for example, an HTTP POST request. This information is transmitted using a secure communication protocol, ensuring data confidentiality and integrity.

[0040] Data storage unit

[0041] The server parses the received information, converts it to the appropriate format, and then stores it in the database. The stored data is managed in an optimized format so that it can be efficiently retrieved and processed later.

[0042] Data acquisition unit

[0043] The server periodically retrieves the latest user information from the database. During this process, it efficiently extracts only the necessary data based on a pre-configured schedule.

[0044] Data Analysis Department

[0045] The server uses AI algorithms to analyze the acquired information. This analysis includes, for example, evaluating the density of schedules, detecting anomalies in health data, and analyzing household income and expenditure balances.

[0046] Information provision department

[0047] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are then sent back to the device and displayed to the user as pop-up notifications or in-app messages.

[0048] Feedback loop

[0049] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback, the AI ​​model is adjusted to improve the accuracy and usefulness of future notifications.

[0050] Specific example

[0051] Schedule management

[0052] When a user enters "Monday 10:00 Doctor's Appointment" into the app, the device sends this information to the server. The server stores this information in a database and uses AI to analyze the user's schedule for the following week to see if there are any busy appointments. Based on the analysis, the server generates a notification saying, "We recommend you take Friday off," and sends it to the device. The device then displays this notification to the user.

[0053] health care

[0054] When a user enters their daily body temperature, weight, and exercise level, the device sends this data to a server. The server analyzes the data stored in the database and detects abnormal temperature increases or health risks. For example, the server might generate a notification such as, "Your recent temperature fluctuations have been significant; we recommend you see a doctor," and send it to the device. The device then displays this notification to the user.

[0055] In this way, the system according to the present invention can improve the user's health and quality of life by efficiently managing user information and providing appropriate advice.

[0056] The following describes the processing flow.

[0057] Step 1:

[0058] The user opens a dedicated application and enters their personal information. This information includes their schedule, health data (body temperature, weight, exercise level, etc.), and household finances.

[0059] Step 2:

[0060] The terminal converts the information entered by the user into JSON format and sends it to the server using a secure communication protocol. The data sent includes the user ID and timestamp.

[0061] Step 3:

[0062] The server parses the received JSON data and saves it to the database. For example, schedule information is saved in the "schedules" table, and health data is saved in the "health_data" table.

[0063] Step 4:

[0064] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule and the latest health data.

[0065] Step 5:

[0066] The server uses AI algorithms to analyze the acquired information. For example, it can identify days with overly busy schedules or detect anomalies in health data. This automatically generates necessary actions and advice.

[0067] Step 6:

[0068] The server converts the analysis results into a reporting format and generates a notification for the user. The generated notification is then converted back into JSON format and sent to the terminal.

[0069] Step 7:

[0070] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[0071] Step 8:

[0072] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings and comments.

[0073] Step 9:

[0074] The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0075] (Example 1)

[0076] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0077] Traditional personal information management systems struggled to integrate and manage diverse data sources, making it difficult to effectively utilize user-generated information. In particular, centrally managing health data, schedules, and household financial information, and analyzing this data to provide appropriate advice to users, proved challenging. Furthermore, the lack of mechanisms for improving the system based on user feedback limited its overall usefulness.

[0078] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0079] In this invention, the server includes means for receiving information entered by the user, means for temporarily storing the received information, means for periodically transmitting the stored information to the server, means for storing the transmitted information to the server in a database, means for periodically retrieving information from the database, means for analyzing the retrieved information with an AI algorithm, means for notifying the user of the analysis results as appropriate advice or reminders, and means for receiving feedback from the user and adjusting the AI ​​model. This makes it possible to integrally manage different data sources and provide appropriate advice to the user based on the analysis results. Furthermore, it is possible to continuously improve the system by utilizing user feedback and improve the user's quality of life.

[0080] A "user" refers to an individual who uses the system to input personal information and receive advice and notifications.

[0081] "Means of receiving information" refers to functions that incorporate various data entered by users into the system. Specific examples include applications and web forms.

[0082] "Means of temporary storage" refers to storage used to temporarily hold information entered on a device. Examples include local storage and temporary files.

[0083] "Means of periodically sending information to the server" refers to the function that allows a device to transfer stored information to a server according to a set schedule. For example, this can be done using an HTTP POST request.

[0084] "Means of saving to a database" refers to the function of a server converting received information into an appropriate format and storing it in a specialized data store. For example, SQL databases are commonly used.

[0085] "Means of periodically retrieving information from a database" refers to a function where a server retrieves the latest data from the database at regular intervals. Cron jobs and scheduled tasks fall into this category.

[0086] "Methods of analysis using AI algorithms" refers to the function of performing analytical processing on collected data using artificial intelligence technology. This includes, for example, machine learning models.

[0087] "Means of notifying as advice or reminders" refers to a function that generates appropriate advice or reminders for the user based on the analysis results and notifies them of these. Pop-ups and in-app notifications fall into this category.

[0088] "Means for receiving feedback and adjusting the AI ​​model" refers to a function that collects responses and opinions from users and uses that data to improve the accuracy of the artificial intelligence model.

[0089] "Health data" refers to physiological information such as the user's body temperature, blood pressure, and exercise level.

[0090] "Household financial information" refers to financial information about a user's income, expenses, assets, etc.

[0091] The system according to the present invention is configured to centrally manage personal information, perform various analyses using the collected data, and provide helpful advice and notifications. This system consists of a series of processes in which the user inputs information using a dedicated application, the server receives that information, stores it, analyzes it, and sends notifications.

[0092] Users use a device with a dedicated application installed to input their schedule, health data (body temperature, blood pressure, exercise level, etc.), household financial information (income, expenses, etc.), life plan, and information about taxes and assets. This device temporarily saves the information entered by the user to local storage. For example, a user might enter "Monday 10:00 Doctor's appointment" on the calendar screen.

[0093] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format. Secure communication protocols such as TLS are used for this API communication, ensuring data confidentiality and integrity. For example, the following JSON data is sent:

[0094] {

[0095] "event": "Doctor's appointment",

[0096] "date": "Monday 10:00"

[0097] }

[0098] The server parses the received data, converts it to the appropriate format, and then stores it in a database (e.g., MySQL® or PostgreSQL). This stored data is optimized for efficient retrieval and processing later.

[0099] The server periodically retrieves the latest user information from the database. This data retrieval is performed automatically based on a pre-configured schedule (e.g., a cron job). The server then analyzes the retrieved data using AI algorithms. Machine learning frameworks such as TENSORFLOW® and PyTorch are used for this analysis. For example, this analysis is performed to evaluate schedule density, detect anomalies in health data, and analyze household income and expenditure balances.

[0100] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent back to the device and displayed to the user as pop-up notifications or in-app messages. For example, advice such as "We recommend you take a rest on Friday" might be displayed.

[0101] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback data, the AI ​​model is continuously refined to improve the accuracy and usefulness of future notifications.

[0102] Example of a prompt

[0103] "Analyze the schedule information entered by the user and generate advice to take a break if the schedule is too busy."

[0104] Thus, the present invention can improve the quality of life for users by efficiently managing user information and providing appropriate advice. Furthermore, by incorporating user feedback and improving the system, continuous value creation becomes possible.

[0105] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0106] Step 1:

[0107] Users input their information using a dedicated application. This includes schedules, health data (body temperature, blood pressure, exercise level, etc.), and household financial information (income, expenses, etc.).

[0108] Input: User information (schedule, health data, household finances, etc.)

[0109] Data processing / calculation: Enter information into the application's input form.

[0110] Output: The entered information is temporarily stored in the device's local storage.

[0111] Step 2:

[0112] The device temporarily stores information entered by the user. This information is stored in local storage or temporary files.

[0113] Input: Information entered by the user

[0114] Data processing / calculation: Save the input data to local storage.

[0115] Output: Data saved to local storage

[0116] Step 3:

[0117] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format.

[0118] Input: Data stored in local storage

[0119] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0120] Output: JSON data sent to the server

[0121] Step 4:

[0122] The server parses the received data, converts it to the appropriate format, and then saves it to the database. This saving is done using SQL statements.

[0123] Input: JSON data sent to the server

[0124] Data processing / calculation: Parse JSON data and save it to the database.

[0125] Output: Data stored in the database

[0126] Step 5:

[0127] The server periodically retrieves the latest user information from the database. This data retrieval is performed according to a pre-configured schedule.

[0128] Input: Data stored in the database

[0129] Data processing / calculations: Retrieve data by executing SQL queries.

[0130] Output: Latest retrieved data

[0131] Step 6:

[0132] The server analyzes the acquired data using AI algorithms. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch.

[0133] Input: Latest data retrieved from the database

[0134] Data processing / calculation: Analyze data using AI algorithms.

[0135] Output: Analysis results

[0136] Step 7:

[0137] The server generates user advice and reminders based on the analysis results. This generation process selects appropriate messages based on the content of the results.

[0138] Input: Analysis results

[0139] Data processing / calculation: Generating advice and reminders.

[0140] Output: Generated advice and reminders

[0141] Step 8:

[0142] The server sends the generated notification to the device. This notification is also sent using an HTTP POST request.

[0143] Input: Generated advice and reminders

[0144] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0145] Output: Notification sent to the device

[0146] Step 9:

[0147] The device displays received notifications to the user. These notifications may appear as pop-up notifications or in-app messages.

[0148] Input: Notification sent from the server

[0149] Data processing / calculation: Converts notification content into a display format.

[0150] Output: Notification displayed to the user

[0151] Step 10:

[0152] Users provide feedback on the advice and notifications they receive. This feedback is sent to the server via their device.

[0153] Input: User feedback

[0154] Data processing / calculation: Feedback is received via an input form.

[0155] Output: Feedback sent to the server

[0156] Step 11:

[0157] The server analyzes the received feedback and uses it to adjust the AI ​​model. This improves the accuracy of future advice.

[0158] Input: Feedback sent to the server

[0159] Data processing / computation: Analyze feedback and retrain the AI ​​model.

[0160] Output: Adjusted AI model

[0161] Through this series of processing steps, it becomes possible to efficiently manage user information and provide appropriate advice.

[0162] (Application Example 1)

[0163] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0164] Employee health management is a critical issue, and real-time monitoring of employee health is particularly necessary in workplaces such as factories. However, current workplaces lack systems that efficiently and accurately collect and analyze individual employee health data and provide appropriate advice. This makes it difficult to prevent health risks in the workplace. Furthermore, the process of employees voluntarily inputting health data and receiving feedback based on that data is cumbersome. This project aims to solve these problems.

[0165] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0166] In this invention, the server includes means for receiving information entered by a user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, means for having a device for collecting worker health data, means for transmitting the collected health data to the server, means for using an AI algorithm to detect anomalies, and means for monitoring the health status of employees in real time and providing advice. This makes it possible to efficiently collect and analyze employee health data and provide appropriate advice in real time.

[0167] - "Means of receiving information" refers to devices or software that have the function of acquiring personal information and health data entered by the user from terminals or sensors.

[0168] "Means of saving to a database" refers to a system for structuring received information in a specific format and recording and storing it in a way that allows for efficient access.

[0169] "Means for periodically retrieving stored information" refers to a process or mechanism that periodically queries information stored in a database and extracts the necessary data.

[0170] "Means of performing analysis based on acquired information" refers to the process of analyzing acquired data using algorithms and AI to detect useful insights and anomalies.

[0171] "Means of notifying the user of analysis results" refers to a device or software that visualizes or provides the user with advice or reminders generated based on the analysis results, or informs them through voice or other means.

[0172] "Means having equipment for collecting workers' health data" refers to sensors, devices, and their functions installed to collect data related to workers' health, such as body temperature, blood pressure, and heart rate.

[0173] "Means for transmitting collected health data to a server" refers to technologies or methods for securely transferring health data collected by sensors or devices to a server.

[0174] "Methods of using AI algorithms to detect outliers" refer to algorithms that analyze collected data using AI or machine learning models to detect outliers or patterns that deviate from standard values.

[0175] "A means of monitoring employees' health status in real time and providing advice" refers to a system that continuously monitors employees' health data and immediately provides countermeasures and advice for any problems discovered.

[0176] This invention relates to a system for monitoring the health status of factory workers in real time and providing appropriate advice. The system is configured as follows:

[0177] 1. Hardware and Software Configuration

[0178] The system consists of the following elements.

[0179] Sensors and devices

[0180] Body temperature measurement sensor

[0181] Blood pressure monitor

[0182] Heart rate sensor

[0183] These devices are used to collect employee health data.

[0184] Dedicated application

[0185] smartphone

[0186] tablet

[0187] An application for employees to input their health data and receive real-time feedback.

[0188] server

[0189] Server software for receiving, storing, retrieving, and analyzing data (e.g., Flask, SQLAlchemy, SQLite)

[0190] Hardware and software for running AI algorithms (e.g., TensorFlow, PyTorch)

[0191] 2. System Operation Description

[0192] 2.1 Data Collection

[0193] Users (employees) input their health data, such as body temperature, blood pressure, and heart rate, using a dedicated application. Sensors and devices installed throughout the factory automatically measure body temperature and heart rate, collecting data.

[0194] 2.2 Data Transmission

[0195] The terminals and sensors transmit the collected data to the server. During this process, a secure communication protocol (e.g., HTTPS) is used to maintain the confidentiality and integrity of the data.

[0196] 2.3 Data Storage

[0197] The server converts the received data into an appropriate format and stores it in the database. The stored data is managed in an optimized format for efficient retrieval and processing.

[0198] 2.4 Data Acquisition and Analysis

[0199] The server periodically retrieves the latest health data from the database and analyzes it using AI algorithms. For example, an anomaly detection algorithm is used to detect abnormal patterns and values ​​in the collected data.

[0200] 2.5 Information provision

[0201] Based on the analysis results, the server generates appropriate advice and notifications for employees. These notifications are sent to the device as a dedicated application or as pop-up notifications.

[0202] 2.6 Feedback Loop

[0203] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server and analyzed by an AI model. Based on this feedback, the accuracy and usefulness of future notifications will be improved.

[0204] 3. Specific Examples

[0205] For example, if an employee enters their body temperature using a dedicated app and their heart rate is measured by a sensor, that data is sent to a server. If the server detects an abnormal value, the employee receives a notification stating, "Your recent body temperature fluctuations have been significant, so we recommend that you see a doctor."

[0206] 4. Examples of prompts for generative AI models

[0207] "Develop an application that uses an anomaly detection algorithm to assess risk based on the health data of factory employees."

[0208] "Implement a system that monitors employees' body temperature, blood pressure, and heart rate in real time and provides appropriate advice."

[0209] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0210] Step 1:

[0211] Users input their health data (body temperature, blood pressure, heart rate, etc.) using a dedicated application. The entered data is temporarily stored on a device such as a smartphone or tablet. Input from the device includes measurements obtained from thermometers, blood pressure monitors, heart rate sensors, etc.

[0212] Input: Health data such as body temperature, blood pressure, and heart rate.

[0213] Output: Temporarily stored health data

[0214] Step 2:

[0215] The device periodically sends the entered health data to the server using HTTP POST requests. The transmitted data is in JSON format. Secure HTTPS is used as the communication protocol to ensure data confidentiality and integrity.

[0216] Input: Temporarily stored health data, HTTP POST request

[0217] Output: Health data sent to the server

[0218] Step 3:

[0219] The server analyzes the received health data, converts it to an appropriate format, and then stores it in a database (SQLite) using SQLAlchemy. The stored data is managed in an optimized format for efficient retrieval and processing.

[0220] Input: Received health data, SQLAlchemy

[0221] Output: Health data stored in the database

[0222] Step 4:

[0223] The server periodically retrieves the latest health data from the database using queries. To ensure efficient data acquisition, only the necessary data is extracted.

[0224] Input: Health data stored in the database, queries

[0225] Output: Latest health data

[0226] Step 5:

[0227] The server analyzes the acquired health data using AI algorithms (e.g., TensorFlow or PyTorch) to detect anomalies. It detects abnormal patterns and values ​​from the collected data based on standard values.

[0228] Input: Latest health data, AI algorithm

[0229] Output: Analysis results (including outliers)

[0230] Step 6:

[0231] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent to a dedicated application in JSON format and displayed on the user's device in real time.

[0232] Input: Analysis results, notification in JSON format

[0233] Output: Advice and reminders displayed to the user.

[0234] Step 7:

[0235] Users provide feedback on the advice and reminders offered. This feedback is sent from the device to the server. Based on this feedback, the server retrains the AI ​​model to improve the accuracy and usefulness of future notifications.

[0236] Input: User feedback

[0237] Output: Adjusted AI model, improved notification accuracy and usefulness.

[0238] Adding specific actions

[0239] Health data entered by the user is collected by sensors, allowing them to understand their own health status.

[0240] When a device transmits health data to a server, encryption technology is used to ensure data integrity.

[0241] When the server processes data, it performs data format verification and filters out abnormal values.

[0242] The server uses AI algorithms for analysis, which is used to detect anomalies and recognize patterns.

[0243] The parameters of the AI ​​model are dynamically adjusted so that improvements in user health can be concretely confirmed through feedback.

[0244] The above is a detailed explanation of each processing step. This system enables efficient management of employee health.

[0245] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0246] The system according to the present invention centrally manages personal information and further recognizes the user's emotions to provide appropriate advice and notifications. Specific embodiments for carrying out the present invention are described below.

[0247] Data Collection Unit

[0248] Users open a dedicated application and enter their personal information, including schedules, health data, and household finances. They can also input emotional information via voice input or text chat. This emotional information is used to recognize the user's everyday emotional state.

[0249] Data transmission unit

[0250] The terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. Sentiment information is also transmitted in the same way.

[0251] Data storage unit

[0252] The server parses the received JSON data and stores it in the database. Schedule information, health data, and household finance information are stored in their respective tables. Sentimental information is also stored in a separate table and used for later analysis.

[0253] Data acquisition unit

[0254] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data.

[0255] Data Analysis Department

[0256] The server uses AI algorithms to analyze the acquired information. Specifically, it evaluates schedule density, detects anomalies in health data, analyzes household income and expenditure balance, and analyzes emotional information. The emotion engine recognizes the user's emotional state and analyzes the results in an integrated manner with other data.

[0257] Information provision department

[0258] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the sentiment engine's analysis. The generated notification is then converted back into JSON format and sent to the device.

[0259] Functions of the Emotion Engine

[0260] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs text such as "I'm depressed," the engine recognizes that emotion as "sad" and generates appropriate advice.

[0261] Feedback loop

[0262] Users provide feedback on the advice and notifications they receive, sending it from their device to the server. This feedback includes ratings and comments. The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0263] Specific example

[0264] Schedule management and sentiment analysis

[0265] Suppose a user enters "Doctor's appointment Monday 10:00" into the app, and simultaneously enters "Feeling a little depressed." This information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off to rest. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[0266] Health management and emotional analysis

[0267] Let's say a user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to a server. The server collects the health and emotional data and confirms that the emotional state is energetic even if there is a slight increase in body temperature. Based on this, it generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotion-based advice such as, "We have confirmed that you are feeling energetic. Don't overdo it, and enjoy yourself."

[0268] In this way, the system according to the present invention recognizes the user's emotions, integrates and analyzes them with other information, and provides more personalized advice. As a result, the user can manage information efficiently and receive emotional support.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The user opens a dedicated application and enters their information. This information includes schedules (e.g., doctor's appointment on Monday at 10:00), health data (e.g., body temperature, weight, exercise level), household finances (e.g., income, expenses), and emotional information (e.g., enter "I'm feeling a little down today" as text).

[0272] Step 2:

[0273] The terminal converts the entered information into JSON format. The converted data includes user ID, time information, schedule, health data, household finance information, and sentiment data.

[0274] Step 3:

[0275] The device sends JSON-formatted data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted during transmission.

[0276] Step 4:

[0277] The server parses (analyzes) the received JSON data, converts it into an appropriate format, and saves each piece of information in the corresponding database table. Schedule information is saved in the "schedules" table, health data in the "health_data" table, and emotion data in the "emotions" table.

[0278] Step 5:

[0279] The server periodically retrieves the latest information of the user from the database. For example, a scheduled job is set to execute an SQL query to obtain the schedule for the next week, the latest health data, and emotion data at 00:00 every morning.

[0280] Step 6:

[0281] Based on the retrieved information, the server performs data analysis using AI algorithms. It evaluates the over-density of the schedule, detects outliers in the health data, and analyzes the income and expenditure balance of the household information. Also, the emotion engine recognizes the user's emotional state and integrates the results with other data for analysis.

[0282] Step 7:

[0283] The server converts the analysis results into a report format and generates the content to be notified to the user. The analysis results of the emotion engine are also reflected in this notification content. For example, it includes specific advice such as "It is recommended to take a break on Friday. It would be good to set aside some time to relax."

[0284] Step 8:

[0285] The generated notification content is converted back to JSON format and sent to the terminal. The communication from the server to the terminal is also carried out using a secure protocol (e.g., HTTPS).

[0286] Step 9:

[0287] The terminal analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it is displayed as a pop-up message within the application or a push notification.

[0288] Step 10:

[0289] The user provides feedback on the provided advice or notification and sends it from the terminal to the server. The feedback includes evaluations (e.g., "helpful" or "unnecessary") and comments.

[0290] Step 11:

[0291] The server analyzes the received feedback and adjusts the AI model. Based on the feedback information, it optimizes the parameters of the algorithm to improve the accuracy and usefulness of subsequent notifications. This continuous feedback loop improves the performance of the entire system.

[0292] (Example 2)

[0293] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0294] In modern society, the information that an individual should manage is diverse. Schedules, health data, household information, etc. are often managed fragmentarily, and in addition to this, it is even more difficult to receive advice considering an individual's emotional state. Conventional information management systems lack the function of integrating and centrally managing these diverse data and analyzing the user's emotional state to provide appropriate advice. There is a demand for the realization of a system that solves such problems and provides more useful information management and support for individuals.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0296] In this invention, the server includes means for receiving schedule, health data, household information, and emotional information entered by the user; means for converting the received information into JSON format and transmitting it to the server using a secure communication protocol; means for the server to parse the received JSON data and store it in a database in a corresponding table; means for the server to periodically retrieve the latest user information from the database by executing SQL queries; means for the server to perform data analysis on the retrieved information using an AI algorithm; means for the server to convert the analysis results into a reporting format, generate notification content, convert it back into JSON format and transmit it to the terminal; and means for the terminal to display the received notification content to the user. This makes it possible to comprehensively manage diverse user information and provide appropriate advice.

[0297] A "user" refers to an individual who uses a system to input and manage information.

[0298] "Schedule" refers to information about appointments and events entered by the user.

[0299] "Health data" refers to information about a user's health, such as their weight, body temperature, and exercise level.

[0300] "Household financial information" refers to economic information about a user's income and expenses.

[0301] "Emotional information" refers to information that indicates the user's emotional state, and includes information entered via voice input or text chat.

[0302] "Terminal" refers to a device used by a user to input information (e.g., a smartphone or tablet).

[0303] A "server" refers to a computer system that receives, stores, and analyzes user input information and generates notifications.

[0304] "JSON format" is short for JavaScript (registered trademark) Object Notation, and refers to a text format for structuring and transferring data.

[0305] "Secure communication protocol" is a communication means that guarantees the secure transfer of data, and refers to, for example, HTTPS.

[0306] "Database" refers to a digital storage system for storing the input information of users.

[0307] "SQL query" is short for Structured Query Language, and refers to an instruction for obtaining information from a database.

[0308] "AI algorithm" refers to a data analysis method using artificial intelligence, and specifically refers to a model constructed with frameworks such as TensorFlow and PyTorch.

[0309] "Notification content" refers to a message including information and advice provided by the server to the user based on the analysis result.

[0310] "Feedback" refers to an evaluation or comment on the advice and notification provided by the user.

[0311] The present invention is a system that centrally manages various information of users, further analyzes their emotional states, and provides appropriate advice. This system provides comprehensive support for individuals by the cooperation of users, terminals, and servers.

[0312] Data collection unit

[0313] Users first launch a dedicated application and input personal information such as their schedule, health data, and household finances. This application can be used on devices such as smartphones and tablets. Users can also input their daily emotional state using voice input or text chat. For example, a user might input "Monday 10:00 Doctor's appointment" and simultaneously input their emotional state, such as "Feeling a little depressed."

[0314] Data transmission unit

[0315] The terminal first converts the information collected from the user into JSON format. This information is then sent to the server using a secure communication protocol (e.g., HTTPS). The data sent includes the user ID and timestamp. For example, it is sent as follows:

[0316] {

[0317] "user_id": "12345",

[0318] "timestamp": "2023-10-04T10:00:00Z",

[0319] "schedule": "Monday 10:00 Doctor's appointment",

[0320] "health_data": {

[0321] "weight": "70kg",

[0322] "temperature": "36.5℃"

[0323] },

[0324] "emotion": "I feel a little depressed."

[0325] }

[0326] Data storage unit

[0327] The server parses the received JSON data and stores each piece of information in the database. The database stores schedule information, health data, household finance information, and emotional information in corresponding tables. For example, schedule information is stored in the schedule table, and health data is stored in the health data table.

[0328] Data acquisition unit

[0329] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data. This ensures that the most up-to-date information is always available for analysis.

[0330] Data Analysis Department

[0331] The server uses AI algorithms (e.g., TensorFlow or PyTorch) to analyze the acquired information. Specifically, the following analyses are performed:

[0332] Evaluation of schedule density

[0333] Detection of abnormal values ​​in health data

[0334] Analysis of household income and expenditure balance

[0335] Analysis of emotional information

[0336] For example, the emotion engine analyzes the text "I feel depressed" entered by the user and recognizes that emotion as "sad." It then integrates and analyzes this emotional information with other data.

[0337] Information provision department

[0338] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. The generated notification is then converted back into JSON format and sent to the device. The device displays this to the user. For example, a notification such as "Considering your busy schedule and emotional state, we recommend you take a rest on Friday" is generated.

[0339] Feedback loop

[0340] Users provide feedback on the advice and notifications they receive. This feedback includes evaluations and comments on the advice. The device sends this feedback to the server. The server analyzes the received feedback and uses it to refine the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0341] Specific example

[0342] Schedule management and sentiment analysis

[0343] A user enters "Doctor's appointment Monday 10:00" into the application and simultaneously enters emotional information such as "Feeling a little depressed." This information is sent from the terminal to the server and stored in a database. The server analyzes this information and generates a notification, such as "We recommend you take Friday off. It would be good to set aside some time to relax," which is then presented to the user.

[0344] Health management and emotional analysis

[0345] The user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to the server. The server analyzes the health and emotional data and generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotionally-based advice such as, "We've confirmed you're feeling great. Don't overdo it, and enjoy yourself."

[0346] In this way, the system of the present invention can comprehensively manage diverse user information and provide personalized advice that also takes into account emotional states.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The user launches a dedicated application and enters schedule, health data, and household financial information. This input data specifically includes schedule information such as "Monday 10:00 Doctor's appointment" and health data such as "Weight: 70kg, Body temperature: 36.5℃". Furthermore, emotional information is entered as text, such as "Feeling a little depressed". The input data is temporarily stored by the application.

[0350] Step 2:

[0351] The terminal converts the collected user input data into JSON format. For example, the following JSON data is generated:

[0352] json

[0353] {

[0354] "user_id": "12345",

[0355] "timestamp": "2023-10-04T10:00:00Z",

[0356] "schedule": "Monday 10:00 Doctor's appointment",

[0357] "health_data": {

[0358] "weight": "70kg",

[0359] "temperature": "36.5℃"

[0360] },

[0361] "emotion": "I feel a little depressed."

[0362] }

[0363] This JSON data will be used as input data to be sent from the terminal to the server.

[0364] Step 3:

[0365] The terminal sends the generated JSON data to the server using a secure communication protocol (e.g., HTTPS). If the transmission is successful, the terminal receives a transmission success status as output.

[0366] Step 4:

[0367] The server parses the received JSON data and saves it to the database. For example, the server generates and executes INSERT statements to save schedule information to the "schedule" table, health data to the "health_data" table, and emotion information to the "emotions" table. The output of this process indicates the status of successful data saving.

[0368] Step 5:

[0369] The server periodically (e.g., every morning at 00:00) retrieves the latest user information from the database using SQL queries. An example of an SQL query executed is:

[0370] SQL

[0371] SELECT FROM schedule WHERE date >= CURDATE() AND user_id = '12345';

[0372] SELECT FROM health_data WHERE date >= CURDATE() AND user_id = '12345';

[0373] SELECT FROM emotions WHERE date >= CURDATE() AND user_id = '12345';

[0374] The acquired data will be used as input data for the next analysis step.

[0375] Step 6:

[0376] The server uses AI algorithms to analyze data based on the latest information obtained via SQL queries. This analysis utilizes TensorFlow and PyTorch to evaluate schedule density, detect anomalies in health data, and assess sentiment. For example, health data is input into an anomaly detection model, and if an anomaly is detected, the result is output.

[0377] Step 7:

[0378] The server converts the analysis results into a reporting format and generates a notification for the user. Specifically, if a busy schedule is identified, it creates a notification message such as, "We recommend you take Friday off." This notification message is converted to JSON format and sent to the terminal. The generated notification message is the output.

[0379] Step 8:

[0380] The device displays the received notification content to the user. For example, the notification may appear as a pop-up message, allowing the user to review the recommendation. The output of this step indicates a successful display status.

[0381] Step 9:

[0382] Users provide feedback on the advice and notifications they receive. For example, a user might comment, "This advice was helpful," and rate it. The feedback entered is temporarily stored on the device.

[0383] Step 10:

[0384] The device converts user feedback into JSON format and sends it to the server. The data sent includes:

[0385] json

[0386] {

[0387] "user_id": "12345",

[0388] "feedback": "This advice was helpful",

[0389] Rating: 5

[0390] }

[0391] It is converted as shown. This JSON data becomes the input data and is sent to the server.

[0392] Step 11:

[0393] The server receives feedback and performs analysis. Based on the received feedback, it updates the parameters of the AI ​​model or adds training data. After the analysis is complete, the model is adjusted to improve accuracy in the next analysis. The analysis results are output.

[0394] Through the process described above, this system can integrate and manage diverse user information and provide personalized advice that takes into account their emotional state.

[0395] (Application Example 2)

[0396] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0397] Modern autonomous vehicles lack personalized driving assistance that takes into account the driver's schedule, health, and emotional state. Furthermore, the absence of a system that can immediately respond to changes in the driver's emotions means that stress reduction and safety improvements during driving are not fully achieved. This invention aims to solve these problems.

[0398] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving information entered by the user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, and means for managing the individual's schedule, health, and emotional state in real time and providing advice for reducing stress and improving safety while driving. This makes it possible to personalize driving assistance according to the driver's emotional state and health state, thereby reducing stress and improving safety while driving.

[0399] "Personal schedule" refers to information that manages the user's daily schedule, event times, and dates.

[0400] "Health status" refers to information about the user's physical health, such as data on body temperature, weight, and exercise level.

[0401] "Emotional state" refers to information that indicates the user's mental state and emotional changes in their daily life, and is obtained from text and voice input.

[0402] "Real-time management" refers to constantly acquiring, analyzing, and processing user information instantly.

[0403] "Reducing stress while driving a vehicle" refers to measures taken to alleviate the mental and physical burden felt when driving a vehicle.

[0404] "Safety improvement" refers to measures taken to reduce risks when driving a vehicle, thereby preventing accidents and promoting safe driving.

[0405] "Providing advice" refers to offering helpful suggestions and guidance to users based on the information they have gathered.

[0406] "Feedback" refers to the act of a user responding to advice or notifications provided by a system by offering their opinion or comment.

[0407] A "generative artificial intelligence model" is an algorithm that learns from user input data and feedback to improve the accuracy and usefulness of the system.

[0408] The system according to the present invention manages an individual's schedule, health status, and emotional state in real time, and provides advice to reduce stress and improve safety while driving a vehicle. Specific embodiments for carrying out the present invention are described below.

[0409] First, users input their information using a dedicated application. This application is installed on smartphones or the vehicle's infotainment system. Users can input their schedule, health data, and emotional information. The schedule includes dates and times, while health data includes body temperature, weight, and activity levels. Emotional information is recorded via text or voice input and used to recognize the user's daily emotional state.

[0410] Next, the terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. The server receives this information and stores it in a database. The stored information is managed in separate tables for schedule data, health data, and sentiment data.

[0411] The server periodically retrieves the latest user information from the database and performs data analysis using an AI algorithm. It evaluates schedule density, abnormal values ​​in health data, and changes in emotional information. The analysis results are converted into a reporting format and notified to the user. This notification also reflects the results of the emotion engine's analysis. The generated notification content is converted back into JSON format and sent to the device.

[0412] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs "I'm feeling a little down," it recognizes that emotion as "sad" and generates appropriate advice.

[0413] For example, if a user enters "Monday 10:00 Doctor's appointment" and simultaneously enters "Feeling a little depressed" into the application, this information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[0414] The hardware used includes smartphones, vehicle infotainment systems, and servers. The software includes dedicated applications, secure communication protocols, database management systems, AI algorithms, and an emotion engine.

[0415] Examples of prompts to input into the generating AI model include: "User input: 'Monday 10:00 Doctor's appointment' 'Feeling a little depressed'" and "Example prompt for generating model output: 'The user's emotional state is low. Please suggest a rest period.'"

[0416] This allows users to receive individually optimized driving assistance while driving, resulting in reduced stress and improved safety.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The user opens the application and enters their schedule, health data, and emotional information. The entered information includes specific appointment times and details (e.g., "Monday 10:00 Doctor's appointment"), body temperature and weight data (e.g., "Body temperature 36.5 degrees, weight 70 kg"), and text describing their emotional state (e.g., "Feeling a little depressed").

[0420] Step 2:

[0421] The terminal converts the information entered by the user into JSON format. The user ID and timestamp are also added during this process. The JSON data is sent to the server using a secure communication protocol (e.g., HTTPS). It receives user data as input and generates JSON formatted data as output.

[0422] Step 3:

[0423] The server parses the JSON data received from the terminal and stores it in the database. The database is structured, with schedule data, health data, and sentiment data stored in separate tables. It takes JSON data as input and stores the information in each table as output.

[0424] Step 4:

[0425] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule, the latest health data, and sentiment data. It inputs an SQL query as a query to the database and outputs the latest user information.

[0426] Step 5:

[0427] The server's AI algorithm performs data analysis based on the acquired information. This includes evaluating schedule density, detecting anomalies in health data, and analyzing emotional information. The emotion engine recognizes emotional states from text data and generates appropriate advice based on that. Specifically, it recognizes the text "I'm a little depressed" as "sad" and creates corresponding advice. It takes user information as input and obtains analysis results as output.

[0428] Step 6:

[0429] The server converts the analysis results into a reporting format and generates notification content. The generated notification content is then converted back into JSON format and sent to the terminal. It receives analysis results as input, generates notification content as output, and converts it into JSON format.

[0430] Step 7:

[0431] The device displays the notification content received from the server to the user. For example, a generated notification message such as "We recommend taking Friday off. It would be good to set aside time to relax" is displayed on the smartphone or vehicle's infotainment system. It receives notification content in JSON format as input and displays it to the user as output.

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0435] [Second Embodiment]

[0436] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0437] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0448] The system according to the present invention centrally manages personal information, performs various analyses, and provides useful advice and notifications. Specific embodiments for carrying out the present invention are described below.

[0449] Data Collection Unit

[0450] Users input their personal information using a dedicated application. This information includes schedules, health data (body temperature, blood pressure, exercise level, etc.), household finances (income, expenses, etc.), life plans, and information about taxes and assets. The information entered in this way is temporarily stored on the device.

[0451] Data transmission unit

[0452] The terminal periodically sends user-entered information to the server. This information is sent to the server in JSON format using, for example, an HTTP POST request. This information is transmitted using a secure communication protocol, ensuring data confidentiality and integrity.

[0453] Data storage unit

[0454] The server parses the received information, converts it to the appropriate format, and then stores it in the database. The stored data is managed in an optimized format so that it can be efficiently retrieved and processed later.

[0455] Data acquisition unit

[0456] The server periodically retrieves the latest user information from the database. During this process, it efficiently extracts only the necessary data based on a pre-configured schedule.

[0457] Data Analysis Department

[0458] The server uses AI algorithms to analyze the acquired information. This analysis includes, for example, evaluating the density of schedules, detecting anomalies in health data, and analyzing household income and expenditure balances.

[0459] Information provision department

[0460] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are then sent back to the device and displayed to the user as pop-up notifications or in-app messages.

[0461] Feedback loop

[0462] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback, the AI ​​model is adjusted to improve the accuracy and usefulness of future notifications.

[0463] Specific example

[0464] Schedule management

[0465] When a user enters "Monday 10:00 Doctor's Appointment" into the app, the device sends this information to the server. The server stores this information in a database and uses AI to analyze the user's schedule for the following week to see if there are any busy appointments. Based on the analysis, the server generates a notification saying, "We recommend you take Friday off," and sends it to the device. The device then displays this notification to the user.

[0466] health care

[0467] When a user enters their daily body temperature, weight, and exercise level, the device sends this data to a server. The server analyzes the data stored in the database and detects abnormal temperature increases or health risks. For example, the server might generate a notification such as, "Your recent temperature fluctuations have been significant; we recommend you see a doctor," and send it to the device. The device then displays this notification to the user.

[0468] In this way, the system according to the present invention can improve the user's health and quality of life by efficiently managing user information and providing appropriate advice.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user opens a dedicated application and enters their personal information. This information includes their schedule, health data (body temperature, weight, exercise level, etc.), and household finances.

[0472] Step 2:

[0473] The terminal converts the information entered by the user into JSON format and sends it to the server using a secure communication protocol. The data sent includes the user ID and timestamp.

[0474] Step 3:

[0475] The server parses the received JSON data and saves it to the database. For example, schedule information is saved in the "schedules" table, and health data is saved in the "health_data" table.

[0476] Step 4:

[0477] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule and the latest health data.

[0478] Step 5:

[0479] The server uses AI algorithms to analyze the acquired information. For example, it can identify days with overly busy schedules or detect anomalies in health data. This automatically generates necessary actions and advice.

[0480] Step 6:

[0481] The server converts the analysis results into a reporting format and generates a notification for the user. The generated notification is then converted back into JSON format and sent to the terminal.

[0482] Step 7:

[0483] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[0484] Step 8:

[0485] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings and comments.

[0486] Step 9:

[0487] The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0488] (Example 1)

[0489] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0490] Traditional personal information management systems struggled to integrate and manage diverse data sources, making it difficult to effectively utilize user-generated information. In particular, centrally managing health data, schedules, and household financial information, and analyzing this data to provide appropriate advice to users, proved challenging. Furthermore, the lack of mechanisms for improving the system based on user feedback limited its overall usefulness.

[0491] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0492] In this invention, the server includes means for receiving information entered by the user, means for temporarily storing the received information, means for periodically transmitting the stored information to the server, means for storing the transmitted information to the server in a database, means for periodically retrieving information from the database, means for analyzing the retrieved information with an AI algorithm, means for notifying the user of the analysis results as appropriate advice or reminders, and means for receiving feedback from the user and adjusting the AI ​​model. This makes it possible to integrally manage different data sources and provide appropriate advice to the user based on the analysis results. Furthermore, it is possible to continuously improve the system by utilizing user feedback and improve the user's quality of life.

[0493] A "user" refers to an individual who uses the system to input personal information and receive advice and notifications.

[0494] "Means of receiving information" refers to functions that incorporate various data entered by users into the system. Specific examples include applications and web forms.

[0495] "Means of temporary storage" refers to storage used to temporarily hold information entered on a device. Examples include local storage and temporary files.

[0496] "Means of periodically sending information to the server" refers to the function that allows a device to transfer stored information to a server according to a set schedule. For example, this can be done using an HTTP POST request.

[0497] "Means of saving to a database" refers to the function of a server converting received information into an appropriate format and storing it in a specialized data store. For example, SQL databases are commonly used.

[0498] "Means of periodically retrieving information from a database" refers to a function where a server retrieves the latest data from the database at regular intervals. Cron jobs and scheduled tasks fall into this category.

[0499] "Methods of analysis using AI algorithms" refers to the function of performing analytical processing on collected data using artificial intelligence technology. This includes, for example, machine learning models.

[0500] "Means of notifying as advice or reminders" refers to a function that generates appropriate advice or reminders for the user based on the analysis results and notifies them of these. Pop-ups and in-app notifications fall into this category.

[0501] "Means for receiving feedback and adjusting the AI ​​model" refers to a function that collects responses and opinions from users and uses that data to improve the accuracy of the artificial intelligence model.

[0502] "Health data" refers to physiological information such as the user's body temperature, blood pressure, and exercise level.

[0503] "Household financial information" refers to financial information about a user's income, expenses, assets, etc.

[0504] The system according to the present invention is configured to centrally manage personal information, perform various analyses using the collected data, and provide helpful advice and notifications. This system consists of a series of processes in which the user inputs information using a dedicated application, the server receives that information, stores it, analyzes it, and sends notifications.

[0505] Users use a device with a dedicated application installed to input their schedule, health data (body temperature, blood pressure, exercise level, etc.), household financial information (income, expenses, etc.), life plan, and information about taxes and assets. This device temporarily saves the information entered by the user to local storage. For example, a user might enter "Monday 10:00 Doctor's appointment" on the calendar screen.

[0506] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format. Secure communication protocols such as TLS are used for this API communication, ensuring data confidentiality and integrity. For example, the following JSON data is sent:

[0507] {

[0508] "event": "Doctor's appointment",

[0509] "date": "Monday 10:00"

[0510] }

[0511] The server parses the received data, converts it to the appropriate format, and then stores it in a database (e.g., MySQL or PostgreSQL). This stored data is optimized for efficient retrieval and processing later.

[0512] The server periodically retrieves the latest user information from the database. This data retrieval is performed automatically based on a pre-configured schedule (e.g., a cron job). The server then analyzes the retrieved data using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis. For example, this analysis is performed to evaluate schedule density, detect anomalies in health data, and analyze household income and expenditure balances.

[0513] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent back to the device and displayed to the user as pop-up notifications or in-app messages. For example, advice such as "We recommend you take a rest on Friday" might be displayed.

[0514] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback data, the AI ​​model is continuously refined to improve the accuracy and usefulness of future notifications.

[0515] Example of a prompt

[0516] "Analyze the schedule information entered by the user and generate advice to take a break if the schedule is too busy."

[0517] Thus, the present invention can improve the quality of life for users by efficiently managing user information and providing appropriate advice. Furthermore, by incorporating user feedback and improving the system, continuous value creation becomes possible.

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] Users input their information using a dedicated application. This includes schedules, health data (body temperature, blood pressure, exercise level, etc.), and household financial information (income, expenses, etc.).

[0521] Input: User information (schedule, health data, household finances, etc.)

[0522] Data processing / calculation: Enter information into the application's input form.

[0523] Output: The entered information is temporarily stored in the device's local storage.

[0524] Step 2:

[0525] The device temporarily stores information entered by the user. This information is stored in local storage or temporary files.

[0526] Input: Information entered by the user

[0527] Data processing / calculation: Save the input data to local storage.

[0528] Output: Data saved to local storage

[0529] Step 3:

[0530] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format.

[0531] Input: Data stored in local storage

[0532] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0533] Output: JSON data sent to the server

[0534] Step 4:

[0535] The server parses the received data, converts it to the appropriate format, and then saves it to the database. This saving is done using SQL statements.

[0536] Input: JSON data sent to the server

[0537] Data processing / calculation: Parse JSON data and save it to the database.

[0538] Output: Data stored in the database

[0539] Step 5:

[0540] The server periodically retrieves the latest user information from the database. This data retrieval is performed according to a pre-configured schedule.

[0541] Input: Data stored in the database

[0542] Data processing / calculations: Retrieve data by executing SQL queries.

[0543] Output: Latest retrieved data

[0544] Step 6:

[0545] The server analyzes the acquired data using AI algorithms. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch.

[0546] Input: Latest data retrieved from the database

[0547] Data processing / calculation: Analyze data using AI algorithms.

[0548] Output: Analysis results

[0549] Step 7:

[0550] The server generates user advice and reminders based on the analysis results. This generation process selects appropriate messages based on the content of the results.

[0551] Input: Analysis results

[0552] Data processing / calculation: Generating advice and reminders.

[0553] Output: Generated advice and reminders

[0554] Step 8:

[0555] The server sends the generated notification to the device. This notification is also sent using an HTTP POST request.

[0556] Input: Generated advice and reminders

[0557] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0558] Output: Notification sent to the device

[0559] Step 9:

[0560] The device displays received notifications to the user. These notifications may appear as pop-up notifications or in-app messages.

[0561] Input: Notification sent from the server

[0562] Data processing / calculation: Converts notification content into a display format.

[0563] Output: Notification displayed to the user

[0564] Step 10:

[0565] Users provide feedback on the advice and notifications they receive. This feedback is sent to the server via their device.

[0566] Input: User feedback

[0567] Data processing / calculation: Feedback is received via an input form.

[0568] Output: Feedback sent to the server

[0569] Step 11:

[0570] The server analyzes the received feedback and uses it to adjust the AI ​​model. This improves the accuracy of future advice.

[0571] Input: Feedback sent to the server

[0572] Data processing / computation: Analyze feedback and retrain the AI ​​model.

[0573] Output: Adjusted AI model

[0574] Through this series of processing steps, it becomes possible to efficiently manage user information and provide appropriate advice.

[0575] (Application Example 1)

[0576] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0577] Employee health management is a critical issue, and real-time monitoring of employee health is particularly necessary in workplaces such as factories. However, current workplaces lack systems that efficiently and accurately collect and analyze individual employee health data and provide appropriate advice. This makes it difficult to prevent health risks in the workplace. Furthermore, the process of employees voluntarily inputting health data and receiving feedback based on that data is cumbersome. This project aims to solve these problems.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0579] In this invention, the server includes means for receiving information entered by a user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, means for having a device for collecting worker health data, means for transmitting the collected health data to the server, means for using an AI algorithm to detect anomalies, and means for monitoring the health status of employees in real time and providing advice. This makes it possible to efficiently collect and analyze employee health data and provide appropriate advice in real time.

[0580] - "Means of receiving information" refers to devices or software that have the function of acquiring personal information and health data entered by the user from terminals or sensors.

[0581] "Means of saving to a database" refers to a system for structuring received information in a specific format and recording and storing it in a way that allows for efficient access.

[0582] "Means for periodically retrieving stored information" refers to a process or mechanism that periodically queries information stored in a database and extracts the necessary data.

[0583] "Means of performing analysis based on acquired information" refers to the process of analyzing acquired data using algorithms and AI to detect useful insights and anomalies.

[0584] "Means of notifying the user of analysis results" refers to a device or software that visualizes or provides the user with advice or reminders generated based on the analysis results, or informs them through voice or other means.

[0585] "Means having equipment for collecting workers' health data" refers to sensors, devices, and their functions installed to collect data related to workers' health, such as body temperature, blood pressure, and heart rate.

[0586] "Means for transmitting collected health data to a server" refers to technologies or methods for securely transferring health data collected by sensors or devices to a server.

[0587] "Methods of using AI algorithms to detect outliers" refer to algorithms that analyze collected data using AI or machine learning models to detect outliers or patterns that deviate from standard values.

[0588] "A means of monitoring employees' health status in real time and providing advice" refers to a system that continuously monitors employees' health data and immediately provides countermeasures and advice for any problems discovered.

[0589] This invention relates to a system for monitoring the health status of factory workers in real time and providing appropriate advice. The system is configured as follows:

[0590] 1. Hardware and Software Configuration

[0591] The system consists of the following elements.

[0592] Sensors and devices

[0593] Body temperature measurement sensor

[0594] Blood pressure monitor

[0595] Heart rate sensor

[0596] These devices are used to collect employee health data.

[0597] Dedicated application

[0598] smartphone

[0599] tablet

[0600] An application for employees to input their health data and receive real-time feedback.

[0601] server

[0602] Server software for receiving, storing, retrieving, and analyzing data (e.g., Flask, SQLAlchemy, SQLite)

[0603] Hardware and software for running AI algorithms (e.g., TensorFlow, PyTorch)

[0604] 2. System Operation Description

[0605] 2.1 Data Collection

[0606] Users (employees) input their health data, such as body temperature, blood pressure, and heart rate, using a dedicated application. Sensors and devices installed throughout the factory automatically measure body temperature and heart rate, collecting data.

[0607] 2.2 Data Transmission

[0608] The terminals and sensors transmit the collected data to the server. During this process, a secure communication protocol (e.g., HTTPS) is used to maintain the confidentiality and integrity of the data.

[0609] 2.3 Data Storage

[0610] The server converts the received data into an appropriate format and stores it in the database. The stored data is managed in an optimized format for efficient retrieval and processing.

[0611] 2.4 Data Acquisition and Analysis

[0612] The server periodically retrieves the latest health data from the database and analyzes it using AI algorithms. For example, an anomaly detection algorithm is used to detect abnormal patterns and values ​​in the collected data.

[0613] 2.5 Information provision

[0614] Based on the analysis results, the server generates appropriate advice and notifications for employees. These notifications are sent to the device as a dedicated application or as pop-up notifications.

[0615] 2.6 Feedback Loop

[0616] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server and analyzed by an AI model. Based on this feedback, the accuracy and usefulness of future notifications will be improved.

[0617] 3. Specific Examples

[0618] For example, if an employee enters their body temperature using a dedicated app and their heart rate is measured by a sensor, that data is sent to a server. If the server detects an abnormal value, the employee receives a notification stating, "Your recent body temperature fluctuations have been significant, so we recommend that you see a doctor."

[0619] 4. Examples of prompts for generative AI models

[0620] "Develop an application that uses an anomaly detection algorithm to assess risk based on the health data of factory employees."

[0621] "Implement a system that monitors employees' body temperature, blood pressure, and heart rate in real time and provides appropriate advice."

[0622] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0623] Step 1:

[0624] Users input their health data (body temperature, blood pressure, heart rate, etc.) using a dedicated application. The entered data is temporarily stored on a device such as a smartphone or tablet. Input from the device includes measurements obtained from thermometers, blood pressure monitors, heart rate sensors, etc.

[0625] Input: Health data such as body temperature, blood pressure, and heart rate.

[0626] Output: Temporarily stored health data

[0627] Step 2:

[0628] The device periodically sends the entered health data to the server using HTTP POST requests. The transmitted data is in JSON format. Secure HTTPS is used as the communication protocol to ensure data confidentiality and integrity.

[0629] Input: Temporarily stored health data, HTTP POST request

[0630] Output: Health data sent to the server

[0631] Step 3:

[0632] The server analyzes the received health data, converts it to an appropriate format, and then stores it in a database (SQLite) using SQLAlchemy. The stored data is managed in an optimized format for efficient retrieval and processing.

[0633] Input: Received health data, SQLAlchemy

[0634] Output: Health data stored in the database

[0635] Step 4:

[0636] The server periodically retrieves the latest health data from the database using queries. To ensure efficient data acquisition, only the necessary data is extracted.

[0637] Input: Health data stored in the database, queries

[0638] Output: Latest health data

[0639] Step 5:

[0640] The server analyzes the acquired health data using AI algorithms (e.g., TensorFlow or PyTorch) to detect anomalies. It detects abnormal patterns and values ​​from the collected data based on standard values.

[0641] Input: Latest health data, AI algorithm

[0642] Output: Analysis results (including outliers)

[0643] Step 6:

[0644] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent to a dedicated application in JSON format and displayed on the user's device in real time.

[0645] Input: Analysis results, notification in JSON format

[0646] Output: Advice and reminders displayed to the user.

[0647] Step 7:

[0648] Users provide feedback on the advice and reminders offered. This feedback is sent from the device to the server. Based on this feedback, the server retrains the AI ​​model to improve the accuracy and usefulness of future notifications.

[0649] Input: User feedback

[0650] Output: Adjusted AI model, improved notification accuracy and usefulness.

[0651] Adding specific actions

[0652] Health data entered by the user is collected by sensors, allowing them to understand their own health status.

[0653] When a device transmits health data to a server, encryption technology is used to ensure data integrity.

[0654] When the server processes data, it performs data format verification and filters out abnormal values.

[0655] The server uses AI algorithms for analysis, which is used to detect anomalies and recognize patterns.

[0656] The parameters of the AI ​​model are dynamically adjusted so that improvements in user health can be concretely confirmed through feedback.

[0657] The above is a detailed explanation of each processing step. This system enables efficient management of employee health.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] The system according to the present invention centrally manages personal information and further recognizes the user's emotions to provide appropriate advice and notifications. Specific embodiments for carrying out the present invention are described below.

[0660] Data Collection Unit

[0661] Users open a dedicated application and enter their personal information, including schedules, health data, and household finances. They can also input emotional information via voice input or text chat. This emotional information is used to recognize the user's everyday emotional state.

[0662] Data transmission unit

[0663] The terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. Sentiment information is also transmitted in the same way.

[0664] Data storage unit

[0665] The server parses the received JSON data and stores it in the database. Schedule information, health data, and household finance information are stored in their respective tables. Sentimental information is also stored in a separate table and used for later analysis.

[0666] Data acquisition unit

[0667] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data.

[0668] Data Analysis Department

[0669] The server uses AI algorithms to analyze the acquired information. Specifically, it evaluates schedule density, detects anomalies in health data, analyzes household income and expenditure balance, and analyzes emotional information. The emotion engine recognizes the user's emotional state and analyzes the results in an integrated manner with other data.

[0670] Information provision department

[0671] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the sentiment engine's analysis. The generated notification is then converted back into JSON format and sent to the device.

[0672] Functions of the Emotion Engine

[0673] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs text such as "I'm depressed," the engine recognizes that emotion as "sad" and generates appropriate advice.

[0674] Feedback loop

[0675] Users provide feedback on the advice and notifications they receive, sending it from their device to the server. This feedback includes ratings and comments. The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0676] Specific example

[0677] Schedule management and sentiment analysis

[0678] Suppose a user enters "Doctor's appointment Monday 10:00" into the app, and simultaneously enters "Feeling a little depressed." This information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off to rest. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[0679] Health management and emotional analysis

[0680] Let's say a user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to a server. The server collects the health and emotional data and confirms that the emotional state is energetic even if there is a slight increase in body temperature. Based on this, it generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotion-based advice such as, "We have confirmed that you are feeling energetic. Don't overdo it, and enjoy yourself."

[0681] In this way, the system according to the present invention recognizes the user's emotions, integrates and analyzes them with other information, and provides more personalized advice. As a result, the user can manage information efficiently and receive emotional support.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user opens a dedicated application and enters their information. This information includes schedules (e.g., doctor's appointment on Monday at 10:00), health data (e.g., body temperature, weight, exercise level), household finances (e.g., income, expenses), and emotional information (e.g., enter "I'm feeling a little down today" as text).

[0685] Step 2:

[0686] The terminal converts the entered information into JSON format. The converted data includes user ID, time information, schedule, health data, household finance information, and sentiment data.

[0687] Step 3:

[0688] The device sends JSON-formatted data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted during transmission.

[0689] Step 4:

[0690] The server parses the received JSON data, converts it to the appropriate format, and stores each piece of information in the corresponding database table. Schedule information is stored in the "schedules" table, health data in the "health_data" table, and emotion data in the "emotions" table.

[0691] Step 5:

[0692] The server periodically retrieves the latest user information from the database. For example, a scheduled job can be set up to execute SQL queries every morning at 00:00 to retrieve the next week's schedule, the latest health data, and sentiment data.

[0693] Step 6:

[0694] The server uses AI algorithms to analyze the acquired information. It evaluates the density of schedules, detects anomalies in health data, and analyzes the balance of income and expenses in household financial information. In addition, an emotion engine recognizes the user's emotional state and integrates the results with other data for analysis.

[0695] Step 7:

[0696] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. For example, it may include specific advice such as, "We recommend taking Friday off. It would be good to set aside time to relax."

[0697] Step 8:

[0698] The generated notification content is converted back into JSON format and sent to the device. Communication from the server to the device is also performed using a secure protocol (e.g., HTTPS).

[0699] Step 9:

[0700] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[0701] Step 10:

[0702] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings (e.g., "helpful" or "unnecessary") and comments.

[0703] Step 11:

[0704] The server analyzes the received feedback and adjusts the AI ​​model. Based on the feedback information, it optimizes the algorithm parameters to improve the accuracy and usefulness of future notifications. This continuous feedback loop improves the overall performance of the system.

[0705] (Example 2)

[0706] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0707] In modern society, individuals must manage a wide range of information. Schedules, health data, and household finances are often managed in fragments, and receiving advice that takes into account an individual's emotional state is even more difficult. Conventional information management systems lack the ability to integrate and centrally manage this diverse data, and to analyze the user's emotional state to provide appropriate advice. There is a need for a system that addresses these challenges and provides more useful information management and support for individuals.

[0708] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0709] In this invention, the server includes means for receiving schedule, health data, household information, and emotional information entered by the user; means for converting the received information into JSON format and transmitting it to the server using a secure communication protocol; means for the server to parse the received JSON data and store it in a database in a corresponding table; means for the server to periodically retrieve the latest user information from the database by executing SQL queries; means for the server to perform data analysis on the retrieved information using an AI algorithm; means for the server to convert the analysis results into a reporting format, generate notification content, convert it back into JSON format and transmit it to the terminal; and means for the terminal to display the received notification content to the user. This makes it possible to comprehensively manage diverse user information and provide appropriate advice.

[0710] A "user" refers to an individual who uses a system to input and manage information.

[0711] "Schedule" refers to information about appointments and events entered by the user.

[0712] "Health data" refers to information about a user's health, such as their weight, body temperature, and exercise level.

[0713] "Household financial information" refers to economic information about a user's income and expenses.

[0714] "Emotional information" refers to information that indicates the user's emotional state, and includes information entered via voice input or text chat.

[0715] "Terminal" refers to a device used by a user to input information (e.g., a smartphone or tablet).

[0716] A "server" refers to a computer system that receives, stores, and analyzes user input information and generates notifications.

[0717] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for structuring and transferring data.

[0718] A "secure communication protocol" is a means of communication that guarantees the safe transfer of data, such as HTTPS.

[0719] A "database" refers to a digital storage system used to store user input information.

[0720] "SQL query" is an abbreviation for Structured Query Language, and refers to a set of commands used to retrieve information from a database.

[0721] "AI algorithms" refer to data analysis methods using artificial intelligence, and specifically to models built with frameworks such as TensorFlow and PyTorch.

[0722] "Notification content" refers to messages containing information and advice that the server provides to the user based on the analysis results.

[0723] "Feedback" refers to the evaluations and comments that users make in response to advice or notifications provided.

[0724] This invention is a system that centrally manages diverse user information and analyzes their emotional state to provide appropriate advice. This system provides comprehensive support to individuals through the coordinated operation of the user, terminal, and server.

[0725] Data Collection Unit

[0726] Users first launch a dedicated application and input personal information such as their schedule, health data, and household finances. This application can be used on devices such as smartphones and tablets. Users can also input their daily emotional state using voice input or text chat. For example, a user might input "Monday 10:00 Doctor's appointment" and simultaneously input their emotional state, such as "Feeling a little depressed."

[0727] Data transmission unit

[0728] The terminal first converts the information collected from the user into JSON format. This information is then sent to the server using a secure communication protocol (e.g., HTTPS). The data sent includes the user ID and timestamp. For example, it is sent as follows:

[0729] {

[0730] "user_id": "12345",

[0731] "timestamp": "2023-10-04T10:00:00Z",

[0732] "schedule": "Monday 10:00 Doctor's appointment",

[0733] "health_data": {

[0734] "weight": "70kg",

[0735] "temperature": "36.5℃"

[0736] },

[0737] "emotion": "I feel a little depressed."

[0738] }

[0739] Data storage unit

[0740] The server parses the received JSON data and stores each piece of information in the database. The database stores schedule information, health data, household finance information, and emotional information in corresponding tables. For example, schedule information is stored in the schedule table, and health data is stored in the health data table.

[0741] Data acquisition unit

[0742] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data. This ensures that the most up-to-date information is always available for analysis.

[0743] Data Analysis Department

[0744] The server uses AI algorithms (e.g., TensorFlow or PyTorch) to analyze the acquired information. Specifically, the following analyses are performed:

[0745] Evaluation of schedule density

[0746] Detection of abnormal values ​​in health data

[0747] Analysis of household income and expenditure balance

[0748] Analysis of emotional information

[0749] For example, the emotion engine analyzes the text "I feel depressed" entered by the user and recognizes that emotion as "sad." It then integrates and analyzes this emotional information with other data.

[0750] Information provision department

[0751] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. The generated notification is then converted back into JSON format and sent to the device. The device displays this to the user. For example, a notification such as "Considering your busy schedule and emotional state, we recommend you take a rest on Friday" is generated.

[0752] Feedback loop

[0753] Users provide feedback on the advice and notifications they receive. This feedback includes evaluations and comments on the advice. The device sends this feedback to the server. The server analyzes the received feedback and uses it to refine the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0754] Specific example

[0755] Schedule management and sentiment analysis

[0756] A user enters "Doctor's appointment Monday 10:00" into the application and simultaneously enters emotional information such as "Feeling a little depressed." This information is sent from the terminal to the server and stored in a database. The server analyzes this information and generates a notification, such as "We recommend you take Friday off. It would be good to set aside some time to relax," which is then presented to the user.

[0757] Health management and emotional analysis

[0758] The user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to the server. The server analyzes the health and emotional data and generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotionally-based advice such as, "We've confirmed you're feeling great. Don't overdo it, and enjoy yourself."

[0759] In this way, the system of the present invention can comprehensively manage diverse user information and provide personalized advice that also takes into account emotional states.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] The user launches a dedicated application and enters schedule, health data, and household financial information. This input data specifically includes schedule information such as "Monday 10:00 Doctor's appointment" and health data such as "Weight: 70kg, Body temperature: 36.5℃". Furthermore, emotional information is entered as text, such as "Feeling a little depressed". The input data is temporarily stored by the application.

[0763] Step 2:

[0764] The terminal converts the collected user input data into JSON format. For example, the following JSON data is generated:

[0765] json

[0766] {

[0767] "user_id": "12345",

[0768] "timestamp": "2023-10-04T10:00:00Z",

[0769] "schedule": "Monday 10:00 Doctor's appointment",

[0770] "health_data": {

[0771] "weight": "70kg",

[0772] "temperature": "36.5℃"

[0773] },

[0774] "emotion": "I feel a little depressed."

[0775] }

[0776] This JSON data will be used as input data to be sent from the terminal to the server.

[0777] Step 3:

[0778] The terminal sends the generated JSON data to the server using a secure communication protocol (e.g., HTTPS). If the transmission is successful, the terminal receives a transmission success status as output.

[0779] Step 4:

[0780] The server parses the received JSON data and saves it to the database. For example, the server generates and executes INSERT statements to save schedule information to the "schedule" table, health data to the "health_data" table, and emotion information to the "emotions" table. The output of this process indicates the status of successful data saving.

[0781] Step 5:

[0782] The server periodically (e.g., every morning at 00:00) retrieves the latest user information from the database using SQL queries. An example of an SQL query executed is:

[0783] SQL

[0784] SELECT FROM schedule WHERE date >= CURDATE() AND user_id = '12345';

[0785] SELECT FROM health_data WHERE date >= CURDATE() AND user_id = '12345';

[0786] SELECT FROM emotions WHERE date >= CURDATE() AND user_id = '12345';

[0787] The acquired data will be used as input data for the next analysis step.

[0788] Step 6:

[0789] The server uses AI algorithms to analyze data based on the latest information obtained via SQL queries. This analysis utilizes TensorFlow and PyTorch to evaluate schedule density, detect anomalies in health data, and assess sentiment. For example, health data is input into an anomaly detection model, and if an anomaly is detected, the result is output.

[0790] Step 7:

[0791] The server converts the analysis results into a reporting format and generates a notification for the user. Specifically, if a busy schedule is identified, it creates a notification message such as, "We recommend you take Friday off." This notification message is converted to JSON format and sent to the terminal. The generated notification message is the output.

[0792] Step 8:

[0793] The device displays the received notification content to the user. For example, the notification may appear as a pop-up message, allowing the user to review the recommendation. The output of this step indicates a successful display status.

[0794] Step 9:

[0795] Users provide feedback on the advice and notifications they receive. For example, a user might comment, "This advice was helpful," and rate it. The feedback entered is temporarily stored on the device.

[0796] Step 10:

[0797] The device converts user feedback into JSON format and sends it to the server. The data sent includes:

[0798] json

[0799] {

[0800] "user_id": "12345",

[0801] "feedback": "This advice was helpful",

[0802] Rating: 5

[0803] }

[0804] It is converted as shown. This JSON data becomes the input data and is sent to the server.

[0805] Step 11:

[0806] The server receives feedback and performs analysis. Based on the received feedback, it updates the parameters of the AI ​​model or adds training data. After the analysis is complete, the model is adjusted to improve accuracy in the next analysis. The analysis results are output.

[0807] Through the process described above, this system can integrate and manage diverse user information and provide personalized advice that takes into account their emotional state.

[0808] (Application Example 2)

[0809] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0810] Modern autonomous vehicles lack personalized driving assistance that takes into account the driver's schedule, health, and emotional state. Furthermore, the absence of a system that can immediately respond to changes in the driver's emotions means that stress reduction and safety improvements during driving are not fully achieved. This invention aims to solve these problems.

[0811] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving information entered by the user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, and means for managing the individual's schedule, health, and emotional state in real time and providing advice for reducing stress and improving safety while driving. This makes it possible to personalize driving assistance according to the driver's emotional state and health state, thereby reducing stress and improving safety while driving.

[0812] "Personal schedule" refers to information that manages the user's daily schedule, event times, and dates.

[0813] "Health status" refers to information about the user's physical health, such as data on body temperature, weight, and exercise level.

[0814] "Emotional state" refers to information that indicates the user's mental state and emotional changes in their daily life, and is obtained from text and voice input.

[0815] "Real-time management" refers to constantly acquiring, analyzing, and processing user information instantly.

[0816] "Reducing stress while driving a vehicle" refers to measures taken to alleviate the mental and physical burden felt when driving a vehicle.

[0817] "Safety improvement" refers to measures taken to reduce risks when driving a vehicle, thereby preventing accidents and promoting safe driving.

[0818] "Providing advice" refers to offering helpful suggestions and guidance to users based on the information they have gathered.

[0819] "Feedback" refers to the act of a user responding to advice or notifications provided by a system by offering their opinion or comment.

[0820] A "generative artificial intelligence model" is an algorithm that learns from user input data and feedback to improve the accuracy and usefulness of the system.

[0821] The system according to the present invention manages an individual's schedule, health status, and emotional state in real time, and provides advice to reduce stress and improve safety while driving a vehicle. Specific embodiments for carrying out the present invention are described below.

[0822] First, users input their information using a dedicated application. This application is installed on smartphones or the vehicle's infotainment system. Users can input their schedule, health data, and emotional information. The schedule includes dates and times, while health data includes body temperature, weight, and activity levels. Emotional information is recorded via text or voice input and used to recognize the user's daily emotional state.

[0823] Next, the terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. The server receives this information and stores it in a database. The stored information is managed in separate tables for schedule data, health data, and sentiment data.

[0824] The server periodically retrieves the latest user information from the database and performs data analysis using an AI algorithm. It evaluates schedule density, abnormal values ​​in health data, and changes in emotional information. The analysis results are converted into a reporting format and notified to the user. This notification also reflects the results of the emotion engine's analysis. The generated notification content is converted back into JSON format and sent to the device.

[0825] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs "I'm feeling a little down," it recognizes that emotion as "sad" and generates appropriate advice.

[0826] For example, if a user enters "Monday 10:00 Doctor's appointment" and simultaneously enters "Feeling a little depressed" into the application, this information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[0827] The hardware used includes smartphones, vehicle infotainment systems, and servers. The software includes dedicated applications, secure communication protocols, database management systems, AI algorithms, and an emotion engine.

[0828] Examples of prompts to input into the generating AI model include: "User input: 'Monday 10:00 Doctor's appointment' 'Feeling a little depressed'" and "Example prompt for generating model output: 'The user's emotional state is low. Please suggest a rest period.'"

[0829] This allows users to receive individually optimized driving assistance while driving, resulting in reduced stress and improved safety.

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The user opens the application and enters their schedule, health data, and emotional information. The entered information includes specific appointment times and details (e.g., "Monday 10:00 Doctor's appointment"), body temperature and weight data (e.g., "Body temperature 36.5 degrees, weight 70 kg"), and text describing their emotional state (e.g., "Feeling a little depressed").

[0833] Step 2:

[0834] The terminal converts the information entered by the user into JSON format. The user ID and timestamp are also added during this process. The JSON data is sent to the server using a secure communication protocol (e.g., HTTPS). It receives user data as input and generates JSON formatted data as output.

[0835] Step 3:

[0836] The server parses the JSON data received from the terminal and stores it in the database. The database is structured, with schedule data, health data, and sentiment data stored in separate tables. It takes JSON data as input and stores the information in each table as output.

[0837] Step 4:

[0838] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule, the latest health data, and sentiment data. It inputs an SQL query as a query to the database and outputs the latest user information.

[0839] Step 5:

[0840] The server's AI algorithm performs data analysis based on the acquired information. This includes evaluating schedule density, detecting anomalies in health data, and analyzing emotional information. The emotion engine recognizes emotional states from text data and generates appropriate advice based on that. Specifically, it recognizes the text "I'm a little depressed" as "sad" and creates corresponding advice. It takes user information as input and obtains analysis results as output.

[0841] Step 6:

[0842] The server converts the analysis results into a reporting format and generates notification content. The generated notification content is then converted back into JSON format and sent to the terminal. It receives analysis results as input, generates notification content as output, and converts it into JSON format.

[0843] Step 7:

[0844] The device displays the notification content received from the server to the user. For example, a generated notification message such as "We recommend taking Friday off. It would be good to set aside time to relax" is displayed on the smartphone or vehicle's infotainment system. It receives notification content in JSON format as input and displays it to the user as output.

[0845] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0848] [Third Embodiment]

[0849] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0850] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0851] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0852] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0853] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0854] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0855] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0856] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0857] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0858] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0859] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0860] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0861] The system according to the present invention centrally manages personal information, performs various analyses, and provides useful advice and notifications. Specific embodiments for carrying out the present invention are described below.

[0862] Data Collection Unit

[0863] Users input their personal information using a dedicated application. This information includes schedules, health data (body temperature, blood pressure, exercise level, etc.), household finances (income, expenses, etc.), life plans, and information about taxes and assets. The information entered in this way is temporarily stored on the device.

[0864] Data transmission unit

[0865] The terminal periodically sends user-entered information to the server. This information is sent to the server in JSON format using, for example, an HTTP POST request. This information is transmitted using a secure communication protocol, ensuring data confidentiality and integrity.

[0866] Data storage unit

[0867] The server parses the received information, converts it to the appropriate format, and then stores it in the database. The stored data is managed in an optimized format so that it can be efficiently retrieved and processed later.

[0868] Data acquisition unit

[0869] The server periodically retrieves the latest user information from the database. During this process, it efficiently extracts only the necessary data based on a pre-configured schedule.

[0870] Data Analysis Department

[0871] The server uses AI algorithms to analyze the acquired information. This analysis includes, for example, evaluating the density of schedules, detecting anomalies in health data, and analyzing household income and expenditure balances.

[0872] Information provision department

[0873] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are then sent back to the device and displayed to the user as pop-up notifications or in-app messages.

[0874] Feedback loop

[0875] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback, the AI ​​model is adjusted to improve the accuracy and usefulness of future notifications.

[0876] Specific example

[0877] Schedule management

[0878] When a user enters "Monday 10:00 Doctor's Appointment" into the app, the device sends this information to the server. The server stores this information in a database and uses AI to analyze the user's schedule for the following week to see if there are any busy appointments. Based on the analysis, the server generates a notification saying, "We recommend you take Friday off," and sends it to the device. The device then displays this notification to the user.

[0879] health care

[0880] When a user enters their daily body temperature, weight, and exercise level, the device sends this data to a server. The server analyzes the data stored in the database and detects abnormal temperature increases or health risks. For example, the server might generate a notification such as, "Your recent temperature fluctuations have been significant; we recommend you see a doctor," and send it to the device. The device then displays this notification to the user.

[0881] In this way, the system according to the present invention can improve the user's health and quality of life by efficiently managing user information and providing appropriate advice.

[0882] The following describes the processing flow.

[0883] Step 1:

[0884] The user opens a dedicated application and enters their personal information. This information includes their schedule, health data (body temperature, weight, exercise level, etc.), and household finances.

[0885] Step 2:

[0886] The terminal converts the information entered by the user into JSON format and sends it to the server using a secure communication protocol. The data sent includes the user ID and timestamp.

[0887] Step 3:

[0888] The server parses the received JSON data and saves it to the database. For example, schedule information is saved in the "schedules" table, and health data is saved in the "health_data" table.

[0889] Step 4:

[0890] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule and the latest health data.

[0891] Step 5:

[0892] The server uses AI algorithms to analyze the acquired information. For example, it can identify days with overly busy schedules or detect anomalies in health data. This automatically generates necessary actions and advice.

[0893] Step 6:

[0894] The server converts the analysis results into a reporting format and generates a notification for the user. The generated notification is then converted back into JSON format and sent to the terminal.

[0895] Step 7:

[0896] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[0897] Step 8:

[0898] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings and comments.

[0899] Step 9:

[0900] The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[0901] (Example 1)

[0902] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0903] Traditional personal information management systems struggled to integrate and manage diverse data sources, making it difficult to effectively utilize user-generated information. In particular, centrally managing health data, schedules, and household financial information, and analyzing this data to provide appropriate advice to users, proved challenging. Furthermore, the lack of mechanisms for improving the system based on user feedback limited its overall usefulness.

[0904] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0905] In this invention, the server includes means for receiving information entered by the user, means for temporarily storing the received information, means for periodically transmitting the stored information to the server, means for storing the transmitted information to the server in a database, means for periodically retrieving information from the database, means for analyzing the retrieved information with an AI algorithm, means for notifying the user of the analysis results as appropriate advice or reminders, and means for receiving feedback from the user and adjusting the AI ​​model. This makes it possible to integrally manage different data sources and provide appropriate advice to the user based on the analysis results. Furthermore, it is possible to continuously improve the system by utilizing user feedback and improve the user's quality of life.

[0906] A "user" refers to an individual who uses the system to input personal information and receive advice and notifications.

[0907] "Means of receiving information" refers to functions that incorporate various data entered by users into the system. Specific examples include applications and web forms.

[0908] "Means of temporary storage" refers to storage used to temporarily hold information entered on a device. Examples include local storage and temporary files.

[0909] "Means of periodically sending information to the server" refers to the function that allows a device to transfer stored information to a server according to a set schedule. For example, this can be done using an HTTP POST request.

[0910] "Means of saving to a database" refers to the function of a server converting received information into an appropriate format and storing it in a specialized data store. For example, SQL databases are commonly used.

[0911] "Means of periodically retrieving information from a database" refers to a function where a server retrieves the latest data from the database at regular intervals. Cron jobs and scheduled tasks fall into this category.

[0912] "Methods of analysis using AI algorithms" refers to the function of performing analytical processing on collected data using artificial intelligence technology. This includes, for example, machine learning models.

[0913] "Means of notifying as advice or reminders" refers to a function that generates appropriate advice or reminders for the user based on the analysis results and notifies them of these. Pop-ups and in-app notifications fall into this category.

[0914] "Means for receiving feedback and adjusting the AI ​​model" refers to a function that collects responses and opinions from users and uses that data to improve the accuracy of the artificial intelligence model.

[0915] "Health data" refers to physiological information such as the user's body temperature, blood pressure, and exercise level.

[0916] "Household financial information" refers to financial information about a user's income, expenses, assets, etc.

[0917] The system according to the present invention is configured to centrally manage personal information, perform various analyses using the collected data, and provide helpful advice and notifications. This system consists of a series of processes in which the user inputs information using a dedicated application, the server receives that information, stores it, analyzes it, and sends notifications.

[0918] Users use a device with a dedicated application installed to input their schedule, health data (body temperature, blood pressure, exercise level, etc.), household financial information (income, expenses, etc.), life plan, and information about taxes and assets. This device temporarily saves the information entered by the user to local storage. For example, a user might enter "Monday 10:00 Doctor's appointment" on the calendar screen.

[0919] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format. Secure communication protocols such as TLS are used for this API communication, ensuring data confidentiality and integrity. For example, the following JSON data is sent:

[0920] {

[0921] "event": "Doctor's appointment",

[0922] "date": "Monday 10:00"

[0923] }

[0924] The server parses the received data, converts it to the appropriate format, and then stores it in a database (e.g., MySQL or PostgreSQL). This stored data is optimized for efficient retrieval and processing later.

[0925] The server periodically retrieves the latest user information from the database. This data retrieval is performed automatically based on a pre-configured schedule (e.g., a cron job). The server then analyzes the retrieved data using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis. For example, this analysis is performed to evaluate schedule density, detect anomalies in health data, and analyze household income and expenditure balances.

[0926] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent back to the device and displayed to the user as pop-up notifications or in-app messages. For example, advice such as "We recommend you take a rest on Friday" might be displayed.

[0927] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback data, the AI ​​model is continuously refined to improve the accuracy and usefulness of future notifications.

[0928] Example of a prompt

[0929] "Analyze the schedule information entered by the user and generate advice to take a break if the schedule is too busy."

[0930] Thus, the present invention can improve the quality of life for users by efficiently managing user information and providing appropriate advice. Furthermore, by incorporating user feedback and improving the system, continuous value creation becomes possible.

[0931] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0932] Step 1:

[0933] Users input their information using a dedicated application. This includes schedules, health data (body temperature, blood pressure, exercise level, etc.), and household financial information (income, expenses, etc.).

[0934] Input: User information (schedule, health data, household finances, etc.)

[0935] Data processing / calculation: Enter information into the application's input form.

[0936] Output: The entered information is temporarily stored in the device's local storage.

[0937] Step 2:

[0938] The device temporarily stores information entered by the user. This information is stored in local storage or temporary files.

[0939] Input: Information entered by the user

[0940] Data processing / calculation: Save the input data to local storage.

[0941] Output: Data saved to local storage

[0942] Step 3:

[0943] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format.

[0944] Input: Data stored in local storage

[0945] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0946] Output: JSON data sent to the server

[0947] Step 4:

[0948] The server parses the received data, converts it to the appropriate format, and then saves it to the database. This saving is done using SQL statements.

[0949] Input: JSON data sent to the server

[0950] Data processing / calculation: Parse JSON data and save it to the database.

[0951] Output: Data stored in the database

[0952] Step 5:

[0953] The server periodically retrieves the latest user information from the database. This data retrieval is performed according to a pre-configured schedule.

[0954] Input: Data stored in the database

[0955] Data processing / calculations: Retrieve data by executing SQL queries.

[0956] Output: Latest retrieved data

[0957] Step 6:

[0958] The server analyzes the acquired data using AI algorithms. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch.

[0959] Input: Latest data retrieved from the database

[0960] Data processing / calculation: Analyze data using AI algorithms.

[0961] Output: Analysis results

[0962] Step 7:

[0963] The server generates user advice and reminders based on the analysis results. This generation process selects appropriate messages based on the content of the results.

[0964] Input: Analysis results

[0965] Data processing / calculation: Generating advice and reminders.

[0966] Output: Generated advice and reminders

[0967] Step 8:

[0968] The server sends the generated notification to the device. This notification is also sent using an HTTP POST request.

[0969] Input: Generated advice and reminders

[0970] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[0971] Output: Notification sent to the device

[0972] Step 9:

[0973] The device displays received notifications to the user. These notifications may appear as pop-up notifications or in-app messages.

[0974] Input: Notification sent from the server

[0975] Data processing / calculation: Converts notification content into a display format.

[0976] Output: Notification displayed to the user

[0977] Step 10:

[0978] Users provide feedback on the advice and notifications they receive. This feedback is sent to the server via their device.

[0979] Input: User feedback

[0980] Data processing / calculation: Feedback is received via an input form.

[0981] Output: Feedback sent to the server

[0982] Step 11:

[0983] The server analyzes the received feedback and uses it to adjust the AI ​​model. This improves the accuracy of future advice.

[0984] Input: Feedback sent to the server

[0985] Data processing / computation: Analyze feedback and retrain the AI ​​model.

[0986] Output: Adjusted AI model

[0987] Through this series of processing steps, it becomes possible to efficiently manage user information and provide appropriate advice.

[0988] (Application Example 1)

[0989] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0990] Employee health management is a critical issue, and real-time monitoring of employee health is particularly necessary in workplaces such as factories. However, current workplaces lack systems that efficiently and accurately collect and analyze individual employee health data and provide appropriate advice. This makes it difficult to prevent health risks in the workplace. Furthermore, the process of employees voluntarily inputting health data and receiving feedback based on that data is cumbersome. This project aims to solve these problems.

[0991] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0992] In this invention, the server includes means for receiving information entered by a user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, means for having a device for collecting worker health data, means for transmitting the collected health data to the server, means for using an AI algorithm to detect anomalies, and means for monitoring the health status of employees in real time and providing advice. This makes it possible to efficiently collect and analyze employee health data and provide appropriate advice in real time.

[0993] - "Means of receiving information" refers to devices or software that have the function of acquiring personal information and health data entered by the user from terminals or sensors.

[0994] "Means of saving to a database" refers to a system for structuring received information in a specific format and recording and storing it in a way that allows for efficient access.

[0995] "Means for periodically retrieving stored information" refers to a process or mechanism that periodically queries information stored in a database and extracts the necessary data.

[0996] "Means of performing analysis based on acquired information" refers to the process of analyzing acquired data using algorithms and AI to detect useful insights and anomalies.

[0997] "Means of notifying the user of analysis results" refers to a device or software that visualizes or provides the user with advice or reminders generated based on the analysis results, or informs them through voice or other means.

[0998] "Means having equipment for collecting workers' health data" refers to sensors, devices, and their functions installed to collect data related to workers' health, such as body temperature, blood pressure, and heart rate.

[0999] "Means for transmitting collected health data to a server" refers to technologies or methods for securely transferring health data collected by sensors or devices to a server.

[1000] "Methods of using AI algorithms to detect outliers" refer to algorithms that analyze collected data using AI or machine learning models to detect outliers or patterns that deviate from standard values.

[1001] "A means of monitoring employees' health status in real time and providing advice" refers to a system that continuously monitors employees' health data and immediately provides countermeasures and advice for any problems discovered.

[1002] This invention relates to a system for monitoring the health status of factory workers in real time and providing appropriate advice. The system is configured as follows:

[1003] 1. Hardware and Software Configuration

[1004] The system consists of the following elements.

[1005] Sensors and devices

[1006] Body temperature measurement sensor

[1007] Blood pressure monitor

[1008] Heart rate sensor

[1009] These devices are used to collect employee health data.

[1010] Dedicated application

[1011] smartphone

[1012] tablet

[1013] An application for employees to input their health data and receive real-time feedback.

[1014] server

[1015] Server software for receiving, storing, retrieving, and analyzing data (e.g., Flask, SQLAlchemy, SQLite)

[1016] Hardware and software for running AI algorithms (e.g., TensorFlow, PyTorch)

[1017] 2. System Operation Description

[1018] 2.1 Data Collection

[1019] Users (employees) input their health data, such as body temperature, blood pressure, and heart rate, using a dedicated application. Sensors and devices installed throughout the factory automatically measure body temperature and heart rate, collecting data.

[1020] 2.2 Data Transmission

[1021] The terminals and sensors transmit the collected data to the server. During this process, a secure communication protocol (e.g., HTTPS) is used to maintain the confidentiality and integrity of the data.

[1022] 2.3 Data Storage

[1023] The server converts the received data into an appropriate format and stores it in the database. The stored data is managed in an optimized format for efficient retrieval and processing.

[1024] 2.4 Data Acquisition and Analysis

[1025] The server periodically retrieves the latest health data from the database and analyzes it using AI algorithms. For example, an anomaly detection algorithm is used to detect abnormal patterns and values ​​in the collected data.

[1026] 2.5 Information provision

[1027] Based on the analysis results, the server generates appropriate advice and notifications for employees. These notifications are sent to the device as a dedicated application or as pop-up notifications.

[1028] 2.6 Feedback Loop

[1029] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server and analyzed by an AI model. Based on this feedback, the accuracy and usefulness of future notifications will be improved.

[1030] 3. Specific Examples

[1031] For example, if an employee enters their body temperature using a dedicated app and their heart rate is measured by a sensor, that data is sent to a server. If the server detects an abnormal value, the employee receives a notification stating, "Your recent body temperature fluctuations have been significant, so we recommend that you see a doctor."

[1032] 4. Examples of prompts for generative AI models

[1033] "Develop an application that uses an anomaly detection algorithm to assess risk based on the health data of factory employees."

[1034] "Implement a system that monitors employees' body temperature, blood pressure, and heart rate in real time and provides appropriate advice."

[1035] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1036] Step 1:

[1037] Users input their health data (body temperature, blood pressure, heart rate, etc.) using a dedicated application. The entered data is temporarily stored on a device such as a smartphone or tablet. Input from the device includes measurements obtained from thermometers, blood pressure monitors, heart rate sensors, etc.

[1038] Input: Health data such as body temperature, blood pressure, and heart rate.

[1039] Output: Temporarily stored health data

[1040] Step 2:

[1041] The device periodically sends the entered health data to the server using HTTP POST requests. The transmitted data is in JSON format. Secure HTTPS is used as the communication protocol to ensure data confidentiality and integrity.

[1042] Input: Temporarily stored health data, HTTP POST request

[1043] Output: Health data sent to the server

[1044] Step 3:

[1045] The server analyzes the received health data, converts it to an appropriate format, and then stores it in a database (SQLite) using SQLAlchemy. The stored data is managed in an optimized format for efficient retrieval and processing.

[1046] Input: Received health data, SQLAlchemy

[1047] Output: Health data stored in the database

[1048] Step 4:

[1049] The server periodically retrieves the latest health data from the database using queries. To ensure efficient data acquisition, only the necessary data is extracted.

[1050] Input: Health data stored in the database, queries

[1051] Output: Latest health data

[1052] Step 5:

[1053] The server analyzes the acquired health data using AI algorithms (e.g., TensorFlow or PyTorch) to detect anomalies. It detects abnormal patterns and values ​​from the collected data based on standard values.

[1054] Input: Latest health data, AI algorithm

[1055] Output: Analysis results (including outliers)

[1056] Step 6:

[1057] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent to a dedicated application in JSON format and displayed on the user's device in real time.

[1058] Input: Analysis results, notification in JSON format

[1059] Output: Advice and reminders displayed to the user.

[1060] Step 7:

[1061] Users provide feedback on the advice and reminders offered. This feedback is sent from the device to the server. Based on this feedback, the server retrains the AI ​​model to improve the accuracy and usefulness of future notifications.

[1062] Input: User feedback

[1063] Output: Adjusted AI model, improved notification accuracy and usefulness.

[1064] Adding specific actions

[1065] Health data entered by the user is collected by sensors, allowing them to understand their own health status.

[1066] When a device transmits health data to a server, encryption technology is used to ensure data integrity.

[1067] When the server processes data, it performs data format verification and filters out abnormal values.

[1068] The server uses AI algorithms for analysis, which is used to detect anomalies and recognize patterns.

[1069] The parameters of the AI ​​model are dynamically adjusted so that improvements in user health can be concretely confirmed through feedback.

[1070] The above is a detailed explanation of each processing step. This system enables efficient management of employee health.

[1071] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1072] The system according to the present invention centrally manages personal information and further recognizes the user's emotions to provide appropriate advice and notifications. Specific embodiments for carrying out the present invention are described below.

[1073] Data Collection Unit

[1074] Users open a dedicated application and enter their personal information, including schedules, health data, and household finances. They can also input emotional information via voice input or text chat. This emotional information is used to recognize the user's everyday emotional state.

[1075] Data transmission unit

[1076] The terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. Sentiment information is also transmitted in the same way.

[1077] Data storage unit

[1078] The server parses the received JSON data and stores it in the database. Schedule information, health data, and household finance information are stored in their respective tables. Sentimental information is also stored in a separate table and used for later analysis.

[1079] Data acquisition unit

[1080] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data.

[1081] Data Analysis Department

[1082] The server uses AI algorithms to analyze the acquired information. Specifically, it evaluates schedule density, detects anomalies in health data, analyzes household income and expenditure balance, and analyzes emotional information. The emotion engine recognizes the user's emotional state and analyzes the results in an integrated manner with other data.

[1083] Information provision department

[1084] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the sentiment engine's analysis. The generated notification is then converted back into JSON format and sent to the device.

[1085] Functions of the Emotion Engine

[1086] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs text such as "I'm depressed," the engine recognizes that emotion as "sad" and generates appropriate advice.

[1087] Feedback loop

[1088] Users provide feedback on the advice and notifications they receive, sending it from their device to the server. This feedback includes ratings and comments. The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[1089] Specific example

[1090] Schedule management and sentiment analysis

[1091] Suppose a user enters "Doctor's appointment Monday 10:00" into the app, and simultaneously enters "Feeling a little depressed." This information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off to rest. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[1092] Health management and emotional analysis

[1093] Let's say a user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to a server. The server collects the health and emotional data and confirms that the emotional state is energetic even if there is a slight increase in body temperature. Based on this, it generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotion-based advice such as, "We have confirmed that you are feeling energetic. Don't overdo it, and enjoy yourself."

[1094] In this way, the system according to the present invention recognizes the user's emotions, integrates and analyzes them with other information, and provides more personalized advice. As a result, the user can manage information efficiently and receive emotional support.

[1095] The following describes the processing flow.

[1096] Step 1:

[1097] The user opens a dedicated application and enters their information. This information includes schedules (e.g., doctor's appointment on Monday at 10:00), health data (e.g., body temperature, weight, exercise level), household finances (e.g., income, expenses), and emotional information (e.g., enter "I'm feeling a little down today" as text).

[1098] Step 2:

[1099] The terminal converts the entered information into JSON format. The converted data includes user ID, time information, schedule, health data, household finance information, and sentiment data.

[1100] Step 3:

[1101] The device sends JSON-formatted data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted during transmission.

[1102] Step 4:

[1103] The server parses the received JSON data, converts it to the appropriate format, and stores each piece of information in the corresponding database table. Schedule information is stored in the "schedules" table, health data in the "health_data" table, and emotion data in the "emotions" table.

[1104] Step 5:

[1105] The server periodically retrieves the latest user information from the database. For example, a scheduled job can be set up to execute SQL queries every morning at 00:00 to retrieve the next week's schedule, the latest health data, and sentiment data.

[1106] Step 6:

[1107] The server uses AI algorithms to analyze the acquired information. It evaluates the density of schedules, detects anomalies in health data, and analyzes the balance of income and expenses in household financial information. In addition, an emotion engine recognizes the user's emotional state and integrates the results with other data for analysis.

[1108] Step 7:

[1109] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. For example, it may include specific advice such as, "We recommend taking Friday off. It would be good to set aside time to relax."

[1110] Step 8:

[1111] The generated notification content is converted back into JSON format and sent to the device. Communication from the server to the device is also performed using a secure protocol (e.g., HTTPS).

[1112] Step 9:

[1113] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[1114] Step 10:

[1115] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings (e.g., "helpful" or "unnecessary") and comments.

[1116] Step 11:

[1117] The server analyzes the received feedback and adjusts the AI ​​model. Based on the feedback information, it optimizes the algorithm parameters to improve the accuracy and usefulness of future notifications. This continuous feedback loop improves the overall performance of the system.

[1118] (Example 2)

[1119] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1120] In modern society, individuals must manage a wide range of information. Schedules, health data, and household finances are often managed in fragments, and receiving advice that takes into account an individual's emotional state is even more difficult. Conventional information management systems lack the ability to integrate and centrally manage this diverse data, and to analyze the user's emotional state to provide appropriate advice. There is a need for a system that addresses these challenges and provides more useful information management and support for individuals.

[1121] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1122] In this invention, the server includes means for receiving schedule, health data, household information, and emotional information entered by the user; means for converting the received information into JSON format and transmitting it to the server using a secure communication protocol; means for the server to parse the received JSON data and store it in a database in a corresponding table; means for the server to periodically retrieve the latest user information from the database by executing SQL queries; means for the server to perform data analysis on the retrieved information using an AI algorithm; means for the server to convert the analysis results into a reporting format, generate notification content, convert it back into JSON format and transmit it to the terminal; and means for the terminal to display the received notification content to the user. This makes it possible to comprehensively manage diverse user information and provide appropriate advice.

[1123] A "user" refers to an individual who uses a system to input and manage information.

[1124] "Schedule" refers to information about appointments and events entered by the user.

[1125] "Health data" refers to information about a user's health, such as their weight, body temperature, and exercise level.

[1126] "Household financial information" refers to economic information about a user's income and expenses.

[1127] "Emotional information" refers to information that indicates the user's emotional state, and includes information entered via voice input or text chat.

[1128] "Terminal" refers to a device used by a user to input information (e.g., a smartphone or tablet).

[1129] A "server" refers to a computer system that receives, stores, and analyzes user input information and generates notifications.

[1130] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for structuring and transferring data.

[1131] A "secure communication protocol" is a means of communication that guarantees the safe transfer of data, such as HTTPS.

[1132] A "database" refers to a digital storage system used to store user input information.

[1133] "SQL query" is an abbreviation for Structured Query Language, and refers to a set of commands used to retrieve information from a database.

[1134] "AI algorithms" refer to data analysis methods using artificial intelligence, and specifically to models built with frameworks such as TensorFlow and PyTorch.

[1135] "Notification content" refers to messages containing information and advice that the server provides to the user based on the analysis results.

[1136] "Feedback" refers to the evaluations and comments that users make in response to advice or notifications provided.

[1137] This invention is a system that centrally manages diverse user information and analyzes their emotional state to provide appropriate advice. This system provides comprehensive support to individuals through the coordinated operation of the user, terminal, and server.

[1138] Data Collection Unit

[1139] Users first launch a dedicated application and input personal information such as their schedule, health data, and household finances. This application can be used on devices such as smartphones and tablets. Users can also input their daily emotional state using voice input or text chat. For example, a user might input "Monday 10:00 Doctor's appointment" and simultaneously input their emotional state, such as "Feeling a little depressed."

[1140] Data transmission unit

[1141] The terminal first converts the information collected from the user into JSON format. This information is then sent to the server using a secure communication protocol (e.g., HTTPS). The data sent includes the user ID and timestamp. For example, it is sent as follows:

[1142] {

[1143] "user_id": "12345",

[1144] "timestamp": "2023-10-04T10:00:00Z",

[1145] "schedule": "Monday 10:00 Doctor's appointment",

[1146] "health_data": {

[1147] "weight": "70kg",

[1148] "temperature": "36.5℃"

[1149] },

[1150] "emotion": "I feel a little depressed."

[1151] }

[1152] Data storage unit

[1153] The server parses the received JSON data and stores each piece of information in the database. The database stores schedule information, health data, household finance information, and emotional information in corresponding tables. For example, schedule information is stored in the schedule table, and health data is stored in the health data table.

[1154] Data acquisition unit

[1155] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data. This ensures that the most up-to-date information is always available for analysis.

[1156] Data Analysis Department

[1157] The server uses AI algorithms (e.g., TensorFlow or PyTorch) to analyze the acquired information. Specifically, the following analyses are performed:

[1158] Evaluation of schedule density

[1159] Detection of abnormal values ​​in health data

[1160] Analysis of household income and expenditure balance

[1161] Analysis of emotional information

[1162] For example, the emotion engine analyzes the text "I feel depressed" entered by the user and recognizes that emotion as "sad." It then integrates and analyzes this emotional information with other data.

[1163] Information provision department

[1164] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. The generated notification is then converted back into JSON format and sent to the device. The device displays this to the user. For example, a notification such as "Considering your busy schedule and emotional state, we recommend you take a rest on Friday" is generated.

[1165] Feedback loop

[1166] Users provide feedback on the advice and notifications they receive. This feedback includes evaluations and comments on the advice. The device sends this feedback to the server. The server analyzes the received feedback and uses it to refine the AI ​​model. This improves the accuracy and usefulness of future notifications.

[1167] Specific example

[1168] Schedule management and sentiment analysis

[1169] A user enters "Doctor's appointment Monday 10:00" into the application and simultaneously enters emotional information such as "Feeling a little depressed." This information is sent from the terminal to the server and stored in a database. The server analyzes this information and generates a notification, such as "We recommend you take Friday off. It would be good to set aside some time to relax," which is then presented to the user.

[1170] Health management and emotional analysis

[1171] The user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to the server. The server analyzes the health and emotional data and generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotionally-based advice such as, "We've confirmed you're feeling great. Don't overdo it, and enjoy yourself."

[1172] In this way, the system of the present invention can comprehensively manage diverse user information and provide personalized advice that also takes into account emotional states.

[1173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1174] Step 1:

[1175] The user launches a dedicated application and enters schedule, health data, and household financial information. This input data specifically includes schedule information such as "Monday 10:00 Doctor's appointment" and health data such as "Weight: 70kg, Body temperature: 36.5℃". Furthermore, emotional information is entered as text, such as "Feeling a little depressed". The input data is temporarily stored by the application.

[1176] Step 2:

[1177] The terminal converts the collected user input data into JSON format. For example, the following JSON data is generated:

[1178] json

[1179] {

[1180] "user_id": "12345",

[1181] "timestamp": "2023-10-04T10:00:00Z",

[1182] "schedule": "Monday 10:00 Doctor's appointment",

[1183] "health_data": {

[1184] "weight": "70kg",

[1185] "temperature": "36.5℃"

[1186] },

[1187] "emotion": "I feel a little depressed."

[1188] }

[1189] This JSON data will be used as input data to be sent from the terminal to the server.

[1190] Step 3:

[1191] The terminal sends the generated JSON data to the server using a secure communication protocol (e.g., HTTPS). If the transmission is successful, the terminal receives a transmission success status as output.

[1192] Step 4:

[1193] The server parses the received JSON data and saves it to the database. For example, the server generates and executes INSERT statements to save schedule information to the "schedule" table, health data to the "health_data" table, and emotion information to the "emotions" table. The output of this process indicates the status of successful data saving.

[1194] Step 5:

[1195] The server periodically (e.g., every morning at 00:00) retrieves the latest user information from the database using SQL queries. An example of an SQL query executed is:

[1196] SQL

[1197] SELECT FROM schedule WHERE date >= CURDATE() AND user_id = '12345';

[1198] SELECT FROM health_data WHERE date >= CURDATE() AND user_id = '12345';

[1199] SELECT FROM emotions WHERE date >= CURDATE() AND user_id = '12345';

[1200] The acquired data will be used as input data for the next analysis step.

[1201] Step 6:

[1202] The server uses AI algorithms to analyze data based on the latest information obtained via SQL queries. This analysis utilizes TensorFlow and PyTorch to evaluate schedule density, detect anomalies in health data, and assess sentiment. For example, health data is input into an anomaly detection model, and if an anomaly is detected, the result is output.

[1203] Step 7:

[1204] The server converts the analysis results into a reporting format and generates a notification for the user. Specifically, if a busy schedule is identified, it creates a notification message such as, "We recommend you take Friday off." This notification message is converted to JSON format and sent to the terminal. The generated notification message is the output.

[1205] Step 8:

[1206] The device displays the received notification content to the user. For example, the notification may appear as a pop-up message, allowing the user to review the recommendation. The output of this step indicates a successful display status.

[1207] Step 9:

[1208] Users provide feedback on the advice and notifications they receive. For example, a user might comment, "This advice was helpful," and rate it. The feedback entered is temporarily stored on the device.

[1209] Step 10:

[1210] The device converts user feedback into JSON format and sends it to the server. The data sent includes:

[1211] json

[1212] {

[1213] "user_id": "12345",

[1214] "feedback": "This advice was helpful",

[1215] Rating: 5

[1216] }

[1217] It is converted as shown. This JSON data becomes the input data and is sent to the server.

[1218] Step 11:

[1219] The server receives feedback and performs analysis. Based on the received feedback, it updates the parameters of the AI ​​model or adds training data. After the analysis is complete, the model is adjusted to improve accuracy in the next analysis. The analysis results are output.

[1220] Through the process described above, this system can integrate and manage diverse user information and provide personalized advice that takes into account their emotional state.

[1221] (Application Example 2)

[1222] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1223] Modern autonomous vehicles lack personalized driving assistance that takes into account the driver's schedule, health, and emotional state. Furthermore, the absence of a system that can immediately respond to changes in the driver's emotions means that stress reduction and safety improvements during driving are not fully achieved. This invention aims to solve these problems.

[1224] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving information entered by the user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, and means for managing the individual's schedule, health, and emotional state in real time and providing advice for reducing stress and improving safety while driving. This makes it possible to personalize driving assistance according to the driver's emotional state and health state, thereby reducing stress and improving safety while driving.

[1225] "Personal schedule" refers to information that manages the user's daily schedule, event times, and dates.

[1226] "Health status" refers to information about the user's physical health, such as data on body temperature, weight, and exercise level.

[1227] "Emotional state" refers to information that indicates the user's mental state and emotional changes in their daily life, and is obtained from text and voice input.

[1228] "Real-time management" refers to constantly acquiring, analyzing, and processing user information instantly.

[1229] "Reducing stress while driving a vehicle" refers to measures taken to alleviate the mental and physical burden felt when driving a vehicle.

[1230] "Safety improvement" refers to measures taken to reduce risks when driving a vehicle, thereby preventing accidents and promoting safe driving.

[1231] "Providing advice" refers to offering helpful suggestions and guidance to users based on the information they have gathered.

[1232] "Feedback" refers to the act of a user responding to advice or notifications provided by a system by offering their opinion or comment.

[1233] A "generative artificial intelligence model" is an algorithm that learns from user input data and feedback to improve the accuracy and usefulness of the system.

[1234] The system according to the present invention manages an individual's schedule, health status, and emotional state in real time, and provides advice to reduce stress and improve safety while driving a vehicle. Specific embodiments for carrying out the present invention are described below.

[1235] First, users input their information using a dedicated application. This application is installed on smartphones or the vehicle's infotainment system. Users can input their schedule, health data, and emotional information. The schedule includes dates and times, while health data includes body temperature, weight, and activity levels. Emotional information is recorded via text or voice input and used to recognize the user's daily emotional state.

[1236] Next, the terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. The server receives this information and stores it in a database. The stored information is managed in separate tables for schedule data, health data, and sentiment data.

[1237] The server periodically retrieves the latest user information from the database and performs data analysis using an AI algorithm. It evaluates schedule density, abnormal values ​​in health data, and changes in emotional information. The analysis results are converted into a reporting format and notified to the user. This notification also reflects the results of the emotion engine's analysis. The generated notification content is converted back into JSON format and sent to the device.

[1238] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs "I'm feeling a little down," it recognizes that emotion as "sad" and generates appropriate advice.

[1239] For example, if a user enters "Monday 10:00 Doctor's appointment" and simultaneously enters "Feeling a little depressed" into the application, this information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[1240] The hardware used includes smartphones, vehicle infotainment systems, and servers. The software includes dedicated applications, secure communication protocols, database management systems, AI algorithms, and an emotion engine.

[1241] Examples of prompts to input into the generating AI model include: "User input: 'Monday 10:00 Doctor's appointment' 'Feeling a little depressed'" and "Example prompt for generating model output: 'The user's emotional state is low. Please suggest a rest period.'"

[1242] This allows users to receive individually optimized driving assistance while driving, resulting in reduced stress and improved safety.

[1243] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1244] Step 1:

[1245] The user opens the application and enters their schedule, health data, and emotional information. The entered information includes specific appointment times and details (e.g., "Monday 10:00 Doctor's appointment"), body temperature and weight data (e.g., "Body temperature 36.5 degrees, weight 70 kg"), and text describing their emotional state (e.g., "Feeling a little depressed").

[1246] Step 2:

[1247] The terminal converts the information entered by the user into JSON format. The user ID and timestamp are also added during this process. The JSON data is sent to the server using a secure communication protocol (e.g., HTTPS). It receives user data as input and generates JSON formatted data as output.

[1248] Step 3:

[1249] The server parses the JSON data received from the terminal and stores it in the database. The database is structured, with schedule data, health data, and sentiment data stored in separate tables. It takes JSON data as input and stores the information in each table as output.

[1250] Step 4:

[1251] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule, the latest health data, and sentiment data. It inputs an SQL query as a query to the database and outputs the latest user information.

[1252] Step 5:

[1253] The server's AI algorithm performs data analysis based on the acquired information. This includes evaluating schedule density, detecting anomalies in health data, and analyzing emotional information. The emotion engine recognizes emotional states from text data and generates appropriate advice based on that. Specifically, it recognizes the text "I'm a little depressed" as "sad" and creates corresponding advice. It takes user information as input and obtains analysis results as output.

[1254] Step 6:

[1255] The server converts the analysis results into a reporting format and generates notification content. The generated notification content is then converted back into JSON format and sent to the terminal. It receives analysis results as input, generates notification content as output, and converts it into JSON format.

[1256] Step 7:

[1257] The device displays the notification content received from the server to the user. For example, a generated notification message such as "We recommend taking Friday off. It would be good to set aside time to relax" is displayed on the smartphone or vehicle's infotainment system. It receives notification content in JSON format as input and displays it to the user as output.

[1258] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1259] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1260] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1261] [Fourth Embodiment]

[1262] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1263] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1264] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1265] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1266] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1267] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1268] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1269] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1270] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1271] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1272] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1273] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1274] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1275] The system according to the present invention centrally manages personal information, performs various analyses, and provides useful advice and notifications. Specific embodiments for carrying out the present invention are described below.

[1276] Data Collection Unit

[1277] Users input their personal information using a dedicated application. This information includes schedules, health data (body temperature, blood pressure, exercise level, etc.), household finances (income, expenses, etc.), life plans, and information about taxes and assets. The information entered in this way is temporarily stored on the device.

[1278] Data transmission unit

[1279] The terminal periodically sends user-entered information to the server. This information is sent to the server in JSON format using, for example, an HTTP POST request. This information is transmitted using a secure communication protocol, ensuring data confidentiality and integrity.

[1280] Data storage unit

[1281] The server parses the received information, converts it to the appropriate format, and then stores it in the database. The stored data is managed in an optimized format so that it can be efficiently retrieved and processed later.

[1282] Data acquisition unit

[1283] The server periodically retrieves the latest user information from the database. During this process, it efficiently extracts only the necessary data based on a pre-configured schedule.

[1284] Data Analysis Department

[1285] The server uses AI algorithms to analyze the acquired information. This analysis includes, for example, evaluating the density of schedules, detecting anomalies in health data, and analyzing household income and expenditure balances.

[1286] Information provision department

[1287] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are then sent back to the device and displayed to the user as pop-up notifications or in-app messages.

[1288] Feedback loop

[1289] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback, the AI ​​model is adjusted to improve the accuracy and usefulness of future notifications.

[1290] Specific example

[1291] Schedule management

[1292] When a user enters "Monday 10:00 Doctor's Appointment" into the app, the device sends this information to the server. The server stores this information in a database and uses AI to analyze the user's schedule for the following week to see if there are any busy appointments. Based on the analysis, the server generates a notification saying, "We recommend you take Friday off," and sends it to the device. The device then displays this notification to the user.

[1293] health care

[1294] When a user enters their daily body temperature, weight, and exercise level, the device sends this data to a server. The server analyzes the data stored in the database and detects abnormal temperature increases or health risks. For example, the server might generate a notification such as, "Your recent temperature fluctuations have been significant; we recommend you see a doctor," and send it to the device. The device then displays this notification to the user.

[1295] In this way, the system according to the present invention can improve the user's health and quality of life by efficiently managing user information and providing appropriate advice.

[1296] The following describes the processing flow.

[1297] Step 1:

[1298] The user opens a dedicated application and enters their personal information. This information includes their schedule, health data (body temperature, weight, exercise level, etc.), and household finances.

[1299] Step 2:

[1300] The terminal converts the information entered by the user into JSON format and sends it to the server using a secure communication protocol. The data sent includes the user ID and timestamp.

[1301] Step 3:

[1302] The server parses the received JSON data and saves it to the database. For example, schedule information is saved in the "schedules" table, and health data is saved in the "health_data" table.

[1303] Step 4:

[1304] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule and the latest health data.

[1305] Step 5:

[1306] The server uses AI algorithms to analyze the acquired information. For example, it can identify days with overly busy schedules or detect anomalies in health data. This automatically generates necessary actions and advice.

[1307] Step 6:

[1308] The server converts the analysis results into a reporting format and generates a notification for the user. The generated notification is then converted back into JSON format and sent to the terminal.

[1309] Step 7:

[1310] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[1311] Step 8:

[1312] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings and comments.

[1313] Step 9:

[1314] The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[1315] (Example 1)

[1316] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1317] Traditional personal information management systems struggled to integrate and manage diverse data sources, making it difficult to effectively utilize user-generated information. In particular, centrally managing health data, schedules, and household financial information, and analyzing this data to provide appropriate advice to users, proved challenging. Furthermore, the lack of mechanisms for improving the system based on user feedback limited its overall usefulness.

[1318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1319] In this invention, the server includes means for receiving information entered by the user, means for temporarily storing the received information, means for periodically transmitting the stored information to the server, means for storing the transmitted information to the server in a database, means for periodically retrieving information from the database, means for analyzing the retrieved information with an AI algorithm, means for notifying the user of the analysis results as appropriate advice or reminders, and means for receiving feedback from the user and adjusting the AI ​​model. This makes it possible to integrally manage different data sources and provide appropriate advice to the user based on the analysis results. Furthermore, it is possible to continuously improve the system by utilizing user feedback and improve the user's quality of life.

[1320] A "user" refers to an individual who uses the system to input personal information and receive advice and notifications.

[1321] "Means of receiving information" refers to functions that incorporate various data entered by users into the system. Specific examples include applications and web forms.

[1322] "Means of temporary storage" refers to storage used to temporarily hold information entered on a device. Examples include local storage and temporary files.

[1323] "Means of periodically sending information to the server" refers to the function that allows a device to transfer stored information to a server according to a set schedule. For example, this can be done using an HTTP POST request.

[1324] "Means of saving to a database" refers to the function of a server converting received information into an appropriate format and storing it in a specialized data store. For example, SQL databases are commonly used.

[1325] "Means of periodically retrieving information from a database" refers to a function where a server retrieves the latest data from the database at regular intervals. Cron jobs and scheduled tasks fall into this category.

[1326] "Methods of analysis using AI algorithms" refers to the function of performing analytical processing on collected data using artificial intelligence technology. This includes, for example, machine learning models.

[1327] "Means of notifying as advice or reminders" refers to a function that generates appropriate advice or reminders for the user based on the analysis results and notifies them of these. Pop-ups and in-app notifications fall into this category.

[1328] "Means for receiving feedback and adjusting the AI ​​model" refers to a function that collects responses and opinions from users and uses that data to improve the accuracy of the artificial intelligence model.

[1329] "Health data" refers to physiological information such as the user's body temperature, blood pressure, and exercise level.

[1330] "Household financial information" refers to financial information about a user's income, expenses, assets, etc.

[1331] The system according to the present invention is configured to centrally manage personal information, perform various analyses using the collected data, and provide helpful advice and notifications. This system consists of a series of processes in which the user inputs information using a dedicated application, the server receives that information, stores it, analyzes it, and sends notifications.

[1332] Users use a device with a dedicated application installed to input their schedule, health data (body temperature, blood pressure, exercise level, etc.), household financial information (income, expenses, etc.), life plan, and information about taxes and assets. This device temporarily saves the information entered by the user to local storage. For example, a user might enter "Monday 10:00 Doctor's appointment" on the calendar screen.

[1333] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format. Secure communication protocols such as TLS are used for this API communication, ensuring data confidentiality and integrity. For example, the following JSON data is sent:

[1334] {

[1335] "event": "Doctor's appointment",

[1336] "date": "Monday 10:00"

[1337] }

[1338] The server parses the received data, converts it to the appropriate format, and then stores it in a database (e.g., MySQL or PostgreSQL). This stored data is optimized for efficient retrieval and processing later.

[1339] The server periodically retrieves the latest user information from the database. This data retrieval is performed automatically based on a pre-configured schedule (e.g., a cron job). The server then analyzes the retrieved data using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis. For example, this analysis is performed to evaluate schedule density, detect anomalies in health data, and analyze household income and expenditure balances.

[1340] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent back to the device and displayed to the user as pop-up notifications or in-app messages. For example, advice such as "We recommend you take a rest on Friday" might be displayed.

[1341] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server, where it is analyzed. Based on this feedback data, the AI ​​model is continuously refined to improve the accuracy and usefulness of future notifications.

[1342] Example of a prompt

[1343] "Analyze the schedule information entered by the user and generate advice to take a break if the schedule is too busy."

[1344] Thus, the present invention can improve the quality of life for users by efficiently managing user information and providing appropriate advice. Furthermore, by incorporating user feedback and improving the system, continuous value creation becomes possible.

[1345] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1346] Step 1:

[1347] Users input their information using a dedicated application. This includes schedules, health data (body temperature, blood pressure, exercise level, etc.), and household financial information (income, expenses, etc.).

[1348] Input: User information (schedule, health data, household finances, etc.)

[1349] Data processing / calculation: Enter information into the application's input form.

[1350] Output: The entered information is temporarily stored in the device's local storage.

[1351] Step 2:

[1352] The device temporarily stores information entered by the user. This information is stored in local storage or temporary files.

[1353] Input: Information entered by the user

[1354] Data processing / calculation: Save the input data to local storage.

[1355] Output: Data saved to local storage

[1356] Step 3:

[1357] The device periodically sends stored information to the server. This transmission uses an HTTP POST request, and the information is sent in JSON format.

[1358] Input: Data stored in local storage

[1359] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[1360] Output: JSON data sent to the server

[1361] Step 4:

[1362] The server parses the received data, converts it to the appropriate format, and then saves it to the database. This saving is done using SQL statements.

[1363] Input: JSON data sent to the server

[1364] Data processing / calculation: Parse JSON data and save it to the database.

[1365] Output: Data stored in the database

[1366] Step 5:

[1367] The server periodically retrieves the latest user information from the database. This data retrieval is performed according to a pre-configured schedule.

[1368] Input: Data stored in the database

[1369] Data processing / calculations: Retrieve data by executing SQL queries.

[1370] Output: Latest retrieved data

[1371] Step 6:

[1372] The server analyzes the acquired data using AI algorithms. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch.

[1373] Input: Latest data retrieved from the database

[1374] Data processing / calculation: Analyze data using AI algorithms.

[1375] Output: Analysis results

[1376] Step 7:

[1377] The server generates user advice and reminders based on the analysis results. This generation process selects appropriate messages based on the content of the results.

[1378] Input: Analysis results

[1379] Data processing / calculation: Generating advice and reminders.

[1380] Output: Generated advice and reminders

[1381] Step 8:

[1382] The server sends the generated notification to the device. This notification is also sent using an HTTP POST request.

[1383] Input: Generated advice and reminders

[1384] Data processing / calculation: Converts data to JSON format and sends an HTTP POST request.

[1385] Output: Notification sent to the device

[1386] Step 9:

[1387] The device displays received notifications to the user. These notifications may appear as pop-up notifications or in-app messages.

[1388] Input: Notification sent from the server

[1389] Data processing / calculation: Converts notification content into a display format.

[1390] Output: Notification displayed to the user

[1391] Step 10:

[1392] Users provide feedback on the advice and notifications they receive. This feedback is sent to the server via their device.

[1393] Input: User feedback

[1394] Data processing / calculation: Feedback is received via an input form.

[1395] Output: Feedback sent to the server

[1396] Step 11:

[1397] The server analyzes the received feedback and uses it to adjust the AI ​​model. This improves the accuracy of future advice.

[1398] Input: Feedback sent to the server

[1399] Data processing / computation: Analyze feedback and retrain the AI ​​model.

[1400] Output: Adjusted AI model

[1401] Through this series of processing steps, it becomes possible to efficiently manage user information and provide appropriate advice.

[1402] (Application Example 1)

[1403] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1404] Employee health management is a critical issue, and real-time monitoring of employee health is particularly necessary in workplaces such as factories. However, current workplaces lack systems that efficiently and accurately collect and analyze individual employee health data and provide appropriate advice. This makes it difficult to prevent health risks in the workplace. Furthermore, the process of employees voluntarily inputting health data and receiving feedback based on that data is cumbersome. This project aims to solve these problems.

[1405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1406] In this invention, the server includes means for receiving information entered by a user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, means for having a device for collecting worker health data, means for transmitting the collected health data to the server, means for using an AI algorithm to detect anomalies, and means for monitoring the health status of employees in real time and providing advice. This makes it possible to efficiently collect and analyze employee health data and provide appropriate advice in real time.

[1407] - "Means of receiving information" refers to devices or software that have the function of acquiring personal information and health data entered by the user from terminals or sensors.

[1408] "Means of saving to a database" refers to a system for structuring received information in a specific format and recording and storing it in a way that allows for efficient access.

[1409] "Means for periodically retrieving stored information" refers to a process or mechanism that periodically queries information stored in a database and extracts the necessary data.

[1410] "Means of performing analysis based on acquired information" refers to the process of analyzing acquired data using algorithms and AI to detect useful insights and anomalies.

[1411] "Means of notifying the user of analysis results" refers to a device or software that visualizes or provides the user with advice or reminders generated based on the analysis results, or informs them through voice or other means.

[1412] "Means having equipment for collecting workers' health data" refers to sensors, devices, and their functions installed to collect data related to workers' health, such as body temperature, blood pressure, and heart rate.

[1413] "Means for transmitting collected health data to a server" refers to technologies or methods for securely transferring health data collected by sensors or devices to a server.

[1414] "Methods of using AI algorithms to detect outliers" refer to algorithms that analyze collected data using AI or machine learning models to detect outliers or patterns that deviate from standard values.

[1415] "A means of monitoring employees' health status in real time and providing advice" refers to a system that continuously monitors employees' health data and immediately provides countermeasures and advice for any problems discovered.

[1416] This invention relates to a system for monitoring the health status of factory workers in real time and providing appropriate advice. The system is configured as follows:

[1417] 1. Hardware and Software Configuration

[1418] The system consists of the following elements.

[1419] Sensors and devices

[1420] Body temperature measurement sensor

[1421] Blood pressure monitor

[1422] Heart rate sensor

[1423] These devices are used to collect employee health data.

[1424] Dedicated application

[1425] smartphone

[1426] tablet

[1427] An application for employees to input their health data and receive real-time feedback.

[1428] server

[1429] Server software for receiving, storing, retrieving, and analyzing data (e.g., Flask, SQLAlchemy, SQLite)

[1430] Hardware and software for running AI algorithms (e.g., TensorFlow, PyTorch)

[1431] 2. System Operation Description

[1432] 2.1 Data Collection

[1433] Users (employees) input their health data, such as body temperature, blood pressure, and heart rate, using a dedicated application. Sensors and devices installed throughout the factory automatically measure body temperature and heart rate, collecting data.

[1434] 2.2 Data Transmission

[1435] The terminals and sensors transmit the collected data to the server. During this process, a secure communication protocol (e.g., HTTPS) is used to maintain the confidentiality and integrity of the data.

[1436] 2.3 Data Storage

[1437] The server converts the received data into an appropriate format and stores it in the database. The stored data is managed in an optimized format for efficient retrieval and processing.

[1438] 2.4 Data Acquisition and Analysis

[1439] The server periodically retrieves the latest health data from the database and analyzes it using AI algorithms. For example, an anomaly detection algorithm is used to detect abnormal patterns and values ​​in the collected data.

[1440] 2.5 Information provision

[1441] Based on the analysis results, the server generates appropriate advice and notifications for employees. These notifications are sent to the device as a dedicated application or as pop-up notifications.

[1442] 2.6 Feedback Loop

[1443] Users can provide feedback on the advice and notifications they receive. This feedback is sent from their device to the server and analyzed by an AI model. Based on this feedback, the accuracy and usefulness of future notifications will be improved.

[1444] 3. Specific Examples

[1445] For example, if an employee enters their body temperature using a dedicated app and their heart rate is measured by a sensor, that data is sent to a server. If the server detects an abnormal value, the employee receives a notification stating, "Your recent body temperature fluctuations have been significant, so we recommend that you see a doctor."

[1446] 4. Examples of prompts for generative AI models

[1447] "Develop an application that uses an anomaly detection algorithm to assess risk based on the health data of factory employees."

[1448] "Implement a system that monitors employees' body temperature, blood pressure, and heart rate in real time and provides appropriate advice."

[1449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1450] Step 1:

[1451] Users input their health data (body temperature, blood pressure, heart rate, etc.) using a dedicated application. The entered data is temporarily stored on a device such as a smartphone or tablet. Input from the device includes measurements obtained from thermometers, blood pressure monitors, heart rate sensors, etc.

[1452] Input: Health data such as body temperature, blood pressure, and heart rate.

[1453] Output: Temporarily stored health data

[1454] Step 2:

[1455] The device periodically sends the entered health data to the server using HTTP POST requests. The transmitted data is in JSON format. Secure HTTPS is used as the communication protocol to ensure data confidentiality and integrity.

[1456] Input: Temporarily stored health data, HTTP POST request

[1457] Output: Health data sent to the server

[1458] Step 3:

[1459] The server analyzes the received health data, converts it to an appropriate format, and then stores it in a database (SQLite) using SQLAlchemy. The stored data is managed in an optimized format for efficient retrieval and processing.

[1460] Input: Received health data, SQLAlchemy

[1461] Output: Health data stored in the database

[1462] Step 4:

[1463] The server periodically retrieves the latest health data from the database using queries. To ensure efficient data acquisition, only the necessary data is extracted.

[1464] Input: Health data stored in the database, queries

[1465] Output: Latest health data

[1466] Step 5:

[1467] The server analyzes the acquired health data using AI algorithms (e.g., TensorFlow or PyTorch) to detect anomalies. It detects abnormal patterns and values ​​from the collected data based on standard values.

[1468] Input: Latest health data, AI algorithm

[1469] Output: Analysis results (including outliers)

[1470] Step 6:

[1471] Based on the analysis results, the server generates appropriate advice and reminders for the user. The generated notifications are sent to a dedicated application in JSON format and displayed on the user's device in real time.

[1472] Input: Analysis results, notification in JSON format

[1473] Output: Advice and reminders displayed to the user.

[1474] Step 7:

[1475] Users provide feedback on the advice and reminders offered. This feedback is sent from the device to the server. Based on this feedback, the server retrains the AI ​​model to improve the accuracy and usefulness of future notifications.

[1476] Input: User feedback

[1477] Output: Adjusted AI model, improved notification accuracy and usefulness.

[1478] Adding specific actions

[1479] Health data entered by the user is collected by sensors, allowing them to understand their own health status.

[1480] When a device transmits health data to a server, encryption technology is used to ensure data integrity.

[1481] When the server processes data, it performs data format verification and filters out abnormal values.

[1482] The server uses AI algorithms for analysis, which is used to detect anomalies and recognize patterns.

[1483] The parameters of the AI ​​model are dynamically adjusted so that improvements in user health can be concretely confirmed through feedback.

[1484] The above is a detailed explanation of each processing step. This system enables efficient management of employee health.

[1485] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1486] The system according to the present invention centrally manages personal information and further recognizes the user's emotions to provide appropriate advice and notifications. Specific embodiments for carrying out the present invention are described below.

[1487] Data Collection Unit

[1488] Users open a dedicated application and enter their personal information, including schedules, health data, and household finances. They can also input emotional information via voice input or text chat. This emotional information is used to recognize the user's everyday emotional state.

[1489] Data transmission unit

[1490] The terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. Sentiment information is also transmitted in the same way.

[1491] Data storage unit

[1492] The server parses the received JSON data and stores it in the database. Schedule information, health data, and household finance information are stored in their respective tables. Sentimental information is also stored in a separate table and used for later analysis.

[1493] Data acquisition unit

[1494] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data.

[1495] Data Analysis Department

[1496] The server uses AI algorithms to analyze the acquired information. Specifically, it evaluates schedule density, detects anomalies in health data, analyzes household income and expenditure balance, and analyzes emotional information. The emotion engine recognizes the user's emotional state and analyzes the results in an integrated manner with other data.

[1497] Information provision department

[1498] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the sentiment engine's analysis. The generated notification is then converted back into JSON format and sent to the device.

[1499] Functions of the Emotion Engine

[1500] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs text such as "I'm depressed," the engine recognizes that emotion as "sad" and generates appropriate advice.

[1501] Feedback loop

[1502] Users provide feedback on the advice and notifications they receive, sending it from their device to the server. This feedback includes ratings and comments. The server analyzes the received feedback and adjusts the AI ​​model. This improves the accuracy and usefulness of future notifications.

[1503] Specific example

[1504] Schedule management and sentiment analysis

[1505] Suppose a user enters "Doctor's appointment Monday 10:00" into the app, and simultaneously enters "Feeling a little depressed." This information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off to rest. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[1506] Health management and emotional analysis

[1507] Let's say a user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to a server. The server collects the health and emotional data and confirms that the emotional state is energetic even if there is a slight increase in body temperature. Based on this, it generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotion-based advice such as, "We have confirmed that you are feeling energetic. Don't overdo it, and enjoy yourself."

[1508] In this way, the system according to the present invention recognizes the user's emotions, integrates and analyzes them with other information, and provides more personalized advice. As a result, the user can manage information efficiently and receive emotional support.

[1509] The following describes the processing flow.

[1510] Step 1:

[1511] The user opens a dedicated application and enters their information. This information includes schedules (e.g., doctor's appointment on Monday at 10:00), health data (e.g., body temperature, weight, exercise level), household finances (e.g., income, expenses), and emotional information (e.g., enter "I'm feeling a little down today" as text).

[1512] Step 2:

[1513] The terminal converts the entered information into JSON format. The converted data includes user ID, time information, schedule, health data, household finance information, and sentiment data.

[1514] Step 3:

[1515] The device sends JSON-formatted data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted during transmission.

[1516] Step 4:

[1517] The server parses the received JSON data, converts it to the appropriate format, and stores each piece of information in the corresponding database table. Schedule information is stored in the "schedules" table, health data in the "health_data" table, and emotion data in the "emotions" table.

[1518] Step 5:

[1519] The server periodically retrieves the latest user information from the database. For example, a scheduled job can be set up to execute SQL queries every morning at 00:00 to retrieve the next week's schedule, the latest health data, and sentiment data.

[1520] Step 6:

[1521] The server uses AI algorithms to analyze the acquired information. It evaluates the density of schedules, detects anomalies in health data, and analyzes the balance of income and expenses in household financial information. In addition, an emotion engine recognizes the user's emotional state and integrates the results with other data for analysis.

[1522] Step 7:

[1523] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. For example, it may include specific advice such as, "We recommend taking Friday off. It would be good to set aside time to relax."

[1524] Step 8:

[1525] The generated notification content is converted back into JSON format and sent to the device. Communication from the server to the device is also performed using a secure protocol (e.g., HTTPS).

[1526] Step 9:

[1527] The device analyzes the notification content received from the server and displays it to the user in an appropriate format. For example, it may be displayed as an in-app pop-up message or a push notification.

[1528] Step 10:

[1529] Users provide feedback on the advice and notifications offered, sending it from their device to the server. This feedback includes ratings (e.g., "helpful" or "unnecessary") and comments.

[1530] Step 11:

[1531] The server analyzes the received feedback and adjusts the AI ​​model. Based on the feedback information, it optimizes the algorithm parameters to improve the accuracy and usefulness of future notifications. This continuous feedback loop improves the overall performance of the system.

[1532] (Example 2)

[1533] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1534] In modern society, individuals must manage a wide range of information. Schedules, health data, and household finances are often managed in fragments, and receiving advice that takes into account an individual's emotional state is even more difficult. Conventional information management systems lack the ability to integrate and centrally manage this diverse data, and to analyze the user's emotional state to provide appropriate advice. There is a need for a system that addresses these challenges and provides more useful information management and support for individuals.

[1535] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1536] In this invention, the server includes means for receiving schedule, health data, household information, and emotional information entered by the user; means for converting the received information into JSON format and transmitting it to the server using a secure communication protocol; means for the server to parse the received JSON data and store it in a database in a corresponding table; means for the server to periodically retrieve the latest user information from the database by executing SQL queries; means for the server to perform data analysis on the retrieved information using an AI algorithm; means for the server to convert the analysis results into a reporting format, generate notification content, convert it back into JSON format and transmit it to the terminal; and means for the terminal to display the received notification content to the user. This makes it possible to comprehensively manage diverse user information and provide appropriate advice.

[1537] A "user" refers to an individual who uses a system to input and manage information.

[1538] "Schedule" refers to information about appointments and events entered by the user.

[1539] "Health data" refers to information about a user's health, such as their weight, body temperature, and exercise level.

[1540] "Household financial information" refers to economic information about a user's income and expenses.

[1541] "Emotional information" refers to information that indicates the user's emotional state, and includes information entered via voice input or text chat.

[1542] "Terminal" refers to a device used by a user to input information (e.g., a smartphone or tablet).

[1543] A "server" refers to a computer system that receives, stores, and analyzes user input information and generates notifications.

[1544] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for structuring and transferring data.

[1545] A "secure communication protocol" is a means of communication that guarantees the safe transfer of data, such as HTTPS.

[1546] A "database" refers to a digital storage system used to store user input information.

[1547] "SQL query" is an abbreviation for Structured Query Language, and refers to a set of commands used to retrieve information from a database.

[1548] "AI algorithms" refer to data analysis methods using artificial intelligence, and specifically to models built with frameworks such as TensorFlow and PyTorch.

[1549] "Notification content" refers to messages containing information and advice that the server provides to the user based on the analysis results.

[1550] "Feedback" refers to the evaluations and comments that users make in response to advice or notifications provided.

[1551] This invention is a system that centrally manages diverse user information and analyzes their emotional state to provide appropriate advice. This system provides comprehensive support to individuals through the coordinated operation of the user, terminal, and server.

[1552] Data Collection Unit

[1553] Users first launch a dedicated application and input personal information such as their schedule, health data, and household finances. This application can be used on devices such as smartphones and tablets. Users can also input their daily emotional state using voice input or text chat. For example, a user might input "Monday 10:00 Doctor's appointment" and simultaneously input their emotional state, such as "Feeling a little depressed."

[1554] Data transmission unit

[1555] The terminal first converts the information collected from the user into JSON format. This information is then sent to the server using a secure communication protocol (e.g., HTTPS). The data sent includes the user ID and timestamp. For example, it is sent as follows:

[1556] {

[1557] "user_id": "12345",

[1558] "timestamp": "2023-10-04T10:00:00Z",

[1559] "schedule": "Monday 10:00 Doctor's appointment",

[1560] "health_data": {

[1561] "weight": "70kg",

[1562] "temperature": "36.5℃"

[1563] },

[1564] "emotion": "I feel a little depressed."

[1565] }

[1566] Data storage unit

[1567] The server parses the received JSON data and stores each piece of information in the database. The database stores schedule information, health data, household finance information, and emotional information in corresponding tables. For example, schedule information is stored in the schedule table, and health data is stored in the health data table.

[1568] Data acquisition unit

[1569] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes SQL queries to retrieve the next week's schedule, the latest health data, and sentiment data. This ensures that the most up-to-date information is always available for analysis.

[1570] Data Analysis Department

[1571] The server uses AI algorithms (e.g., TensorFlow or PyTorch) to analyze the acquired information. Specifically, the following analyses are performed:

[1572] Evaluation of schedule density

[1573] Detection of abnormal values ​​in health data

[1574] Analysis of household income and expenditure balance

[1575] Analysis of emotional information

[1576] For example, the emotion engine analyzes the text "I feel depressed" entered by the user and recognizes that emotion as "sad." It then integrates and analyzes this emotional information with other data.

[1577] Information provision department

[1578] The server converts the analysis results into a reporting format and generates a notification for the user. This notification also reflects the results of the emotion engine's analysis. The generated notification is then converted back into JSON format and sent to the device. The device displays this to the user. For example, a notification such as "Considering your busy schedule and emotional state, we recommend you take a rest on Friday" is generated.

[1579] Feedback loop

[1580] Users provide feedback on the advice and notifications they receive. This feedback includes evaluations and comments on the advice. The device sends this feedback to the server. The server analyzes the received feedback and uses it to refine the AI ​​model. This improves the accuracy and usefulness of future notifications.

[1581] Specific example

[1582] Schedule management and sentiment analysis

[1583] A user enters "Doctor's appointment Monday 10:00" into the application and simultaneously enters emotional information such as "Feeling a little depressed." This information is sent from the terminal to the server and stored in a database. The server analyzes this information and generates a notification, such as "We recommend you take Friday off. It would be good to set aside some time to relax," which is then presented to the user.

[1584] Health management and emotional analysis

[1585] The user inputs their daily body temperature, weight, and exercise level, and simultaneously enters their emotional state as "I feel very energetic today." The device sends this information to the server. The server analyzes the health and emotional data and generates a notification such as, "Your exercise level this week is appropriate. Keep it up." The notification also includes emotionally-based advice such as, "We've confirmed you're feeling great. Don't overdo it, and enjoy yourself."

[1586] In this way, the system of the present invention can comprehensively manage diverse user information and provide personalized advice that also takes into account emotional states.

[1587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1588] Step 1:

[1589] The user launches a dedicated application and enters schedule, health data, and household financial information. This input data specifically includes schedule information such as "Monday 10:00 Doctor's appointment" and health data such as "Weight: 70kg, Body temperature: 36.5℃". Furthermore, emotional information is entered as text, such as "Feeling a little depressed". The input data is temporarily stored by the application.

[1590] Step 2:

[1591] The terminal converts the collected user input data into JSON format. For example, the following JSON data is generated:

[1592] json

[1593] {

[1594] "user_id": "12345",

[1595] "timestamp": "2023-10-04T10:00:00Z",

[1596] "schedule": "Monday 10:00 Doctor's appointment",

[1597] "health_data": {

[1598] "weight": "70kg",

[1599] "temperature": "36.5℃"

[1600] },

[1601] "emotion": "I feel a little depressed."

[1602] }

[1603] This JSON data will be used as input data to be sent from the terminal to the server.

[1604] Step 3:

[1605] The terminal sends the generated JSON data to the server using a secure communication protocol (e.g., HTTPS). If the transmission is successful, the terminal receives a transmission success status as output.

[1606] Step 4:

[1607] The server parses the received JSON data and saves it to the database. For example, the server generates and executes INSERT statements to save schedule information to the "schedule" table, health data to the "health_data" table, and emotion information to the "emotions" table. The output of this process indicates the status of successful data saving.

[1608] Step 5:

[1609] The server periodically (e.g., every morning at 00:00) retrieves the latest user information from the database using SQL queries. An example of an SQL query executed is:

[1610] SQL

[1611] SELECT FROM schedule WHERE date >= CURDATE() AND user_id = '12345';

[1612] SELECT FROM health_data WHERE date >= CURDATE() AND user_id = '12345';

[1613] SELECT FROM emotions WHERE date >= CURDATE() AND user_id = '12345';

[1614] The acquired data will be used as input data for the next analysis step.

[1615] Step 6:

[1616] The server uses AI algorithms to analyze data based on the latest information obtained via SQL queries. This analysis utilizes TensorFlow and PyTorch to evaluate schedule density, detect anomalies in health data, and assess sentiment. For example, health data is input into an anomaly detection model, and if an anomaly is detected, the result is output.

[1617] Step 7:

[1618] The server converts the analysis results into a reporting format and generates a notification for the user. Specifically, if a busy schedule is identified, it creates a notification message such as, "We recommend you take Friday off." This notification message is converted to JSON format and sent to the terminal. The generated notification message is the output.

[1619] Step 8:

[1620] The device displays the received notification content to the user. For example, the notification may appear as a pop-up message, allowing the user to review the recommendation. The output of this step indicates a successful display status.

[1621] Step 9:

[1622] Users provide feedback on the advice and notifications they receive. For example, a user might comment, "This advice was helpful," and rate it. The feedback entered is temporarily stored on the device.

[1623] Step 10:

[1624] The device converts user feedback into JSON format and sends it to the server. The data sent includes:

[1625] json

[1626] {

[1627] "user_id": "12345",

[1628] "feedback": "This advice was helpful",

[1629] Rating: 5

[1630] }

[1631] It is converted as shown. This JSON data becomes the input data and is sent to the server.

[1632] Step 11:

[1633] The server receives feedback and performs analysis. Based on the received feedback, it updates the parameters of the AI ​​model or adds training data. After the analysis is complete, the model is adjusted to improve accuracy in the next analysis. The analysis results are output.

[1634] Through the process described above, this system can integrate and manage diverse user information and provide personalized advice that takes into account their emotional state.

[1635] (Application Example 2)

[1636] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1637] Modern autonomous vehicles lack personalized driving assistance that takes into account the driver's schedule, health, and emotional state. Furthermore, the absence of a system that can immediately respond to changes in the driver's emotions means that stress reduction and safety improvements during driving are not fully achieved. This invention aims to solve these problems.

[1638] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving information entered by the user, means for storing the received information in a database, means for periodically retrieving the stored information, means for performing analysis based on the retrieved information, means for notifying the user of the analysis results, and means for managing the individual's schedule, health, and emotional state in real time and providing advice for reducing stress and improving safety while driving. This makes it possible to personalize driving assistance according to the driver's emotional state and health state, thereby reducing stress and improving safety while driving.

[1639] "Personal schedule" refers to information that manages the user's daily schedule, event times, and dates.

[1640] "Health status" refers to information about the user's physical health, such as data on body temperature, weight, and exercise level.

[1641] "Emotional state" refers to information that indicates the user's mental state and emotional changes in their daily life, and is obtained from text and voice input.

[1642] "Real-time management" refers to constantly acquiring, analyzing, and processing user information instantly.

[1643] "Reducing stress while driving a vehicle" refers to measures taken to alleviate the mental and physical burden felt when driving a vehicle.

[1644] "Safety improvement" refers to measures taken to reduce risks when driving a vehicle, thereby preventing accidents and promoting safe driving.

[1645] "Providing advice" refers to offering helpful suggestions and guidance to users based on the information they have gathered.

[1646] "Feedback" refers to the act of a user responding to advice or notifications provided by a system by offering their opinion or comment.

[1647] A "generative artificial intelligence model" is an algorithm that learns from user input data and feedback to improve the accuracy and usefulness of the system.

[1648] The system according to the present invention manages an individual's schedule, health status, and emotional state in real time, and provides advice to reduce stress and improve safety while driving a vehicle. Specific embodiments for carrying out the present invention are described below.

[1649] First, users input their information using a dedicated application. This application is installed on smartphones or the vehicle's infotainment system. Users can input their schedule, health data, and emotional information. The schedule includes dates and times, while health data includes body temperature, weight, and activity levels. Emotional information is recorded via text or voice input and used to recognize the user's daily emotional state.

[1650] Next, the terminal converts the user's input into JSON format and sends it to the server using a secure communication protocol. The transmitted data includes the user ID and timestamp. The server receives this information and stores it in a database. The stored information is managed in separate tables for schedule data, health data, and sentiment data.

[1651] The server periodically retrieves the latest user information from the database and performs data analysis using an AI algorithm. It evaluates schedule density, abnormal values ​​in health data, and changes in emotional information. The analysis results are converted into a reporting format and notified to the user. This notification also reflects the results of the emotion engine's analysis. The generated notification content is converted back into JSON format and sent to the device.

[1652] The emotion engine is a model for recognizing a user's emotional state from voice or text. For example, if a user inputs "I'm feeling a little down," it recognizes that emotion as "sad" and generates appropriate advice.

[1653] For example, if a user enters "Monday 10:00 Doctor's appointment" and simultaneously enters "Feeling a little depressed" into the application, this information is sent from the device to the server and stored in the database. The server analyzes the information, taking into account the busy schedule and emotional state, and generates a notification saying, "We recommend taking Friday off. It would be good to set aside some time to relax," and sends it to the device. The device then displays this notification to the user.

[1654] The hardware used includes smartphones, vehicle infotainment systems, and servers. The software includes dedicated applications, secure communication protocols, database management systems, AI algorithms, and an emotion engine.

[1655] Examples of prompts to input into the generating AI model include: "User input: 'Monday 10:00 Doctor's appointment' 'Feeling a little depressed'" and "Example prompt for generating model output: 'The user's emotional state is low. Please suggest a rest period.'"

[1656] This allows users to receive individually optimized driving assistance while driving, resulting in reduced stress and improved safety.

[1657] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1658] Step 1:

[1659] The user opens the application and enters their schedule, health data, and emotional information. The entered information includes specific appointment times and details (e.g., "Monday 10:00 Doctor's appointment"), body temperature and weight data (e.g., "Body temperature 36.5 degrees, weight 70 kg"), and text describing their emotional state (e.g., "Feeling a little depressed").

[1660] Step 2:

[1661] The terminal converts the information entered by the user into JSON format. The user ID and timestamp are also added during this process. The JSON data is sent to the server using a secure communication protocol (e.g., HTTPS). It receives user data as input and generates JSON formatted data as output.

[1662] Step 3:

[1663] The server parses the JSON data received from the terminal and stores it in the database. The database is structured, with schedule data, health data, and sentiment data stored in separate tables. It takes JSON data as input and stores the information in each table as output.

[1664] Step 4:

[1665] The server periodically retrieves the latest user information from the database. For example, every morning at 00:00, it executes an SQL query to retrieve the next week's schedule, the latest health data, and sentiment data. It inputs an SQL query as a query to the database and outputs the latest user information.

[1666] Step 5:

[1667] The server's AI algorithm performs data analysis based on the acquired information. This includes evaluating schedule density, detecting anomalies in health data, and analyzing emotional information. The emotion engine recognizes emotional states from text data and generates appropriate advice based on that. Specifically, it recognizes the text "I'm a little depressed" as "sad" and creates corresponding advice. It takes user information as input and obtains analysis results as output.

[1668] Step 6:

[1669] The server converts the analysis results into a reporting format and generates notification content. The generated notification content is then converted back into JSON format and sent to the terminal. It receives analysis results as input, generates notification content as output, and converts it into JSON format.

[1670] Step 7:

[1671] The device displays the notification content received from the server to the user. For example, a generated notification message such as "We recommend taking Friday off. It would be good to set aside time to relax" is displayed on the smartphone or vehicle's infotainment system. It receives notification content in JSON format as input and displays it to the user as output.

[1672] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1673] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1674] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1675] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1676] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1677] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1678] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1679] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1680] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1681] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1682] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1683] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1684] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1685] 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.

[1686] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1687] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1688] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1689] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1690] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1691] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1692] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1693] The following is further disclosed regarding the embodiments described above.

[1694] (Claim 1)

[1695] It is a system for centrally managing personal information.

[1696] A means of receiving information entered by the user,

[1697] A means of saving the received information to a database,

[1698] A means of periodically retrieving stored information,

[1699] A means of performing analysis based on the acquired information,

[1700] A system that includes means for notifying the user of the analysis results.

[1701] (Claim 2)

[1702] The system according to claim 1, which provides appropriate advice to the user based on the analysis results.

[1703] (Claim 3)

[1704] The system according to claim 1, which receives user feedback and adjusts the AI ​​model.

[1705] "Example 1"

[1706] (Claim 1)

[1707] It is a system for centrally managing personal information.

[1708] A means of receiving information entered by the user,

[1709] A means of temporarily storing the received information,

[1710] A means of periodically sending the stored information to the server,

[1711] A means of storing information sent to the server in a database,

[1712] A means of periodically retrieving information from a database,

[1713] Methods for analyzing acquired information using AI algorithms,

[1714] A means of notifying users of the analysis results as appropriate advice or reminders,

[1715] A system that includes means for receiving user feedback and adjusting the AI ​​model.

[1716] (Claim 2)

[1717] The system according to claim 1, which displays the generated notification as a pop-up or an in-app message.

[1718] (Claim 3)

[1719] The system according to claim 1, which analyzes health data and household financial information to provide advice to improve the user's quality of life.

[1720] "Application Example 1"

[1721] (Claim 1)

[1722] It is a system for centrally managing personal information.

[1723] A means of receiving information entered by the user,

[1724] A means of saving the received information to a database,

[1725] A means of periodically retrieving stored information,

[1726] A means of performing analysis based on the acquired information,

[1727] A means of notifying the user of the analysis results,

[1728] A means having a device for collecting workers' health data,

[1729] A means of transmitting the collected health data to a server,

[1730] Methods for using AI algorithms to detect anomalies,

[1731] A means of monitoring and advising on the health status of employees in real time,

[1732] A system that includes this.

[1733] (Claim 2)

[1734] The system according to claim 1, which provides appropriate advice to the user based on the analysis results.

[1735] (Claim 3)

[1736] The system according to claim 1, which receives user feedback and adjusts the AI ​​model.

[1737] "Example 2 of combining an emotion engine"

[1738] (Claim 1)

[1739] A means of receiving schedules, health data, household financial information, and emotional information entered by the user,

[1740] A means of converting received information into JSON format and sending it to the server using a secure communication protocol,

[1741] A means of parsing the JSON data received by the server and saving it to the corresponding table in the database,

[1742] A means for the server to periodically retrieve the latest user information from the database by executing SQL queries,

[1743] A means of performing data analysis using AI algorithms on information acquired by a server,

[1744] A means by which the server converts the analysis results into a reporting format, generates notification content, converts it back into JSON format, and sends it to the terminal,

[1745] A system that includes means for displaying the content of notifications received by a terminal to the user.

[1746] (Claim 2)

[1747] The system according to claim 1, which provides appropriate advice to the user based on the analysis results.

[1748] (Claim 3)

[1749] The system according to claim 1, which receives user feedback and adjusts the AI ​​model.

[1750] "Application example 2 when combining with an emotional engine"

[1751] (Claim 1)

[1752] A means of receiving information entered by the user,

[1753] A means of saving the received information to a database,

[1754] A means of periodically retrieving stored information,

[1755] A means of performing analysis based on the acquired information,

[1756] A means of notifying the user of the analysis results,

[1757] A system that manages an individual's schedule, health, and emotional state in real time, and includes means to provide advice for reducing stress and improving safety while driving a vehicle.

[1758] (Claim 2)

[1759] The system according to claim 1, which provides appropriate advice to the user based on the analysis results.

[1760] (Claim 3)

[1761] The system according to claim 1, which receives user feedback and adjusts the generative artificial intelligence model. [Explanation of Symbols]

[1762] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. It is a system for centrally managing personal information. A means of receiving information entered by the user, A means of saving the received information to a database, A means of periodically retrieving stored information, A means of performing analysis based on the acquired information, A system that includes means for notifying the user of the analysis results.

2. The system according to claim 1, which provides appropriate advice to the user based on the analysis results.

3. The system according to claim 1, which receives user feedback and adjusts the AI ​​model.

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