system

Abstract

We provide the system. [Solution] A means for the user to answer mental health check questions using a terminal, A means for the server to receive and store user responses, The server analyzes the stored response data and provides a means to evaluate the mental state. A means by which the server sends feedback to the terminal based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, mental health problems are becoming increasingly important. However, with conventional methods, it has been difficult for users to continuously monitor their own mental state and detect problems early. In particular, it is difficult for general users without specialized knowledge to evaluate their own mental health and they need to receive support from experts, but such support usually has problems of high cost and limited access.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system in which a user answers mental check questions using a terminal, and a server analyzes the answer data to evaluate the user's mental state. Specifically, the system includes means for the server to receive, store, and analyze the answer data entered by the user. Furthermore, the server includes means for sending feedback to the terminal based on the analysis results. In addition, the server includes means for authenticating the user's login information and means for analyzing trends in the user's mental state using past answer data, thereby providing a system that enables continuous monitoring of the user's mental health and early detection of problems.

[0006] A "user" refers to a person who uses the system to answer mental health check questions.

[0007] "Terminal" refers to an electronic device used by a user, such as a personal computer, smartphone, or tablet.

[0008] A "server" refers to a computer system that receives, stores, and analyzes user responses and provides necessary feedback.

[0009] "Mental check" refers to questions or inquiries used to assess a user's mental state.

[0010] "Answer" refers to the response that a user provides to the questions in a mental health check.

[0011] "Analysis" refers to the process by which the server evaluates user response data collected and determines the user's mental state.

[0012] "Feedback" refers to the information and advice that the server provides to the user based on its analysis results.

[0013] "Authentication" refers to the process by which a server verifies a user's login information and confirms that the user is a legitimate user.

[0014] "Trend" refers to analyzing the results of a user's past mental checks to identify fluctuations and patterns over time.

[0015] "Mental state" refers to the mental health state of the user, including stress and mood states, etc.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory where information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for monitoring a user's mental state. The system aims to provide feedback by having the user answer a mental check, and then having the server analyze the response data. This system consists of three main elements: the user, the terminal, and the server.

[0038] User actions

[0039] First, the user logs into the system using their own device. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user logs in successfully, mental health check questions will be displayed on the device.

[0040] Terminal processing

[0041] The device receives input from the user, encrypts the information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0042] Server Processing

[0043] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0044] The analysis process uses techniques such as natural language processing to text-mine user responses and calculate sentiment scores. This quantifies the level of stress and anxiety a user is experiencing. The server also analyzes trends in the user's mental state using past response data. This allows the system to understand whether the user's condition is improving or worsening.

[0045] Ultimately, the server generates appropriate feedback for the user based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional," is generated and sent to the device. The device receives this feedback and displays it to the user.

[0046] Specific example

[0047] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed. The analysis results indicate that the user is feeling down, and a message is sent to the user's terminal as feedback: "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0048] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[0052] Step 2:

[0053] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[0054] Step 3:

[0055] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[0056] Step 4:

[0057] The terminal receives a login success message and displays mental health check questions on the user's screen.

[0058] Step 5:

[0059] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[0060] Step 6:

[0061] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[0062] Step 7:

[0063] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine.

[0064] Step 8:

[0065] The server uses an analysis engine to analyze the response data. Specifically, it uses text mining and natural language processing to calculate sentiment scores and evaluate the user's mental state.

[0066] Step 9:

[0067] Based on the analysis results, the server analyzes trends in the user's mental state by comparing them with the user's past response data.

[0068] Step 10:

[0069] The server generates an appropriate feedback message based on the analysis results. Example: "Your stress level appears high. We recommend taking some time to relax or consulting a professional."

[0070] Step 11:

[0071] The server sends the generated feedback message to the terminal.

[0072] Step 12:

[0073] The device receives feedback messages and displays them to the user. The user reviews the displayed feedback and takes the necessary actions.

[0074] Through this series of steps, the system monitors the user's mental state and supports early problem detection and appropriate countermeasures.

[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] In modern society, mental health problems are on the rise, and early detection and appropriate treatment are crucial. However, conventional mental health check systems have struggled to monitor users' mental states in real time and provide appropriate feedback. Furthermore, they lacked the functionality to analyze trends in mental state using users' past data. As a result, the effectiveness of mental healthcare was limited, and useful support could not be provided to users.

[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 authenticating the user by matching their login information with a database, means for passing the stored response data to an analysis engine and evaluating the user's mental state using natural language processing technology, and means for sending feedback to the terminal based on the analysis results. This allows the user to check their mental state in real time and receive appropriate feedback, enabling early detection and response to mental health problems.

[0080] A "terminal" is a device that a user uses to access a system and perform input and reception. Specific examples include personal computers and smartphones.

[0081] "Authentication information" refers to the information a user needs to log in to a system, and typically consists of a user ID and password.

[0082] A "server" is a central computing system that receives, stores, and analyzes user authentication information and response data, generates feedback, and sends it to the terminal.

[0083] A "database" is a place where a server stores user login information and response data, and uses that information for later verification and analysis.

[0084] "Authentication" is the process by which a server verifies a user's login information to confirm that the user is a legitimate user.

[0085] "Encryption" is a technology that transforms data so that a device can securely transmit user response data to a server, preventing unauthorized access by third parties.

[0086] "Natural language processing technology" refers to techniques that allow servers to analyze user response data and perform text mining and sentiment analysis. Specific examples include technologies such as NLTK and spaCy.

[0087] "Feedback" refers to messages generated by the server based on analysis results, offering advice and suggestions for the next steps to the user.

[0088] "Trend analysis" is a method used by servers to evaluate changes and trends in users' mental states by utilizing past response data.

[0089] This invention is a system for monitoring a user's mental state. The system involves the user answering a mental check, and a server analyzing the response data to provide feedback. This system consists of three main elements: the user, the terminal, and the server.

[0090] User actions

[0091] First, the user logs into the system using their own device (computer or smartphone). The login screen displays input fields for user ID and password, which the user enters and clicks the "Login" button. If the login is successful, the device displays mental health check questions to the user. For example, a question such as "Have you been feeling down a lot lately?" may be displayed.

[0092] Terminal processing

[0093] The device receives user response data, encrypts it, and sends it to the server. AES (Advanced Encryption Standard) is used for encryption, and communication takes place via the HTTPS protocol. For example, if the user responds "yes," that data is encrypted and securely sent to the server. The device also receives feedback messages from the server in real time and displays them to the user.

[0094] Server Processing

[0095] The server first authenticates the user by matching their login information against a database (e.g., MySQL® or PostgreSQL). If authentication is successful, it then receives the mental check response data submitted by the user and stores it in the database. This data is passed to an analysis engine, which uses natural language processing techniques (e.g., NLTK or spaCy) to perform text mining and calculate a sentiment score. Based on this score, the server evaluates the user's mental state. Furthermore, the server uses past response data to analyze trends in the user's mental state. This trend analysis allows the server to determine whether the user's condition is improving or worsening.

[0096] Feedback generation

[0097] The server generates a feedback message based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend taking some time to relax or consulting a professional," is generated and sent to the terminal. The terminal receives this message and displays it to the user in real time.

[0098] Specific example

[0099] When a user logs into the system using their home computer and answers "yes" to the question, "I've been feeling down a lot lately," this response data is encrypted and sent to the server. The server analyzes this data, assesses that the user is feeling down, and generates and sends a feedback message to the user's device stating, "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0100] Example of a prompt

[0101] "Please describe the detailed process by which a user logs into the system, performs a mental health check, and the results are analyzed on the server to generate feedback."

[0102] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0103] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0104] Step 1:

[0105] The user uses a device to enter authentication information and log in to the system.

[0106] Input: User ID, Password

[0107] Specific steps: The user enters their user ID and password on their computer or smartphone screen and clicks the "Login" button.

[0108] Output: Sending authentication information from the terminal to the server

[0109] Step 2:

[0110] The server authenticates the user by comparing their login information with the database.

[0111] Input: User ID and password sent from the device.

[0112] Specific operation: The server compares the received user ID and password with the database to check if they match. If authentication is successful, the user can log in to the system.

[0113] Output: Authentication result (success or failure) is sent back to the terminal.

[0114] Step 3:

[0115] The user uses a device to answer questions for a mental health check.

[0116] Input: Question displayed after logging in

[0117] Specific operation: The user selects an answer from the given options for each mental health check question displayed on the device and enters the answer.

[0118] Output: User response data

[0119] Step 4:

[0120] The device encrypts the user's response data and sends it to the server.

[0121] Input: User response data

[0122] Specific operation: The device encrypts the response data using AES (Advanced Encryption Standard) and sends it to the server using the HTTPS protocol.

[0123] Output: Encrypted response data sent to the server

[0124] Step 5:

[0125] The server receives the user's response data and saves it to the database.

[0126] Input: Encrypted response data sent from the device

[0127] Specific operation: The server receives encrypted data and decrypts it using AES. The decrypted data is then saved to the database.

[0128] Output: Saving response data to the database

[0129] Step 6:

[0130] The server passes the stored response data to the analysis engine, which then uses natural language processing technology to evaluate the mental state.

[0131] Input: Saved response data

[0132] Specific operation: The server passes the stored response data to an analysis engine (e.g., NLTK or spaCy) for text mining and sentiment analysis. This quantifies the user's stress level, anxiety index, and other metrics.

[0133] Output: Analysis results (emotion score, evaluation results)

[0134] Step 7:

[0135] The server generates feedback based on the analysis results and sends it to the terminal.

[0136] Input: Analysis results (emotion score, evaluation results)

[0137] Specific operation: The server generates a feedback message based on the analysis results (e.g., "Your stress level appears high. We recommend you take some time to relax or consult a professional"). The generated feedback is then sent to the terminal.

[0138] Output: Feedback message

[0139] Step 8:

[0140] The device displays feedback messages sent from the server in real time.

[0141] Input: Feedback message sent from the server

[0142] Specific operation: The terminal receives feedback messages from the server and displays them to the user in real time.

[0143] Output: Display of feedback message to the user

[0144] (Application Example 1)

[0145] 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."

[0146] In modern factories, workers are often exposed to harsh working conditions and high levels of stress, which contribute to decreased productivity and health problems. Traditional mental health monitoring systems rely solely on self-reporting by individual workers, making real-time monitoring and immediate feedback difficult. As a result, there is a challenge in that serious mental health problems often go undetected for a long time.

[0147] 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.

[0148] In this invention, the server includes means for a user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the mental state, and means for a supervisory robot to collect mental check data of workers in the factory and transmit it to the server. This makes it possible to monitor the mental state of workers in the factory in real time and provide rapid feedback.

[0149] A "user" is a person who logs into the system and answers the mental health check questions.

[0150] A "terminal" is a device used by users to answer mental health check questions and is responsible for transmitting data to the server.

[0151] A "server" is a central processing unit that receives, analyzes, and generates feedback data from users' mental health check responses, then sends it to the terminal.

[0152] A "mental check" is a series of questions that users answer to assess their own mental state.

[0153] "Answers" refer to the data that users enter in response to questions in a mental health check.

[0154] "Saving" refers to the act of a server accumulating user response data that it has received.

[0155] "Analysis" is the process by which a server processes stored response data and evaluates the user's mental state.

[0156] "Feedback" refers to advice and notifications that the server generates and provides to the user based on its analysis results.

[0157] A "supervising robot" is a robot whose role is to collect mental health check data from workers within a factory and send it to a server.

[0158] A "worker" is a person who works in a factory and is subject to mental health checks.

[0159] This invention is a system for monitoring the mental state of factory workers in real time and providing rapid feedback. The system consists of four main components: users, terminals, a server, and a supervisory robot.

[0160] User actions

[0161] First, the user (worker) logs into the system using a terminal within the factory. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user successfully logs in, mental health check questions will be displayed on the terminal. For example, the user will answer questions such as, "Have you been feeling stressed lately?"

[0162] Terminal processing

[0163] The device receives input from the user, encrypts the information, and sends it to the server. For example, if the user answers "yes," that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0164] Server Processing

[0165] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0166] The analysis process uses natural language processing techniques to text-mine user responses and calculate a sentiment score. This quantifies the level of stress and anxiety the user is experiencing. The server also analyzes trends in mental state using past response data, allowing it to determine whether the user's condition is improving or worsening. Finally, based on the analysis results, the server generates appropriate feedback for the user and sends it to their device.

[0167] Functions of the supervisor robot

[0168] Supervising robots are stationed throughout the factory. These robots are responsible for collecting mental health check data from workers and sending it to a server. The supervisory robots patrol the factory, presenting workers with mental health check questions and collecting their responses.

[0169] Hardware and software used

[0170] The following hardware and software are used to implement the system:

[0171] User's device: A smartphone or dedicated device used by the worker.

[0172] Server: A central processing unit containing the database and analysis engine (e.g., MySQL database and TENSORFLOW® analysis engine).

[0173] Supervising robot: A robot that collects mental health check data from workers and sends it to a server.

[0174] Specific example

[0175] For example, if a worker answers "yes" to the question, "I've been feeling down a lot lately," this response is sent from the terminal to the server, and the analysis results indicate that the worker is "feeling down." The server then generates a feedback message, "If this continues, please consult a professional," and sends it to the terminal. The worker can then review this message and take appropriate action.

[0176] Examples of prompts for generative AI models

[0177] Please analyze the following mental check data and generate an appropriate feedback message:

[0178] High stress levels

[0179] I've been feeling down lately.

[0180] By accurately understanding the mental state of workers throughout the entire system and providing prompt feedback, improvements in the work environment and increased productivity can be expected.

[0181] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0182] Step 1:

[0183] The user logs into the system using their device.

[0184] Enter: ID and password

[0185] Output: Authentication result (success or failure)

[0186] Specific operation: The user enters their ID and password on the login screen and clicks the "Login" button. The device sends this information to the server. The server verifies the information against the database, performs authentication, and sends the authentication result to the device.

[0187] Step 2:

[0188] The user answers questions for a mental health check.

[0189] Input: Mental health check questions and user responses

[0190] Output: Encrypted response data

[0191] Specific operation: Mental health check questions are displayed on the user's device. The user answers each question, and the answers are encrypted on the device.

[0192] Step 3:

[0193] The device sends encrypted response data to the server.

[0194] Input: Encrypted response data

[0195] Output: Response data stored on the server

[0196] Specific operation: The device sends encrypted response data to the server. The server receives this data and stores it securely.

[0197] Step 4:

[0198] The server analyzes the response data it receives using an analysis engine.

[0199] Input: Saved response data

[0200] Output: Analysis results (evaluation of mental state)

[0201] Specific operation: The server passes the received response data to the analysis engine, which uses natural language processing technology to calculate an emotion score. This process quantifies how much stress or anxiety the user is experiencing.

[0202] Step 5:

[0203] The server generates a feedback message based on the analysis results.

[0204] Input: Analysis results

[0205] Output: Feedback message

[0206] Specific operation: Based on the analysis results, the server generates an appropriate feedback message. For example, it might create a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional."

[0207] Step 6:

[0208] A supervisory robot collects mental health check data from factory workers and sends it to a server.

[0209] Input: Responses to the worker's mental health check

[0210] Output: Check data sent to the server

[0211] Specific operation: The supervisory robot patrols the factory and presents workers with mental health check questions. It collects the workers' responses and sends the data to a server. The server receives this data and proceeds with analysis.

[0212] Step 7:

[0213] A feedback message is sent to the device and displayed to the user.

[0214] Input: Feedback message

[0215] Output: Notification to the user

[0216] Specific operation: The server generates a feedback message and sends it to the terminal. The terminal receives this message and displays it to the user in real time. The user can then review the message and take appropriate action.

[0217] 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.

[0218] This invention provides a system for monitoring a user's mental state and providing feedback, incorporating an emotion engine that recognizes the user's emotions to achieve more accurate and data-driven mental care. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0219] User actions

[0220] Users log in to the system using their own devices. The login screen provides input fields for ID and password, and users enter this authentication information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[0221] Terminal processing

[0222] The device receives input data from the user, encrypts that information, and sends it to the server. If the user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also displays feedback messages from the server to the user in real time.

[0223] Server Processing

[0224] The server first authenticates the user by comparing their login information with the database. If authentication is successful, the server receives and stores the user's response data. The stored data is then passed to the analysis engine and the sentiment engine.

[0225] Emotional Engine Processing

[0226] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score quantifies whether the user's response is positive, negative, or neutral. For example, the emotion engine would rate the response "I've been feeling down a lot lately" as "negative."

[0227] The emotion engine further uses this emotion score to analyze the user's emotional trends by comparing it with past response data. This allows it to understand whether the user's mental state is improving or worsening.

[0228] Server Feedback Generation

[0229] The server generates appropriate feedback for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, a message such as, "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional," is automatically generated. The server then sends this feedback message to the device.

[0230] Device feedback display

[0231] The device receives feedback messages and displays them to the user in real time. The user can review this feedback and take the necessary actions.

[0232] Specific example

[0233] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This answer is sent from the device to the server, where it is analyzed and an emotion engine calculates an emotion score. The analysis results in an assessment that the user is "feeling down," and the emotion trend concludes that "negative emotions have increased over the past month." As feedback, a message is sent to the user's device stating, "You seem to have been feeling down lately. We recommend you consult a professional." The user can then review this message and take appropriate action.

[0234] Thus, the system of the present invention allows users to easily monitor their own mental state, and by using an emotion engine, it is possible to provide more advanced and accurate feedback.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[0238] Step 2:

[0239] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[0240] Step 3:

[0241] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[0242] Step 4:

[0243] The terminal receives a login success message and displays mental health check questions on the user's screen.

[0244] Step 5:

[0245] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[0246] Step 6:

[0247] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[0248] Step 7:

[0249] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine and the sentiment engine.

[0250] Step 8:

[0251] The sentiment engine uses natural language processing to analyze the user's response and generate a sentiment score. This score quantifies whether the response is positive, negative, or neutral.

[0252] Step 9:

[0253] By combining data from the analysis engine and the emotion engine, the server comprehensively evaluates the user's mental state.

[0254] Step 10:

[0255] The server analyzes sentiment trends by comparing the analysis results and sentiment scores with the user's past response data.

[0256] Step 11:

[0257] The server generates appropriate feedback messages based on analysis results and sentiment trends. Example: "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional."

[0258] Step 12:

[0259] The server sends the generated feedback message to the terminal.

[0260] Step 13:

[0261] The device receives the feedback message and displays it to the user. The user reviews the feedback and takes the necessary action.

[0262] (Example 2)

[0263] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0264] Traditional mental healthcare systems have struggled to accurately recognize users' emotions and provide appropriate feedback. Furthermore, they lacked the ability to track trends in users' mental states, making long-term mental healthcare for individual users difficult.

[0265] 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.

[0266] In this invention, the server includes means for receiving and securely storing user responses, means for analyzing the stored response data using a natural language processing engine to generate an emotion score and evaluate the user's mental state, and means for generating feedback based on the evaluation results and transmitting it to a communication device. This makes it possible to accurately recognize the user's mental state and provide individualized feedback, and further enables comprehensive mental care through analysis of long-term trends.

[0267] "Communication equipment" refers to devices used by users to access the system and answer questions in a mental health check, such as personal computers and smartphones.

[0268] A "server" is a central system that receives data sent from users, analyzes it, generates feedback, and sends it back.

[0269] A "natural language processing engine" is an artificial intelligence technology used to analyze user response data and generate sentiment scores, and includes models such as BERT and GPT-3(registered trademark).

[0270] The "emotion score" is a numerical representation of whether a user's response is positive, negative, or neutral.

[0271] "Feedback" refers to advice and recommendations provided to the user based on the evaluation results generated by the server.

[0272] "Emotional trends" refer to long-term patterns of mental state obtained by analyzing changes in users' emotions based on past response data.

[0273] "Login information" refers to authentication data that a user uses to access the system, and includes, for example, an ID and password.

[0274] "Analysis results" refer to the results of data analysis obtained by the natural language processing engine, and specifically include information such as sentiment scores and sentiment trends.

[0275] "Saving" refers to securely recording received data in a database so that it can be analyzed and referenced later.

[0276] Modes for carrying out the invention

[0277] This invention relates to a system that monitors a user's mental state and provides appropriate feedback. This system consists of four main elements: the user, the terminal, the server, and the natural language processing engine. Each of these elements is described in detail below.

[0278] User actions

[0279] First, users access the system using a communication device (such as a personal computer or smartphone). The login screen displays fields for entering an ID and password, and users log in by entering this authentication information. Upon successful authentication, mental health check questions are displayed.

[0280] Terminal processing

[0281] The device receives response data from the user. This data is encrypted using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). The device also displays feedback messages sent from the server to the user in real time.

[0282] Server Processing

[0283] The server first verifies the user's login information against a database (e.g., MySQL) for authentication. If the authentication is successful, it decrypts the encrypted user response data and saves it in the database. The saved data is then passed to an analysis engine and a natural language processing engine.

[0284] Processing by the natural language processing engine

[0285] The natural language processing engine analyzes the user's response using models such as BERT or GPT-3. An emotion score is generated from this analysis result. For example, for a response like "I've been feeling down lately," the natural language processing engine evaluates it as "negative" and assigns a corresponding emotion score.

[0286] Server feedback generation

[0287] The server receives the emotion score and analysis result obtained from the natural language processing engine, analyzes the user's emotion trend by comparing it with past data. Based on this result, the server generates appropriate feedback to provide to the user. For example, a message like "From your recent responses, it seems you have a high stress level. I recommend taking some time to relax or consulting a professional." is generated.

[0288] Terminal feedback display

[0289] The server re-encrypts the generated feedback message and sends it to the terminal. The terminal decrypts it and displays it to the user in real time. The user can view this feedback and take necessary actions.

[0290] Specific example

[0291] Consider a scenario where a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed and a sentiment score is calculated using a natural language processing engine. The analysis results in an assessment of "feeling down," and the sentiment trend is concluded to be "negative emotions have increased over the past month." A feedback message is sent to the user's terminal stating, "You seem to have been feeling down lately. We recommend consulting a professional." The user can then review this message and take appropriate action.

[0292] Examples of prompt statements include the following:

[0293] "Have you been feeling down lately? Yes No"

[0294] In this way, the system of the present invention can accurately monitor the user's mental state and provide individualized feedback.

[0295] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0296] Step 1:

[0297] Users access the system using communication devices and log in by entering their ID and password. The input includes the user's "ID" and "password." The system receives this authentication information and sends a login request to the server. At this time, the terminal sends the user's input data to the server. The system returns information indicating whether the login was successful or unsuccessful.

[0298] Step 2:

[0299] The server verifies the received login information in the database for authentication. Query the user's "ID" and "password" in the database (e.g., MySQL) and output the verification result. If the authentication is successful, the server generates a login success status and sends it to the terminal. The output is the "login success or failure status".

[0300] Step 3:

[0301] When the user successfully logs in, a mental check question is displayed on the terminal. For example, "Do you often feel depressed recently?". The user answers this question with "Yes" or "No". The input at this time is the user's answer ("Yes", "No").

[0302] Step 4:

[0303] The terminal encrypts the user's input data using the AES encryption technology and sends the encrypted data to the server using the HTTPS protocol. The input is the "user's answer", and the output is the "encrypted user's answer". Specifically, the encrypted "Yes" or "No" is sent to the server.

[0304] Step 5:

[0305] The server receives the encrypted user's answer and decrypts it. The input is the "encrypted answer", and the output is the "decrypted user's answer". After decrypting, the server saves the answer data in the database.

[0306] Step 6:

[0307] The server passes the saved data to a natural language processing engine (e.g., BERT or GPT-3) for analysis. The input is the "user's answer data", and the output is the "sentiment score". Specifically, if the answer data is "I often feel depressed recently", the natural language processing engine generates a sentiment score indicating "negative".

[0308] Step 7:

[0309] The server analyzes the user's emotional trends by comparing the generated emotional score with past data. The inputs are the "emotional score" and "past response data," and the output is the "emotional trend analysis result." Specifically, the emotional trend is assessed as "negative emotions have increased over the past month."

[0310] Step 8:

[0311] The server generates appropriate feedback messages for the user based on the results of the sentiment trend analysis. The input is the "sentiment trend analysis results," and the output is the "feedback message." An example message might be: "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional."

[0312] Step 9:

[0313] The server re-encrypts the generated feedback message and sends it to the terminal using the HTTPS protocol. The input is the "feedback message," and the output is the "encrypted feedback message."

[0314] Step 10:

[0315] The terminal receives encrypted feedback messages sent from the server, decrypts them, and displays them to the user in real time. The user reviews the displayed feedback and takes the necessary actions. The input is the "encrypted feedback message," and the output is the "feedback message displayed to the user."

[0316] (Application Example 2)

[0317] 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".

[0318] Traditional mental healthcare systems primarily focused on providing appropriate feedback based on the user's emotional state, lacking specific action suggestions tailored to the user's mental condition and integration with external services. As a result, users struggled to find concrete coping mechanisms suited to their emotions, leading to limited effectiveness of mental healthcare. In particular, there was a lack of feedback directly relevant to daily life, such as suggestions for foods and drinks to reduce stress and anxiety.

[0319] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0320] In this invention, the server includes means for the user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the user's mental state, means for the server to send feedback to the terminal based on the analysis results, and means for suggesting appropriate meals based on the user's mental state. This allows the user to receive specific meal suggestions that can help improve their mental state, thereby achieving more effective mental care.

[0321] A "user" refers to an individual who uses this system to perform a mental health check.

[0322] "Device" refers to the device that a user uses to answer questions in a mental health check. Examples include smartphones, tablets, and computers.

[0323] A "server" refers to a computer system used to receive, store, analyze, and generate feedback from users.

[0324] A "mental check" refers to a set of questions used to assess a user's emotions and stress levels.

[0325] "User responses" refer to the user's responses to the mental health check questions.

[0326] "Means of evaluation" refers to the algorithms and technologies used by the server to analyze the user's responses and evaluate their mental state.

[0327] "Feedback" refers to recommendations and advice that a server generates based on the user's mental state.

[0328] "Methods for suggesting meals" refers to algorithms and technologies used by servers to recommend the most suitable food and drink based on the user's mental state.

[0329] "Analysis results" refer to the evaluation results obtained by the server analyzing the user's response data.

[0330] "Data" refers to information that is stored and analyzed, including user responses and evaluations of their mental state.

[0331] An "emotion engine" refers to a program or system that uses technologies such as natural language processing to analyze user responses and generate an emotion score.

[0332] "Sentiment score" refers to a numerical evaluation value that quantifies whether a user's response is positive, negative, or neutral.

[0333] "Trend" refers to the tendency that indicates fluctuations in a user's mental state based on past data.

[0334] System Overview

[0335] This system allows users to perform mental health checks using a smartphone or other device, and the server receives, analyzes, and provides feedback. Furthermore, it suggests appropriate meals based on the user's mental state. This enables users to receive specific meal suggestions that can help improve their mental state, resulting in more effective mental care.

[0336] Hardware and software to be used

[0337] Hardware: Devices such as smartphones, tablets, and computers.

[0338] software:

[0339] Python: a programming language

[0340] Requests: HTTP Request Sending Library

[0341] EmotionEngine: Emotion Analysis Engine

[0342] Detailed processing of the system

[0343] 1. User actions:

[0344] Users log in to the system using their smartphones or tablets. The login screen has input fields for ID and password, and users enter this information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[0345] 2. Terminal processing:

[0346] The device receives input data from the user (responses to a mental health check), encrypts that information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that response data is encrypted and sent to the server. The device also displays feedback messages and meal suggestions sent from the server to the user in real time.

[0347] 3. Server processing:

[0348] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server receives and saves the user's response data. The saved data is then passed to the analysis engine and the emotion engine, which evaluate the user's mental state and generate an emotion score.

[0349] 4. Emotional engine processing:

[0350] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score is a numerical evaluation value that indicates whether the user's response is positive, negative, or neutral. The emotion engine also uses this emotion score to compare it with past response data and analyze the user's emotional trends. This allows for understanding fluctuations in the user's mental state.

[0351] 5. Server feedback and meal suggestion generation:

[0352] The server automatically generates appropriate feedback and meal suggestions for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, it might generate a message such as, "Your recent responses indicate a high stress level. We recommend a relaxing herbal tea or a healthy soup." The server then sends this feedback message and meal suggestion to the user's device.

[0353] 6. Displaying feedback on the device:

[0354] The device displays feedback messages and meal suggestions received from the server to the user in real time. The user can review this feedback and take appropriate action.

[0355] Specific example

[0356] For example, if a user answers "yes" to the question, "Have you been feeling down lately?", this response data is sent from the device to the server. The server analyzes the data, and the emotion engine evaluates it as "negative." Furthermore, it concludes that the user's emotional trend is "an increase in negative emotions over the past month." A feedback message is generated and sent to the user's device, such as, "It seems you've been feeling down lately. We recommend consulting a professional. You might also want to try relaxing herbal tea or a healthy soup." The user can then review this message and order the recommended meal.

[0357] Examples of prompts to input into a generative AI model

[0358] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[0359] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0360] Step 1:

[0361] The user logs into the system using a device.

[0362] The user accesses the system's login screen using a device (e.g., a smartphone). Here, they enter their user ID and password. The entered information is sent to the server for login authentication.

[0363] Input: User ID, Password

[0364] Output: Authentication token (upon successful login) or error message (upon login failure)

[0365] Data processing and calculation: The server compares the transmitted user ID and password with the information in the database to perform authentication.

[0366] Step 2:

[0367] The device displays mental health check questions.

[0368] Once the user successfully logs in, the terminal retrieves mental health check questions from the server and displays them on the screen. The user then answers these questions.

[0369] Input: Authenticated User ID, Authentication Token

[0370] Output: Mental health check questionnaire list

[0371] Data processing and calculation: The server verifies the authentication token and provides the user with mental check questions.

[0372] Step 3:

[0373] The user answers questions for a mental health check.

[0374] The user answers the displayed mental health check questions. These answers are collected by the device, encrypted, and sent to the server.

[0375] Input: User's mental health check responses

[0376] Output: Encrypted response data

[0377] Data processing and calculation: The terminal encrypts the user's response to ensure security before sending it to the server.

[0378] Step 4:

[0379] The server receives and stores the response data.

[0380] The server receives encrypted response data and stores it in a database. After storage, this data is passed to the analysis engine and the emotion engine.

[0381] Input: Encrypted response data

[0382] Output: Response data stored in the database

[0383] Data processing and calculation: The server decrypts the data and stores and manages the response data in the database.

[0384] Step 5:

[0385] The emotion engine analyzes the response data and generates an emotion score.

[0386] The emotion engine uses stored response data to perform natural language processing and calculate the user's emotion score.

[0387] Input: Response data

[0388] Output: Emotion score

[0389] Data Processing and Calculation: The emotion engine generates positive, negative, or neutral emotion scores from response data based on natural language processing technology.

[0390] Step 6:

[0391] The server generates feedback based on the analysis results and sentiment score.

[0392] Based on the emotional score and past response data, the server assesses the user's mental state and generates appropriate feedback messages and meal suggestions.

[0393] Input: Sentiment score, past response data

[0394] Output: Feedback message, meal suggestion

[0395] Data processing and calculation: The server automatically generates feedback based on the results obtained from the analysis engine and also provides meal suggestions tailored to the user's mental state.

[0396] Step 7:

[0397] The device displays feedback and meal suggestions received from the server.

[0398] The device displays feedback messages and meal suggestions sent from the server to the user in real time.

[0399] Input: Feedback message, meal suggestion

[0400] Output: Feedback message and meal suggestion displayed on the device screen.

[0401] Data processing and calculation: The terminal formats the received data appropriately and provides it to the user in an easy-to-read format.

[0402] Examples of prompts to input into a generative AI model

[0403] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Second Embodiment]

[0408] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention is a system for monitoring a user's mental state. The system aims to provide feedback by having the user answer a mental check, and then having the server analyze the response data. This system consists of three main elements: the user, the terminal, and the server.

[0421] User actions

[0422] First, the user logs into the system using their own device. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user logs in successfully, mental health check questions will be displayed on the device.

[0423] Terminal processing

[0424] The device receives input from the user, encrypts the information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0425] Server Processing

[0426] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0427] The analysis process uses techniques such as natural language processing to text-mine user responses and calculate sentiment scores. This quantifies the level of stress and anxiety a user is experiencing. The server also analyzes trends in the user's mental state using past response data. This allows the system to understand whether the user's condition is improving or worsening.

[0428] Ultimately, the server generates appropriate feedback for the user based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional," is generated and sent to the device. The device receives this feedback and displays it to the user.

[0429] Specific example

[0430] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed. The analysis results indicate that the user is feeling down, and a message is sent to the user's terminal as feedback: "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0431] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[0435] Step 2:

[0436] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[0437] Step 3:

[0438] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[0439] Step 4:

[0440] The terminal receives a login success message and displays mental health check questions on the user's screen.

[0441] Step 5:

[0442] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[0443] Step 6:

[0444] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[0445] Step 7:

[0446] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine.

[0447] Step 8:

[0448] The server uses an analysis engine to analyze the response data. Specifically, it uses text mining and natural language processing to calculate sentiment scores and evaluate the user's mental state.

[0449] Step 9:

[0450] Based on the analysis results, the server analyzes trends in the user's mental state by comparing them with the user's past response data.

[0451] Step 10:

[0452] The server generates an appropriate feedback message based on the analysis results. Example: "Your stress level appears high. We recommend taking some time to relax or consulting a professional."

[0453] Step 11:

[0454] The server sends the generated feedback message to the terminal.

[0455] Step 12:

[0456] The device receives feedback messages and displays them to the user. The user reviews the displayed feedback and takes the necessary actions.

[0457] Through this series of steps, the system monitors the user's mental state and supports early problem detection and appropriate countermeasures.

[0458] (Example 1)

[0459] 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."

[0460] In modern society, mental health problems are on the rise, and early detection and appropriate treatment are crucial. However, conventional mental health check systems have struggled to monitor users' mental states in real time and provide appropriate feedback. Furthermore, they lacked the functionality to analyze trends in mental state using users' past data. As a result, the effectiveness of mental healthcare was limited, and useful support could not be provided to users.

[0461] 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.

[0462] In this invention, the server includes means for authenticating the user by matching their login information with a database, means for passing the stored response data to an analysis engine and evaluating the user's mental state using natural language processing technology, and means for sending feedback to the terminal based on the analysis results. This allows the user to check their mental state in real time and receive appropriate feedback, enabling early detection and response to mental health problems.

[0463] A "terminal" is a device that a user uses to access a system and perform input and reception. Specific examples include personal computers and smartphones.

[0464] "Authentication information" refers to the information a user needs to log in to a system, and typically consists of a user ID and password.

[0465] A "server" is a central computing system that receives, stores, and analyzes user authentication information and response data, generates feedback, and sends it to the terminal.

[0466] A "database" is a place where a server stores user login information and response data, and uses that information for later verification and analysis.

[0467] "Authentication" is the process by which a server verifies a user's login information to confirm that the user is a legitimate user.

[0468] "Encryption" is a technology that transforms data so that a device can securely transmit user response data to a server, preventing unauthorized access by third parties.

[0469] "Natural language processing technology" refers to techniques that allow servers to analyze user response data and perform text mining and sentiment analysis. Specific examples include technologies such as NLTK and spaCy.

[0470] "Feedback" refers to messages generated by the server based on analysis results, offering advice and suggestions for the next steps to the user.

[0471] "Trend analysis" is a method used by servers to evaluate changes and trends in users' mental states by utilizing past response data.

[0472] This invention is a system for monitoring a user's mental state. The system involves the user answering a mental check, and a server analyzing the response data to provide feedback. This system consists of three main elements: the user, the terminal, and the server.

[0473] User actions

[0474] First, the user logs into the system using their own device (computer or smartphone). The login screen displays input fields for user ID and password, which the user enters and clicks the "Login" button. If the login is successful, the device displays mental health check questions to the user. For example, a question such as "Have you been feeling down a lot lately?" may be displayed.

[0475] Terminal processing

[0476] The device receives user response data, encrypts it, and sends it to the server. AES (Advanced Encryption Standard) is used for encryption, and communication takes place via the HTTPS protocol. For example, if the user responds "yes," that data is encrypted and securely sent to the server. The device also receives feedback messages from the server in real time and displays them to the user.

[0477] Server Processing

[0478] The server first authenticates the user by matching their login information against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it then receives the mental check responses submitted by the user and stores them in the database. This data is passed to an analysis engine, which uses natural language processing techniques (e.g., NLTK or spaCy) to perform text mining and calculate a sentiment score. Based on this score, the server evaluates the user's mental state. Furthermore, the server uses past response data to analyze trends in the user's mental state. This trend analysis allows the server to determine whether the user's condition is improving or worsening.

[0479] Feedback generation

[0480] The server generates a feedback message based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend taking some time to relax or consulting a professional," is generated and sent to the terminal. The terminal receives this message and displays it to the user in real time.

[0481] Specific example

[0482] When a user logs into the system using their home computer and answers "yes" to the question, "I've been feeling down a lot lately," this response data is encrypted and sent to the server. The server analyzes this data, assesses that the user is feeling down, and generates and sends a feedback message to the user's device stating, "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0483] Example of a prompt

[0484] "Please describe the detailed process by which a user logs into the system, performs a mental health check, and the results are analyzed on the server to generate feedback."

[0485] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user uses a device to enter authentication information and log in to the system.

[0489] Input: User ID, Password

[0490] Specific steps: The user enters their user ID and password on their computer or smartphone screen and clicks the "Login" button.

[0491] Output: Sending authentication information from the terminal to the server

[0492] Step 2:

[0493] The server authenticates the user by comparing their login information with the database.

[0494] Input: User ID and password sent from the device.

[0495] Specific operation: The server compares the received user ID and password with the database to check if they match. If authentication is successful, the user can log in to the system.

[0496] Output: Authentication result (success or failure) is sent back to the terminal.

[0497] Step 3:

[0498] The user uses a device to answer questions for a mental health check.

[0499] Input: Question displayed after logging in

[0500] Specific operation: The user selects an answer from the given options for each mental health check question displayed on the device and enters the answer.

[0501] Output: User response data

[0502] Step 4:

[0503] The device encrypts the user's response data and sends it to the server.

[0504] Input: User response data

[0505] Specific operation: The device encrypts the response data using AES (Advanced Encryption Standard) and sends it to the server using the HTTPS protocol.

[0506] Output: Encrypted response data sent to the server

[0507] Step 5:

[0508] The server receives the user's response data and saves it to the database.

[0509] Input: Encrypted response data sent from the device

[0510] Specific operation: The server receives encrypted data and decrypts it using AES. The decrypted data is then saved to the database.

[0511] Output: Saving response data to the database

[0512] Step 6:

[0513] The server passes the stored response data to the analysis engine, which then uses natural language processing technology to evaluate the mental state.

[0514] Input: Saved response data

[0515] Specific operation: The server passes the stored response data to an analysis engine (e.g., NLTK or spaCy) for text mining and sentiment analysis. This quantifies the user's stress level, anxiety index, and other metrics.

[0516] Output: Analysis results (emotion score, evaluation results)

[0517] Step 7:

[0518] The server generates feedback based on the analysis results and sends it to the terminal.

[0519] Input: Analysis results (emotion score, evaluation results)

[0520] Specific operation: The server generates a feedback message based on the analysis results (e.g., "Your stress level appears high. We recommend you take some time to relax or consult a professional"). The generated feedback is then sent to the terminal.

[0521] Output: Feedback message

[0522] Step 8:

[0523] The device displays feedback messages sent from the server in real time.

[0524] Input: Feedback message sent from the server

[0525] Specific operation: The terminal receives feedback messages from the server and displays them to the user in real time.

[0526] Output: Display of feedback message to the user

[0527] (Application Example 1)

[0528] 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."

[0529] In modern factories, workers are often exposed to harsh working conditions and high levels of stress, which contribute to decreased productivity and health problems. Traditional mental health monitoring systems rely solely on self-reporting by individual workers, making real-time monitoring and immediate feedback difficult. As a result, there is a challenge in that serious mental health problems often go undetected for a long time.

[0530] 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.

[0531] In this invention, the server includes means for a user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the mental state, and means for a supervisory robot to collect mental check data of workers in the factory and transmit it to the server. This makes it possible to monitor the mental state of workers in the factory in real time and provide rapid feedback.

[0532] A "user" is a person who logs into the system and answers the mental health check questions.

[0533] A "terminal" is a device used by users to answer mental health check questions and is responsible for transmitting data to the server.

[0534] A "server" is a central processing unit that receives, analyzes, and generates feedback data from users' mental health check responses, then sends it to the terminal.

[0535] A "mental check" is a series of questions that users answer to assess their own mental state.

[0536] "Answers" refer to the data that users enter in response to questions in a mental health check.

[0537] "Saving" refers to the act of a server accumulating user response data that it has received.

[0538] "Analysis" is the process by which a server processes stored response data and evaluates the user's mental state.

[0539] "Feedback" refers to advice and notifications that the server generates and provides to the user based on its analysis results.

[0540] A "supervising robot" is a robot whose role is to collect mental health check data from workers within a factory and send it to a server.

[0541] A "worker" is a person who works in a factory and is subject to mental health checks.

[0542] This invention is a system for monitoring the mental state of factory workers in real time and providing rapid feedback. The system consists of four main components: users, terminals, a server, and a supervisory robot.

[0543] User actions

[0544] First, the user (worker) logs into the system using a terminal within the factory. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user successfully logs in, mental health check questions will be displayed on the terminal. For example, the user will answer questions such as, "Have you been feeling stressed lately?"

[0545] Terminal processing

[0546] The device receives input from the user, encrypts the information, and sends it to the server. For example, if the user answers "yes," that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0547] Server Processing

[0548] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0549] The analysis process uses natural language processing techniques to text-mine user responses and calculate a sentiment score. This quantifies the level of stress and anxiety the user is experiencing. The server also analyzes trends in mental state using past response data, allowing it to determine whether the user's condition is improving or worsening. Finally, based on the analysis results, the server generates appropriate feedback for the user and sends it to their device.

[0550] Functions of the supervisor robot

[0551] Supervising robots are stationed throughout the factory. These robots are responsible for collecting mental health check data from workers and sending it to a server. The supervisory robots patrol the factory, presenting workers with mental health check questions and collecting their responses.

[0552] Hardware and software used

[0553] The following hardware and software are used to implement the system:

[0554] User's device: A smartphone or dedicated device used by the worker.

[0555] Server: A central processing unit containing the database and analysis engine (e.g., MySQL database and TensorFlow analysis engine).

[0556] Supervising robot: A robot that collects mental health check data from workers and sends it to a server.

[0557] Specific example

[0558] For example, if a worker answers "yes" to the question, "I've been feeling down a lot lately," this response is sent from the terminal to the server, and the analysis results indicate that the worker is "feeling down." The server then generates a feedback message, "If this continues, please consult a professional," and sends it to the terminal. The worker can then review this message and take appropriate action.

[0559] Examples of prompts for generative AI models

[0560] Please analyze the following mental check data and generate an appropriate feedback message:

[0561] High stress levels

[0562] I've been feeling down lately.

[0563] By accurately understanding the mental state of workers throughout the entire system and providing prompt feedback, improvements in the work environment and increased productivity can be expected.

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The user logs into the system using their device.

[0567] Enter: ID and password

[0568] Output: Authentication result (success or failure)

[0569] Specific operation: The user enters their ID and password on the login screen and clicks the "Login" button. The device sends this information to the server. The server verifies the information against the database, performs authentication, and sends the authentication result to the device.

[0570] Step 2:

[0571] The user answers questions for a mental health check.

[0572] Input: Mental health check questions and user responses

[0573] Output: Encrypted response data

[0574] Specific operation: Mental health check questions are displayed on the user's device. The user answers each question, and the answers are encrypted on the device.

[0575] Step 3:

[0576] The device sends encrypted response data to the server.

[0577] Input: Encrypted response data

[0578] Output: Response data stored on the server

[0579] Specific operation: The device sends encrypted response data to the server. The server receives this data and stores it securely.

[0580] Step 4:

[0581] The server analyzes the response data it receives using an analysis engine.

[0582] Input: Saved response data

[0583] Output: Analysis results (evaluation of mental state)

[0584] Specific operation: The server passes the received response data to the analysis engine, which uses natural language processing technology to calculate an emotion score. This process quantifies how much stress or anxiety the user is experiencing.

[0585] Step 5:

[0586] The server generates a feedback message based on the analysis results.

[0587] Input: Analysis results

[0588] Output: Feedback message

[0589] Specific operation: Based on the analysis results, the server generates an appropriate feedback message. For example, it might create a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional."

[0590] Step 6:

[0591] A supervisory robot collects mental health check data from factory workers and sends it to a server.

[0592] Input: Responses to the worker's mental health check

[0593] Output: Check data sent to the server

[0594] Specific operation: The supervisory robot patrols the factory and presents workers with mental health check questions. It collects the workers' responses and sends the data to a server. The server receives this data and proceeds with analysis.

[0595] Step 7:

[0596] A feedback message is sent to the device and displayed to the user.

[0597] Input: Feedback message

[0598] Output: Notification to the user

[0599] Specific operation: The server generates a feedback message and sends it to the terminal. The terminal receives this message and displays it to the user in real time. The user can then review the message and take appropriate action.

[0600] 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.

[0601] This invention provides a system for monitoring a user's mental state and providing feedback, incorporating an emotion engine that recognizes the user's emotions to achieve more accurate and data-driven mental care. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0602] User actions

[0603] Users log in to the system using their own devices. The login screen provides input fields for ID and password, and users enter this authentication information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[0604] Terminal processing

[0605] The device receives input data from the user, encrypts that information, and sends it to the server. If the user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also displays feedback messages from the server to the user in real time.

[0606] Server Processing

[0607] The server first authenticates the user by comparing their login information with the database. If authentication is successful, the server receives and stores the user's response data. The stored data is then passed to the analysis engine and the sentiment engine.

[0608] Emotional Engine Processing

[0609] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score quantifies whether the user's response is positive, negative, or neutral. For example, the emotion engine would rate the response "I've been feeling down a lot lately" as "negative."

[0610] The emotion engine further uses this emotion score to analyze the user's emotional trends by comparing it with past response data. This allows it to understand whether the user's mental state is improving or worsening.

[0611] Server Feedback Generation

[0612] The server generates appropriate feedback for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, a message such as, "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional," is automatically generated. The server then sends this feedback message to the device.

[0613] Device feedback display

[0614] The device receives feedback messages and displays them to the user in real time. The user can review this feedback and take the necessary actions.

[0615] Specific example

[0616] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This answer is sent from the device to the server, where it is analyzed and an emotion engine calculates an emotion score. The analysis results in an assessment that the user is "feeling down," and the emotion trend concludes that "negative emotions have increased over the past month." As feedback, a message is sent to the user's device stating, "You seem to have been feeling down lately. We recommend you consult a professional." The user can then review this message and take appropriate action.

[0617] Thus, the system of the present invention allows users to easily monitor their own mental state, and by using an emotion engine, it is possible to provide more advanced and accurate feedback.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[0621] Step 2:

[0622] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[0623] Step 3:

[0624] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[0625] Step 4:

[0626] The terminal receives a login success message and displays mental health check questions on the user's screen.

[0627] Step 5:

[0628] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[0629] Step 6:

[0630] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[0631] Step 7:

[0632] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine and the sentiment engine.

[0633] Step 8:

[0634] The sentiment engine uses natural language processing to analyze the user's response and generate a sentiment score. This score quantifies whether the response is positive, negative, or neutral.

[0635] Step 9:

[0636] By combining data from the analysis engine and the emotion engine, the server comprehensively evaluates the user's mental state.

[0637] Step 10:

[0638] The server analyzes sentiment trends by comparing the analysis results and sentiment scores with the user's past response data.

[0639] Step 11:

[0640] The server generates appropriate feedback messages based on analysis results and sentiment trends. Example: "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional."

[0641] Step 12:

[0642] The server sends the generated feedback message to the terminal.

[0643] Step 13:

[0644] The device receives the feedback message and displays it to the user. The user reviews the feedback and takes the necessary action.

[0645] (Example 2)

[0646] 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".

[0647] Traditional mental healthcare systems have struggled to accurately recognize users' emotions and provide appropriate feedback. Furthermore, they lacked the ability to track trends in users' mental states, making long-term mental healthcare for individual users difficult.

[0648] 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.

[0649] In this invention, the server includes means for receiving and securely storing user responses, means for analyzing the stored response data using a natural language processing engine to generate an emotion score and evaluate the user's mental state, and means for generating feedback based on the evaluation results and transmitting it to a communication device. This makes it possible to accurately recognize the user's mental state and provide individualized feedback, and further enables comprehensive mental care through analysis of long-term trends.

[0650] "Communication equipment" refers to devices used by users to access the system and answer questions in a mental health check, such as personal computers and smartphones.

[0651] A "server" is a central system that receives data sent from users, analyzes it, generates feedback, and sends it back.

[0652] A "natural language processing engine" is an artificial intelligence technology used to analyze user response data and generate sentiment scores, and includes models such as BERT and GPT-3.

[0653] The "emotion score" is a numerical representation of whether a user's response is positive, negative, or neutral.

[0654] "Feedback" refers to advice and recommendations provided to the user based on the evaluation results generated by the server.

[0655] "Emotional trends" refer to long-term patterns of mental state obtained by analyzing changes in users' emotions based on past response data.

[0656] "Login information" refers to authentication data that a user uses to access the system, and includes, for example, an ID and password.

[0657] "Analysis results" refer to the results of data analysis obtained by the natural language processing engine, and specifically include information such as sentiment scores and sentiment trends.

[0658] "Saving" refers to securely recording received data in a database so that it can be analyzed and referenced later.

[0659] Modes for carrying out the invention

[0660] This invention relates to a system that monitors a user's mental state and provides appropriate feedback. This system consists of four main elements: the user, the terminal, the server, and the natural language processing engine. Each of these elements is described in detail below.

[0661] User actions

[0662] First, users access the system using a communication device (such as a personal computer or smartphone). The login screen displays fields for entering an ID and password, and users log in by entering this authentication information. Upon successful authentication, mental health check questions are displayed.

[0663] Terminal processing

[0664] The device receives response data from the user. This data is encrypted using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). The device also displays feedback messages sent from the server to the user in real time.

[0665] Server Processing

[0666] The server first verifies the user's login information against a database (e.g., MySQL) to perform authentication. If authentication is successful, it decrypts the encrypted user response data and stores it in the database. The stored data is then passed to the parsing engine and the natural language processing engine.

[0667] Processing by a natural language processing engine

[0668] The natural language processing engine analyzes the user's responses using models such as BERT and GPT-3. A sentiment score is generated from the results of this analysis. For example, in response to the answer "I've been feeling down a lot lately," the natural language processing engine evaluates it as "negative" and assigns a corresponding sentiment score.

[0669] Server Feedback Generation

[0670] The server receives the sentiment score and analysis results from the natural language processing engine and analyzes the user's sentiment trends by comparing them with past data. Based on these results, the server generates appropriate feedback to provide to the user. For example, a message such as, "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional," might be generated.

[0671] Device feedback display

[0672] The server re-encrypts the generated feedback message and sends it to the terminal. The terminal decrypts it and displays it to the user in real time. The user can then review this feedback and take any necessary actions.

[0673] Specific example

[0674] Consider a scenario where a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed and a sentiment score is calculated using a natural language processing engine. The analysis results in an assessment of "feeling down," and the sentiment trend is concluded to be "negative emotions have increased over the past month." A feedback message is sent to the user's terminal stating, "You seem to have been feeling down lately. We recommend consulting a professional." The user can then review this message and take appropriate action.

[0675] Examples of prompt statements include the following:

[0676] "Have you been feeling down lately? Yes No"

[0677] In this way, the system of the present invention can accurately monitor the user's mental state and provide individualized feedback.

[0678] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0679] Step 1:

[0680] Users access the system using communication devices and log in by entering their ID and password. The input includes the user's "ID" and "password." The system receives this authentication information and sends a login request to the server. At this time, the terminal sends the user's input data to the server. The system returns information indicating whether the login was successful or unsuccessful.

[0681] Step 2:

[0682] The server verifies the received login information against the database and performs authentication. It queries the database (e.g., MySQL) for the user's "ID" and "password" and outputs the verification result. If authentication is successful, the server generates a login success status and sends it to the terminal. The output is either "Login Success or Failure Status".

[0683] Step 3:

[0684] Once a user successfully logs in, a mental health check question will appear on their device. For example: "Have you been feeling down lately?" The user answers this question with "yes" or "no." The input at this time is the user's answer ("yes" or "no").

[0685] Step 4:

[0686] The terminal encrypts the user's input data using AES encryption technology and sends the encrypted data to the server using the HTTPS protocol. The input is the "user's response," and the output is the "encrypted user's response." Specifically, an encrypted "yes" or "no" is sent to the server.

[0687] Step 5:

[0688] The server receives encrypted user responses and decrypts them. The input is the "encrypted response," and the output is the "decrypted user response." After decryption, the server saves the response data to a database.

[0689] Step 6:

[0690] The server passes the stored data to a natural language processing engine (e.g., BERT or GPT-3) for analysis. The input is "user response data," and the output is a "sentiment score." Specifically, if the response data is "I've been feeling down a lot lately," the natural language processing engine will generate a sentiment score indicating "negative."

[0691] Step 7:

[0692] The server analyzes the user's emotional trends by comparing the generated emotional score with past data. The inputs are the "emotional score" and "past response data," and the output is the "emotional trend analysis result." Specifically, the emotional trend is assessed as "negative emotions have increased over the past month."

[0693] Step 8:

[0694] The server generates appropriate feedback messages for the user based on the results of the sentiment trend analysis. The input is the "sentiment trend analysis results," and the output is the "feedback message." An example message might be: "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional."

[0695] Step 9:

[0696] The server re-encrypts the generated feedback message and sends it to the terminal using the HTTPS protocol. The input is the "feedback message," and the output is the "encrypted feedback message."

[0697] Step 10:

[0698] The terminal receives encrypted feedback messages sent from the server, decrypts them, and displays them to the user in real time. The user reviews the displayed feedback and takes the necessary actions. The input is the "encrypted feedback message," and the output is the "feedback message displayed to the user."

[0699] (Application Example 2)

[0700] 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."

[0701] Traditional mental healthcare systems primarily focused on providing appropriate feedback based on the user's emotional state, lacking specific action suggestions tailored to the user's mental condition and integration with external services. As a result, users struggled to find concrete coping mechanisms suited to their emotions, leading to limited effectiveness of mental healthcare. In particular, there was a lack of feedback directly relevant to daily life, such as suggestions for foods and drinks to reduce stress and anxiety.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0703] In this invention, the server includes means for the user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the user's mental state, means for the server to send feedback to the terminal based on the analysis results, and means for suggesting appropriate meals based on the user's mental state. This allows the user to receive specific meal suggestions that can help improve their mental state, thereby achieving more effective mental care.

[0704] A "user" refers to an individual who uses this system to perform a mental health check.

[0705] "Device" refers to the device that a user uses to answer questions in a mental health check. Examples include smartphones, tablets, and computers.

[0706] A "server" refers to a computer system used to receive, store, analyze, and generate feedback from users.

[0707] A "mental check" refers to a set of questions used to assess a user's emotions and stress levels.

[0708] "User responses" refer to the user's responses to the mental health check questions.

[0709] "Means of evaluation" refers to the algorithms and technologies used by the server to analyze the user's responses and evaluate their mental state.

[0710] "Feedback" refers to recommendations and advice that a server generates based on the user's mental state.

[0711] "Methods for suggesting meals" refers to algorithms and technologies used by servers to recommend the most suitable food and drink based on the user's mental state.

[0712] "Analysis results" refer to the evaluation results obtained by the server analyzing the user's response data.

[0713] "Data" refers to information that is stored and analyzed, including user responses and evaluations of their mental state.

[0714] An "emotion engine" refers to a program or system that uses technologies such as natural language processing to analyze user responses and generate an emotion score.

[0715] "Sentiment score" refers to a numerical evaluation value that quantifies whether a user's response is positive, negative, or neutral.

[0716] "Trend" refers to the tendency that indicates fluctuations in a user's mental state based on past data.

[0717] System Overview

[0718] This system allows users to perform mental health checks using a smartphone or other device, and the server receives, analyzes, and provides feedback. Furthermore, it suggests appropriate meals based on the user's mental state. This enables users to receive specific meal suggestions that can help improve their mental state, resulting in more effective mental care.

[0719] Hardware and software to be used

[0720] Hardware: Devices such as smartphones, tablets, and computers.

[0721] software:

[0722] Python: a programming language

[0723] Requests: HTTP Request Sending Library

[0724] EmotionEngine: Emotion Analysis Engine

[0725] Detailed processing of the system

[0726] 1. User actions:

[0727] Users log in to the system using their smartphones or tablets. The login screen has input fields for ID and password, and users enter this information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[0728] 2. Terminal processing:

[0729] The device receives input data from the user (responses to a mental health check), encrypts that information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that response data is encrypted and sent to the server. The device also displays feedback messages and meal suggestions sent from the server to the user in real time.

[0730] 3. Server processing:

[0731] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server receives and saves the user's response data. The saved data is then passed to the analysis engine and the emotion engine, which evaluate the user's mental state and generate an emotion score.

[0732] 4. Emotional engine processing:

[0733] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score is a numerical evaluation value that indicates whether the user's response is positive, negative, or neutral. The emotion engine also uses this emotion score to compare it with past response data and analyze the user's emotional trends. This allows for understanding fluctuations in the user's mental state.

[0734] 5. Server feedback and meal suggestion generation:

[0735] The server automatically generates appropriate feedback and meal suggestions for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, it might generate a message such as, "Your recent responses indicate a high stress level. We recommend a relaxing herbal tea or a healthy soup." The server then sends this feedback message and meal suggestion to the user's device.

[0736] 6. Displaying feedback on the device:

[0737] The device displays feedback messages and meal suggestions received from the server to the user in real time. The user can review this feedback and take appropriate action.

[0738] Specific example

[0739] For example, if a user answers "yes" to the question, "Have you been feeling down lately?", this response data is sent from the device to the server. The server analyzes the data, and the emotion engine evaluates it as "negative." Furthermore, it concludes that the user's emotional trend is "an increase in negative emotions over the past month." A feedback message is generated and sent to the user's device, such as, "It seems you've been feeling down lately. We recommend consulting a professional. You might also want to try relaxing herbal tea or a healthy soup." The user can then review this message and order the recommended meal.

[0740] Examples of prompts to input into a generative AI model

[0741] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The user logs into the system using a device.

[0745] The user accesses the system's login screen using a device (e.g., a smartphone). Here, they enter their user ID and password. The entered information is sent to the server for login authentication.

[0746] Input: User ID, Password

[0747] Output: Authentication token (upon successful login) or error message (upon login failure)

[0748] Data processing and calculation: The server compares the transmitted user ID and password with the information in the database to perform authentication.

[0749] Step 2:

[0750] The device displays mental health check questions.

[0751] Once the user successfully logs in, the terminal retrieves mental health check questions from the server and displays them on the screen. The user then answers these questions.

[0752] Input: Authenticated User ID, Authentication Token

[0753] Output: Mental health check questionnaire list

[0754] Data processing and calculation: The server verifies the authentication token and provides the user with mental check questions.

[0755] Step 3:

[0756] The user answers questions for a mental health check.

[0757] The user answers the displayed mental health check questions. These answers are collected by the device, encrypted, and sent to the server.

[0758] Input: User's mental health check responses

[0759] Output: Encrypted response data

[0760] Data processing and calculation: The terminal encrypts the user's response to ensure security before sending it to the server.

[0761] Step 4:

[0762] The server receives and stores the response data.

[0763] The server receives encrypted response data and stores it in a database. After storage, this data is passed to the analysis engine and the emotion engine.

[0764] Input: Encrypted response data

[0765] Output: Response data stored in the database

[0766] Data processing and calculation: The server decrypts the data and stores and manages the response data in the database.

[0767] Step 5:

[0768] The emotion engine analyzes the response data and generates an emotion score.

[0769] The emotion engine uses stored response data to perform natural language processing and calculate the user's emotion score.

[0770] Input: Response data

[0771] Output: Emotion score

[0772] Data Processing and Calculation: The emotion engine generates positive, negative, or neutral emotion scores from response data based on natural language processing technology.

[0773] Step 6:

[0774] The server generates feedback based on the analysis results and sentiment score.

[0775] Based on the emotional score and past response data, the server assesses the user's mental state and generates appropriate feedback messages and meal suggestions.

[0776] Input: Sentiment score, past response data

[0777] Output: Feedback message, meal suggestion

[0778] Data processing and calculation: The server automatically generates feedback based on the results obtained from the analysis engine and also provides meal suggestions tailored to the user's mental state.

[0779] Step 7:

[0780] The device displays feedback and meal suggestions received from the server.

[0781] The device displays feedback messages and meal suggestions sent from the server to the user in real time.

[0782] Input: Feedback message, meal suggestion

[0783] Output: Feedback message and meal suggestion displayed on the device screen.

[0784] Data processing and calculation: The terminal formats the received data appropriately and provides it to the user in an easy-to-read format.

[0785] Examples of prompts to input into a generative AI model

[0786] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[0787] 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.

[0788] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0789] 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.

[0790] [Third Embodiment]

[0791] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0792] 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.

[0793] 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).

[0794] 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.

[0795] 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.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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".

[0803] This invention is a system for monitoring a user's mental state. The system aims to provide feedback by having the user answer a mental check, and then having the server analyze the response data. This system consists of three main elements: the user, the terminal, and the server.

[0804] User actions

[0805] First, the user logs into the system using their own device. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user logs in successfully, mental health check questions will be displayed on the device.

[0806] Terminal processing

[0807] The device receives input from the user, encrypts the information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0808] Server Processing

[0809] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0810] The analysis process uses techniques such as natural language processing to text-mine user responses and calculate sentiment scores. This quantifies the level of stress and anxiety a user is experiencing. The server also analyzes trends in the user's mental state using past response data. This allows the system to understand whether the user's condition is improving or worsening.

[0811] Ultimately, the server generates appropriate feedback for the user based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional," is generated and sent to the device. The device receives this feedback and displays it to the user.

[0812] Specific example

[0813] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed. The analysis results indicate that the user is feeling down, and a message is sent to the user's terminal as feedback: "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0814] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0815] The following describes the processing flow.

[0816] Step 1:

[0817] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[0818] Step 2:

[0819] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[0820] Step 3:

[0821] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[0822] Step 4:

[0823] The terminal receives a login success message and displays mental health check questions on the user's screen.

[0824] Step 5:

[0825] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[0826] Step 6:

[0827] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[0828] Step 7:

[0829] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine.

[0830] Step 8:

[0831] The server uses an analysis engine to analyze the response data. Specifically, it uses text mining and natural language processing to calculate sentiment scores and evaluate the user's mental state.

[0832] Step 9:

[0833] Based on the analysis results, the server analyzes trends in the user's mental state by comparing them with the user's past response data.

[0834] Step 10:

[0835] The server generates an appropriate feedback message based on the analysis results. Example: "Your stress level appears high. We recommend taking some time to relax or consulting a professional."

[0836] Step 11:

[0837] The server sends the generated feedback message to the terminal.

[0838] Step 12:

[0839] The device receives feedback messages and displays them to the user. The user reviews the displayed feedback and takes the necessary actions.

[0840] Through this series of steps, the system monitors the user's mental state and supports early problem detection and appropriate countermeasures.

[0841] (Example 1)

[0842] 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."

[0843] In modern society, mental health problems are on the rise, and early detection and appropriate treatment are crucial. However, conventional mental health check systems have struggled to monitor users' mental states in real time and provide appropriate feedback. Furthermore, they lacked the functionality to analyze trends in mental state using users' past data. As a result, the effectiveness of mental healthcare was limited, and useful support could not be provided to users.

[0844] 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.

[0845] In this invention, the server includes means for authenticating the user by matching their login information with a database, means for passing the stored response data to an analysis engine and evaluating the user's mental state using natural language processing technology, and means for sending feedback to the terminal based on the analysis results. This allows the user to check their mental state in real time and receive appropriate feedback, enabling early detection and response to mental health problems.

[0846] A "terminal" is a device that a user uses to access a system and perform input and reception. Specific examples include personal computers and smartphones.

[0847] "Authentication information" refers to the information a user needs to log in to a system, and typically consists of a user ID and password.

[0848] A "server" is a central computing system that receives, stores, and analyzes user authentication information and response data, generates feedback, and sends it to the terminal.

[0849] A "database" is a place where a server stores user login information and response data, and uses that information for later verification and analysis.

[0850] "Authentication" is the process by which a server verifies a user's login information to confirm that the user is a legitimate user.

[0851] "Encryption" is a technology that transforms data so that a device can securely transmit user response data to a server, preventing unauthorized access by third parties.

[0852] "Natural language processing technology" refers to techniques that allow servers to analyze user response data and perform text mining and sentiment analysis. Specific examples include technologies such as NLTK and spaCy.

[0853] "Feedback" refers to messages generated by the server based on analysis results, offering advice and suggestions for the next steps to the user.

[0854] "Trend analysis" is a method used by servers to evaluate changes and trends in users' mental states by utilizing past response data.

[0855] This invention is a system for monitoring a user's mental state. The system involves the user answering a mental check, and a server analyzing the response data to provide feedback. This system consists of three main elements: the user, the terminal, and the server.

[0856] User actions

[0857] First, the user logs into the system using their own device (computer or smartphone). The login screen displays input fields for user ID and password, which the user enters and clicks the "Login" button. If the login is successful, the device displays mental health check questions to the user. For example, a question such as "Have you been feeling down a lot lately?" may be displayed.

[0858] Terminal processing

[0859] The device receives user response data, encrypts it, and sends it to the server. AES (Advanced Encryption Standard) is used for encryption, and communication takes place via the HTTPS protocol. For example, if the user responds "yes," that data is encrypted and securely sent to the server. The device also receives feedback messages from the server in real time and displays them to the user.

[0860] Server Processing

[0861] The server first authenticates the user by matching their login information against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it then receives the mental check responses submitted by the user and stores them in the database. This data is passed to an analysis engine, which uses natural language processing techniques (e.g., NLTK or spaCy) to perform text mining and calculate a sentiment score. Based on this score, the server evaluates the user's mental state. Furthermore, the server uses past response data to analyze trends in the user's mental state. This trend analysis allows the server to determine whether the user's condition is improving or worsening.

[0862] Feedback generation

[0863] The server generates a feedback message based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend taking some time to relax or consulting a professional," is generated and sent to the terminal. The terminal receives this message and displays it to the user in real time.

[0864] Specific example

[0865] When a user logs into the system using their home computer and answers "yes" to the question, "I've been feeling down a lot lately," this response data is encrypted and sent to the server. The server analyzes this data, assesses that the user is feeling down, and generates and sends a feedback message to the user's device stating, "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[0866] Example of a prompt

[0867] "Please describe the detailed process by which a user logs into the system, performs a mental health check, and the results are analyzed on the server to generate feedback."

[0868] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[0869] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0870] Step 1:

[0871] The user uses a device to enter authentication information and log in to the system.

[0872] Input: User ID, Password

[0873] Specific steps: The user enters their user ID and password on their computer or smartphone screen and clicks the "Login" button.

[0874] Output: Sending authentication information from the terminal to the server

[0875] Step 2:

[0876] The server authenticates the user by comparing their login information with the database.

[0877] Input: User ID and password sent from the device.

[0878] Specific operation: The server compares the received user ID and password with the database to check if they match. If authentication is successful, the user can log in to the system.

[0879] Output: Authentication result (success or failure) is sent back to the terminal.

[0880] Step 3:

[0881] The user uses a device to answer questions for a mental health check.

[0882] Input: Question displayed after logging in

[0883] Specific operation: The user selects an answer from the given options for each mental health check question displayed on the device and enters the answer.

[0884] Output: User response data

[0885] Step 4:

[0886] The device encrypts the user's response data and sends it to the server.

[0887] Input: User response data

[0888] Specific operation: The device encrypts the response data using AES (Advanced Encryption Standard) and sends it to the server using the HTTPS protocol.

[0889] Output: Encrypted response data sent to the server

[0890] Step 5:

[0891] The server receives the user's response data and saves it to the database.

[0892] Input: Encrypted response data sent from the device

[0893] Specific operation: The server receives encrypted data and decrypts it using AES. The decrypted data is then saved to the database.

[0894] Output: Saving response data to the database

[0895] Step 6:

[0896] The server passes the stored response data to the analysis engine, which then uses natural language processing technology to evaluate the mental state.

[0897] Input: Saved response data

[0898] Specific operation: The server passes the stored response data to an analysis engine (e.g., NLTK or spaCy) for text mining and sentiment analysis. This quantifies the user's stress level, anxiety index, and other metrics.

[0899] Output: Analysis results (emotion score, evaluation results)

[0900] Step 7:

[0901] The server generates feedback based on the analysis results and sends it to the terminal.

[0902] Input: Analysis results (emotion score, evaluation results)

[0903] Specific operation: The server generates a feedback message based on the analysis results (e.g., "Your stress level appears high. We recommend you take some time to relax or consult a professional"). The generated feedback is then sent to the terminal.

[0904] Output: Feedback message

[0905] Step 8:

[0906] The device displays feedback messages sent from the server in real time.

[0907] Input: Feedback message sent from the server

[0908] Specific operation: The terminal receives feedback messages from the server and displays them to the user in real time.

[0909] Output: Display of feedback message to the user

[0910] (Application Example 1)

[0911] 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."

[0912] In modern factories, workers are often exposed to harsh working conditions and high levels of stress, which contribute to decreased productivity and health problems. Traditional mental health monitoring systems rely solely on self-reporting by individual workers, making real-time monitoring and immediate feedback difficult. As a result, there is a challenge in that serious mental health problems often go undetected for a long time.

[0913] 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.

[0914] In this invention, the server includes means for a user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the mental state, and means for a supervisory robot to collect mental check data of workers in the factory and transmit it to the server. This makes it possible to monitor the mental state of workers in the factory in real time and provide rapid feedback.

[0915] A "user" is a person who logs into the system and answers the mental health check questions.

[0916] A "terminal" is a device used by users to answer mental health check questions and is responsible for transmitting data to the server.

[0917] A "server" is a central processing unit that receives, analyzes, and generates feedback data from users' mental health check responses, then sends it to the terminal.

[0918] A "mental check" is a series of questions that users answer to assess their own mental state.

[0919] "Answers" refer to the data that users enter in response to questions in a mental health check.

[0920] "Saving" refers to the act of a server accumulating user response data that it has received.

[0921] "Analysis" is the process by which a server processes stored response data and evaluates the user's mental state.

[0922] "Feedback" refers to advice and notifications that the server generates and provides to the user based on its analysis results.

[0923] A "supervising robot" is a robot whose role is to collect mental health check data from workers within a factory and send it to a server.

[0924] A "worker" is a person who works in a factory and is subject to mental health checks.

[0925] This invention is a system for monitoring the mental state of factory workers in real time and providing rapid feedback. The system consists of four main components: users, terminals, a server, and a supervisory robot.

[0926] User actions

[0927] First, the user (worker) logs into the system using a terminal within the factory. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user successfully logs in, mental health check questions will be displayed on the terminal. For example, the user will answer questions such as, "Have you been feeling stressed lately?"

[0928] Terminal processing

[0929] The device receives input from the user, encrypts the information, and sends it to the server. For example, if the user answers "yes," that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[0930] Server Processing

[0931] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[0932] The analysis process uses natural language processing techniques to text-mine user responses and calculate a sentiment score. This quantifies the level of stress and anxiety the user is experiencing. The server also analyzes trends in mental state using past response data, allowing it to determine whether the user's condition is improving or worsening. Finally, based on the analysis results, the server generates appropriate feedback for the user and sends it to their device.

[0933] Functions of the supervisor robot

[0934] Supervising robots are stationed throughout the factory. These robots are responsible for collecting mental health check data from workers and sending it to a server. The supervisory robots patrol the factory, presenting workers with mental health check questions and collecting their responses.

[0935] Hardware and software used

[0936] The following hardware and software are used to implement the system:

[0937] User's device: A smartphone or dedicated device used by the worker.

[0938] Server: A central processing unit containing the database and analysis engine (e.g., MySQL database and TensorFlow analysis engine).

[0939] Supervising robot: A robot that collects mental health check data from workers and sends it to a server.

[0940] Specific example

[0941] For example, if a worker answers "yes" to the question, "I've been feeling down a lot lately," this response is sent from the terminal to the server, and the analysis results indicate that the worker is "feeling down." The server then generates a feedback message, "If this continues, please consult a professional," and sends it to the terminal. The worker can then review this message and take appropriate action.

[0942] Examples of prompts for generative AI models

[0943] Please analyze the following mental check data and generate an appropriate feedback message:

[0944] High stress levels

[0945] I've been feeling down lately.

[0946] By accurately understanding the mental state of workers throughout the entire system and providing prompt feedback, improvements in the work environment and increased productivity can be expected.

[0947] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0948] Step 1:

[0949] The user logs into the system using their device.

[0950] Enter: ID and password

[0951] Output: Authentication result (success or failure)

[0952] Specific operation: The user enters their ID and password on the login screen and clicks the "Login" button. The device sends this information to the server. The server verifies the information against the database, performs authentication, and sends the authentication result to the device.

[0953] Step 2:

[0954] The user answers questions for a mental health check.

[0955] Input: Mental health check questions and user responses

[0956] Output: Encrypted response data

[0957] Specific operation: Mental health check questions are displayed on the user's device. The user answers each question, and the answers are encrypted on the device.

[0958] Step 3:

[0959] The device sends encrypted response data to the server.

[0960] Input: Encrypted response data

[0961] Output: Response data stored on the server

[0962] Specific operation: The device sends encrypted response data to the server. The server receives this data and stores it securely.

[0963] Step 4:

[0964] The server analyzes the response data it receives using an analysis engine.

[0965] Input: Saved response data

[0966] Output: Analysis results (evaluation of mental state)

[0967] Specific operation: The server passes the received response data to the analysis engine, which uses natural language processing technology to calculate an emotion score. This process quantifies how much stress or anxiety the user is experiencing.

[0968] Step 5:

[0969] The server generates a feedback message based on the analysis results.

[0970] Input: Analysis results

[0971] Output: Feedback message

[0972] Specific operation: Based on the analysis results, the server generates an appropriate feedback message. For example, it might create a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional."

[0973] Step 6:

[0974] A supervisory robot collects mental health check data from factory workers and sends it to a server.

[0975] Input: Responses to the worker's mental health check

[0976] Output: Check data sent to the server

[0977] Specific operation: The supervisory robot patrols the factory and presents workers with mental health check questions. It collects the workers' responses and sends the data to a server. The server receives this data and proceeds with analysis.

[0978] Step 7:

[0979] A feedback message is sent to the device and displayed to the user.

[0980] Input: Feedback message

[0981] Output: Notification to the user

[0982] Specific operation: The server generates a feedback message and sends it to the terminal. The terminal receives this message and displays it to the user in real time. The user can then review the message and take appropriate action.

[0983] 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.

[0984] This invention provides a system for monitoring a user's mental state and providing feedback, incorporating an emotion engine that recognizes the user's emotions to achieve more accurate and data-driven mental care. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0985] User actions

[0986] Users log in to the system using their own devices. The login screen provides input fields for ID and password, and users enter this authentication information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[0987] Terminal processing

[0988] The device receives input data from the user, encrypts that information, and sends it to the server. If the user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also displays feedback messages from the server to the user in real time.

[0989] Server Processing

[0990] The server first authenticates the user by comparing their login information with the database. If authentication is successful, the server receives and stores the user's response data. The stored data is then passed to the analysis engine and the sentiment engine.

[0991] Emotional Engine Processing

[0992] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score quantifies whether the user's response is positive, negative, or neutral. For example, the emotion engine would rate the response "I've been feeling down a lot lately" as "negative."

[0993] The emotion engine further uses this emotion score to analyze the user's emotional trends by comparing it with past response data. This allows it to understand whether the user's mental state is improving or worsening.

[0994] Server Feedback Generation

[0995] The server generates appropriate feedback for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, a message such as, "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional," is automatically generated. The server then sends this feedback message to the device.

[0996] Device feedback display

[0997] The device receives feedback messages and displays them to the user in real time. The user can review this feedback and take the necessary actions.

[0998] Specific example

[0999] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This answer is sent from the device to the server, where it is analyzed and an emotion engine calculates an emotion score. The analysis results in an assessment that the user is "feeling down," and the emotion trend concludes that "negative emotions have increased over the past month." As feedback, a message is sent to the user's device stating, "You seem to have been feeling down lately. We recommend you consult a professional." The user can then review this message and take appropriate action.

[1000] Thus, the system of the present invention allows users to easily monitor their own mental state, and by using an emotion engine, it is possible to provide more advanced and accurate feedback.

[1001] The following describes the processing flow.

[1002] Step 1:

[1003] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[1004] Step 2:

[1005] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[1006] Step 3:

[1007] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[1008] Step 4:

[1009] The terminal receives a login success message and displays mental health check questions on the user's screen.

[1010] Step 5:

[1011] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[1012] Step 6:

[1013] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[1014] Step 7:

[1015] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine and the sentiment engine.

[1016] Step 8:

[1017] The sentiment engine uses natural language processing to analyze the user's response and generate a sentiment score. This score quantifies whether the response is positive, negative, or neutral.

[1018] Step 9:

[1019] By combining data from the analysis engine and the emotion engine, the server comprehensively evaluates the user's mental state.

[1020] Step 10:

[1021] The server analyzes sentiment trends by comparing the analysis results and sentiment scores with the user's past response data.

[1022] Step 11:

[1023] The server generates appropriate feedback messages based on analysis results and sentiment trends. Example: "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional."

[1024] Step 12:

[1025] The server sends the generated feedback message to the terminal.

[1026] Step 13:

[1027] The device receives the feedback message and displays it to the user. The user reviews the feedback and takes the necessary action.

[1028] (Example 2)

[1029] 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."

[1030] Traditional mental healthcare systems have struggled to accurately recognize users' emotions and provide appropriate feedback. Furthermore, they lacked the ability to track trends in users' mental states, making long-term mental healthcare for individual users difficult.

[1031] 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.

[1032] In this invention, the server includes means for receiving and securely storing user responses, means for analyzing the stored response data using a natural language processing engine to generate an emotion score and evaluate the user's mental state, and means for generating feedback based on the evaluation results and transmitting it to a communication device. This makes it possible to accurately recognize the user's mental state and provide individualized feedback, and further enables comprehensive mental care through analysis of long-term trends.

[1033] "Communication equipment" refers to devices used by users to access the system and answer questions in a mental health check, such as personal computers and smartphones.

[1034] A "server" is a central system that receives data sent from users, analyzes it, generates feedback, and sends it back.

[1035] A "natural language processing engine" is an artificial intelligence technology used to analyze user response data and generate sentiment scores, and includes models such as BERT and GPT-3.

[1036] The "emotion score" is a numerical representation of whether a user's response is positive, negative, or neutral.

[1037] "Feedback" refers to advice and recommendations provided to the user based on the evaluation results generated by the server.

[1038] "Emotional trends" refer to long-term patterns of mental state obtained by analyzing changes in users' emotions based on past response data.

[1039] "Login information" refers to authentication data that a user uses to access the system, and includes, for example, an ID and password.

[1040] "Analysis results" refer to the results of data analysis obtained by the natural language processing engine, and specifically include information such as sentiment scores and sentiment trends.

[1041] "Saving" refers to securely recording received data in a database so that it can be analyzed and referenced later.

[1042] Modes for carrying out the invention

[1043] This invention relates to a system that monitors a user's mental state and provides appropriate feedback. This system consists of four main elements: the user, the terminal, the server, and the natural language processing engine. Each of these elements is described in detail below.

[1044] User actions

[1045] First, users access the system using a communication device (such as a personal computer or smartphone). The login screen displays fields for entering an ID and password, and users log in by entering this authentication information. Upon successful authentication, mental health check questions are displayed.

[1046] Terminal processing

[1047] The device receives response data from the user. This data is encrypted using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). The device also displays feedback messages sent from the server to the user in real time.

[1048] Server Processing

[1049] The server first verifies the user's login information against a database (e.g., MySQL) to perform authentication. If authentication is successful, it decrypts the encrypted user response data and stores it in the database. The stored data is then passed to the parsing engine and the natural language processing engine.

[1050] Processing by a natural language processing engine

[1051] The natural language processing engine analyzes the user's responses using models such as BERT and GPT-3. A sentiment score is generated from the results of this analysis. For example, in response to the answer "I've been feeling down a lot lately," the natural language processing engine evaluates it as "negative" and assigns a corresponding sentiment score.

[1052] Server Feedback Generation

[1053] The server receives the sentiment score and analysis results from the natural language processing engine and analyzes the user's sentiment trends by comparing them with past data. Based on these results, the server generates appropriate feedback to provide to the user. For example, a message such as, "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional," might be generated.

[1054] Device feedback display

[1055] The server re-encrypts the generated feedback message and sends it to the terminal. The terminal decrypts it and displays it to the user in real time. The user can then review this feedback and take any necessary actions.

[1056] Specific example

[1057] Consider a scenario where a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed and a sentiment score is calculated using a natural language processing engine. The analysis results in an assessment of "feeling down," and the sentiment trend is concluded to be "negative emotions have increased over the past month." A feedback message is sent to the user's terminal stating, "You seem to have been feeling down lately. We recommend consulting a professional." The user can then review this message and take appropriate action.

[1058] Examples of prompt statements include the following:

[1059] "Have you been feeling down lately? Yes No"

[1060] In this way, the system of the present invention can accurately monitor the user's mental state and provide individualized feedback.

[1061] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1062] Step 1:

[1063] Users access the system using communication devices and log in by entering their ID and password. The input includes the user's "ID" and "password." The system receives this authentication information and sends a login request to the server. At this time, the terminal sends the user's input data to the server. The system returns information indicating whether the login was successful or unsuccessful.

[1064] Step 2:

[1065] The server verifies the received login information against the database and performs authentication. It queries the database (e.g., MySQL) for the user's "ID" and "password" and outputs the verification result. If authentication is successful, the server generates a login success status and sends it to the terminal. The output is either "Login Success or Failure Status".

[1066] Step 3:

[1067] Once a user successfully logs in, a mental health check question will appear on their device. For example: "Have you been feeling down lately?" The user answers this question with "yes" or "no." The input at this time is the user's answer ("yes" or "no").

[1068] Step 4:

[1069] The terminal encrypts the user's input data using AES encryption technology and sends the encrypted data to the server using the HTTPS protocol. The input is the "user's response," and the output is the "encrypted user's response." Specifically, an encrypted "yes" or "no" is sent to the server.

[1070] Step 5:

[1071] The server receives encrypted user responses and decrypts them. The input is the "encrypted response," and the output is the "decrypted user response." After decryption, the server saves the response data to a database.

[1072] Step 6:

[1073] The server passes the stored data to a natural language processing engine (e.g., BERT or GPT-3) for analysis. The input is "user response data," and the output is a "sentiment score." Specifically, if the response data is "I've been feeling down a lot lately," the natural language processing engine will generate a sentiment score indicating "negative."

[1074] Step 7:

[1075] The server analyzes the user's emotional trends by comparing the generated emotional score with past data. The inputs are the "emotional score" and "past response data," and the output is the "emotional trend analysis result." Specifically, the emotional trend is assessed as "negative emotions have increased over the past month."

[1076] Step 8:

[1077] The server generates appropriate feedback messages for the user based on the results of the sentiment trend analysis. The input is the "sentiment trend analysis results," and the output is the "feedback message." An example message might be: "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional."

[1078] Step 9:

[1079] The server re-encrypts the generated feedback message and sends it to the terminal using the HTTPS protocol. The input is the "feedback message," and the output is the "encrypted feedback message."

[1080] Step 10:

[1081] The terminal receives encrypted feedback messages sent from the server, decrypts them, and displays them to the user in real time. The user reviews the displayed feedback and takes the necessary actions. The input is the "encrypted feedback message," and the output is the "feedback message displayed to the user."

[1082] (Application Example 2)

[1083] 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."

[1084] Traditional mental healthcare systems primarily focused on providing appropriate feedback based on the user's emotional state, lacking specific action suggestions tailored to the user's mental condition and integration with external services. As a result, users struggled to find concrete coping mechanisms suited to their emotions, leading to limited effectiveness of mental healthcare. In particular, there was a lack of feedback directly relevant to daily life, such as suggestions for foods and drinks to reduce stress and anxiety.

[1085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1086] In this invention, the server includes means for the user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the user's mental state, means for the server to send feedback to the terminal based on the analysis results, and means for suggesting appropriate meals based on the user's mental state. This allows the user to receive specific meal suggestions that can help improve their mental state, thereby achieving more effective mental care.

[1087] A "user" refers to an individual who uses this system to perform a mental health check.

[1088] "Device" refers to the device that a user uses to answer questions in a mental health check. Examples include smartphones, tablets, and computers.

[1089] A "server" refers to a computer system used to receive, store, analyze, and generate feedback from users.

[1090] A "mental check" refers to a set of questions used to assess a user's emotions and stress levels.

[1091] "User responses" refer to the user's responses to the mental health check questions.

[1092] "Means of evaluation" refers to the algorithms and technologies used by the server to analyze the user's responses and evaluate their mental state.

[1093] "Feedback" refers to recommendations and advice that a server generates based on the user's mental state.

[1094] "Methods for suggesting meals" refers to algorithms and technologies used by servers to recommend the most suitable food and drink based on the user's mental state.

[1095] "Analysis results" refer to the evaluation results obtained by the server analyzing the user's response data.

[1096] "Data" refers to information that is stored and analyzed, including user responses and evaluations of their mental state.

[1097] An "emotion engine" refers to a program or system that uses technologies such as natural language processing to analyze user responses and generate an emotion score.

[1098] "Sentiment score" refers to a numerical evaluation value that quantifies whether a user's response is positive, negative, or neutral.

[1099] "Trend" refers to the tendency that indicates fluctuations in a user's mental state based on past data.

[1100] System Overview

[1101] This system allows users to perform mental health checks using a smartphone or other device, and the server receives, analyzes, and provides feedback. Furthermore, it suggests appropriate meals based on the user's mental state. This enables users to receive specific meal suggestions that can help improve their mental state, resulting in more effective mental care.

[1102] Hardware and software to be used

[1103] Hardware: Devices such as smartphones, tablets, and computers.

[1104] software:

[1105] Python: a programming language

[1106] Requests: HTTP Request Sending Library

[1107] EmotionEngine: Emotion Analysis Engine

[1108] Detailed processing of the system

[1109] 1. User actions:

[1110] Users log in to the system using their smartphones or tablets. The login screen has input fields for ID and password, and users enter this information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[1111] 2. Terminal processing:

[1112] The device receives input data from the user (responses to a mental health check), encrypts that information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that response data is encrypted and sent to the server. The device also displays feedback messages and meal suggestions sent from the server to the user in real time.

[1113] 3. Server processing:

[1114] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server receives and saves the user's response data. The saved data is then passed to the analysis engine and the emotion engine, which evaluate the user's mental state and generate an emotion score.

[1115] 4. Emotional engine processing:

[1116] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score is a numerical evaluation value that indicates whether the user's response is positive, negative, or neutral. The emotion engine also uses this emotion score to compare it with past response data and analyze the user's emotional trends. This allows for understanding fluctuations in the user's mental state.

[1117] 5. Server feedback and meal suggestion generation:

[1118] The server automatically generates appropriate feedback and meal suggestions for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, it might generate a message such as, "Your recent responses indicate a high stress level. We recommend a relaxing herbal tea or a healthy soup." The server then sends this feedback message and meal suggestion to the user's device.

[1119] 6. Displaying feedback on the device:

[1120] The device displays feedback messages and meal suggestions received from the server to the user in real time. The user can review this feedback and take appropriate action.

[1121] Specific example

[1122] For example, if a user answers "yes" to the question, "Have you been feeling down lately?", this response data is sent from the device to the server. The server analyzes the data, and the emotion engine evaluates it as "negative." Furthermore, it concludes that the user's emotional trend is "an increase in negative emotions over the past month." A feedback message is generated and sent to the user's device, such as, "It seems you've been feeling down lately. We recommend consulting a professional. You might also want to try relaxing herbal tea or a healthy soup." The user can then review this message and order the recommended meal.

[1123] Examples of prompts to input into a generative AI model

[1124] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[1125] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1126] Step 1:

[1127] The user logs into the system using a device.

[1128] The user accesses the system's login screen using a device (e.g., a smartphone). Here, they enter their user ID and password. The entered information is sent to the server for login authentication.

[1129] Input: User ID, Password

[1130] Output: Authentication token (upon successful login) or error message (upon login failure)

[1131] Data processing and calculation: The server compares the transmitted user ID and password with the information in the database to perform authentication.

[1132] Step 2:

[1133] The device displays mental health check questions.

[1134] Once the user successfully logs in, the terminal retrieves mental health check questions from the server and displays them on the screen. The user then answers these questions.

[1135] Input: Authenticated User ID, Authentication Token

[1136] Output: Mental health check questionnaire list

[1137] Data processing and calculation: The server verifies the authentication token and provides the user with mental check questions.

[1138] Step 3:

[1139] The user answers questions for a mental health check.

[1140] The user answers the displayed mental health check questions. These answers are collected by the device, encrypted, and sent to the server.

[1141] Input: User's mental health check responses

[1142] Output: Encrypted response data

[1143] Data processing and calculation: The terminal encrypts the user's response to ensure security before sending it to the server.

[1144] Step 4:

[1145] The server receives and stores the response data.

[1146] The server receives encrypted response data and stores it in a database. After storage, this data is passed to the analysis engine and the emotion engine.

[1147] Input: Encrypted response data

[1148] Output: Response data stored in the database

[1149] Data processing and calculation: The server decrypts the data and stores and manages the response data in the database.

[1150] Step 5:

[1151] The emotion engine analyzes the response data and generates an emotion score.

[1152] The emotion engine uses stored response data to perform natural language processing and calculate the user's emotion score.

[1153] Input: Response data

[1154] Output: Emotion score

[1155] Data Processing and Calculation: The emotion engine generates positive, negative, or neutral emotion scores from response data based on natural language processing technology.

[1156] Step 6:

[1157] The server generates feedback based on the analysis results and sentiment score.

[1158] Based on the emotional score and past response data, the server assesses the user's mental state and generates appropriate feedback messages and meal suggestions.

[1159] Input: Sentiment score, past response data

[1160] Output: Feedback message, meal suggestion

[1161] Data processing and calculation: The server automatically generates feedback based on the results obtained from the analysis engine and also provides meal suggestions tailored to the user's mental state.

[1162] Step 7:

[1163] The device displays feedback and meal suggestions received from the server.

[1164] The device displays feedback messages and meal suggestions sent from the server to the user in real time.

[1165] Input: Feedback message, meal suggestion

[1166] Output: Feedback message and meal suggestion displayed on the device screen.

[1167] Data processing and calculation: The terminal formats the received data appropriately and provides it to the user in an easy-to-read format.

[1168] Examples of prompts to input into a generative AI model

[1169] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[1170] 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.

[1171] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[1172] 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.

[1173] [Fourth Embodiment]

[1174] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1175] 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.

[1176] 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).

[1177] 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.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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".

[1187] This invention is a system for monitoring a user's mental state. The system aims to provide feedback by having the user answer a mental check, and then having the server analyze the response data. This system consists of three main elements: the user, the terminal, and the server.

[1188] User actions

[1189] First, the user logs into the system using their own device. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user logs in successfully, mental health check questions will be displayed on the device.

[1190] Terminal processing

[1191] The device receives input from the user, encrypts the information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[1192] Server Processing

[1193] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[1194] The analysis process uses techniques such as natural language processing to text-mine user responses and calculate sentiment scores. This quantifies the level of stress and anxiety a user is experiencing. The server also analyzes trends in the user's mental state using past response data. This allows the system to understand whether the user's condition is improving or worsening.

[1195] Ultimately, the server generates appropriate feedback for the user based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional," is generated and sent to the device. The device receives this feedback and displays it to the user.

[1196] Specific example

[1197] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed. The analysis results indicate that the user is feeling down, and a message is sent to the user's terminal as feedback: "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[1198] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[1199] The following describes the processing flow.

[1200] Step 1:

[1201] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[1202] Step 2:

[1203] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[1204] Step 3:

[1205] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[1206] Step 4:

[1207] The terminal receives a login success message and displays mental health check questions on the user's screen.

[1208] Step 5:

[1209] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[1210] Step 6:

[1211] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[1212] Step 7:

[1213] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine.

[1214] Step 8:

[1215] The server uses an analysis engine to analyze the response data. Specifically, it uses text mining and natural language processing to calculate sentiment scores and evaluate the user's mental state.

[1216] Step 9:

[1217] Based on the analysis results, the server analyzes trends in the user's mental state by comparing them with the user's past response data.

[1218] Step 10:

[1219] The server generates an appropriate feedback message based on the analysis results. Example: "Your stress level appears high. We recommend taking some time to relax or consulting a professional."

[1220] Step 11:

[1221] The server sends the generated feedback message to the terminal.

[1222] Step 12:

[1223] The device receives feedback messages and displays them to the user. The user reviews the displayed feedback and takes the necessary actions.

[1224] Through this series of steps, the system monitors the user's mental state and supports early problem detection and appropriate countermeasures.

[1225] (Example 1)

[1226] 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".

[1227] In modern society, mental health problems are on the rise, and early detection and appropriate treatment are crucial. However, conventional mental health check systems have struggled to monitor users' mental states in real time and provide appropriate feedback. Furthermore, they lacked the functionality to analyze trends in mental state using users' past data. As a result, the effectiveness of mental healthcare was limited, and useful support could not be provided to users.

[1228] 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.

[1229] In this invention, the server includes means for authenticating the user by matching their login information with a database, means for passing the stored response data to an analysis engine and evaluating the user's mental state using natural language processing technology, and means for sending feedback to the terminal based on the analysis results. This allows the user to check their mental state in real time and receive appropriate feedback, enabling early detection and response to mental health problems.

[1230] A "terminal" is a device that a user uses to access a system and perform input and reception. Specific examples include personal computers and smartphones.

[1231] "Authentication information" refers to the information a user needs to log in to a system, and typically consists of a user ID and password.

[1232] A "server" is a central computing system that receives, stores, and analyzes user authentication information and response data, generates feedback, and sends it to the terminal.

[1233] A "database" is a place where a server stores user login information and response data, and uses that information for later verification and analysis.

[1234] "Authentication" is the process by which a server verifies a user's login information to confirm that the user is a legitimate user.

[1235] "Encryption" is a technology that transforms data so that a device can securely transmit user response data to a server, preventing unauthorized access by third parties.

[1236] "Natural language processing technology" refers to techniques that allow servers to analyze user response data and perform text mining and sentiment analysis. Specific examples include technologies such as NLTK and spaCy.

[1237] "Feedback" refers to messages generated by the server based on analysis results, offering advice and suggestions for the next steps to the user.

[1238] "Trend analysis" is a method used by servers to evaluate changes and trends in users' mental states by utilizing past response data.

[1239] This invention is a system for monitoring a user's mental state. The system involves the user answering a mental check, and a server analyzing the response data to provide feedback. This system consists of three main elements: the user, the terminal, and the server.

[1240] User actions

[1241] First, the user logs into the system using their own device (computer or smartphone). The login screen displays input fields for user ID and password, which the user enters and clicks the "Login" button. If the login is successful, the device displays mental health check questions to the user. For example, a question such as "Have you been feeling down a lot lately?" may be displayed.

[1242] Terminal processing

[1243] The device receives user response data, encrypts it, and sends it to the server. AES (Advanced Encryption Standard) is used for encryption, and communication takes place via the HTTPS protocol. For example, if the user responds "yes," that data is encrypted and securely sent to the server. The device also receives feedback messages from the server in real time and displays them to the user.

[1244] Server Processing

[1245] The server first authenticates the user by matching their login information against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it then receives the mental check responses submitted by the user and stores them in the database. This data is passed to an analysis engine, which uses natural language processing techniques (e.g., NLTK or spaCy) to perform text mining and calculate a sentiment score. Based on this score, the server evaluates the user's mental state. Furthermore, the server uses past response data to analyze trends in the user's mental state. This trend analysis allows the server to determine whether the user's condition is improving or worsening.

[1246] Feedback generation

[1247] The server generates a feedback message based on the analysis results. For example, a message such as, "Your stress level appears high. We recommend taking some time to relax or consulting a professional," is generated and sent to the terminal. The terminal receives this message and displays it to the user in real time.

[1248] Specific example

[1249] When a user logs into the system using their home computer and answers "yes" to the question, "I've been feeling down a lot lately," this response data is encrypted and sent to the server. The server analyzes this data, assesses that the user is feeling down, and generates and sends a feedback message to the user's device stating, "If this continues, please consult a professional." The user can then review this message and take appropriate action.

[1250] Example of a prompt

[1251] "Please describe the detailed process by which a user logs into the system, performs a mental health check, and the results are analyzed on the server to generate feedback."

[1252] In this way, the system of the present invention allows users to easily check their own mental state, identify problems early, and take appropriate action.

[1253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1254] Step 1:

[1255] The user uses a device to enter authentication information and log in to the system.

[1256] Input: User ID, Password

[1257] Specific steps: The user enters their user ID and password on their computer or smartphone screen and clicks the "Login" button.

[1258] Output: Sending authentication information from the terminal to the server

[1259] Step 2:

[1260] The server authenticates the user by comparing their login information with the database.

[1261] Input: User ID and password sent from the device.

[1262] Specific operation: The server compares the received user ID and password with the database to check if they match. If authentication is successful, the user can log in to the system.

[1263] Output: Authentication result (success or failure) is sent back to the terminal.

[1264] Step 3:

[1265] The user uses a device to answer questions for a mental health check.

[1266] Input: Question displayed after logging in

[1267] Specific operation: The user selects an answer from the given options for each mental health check question displayed on the device and enters the answer.

[1268] Output: User response data

[1269] Step 4:

[1270] The device encrypts the user's response data and sends it to the server.

[1271] Input: User response data

[1272] Specific operation: The device encrypts the response data using AES (Advanced Encryption Standard) and sends it to the server using the HTTPS protocol.

[1273] Output: Encrypted response data sent to the server

[1274] Step 5:

[1275] The server receives the user's response data and saves it to the database.

[1276] Input: Encrypted response data sent from the device

[1277] Specific operation: The server receives encrypted data and decrypts it using AES. The decrypted data is then saved to the database.

[1278] Output: Saving response data to the database

[1279] Step 6:

[1280] The server passes the stored response data to the analysis engine, which then uses natural language processing technology to evaluate the mental state.

[1281] Input: Saved response data

[1282] Specific operation: The server passes the stored response data to an analysis engine (e.g., NLTK or spaCy) for text mining and sentiment analysis. This quantifies the user's stress level, anxiety index, and other metrics.

[1283] Output: Analysis results (emotion score, evaluation results)

[1284] Step 7:

[1285] The server generates feedback based on the analysis results and sends it to the terminal.

[1286] Input: Analysis results (emotion score, evaluation results)

[1287] Specific operation: The server generates a feedback message based on the analysis results (e.g., "Your stress level appears high. We recommend you take some time to relax or consult a professional"). The generated feedback is then sent to the terminal.

[1288] Output: Feedback message

[1289] Step 8:

[1290] The device displays feedback messages sent from the server in real time.

[1291] Input: Feedback message sent from the server

[1292] Specific operation: The terminal receives feedback messages from the server and displays them to the user in real time.

[1293] Output: Display of feedback message to the user

[1294] (Application Example 1)

[1295] 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".

[1296] In modern factories, workers are often exposed to harsh working conditions and high levels of stress, which contribute to decreased productivity and health problems. Traditional mental health monitoring systems rely solely on self-reporting by individual workers, making real-time monitoring and immediate feedback difficult. As a result, there is a challenge in that serious mental health problems often go undetected for a long time.

[1297] 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.

[1298] In this invention, the server includes means for a user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the mental state, and means for a supervisory robot to collect mental check data of workers in the factory and transmit it to the server. This makes it possible to monitor the mental state of workers in the factory in real time and provide rapid feedback.

[1299] A "user" is a person who logs into the system and answers the mental health check questions.

[1300] A "terminal" is a device used by users to answer mental health check questions and is responsible for transmitting data to the server.

[1301] A "server" is a central processing unit that receives, analyzes, and generates feedback data from users' mental health check responses, then sends it to the terminal.

[1302] A "mental check" is a series of questions that users answer to assess their own mental state.

[1303] "Answers" refer to the data that users enter in response to questions in a mental health check.

[1304] "Saving" refers to the act of a server accumulating user response data that it has received.

[1305] "Analysis" is the process by which a server processes stored response data and evaluates the user's mental state.

[1306] "Feedback" refers to advice and notifications that the server generates and provides to the user based on its analysis results.

[1307] A "supervising robot" is a robot whose role is to collect mental health check data from workers within a factory and send it to a server.

[1308] A "worker" is a person who works in a factory and is subject to mental health checks.

[1309] This invention is a system for monitoring the mental state of factory workers in real time and providing rapid feedback. The system consists of four main components: users, terminals, a server, and a supervisory robot.

[1310] User actions

[1311] First, the user (worker) logs into the system using a terminal within the factory. The login screen has input fields for ID and password, and the user enters their authentication information and clicks the "Login" button. If the user successfully logs in, mental health check questions will be displayed on the terminal. For example, the user will answer questions such as, "Have you been feeling stressed lately?"

[1312] Terminal processing

[1313] The device receives input from the user, encrypts the information, and sends it to the server. For example, if the user answers "yes," that answer is securely sent from the device to the server. The device also receives feedback messages from the server and displays them to the user in real time.

[1314] Server Processing

[1315] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server then receives and stores the mental check data that the user has answered. The stored data is then passed to the analysis engine, which evaluates the user's mental state in detail.

[1316] The analysis process uses natural language processing techniques to text-mine user responses and calculate a sentiment score. This quantifies the level of stress and anxiety the user is experiencing. The server also analyzes trends in mental state using past response data, allowing it to determine whether the user's condition is improving or worsening. Finally, based on the analysis results, the server generates appropriate feedback for the user and sends it to their device.

[1317] Functions of the supervisor robot

[1318] Supervising robots are stationed throughout the factory. These robots are responsible for collecting mental health check data from workers and sending it to a server. The supervisory robots patrol the factory, presenting workers with mental health check questions and collecting their responses.

[1319] Hardware and software used

[1320] The following hardware and software are used to implement the system:

[1321] User's device: A smartphone or dedicated device used by the worker.

[1322] Server: A central processing unit containing the database and analysis engine (e.g., MySQL database and TensorFlow analysis engine).

[1323] Supervising robot: A robot that collects mental health check data from workers and sends it to a server.

[1324] Specific example

[1325] For example, if a worker answers "yes" to the question, "I've been feeling down a lot lately," this response is sent from the terminal to the server, and the analysis results indicate that the worker is "feeling down." The server then generates a feedback message, "If this continues, please consult a professional," and sends it to the terminal. The worker can then review this message and take appropriate action.

[1326] Examples of prompts for generative AI models

[1327] Please analyze the following mental check data and generate an appropriate feedback message:

[1328] High stress levels

[1329] I've been feeling down lately.

[1330] By accurately understanding the mental state of workers throughout the entire system and providing prompt feedback, improvements in the work environment and increased productivity can be expected.

[1331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1332] Step 1:

[1333] The user logs into the system using their device.

[1334] Enter: ID and password

[1335] Output: Authentication result (success or failure)

[1336] Specific operation: The user enters their ID and password on the login screen and clicks the "Login" button. The device sends this information to the server. The server verifies the information against the database, performs authentication, and sends the authentication result to the device.

[1337] Step 2:

[1338] The user answers questions for a mental health check.

[1339] Input: Mental health check questions and user responses

[1340] Output: Encrypted response data

[1341] Specific operation: Mental health check questions are displayed on the user's device. The user answers each question, and the answers are encrypted on the device.

[1342] Step 3:

[1343] The device sends encrypted response data to the server.

[1344] Input: Encrypted response data

[1345] Output: Response data stored on the server

[1346] Specific operation: The device sends encrypted response data to the server. The server receives this data and stores it securely.

[1347] Step 4:

[1348] The server analyzes the response data it receives using an analysis engine.

[1349] Input: Saved response data

[1350] Output: Analysis results (evaluation of mental state)

[1351] Specific operation: The server passes the received response data to the analysis engine, which uses natural language processing technology to calculate an emotion score. This process quantifies how much stress or anxiety the user is experiencing.

[1352] Step 5:

[1353] The server generates a feedback message based on the analysis results.

[1354] Input: Analysis results

[1355] Output: Feedback message

[1356] Specific operation: Based on the analysis results, the server generates an appropriate feedback message. For example, it might create a message such as, "Your stress level appears high. We recommend you take some time to relax or consult a professional."

[1357] Step 6:

[1358] A supervisory robot collects mental health check data from factory workers and sends it to a server.

[1359] Input: Responses to the worker's mental health check

[1360] Output: Check data sent to the server

[1361] Specific operation: The supervisory robot patrols the factory and presents workers with mental health check questions. It collects the workers' responses and sends the data to a server. The server receives this data and proceeds with analysis.

[1362] Step 7:

[1363] A feedback message is sent to the device and displayed to the user.

[1364] Input: Feedback message

[1365] Output: Notification to the user

[1366] Specific operation: The server generates a feedback message and sends it to the terminal. The terminal receives this message and displays it to the user in real time. The user can then review the message and take appropriate action.

[1367] 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.

[1368] This invention provides a system for monitoring a user's mental state and providing feedback, incorporating an emotion engine that recognizes the user's emotions to achieve more accurate and data-driven mental care. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[1369] User actions

[1370] Users log in to the system using their own devices. The login screen provides input fields for ID and password, and users enter this authentication information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[1371] Terminal processing

[1372] The device receives input data from the user, encrypts that information, and sends it to the server. If the user answers "yes" to the question "Have you been feeling stressed lately?", that answer is securely sent from the device to the server. The device also displays feedback messages from the server to the user in real time.

[1373] Server Processing

[1374] The server first authenticates the user by comparing their login information with the database. If authentication is successful, the server receives and stores the user's response data. The stored data is then passed to the analysis engine and the sentiment engine.

[1375] Emotional Engine Processing

[1376] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score quantifies whether the user's response is positive, negative, or neutral. For example, the emotion engine would rate the response "I've been feeling down a lot lately" as "negative."

[1377] The emotion engine further uses this emotion score to analyze the user's emotional trends by comparing it with past response data. This allows it to understand whether the user's mental state is improving or worsening.

[1378] Server Feedback Generation

[1379] The server generates appropriate feedback for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, a message such as, "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional," is automatically generated. The server then sends this feedback message to the device.

[1380] Device feedback display

[1381] The device receives feedback messages and displays them to the user in real time. The user can review this feedback and take the necessary actions.

[1382] Specific example

[1383] Let's say a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This answer is sent from the device to the server, where it is analyzed and an emotion engine calculates an emotion score. The analysis results in an assessment that the user is "feeling down," and the emotion trend concludes that "negative emotions have increased over the past month." As feedback, a message is sent to the user's device stating, "You seem to have been feeling down lately. We recommend you consult a professional." The user can then review this message and take appropriate action.

[1384] Thus, the system of the present invention allows users to easily monitor their own mental state, and by using an emotion engine, it is possible to provide more advanced and accurate feedback.

[1385] The following describes the processing flow.

[1386] Step 1:

[1387] The user opens the login screen using their device. The device displays the user interface and provides input fields for ID and password.

[1388] Step 2:

[1389] The user enters their login information and clicks the "Login" button. The terminal encrypts the entered information and sends it to the server.

[1390] Step 3:

[1391] The server verifies the received login information against its database and performs authentication. If authentication is successful, the server sends a login success message to the terminal. If authentication fails, the server sends an error message to the terminal.

[1392] Step 4:

[1393] The terminal receives a login success message and displays mental health check questions on the user's screen.

[1394] Step 5:

[1395] The user answers mental health check questions displayed on the device. For example, to the question, "Have you been feeling stressed lately?", they answer "Yes" or "No".

[1396] Step 6:

[1397] The device receives the user's response, formats the data, encrypts it, and then sends it to the server.

[1398] Step 7:

[1399] The server receives the response data and stores it securely. The stored data is then passed to the analysis engine and the sentiment engine.

[1400] Step 8:

[1401] The sentiment engine uses natural language processing to analyze the user's response and generate a sentiment score. This score quantifies whether the response is positive, negative, or neutral.

[1402] Step 9:

[1403] By combining data from the analysis engine and the emotion engine, the server comprehensively evaluates the user's mental state.

[1404] Step 10:

[1405] The server analyzes sentiment trends by comparing the analysis results and sentiment scores with the user's past response data.

[1406] Step 11:

[1407] The server generates appropriate feedback messages based on analysis results and sentiment trends. Example: "Your recent responses indicate a high stress level. We recommend taking some time to relax or consulting a professional."

[1408] Step 12:

[1409] The server sends the generated feedback message to the terminal.

[1410] Step 13:

[1411] The device receives the feedback message and displays it to the user. The user reviews the feedback and takes the necessary action.

[1412] (Example 2)

[1413] 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".

[1414] Traditional mental healthcare systems have struggled to accurately recognize users' emotions and provide appropriate feedback. Furthermore, they lacked the ability to track trends in users' mental states, making long-term mental healthcare for individual users difficult.

[1415] 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.

[1416] In this invention, the server includes means for receiving and securely storing user responses, means for analyzing the stored response data using a natural language processing engine to generate an emotion score and evaluate the user's mental state, and means for generating feedback based on the evaluation results and transmitting it to a communication device. This makes it possible to accurately recognize the user's mental state and provide individualized feedback, and further enables comprehensive mental care through analysis of long-term trends.

[1417] "Communication equipment" refers to devices used by users to access the system and answer questions in a mental health check, such as personal computers and smartphones.

[1418] A "server" is a central system that receives data sent from users, analyzes it, generates feedback, and sends it back.

[1419] A "natural language processing engine" is an artificial intelligence technology used to analyze user response data and generate sentiment scores, and includes models such as BERT and GPT-3.

[1420] The "emotion score" is a numerical representation of whether a user's response is positive, negative, or neutral.

[1421] "Feedback" refers to advice and recommendations provided to the user based on the evaluation results generated by the server.

[1422] "Emotional trends" refer to long-term patterns of mental state obtained by analyzing changes in users' emotions based on past response data.

[1423] "Login information" refers to authentication data that a user uses to access the system, and includes, for example, an ID and password.

[1424] "Analysis results" refer to the results of data analysis obtained by the natural language processing engine, and specifically include information such as sentiment scores and sentiment trends.

[1425] "Saving" refers to securely recording received data in a database so that it can be analyzed and referenced later.

[1426] Modes for carrying out the invention

[1427] This invention relates to a system that monitors a user's mental state and provides appropriate feedback. This system consists of four main elements: the user, the terminal, the server, and the natural language processing engine. Each of these elements is described in detail below.

[1428] User actions

[1429] First, users access the system using a communication device (such as a personal computer or smartphone). The login screen displays fields for entering an ID and password, and users log in by entering this authentication information. Upon successful authentication, mental health check questions are displayed.

[1430] Terminal processing

[1431] The device receives response data from the user. This data is encrypted using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). The device also displays feedback messages sent from the server to the user in real time.

[1432] Server Processing

[1433] The server first verifies the user's login information against a database (e.g., MySQL) to perform authentication. If authentication is successful, it decrypts the encrypted user response data and stores it in the database. The stored data is then passed to the parsing engine and the natural language processing engine.

[1434] Processing by a natural language processing engine

[1435] The natural language processing engine analyzes the user's responses using models such as BERT and GPT-3. A sentiment score is generated from the results of this analysis. For example, in response to the answer "I've been feeling down a lot lately," the natural language processing engine evaluates it as "negative" and assigns a corresponding sentiment score.

[1436] Server Feedback Generation

[1437] The server receives the sentiment score and analysis results from the natural language processing engine and analyzes the user's sentiment trends by comparing them with past data. Based on these results, the server generates appropriate feedback to provide to the user. For example, a message such as, "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional," might be generated.

[1438] Device feedback display

[1439] The server re-encrypts the generated feedback message and sends it to the terminal. The terminal decrypts it and displays it to the user in real time. The user can then review this feedback and take any necessary actions.

[1440] Specific example

[1441] Consider a scenario where a user logs into the system using their home computer and answers "yes" to the question, "Have you been feeling down lately?" This response is sent from the terminal to the server, where it is analyzed and a sentiment score is calculated using a natural language processing engine. The analysis results in an assessment of "feeling down," and the sentiment trend is concluded to be "negative emotions have increased over the past month." A feedback message is sent to the user's terminal stating, "You seem to have been feeling down lately. We recommend consulting a professional." The user can then review this message and take appropriate action.

[1442] Examples of prompt statements include the following:

[1443] "Have you been feeling down lately? Yes No"

[1444] In this way, the system of the present invention can accurately monitor the user's mental state and provide individualized feedback.

[1445] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1446] Step 1:

[1447] Users access the system using communication devices and log in by entering their ID and password. The input includes the user's "ID" and "password." The system receives this authentication information and sends a login request to the server. At this time, the terminal sends the user's input data to the server. The system returns information indicating whether the login was successful or unsuccessful.

[1448] Step 2:

[1449] The server verifies the received login information against the database and performs authentication. It queries the database (e.g., MySQL) for the user's "ID" and "password" and outputs the verification result. If authentication is successful, the server generates a login success status and sends it to the terminal. The output is either "Login Success or Failure Status".

[1450] Step 3:

[1451] Once a user successfully logs in, a mental health check question will appear on their device. For example: "Have you been feeling down lately?" The user answers this question with "yes" or "no." The input at this time is the user's answer ("yes" or "no").

[1452] Step 4:

[1453] The terminal encrypts the user's input data using AES encryption technology and sends the encrypted data to the server using the HTTPS protocol. The input is the "user's response," and the output is the "encrypted user's response." Specifically, an encrypted "yes" or "no" is sent to the server.

[1454] Step 5:

[1455] The server receives encrypted user responses and decrypts them. The input is the "encrypted response," and the output is the "decrypted user response." After decryption, the server saves the response data to a database.

[1456] Step 6:

[1457] The server passes the stored data to a natural language processing engine (e.g., BERT or GPT-3) for analysis. The input is "user response data," and the output is a "sentiment score." Specifically, if the response data is "I've been feeling down a lot lately," the natural language processing engine will generate a sentiment score indicating "negative."

[1458] Step 7:

[1459] The server analyzes the user's emotional trends by comparing the generated emotional score with past data. The inputs are the "emotional score" and "past response data," and the output is the "emotional trend analysis result." Specifically, the emotional trend is assessed as "negative emotions have increased over the past month."

[1460] Step 8:

[1461] The server generates appropriate feedback messages for the user based on the results of the sentiment trend analysis. The input is the "sentiment trend analysis results," and the output is the "feedback message." An example message might be: "Your recent responses indicate a high stress level. We recommend you take some time to relax or consult a professional."

[1462] Step 9:

[1463] The server re-encrypts the generated feedback message and sends it to the terminal using the HTTPS protocol. The input is the "feedback message," and the output is the "encrypted feedback message."

[1464] Step 10:

[1465] The terminal receives encrypted feedback messages sent from the server, decrypts them, and displays them to the user in real time. The user reviews the displayed feedback and takes the necessary actions. The input is the "encrypted feedback message," and the output is the "feedback message displayed to the user."

[1466] (Application Example 2)

[1467] 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".

[1468] Traditional mental healthcare systems primarily focused on providing appropriate feedback based on the user's emotional state, lacking specific action suggestions tailored to the user's mental condition and integration with external services. As a result, users struggled to find concrete coping mechanisms suited to their emotions, leading to limited effectiveness of mental healthcare. In particular, there was a lack of feedback directly relevant to daily life, such as suggestions for foods and drinks to reduce stress and anxiety.

[1469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1470] In this invention, the server includes means for the user to answer mental check questions using a terminal, means for the server to receive and store the user's answers, means for the server to analyze the stored answer data and evaluate the user's mental state, means for the server to send feedback to the terminal based on the analysis results, and means for suggesting appropriate meals based on the user's mental state. This allows the user to receive specific meal suggestions that can help improve their mental state, thereby achieving more effective mental care.

[1471] A "user" refers to an individual who uses this system to perform a mental health check.

[1472] "Device" refers to the device that a user uses to answer questions in a mental health check. Examples include smartphones, tablets, and computers.

[1473] A "server" refers to a computer system used to receive, store, analyze, and generate feedback from users.

[1474] A "mental check" refers to a set of questions used to assess a user's emotions and stress levels.

[1475] "User responses" refer to the user's responses to the mental health check questions.

[1476] "Means of evaluation" refers to the algorithms and technologies used by the server to analyze the user's responses and evaluate their mental state.

[1477] "Feedback" refers to recommendations and advice that a server generates based on the user's mental state.

[1478] "Methods for suggesting meals" refers to algorithms and technologies used by servers to recommend the most suitable food and drink based on the user's mental state.

[1479] "Analysis results" refer to the evaluation results obtained by the server analyzing the user's response data.

[1480] "Data" refers to information that is stored and analyzed, including user responses and evaluations of their mental state.

[1481] An "emotion engine" refers to a program or system that uses technologies such as natural language processing to analyze user responses and generate an emotion score.

[1482] "Sentiment score" refers to a numerical evaluation value that quantifies whether a user's response is positive, negative, or neutral.

[1483] "Trend" refers to the tendency that indicates fluctuations in a user's mental state based on past data.

[1484] System Overview

[1485] This system allows users to perform mental health checks using a smartphone or other device, and the server receives, analyzes, and provides feedback. Furthermore, it suggests appropriate meals based on the user's mental state. This enables users to receive specific meal suggestions that can help improve their mental state, resulting in more effective mental care.

[1486] Hardware and software to be used

[1487] Hardware: Devices such as smartphones, tablets, and computers.

[1488] software:

[1489] Python: a programming language

[1490] Requests: HTTP Request Sending Library

[1491] EmotionEngine: Emotion Analysis Engine

[1492] Detailed processing of the system

[1493] 1. User actions:

[1494] Users log in to the system using their smartphones or tablets. The login screen has input fields for ID and password, and users enter this information and click the "Login" button. Upon successful login, mental health check questions are displayed on the device.

[1495] 2. Terminal processing:

[1496] The device receives input data from the user (responses to a mental health check), encrypts that information, and sends it to the server. For example, if a user answers "yes" to the question "Have you been feeling stressed lately?", that response data is encrypted and sent to the server. The device also displays feedback messages and meal suggestions sent from the server to the user in real time.

[1497] 3. Server processing:

[1498] The server first authenticates the user by matching their login information against the database. If authentication is successful, the server receives and saves the user's response data. The saved data is then passed to the analysis engine and the emotion engine, which evaluate the user's mental state and generate an emotion score.

[1499] 4. Emotional engine processing:

[1500] The emotion engine uses natural language processing to analyze user responses and generate an emotion score. This emotion score is a numerical evaluation value that indicates whether the user's response is positive, negative, or neutral. The emotion engine also uses this emotion score to compare it with past response data and analyze the user's emotional trends. This allows for understanding fluctuations in the user's mental state.

[1501] 5. Server feedback and meal suggestion generation:

[1502] The server automatically generates appropriate feedback and meal suggestions for the user based on analysis results, emotion scores, and trend information obtained from the emotion engine. For example, it might generate a message such as, "Your recent responses indicate a high stress level. We recommend a relaxing herbal tea or a healthy soup." The server then sends this feedback message and meal suggestion to the user's device.

[1503] 6. Displaying feedback on the device:

[1504] The device displays feedback messages and meal suggestions received from the server to the user in real time. The user can review this feedback and take appropriate action.

[1505] Specific example

[1506] For example, if a user answers "yes" to the question, "Have you been feeling down lately?", this response data is sent from the device to the server. The server analyzes the data, and the emotion engine evaluates it as "negative." Furthermore, it concludes that the user's emotional trend is "an increase in negative emotions over the past month." A feedback message is generated and sent to the user's device, such as, "It seems you've been feeling down lately. We recommend consulting a professional. You might also want to try relaxing herbal tea or a healthy soup." The user can then review this message and order the recommended meal.

[1507] Examples of prompts to input into a generative AI model

[1508] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[1509] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1510] Step 1:

[1511] The user logs into the system using a device.

[1512] The user accesses the system's login screen using a device (e.g., a smartphone). Here, they enter their user ID and password. The entered information is sent to the server for login authentication.

[1513] Input: User ID, Password

[1514] Output: Authentication token (upon successful login) or error message (upon login failure)

[1515] Data processing and calculation: The server compares the transmitted user ID and password with the information in the database to perform authentication.

[1516] Step 2:

[1517] The device displays mental health check questions.

[1518] Once the user successfully logs in, the terminal retrieves mental health check questions from the server and displays them on the screen. The user then answers these questions.

[1519] Input: Authenticated User ID, Authentication Token

[1520] Output: Mental health check questionnaire list

[1521] Data processing and calculation: The server verifies the authentication token and provides the user with mental check questions.

[1522] Step 3:

[1523] The user answers questions for a mental health check.

[1524] The user answers the displayed mental health check questions. These answers are collected by the device, encrypted, and sent to the server.

[1525] Input: User's mental health check responses

[1526] Output: Encrypted response data

[1527] Data processing and calculation: The terminal encrypts the user's response to ensure security before sending it to the server.

[1528] Step 4:

[1529] The server receives and stores the response data.

[1530] The server receives encrypted response data and stores it in a database. After storage, this data is passed to the analysis engine and the emotion engine.

[1531] Input: Encrypted response data

[1532] Output: Response data stored in the database

[1533] Data processing and calculation: The server decrypts the data and stores and manages the response data in the database.

[1534] Step 5:

[1535] The emotion engine analyzes the response data and generates an emotion score.

[1536] The emotion engine uses stored response data to perform natural language processing and calculate the user's emotion score.

[1537] Input: Response data

[1538] Output: Emotion score

[1539] Data Processing and Calculation: The emotion engine generates positive, negative, or neutral emotion scores from response data based on natural language processing technology.

[1540] Step 6:

[1541] The server generates feedback based on the analysis results and sentiment score.

[1542] Based on the emotional score and past response data, the server assesses the user's mental state and generates appropriate feedback messages and meal suggestions.

[1543] Input: Sentiment score, past response data

[1544] Output: Feedback message, meal suggestion

[1545] Data processing and calculation: The server automatically generates feedback based on the results obtained from the analysis engine and also provides meal suggestions tailored to the user's mental state.

[1546] Step 7:

[1547] The device displays feedback and meal suggestions received from the server.

[1548] The device displays feedback messages and meal suggestions sent from the server to the user in real time.

[1549] Input: Feedback message, meal suggestion

[1550] Output: Feedback message and meal suggestion displayed on the device screen.

[1551] Data processing and calculation: The terminal formats the received data appropriately and provides it to the user in an easy-to-read format.

[1552] Examples of prompts to input into a generative AI model

[1553] "Please tell me about suggestions for relaxing meals that should be offered to users who are feeling stressed."

[1554] 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.

[1555] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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."

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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 as being incorporated by reference.

[1575] The following is further disclosed regarding the embodiments described above.

[1576] (Claim 1)

[1577] A means for users to answer mental health check questions using a device,

[1578] A means for the server to receive and store user responses,

[1579] The server analyzes the stored response data and provides a means to evaluate the mental state.

[1580] A means by which the server sends feedback to the terminal based on the analysis results,

[1581] A system that includes this.

[1582] (Claim 2)

[1583] The system according to claim 1, further comprising means for the server to authenticate the user's login information.

[1584] (Claim 3)

[1585] The system according to claim 1, further comprising means by which the server uses past response data to analyze trends in mental state.

[1586] "Example 1"

[1587] (Claim 1)

[1588] A means for the user to use a device to enter authentication information and log in to the system,

[1589] A means for users to answer mental health check questions using a device,

[1590] A means by which the terminal encrypts the user's response data and sends it to the server,

[1591] A means by which the server authenticates a user by comparing their login information with a database,

[1592] A means for the server to receive and store user responses,

[1593] A server passes the stored response data to an analysis engine, and a means of evaluating the mental state using natural language processing technology,

[1594] A means by which the server sends feedback to the terminal based on the analysis results,

[1595] A system that includes this.

[1596] (Claim 2)

[1597] The system according to claim 1, further comprising means by which the server uses the user's past response data to analyze trends in their mental state.

[1598] (Claim 3)

[1599] The system according to claim 1, further comprising means for the terminal to display feedback messages sent from the server in real time.

[1600] "Application Example 1"

[1601] (Claim 1)

[1602] A means for users to answer mental health check questions using a device,

[1603] A means for the server to receive and store user responses,

[1604] The server analyzes the stored response data and provides a means to evaluate the mental state.

[1605] A means by which the server sends feedback to the terminal based on the analysis results,

[1606] A means by which a supervisory robot collects mental health check data of factory workers and transmits it to a server,

[1607] A system that includes this.

[1608] (Claim 2)

[1609] The system according to claim 1, further comprising means for the server to authenticate the user's login information.

[1610] (Claim 3)

[1611] The system according to claim 1, further comprising means by which the server uses past response data to analyze trends in mental state.

[1612] "Example 2 of combining an emotion engine"

[1613] (Claim 1)

[1614] A means for users to answer mental health check questions using communication devices,

[1615] A means for the server to receive and securely store user responses,

[1616] A method for evaluating mental state by analyzing the response data stored on the server using a natural language processing engine, generating an emotion score, and

[1617] A means by which the server generates feedback based on the evaluation results and transmits it to a communication device,

[1618] A system that includes this.

[1619] (Claim 2)

[1620] The system according to claim 1, further comprising means for the server to authenticate the user's login information.

[1621] (Claim 3)

[1622] The system according to claim 1, further comprising means by which the server uses past response data to analyze sentiment trends.

[1623] "Application example 2 when combining with an emotional engine"

[1624] (Claim 1)

[1625] A means for users to answer mental health check questions using a device,

[1626] A means for the server to receive and store user responses,

[1627] The server analyzes the stored response data and provides a means to evaluate the mental state.

[1628] A means by which the server sends feedback to the terminal based on the analysis results,

[1629] A means of suggesting appropriate meals based on the user's mental state,

[1630] ...

[1631] A system that includes this.

[1632] (Claim 2)

[1633] The system according to claim 1, further comprising means for the server to authenticate the user's login information.

[1634] (Claim 3)

[1635] The system according to claim 1, further comprising means by which the server uses past response data to analyze trends in mental state. [Explanation of symbols]

[1636] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to answer mental health check questions using a device, A means for the server to receive and store user responses, The server analyzes the stored response data and provides a means to evaluate the mental state. A means by which the server sends feedback to the terminal based on the analysis results, A system that includes this.

2. The system according to claim 1, further comprising means for the server to authenticate the user's login information.

3. The system according to claim 1, further comprising means by which the server uses past response data to analyze trends in mental state.

Images