System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting information from a terminal by a user, a means for transmitting data to a server by the terminal, a means for processing and storing the data by the server, a means for transmitting confirmation mail by the server, and a means for confirming the mail and clicking a link by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, mental health issues are a significant challenge for many people. However, traditional mental health services are expensive, often associated with stigma, and can be difficult to access. As a result, many people are unable to receive the support they need. This invention aims to provide affordable, stigma-free mental health support that is available anywhere, anytime. [Means for solving the problem]

[0005] The present invention provides a system that allows users to input information from their devices, which then send the data to a server. The server processes and stores the received data and completes the user's registration by sending a confirmation email. The server also generates and sends a list of questions to users who log in using their devices, and evaluates their mental health by analyzing the data the users provide. Based on the evaluation, the server creates a personalized support plan and provides it to the user. Furthermore, the server collects feedback from users, analyzes it, and updates the AI ​​model to continuously improve the plan. This series of measures enables the realization of a system that provides affordable, accessible, and personalized mental health support.

[0006] "User" refers to an individual who uses the system.

[0007] "Terminal" refers to a device through which a user accesses and operates the system.

[0008] "Server" refers to a computer system that receives data from users and provides various functions such as processing, storing, and transmitting data.

[0009] "Data" refers to all information entered by users and generated by systems.

[0010] "Processing data" refers to performing operations such as analyzing, evaluating, and storing data.

[0011] "Confirmation email" refers to a confirmation email sent to the email address registered by the user.

[0012] "Questionnaire" refers to a set of questions for assessing a user's mental health.

[0013] "Feedback" refers to opinions and evaluations provided by users regarding the support they have used.

[0014] "Support Plan" refers to specific plans and suggestions provided by the server to support the user's mental health.

[0015] "Guided meditation" refers to a meditation session accompanied by audio or video instruction.

[0016] "Cognitive behavioral therapy (CBT) exercises" refer to exercises based on cognitive behavioral therapy techniques.

[0017] "Natural language processing (NLP)" refers to the technology of using computers to analyze and understand human language.

[0018] "AI model" refers to an artificial intelligence model built using machine learning algorithms.

[0019] "Training data" refers to the data used to train an AI model. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention relates to a system that provides personalized mental health support by having a server process information entered by a user via a device. The system assesses the user's mental health status, provides an appropriate support plan, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0042] User Registration

[0043] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0044] Initial diagnosis

[0045] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0046] Ongoing support

[0047] Based on the assessment results, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0048] Feedback and AI Learning

[0049] Users provide feedback on the support plan from their device, which is then sent to the server, where it is analyzed and used to update the AI ​​model, allowing the server to continually provide more appropriate and effective support to users.

[0050] Specific examples

[0051] For example, if a user inputs information about their recent stress levels, the server will analyze that data and suggest guided meditation sessions to help reduce stress, adjusting the content and frequency of these sessions based on user feedback.

[0052] In this way, the system of the present invention effectively supports users' mental health conditions and provides personalized plans, enabling affordable and accessible solutions to mental health problems.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0056] Step 2:

[0057] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0058] Step 3:

[0059] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0060] Step 4:

[0061] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0062] Step 5:

[0063] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0064] Step 6:

[0065] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0066] Step 7:

[0067] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0068] Step 8:

[0069] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0070] Step 9:

[0071] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0072] Step 10:

[0073] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0074] Step 11:

[0075] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0076] Step 12:

[0077] The server then creates a support plan based on the assessment results, including stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0078] Step 13:

[0079] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0080] Step 14:

[0081] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0082] Step 15:

[0083] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0084] Step 16:

[0085] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0086] Step 17:

[0087] The server analyzes the feedback and updates the AI ​​model, specifically using the feedback data as training data for the AI ​​model and retraining the model with machine learning algorithms.

[0088] Example 1

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

[0090] In modern society, the number of users experiencing a deterioration in their mental health is increasing, but there are limited means to effectively provide personalized support. Furthermore, conventional methods make it difficult to accurately assess a user's mental health and create an appropriate support plan based on that assessment. Furthermore, there is a lack of means to effectively improve the system by utilizing subsequent feedback.

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

[0092] In this invention, the server includes a means for a user to input information from a terminal, a means for the terminal to send data to the server, a means for the server to process and store the data, a means for the server to send a confirmation message, and a means for the user to check the message and click a link. The system also includes a means for a user to log in using a terminal, a means for the server to generate and send a list of questions, a means for the user to answer the questions, a means for the terminal to send the answers to the server, and a means for the server to analyze and evaluate the answers using natural language processing technology and a machine learning algorithm. The system also includes a means for the server to create a support plan based on the evaluation results, a means for the server to send the created plan to the terminal, a means for the user to follow the plan, a means for the user to provide feedback, and a means for the terminal to send the feedback to the server, the server to analyze the feedback using a machine learning algorithm, and update the support plan. This not only enables the system to accurately evaluate a user's mental health status and provide a personalized support plan, but also enables the system to continuously improve using feedback.

[0093] A "user" is an individual who utilizes the system to input information and receive mental health support.

[0094] A "terminal" is an electronic device that a user uses to enter information, answer questionnaires, and provide feedback.

[0095] The "server" is a central processing unit that processes and stores data sent by users, generates question lists, creates support plans, analyzes feedback, and so on.

[0096] "Information" refers to all data that users enter on their devices, such as their name, email address, password, and data related to their mental health.

[0097] "Data" refers to all information handled within the system, including information provided by users from their terminals and the results of analysis by the server.

[0098] "Confirmation Message" means an email or notification sent to a user when they register or perform other actions, such as activating an account.

[0099] A "link" is a URL included in the confirmation message, and is a means for a user to click to perform a specific process or confirmation.

[0100] A "questionnaire" is a series of questions generated by the server and sent to the terminal to assess the user's mental health status.

[0101] "Natural language processing technology" is a technology that processes and understands language data that the server uses to analyze user responses.

[0102] A "machine learning algorithm" is a computational method used by the server to analyze data and automatically learn and improve models.

[0103] A "support plan" is a personalized support plan created by the server based on the user's mental health status, and includes guided meditation and cognitive behavioral therapy exercises.

[0104] "Analysis" is the process by which the server evaluates and classifies the data and feedback it receives, using natural language processing techniques and machine learning algorithms.

[0105] "Feedback" refers to information such as opinions, impressions, and results regarding the support plan implemented by the user.

[0106] "Update" is the process by which the server readjusts and improves support plans and AI models based on feedback.

[0107] This invention relates to a system in which a server processes information entered by a user through a terminal and provides personalized mental health support. This system has the following main functions:

[0108] User Registration

[0109] The user enters basic information such as name, email address, and password from the terminal. The terminal sends the entered information to the server, which stores it in a database. The server then sends a confirmation message to the user's email address, and the user activates the account by checking the email and clicking a link. This process typically uses MySQL as the database and a service such as SendGrid or Amazon SES to send emails.

[0110] Initial diagnosis

[0111] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP techniques and machine learning algorithms (e.g., TensorFlow and PyTorch) to assess the user's mental health.

[0112] Ongoing support

[0113] Based on the assessment results, the server creates an optimal support plan for the user. The plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends the plan to the user's device, and the user follows the instructions to put it into practice. This allows the user to receive appropriate mental health support at any time, whether at home or at work.

[0114] Feedback and AI Learning

[0115] Users provide feedback on support plans from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. This allows the server to continually provide more appropriate and effective support to users. Specifically, the feedback is used to retrain the machine learning model and improve the quality of future support plans.

[0116] Specific examples

[0117] For example, if a user inputs information about their recent stress levels, the server will analyze that data using NLP techniques and machine learning algorithms to suggest effective guided meditation sessions to reduce stress, with the content and frequency of these sessions adjusted accordingly based on feedback provided by the user via their device.

[0118] Example prompts for generative AI models

[0119] "We've asked you to provide information about your current stress levels. Please analyze this data and suggest effective guided meditation sessions."

[0120] Thus, the invention provides an affordable and accessible solution to resolving a user's mental health issues by effectively assessing their mental health status and providing a personalized support plan.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: User enters basic information

[0123] The user enters basic information such as name, email address, and password into the terminal. The entered basic information is temporarily stored in the terminal's memory. The terminal then sends this entered data to the server. The basic information provided by the user is the input, and the basic information sent to the server is the output.

[0124] Step 2: The server saves the data and sends a confirmation message

[0125] The server receives the basic information sent from the device and stores it in a database (Database: MySQL for example). Once the storage is complete, the server sends a confirmation message to the user's email address. This confirmation message contains a link that the user can click to activate their account. As input, we have the basic information received from the device, and as output, we have the data stored in the database and the confirmation message sent to the user.

[0126] Step 3: User clicks link to activate account

[0127] The user clicks the link in the confirmation message. Accessing the link causes the server to update the user's account status to "valid." The input is the user's click, and the output is the status change of the user's account information in the database.

[0128] Step 4: User logs in at terminal

[0129] A user logs in by entering their email address and password on the device. The device sends the input information to the server, which then authenticates the user by comparing it with information in a database. If authentication is successful, the server generates a list of diagnostic questions for the user and sends it to the device. The input is login information, and the output is the authentication result and the list of diagnostic questions.

[0130] Step 5: User answers questionnaire

[0131] The user answers a list of diagnostic questions on the terminal. Each answer is sent from the terminal to the server in real time. The input is the user's answers to the diagnostic questions, and the output is the answer data sent to the server.

[0132] Step 6: The server analyzes the response data and evaluates the health status

[0133] The server analyzes the received response data using NLP technology and machine learning algorithms. The analysis results are used to evaluate the user's mental health. The response data is input, and the analysis results and health status evaluation are obtained as output.

[0134] Step 7: The server creates a support plan and sends it to the device

[0135] The server generates an optimal support plan for the user based on the assessment results. The support plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The generated support plan is sent to the device and made available to the user. The assessment results are input, and the generated support plan is output.

[0136] Step 8: Users follow the support plan and provide feedback

[0137] The user follows the instructions in the support plan through the terminal, and inputs the results and impressions as feedback into the terminal. The feedback data is sent from the terminal to the server. The user's feedback is the input, and the feedback data sent to the server is the output.

[0138] Step 9: The server analyzes the feedback and updates the AI ​​model

[0139] The server analyzes the received feedback and uses a generative AI model to make necessary adjustments to improve the quality of the support plan. It leverages machine learning algorithms to retrain the model and improve future support plans. The input is the feedback data, and the output is an updated AI model and an improved support plan.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Stress and mental health problems are on the rise in modern society, and there is a growing need for personalized mental support systems to effectively address these issues. However, conventional systems have difficulty providing appropriate relaxation menus in real time based on information provided by users, and they lack the functionality to efficiently collect user feedback and continuously improve the system. This has led to problems such as a decline in user satisfaction and system effectiveness.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes: a means for a user to input information from an electronic device; a means for the electronic device to transmit data to the server; a means for the server to process and store the data; a means for the server to transmit a confirmation message; a means for the user to check the message and click a link; a means for the user to input mental health status information provided by the electronic device; a means for the electronic device to transmit the input information to the server; a means for the server to analyze and evaluate the input information; a means for the server to generate a relaxation menu based on the evaluation results and transmit it to the electronic device; and a means for the user to confirm the relaxation menu transmitted by the electronic device. This allows users to evaluate their own mental health status in real time and provide them with an appropriate personalized relaxation menu. Furthermore, user feedback can be efficiently collected, enabling continuous improvement of the system.

[0145] "User" refers to an individual who uses the system and provides mental health information.

[0146] "Electronic devices" refer to terminal devices that users use to input and receive information, including smartphones and robots.

[0147] "Server" refers to a computer system installed on a network to process, store, and analyze data submitted by users.

[0148] "Information" refers to data entered by users through electronic devices, specifically data related to mental health status.

[0149] A "relaxation menu" refers to plans and activities for improving mental health that are generated by the server based on the analysis results and provided to the user.

[0150] "Feedback" refers to data on evaluations and opinions of the relaxation menu provided by users.

[0151] "Diagnostic list" refers to a list of questions generated and sent by the server to assess the user's mental health status.

[0152] "Analysis" refers to the process by which the server evaluates data collected from users using machine learning models and natural language processing techniques.

[0153] The present invention relates to a system that allows a user to evaluate their mental health status and provide a personalized support plan. A specific implementation method thereof will be described below.

[0154] Hardware used

[0155] The present invention uses the following hardware:

[0156] Smartphone: A mobile device that allows users to enter information.

[0157] Automated reception robots: Used as an interface for users to enter information in brick-and-mortar establishments such as cafes and relaxation salons.

[0158] Server: Contains the database, NLP engine, and machine learning models to process, analyze, and store data.

[0159] Software used

[0160] The present invention uses the following software:

[0161] Server-side scripts (Python / Flask): process data and provide API.

[0162] Database (PostgreSQL): Stores user information, diagnostic results, and feedback.

[0163] Machine learning models (TensorFlow, PyTorch): Analyze input data from users and generate appropriate support plans.

[0164] User Interface (React Native): Front-end development for smartphone apps.

[0165] Node.js: Backend processing for robot integration.

[0166] Example of a system

[0167] 1. User Registration

[0168] A user enters their basic information, including name, email address, and password, into a smartphone app or automated reception robot. This information is sent to a server and stored in a database. The server then sends a confirmation email, and the user clicks on a link to activate their account.

[0169] 2. Initial diagnosis

[0170] When a user logs in, the server generates a diagnostic list and sends it to a smartphone or robot. The user answers the questions on the diagnostic list and sends the answers to the server, which then uses NLP technology to analyze the answers and evaluate the user's mental health.

[0171] Prompt Sentence Examples

[0172] "How stressed have you been lately? For example, on a scale of 1-10, please answer. Also, please tell us specifically what is causing you stress."

[0173] "Do you want guided meditation sessions? If so, how often would you like them?"

[0174] 3. Ongoing support

[0175] Based on the analysis results, the server generates an optimal relaxation menu for the user and sends it to the smartphone or robot. The user then follows the suggested menu to relax. For example, a specific aromatherapy or healing music session may be suggested.

[0176] 4. Feedback and AI Learning

[0177] After completing the relaxation menu, the user provides feedback, which is sent to the server via smartphone or robot. The server analyzes the feedback and updates the machine learning model to improve the support plan for future visits.

[0178] As can be seen, the system of the present invention efficiently supports the user's mental health and provides a personalized relaxation menu, providing an effective and accessible solution to mental health issues.

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

[0180] Step 1: User Registration

[0181] A user uses an electronic device (smartphone or robot) to input basic information such as name, email address, and password. This information is sent from the electronic device to a server. The server receives it and stores it in a database. The server then sends a confirmation email to the user's email address. When the user clicks on the link in the email, the account is activated. The input data is user information (a set of basic information), and the output data is a new user entry in the database.

[0182] Step 2: Initial diagnosis

[0183] When a user logs in with their electronic device, the server generates a diagnosis list and sends it to the electronic device. The user answers the questions in the diagnosis list and sends the answers to the server. The server analyzes the answers using NLP technology and evaluates the user's mental health status. The input data is the user's diagnosis answers, and the output data is the evaluation result of the mental health status.

[0184] Step 3: Relaxation menu provided

[0185] The server generates an optimal relaxation menu for the user based on the evaluation results. The generated menu is sent to the electronic device, where the user can review it. For example, a specific aromatherapy or healing music session may be suggested. The input data is the evaluation results, and the output data is the relaxation menu.

[0186] Step 4: Relaxation

[0187] The user follows the provided relaxation menu and practices. The electronic device records the operation log and the implementation status during this process, but no data is exchanged between the server and the user during this step. The input data is the relaxation menu, and the output data is the implementation log.

[0188] Step 5: Provide feedback

[0189] After completing the relaxation menu, the user provides feedback via the electronic device. The feedback is sent from the electronic device to the server. The input data is the feedback content, and the output data is the feedback entry.

[0190] Step 6: Feedback analysis and AI model update

[0191] The server receives and analyzes the feedback provided by the user. Based on the analysis results, the server updates the generative AI model to improve the accuracy of future support plans. The input data is the feedback, and the output data is the updated generative AI model.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0194] User Registration

[0195] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0196] Initial diagnosis

[0197] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0198] emotion recognition

[0199] The emotion engine recognizes the user's emotions using the user's input data, the device's camera, microphone, etc. The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state, which further personalizes the support plan and optimizes it to fit the user's current emotional state.

[0200] Ongoing support

[0201] Based on the evaluation results and emotional data, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0202] Feedback and AI Learning

[0203] Users provide feedback on the support plan from their device. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. Emotion data from the emotion engine is also used as training data for the AI ​​model, continuously improving it. This allows the server to provide more appropriate and effective support to users.

[0204] Specific examples

[0205] For example, when a user describes a stressful situation, the emotion engine analyzes the user's facial expressions and tone of voice to assess the level of stress. Combining this emotion data with the initial assessment responses, the server then recommends specific guided meditation sessions or relaxation exercises for the user. The content and frequency of these plans are adjusted by the server based on the user's feedback and emotion data.

[0206] In this way, the system of the present invention provides comprehensive support for a user's mental health and emotional state, offering personalized plans and providing affordable and accessible solutions to mental health issues.

[0207] The processing flow will be explained below.

[0208] Step 1:

[0209] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0210] Step 2:

[0211] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0212] Step 3:

[0213] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0214] Step 4:

[0215] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0216] Step 5:

[0217] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0218] Step 6:

[0219] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0220] Step 7:

[0221] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0222] Step 8:

[0223] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0224] Step 9:

[0225] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0226] Step 10:

[0227] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0228] Step 11:

[0229] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0230] Step 12:

[0231] The emotion engine recognizes the user's emotions by analyzing the user's facial expressions, tone of voice, and the emotion of the text using the device's camera, microphone, and input text.

[0232] Step 13:

[0233] The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state. Specifically, it uses an emotion evaluation algorithm to quantify emotions such as stress, joy, and sadness.

[0234] Step 14:

[0235] The server creates a support plan based on the evaluation results and emotional data, specifically, stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0236] Step 15:

[0237] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0238] Step 16:

[0239] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0240] Step 17:

[0241] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0242] Step 18:

[0243] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0244] Step 19:

[0245] The server analyzes the feedback and updates the plan, specifically using the feedback data to adjust the support plan and taking emotional data into account for optimization.

[0246] Step 20:

[0247] The server uses the feedback as training data for the AI ​​model and updates it, specifically retraining it with machine learning algorithms to continuously improve it.

[0248] Example 2

[0249] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] In modern society, many people suffer from stress, anxiety, and other mental health issues. These issues can significantly reduce an individual's quality of life if not addressed early and appropriately. However, few systems offer affordable, accessible, and effective mental health support. The present invention aims to address these issues by comprehensively assessing a user's mental health and emotional state and providing a personalized support plan.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input information from a terminal; means for the terminal to send data to the server; means for the server to process and store data; means for the server to send a confirmation email; means for the user to check the email and click a link; means for the server to generate a diagnostic question list and send it to the terminal; means for the server to analyze the answers using natural language processing technology; means for the server to acquire emotion data using a camera or microphone of the terminal; means for the server to evaluate the emotion data using an emotion recognition engine; and means for the server to continuously analyze feedback and improve the AI ​​model. This makes it possible to provide personalized mental health support at an affordable price and effectively improve the user's mental health status.

[0252] "User" means an individual or organization that uses the system.

[0253] A "terminal" is an electronic device that allows a user to input information and interact with the system. Examples include smartphones, tablets, and personal computers.

[0254] "Means for inputting information" refers to the method by which a user inputs data such as text information, voice, or images into a terminal.

[0255] "Means for transmitting data" refers to a method by which the terminal transmits information input by the user to the server via the network.

[0256] A "server" is a computing device or system for processing and storing data sent by users and providing the required response.

[0257] "Means for processing and storing data" refers to the method by which the server appropriately analyzes the data it receives and stores it in a storage device in the required form.

[0258] "Means for sending a confirmation email" refers to the method by which the server sends an email to the user's email address for the purpose of account confirmation or authentication.

[0259] A "diagnostic questionnaire" is a series of questions used to assess a user's mental health.

[0260] "Natural language processing technology" is a technology for mechanically understanding and analyzing text information entered by users. Examples include text classification and sentiment analysis.

[0261] "Emotion data" is data that indicates the user's emotional state, and is based on information such as facial expressions and tone of voice.

[0262] An "emotion recognition engine" is software or algorithms for analyzing emotion data and assessing a user's emotional state.

[0263] A "support plan" is a set of actions or suggestions developed by the server to improve a user's mental health, such as guided meditation or cognitive behavioral therapy (CBT) exercises.

[0264] "Feedback" refers to opinions and reactions provided by users regarding the effectiveness of the support plan and the proposed content.

[0265] An "AI model" is a computational model that uses machine learning algorithms to learn from data and perform specific tasks or decisions.

[0266] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0267] First, a user accesses the system using a terminal. A terminal refers to a common electronic device such as a smartphone, tablet, or PC. The user enters basic information such as their name, email address, and password, and sends it from the terminal to the server. The server receives this information and processes it to store it in a database. The server uses a database management system such as MySQL or PostgreSQL to store the information. Next, the server sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0268] When a user logs in from a device, the server generates a diagnostic questionnaire and sends it to the device. This questionnaire contains questions to assess mental health status. For example, it can refer to standard diagnostic tools such as the Anxiety Inventory or the Beck Depression Inventory. The user answers the questions on the device and sends the answers to the server. The server uses NLP technology (e.g., SpaCy or a custom-trained model) to analyze these answers and assess the user's mental health status.

[0269] Furthermore, the device's camera and microphone are used to capture the user's facial expressions and tone of voice. These data are used as emotion data to indicate the user's emotional state. The device then transmits this emotion data to a server, which then uses an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) to analyze the emotion data and evaluate the user's emotional state.

[0270] Based on the assessment results and emotional data, the server creates a personalized support plan. This plan may include guided meditations and cognitive behavioral therapy (CBT) exercises. The server then sends the created support plan to the device, which the user follows. The user provides feedback on the support plan from the device, which then sends the feedback to the server. The server analyzes this feedback and updates the AI ​​model, allowing the server to continuously improve the support plan.

[0271] For example, if a user types "My boss got angry at me at work today" into a device, the device sends this input to a server. The server uses an emotion engine to evaluate the user's emotional state and determines that the stress level is high. Based on this evaluation, the server creates a support plan including a "5-minute guided meditation" and sends it to the device. The user then executes this plan and provides feedback on its effectiveness, allowing the server to provide more appropriate support.

[0272] An example of a prompt to input to a generative AI model is as follows:

[0273] "Please record your mental health status today. Based on that, we will suggest an appropriate support plan. For example, 'My boss got angry at me at work.'"

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

[0275] Step 1:

[0276] The user enters basic information such as name, email address, and password from the terminal. The entered basic information is formatted by the terminal and sent to the server. Specifically, the terminal sends the input data to the server as an HTTP request.

[0277] Input: Basic information such as name, email address, and password

[0278] Output: Send basic information to the server

[0279] Step 2:

[0280] The server verifies the received basic information and stores it in a database, for example, using MySQL or PostgreSQL. Once the basic information has been stored, the server generates a confirmation email and sends it to the user's email address.

[0281] Input: User basic information

[0282] Output: Save basic information to database and send confirmation email

[0283] Step 3:

[0284] The user receives a confirmation email and clicks on the link in the email. When the link is clicked, the device sends an account activation request to the server. The server receives this request and activates the user's account.

[0285] Input: Click the link in the confirmation email

[0286] Output: Account activation request to server and account activation

[0287] Step 4:

[0288] When a user logs in at a device, the server generates and sends to the device a diagnostic questionnaire containing questions to assess mental health, such as the Anxiety Inventory and the Beck Depression Inventory.

[0289] Input: User login information

[0290] Output: Diagnostic Question List sent to terminal

[0291] Step 5:

[0292] The user answers questions on the device and sends the answers to the server, which then converts the answers into JSON format and sends it to the server as an HTTP request.

[0293] Input: Answers to the diagnostic questionnaire

[0294] Output: Sending response data to the server

[0295] Step 6:

[0296] The server analyzes the received data using natural language processing technology (e.g., SpaCy) and machine learning algorithms to evaluate the user's mental health status, and stores the evaluation results in a database.

[0297] Input: Answer data

[0298] Output: Mental health assessment results and storage in a database

[0299] Step 7:

[0300] The device's camera and microphone are used to capture emotional data such as the user's facial expressions and tone of voice, which are then processed by the device and sent to a server.

[0301] Input: Facial expressions and tone of voice

[0302] Output: Sending emotion data to the server

[0303] Step 8:

[0304] The server analyzes the received emotion data using an emotion recognition engine (e.g., Affectiva or Microsoft Azure Emotion API) to evaluate the user's emotional state and stores the evaluation results in a database.

[0305] Input: Emotion data

[0306] Output: Evaluation results of emotional state and storage in a database

[0307] Step 9:

[0308] The server creates a personalized support plan based on the assessment results and emotional data, which may include guided meditation and cognitive behavioral therapy (CBT) exercises, and sends the plan in JSON format to the device.

[0309] Input: Evaluation results and emotion data

[0310] Output: Sending the support plan to the device

[0311] Step 10:

[0312] The user checks the support plan on the device and implements it according to its contents. After implementing it, the user inputs feedback into the device, which then sends this feedback to the server.

[0313] Input: Feedback data

[0314] Output: Sending feedback to the server

[0315] Step 11:

[0316] The server analyzes the received feedback and updates the AI ​​model, which will improve future support plans more effectively.

[0317] Input: Feedback data

[0318] Output: Updated AI models and improved support plans

[0319] In this way, by clearly indicating the specific actions performed at each step and their inputs and outputs, it becomes clear how the system of the present invention supports the mental health of the user.

[0320] (Application example 2)

[0321] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] Conventional mental health support systems have had problems such as users being unable to accurately communicate their mental state and difficulty in providing personalized support plans. This has made it difficult for users to receive effective mental health support. Another issue, particularly in physical stores, is that customers are unable to make effective use of their waiting time.

[0323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0324] In this invention, the server includes means for a user to input information from a terminal, means by the terminal to send data to the server, means by the server to process and store the data, means by the server to send a confirmation email, means by the user to check the email and click a link, means by the terminal to recognize emotions using the user's input data, means by the server to process and store emotional data, means by the server to provide a support plan based on the user's emotional data, and means by the terminal to display the contents of the support plan to the user. This makes it possible to provide an individualized support plan that takes into account the user's emotional state, and further enables customers to receive mental health support in physical stores by effectively using their waiting time.

[0325] A "terminal" is a digital device through which a user inputs information and communicates data with a server.

[0326] "Server" means a central processing unit that processes, stores, analyzes data, and provides services to users.

[0327] "Data" is a general term for information processed by the system, such as information entered by the user and the results of the emotion recognition engine.

[0328] "Input Data" refers to information provided by a user to the system through a terminal, such as text input or audio / visual data.

[0329] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and text input to identify their emotional state.

[0330] "Emotion data" is data about a user's emotional state obtained using emotion recognition technology.

[0331] A "support plan" is a set of specific actions, exercises, and advice provided to support a user's mental health.

[0332] "Feedback" refers to information about the effectiveness and satisfaction of the support plan provided by the user.

[0333] An "AI model" refers to an algorithm that uses artificial intelligence to analyze data and is continuously improved.

[0334] A "brick and mortar store" is a store located in a physical location where customers can receive in-person service.

[0335] "Waiting time" refers to the time a customer spends waiting to receive service in a physical store.

[0336] This invention is a system that effectively utilizes customer waiting time in a physical store to provide mental health support. The system includes the following main hardware and software components:

[0337] Hardware

[0338] 1. Terminal: A digital device (e.g., smartphone, tablet) through which a user inputs information and communicates data with a server.

[0339] 2. Server: A central processing unit (cloud server) that processes, stores, analyzes data, and provides services to users.

[0340] software

[0341] 1. User registration: The user enters basic information such as name, email address, and password on the device. The device sends this information to the server, which stores it in a database. The server then sends a confirmation email, and the user clicks on the link to activate their account.

[0342] 2. Initial diagnosis: When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server uses NLP technology and machine learning algorithms to analyze the answers and evaluate the user's mental health.

[0343] 3. Emotion Recognition: The device's camera and microphone are used to collect the user's facial expressions and tone of voice. This data is analyzed by the emotion recognition engine and sent to the server as emotional data.

[0344] 4. Providing a support plan: The server creates a support plan tailored to the user based on the analysis results and emotional data. The plan may include guided meditations and relaxation exercises. The server then sends the plan to the device, and the user follows the instructions.

[0345] 5. Feedback and AI learning: Users can provide feedback on the support plan from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model, allowing the server to provide more appropriate and effective support to users.

[0346] Specific examples

[0347] For example, a customer waiting in a physical store opens the app and enters information about their stress level. The device's camera captures their facial expressions and the microphone records their tone of voice. An emotion recognition engine analyzes this data and sends the emotional data to a server. The server uses this data to suggest specific guided meditation sessions or relaxation exercises. The user tries them out and then provides feedback. The feedback is sent to the server, and the AI ​​model is updated accordingly.

[0348] Example prompts for generative AI models

[0349] "Users enter their stress levels while waiting in line at a physical store. Through facial recognition and voice analysis, the emotion engine analyzes their stress levels. Recommend personalized guided meditations and relaxation exercises."

[0350] In this way, the system of the present invention can enhance the customer experience in physical stores by providing comprehensive support for the user's mental health and emotional state and providing personalized plans.

[0351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0352] Step 1:

[0353] User Registration:

[0354] The user enters basic information (name, email address, password) from the terminal, and the terminal sends this data to the server. The server stores the received data in a database and sends a confirmation email to the user. The user clicks on the link in the confirmation email to activate the account. The input data is basic information, and the output is the sending of a confirmation email.

[0355] Step 2:

[0356] Initial diagnosis:

[0357] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answer data to the server. The server then analyzes the answer data using NLP technology and machine learning algorithms to evaluate the user's mental health status. The input data is the answer data, and the output is the mental health status evaluation result.

[0358] Step 3:

[0359] Emotion recognition:

[0360] The user uses the device's camera and microphone to capture and record facial expressions and tone of voice. The device then sends this data to the emotion recognition engine to obtain emotion data. The emotion data is then sent to the server. The input data is facial expression data and tone of voice data, and the output is emotion data.

[0361] Step 4:

[0362] Support plans offered:

[0363] The server creates an optimal support plan for the user based on the mental health assessment results from step 2 and the emotional data from step 3. This plan includes guided meditation and relaxation exercises. The created support plan is sent to the device. The input data are the mental health assessment results and emotional data, and the output is the support plan.

[0364] Step 5:

[0365] Support plan execution:

[0366] The user follows the support plan provided on the device and performs guided meditation and relaxation exercises. The execution results are input as feedback. The input data are the support plan and the execution results, and the output is feedback.

[0367] Step 6:

[0368] Send feedback and update the AI ​​model:

[0369] The user's feedback is sent to the server via the device. The server updates the AI ​​model based on the feedback and the continuous emotional data obtained in step 3. This process enables the server to provide a more effective support plan from the next time onwards. The input data are the feedback and emotional data, and the output is the updated AI model.

[0370] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0371] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0372] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0373] [Second embodiment]

[0374] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0375] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0376] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0377] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0378] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0380] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0381] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0382] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0383] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0384] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0385] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0386] The present invention relates to a system that provides personalized mental health support by having a server process information entered by a user via a device. The system assesses the user's mental health status, provides an appropriate support plan, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0387] User Registration

[0388] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0389] Initial diagnosis

[0390] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0391] Ongoing support

[0392] Based on the assessment results, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0393] Feedback and AI Learning

[0394] Users provide feedback on the support plan from their device, which is then sent to the server, where it is analyzed and used to update the AI ​​model, allowing the server to continually provide more appropriate and effective support to users.

[0395] Specific examples

[0396] For example, if a user inputs information about their recent stress levels, the server will analyze that data and suggest guided meditation sessions to help reduce stress, adjusting the content and frequency of these sessions based on user feedback.

[0397] In this way, the system of the present invention effectively supports users' mental health conditions and provides personalized plans, enabling affordable and accessible solutions to mental health problems.

[0398] The processing flow will be explained below.

[0399] Step 1:

[0400] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0401] Step 2:

[0402] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0403] Step 3:

[0404] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0405] Step 4:

[0406] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0407] Step 5:

[0408] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0409] Step 6:

[0410] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0411] Step 7:

[0412] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0413] Step 8:

[0414] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0415] Step 9:

[0416] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0417] Step 10:

[0418] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0419] Step 11:

[0420] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0421] Step 12:

[0422] The server then creates a support plan based on the assessment results, including stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0423] Step 13:

[0424] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0425] Step 14:

[0426] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0427] Step 15:

[0428] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0429] Step 16:

[0430] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0431] Step 17:

[0432] The server analyzes the feedback and updates the AI ​​model, specifically using the feedback data as training data for the AI ​​model and retraining the model with machine learning algorithms.

[0433] Example 1

[0434] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0435] In modern society, the number of users experiencing a deterioration in their mental health is increasing, but there are limited means to effectively provide personalized support. Furthermore, conventional methods make it difficult to accurately assess a user's mental health and create an appropriate support plan based on that assessment. Furthermore, there is a lack of means to effectively improve the system by utilizing subsequent feedback.

[0436] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0437] In this invention, the server includes a means for a user to input information from a terminal, a means for the terminal to send data to the server, a means for the server to process and store the data, a means for the server to send a confirmation message, and a means for the user to check the message and click a link. The system also includes a means for a user to log in using a terminal, a means for the server to generate and send a list of questions, a means for the user to answer the questions, a means for the terminal to send the answers to the server, and a means for the server to analyze and evaluate the answers using natural language processing technology and a machine learning algorithm. The system also includes a means for the server to create a support plan based on the evaluation results, a means for the server to send the created plan to the terminal, a means for the user to follow the plan, a means for the user to provide feedback, and a means for the terminal to send the feedback to the server, the server to analyze the feedback using a machine learning algorithm, and update the support plan. This not only enables the system to accurately evaluate a user's mental health status and provide a personalized support plan, but also enables the system to continuously improve using feedback.

[0438] A "user" is an individual who utilizes the system to input information and receive mental health support.

[0439] A "terminal" is an electronic device that a user uses to enter information, answer questionnaires, and provide feedback.

[0440] The "server" is a central processing unit that processes and stores data sent by users, generates question lists, creates support plans, analyzes feedback, and so on.

[0441] "Information" refers to all data that users enter on their devices, such as their name, email address, password, and data related to their mental health.

[0442] "Data" refers to all information handled within the system, including information provided by users from their terminals and the results of analysis by the server.

[0443] "Confirmation Message" means an email or notification sent to a user when they register or perform other actions, such as activating an account.

[0444] A "link" is a URL included in the confirmation message, and is a means for a user to click to perform a specific process or confirmation.

[0445] A "questionnaire" is a series of questions generated by the server and sent to the terminal to assess the user's mental health status.

[0446] "Natural language processing technology" is a technology that processes and understands language data that the server uses to analyze user responses.

[0447] A "machine learning algorithm" is a computational method used by the server to analyze data and automatically learn and improve models.

[0448] A "support plan" is a personalized support plan created by the server based on the user's mental health status, and includes guided meditation and cognitive behavioral therapy exercises.

[0449] "Analysis" is the process by which the server evaluates and classifies the data and feedback it receives, using natural language processing techniques and machine learning algorithms.

[0450] "Feedback" refers to information such as opinions, impressions, and results regarding the support plan implemented by the user.

[0451] "Update" is the process by which the server readjusts and improves support plans and AI models based on feedback.

[0452] This invention relates to a system in which a server processes information entered by a user through a terminal and provides personalized mental health support. This system has the following main functions:

[0453] User Registration

[0454] The user enters basic information such as name, email address, and password from the terminal. The terminal sends the entered information to the server, which stores it in a database. The server then sends a confirmation message to the user's email address, and the user activates the account by checking the email and clicking a link. This process typically uses MySQL as the database and a service such as SendGrid or Amazon SES to send emails.

[0455] Initial diagnosis

[0456] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP techniques and machine learning algorithms (e.g., TensorFlow and PyTorch) to assess the user's mental health.

[0457] Ongoing support

[0458] Based on the assessment results, the server creates an optimal support plan for the user. The plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends the plan to the user's device, and the user follows the instructions to put it into practice. This allows the user to receive appropriate mental health support at any time, whether at home or at work.

[0459] Feedback and AI Learning

[0460] Users provide feedback on support plans from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. This allows the server to continually provide more appropriate and effective support to users. Specifically, the feedback is used to retrain the machine learning model and improve the quality of future support plans.

[0461] Specific examples

[0462] For example, if a user inputs information about their recent stress levels, the server will analyze that data using NLP techniques and machine learning algorithms to suggest effective guided meditation sessions to reduce stress, with the content and frequency of these sessions adjusted accordingly based on feedback provided by the user via their device.

[0463] Example prompts for generative AI models

[0464] "We've asked you to provide information about your current stress levels. Please analyze this data and suggest effective guided meditation sessions."

[0465] Thus, the invention provides an affordable and accessible solution to resolving a user's mental health issues by effectively assessing their mental health status and providing a personalized support plan.

[0466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0467] Step 1: User enters basic information

[0468] The user enters basic information such as name, email address, and password into the terminal. The entered basic information is temporarily stored in the terminal's memory. The terminal then sends this entered data to the server. The basic information provided by the user is the input, and the basic information sent to the server is the output.

[0469] Step 2: The server saves the data and sends a confirmation message

[0470] The server receives the basic information sent from the device and stores it in a database (Database: MySQL for example). Once the storage is complete, the server sends a confirmation message to the user's email address. This confirmation message contains a link that the user can click to activate their account. As input, we have the basic information received from the device, and as output, we have the data stored in the database and the confirmation message sent to the user.

[0471] Step 3: User clicks link to activate account

[0472] The user clicks the link in the confirmation message. Accessing the link causes the server to update the user's account status to "valid." The input is the user's click, and the output is the status change of the user's account information in the database.

[0473] Step 4: User logs in at terminal

[0474] A user logs in by entering their email address and password on the device. The device sends the input information to the server, which then authenticates the user by comparing it with information in a database. If authentication is successful, the server generates a list of diagnostic questions for the user and sends it to the device. The input is login information, and the output is the authentication result and the list of diagnostic questions.

[0475] Step 5: User answers questionnaire

[0476] The user answers a list of diagnostic questions on the terminal. Each answer is sent from the terminal to the server in real time. The input is the user's answers to the diagnostic questions, and the output is the answer data sent to the server.

[0477] Step 6: The server analyzes the response data and evaluates the health status

[0478] The server analyzes the received response data using NLP technology and machine learning algorithms. The analysis results are used to evaluate the user's mental health. The response data is input, and the analysis results and health status evaluation are obtained as output.

[0479] Step 7: The server creates a support plan and sends it to the device

[0480] The server generates an optimal support plan for the user based on the assessment results. The support plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The generated support plan is sent to the device and made available to the user. The assessment results are input, and the generated support plan is output.

[0481] Step 8: Users follow the support plan and provide feedback

[0482] The user follows the instructions in the support plan through the terminal, and inputs the results and impressions as feedback into the terminal. The feedback data is sent from the terminal to the server. The user's feedback is the input, and the feedback data sent to the server is the output.

[0483] Step 9: The server analyzes the feedback and updates the AI ​​model

[0484] The server analyzes the received feedback and uses a generative AI model to make necessary adjustments to improve the quality of the support plan. It leverages machine learning algorithms to retrain the model and improve future support plans. The input is the feedback data, and the output is an updated AI model and an improved support plan.

[0485] (Application example 1)

[0486] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] Stress and mental health problems are on the rise in modern society, and there is a growing need for personalized mental support systems to effectively address these issues. However, conventional systems have difficulty providing appropriate relaxation menus in real time based on information provided by users, and they lack the functionality to efficiently collect user feedback and continuously improve the system. This has led to problems such as a decline in user satisfaction and system effectiveness.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0489] In this invention, the server includes: a means for a user to input information from an electronic device; a means for the electronic device to transmit data to the server; a means for the server to process and store the data; a means for the server to transmit a confirmation message; a means for the user to check the message and click a link; a means for the user to input mental health status information provided by the electronic device; a means for the electronic device to transmit the input information to the server; a means for the server to analyze and evaluate the input information; a means for the server to generate a relaxation menu based on the evaluation results and transmit it to the electronic device; and a means for the user to confirm the relaxation menu transmitted by the electronic device. This allows users to evaluate their own mental health status in real time and provide them with an appropriate personalized relaxation menu. Furthermore, user feedback can be efficiently collected, enabling continuous improvement of the system.

[0490] "User" refers to an individual who uses the system and provides mental health information.

[0491] "Electronic devices" refer to terminal devices that users use to input and receive information, including smartphones and robots.

[0492] "Server" refers to a computer system installed on a network to process, store, and analyze data submitted by users.

[0493] "Information" refers to data entered by users through electronic devices, specifically data related to mental health status.

[0494] A "relaxation menu" refers to plans and activities for improving mental health that are generated by the server based on the analysis results and provided to the user.

[0495] "Feedback" refers to data on evaluations and opinions of the relaxation menu provided by users.

[0496] "Diagnostic list" refers to a list of questions generated and sent by the server to assess the user's mental health status.

[0497] "Analysis" refers to the process by which the server evaluates data collected from users using machine learning models and natural language processing techniques.

[0498] The present invention relates to a system that allows a user to evaluate their mental health status and provide a personalized support plan. A specific implementation method thereof will be described below.

[0499] Hardware used

[0500] The present invention uses the following hardware:

[0501] Smartphone: A mobile device that allows users to enter information.

[0502] Automated reception robots: Used as an interface for users to enter information in brick-and-mortar establishments such as cafes and relaxation salons.

[0503] Server: Contains the database, NLP engine, and machine learning models to process, analyze, and store data.

[0504] Software used

[0505] The present invention uses the following software:

[0506] Server-side scripts (Python / Flask): process data and provide API.

[0507] Database (PostgreSQL): Stores user information, diagnostic results, and feedback.

[0508] Machine learning models (TensorFlow, PyTorch): Analyze input data from users and generate appropriate support plans.

[0509] User Interface (React Native): Front-end development for smartphone apps.

[0510] Node.js: Backend processing for robot integration.

[0511] Example of a system

[0512] 1. User Registration

[0513] A user enters their basic information, including name, email address, and password, into a smartphone app or automated reception robot. This information is sent to a server and stored in a database. The server then sends a confirmation email, and the user clicks on a link to activate their account.

[0514] 2. Initial diagnosis

[0515] When a user logs in, the server generates a diagnostic list and sends it to a smartphone or robot. The user answers the questions on the diagnostic list and sends the answers to the server, which then uses NLP technology to analyze the answers and evaluate the user's mental health.

[0516] Prompt Sentence Examples

[0517] "How stressed have you been lately? For example, on a scale of 1-10, please answer. Also, please tell us specifically what is causing you stress."

[0518] "Do you want guided meditation sessions? If so, how often would you like them?"

[0519] 3. Ongoing support

[0520] Based on the analysis results, the server generates an optimal relaxation menu for the user and sends it to the smartphone or robot. The user then follows the suggested menu to relax. For example, a specific aromatherapy or healing music session may be suggested.

[0521] 4. Feedback and AI Learning

[0522] After completing the relaxation menu, the user provides feedback, which is sent to the server via smartphone or robot. The server analyzes the feedback and updates the machine learning model to improve the support plan for future visits.

[0523] As can be seen, the system of the present invention efficiently supports the user's mental health and provides a personalized relaxation menu, providing an effective and accessible solution to mental health issues.

[0524] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0525] Step 1: User Registration

[0526] A user uses an electronic device (smartphone or robot) to input basic information such as name, email address, and password. This information is sent from the electronic device to a server. The server receives it and stores it in a database. The server then sends a confirmation email to the user's email address. When the user clicks on the link in the email, the account is activated. The input data is user information (a set of basic information), and the output data is a new user entry in the database.

[0527] Step 2: Initial diagnosis

[0528] When a user logs in with their electronic device, the server generates a diagnosis list and sends it to the electronic device. The user answers the questions in the diagnosis list and sends the answers to the server. The server analyzes the answers using NLP technology and evaluates the user's mental health status. The input data is the user's diagnosis answers, and the output data is the evaluation result of the mental health status.

[0529] Step 3: Relaxation menu provided

[0530] The server generates an optimal relaxation menu for the user based on the evaluation results. The generated menu is sent to the electronic device, where the user can review it. For example, a specific aromatherapy or healing music session may be suggested. The input data is the evaluation results, and the output data is the relaxation menu.

[0531] Step 4: Relaxation

[0532] The user follows the provided relaxation menu and practices. The electronic device records the operation log and the implementation status during this process, but no data is exchanged between the server and the user during this step. The input data is the relaxation menu, and the output data is the implementation log.

[0533] Step 5: Provide feedback

[0534] After completing the relaxation menu, the user provides feedback via the electronic device. The feedback is sent from the electronic device to the server. The input data is the feedback content, and the output data is the feedback entry.

[0535] Step 6: Feedback analysis and AI model update

[0536] The server receives and analyzes the feedback provided by the user. Based on the analysis results, the server updates the generative AI model to improve the accuracy of future support plans. The input data is the feedback, and the output data is the updated generative AI model.

[0537] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0538] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0539] User Registration

[0540] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0541] Initial diagnosis

[0542] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0543] emotion recognition

[0544] The emotion engine recognizes the user's emotions using the user's input data, the device's camera, microphone, etc. The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state, which further personalizes the support plan and optimizes it to fit the user's current emotional state.

[0545] Ongoing support

[0546] Based on the evaluation results and emotional data, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0547] Feedback and AI Learning

[0548] Users provide feedback on the support plan from their device. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. Emotion data from the emotion engine is also used as training data for the AI ​​model, continuously improving it. This allows the server to provide more appropriate and effective support to users.

[0549] Specific examples

[0550] For example, when a user describes a stressful situation, the emotion engine analyzes the user's facial expressions and tone of voice to assess the level of stress. Combining this emotion data with the initial assessment responses, the server then recommends specific guided meditation sessions or relaxation exercises for the user. The content and frequency of these plans are adjusted by the server based on the user's feedback and emotion data.

[0551] In this way, the system of the present invention provides comprehensive support for a user's mental health and emotional state, offering personalized plans and providing affordable and accessible solutions to mental health issues.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0555] Step 2:

[0556] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0557] Step 3:

[0558] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0559] Step 4:

[0560] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0561] Step 5:

[0562] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0563] Step 6:

[0564] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0565] Step 7:

[0566] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0567] Step 8:

[0568] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0569] Step 9:

[0570] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0571] Step 10:

[0572] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0573] Step 11:

[0574] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0575] Step 12:

[0576] The emotion engine recognizes the user's emotions by analyzing the user's facial expressions, tone of voice, and the emotion of the text using the device's camera, microphone, and input text.

[0577] Step 13:

[0578] The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state. Specifically, it uses an emotion evaluation algorithm to quantify emotions such as stress, joy, and sadness.

[0579] Step 14:

[0580] The server creates a support plan based on the evaluation results and emotional data, specifically, stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0581] Step 15:

[0582] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0583] Step 16:

[0584] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0585] Step 17:

[0586] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0587] Step 18:

[0588] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0589] Step 19:

[0590] The server analyzes the feedback and updates the plan, specifically using the feedback data to adjust the support plan and taking emotional data into account for optimization.

[0591] Step 20:

[0592] The server uses the feedback as training data for the AI ​​model and updates it, specifically retraining it with machine learning algorithms to continuously improve it.

[0593] Example 2

[0594] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0595] In modern society, many people suffer from stress, anxiety, and other mental health issues. These issues can significantly reduce an individual's quality of life if not addressed early and appropriately. However, few systems offer affordable, accessible, and effective mental health support. The present invention aims to address these issues by comprehensively assessing a user's mental health and emotional state and providing a personalized support plan.

[0596] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input information from a terminal; means for the terminal to send data to the server; means for the server to process and store data; means for the server to send a confirmation email; means for the user to check the email and click a link; means for the server to generate a diagnostic question list and send it to the terminal; means for the server to analyze the answers using natural language processing technology; means for the server to acquire emotion data using a camera or microphone of the terminal; means for the server to evaluate the emotion data using an emotion recognition engine; and means for the server to continuously analyze feedback and improve the AI ​​model. This makes it possible to provide personalized mental health support at an affordable price and effectively improve the user's mental health status.

[0597] "User" means an individual or organization that uses the system.

[0598] A "terminal" is an electronic device that allows a user to input information and interact with the system. Examples include smartphones, tablets, and personal computers.

[0599] "Means for inputting information" refers to the method by which a user inputs data such as text information, voice, or images into a terminal.

[0600] "Means for transmitting data" refers to a method by which the terminal transmits information input by the user to the server via the network.

[0601] A "server" is a computing device or system for processing and storing data sent by users and providing the required response.

[0602] "Means for processing and storing data" refers to the method by which the server appropriately analyzes the data it receives and stores it in a storage device in the required form.

[0603] "Means for sending a confirmation email" refers to the method by which the server sends an email to the user's email address for the purpose of account confirmation or authentication.

[0604] A "diagnostic questionnaire" is a series of questions used to assess a user's mental health.

[0605] "Natural language processing technology" is a technology for mechanically understanding and analyzing text information entered by users. Examples include text classification and sentiment analysis.

[0606] "Emotion data" is data that indicates the user's emotional state, and is based on information such as facial expressions and tone of voice.

[0607] An "emotion recognition engine" is software or algorithms for analyzing emotion data and assessing a user's emotional state.

[0608] A "support plan" is a set of actions or suggestions developed by the server to improve a user's mental health, such as guided meditation or cognitive behavioral therapy (CBT) exercises.

[0609] "Feedback" refers to opinions and reactions provided by users regarding the effectiveness of the support plan and the proposed content.

[0610] An "AI model" is a computational model that uses machine learning algorithms to learn from data and perform specific tasks or decisions.

[0611] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0612] First, a user accesses the system using a terminal. A terminal refers to a common electronic device such as a smartphone, tablet, or PC. The user enters basic information such as their name, email address, and password, and sends it from the terminal to the server. The server receives this information and processes it to store it in a database. The server uses a database management system such as MySQL or PostgreSQL to store the information. Next, the server sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0613] When a user logs in from a device, the server generates a diagnostic questionnaire and sends it to the device. This questionnaire contains questions to assess mental health status. For example, it can refer to standard diagnostic tools such as the Anxiety Inventory or the Beck Depression Inventory. The user answers the questions on the device and sends the answers to the server. The server uses NLP technology (e.g., SpaCy or a custom-trained model) to analyze these answers and assess the user's mental health status.

[0614] Furthermore, the device's camera and microphone are used to capture the user's facial expressions and tone of voice. These data are used as emotion data to indicate the user's emotional state. The device then transmits this emotion data to a server, which then uses an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) to analyze the emotion data and evaluate the user's emotional state.

[0615] Based on the assessment results and emotional data, the server creates a personalized support plan. This plan may include guided meditations and cognitive behavioral therapy (CBT) exercises. The server then sends the created support plan to the device, which the user follows. The user provides feedback on the support plan from the device, which then sends the feedback to the server. The server analyzes this feedback and updates the AI ​​model, allowing the server to continuously improve the support plan.

[0616] For example, if a user types "My boss got angry at me at work today" into a device, the device sends this input to a server. The server uses an emotion engine to evaluate the user's emotional state and determines that the stress level is high. Based on this evaluation, the server creates a support plan including a "5-minute guided meditation" and sends it to the device. The user then executes this plan and provides feedback on its effectiveness, allowing the server to provide more appropriate support.

[0617] An example of a prompt to input to a generative AI model is as follows:

[0618] "Please record your mental health status today. Based on that, we will suggest an appropriate support plan. For example, 'My boss got angry at me at work.'"

[0619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] The user enters basic information such as name, email address, and password from the terminal. The entered basic information is formatted by the terminal and sent to the server. Specifically, the terminal sends the input data to the server as an HTTP request.

[0622] Input: Basic information such as name, email address, and password

[0623] Output: Send basic information to the server

[0624] Step 2:

[0625] The server verifies the received basic information and stores it in a database, for example, using MySQL or PostgreSQL. Once the basic information has been stored, the server generates a confirmation email and sends it to the user's email address.

[0626] Input: User basic information

[0627] Output: Save basic information to database and send confirmation email

[0628] Step 3:

[0629] The user receives a confirmation email and clicks on the link in the email. When the link is clicked, the device sends an account activation request to the server. The server receives this request and activates the user's account.

[0630] Input: Click the link in the confirmation email

[0631] Output: Account activation request to server and account activation

[0632] Step 4:

[0633] When a user logs in at a device, the server generates and sends to the device a diagnostic questionnaire containing questions to assess mental health, such as the Anxiety Inventory and the Beck Depression Inventory.

[0634] Input: User login information

[0635] Output: Diagnostic Question List sent to terminal

[0636] Step 5:

[0637] The user answers questions on the device and sends the answers to the server, which then converts the answers into JSON format and sends it to the server as an HTTP request.

[0638] Input: Answers to the diagnostic questionnaire

[0639] Output: Sending response data to the server

[0640] Step 6:

[0641] The server analyzes the received data using natural language processing technology (e.g., SpaCy) and machine learning algorithms to evaluate the user's mental health status, and stores the evaluation results in a database.

[0642] Input: Answer data

[0643] Output: Mental health assessment results and storage in a database

[0644] Step 7:

[0645] The device's camera and microphone are used to capture emotional data such as the user's facial expressions and tone of voice, which are then processed by the device and sent to a server.

[0646] Input: Facial expressions and tone of voice

[0647] Output: Sending emotion data to the server

[0648] Step 8:

[0649] The server analyzes the received emotion data using an emotion recognition engine (e.g., Affectiva or Microsoft Azure Emotion API) to evaluate the user's emotional state and stores the evaluation results in a database.

[0650] Input: Emotion data

[0651] Output: Evaluation results of emotional state and storage in a database

[0652] Step 9:

[0653] The server creates a personalized support plan based on the assessment results and emotional data, which may include guided meditation and cognitive behavioral therapy (CBT) exercises, and sends the plan in JSON format to the device.

[0654] Input: Evaluation results and emotion data

[0655] Output: Sending the support plan to the device

[0656] Step 10:

[0657] The user checks the support plan on the device and implements it according to its contents. After implementing it, the user inputs feedback into the device, which then sends this feedback to the server.

[0658] Input: Feedback data

[0659] Output: Sending feedback to the server

[0660] Step 11:

[0661] The server analyzes the received feedback and updates the AI ​​model, which will improve future support plans more effectively.

[0662] Input: Feedback data

[0663] Output: Updated AI models and improved support plans

[0664] In this way, by clearly indicating the specific actions performed at each step and their inputs and outputs, it becomes clear how the system of the present invention supports the mental health of the user.

[0665] (Application example 2)

[0666] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0667] Conventional mental health support systems have had problems such as users being unable to accurately communicate their mental state and difficulty in providing personalized support plans. This has made it difficult for users to receive effective mental health support. Another issue, particularly in physical stores, is that customers are unable to make effective use of their waiting time.

[0668] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0669] In this invention, the server includes means for a user to input information from a terminal, means by the terminal to send data to the server, means by the server to process and store the data, means by the server to send a confirmation email, means by the user to check the email and click a link, means by the terminal to recognize emotions using the user's input data, means by the server to process and store emotional data, means by the server to provide a support plan based on the user's emotional data, and means by the terminal to display the contents of the support plan to the user. This makes it possible to provide an individualized support plan that takes into account the user's emotional state, and further enables customers to receive mental health support in physical stores by effectively using their waiting time.

[0670] A "terminal" is a digital device through which a user inputs information and communicates data with a server.

[0671] "Server" means a central processing unit that processes, stores, analyzes data, and provides services to users.

[0672] "Data" is a general term for information processed by the system, such as information entered by the user and the results of the emotion recognition engine.

[0673] "Input Data" refers to information provided by a user to the system through a terminal, such as text input or audio / visual data.

[0674] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and text input to identify their emotional state.

[0675] "Emotion data" is data about a user's emotional state obtained using emotion recognition technology.

[0676] A "support plan" is a set of specific actions, exercises, and advice provided to support a user's mental health.

[0677] "Feedback" refers to information about the effectiveness and satisfaction of the support plan provided by the user.

[0678] An "AI model" refers to an algorithm that uses artificial intelligence to analyze data and is continuously improved.

[0679] A "brick and mortar store" is a store located in a physical location where customers can receive in-person service.

[0680] "Waiting time" refers to the time a customer spends waiting to receive service in a physical store.

[0681] This invention is a system that effectively utilizes customer waiting time in a physical store to provide mental health support. The system includes the following main hardware and software components:

[0682] Hardware

[0683] 1. Terminal: A digital device (e.g., smartphone, tablet) through which a user inputs information and communicates data with a server.

[0684] 2. Server: A central processing unit (cloud server) that processes, stores, analyzes data, and provides services to users.

[0685] software

[0686] 1. User registration: The user enters basic information such as name, email address, and password on the device. The device sends this information to the server, which stores it in a database. The server then sends a confirmation email, and the user clicks on the link to activate their account.

[0687] 2. Initial diagnosis: When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server uses NLP technology and machine learning algorithms to analyze the answers and evaluate the user's mental health.

[0688] 3. Emotion Recognition: The device's camera and microphone are used to collect the user's facial expressions and tone of voice. This data is analyzed by the emotion recognition engine and sent to the server as emotional data.

[0689] 4. Providing a support plan: The server creates a support plan tailored to the user based on the analysis results and emotional data. The plan may include guided meditations and relaxation exercises. The server then sends the plan to the device, and the user follows the instructions.

[0690] 5. Feedback and AI learning: Users can provide feedback on the support plan from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model, allowing the server to provide more appropriate and effective support to users.

[0691] Specific examples

[0692] For example, a customer waiting in a physical store opens the app and enters information about their stress level. The device's camera captures their facial expressions and the microphone records their tone of voice. An emotion recognition engine analyzes this data and sends the emotional data to a server. The server uses this data to suggest specific guided meditation sessions or relaxation exercises. The user tries them out and then provides feedback. The feedback is sent to the server, and the AI ​​model is updated accordingly.

[0693] Example prompts for generative AI models

[0694] "Users enter their stress levels while waiting in line at a physical store. Through facial recognition and voice analysis, the emotion engine analyzes their stress levels. Recommend personalized guided meditations and relaxation exercises."

[0695] In this way, the system of the present invention can enhance the customer experience in physical stores by providing comprehensive support for the user's mental health and emotional state and providing personalized plans.

[0696] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0697] Step 1:

[0698] User Registration:

[0699] The user enters basic information (name, email address, password) from the terminal, and the terminal sends this data to the server. The server stores the received data in a database and sends a confirmation email to the user. The user clicks on the link in the confirmation email to activate the account. The input data is basic information, and the output is the sending of a confirmation email.

[0700] Step 2:

[0701] Initial diagnosis:

[0702] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answer data to the server. The server then analyzes the answer data using NLP technology and machine learning algorithms to evaluate the user's mental health status. The input data is the answer data, and the output is the mental health status evaluation result.

[0703] Step 3:

[0704] Emotion recognition:

[0705] The user uses the device's camera and microphone to capture and record facial expressions and tone of voice. The device then sends this data to the emotion recognition engine to obtain emotion data. The emotion data is then sent to the server. The input data is facial expression data and tone of voice data, and the output is emotion data.

[0706] Step 4:

[0707] Support plans offered:

[0708] The server creates an optimal support plan for the user based on the mental health assessment results from step 2 and the emotional data from step 3. This plan includes guided meditation and relaxation exercises. The created support plan is sent to the device. The input data are the mental health assessment results and emotional data, and the output is the support plan.

[0709] Step 5:

[0710] Support plan execution:

[0711] The user follows the support plan provided on the device and performs guided meditation and relaxation exercises. The execution results are input as feedback. The input data are the support plan and the execution results, and the output is feedback.

[0712] Step 6:

[0713] Send feedback and update the AI ​​model:

[0714] The user's feedback is sent to the server via the device. The server updates the AI ​​model based on the feedback and the continuous emotional data obtained in step 3. This process enables the server to provide a more effective support plan from the next time onwards. The input data are the feedback and emotional data, and the output is the updated AI model.

[0715] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0716] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0717] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0718] [Third embodiment]

[0719] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0720] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0721] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0722] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0723] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0724] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0725] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0726] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0727] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0728] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0729] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0730] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0731] The present invention relates to a system that provides personalized mental health support by having a server process information entered by a user via a device. The system assesses the user's mental health status, provides an appropriate support plan, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0732] User Registration

[0733] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0734] Initial diagnosis

[0735] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0736] Ongoing support

[0737] Based on the assessment results, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0738] Feedback and AI Learning

[0739] Users provide feedback on the support plan from their device, which is then sent to the server, where it is analyzed and used to update the AI ​​model, allowing the server to continually provide more appropriate and effective support to users.

[0740] Specific examples

[0741] For example, if a user inputs information about their recent stress levels, the server will analyze that data and suggest guided meditation sessions to help reduce stress, adjusting the content and frequency of these sessions based on user feedback.

[0742] In this way, the system of the present invention effectively supports users' mental health conditions and provides personalized plans, enabling affordable and accessible solutions to mental health problems.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0746] Step 2:

[0747] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0748] Step 3:

[0749] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0750] Step 4:

[0751] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0752] Step 5:

[0753] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0754] Step 6:

[0755] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0756] Step 7:

[0757] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0758] Step 8:

[0759] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0760] Step 9:

[0761] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0762] Step 10:

[0763] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0764] Step 11:

[0765] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0766] Step 12:

[0767] The server then creates a support plan based on the assessment results, including stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0768] Step 13:

[0769] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0770] Step 14:

[0771] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0772] Step 15:

[0773] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0774] Step 16:

[0775] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0776] Step 17:

[0777] The server analyzes the feedback and updates the AI ​​model, specifically using the feedback data as training data for the AI ​​model and retraining the model with machine learning algorithms.

[0778] Example 1

[0779] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0780] In modern society, the number of users experiencing a deterioration in their mental health is increasing, but there are limited means to effectively provide personalized support. Furthermore, conventional methods make it difficult to accurately assess a user's mental health and create an appropriate support plan based on that assessment. Furthermore, there is a lack of means to effectively improve the system by utilizing subsequent feedback.

[0781] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0782] In this invention, the server includes a means for a user to input information from a terminal, a means for the terminal to send data to the server, a means for the server to process and store the data, a means for the server to send a confirmation message, and a means for the user to check the message and click a link. The system also includes a means for a user to log in using a terminal, a means for the server to generate and send a list of questions, a means for the user to answer the questions, a means for the terminal to send the answers to the server, and a means for the server to analyze and evaluate the answers using natural language processing technology and a machine learning algorithm. The system also includes a means for the server to create a support plan based on the evaluation results, a means for the server to send the created plan to the terminal, a means for the user to follow the plan, a means for the user to provide feedback, and a means for the terminal to send the feedback to the server, the server to analyze the feedback using a machine learning algorithm, and update the support plan. This not only enables the system to accurately evaluate a user's mental health status and provide a personalized support plan, but also enables the system to continuously improve using feedback.

[0783] A "user" is an individual who utilizes the system to input information and receive mental health support.

[0784] A "terminal" is an electronic device that a user uses to enter information, answer questionnaires, and provide feedback.

[0785] The "server" is a central processing unit that processes and stores data sent by users, generates question lists, creates support plans, analyzes feedback, and so on.

[0786] "Information" refers to all data that users enter on their devices, such as their name, email address, password, and data related to their mental health.

[0787] "Data" refers to all information handled within the system, including information provided by users from their terminals and the results of analysis by the server.

[0788] "Confirmation Message" means an email or notification sent to a user when they register or perform other actions, such as activating an account.

[0789] A "link" is a URL included in the confirmation message, and is a means for a user to click to perform a specific process or confirmation.

[0790] A "questionnaire" is a series of questions generated by the server and sent to the terminal to assess the user's mental health status.

[0791] "Natural language processing technology" is a technology that processes and understands language data that the server uses to analyze user responses.

[0792] A "machine learning algorithm" is a computational method used by the server to analyze data and automatically learn and improve models.

[0793] A "support plan" is a personalized support plan created by the server based on the user's mental health status, and includes guided meditation and cognitive behavioral therapy exercises.

[0794] "Analysis" is the process by which the server evaluates and classifies the data and feedback it receives, using natural language processing techniques and machine learning algorithms.

[0795] "Feedback" refers to information such as opinions, impressions, and results regarding the support plan implemented by the user.

[0796] "Update" is the process by which the server readjusts and improves support plans and AI models based on feedback.

[0797] This invention relates to a system in which a server processes information entered by a user through a terminal and provides personalized mental health support. This system has the following main functions:

[0798] User Registration

[0799] The user enters basic information such as name, email address, and password from the terminal. The terminal sends the entered information to the server, which stores it in a database. The server then sends a confirmation message to the user's email address, and the user activates the account by checking the email and clicking a link. This process typically uses MySQL as the database and a service such as SendGrid or Amazon SES to send emails.

[0800] Initial diagnosis

[0801] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP techniques and machine learning algorithms (e.g., TensorFlow and PyTorch) to assess the user's mental health.

[0802] Ongoing support

[0803] Based on the assessment results, the server creates an optimal support plan for the user. The plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends the plan to the user's device, and the user follows the instructions to put it into practice. This allows the user to receive appropriate mental health support at any time, whether at home or at work.

[0804] Feedback and AI Learning

[0805] Users provide feedback on support plans from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. This allows the server to continually provide more appropriate and effective support to users. Specifically, the feedback is used to retrain the machine learning model and improve the quality of future support plans.

[0806] Specific examples

[0807] For example, if a user inputs information about their recent stress levels, the server will analyze that data using NLP techniques and machine learning algorithms to suggest effective guided meditation sessions to reduce stress, with the content and frequency of these sessions adjusted accordingly based on feedback provided by the user via their device.

[0808] Example prompts for generative AI models

[0809] "We've asked you to provide information about your current stress levels. Please analyze this data and suggest effective guided meditation sessions."

[0810] Thus, the invention provides an affordable and accessible solution to resolving a user's mental health issues by effectively assessing their mental health status and providing a personalized support plan.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1: User enters basic information

[0813] The user enters basic information such as name, email address, and password into the terminal. The entered basic information is temporarily stored in the terminal's memory. The terminal then sends this entered data to the server. The basic information provided by the user is the input, and the basic information sent to the server is the output.

[0814] Step 2: The server saves the data and sends a confirmation message

[0815] The server receives the basic information sent from the device and stores it in a database (Database: MySQL for example). Once the storage is complete, the server sends a confirmation message to the user's email address. This confirmation message contains a link that the user can click to activate their account. As input, we have the basic information received from the device, and as output, we have the data stored in the database and the confirmation message sent to the user.

[0816] Step 3: User clicks link to activate account

[0817] The user clicks the link in the confirmation message. Accessing the link causes the server to update the user's account status to "valid." The input is the user's click, and the output is the status change of the user's account information in the database.

[0818] Step 4: User logs in at terminal

[0819] A user logs in by entering their email address and password on the device. The device sends the input information to the server, which then authenticates the user by comparing it with information in a database. If authentication is successful, the server generates a list of diagnostic questions for the user and sends it to the device. The input is login information, and the output is the authentication result and the list of diagnostic questions.

[0820] Step 5: User answers questionnaire

[0821] The user answers a list of diagnostic questions on the terminal. Each answer is sent from the terminal to the server in real time. The input is the user's answers to the diagnostic questions, and the output is the answer data sent to the server.

[0822] Step 6: The server analyzes the response data and evaluates the health status

[0823] The server analyzes the received response data using NLP technology and machine learning algorithms. The analysis results are used to evaluate the user's mental health. The response data is input, and the analysis results and health status evaluation are obtained as output.

[0824] Step 7: The server creates a support plan and sends it to the device

[0825] The server generates an optimal support plan for the user based on the assessment results. The support plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The generated support plan is sent to the device and made available to the user. The assessment results are input, and the generated support plan is output.

[0826] Step 8: Users follow the support plan and provide feedback

[0827] The user follows the instructions in the support plan through the terminal, and inputs the results and impressions as feedback into the terminal. The feedback data is sent from the terminal to the server. The user's feedback is the input, and the feedback data sent to the server is the output.

[0828] Step 9: The server analyzes the feedback and updates the AI ​​model

[0829] The server analyzes the received feedback and uses a generative AI model to make necessary adjustments to improve the quality of the support plan. It leverages machine learning algorithms to retrain the model and improve future support plans. The input is the feedback data, and the output is an updated AI model and an improved support plan.

[0830] (Application example 1)

[0831] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0832] Stress and mental health problems are on the rise in modern society, and there is a growing need for personalized mental support systems to effectively address these issues. However, conventional systems have difficulty providing appropriate relaxation menus in real time based on information provided by users, and they lack the functionality to efficiently collect user feedback and continuously improve the system. This has led to problems such as a decline in user satisfaction and system effectiveness.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0834] In this invention, the server includes: a means for a user to input information from an electronic device; a means for the electronic device to transmit data to the server; a means for the server to process and store the data; a means for the server to transmit a confirmation message; a means for the user to check the message and click a link; a means for the user to input mental health status information provided by the electronic device; a means for the electronic device to transmit the input information to the server; a means for the server to analyze and evaluate the input information; a means for the server to generate a relaxation menu based on the evaluation results and transmit it to the electronic device; and a means for the user to confirm the relaxation menu transmitted by the electronic device. This allows users to evaluate their own mental health status in real time and provide them with an appropriate personalized relaxation menu. Furthermore, user feedback can be efficiently collected, enabling continuous improvement of the system.

[0835] "User" refers to an individual who uses the system and provides mental health information.

[0836] "Electronic devices" refer to terminal devices that users use to input and receive information, including smartphones and robots.

[0837] "Server" refers to a computer system installed on a network to process, store, and analyze data submitted by users.

[0838] "Information" refers to data entered by users through electronic devices, specifically data related to mental health status.

[0839] A "relaxation menu" refers to plans and activities for improving mental health that are generated by the server based on the analysis results and provided to the user.

[0840] "Feedback" refers to data on evaluations and opinions of the relaxation menu provided by users.

[0841] "Diagnostic list" refers to a list of questions generated and sent by the server to assess the user's mental health status.

[0842] "Analysis" refers to the process by which the server evaluates data collected from users using machine learning models and natural language processing techniques.

[0843] The present invention relates to a system that allows a user to evaluate their mental health status and provide a personalized support plan. A specific implementation method thereof will be described below.

[0844] Hardware used

[0845] The present invention uses the following hardware:

[0846] Smartphone: A mobile device that allows users to enter information.

[0847] Automated reception robots: Used as an interface for users to enter information in brick-and-mortar establishments such as cafes and relaxation salons.

[0848] Server: Contains the database, NLP engine, and machine learning models to process, analyze, and store data.

[0849] Software used

[0850] The present invention uses the following software:

[0851] Server-side scripts (Python / Flask): process data and provide API.

[0852] Database (PostgreSQL): Stores user information, diagnostic results, and feedback.

[0853] Machine learning models (TensorFlow, PyTorch): Analyze input data from users and generate appropriate support plans.

[0854] User Interface (React Native): Front-end development for smartphone apps.

[0855] Node.js: Backend processing for robot integration.

[0856] Example of a system

[0857] 1. User Registration

[0858] A user enters their basic information, including name, email address, and password, into a smartphone app or automated reception robot. This information is sent to a server and stored in a database. The server then sends a confirmation email, and the user clicks on a link to activate their account.

[0859] 2. Initial diagnosis

[0860] When a user logs in, the server generates a diagnostic list and sends it to a smartphone or robot. The user answers the questions on the diagnostic list and sends the answers to the server, which then uses NLP technology to analyze the answers and evaluate the user's mental health.

[0861] Prompt Sentence Examples

[0862] "How stressed have you been lately? For example, on a scale of 1-10, please answer. Also, please tell us specifically what is causing you stress."

[0863] "Do you want guided meditation sessions? If so, how often would you like them?"

[0864] 3. Ongoing support

[0865] Based on the analysis results, the server generates an optimal relaxation menu for the user and sends it to the smartphone or robot. The user then follows the suggested menu to relax. For example, a specific aromatherapy or healing music session may be suggested.

[0866] 4. Feedback and AI Learning

[0867] After completing the relaxation menu, the user provides feedback, which is sent to the server via smartphone or robot. The server analyzes the feedback and updates the machine learning model to improve the support plan for future visits.

[0868] As can be seen, the system of the present invention efficiently supports the user's mental health and provides a personalized relaxation menu, providing an effective and accessible solution to mental health issues.

[0869] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0870] Step 1: User Registration

[0871] A user uses an electronic device (smartphone or robot) to input basic information such as name, email address, and password. This information is sent from the electronic device to a server. The server receives it and stores it in a database. The server then sends a confirmation email to the user's email address. When the user clicks on the link in the email, the account is activated. The input data is user information (a set of basic information), and the output data is a new user entry in the database.

[0872] Step 2: Initial diagnosis

[0873] When a user logs in with their electronic device, the server generates a diagnosis list and sends it to the electronic device. The user answers the questions in the diagnosis list and sends the answers to the server. The server analyzes the answers using NLP technology and evaluates the user's mental health status. The input data is the user's diagnosis answers, and the output data is the evaluation result of the mental health status.

[0874] Step 3: Relaxation menu provided

[0875] The server generates an optimal relaxation menu for the user based on the evaluation results. The generated menu is sent to the electronic device, where the user can review it. For example, a specific aromatherapy or healing music session may be suggested. The input data is the evaluation results, and the output data is the relaxation menu.

[0876] Step 4: Relaxation

[0877] The user follows the provided relaxation menu and practices. The electronic device records the operation log and the implementation status during this process, but no data is exchanged between the server and the user during this step. The input data is the relaxation menu, and the output data is the implementation log.

[0878] Step 5: Provide feedback

[0879] After completing the relaxation menu, the user provides feedback via the electronic device. The feedback is sent from the electronic device to the server. The input data is the feedback content, and the output data is the feedback entry.

[0880] Step 6: Feedback analysis and AI model update

[0881] The server receives and analyzes the feedback provided by the user. Based on the analysis results, the server updates the generative AI model to improve the accuracy of future support plans. The input data is the feedback, and the output data is the updated generative AI model.

[0882] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0883] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0884] User Registration

[0885] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0886] Initial diagnosis

[0887] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[0888] emotion recognition

[0889] The emotion engine recognizes the user's emotions using the user's input data, the device's camera, microphone, etc. The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state, which further personalizes the support plan and optimizes it to fit the user's current emotional state.

[0890] Ongoing support

[0891] Based on the evaluation results and emotional data, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[0892] Feedback and AI Learning

[0893] Users provide feedback on the support plan from their device. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. Emotion data from the emotion engine is also used as training data for the AI ​​model, continuously improving it. This allows the server to provide more appropriate and effective support to users.

[0894] Specific examples

[0895] For example, when a user describes a stressful situation, the emotion engine analyzes the user's facial expressions and tone of voice to assess the level of stress. Combining this emotion data with the initial assessment responses, the server then recommends specific guided meditation sessions or relaxation exercises for the user. The content and frequency of these plans are adjusted by the server based on the user's feedback and emotion data.

[0896] In this way, the system of the present invention provides comprehensive support for a user's mental health and emotional state, offering personalized plans and providing affordable and accessible solutions to mental health issues.

[0897] The processing flow will be explained below.

[0898] Step 1:

[0899] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[0900] Step 2:

[0901] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[0902] Step 3:

[0903] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[0904] Step 4:

[0905] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[0906] Step 5:

[0907] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[0908] Step 6:

[0909] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[0910] Step 7:

[0911] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[0912] Step 8:

[0913] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[0914] Step 9:

[0915] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[0916] Step 10:

[0917] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[0918] Step 11:

[0919] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[0920] Step 12:

[0921] The emotion engine recognizes the user's emotions by analyzing the user's facial expressions, tone of voice, and the emotion of the text using the device's camera, microphone, and input text.

[0922] Step 13:

[0923] The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state. Specifically, it uses an emotion evaluation algorithm to quantify emotions such as stress, joy, and sadness.

[0924] Step 14:

[0925] The server creates a support plan based on the evaluation results and emotional data, specifically, stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[0926] Step 15:

[0927] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[0928] Step 16:

[0929] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[0930] Step 17:

[0931] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[0932] Step 18:

[0933] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[0934] Step 19:

[0935] The server analyzes the feedback and updates the plan, specifically using the feedback data to adjust the support plan and taking emotional data into account for optimization.

[0936] Step 20:

[0937] The server uses the feedback as training data for the AI ​​model and updates it, specifically retraining it with machine learning algorithms to continuously improve it.

[0938] Example 2

[0939] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0940] In modern society, many people suffer from stress, anxiety, and other mental health issues. These issues can significantly reduce an individual's quality of life if not addressed early and appropriately. However, few systems offer affordable, accessible, and effective mental health support. The present invention aims to address these issues by comprehensively assessing a user's mental health and emotional state and providing a personalized support plan.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input information from a terminal; means for the terminal to send data to the server; means for the server to process and store data; means for the server to send a confirmation email; means for the user to check the email and click a link; means for the server to generate a diagnostic question list and send it to the terminal; means for the server to analyze the answers using natural language processing technology; means for the server to acquire emotion data using a camera or microphone of the terminal; means for the server to evaluate the emotion data using an emotion recognition engine; and means for the server to continuously analyze feedback and improve the AI ​​model. This makes it possible to provide personalized mental health support at an affordable price and effectively improve the user's mental health status.

[0942] "User" means an individual or organization that uses the system.

[0943] A "terminal" is an electronic device that allows a user to input information and interact with the system. Examples include smartphones, tablets, and personal computers.

[0944] "Means for inputting information" refers to the method by which a user inputs data such as text information, voice, or images into a terminal.

[0945] "Means for transmitting data" refers to a method by which the terminal transmits information input by the user to the server via the network.

[0946] A "server" is a computing device or system for processing and storing data sent by users and providing the required response.

[0947] "Means for processing and storing data" refers to the method by which the server appropriately analyzes the data it receives and stores it in a storage device in the required form.

[0948] "Means for sending a confirmation email" refers to the method by which the server sends an email to the user's email address for the purpose of account confirmation or authentication.

[0949] A "diagnostic questionnaire" is a series of questions used to assess a user's mental health.

[0950] "Natural language processing technology" is a technology for mechanically understanding and analyzing text information entered by users. Examples include text classification and sentiment analysis.

[0951] "Emotion data" is data that indicates the user's emotional state, and is based on information such as facial expressions and tone of voice.

[0952] An "emotion recognition engine" is software or algorithms for analyzing emotion data and assessing a user's emotional state.

[0953] A "support plan" is a set of actions or suggestions developed by the server to improve a user's mental health, such as guided meditation or cognitive behavioral therapy (CBT) exercises.

[0954] "Feedback" refers to opinions and reactions provided by users regarding the effectiveness of the support plan and the proposed content.

[0955] An "AI model" is a computational model that uses machine learning algorithms to learn from data and perform specific tasks or decisions.

[0956] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[0957] First, a user accesses the system using a terminal. A terminal refers to a common electronic device such as a smartphone, tablet, or PC. The user enters basic information such as their name, email address, and password, and sends it from the terminal to the server. The server receives this information and processes it to store it in a database. The server uses a database management system such as MySQL or PostgreSQL to store the information. Next, the server sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[0958] When a user logs in from a device, the server generates a diagnostic questionnaire and sends it to the device. This questionnaire contains questions to assess mental health status. For example, it can refer to standard diagnostic tools such as the Anxiety Inventory or the Beck Depression Inventory. The user answers the questions on the device and sends the answers to the server. The server uses NLP technology (e.g., SpaCy or a custom-trained model) to analyze these answers and assess the user's mental health status.

[0959] Furthermore, the device's camera and microphone are used to capture the user's facial expressions and tone of voice. These data are used as emotion data to indicate the user's emotional state. The device then transmits this emotion data to a server, which then uses an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) to analyze the emotion data and evaluate the user's emotional state.

[0960] Based on the assessment results and emotional data, the server creates a personalized support plan. This plan may include guided meditations and cognitive behavioral therapy (CBT) exercises. The server then sends the created support plan to the device, which the user follows. The user provides feedback on the support plan from the device, which then sends the feedback to the server. The server analyzes this feedback and updates the AI ​​model, allowing the server to continuously improve the support plan.

[0961] For example, if a user types "My boss got angry at me at work today" into a device, the device sends this input to a server. The server uses an emotion engine to evaluate the user's emotional state and determines that the stress level is high. Based on this evaluation, the server creates a support plan including a "5-minute guided meditation" and sends it to the device. The user then executes this plan and provides feedback on its effectiveness, allowing the server to provide more appropriate support.

[0962] An example of a prompt to input to a generative AI model is as follows:

[0963] "Please record your mental health status today. Based on that, we will suggest an appropriate support plan. For example, 'My boss got angry at me at work.'"

[0964] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0965] Step 1:

[0966] The user enters basic information such as name, email address, and password from the terminal. The entered basic information is formatted by the terminal and sent to the server. Specifically, the terminal sends the input data to the server as an HTTP request.

[0967] Input: Basic information such as name, email address, and password

[0968] Output: Send basic information to the server

[0969] Step 2:

[0970] The server verifies the received basic information and stores it in a database, for example, using MySQL or PostgreSQL. Once the basic information has been stored, the server generates a confirmation email and sends it to the user's email address.

[0971] Input: User basic information

[0972] Output: Save basic information to database and send confirmation email

[0973] Step 3:

[0974] The user receives a confirmation email and clicks on the link in the email. When the link is clicked, the device sends an account activation request to the server. The server receives this request and activates the user's account.

[0975] Input: Click the link in the confirmation email

[0976] Output: Account activation request to server and account activation

[0977] Step 4:

[0978] When a user logs in at a device, the server generates and sends to the device a diagnostic questionnaire containing questions to assess mental health, such as the Anxiety Inventory and the Beck Depression Inventory.

[0979] Input: User login information

[0980] Output: Diagnostic Question List sent to terminal

[0981] Step 5:

[0982] The user answers questions on the device and sends the answers to the server, which then converts the answers into JSON format and sends it to the server as an HTTP request.

[0983] Input: Answers to the diagnostic questionnaire

[0984] Output: Sending response data to the server

[0985] Step 6:

[0986] The server analyzes the received data using natural language processing technology (e.g., SpaCy) and machine learning algorithms to evaluate the user's mental health status, and stores the evaluation results in a database.

[0987] Input: Answer data

[0988] Output: Mental health assessment results and storage in a database

[0989] Step 7:

[0990] The device's camera and microphone are used to capture emotional data such as the user's facial expressions and tone of voice, which are then processed by the device and sent to a server.

[0991] Input: Facial expressions and tone of voice

[0992] Output: Sending emotion data to the server

[0993] Step 8:

[0994] The server analyzes the received emotion data using an emotion recognition engine (e.g., Affectiva or Microsoft Azure Emotion API) to evaluate the user's emotional state and stores the evaluation results in a database.

[0995] Input: Emotion data

[0996] Output: Evaluation results of emotional state and storage in a database

[0997] Step 9:

[0998] The server creates a personalized support plan based on the assessment results and emotional data, which may include guided meditation and cognitive behavioral therapy (CBT) exercises, and sends the plan in JSON format to the device.

[0999] Input: Evaluation results and emotion data

[1000] Output: Sending the support plan to the device

[1001] Step 10:

[1002] The user checks the support plan on the device and implements it according to its contents. After implementing it, the user inputs feedback into the device, which then sends this feedback to the server.

[1003] Input: Feedback data

[1004] Output: Sending feedback to the server

[1005] Step 11:

[1006] The server analyzes the received feedback and updates the AI ​​model, which will improve future support plans more effectively.

[1007] Input: Feedback data

[1008] Output: Updated AI models and improved support plans

[1009] In this way, by clearly indicating the specific actions performed at each step and their inputs and outputs, it becomes clear how the system of the present invention supports the mental health of the user.

[1010] (Application example 2)

[1011] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1012] Conventional mental health support systems have had problems such as users being unable to accurately communicate their mental state and difficulty in providing personalized support plans. This has made it difficult for users to receive effective mental health support. Another issue, particularly in physical stores, is that customers are unable to make effective use of their waiting time.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1014] In this invention, the server includes means for a user to input information from a terminal, means by the terminal to send data to the server, means by the server to process and store the data, means by the server to send a confirmation email, means by the user to check the email and click a link, means by the terminal to recognize emotions using the user's input data, means by the server to process and store emotional data, means by the server to provide a support plan based on the user's emotional data, and means by the terminal to display the contents of the support plan to the user. This makes it possible to provide an individualized support plan that takes into account the user's emotional state, and further enables customers to receive mental health support in physical stores by effectively using their waiting time.

[1015] A "terminal" is a digital device through which a user inputs information and communicates data with a server.

[1016] "Server" means a central processing unit that processes, stores, analyzes data, and provides services to users.

[1017] "Data" is a general term for information processed by the system, such as information entered by the user and the results of the emotion recognition engine.

[1018] "Input Data" refers to information provided by a user to the system through a terminal, such as text input or audio / visual data.

[1019] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and text input to identify their emotional state.

[1020] "Emotion data" is data about a user's emotional state obtained using emotion recognition technology.

[1021] A "support plan" is a set of specific actions, exercises, and advice provided to support a user's mental health.

[1022] "Feedback" refers to information about the effectiveness and satisfaction of the support plan provided by the user.

[1023] An "AI model" refers to an algorithm that uses artificial intelligence to analyze data and is continuously improved.

[1024] A "brick and mortar store" is a store located in a physical location where customers can receive in-person service.

[1025] "Waiting time" refers to the time a customer spends waiting to receive service in a physical store.

[1026] This invention is a system that effectively utilizes customer waiting time in a physical store to provide mental health support. The system includes the following main hardware and software components:

[1027] Hardware

[1028] 1. Terminal: A digital device (e.g., smartphone, tablet) through which a user inputs information and communicates data with a server.

[1029] 2. Server: A central processing unit (cloud server) that processes, stores, analyzes data, and provides services to users.

[1030] software

[1031] 1. User registration: The user enters basic information such as name, email address, and password on the device. The device sends this information to the server, which stores it in a database. The server then sends a confirmation email, and the user clicks on the link to activate their account.

[1032] 2. Initial diagnosis: When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server uses NLP technology and machine learning algorithms to analyze the answers and evaluate the user's mental health.

[1033] 3. Emotion Recognition: The device's camera and microphone are used to collect the user's facial expressions and tone of voice. This data is analyzed by the emotion recognition engine and sent to the server as emotional data.

[1034] 4. Providing a support plan: The server creates a support plan tailored to the user based on the analysis results and emotional data. The plan may include guided meditations and relaxation exercises. The server then sends the plan to the device, and the user follows the instructions.

[1035] 5. Feedback and AI learning: Users can provide feedback on the support plan from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model, allowing the server to provide more appropriate and effective support to users.

[1036] Specific examples

[1037] For example, a customer waiting in a physical store opens the app and enters information about their stress level. The device's camera captures their facial expressions and the microphone records their tone of voice. An emotion recognition engine analyzes this data and sends the emotional data to a server. The server uses this data to suggest specific guided meditation sessions or relaxation exercises. The user tries them out and then provides feedback. The feedback is sent to the server, and the AI ​​model is updated accordingly.

[1038] Example prompts for generative AI models

[1039] "Users enter their stress levels while waiting in line at a physical store. Through facial recognition and voice analysis, the emotion engine analyzes their stress levels. Recommend personalized guided meditations and relaxation exercises."

[1040] In this way, the system of the present invention can enhance the customer experience in physical stores by providing comprehensive support for the user's mental health and emotional state and providing personalized plans.

[1041] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1042] Step 1:

[1043] User Registration:

[1044] The user enters basic information (name, email address, password) from the terminal, and the terminal sends this data to the server. The server stores the received data in a database and sends a confirmation email to the user. The user clicks on the link in the confirmation email to activate the account. The input data is basic information, and the output is the sending of a confirmation email.

[1045] Step 2:

[1046] Initial diagnosis:

[1047] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answer data to the server. The server then analyzes the answer data using NLP technology and machine learning algorithms to evaluate the user's mental health status. The input data is the answer data, and the output is the mental health status evaluation result.

[1048] Step 3:

[1049] Emotion recognition:

[1050] The user uses the device's camera and microphone to capture and record facial expressions and tone of voice. The device then sends this data to the emotion recognition engine to obtain emotion data. The emotion data is then sent to the server. The input data is facial expression data and tone of voice data, and the output is emotion data.

[1051] Step 4:

[1052] Support plans offered:

[1053] The server creates an optimal support plan for the user based on the mental health assessment results from step 2 and the emotional data from step 3. This plan includes guided meditation and relaxation exercises. The created support plan is sent to the device. The input data are the mental health assessment results and emotional data, and the output is the support plan.

[1054] Step 5:

[1055] Support plan execution:

[1056] The user follows the support plan provided on the device and performs guided meditation and relaxation exercises. The execution results are input as feedback. The input data are the support plan and the execution results, and the output is feedback.

[1057] Step 6:

[1058] Send feedback and update the AI ​​model:

[1059] The user's feedback is sent to the server via the device. The server updates the AI ​​model based on the feedback and the continuous emotional data obtained in step 3. This process enables the server to provide a more effective support plan from the next time onwards. The input data are the feedback and emotional data, and the output is the updated AI model.

[1060] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1061] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1062] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1063] [Fourth embodiment]

[1064] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1065] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1066] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1067] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1068] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1069] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1070] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1071] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1072] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1073] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1074] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1075] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1076] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1077] The present invention relates to a system that provides personalized mental health support by having a server process information entered by a user via a device. The system assesses the user's mental health status, provides an appropriate support plan, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[1078] User Registration

[1079] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[1080] Initial diagnosis

[1081] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[1082] Ongoing support

[1083] Based on the assessment results, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[1084] Feedback and AI Learning

[1085] Users provide feedback on the support plan from their device, which is then sent to the server, where it is analyzed and used to update the AI ​​model, allowing the server to continually provide more appropriate and effective support to users.

[1086] Specific examples

[1087] For example, if a user inputs information about their recent stress levels, the server will analyze that data and suggest guided meditation sessions to help reduce stress, adjusting the content and frequency of these sessions based on user feedback.

[1088] In this way, the system of the present invention effectively supports users' mental health conditions and provides personalized plans, enabling affordable and accessible solutions to mental health problems.

[1089] The processing flow will be explained below.

[1090] Step 1:

[1091] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[1092] Step 2:

[1093] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[1094] Step 3:

[1095] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[1096] Step 4:

[1097] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[1098] Step 5:

[1099] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[1100] Step 6:

[1101] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[1102] Step 7:

[1103] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[1104] Step 8:

[1105] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[1106] Step 9:

[1107] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[1108] Step 10:

[1109] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[1110] Step 11:

[1111] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[1112] Step 12:

[1113] The server then creates a support plan based on the assessment results, including stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[1114] Step 13:

[1115] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[1116] Step 14:

[1117] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[1118] Step 15:

[1119] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[1120] Step 16:

[1121] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[1122] Step 17:

[1123] The server analyzes the feedback and updates the AI ​​model, specifically using the feedback data as training data for the AI ​​model and retraining the model with machine learning algorithms.

[1124] Example 1

[1125] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1126] In modern society, the number of users experiencing a deterioration in their mental health is increasing, but there are limited means to effectively provide personalized support. Furthermore, conventional methods make it difficult to accurately assess a user's mental health and create an appropriate support plan based on that assessment. Furthermore, there is a lack of means to effectively improve the system by utilizing subsequent feedback.

[1127] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1128] In this invention, the server includes a means for a user to input information from a terminal, a means for the terminal to send data to the server, a means for the server to process and store the data, a means for the server to send a confirmation message, and a means for the user to check the message and click a link. The system also includes a means for a user to log in using a terminal, a means for the server to generate and send a list of questions, a means for the user to answer the questions, a means for the terminal to send the answers to the server, and a means for the server to analyze and evaluate the answers using natural language processing technology and a machine learning algorithm. The system also includes a means for the server to create a support plan based on the evaluation results, a means for the server to send the created plan to the terminal, a means for the user to follow the plan, a means for the user to provide feedback, and a means for the terminal to send the feedback to the server, the server to analyze the feedback using a machine learning algorithm, and update the support plan. This not only enables the system to accurately evaluate a user's mental health status and provide a personalized support plan, but also enables the system to continuously improve using feedback.

[1129] A "user" is an individual who utilizes the system to input information and receive mental health support.

[1130] A "terminal" is an electronic device that a user uses to enter information, answer questionnaires, and provide feedback.

[1131] The "server" is a central processing unit that processes and stores data sent by users, generates question lists, creates support plans, analyzes feedback, and so on.

[1132] "Information" refers to all data that users enter on their devices, such as their name, email address, password, and data related to their mental health.

[1133] "Data" refers to all information handled within the system, including information provided by users from their terminals and the results of analysis by the server.

[1134] "Confirmation Message" means an email or notification sent to a user when they register or perform other actions, such as activating an account.

[1135] A "link" is a URL included in the confirmation message, and is a means for a user to click to perform a specific process or confirmation.

[1136] A "questionnaire" is a series of questions generated by the server and sent to the terminal to assess the user's mental health status.

[1137] "Natural language processing technology" is a technology that processes and understands language data that the server uses to analyze user responses.

[1138] A "machine learning algorithm" is a computational method used by the server to analyze data and automatically learn and improve models.

[1139] A "support plan" is a personalized support plan created by the server based on the user's mental health status, and includes guided meditation and cognitive behavioral therapy exercises.

[1140] "Analysis" is the process by which the server evaluates and classifies the data and feedback it receives, using natural language processing techniques and machine learning algorithms.

[1141] "Feedback" refers to information such as opinions, impressions, and results regarding the support plan implemented by the user.

[1142] "Update" is the process by which the server readjusts and improves support plans and AI models based on feedback.

[1143] This invention relates to a system in which a server processes information entered by a user through a terminal and provides personalized mental health support. This system has the following main functions:

[1144] User Registration

[1145] The user enters basic information such as name, email address, and password from the terminal. The terminal sends the entered information to the server, which stores it in a database. The server then sends a confirmation message to the user's email address, and the user activates the account by checking the email and clicking a link. This process typically uses MySQL as the database and a service such as SendGrid or Amazon SES to send emails.

[1146] Initial diagnosis

[1147] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP techniques and machine learning algorithms (e.g., TensorFlow and PyTorch) to assess the user's mental health.

[1148] Ongoing support

[1149] Based on the assessment results, the server creates an optimal support plan for the user. The plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends the plan to the user's device, and the user follows the instructions to put it into practice. This allows the user to receive appropriate mental health support at any time, whether at home or at work.

[1150] Feedback and AI Learning

[1151] Users provide feedback on support plans from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. This allows the server to continually provide more appropriate and effective support to users. Specifically, the feedback is used to retrain the machine learning model and improve the quality of future support plans.

[1152] Specific examples

[1153] For example, if a user inputs information about their recent stress levels, the server will analyze that data using NLP techniques and machine learning algorithms to suggest effective guided meditation sessions to reduce stress, with the content and frequency of these sessions adjusted accordingly based on feedback provided by the user via their device.

[1154] Example prompts for generative AI models

[1155] "We've asked you to provide information about your current stress levels. Please analyze this data and suggest effective guided meditation sessions."

[1156] Thus, the invention provides an affordable and accessible solution to resolving a user's mental health issues by effectively assessing their mental health status and providing a personalized support plan.

[1157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1158] Step 1: User enters basic information

[1159] The user enters basic information such as name, email address, and password into the terminal. The entered basic information is temporarily stored in the terminal's memory. The terminal then sends this entered data to the server. The basic information provided by the user is the input, and the basic information sent to the server is the output.

[1160] Step 2: The server saves the data and sends a confirmation message

[1161] The server receives the basic information sent from the device and stores it in a database (Database: MySQL for example). Once the storage is complete, the server sends a confirmation message to the user's email address. This confirmation message contains a link that the user can click to activate their account. As input, we have the basic information received from the device, and as output, we have the data stored in the database and the confirmation message sent to the user.

[1162] Step 3: User clicks link to activate account

[1163] The user clicks the link in the confirmation message. Accessing the link causes the server to update the user's account status to "valid." The input is the user's click, and the output is the status change of the user's account information in the database.

[1164] Step 4: User logs in at terminal

[1165] A user logs in by entering their email address and password on the device. The device sends the input information to the server, which then authenticates the user by comparing it with information in a database. If authentication is successful, the server generates a list of diagnostic questions for the user and sends it to the device. The input is login information, and the output is the authentication result and the list of diagnostic questions.

[1166] Step 5: User answers questionnaire

[1167] The user answers a list of diagnostic questions on the terminal. Each answer is sent from the terminal to the server in real time. The input is the user's answers to the diagnostic questions, and the output is the answer data sent to the server.

[1168] Step 6: The server analyzes the response data and evaluates the health status

[1169] The server analyzes the received response data using NLP technology and machine learning algorithms. The analysis results are used to evaluate the user's mental health. The response data is input, and the analysis results and health status evaluation are obtained as output.

[1170] Step 7: The server creates a support plan and sends it to the device

[1171] The server generates an optimal support plan for the user based on the assessment results. The support plan may include guided meditation and cognitive behavioral therapy (CBT) exercises. The generated support plan is sent to the device and made available to the user. The assessment results are input, and the generated support plan is output.

[1172] Step 8: Users follow the support plan and provide feedback

[1173] The user follows the instructions in the support plan through the terminal, and inputs the results and impressions as feedback into the terminal. The feedback data is sent from the terminal to the server. The user's feedback is the input, and the feedback data sent to the server is the output.

[1174] Step 9: The server analyzes the feedback and updates the AI ​​model

[1175] The server analyzes the received feedback and uses a generative AI model to make necessary adjustments to improve the quality of the support plan. It leverages machine learning algorithms to retrain the model and improve future support plans. The input is the feedback data, and the output is an updated AI model and an improved support plan.

[1176] (Application example 1)

[1177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1178] Stress and mental health problems are on the rise in modern society, and there is a growing need for personalized mental support systems to effectively address these issues. However, conventional systems have difficulty providing appropriate relaxation menus in real time based on information provided by users, and they lack the functionality to efficiently collect user feedback and continuously improve the system. This has led to problems such as a decline in user satisfaction and system effectiveness.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1180] In this invention, the server includes: a means for a user to input information from an electronic device; a means for the electronic device to transmit data to the server; a means for the server to process and store the data; a means for the server to transmit a confirmation message; a means for the user to check the message and click a link; a means for the user to input mental health status information provided by the electronic device; a means for the electronic device to transmit the input information to the server; a means for the server to analyze and evaluate the input information; a means for the server to generate a relaxation menu based on the evaluation results and transmit it to the electronic device; and a means for the user to confirm the relaxation menu transmitted by the electronic device. This allows users to evaluate their own mental health status in real time and provide them with an appropriate personalized relaxation menu. Furthermore, user feedback can be efficiently collected, enabling continuous improvement of the system.

[1181] "User" refers to an individual who uses the system and provides mental health information.

[1182] "Electronic devices" refer to terminal devices that users use to input and receive information, including smartphones and robots.

[1183] "Server" refers to a computer system installed on a network to process, store, and analyze data submitted by users.

[1184] "Information" refers to data entered by users through electronic devices, specifically data related to mental health status.

[1185] A "relaxation menu" refers to plans and activities for improving mental health that are generated by the server based on the analysis results and provided to the user.

[1186] "Feedback" refers to data on evaluations and opinions of the relaxation menu provided by users.

[1187] "Diagnostic list" refers to a list of questions generated and sent by the server to assess the user's mental health status.

[1188] "Analysis" refers to the process by which the server evaluates data collected from users using machine learning models and natural language processing techniques.

[1189] The present invention relates to a system that allows a user to evaluate their mental health status and provide a personalized support plan. A specific implementation method thereof will be described below.

[1190] Hardware used

[1191] The present invention uses the following hardware:

[1192] Smartphone: A mobile device that allows users to enter information.

[1193] Automated reception robots: Used as an interface for users to enter information in brick-and-mortar establishments such as cafes and relaxation salons.

[1194] Server: Contains the database, NLP engine, and machine learning models to process, analyze, and store data.

[1195] Software used

[1196] The present invention uses the following software:

[1197] Server-side scripts (Python / Flask): process data and provide API.

[1198] Database (PostgreSQL): Stores user information, diagnostic results, and feedback.

[1199] Machine learning models (TensorFlow, PyTorch): Analyze input data from users and generate appropriate support plans.

[1200] User Interface (React Native): Front-end development for smartphone apps.

[1201] Node.js: Backend processing for robot integration.

[1202] Example of a system

[1203] 1. User Registration

[1204] A user enters their basic information, including name, email address, and password, into a smartphone app or automated reception robot. This information is sent to a server and stored in a database. The server then sends a confirmation email, and the user clicks on a link to activate their account.

[1205] 2. Initial diagnosis

[1206] When a user logs in, the server generates a diagnostic list and sends it to a smartphone or robot. The user answers the questions on the diagnostic list and sends the answers to the server, which then uses NLP technology to analyze the answers and evaluate the user's mental health.

[1207] Prompt Sentence Examples

[1208] "How stressed have you been lately? For example, on a scale of 1-10, please answer. Also, please tell us specifically what is causing you stress."

[1209] "Do you want guided meditation sessions? If so, how often would you like them?"

[1210] 3. Ongoing support

[1211] Based on the analysis results, the server generates an optimal relaxation menu for the user and sends it to the smartphone or robot. The user then follows the suggested menu to relax. For example, a specific aromatherapy or healing music session may be suggested.

[1212] 4. Feedback and AI Learning

[1213] After completing the relaxation menu, the user provides feedback, which is sent to the server via smartphone or robot. The server analyzes the feedback and updates the machine learning model to improve the support plan for future visits.

[1214] As can be seen, the system of the present invention efficiently supports the user's mental health and provides a personalized relaxation menu, providing an effective and accessible solution to mental health issues.

[1215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1216] Step 1: User Registration

[1217] A user uses an electronic device (smartphone or robot) to input basic information such as name, email address, and password. This information is sent from the electronic device to a server. The server receives it and stores it in a database. The server then sends a confirmation email to the user's email address. When the user clicks on the link in the email, the account is activated. The input data is user information (a set of basic information), and the output data is a new user entry in the database.

[1218] Step 2: Initial diagnosis

[1219] When a user logs in with their electronic device, the server generates a diagnosis list and sends it to the electronic device. The user answers the questions in the diagnosis list and sends the answers to the server. The server analyzes the answers using NLP technology and evaluates the user's mental health status. The input data is the user's diagnosis answers, and the output data is the evaluation result of the mental health status.

[1220] Step 3: Relaxation menu provided

[1221] The server generates an optimal relaxation menu for the user based on the evaluation results. The generated menu is sent to the electronic device, where the user can review it. For example, a specific aromatherapy or healing music session may be suggested. The input data is the evaluation results, and the output data is the relaxation menu.

[1222] Step 4: Relaxation

[1223] The user follows the provided relaxation menu and practices. The electronic device records the operation log and the implementation status during this process, but no data is exchanged between the server and the user during this step. The input data is the relaxation menu, and the output data is the implementation log.

[1224] Step 5: Provide feedback

[1225] After completing the relaxation menu, the user provides feedback via the electronic device. The feedback is sent from the electronic device to the server. The input data is the feedback content, and the output data is the feedback entry.

[1226] Step 6: Feedback analysis and AI model update

[1227] The server receives and analyzes the feedback provided by the user. Based on the analysis results, the server updates the generative AI model to improve the accuracy of future support plans. The input data is the feedback, and the output data is the updated generative AI model.

[1228] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1229] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[1230] User Registration

[1231] The user enters basic information such as name, email address, and password from the device. The device sends this information to the server, which stores the received data in a database. The server then sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[1232] Initial diagnosis

[1233] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server then analyzes the answers using NLP technology and machine learning algorithms to assess the user's mental health.

[1234] emotion recognition

[1235] The emotion engine recognizes the user's emotions using the user's input data, the device's camera, microphone, etc. The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state, which further personalizes the support plan and optimizes it to fit the user's current emotional state.

[1236] Ongoing support

[1237] Based on the evaluation results and emotional data, the server creates an optimal support plan for the user, which may include guided meditation and cognitive behavioral therapy (CBT) exercises. The server then sends this plan to the device, and the user follows the instructions.

[1238] Feedback and AI Learning

[1239] Users provide feedback on the support plan from their device. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model. Emotion data from the emotion engine is also used as training data for the AI ​​model, continuously improving it. This allows the server to provide more appropriate and effective support to users.

[1240] Specific examples

[1241] For example, when a user describes a stressful situation, the emotion engine analyzes the user's facial expressions and tone of voice to assess the level of stress. Combining this emotion data with the initial assessment responses, the server then recommends specific guided meditation sessions or relaxation exercises for the user. The content and frequency of these plans are adjusted by the server based on the user's feedback and emotion data.

[1242] In this way, the system of the present invention provides comprehensive support for a user's mental health and emotional state, offering personalized plans and providing affordable and accessible solutions to mental health issues.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] The user enters basic information from the terminal. Specifically, the user enters their name, email address, password, etc. into the form and clicks the submit button.

[1246] Step 2:

[1247] The device sends the entered information to the server. Specifically, the data is packaged as an API request and sent to the server's registration processing endpoint.

[1248] Step 3:

[1249] The server processes and stores the received data, specifically creating a new user record in the database and storing the entered information.

[1250] Step 4:

[1251] The server sends a confirmation email, specifically using an email sending library to send an email containing a confirmation link to the user's email address.

[1252] Step 5:

[1253] The user checks the received email and clicks on the link. Specifically, by opening the email and clicking on the confirmation link, the account is activated.

[1254] Step 6:

[1255] The user logs in on the device. Specifically, they enter their email address and password into the form and click the login button.

[1256] Step 7:

[1257] The server receives the login information and performs user authentication. Specifically, it checks the user information against a database and starts a session if authentication is successful.

[1258] Step 8:

[1259] The server generates a diagnostic question list and sends it to the device. Specifically, it packages a set of questions prepared in advance in JSON format and sends it to the device as an API response.

[1260] Step 9:

[1261] The user answers the questions on the device by entering options or text in response to the displayed questions and clicking the submit button.

[1262] Step 10:

[1263] The device sends the answer to the server. Specifically, the input answer is packaged in JSON format and sent to the server's analysis processing endpoint.

[1264] Step 11:

[1265] The server analyzes and evaluates the answers, using natural language processing (NLP) and machine learning algorithms to assess the user's mental health.

[1266] Step 12:

[1267] The emotion engine recognizes the user's emotions by analyzing the user's facial expressions, tone of voice, and the emotion of the text using the device's camera, microphone, and input text.

[1268] Step 13:

[1269] The server processes the emotion data obtained from the emotion engine and evaluates the user's emotional state. Specifically, it uses an emotion evaluation algorithm to quantify emotions such as stress, joy, and sadness.

[1270] Step 14:

[1271] The server creates a support plan based on the evaluation results and emotional data, specifically, stress reduction and cognitive behavioral therapy (CBT) exercises, and customizes the plan for each user.

[1272] Step 15:

[1273] The server sends the support plan to the device. Specifically, the generated plan is packaged in JSON format and sent to the device as an API response.

[1274] Step 16:

[1275] The user then executes the actions in accordance with the plan, specifically by performing suggested guided meditations or CBT exercises on the device.

[1276] Step 17:

[1277] The user provides feedback from the terminal by inputting their opinion and effect regarding the support plan and clicking the submit button.

[1278] Step 18:

[1279] The device sends the feedback to the server. Specifically, the input feedback is packaged in JSON format and sent to the server's feedback processing endpoint.

[1280] Step 19:

[1281] The server analyzes the feedback and updates the plan, specifically using the feedback data to adjust the support plan and taking emotional data into account for optimization.

[1282] Step 20:

[1283] The server uses the feedback as training data for the AI ​​model and updates it, specifically retraining it with machine learning algorithms to continuously improve it.

[1284] Example 2

[1285] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1286] In modern society, many people suffer from stress, anxiety, and other mental health issues. These issues can significantly reduce an individual's quality of life if not addressed early and appropriately. However, few systems offer affordable, accessible, and effective mental health support. The present invention aims to address these issues by comprehensively assessing a user's mental health and emotional state and providing a personalized support plan.

[1287] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input information from a terminal; means for the terminal to send data to the server; means for the server to process and store data; means for the server to send a confirmation email; means for the user to check the email and click a link; means for the server to generate a diagnostic question list and send it to the terminal; means for the server to analyze the answers using natural language processing technology; means for the server to acquire emotion data using a camera or microphone of the terminal; means for the server to evaluate the emotion data using an emotion recognition engine; and means for the server to continuously analyze feedback and improve the AI ​​model. This makes it possible to provide personalized mental health support at an affordable price and effectively improve the user's mental health status.

[1288] "User" means an individual or organization that uses the system.

[1289] A "terminal" is an electronic device that allows a user to input information and interact with the system. Examples include smartphones, tablets, and personal computers.

[1290] "Means for inputting information" refers to the method by which a user inputs data such as text information, voice, or images into a terminal.

[1291] "Means for transmitting data" refers to a method by which the terminal transmits information input by the user to the server via the network.

[1292] A "server" is a computing device or system for processing and storing data sent by users and providing the required response.

[1293] "Means for processing and storing data" refers to the method by which the server appropriately analyzes the data it receives and stores it in a storage device in the required form.

[1294] "Means for sending a confirmation email" refers to the method by which the server sends an email to the user's email address for the purpose of account confirmation or authentication.

[1295] A "diagnostic questionnaire" is a series of questions used to assess a user's mental health.

[1296] "Natural language processing technology" is a technology for mechanically understanding and analyzing text information entered by users. Examples include text classification and sentiment analysis.

[1297] "Emotion data" is data that indicates the user's emotional state, and is based on information such as facial expressions and tone of voice.

[1298] An "emotion recognition engine" is software or algorithms for analyzing emotion data and assessing a user's emotional state.

[1299] A "support plan" is a set of actions or suggestions developed by the server to improve a user's mental health, such as guided meditation or cognitive behavioral therapy (CBT) exercises.

[1300] "Feedback" refers to opinions and reactions provided by users regarding the effectiveness of the support plan and the proposed content.

[1301] An "AI model" is a computational model that uses machine learning algorithms to learn from data and perform specific tasks or decisions.

[1302] The present invention relates to a system that provides personalized mental health support by combining information input by users via their devices with emotional data generated by an emotion engine. The system allows users to assess their mental health status, provides an appropriate support plan based on the emotional data, and collects continuous feedback to improve the AI ​​model, thereby providing affordable, accessible, and effective mental health support.

[1303] First, a user accesses the system using a terminal. A terminal refers to a common electronic device such as a smartphone, tablet, or PC. The user enters basic information such as their name, email address, and password, and sends it from the terminal to the server. The server receives this information and processes it to store it in a database. The server uses a database management system such as MySQL or PostgreSQL to store the information. Next, the server sends a confirmation email to the user's email address, and the user clicks on the link to activate their account.

[1304] When a user logs in from a device, the server generates a diagnostic questionnaire and sends it to the device. This questionnaire contains questions to assess mental health status. For example, it can refer to standard diagnostic tools such as the Anxiety Inventory or the Beck Depression Inventory. The user answers the questions on the device and sends the answers to the server. The server uses NLP technology (e.g., SpaCy or a custom-trained model) to analyze these answers and assess the user's mental health status.

[1305] Furthermore, the device's camera and microphone are used to capture the user's facial expressions and tone of voice. These data are used as emotion data to indicate the user's emotional state. The device then transmits this emotion data to a server, which then uses an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) to analyze the emotion data and evaluate the user's emotional state.

[1306] Based on the assessment results and emotional data, the server creates a personalized support plan. This plan may include guided meditations and cognitive behavioral therapy (CBT) exercises. The server then sends the created support plan to the device, which the user follows. The user provides feedback on the support plan from the device, which then sends the feedback to the server. The server analyzes this feedback and updates the AI ​​model, allowing the server to continuously improve the support plan.

[1307] For example, if a user types "My boss got angry at me at work today" into a device, the device sends this input to a server. The server uses an emotion engine to evaluate the user's emotional state and determines that the stress level is high. Based on this evaluation, the server creates a support plan including a "5-minute guided meditation" and sends it to the device. The user then executes this plan and provides feedback on its effectiveness, allowing the server to provide more appropriate support.

[1308] An example of a prompt to input to a generative AI model is as follows:

[1309] "Please record your mental health status today. Based on that, we will suggest an appropriate support plan. For example, 'My boss got angry at me at work.'"

[1310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] The user enters basic information such as name, email address, and password from the terminal. The entered basic information is formatted by the terminal and sent to the server. Specifically, the terminal sends the input data to the server as an HTTP request.

[1313] Input: Basic information such as name, email address, and password

[1314] Output: Send basic information to the server

[1315] Step 2:

[1316] The server verifies the received basic information and stores it in a database, for example, using MySQL or PostgreSQL. Once the basic information has been stored, the server generates a confirmation email and sends it to the user's email address.

[1317] Input: User basic information

[1318] Output: Save basic information to database and send confirmation email

[1319] Step 3:

[1320] The user receives a confirmation email and clicks on the link in the email. When the link is clicked, the device sends an account activation request to the server. The server receives this request and activates the user's account.

[1321] Input: Click the link in the confirmation email

[1322] Output: Account activation request to server and account activation

[1323] Step 4:

[1324] When a user logs in at a device, the server generates and sends to the device a diagnostic questionnaire containing questions to assess mental health, such as the Anxiety Inventory and the Beck Depression Inventory.

[1325] Input: User login information

[1326] Output: Diagnostic Question List sent to terminal

[1327] Step 5:

[1328] The user answers questions on the device and sends the answers to the server, which then converts the answers into JSON format and sends it to the server as an HTTP request.

[1329] Input: Answers to the diagnostic questionnaire

[1330] Output: Sending response data to the server

[1331] Step 6:

[1332] The server analyzes the received data using natural language processing technology (e.g., SpaCy) and machine learning algorithms to evaluate the user's mental health status, and stores the evaluation results in a database.

[1333] Input: Answer data

[1334] Output: Mental health assessment results and storage in a database

[1335] Step 7:

[1336] The device's camera and microphone are used to capture emotional data such as the user's facial expressions and tone of voice, which are then processed by the device and sent to a server.

[1337] Input: Facial expressions and tone of voice

[1338] Output: Sending emotion data to the server

[1339] Step 8:

[1340] The server analyzes the received emotion data using an emotion recognition engine (e.g., Affectiva or Microsoft Azure Emotion API) to evaluate the user's emotional state and stores the evaluation results in a database.

[1341] Input: Emotion data

[1342] Output: Evaluation results of emotional state and storage in a database

[1343] Step 9:

[1344] The server creates a personalized support plan based on the assessment results and emotional data, which may include guided meditation and cognitive behavioral therapy (CBT) exercises, and sends the plan in JSON format to the device.

[1345] Input: Evaluation results and emotion data

[1346] Output: Sending the support plan to the device

[1347] Step 10:

[1348] The user checks the support plan on the device and implements it according to its contents. After implementing it, the user inputs feedback into the device, which then sends this feedback to the server.

[1349] Input: Feedback data

[1350] Output: Sending feedback to the server

[1351] Step 11:

[1352] The server analyzes the received feedback and updates the AI ​​model, which will improve future support plans more effectively.

[1353] Input: Feedback data

[1354] Output: Updated AI models and improved support plans

[1355] In this way, by clearly indicating the specific actions performed at each step and their inputs and outputs, it becomes clear how the system of the present invention supports the mental health of the user.

[1356] (Application example 2)

[1357] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] Conventional mental health support systems have had problems such as users being unable to accurately communicate their mental state and difficulty in providing personalized support plans. This has made it difficult for users to receive effective mental health support. Another issue, particularly in physical stores, is that customers are unable to make effective use of their waiting time.

[1359] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1360] In this invention, the server includes means for a user to input information from a terminal, means by the terminal to send data to the server, means by the server to process and store the data, means by the server to send a confirmation email, means by the user to check the email and click a link, means by the terminal to recognize emotions using the user's input data, means by the server to process and store emotional data, means by the server to provide a support plan based on the user's emotional data, and means by the terminal to display the contents of the support plan to the user. This makes it possible to provide an individualized support plan that takes into account the user's emotional state, and further enables customers to receive mental health support in physical stores by effectively using their waiting time.

[1361] A "terminal" is a digital device through which a user inputs information and communicates data with a server.

[1362] "Server" means a central processing unit that processes, stores, analyzes data, and provides services to users.

[1363] "Data" is a general term for information processed by the system, such as information entered by the user and the results of the emotion recognition engine.

[1364] "Input Data" refers to information provided by a user to the system through a terminal, such as text input or audio / visual data.

[1365] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and text input to identify their emotional state.

[1366] "Emotion data" is data about a user's emotional state obtained using emotion recognition technology.

[1367] A "support plan" is a set of specific actions, exercises, and advice provided to support a user's mental health.

[1368] "Feedback" refers to information about the effectiveness and satisfaction of the support plan provided by the user.

[1369] An "AI model" refers to an algorithm that uses artificial intelligence to analyze data and is continuously improved.

[1370] A "brick and mortar store" is a store located in a physical location where customers can receive in-person service.

[1371] "Waiting time" refers to the time a customer spends waiting to receive service in a physical store.

[1372] This invention is a system that effectively utilizes customer waiting time in a physical store to provide mental health support. The system includes the following main hardware and software components:

[1373] Hardware

[1374] 1. Terminal: A digital device (e.g., smartphone, tablet) through which a user inputs information and communicates data with a server.

[1375] 2. Server: A central processing unit (cloud server) that processes, stores, analyzes data, and provides services to users.

[1376] software

[1377] 1. User registration: The user enters basic information such as name, email address, and password on the device. The device sends this information to the server, which stores it in a database. The server then sends a confirmation email, and the user clicks on the link to activate their account.

[1378] 2. Initial diagnosis: When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answers to the server. The server uses NLP technology and machine learning algorithms to analyze the answers and evaluate the user's mental health.

[1379] 3. Emotion Recognition: The device's camera and microphone are used to collect the user's facial expressions and tone of voice. This data is analyzed by the emotion recognition engine and sent to the server as emotional data.

[1380] 4. Providing a support plan: The server creates a support plan tailored to the user based on the analysis results and emotional data. The plan may include guided meditations and relaxation exercises. The server then sends the plan to the device, and the user follows the instructions.

[1381] 5. Feedback and AI learning: Users can provide feedback on the support plan from their devices. The feedback is sent from the device to the server, which analyzes it and updates the AI ​​model, allowing the server to provide more appropriate and effective support to users.

[1382] Specific examples

[1383] For example, a customer waiting in a physical store opens the app and enters information about their stress level. The device's camera captures their facial expressions and the microphone records their tone of voice. An emotion recognition engine analyzes this data and sends the emotional data to a server. The server uses this data to suggest specific guided meditation sessions or relaxation exercises. The user tries them out and then provides feedback. The feedback is sent to the server, and the AI ​​model is updated accordingly.

[1384] Example prompts for generative AI models

[1385] "Users enter their stress levels while waiting in line at a physical store. Through facial recognition and voice analysis, the emotion engine analyzes their stress levels. Recommend personalized guided meditations and relaxation exercises."

[1386] In this way, the system of the present invention can enhance the customer experience in physical stores by providing comprehensive support for the user's mental health and emotional state and providing personalized plans.

[1387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1388] Step 1:

[1389] User Registration:

[1390] The user enters basic information (name, email address, password) from the terminal, and the terminal sends this data to the server. The server stores the received data in a database and sends a confirmation email to the user. The user clicks on the link in the confirmation email to activate the account. The input data is basic information, and the output is the sending of a confirmation email.

[1391] Step 2:

[1392] Initial diagnosis:

[1393] When a user logs in on their device, the server generates a list of diagnostic questions and sends them to the device. The user answers the questions on their device and sends the answer data to the server. The server then analyzes the answer data using NLP technology and machine learning algorithms to evaluate the user's mental health status. The input data is the answer data, and the output is the mental health status evaluation result.

[1394] Step 3:

[1395] Emotion recognition:

[1396] The user uses the device's camera and microphone to capture and record facial expressions and tone of voice. The device then sends this data to the emotion recognition engine to obtain emotion data. The emotion data is then sent to the server. The input data is facial expression data and tone of voice data, and the output is emotion data.

[1397] Step 4:

[1398] Support plans offered:

[1399] The server creates an optimal support plan for the user based on the mental health assessment results from step 2 and the emotional data from step 3. This plan includes guided meditation and relaxation exercises. The created support plan is sent to the device. The input data are the mental health assessment results and emotional data, and the output is the support plan.

[1400] Step 5:

[1401] Support plan execution:

[1402] The user follows the support plan provided on the device and performs guided meditation and relaxation exercises. The execution results are input as feedback. The input data are the support plan and the execution results, and the output is feedback.

[1403] Step 6:

[1404] Send feedback and update the AI ​​model:

[1405] The user's feedback is sent to the server via the device. The server updates the AI ​​model based on the feedback and the continuous emotional data obtained in step 3. This process enables the server to provide a more effective support plan from the next time onwards. The input data are the feedback and emotional data, and the output is the updated AI model.

[1406] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1408] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1409] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1410] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1411] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1412] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1413] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1414] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1415] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1416] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1417] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1418] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1419] 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.

[1420] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1421] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1422] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1423] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1424] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1425] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1426] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1427] The following is further disclosed regarding the above embodiment.

[1428] (Claim 1)

[1429] a means for a user to input information from a terminal;

[1430] A means for the terminal to transmit data to the server;

[1431] The means by which the server processes and stores the data;

[1432] a means by which the server sends a confirmation email;

[1433] The system includes a means for users to view emails and click links.

[1434] (Claim 2)

[1435] a means for a user to log in at a terminal;

[1436] means for the server to generate and transmit a list of questions;

[1437] a means for users to answer questions;

[1438] means for the terminal to transmit a response to the server;

[1439] 10. The system of claim 1, wherein the server includes means for analyzing and evaluating the answers.

[1440] (Claim 3)

[1441] a means by which the server creates a support plan;

[1442] A means for the server to transmit the plan to the terminal;

[1443] a means by which users follow the plan;

[1444] a means for users to provide feedback;

[1445] means for the device to send feedback to a server;

[1446] 10. The system of claim 1, wherein the server includes means for analyzing the feedback and updating the plan.

[1447] (Claim 4)

[1448] a means by which the server stores and analyzes the feedback;

[1449] 10. The system of claim 1, wherein the server includes means for updating the AI ​​model.

[1450] "Example 1"

[1451] (Claim 1)

[1452] a means for a user to input information from a terminal;

[1453] A means for the terminal to transmit data to the server;

[1454] The means by which the server processes and stores the data;

[1455] a means for the server to send a confirmation message;

[1456] The system includes a means for the user to view the message and click on the link.

[1457] (Claim 2)

[1458] a means for a user to log in at a terminal;

[1459] means for the server to generate and transmit a list of questions;

[1460] a means for users to answer questions;

[1461] means for the terminal to transmit a response to the server;

[1462] 10. The system of claim 1, wherein the server includes means for analyzing and evaluating the answers using natural language processing techniques and machine learning algorithms.

[1463] (Claim 3)

[1464] a means for the server to create a support plan based on the evaluation results;

[1465] A means for transmitting the plan created by the server to the terminal;

[1466] a means by which users follow the plan;

[1467] a means for users to provide feedback;

[1468] means for the device to send feedback to a server;

[1469] 10. The system of claim 1, wherein the server includes means for analyzing the feedback using a machine learning algorithm and updating the support plan.

[1470] "Application Example 1"

[1471] (Claim 1)

[1472] a means for a user to input information from an electronic device;

[1473] means for the electronic device to transmit data to a server;

[1474] The means by which the server processes and stores the data;

[1475] a means for the server to send a confirmation message;

[1476] A means for users to view the message and click on the link;

[1477] a means for a user to input electronically provided mental health status information;

[1478] A means for transmitting input information to a server by the electronic device;

[1479] means for the server to analyze and evaluate the input information;

[1480] A means for the server to generate a relaxation menu based on the evaluation results and transmit the menu to the electronic device;

[1481] A means for the user to check the transmitted relaxation menu on an electronic device;

[1482] A system including:

[1483] (Claim 2)

[1484] a means for a user to log in using an electronic device;

[1485] means for the server to generate and transmit a diagnostic list;

[1486] a means for the user to respond to the diagnostic list;

[1487] means for the electronic device to transmit the response to the server;

[1488] a means by which the server analyzes and evaluates the response;

[1489] The server includes means for generating and transmitting a relaxation menu based on the evaluation.

[1490] 10. The system of claim 1.

[1491] (Claim 3)

[1492] A means for the user to follow a relaxation menu based on the answers to the diagnostic list;

[1493] a means for users to provide feedback;

[1494] means for the electronic device to transmit feedback to the server;

[1495] Includes a means for the server to analyze feedback and make improvements

[1496] 10. The system of claim 1.

[1497] "Example 2: Combining Emotion Engines"

[1498] (Claim 1)

[1499] a means for a user to input information from a terminal;

[1500] A means for the terminal to transmit data to the server;

[1501] The means by which the server processes and stores the data;

[1502] a means by which the server sends a confirmation email;

[1503] A way for users to check their email and click on the link;

[1504] A means for the server to generate a diagnostic question list and transmit it to the terminal;

[1505] A means for the server to analyze the response using natural language processing technology;

[1506] A means of acquiring emotional data using the device's camera and microphone,

[1507] a means for the server to evaluate the emotion data using an emotion recognition engine;

[1508] A means for the server to continuously analyze feedback and improve the AI ​​model;

[1509] A system including:

[1510] (Claim 2)

[1511] a means for a user to log in at a terminal;

[1512] means for the server to generate and transmit a list of questions;

[1513] a means for users to answer questions;

[1514] means for the terminal to transmit a response to the server;

[1515] 10. The system of claim 1, wherein the server includes means for analyzing and evaluating the answers.

[1516] (Claim 3)

[1517] a means by which the server creates a support plan;

[1518] A means for the server to transmit the plan to the terminal;

[1519] a means by which users follow the plan;

[1520] a means for users to provide feedback;

[1521] means for the device to send feedback to a server;

[1522] 10. The system of claim 1, wherein the server includes means for analyzing the feedback and updating the plan.

[1523] "Application example 2 when combining emotion engines"

[1524] (Claim 1)

[1525] a means for a user to input information from a terminal;

[1526] A means for the terminal to transmit data to the server;

[1527] The means by which the server processes and stores the data;

[1528] a means by which the server sends a confirmation email;

[1529] A way for users to check their email and click on the link;

[1530] means for the terminal to recognize emotions using user input data;

[1531] a means for the server to process and store the emotion data;

[1532] A means for the server to provide a support plan based on the user's emotional data;

[1533] The system includes a means for the terminal to display the contents of the support plan to the user.

[1534] (Claim 2)

[1535] a means for a user to log in at a terminal;

[1536] means for the server to generate and transmit a list of questions;

[1537] a means for users to answer questions;

[1538] means for the terminal to transmit a response to the server;

[1539] a means by which the server analyzes and evaluates the response;

[1540] 2. The system of claim 1, wherein the terminal comprises means for recognizing a user's emotion.

[1541] (Claim 3)

[1542] a means by which the server creates a support plan;

[1543] A means for the server to transmit the plan to the terminal;

[1544] a means by which users follow the plan;

[1545] a means for users to provide feedback;

[1546] means for the device to send feedback to a server;

[1547] a means for the server to analyze the feedback and update the plan;

[1548] A means for the terminal to continuously collect and transmit user emotion data;

[1549] 10. The system of claim 1, wherein the server includes means for updating the AI ​​model based on the emotion data and feedback. [Explanation of symbols]

[1550] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input information from a terminal; A means for the terminal to transmit data to the server; The means by which the server processes and stores the data; a means by which the server sends a confirmation email; The system includes a means for users to view emails and click links.

2. a means for a user to log in at a terminal; means for the server to generate and transmit a list of questions; a means for users to answer questions; means for the terminal to transmit a response to the server; 2. The system of claim 1, wherein the server includes means for analyzing and evaluating the answers.

3. a means by which the server creates a support plan; A means for the server to transmit the plan to the terminal; a means by which users follow the plan; a means for users to provide feedback; means for the device to send feedback to a server; 10. The system of claim 1, wherein the server includes means for analyzing the feedback and updating the plan.

4. a means by which the server stores and analyzes the feedback; The system of claim 1 , wherein the server includes means for updating the AI ​​model.

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