system

Abstract

We provide the system. [Solution] A system comprising means for having the user input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's answers to the medical questionnaire form, means for analyzing the received answers to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for conducting a dialogue with the user based on the generated dialogue scenario, means for storing the content of the dialogue, means for generating an action plan based on the content of the dialogue, means for presenting the generated action plan to the user, and means for following up on the progress of the action plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people have mental and physical troubles, and solving them requires specialized knowledge and experience. However, systems for providing effective and personal support to individual users are limited. Also, existing support systems are insufficient in responding to individual needs and lack empathetic dialogue and continuous follow-up to enhance self-affirmation. Against this background, there is a need to develop a system that addresses specific troubles of users and supports their mental and physical well-being through introduction of experts and continuous follow-up.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following means: a system equipped with means for receiving and storing basic information from a user; a means for generating a medical questionnaire form based on the stored basic information and receiving responses to the questionnaire form from the user; a means for analyzing the received responses to the questionnaire form and generating a dialogue scenario based on the analysis results; a means for engaging in dialogue with the user based on the generated dialogue scenario, storing the content of the dialogue, and generating an action plan based on the content of the dialogue; and finally, a means for presenting the generated action plan to the user and following up on the progress of the action plan. This system enables attentive and continuous support for the user's concerns.

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

[0007] "Basic information" refers to information necessary for user identification and system use, such as the user's name, email address, and password.

[0008] A "medical questionnaire form" refers to a set of questions used to input the user's specific concerns and circumstances.

[0009] "Analysis" refers to the process by which a generative artificial intelligence understands and addresses the user's concerns based on the received data.

[0010] "Generative artificial intelligence" refers to artificial intelligence technology used to analyze user input data and generate appropriate dialogues and action plans.

[0011] A "dialogue scenario" refers to a sequence of questions and answers, constructed by generative artificial intelligence, designed to facilitate a conversation with the user.

[0012] An "action plan" refers to a plan that proposes specific actions to solve the user's problems.

[0013] A "reminder" refers to a message used to inform a user of a specific action plan or follow-up time.

[0014] "Follow-up" refers to continuously monitoring the user's progress after the initial session and providing additional support.

[0015] A "system" refers to a collection of programs and devices configured to perform all of these functions. [Brief explanation of the drawing]

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

Modes for Carrying Out the Invention

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

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system's processing is described below in natural language.

[0038] System Program Overview

[0039] 1. User Registration

[0040] In order for a user to use the system, they must first register.

[0041] The terminal prompts the user to enter basic information (name, email address, password, etc.).

[0042] The terminal sends the entered basic information to the server.

[0043] The server saves the user's basic information to a database and sends a registration completion notification to the device.

[0044] The device displays a registration completion message to the user.

[0045] 2. Initial consultation

[0046] After user registration is complete, the device will display a medical questionnaire form to the user.

[0047] The user selects a category of their problem (for example, work stress) and enters their specific problem.

[0048] The terminal sends user input to the server.

[0049] The server receives the input data and passes it to the generative artificial intelligence for analysis.

[0050] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0051] 3. Dialogue Session

[0052] The server sends the generated dialogue scenario to the terminal.

[0053] The device displays a dialogue screen and initiates a conversation with the generative artificial intelligence for the user.

[0054] The terminal sends user input to the server, which then uses generative artificial intelligence to generate an appropriate response.

[0055] The response is sent to the terminal and displayed to the user.

[0056] 4. Generating an action plan

[0057] The server records and analyzes the information obtained through the dialogue session.

[0058] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation.

[0059] The server sends the generated action plan to the terminal and displays it to the user.

[0060] The user reviews the action plan and proceeds to the next step.

[0061] 5. After-sales support

[0062] The server generates periodic reminders to follow up on the progress of the action plan.

[0063] The device sends a reminder notification to the user, and the user enters the progress of the action plan.

[0064] The server receives the input results, a generative artificial intelligence analyzes them, and proposes additional dialogue or action plans as needed.

[0065] Specific example

[0066] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[0067] User Registration

[0068] User A enters basic information on the terminal and presses the registration button.

[0069] The entered information is sent to the server and stored in the database.

[0070] A registration completion message is displayed to user A.

[0071] Initial consultation

[0072] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[0073] The input data is sent to the server, where a generative artificial intelligence performs the analysis.

[0074] A dialogue scenario is created and sent to the terminal.

[0075] Dialogue session

[0076] Based on the dialogue scenario sent from the server, the terminal displays the question, "What exactly is the problem?"

[0077] User A responded, "My boss doesn't listen to my opinions at all."

[0078] The generative artificial intelligence analyzed the message and generated the response, "That sounds tough. Do you have an opportunity to talk to your supervisor?"

[0079] The response is displayed to user A.

[0080] Generating an action plan

[0081] The server records the conversation, and a generative artificial intelligence generates an action plan: "Let's schedule a one-on-one meeting with your boss."

[0082] The action plan is displayed on the device, and User A accepts it.

[0083] After-sales support

[0084] The server generates a reminder based on the action plan, and the device sends a notification one week later.

[0085] User A entered the results of the meeting and reported, "I discussed it with my supervisor, but it hasn't improved yet."

[0086] The generative artificial intelligence re-analyzed the text and suggested, "Next, let's practice expressing your feelings honestly together."

[0087] In this way, the system addresses users' concerns step by step, providing attentive support and continuous follow-up.

[0088] The following describes the processing flow.

[0089] User Registration

[0090] Step 1:

[0091] The user opens a website or app to access the system.

[0092] Step 2:

[0093] The device displays a new registration form to the user.

[0094] Step 3:

[0095] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[0096] Step 4:

[0097] The terminal sends the entered basic information to the server.

[0098] Step 5:

[0099] The server saves the basic information it receives to the database.

[0100] Step 6:

[0101] The server generates a registration completion message and sends it to the terminal.

[0102] Step 7:

[0103] The device displays a registration completion message to the user.

[0104] Initial consultation

[0105] Step 1:

[0106] After user registration is complete, the device will display a medical questionnaire form to the user.

[0107] Step 2:

[0108] The user selects a category for their problem and enters their specific problem.

[0109] Step 3:

[0110] The terminal sends the user's input to the server.

[0111] Step 4:

[0112] The server sends the received data to a generative artificial intelligence.

[0113] Step 5:

[0114] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0115] Step 6:

[0116] The server sends the constructed dialogue scenario to the terminal.

[0117] Dialogue session

[0118] Step 1:

[0119] The device displays a dialogue screen to the user.

[0120] Step 2:

[0121] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[0122] Step 3:

[0123] The user enters a specific answer to the question.

[0124] Step 4:

[0125] The terminal sends user input to the server.

[0126] Step 5:

[0127] The server sends the user's input data to a generative artificial intelligence system and generates the following response.

[0128] Step 6:

[0129] The generative artificial intelligence analyzes the data, generates an appropriate response, and sends it to the server.

[0130] Step 7:

[0131] The server sends the generated response to the terminal.

[0132] Step 8:

[0133] The device displays a response to the user.

[0134] Generating an action plan

[0135] Step 1:

[0136] The server records the contents of the conversation session.

[0137] Step 2:

[0138] Generative artificial intelligence analyzes the conversation content and generates the optimal action plan for the user.

[0139] Step 3:

[0140] The server sends the generated action plan to the terminal.

[0141] Step 4:

[0142] The device displays an action plan to the user.

[0143] Step 5:

[0144] The user reviews the action plan and approves to proceed to the next step.

[0145] After-sales support

[0146] Step 1:

[0147] The server generates reminders to follow up on the progress of the action plan.

[0148] Step 2:

[0149] The device periodically sends reminder notifications to the user.

[0150] Step 3:

[0151] The user enters the progress of the action plan.

[0152] Step 4:

[0153] The terminal sends the user's input results to the server.

[0154] Step 5:

[0155] The server sends the input results to a generative artificial intelligence system for analysis.

[0156] Step 6:

[0157] Generative artificial intelligence analyzes the progress and generates additional dialogues and action plans as needed.

[0158] Step 7:

[0159] The server sends any additional dialogue or action plans it has generated to the terminal.

[0160] Step 8:

[0161] The device displays additional dialogues or action plans to the user.

[0162] As described above, the "LifeBridge" system provides gradual and continuous support for users' physical and mental health concerns.

[0163] (Example 1)

[0164] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0165] Conventional lifestyle platform systems have not provided sufficient, empathetic support for users' physical and mental health concerns, particularly inadequate, continuous follow-up. As a result, effective solutions to the problems users face may not be provided, potentially leading to decreased user satisfaction. Furthermore, the inability to effectively utilize artificial intelligence in dialogue and action plan generation could result in inconsistent support for users.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0167] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's answers to the medical questionnaire form, means for analyzing the received answers to the medical questionnaire form using generative artificial intelligence, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the content of the dialogue, means for generating an action plan based on the content of the dialogue, means for presenting the generated action plan to the user, means for generating and sending periodic reminders to the user to follow up on the progress of the action plan, and means for receiving progress information from the user, re-analyzing it, and proposing additional dialogues or action plans as necessary. This enables empathetic support and continuous follow-up for the user's physical and mental health concerns, thereby improving user satisfaction.

[0168] "Basic information" refers to personal information necessary to start using the system, such as the user's name, email address, and password.

[0169] A "database" is a digital storage device used to store and manage information such as a user's basic information, conversation history, and action plans.

[0170] A "medical questionnaire form" is a question-based input screen used to gain a detailed understanding of a user's physical and mental health concerns.

[0171] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to analyze input data and generate appropriate dialogue scenarios and action plans.

[0172] A "dialogue scenario" refers to the pre-conversation flow and questions created by generative artificial intelligence to facilitate smooth interaction with the user.

[0173] An "action plan" refers to specific measures and action plans proposed by a generative artificial intelligence system to resolve the user's mental and physical problems.

[0174] A "reminder" is a notification or message that is sent periodically to a user to check and input the progress of their action plan.

[0175] "Follow-up" refers to a series of activities and actions taken to check the progress of the action plan and provide additional support as needed.

[0176] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system requires a server, terminals, and a generative AI model to be implemented.

[0177] The system operates as follows: First, the user enters basic information using a terminal. The terminal has a function to display a form for entering information such as name, email address, and password. The terminal then sends the entered basic information to the server. The server saves the received basic information to a database and sends a registration completion notification to the terminal. The terminal displays this notification to the user.

[0178] Once user registration is complete, the device displays a questionnaire form to the user. This questionnaire includes areas where the user can select a category related to their mental or physical health concerns and enter their specific concerns. For example, the user might select the category "work stress" and enter, "Recently, my relationship with my boss hasn't been going well." The device then sends this input data to the server.

[0179] The server passes the received data to a generative artificial intelligence model. This generative AI model analyzes the input data and constructs an appropriate dialogue scenario. The generated dialogue scenario is sent from the server to the terminal and displayed to the user on the terminal.

[0180] Once a dialogue session begins, the server generates questions based on a pre-defined dialogue scenario. The user's input through the terminal is sent to the server, where a generative artificial intelligence analyzes it and generates an appropriate response. The generated response is displayed on the terminal, and the user responds to it. This dialogue process continues.

[0181] The entire content of the conversation session is recorded on the server. Based on the conversation, the server uses generative artificial intelligence to generate an appropriate action plan. For example, an action plan such as "Let's schedule a one-on-one meeting with your boss" is generated and displayed on the terminal. The user can review this action plan and proceed to the next step.

[0182] To track the progress of the action plan, the server generates periodic reminders. These reminder notifications are sent to the device, and the user receives them. The user then inputs the progress of the action plan according to the reminder and sends it from the device to the server. The server re-analyzes this progress information and, if necessary, uses generative artificial intelligence to suggest additional dialogues or action plans.

[0183] For example, if user A is suffering from work-related stress, the following dialogue may take place:

[0184] User A: "Lately, my relationship with my boss hasn't been going well."

[0185] Generative AI: "That sounds tough. Do you have a chance to talk to your supervisor about it?"

[0186] In this way, the system approaches users' problems step by step, providing attentive support and continuous follow-up.

[0187] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0188] Step 1:

[0189] User registration (entering basic information)

[0190] The user enters basic information using a device. The device displays a form for entering basic information such as name, email address, and password. This form includes required fields for name, email address, and password. The entered data is sent from the device to the server.

[0191] Input: Name, email address, password

[0192] Output: Basic information is sent to the server.

[0193] Step 2:

[0194] Basic information saving

[0195] The server receives basic information sent from the terminal. The received basic information is stored in the database. The server notifies the terminal when the saving is complete.

[0196] Input: Basic Information

[0197] Output: Basic information is saved to the database, and a registration completion notification is sent to the device.

[0198] Step 3:

[0199] Display of registration completion message

[0200] The device receives a registration completion notification sent from the server. The device displays a registration completion message to the user. This allows the user to confirm that registration was successful.

[0201] Input: Registration completion notification

[0202] Output: A registration complete message is displayed to the user.

[0203] Step 4:

[0204] Display of the medical questionnaire form

[0205] After user registration is complete, the device displays a questionnaire form to the user. This questionnaire form includes a category selection field for mental and physical health concerns and a text box for entering specific concerns.

[0206] Input: None (A questionnaire form is displayed as a prompt)

[0207] Output: The medical questionnaire form is displayed to the user.

[0208] Step 5:

[0209] Enter category and problem

[0210] The user selects a category of their problem in a questionnaire form and enters their specific problem. For example, the user might select the category "Work Stress" and enter, "Recently, my relationship with my boss hasn't been going well." This information is sent from the device to the server.

[0211] Input: Category of problem and specific problem

[0212] Output: Category and problem information is sent to the server.

[0213] Step 6:

[0214] Data Analysis

[0215] The server provides the data from the received questionnaire form to a generative artificial intelligence model. The generative AI model analyzes the data and generates an appropriate dialogue scenario. For example, it might take a prompt such as "The user is suffering from work-related stress" and generate a scenario such as "Please tell me more."

[0216] Input: User's problem data

[0217] Output: Dialogue scenario generated by generative artificial intelligence

[0218] Step 7:

[0219] Sending and displaying dialogue scenarios

[0220] The server sends a generated dialogue scenario to the terminal. The terminal displays the dialogue scenario to the user and begins the dialogue. At this time, a question such as "What specific problems are you experiencing?" is presented.

[0221] Input: Dialogue scenario

[0222] Output: The dialogue scenario is displayed to the user.

[0223] Step 8:

[0224] Submitting user input

[0225] The user enters a response according to the dialogue scenario. For example, they might enter a response such as, "My boss doesn't listen to my opinion at all." This input is sent from the terminal to the server.

[0226] Input: User's response

[0227] Output: The answer is sent to the server.

[0228] Step 9:

[0229] Response generation

[0230] The server passes the user's response to a generative artificial intelligence model, which then generates an appropriate response. For example, a response such as, "That's tough. Do you have a chance to talk to your supervisor?" might be generated.

[0231] Input: User response data

[0232] Output: Generated response

[0233] Step 10:

[0234] Display of response

[0235] The server sends the generated response to the terminal, which then displays it to the user. The response acts as a trigger for the user to enter the next answer.

[0236] Input: Generated response

[0237] Output: Response is lowered to the user.

[0238] Step 11:

[0239] Record of the conversation

[0240] The content of the dialogue session is recorded on the server. This includes all questions and answers. The recorded data is used to generate an action plan.

[0241] Input: Content of the dialogue session

[0242] Output: The conversation content is saved to the database.

[0243] Step 12:

[0244] Generating an action plan

[0245] The server passes the conversation content to a generative artificial intelligence model, which then generates an appropriate action plan. For example, a specific action plan such as "Let's schedule a one-on-one meeting with your boss" might be generated.

[0246] Input: Dialogue content data

[0247] Output: Generated action plan

[0248] Step 13:

[0249] Sending and displaying action plans

[0250] The generated action plan is sent from the server to the terminal and displayed to the user. The user can review the action plan and proceed to the next step.

[0251] Input: Action Plan

[0252] Output: The action plan is displayed to the user.

[0253] Step 14:

[0254] Creating and sending reminders

[0255] To follow up on the progress of the action plan, the server generates periodic reminders. These reminders are sent to the device and notified to the user.

[0256] Input: Action plan execution schedule

[0257] Output: A reminder is sent to the user.

[0258] Step 15:

[0259] Inputting progress and reanalysis

[0260] The user enters the progress of their action plan via their device, following a reminder. The entered progress information is sent to the server and re-analyzed by generative artificial intelligence. Additional dialogues and action plans are suggested as needed.

[0261] Input: User progress information

[0262] Output: Additional dialogue or action plan

[0263] This series of steps enables the system to provide attentive support and ongoing follow-up for users' concerns.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0266] In modern society, users often experience mental and physical stress and anxieties, which can prevent them from receiving professional support. However, conventional mental health care systems lack concrete action plans and follow-up to effectively and sustainably resolve users' problems. In particular, there is a need for a method that provides appropriate dialogue sessions for each user's individual concerns and automatically follows up on their progress.

[0267] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0268] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for notifying a smart device of a reminder, means for analyzing the medical questionnaire data and dialogue data with a generative artificial intelligence system, means for generating prompt sentences, means for presenting the generated action plan to the user, and means for following up on the progress of the action plan. As a result, the user can receive prompt and appropriate support for their physical and mental health concerns, and continuous follow-up is also possible.

[0269] "Basic information" refers to information necessary to identify a user, such as the user's name, email address, and password.

[0270] "To store" means to save received data in an information management device.

[0271] A "medical questionnaire form" is a questionnaire used to collect information about a user's mental and physical health concerns and stress levels.

[0272] "Analyzing" means processing received data to extract useful information.

[0273] A "dialogue scenario" is a plan of a series of questions and answers used when interacting with a user.

[0274] "Generative artificial intelligence" is an advanced computational technique that generates optimal responses and scenarios based on large amounts of data.

[0275] An "action plan" is a set of specific guidelines for actions taken to solve a user's problem.

[0276] A "smart device" is a highly functional portable device capable of connecting to the internet and launching applications.

[0277] A "reminder" is a notification that prompts a user to take a specific action or confirm something.

[0278] A "prompt" is the input text for a generative artificial intelligence system, and a response is generated based on it.

[0279] "Follow-up" is the process of evaluating the progress of the established action plan and taking additional measures as needed.

[0280] This invention is a mental health care system that supports users with their physical and mental health concerns. The system inputs and records the user's basic information, generates a questionnaire form, generates and executes dialogue scenarios using generative artificial intelligence (AI) based on that form, and has the function of following up on the progress of the action plan. The implementation method of this system is described in detail below.

[0281] System Configuration

[0282] 1. Hardware and software

[0283] The system consists of a smartphone and a server. A mental health care application is installed on the smartphone, and through the operation of this application, the user inputs basic information and answers a questionnaire form. The server is a cloud platform equipped with a database, a generative artificial intelligence (e.g., GPT-4 of OpenAI (registered trademark)), a notification system, etc.

[0284] 2. Data Processing and Data Calculation

[0285] User Registration:

[0286] When the user inputs basic information through the application on the smartphone, the information is sent to the server and stored in the database. The server generates a registration completion notification and sends it to the smartphone to notify the user.

[0287] Generation and Analysis of Questionnaire Form:

[0288] The server generates a questionnaire form based on the stored basic information and presents it to the user. When the user answers the questionnaire form, the data is sent to the server. The server uses generative AI to analyze the answers and generate a personalized dialogue scenario.

[0289] Dialogue Session:

[0290] Based on the dialogue scenario sent from the server, the smartphone application displays a dialogue screen and starts a dialogue with the user. The user's input is sent to the server again, and the generative AI generates an appropriate response and sends it to the smartphone for display.

[0291] Generation and Presentation of Action Plan:

[0292] Based on the information gathered through the dialogue session, the server uses generative AI to generate a user-specific action plan. This generated action plan is then presented to the user via a smartphone application.

[0293] After-sales support:

[0294] The server generates periodic reminders and sends notifications to the user's smartphone. The user inputs the progress of their action plan, and this data is sent to the server. If necessary, a generative AI re-analyzes the data and suggests additional dialogue or action plans.

[0295] Specific example

[0296] For example, if user A is suffering from work-related stress, the following prompt message will be generated.

[0297] Example of a prompt:

[0298] Specific problems related to work stress include:

[0299] Lately, I've been having trouble with my boss at work. Specifically, my boss completely ignores my opinions.

[0300] Please provide advice on this issue.

[0301] Based on this prompt, the generative AI analyzes and responds, providing user A with interactive support. Ultimately, user A is given actionable plans (e.g., scheduling a one-on-one meeting with their supervisor) and follow-up support based on those plans.

[0302] This allows users to receive immediate and appropriate feedback on their concerns, ensuring continuous support.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] User registration

[0306] The user launches the smartphone application and enters basic information (name, email address, password). The terminal sends this input data to the server. The server saves the received information in the database and generates a registration completion notification. The server sends the registration completion notification to the terminal, and the terminal displays a completion message to the user.

[0307] Input: Name, email address, password

[0308] Data processing: Save the input data in the database

[0309] Output: Registration completion notification

[0310] Step 2:

[0311] Generation of the questionnaire form and reception of answers

[0312] The server generates a questionnaire form based on the stored basic information. This questionnaire form includes questions for entering the user's category of worries and specific problems. The questionnaire form is sent to the terminal, and the user enters it. The answers entered by the user are sent to the server through the terminal.

[0313] Input: User's basic information

[0314] Data processing: Generation of the questionnaire form

[0315] Output: Questionnaire form answer data

[0316] Step 3:

[0317] Analysis of the answer data

[0318] The server passes the responses from the received questionnaire form to a generative artificial intelligence (AI) system for analysis. The AI ​​then generates an appropriate dialogue scenario based on the response data. This scenario is specifically designed to address the user's concerns.

[0319] Input: Response data from the medical questionnaire

[0320] Data processing: Analysis using generative artificial intelligence

[0321] Output: Dialogue Scenario

[0322] Step 4:

[0323] Executing a dialogue session

[0324] The server sends the generated dialogue scenario to the terminal. The terminal displays a dialogue screen based on the dialogue scenario and begins interacting with the user. User input is sent to the server through the terminal, and the server uses generative artificial intelligence to generate an appropriate response, which is then sent to the terminal for display.

[0325] Input: Dialogue scenario, user dialogue input

[0326] Data processing: Response generation using generative artificial intelligence.

[0327] Output: Response to the user

[0328] Step 5:

[0329] Generating an action plan

[0330] Based on the information obtained through the dialogue session, the server uses generative artificial intelligence to generate a user-specific action plan. The generated action plan is sent to the terminal and presented to the user.

[0331] Input: Dialogue content

[0332] Data processing: Generation of action plans using generative artificial intelligence.

[0333] Output: Action Plan

[0334] Step 6:

[0335] After-sales support

[0336] The server generates periodic reminders to follow up on the progress of the action plan. These reminders are sent to the device, and the user enters the progress. The entered data is sent to the server, where generative artificial intelligence re-analyzes it and suggests additional dialogue or action plans as needed.

[0337] Input: Action plan, user progress input

[0338] Data processing: Reminder generation and reanalysis using generative artificial intelligence.

[0339] Output: Reminder notifications and additional interaction / action plans

[0340] Through these steps, users can receive prompt and appropriate support for their physical and mental health concerns, and ongoing follow-up is also possible.

[0341] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0342] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The system's processing is described below in natural language.

[0343] System Program Overview

[0344] User Registration

[0345] 1. The user accesses the system and opens a website or app.

[0346] 2. The device displays a new registration form to the user.

[0347] 3. The user enters basic information such as their name, email address, and password, and then presses the submit button.

[0348] 4. The terminal sends the entered basic information to the server.

[0349] 5. The server saves the basic information it receives to the database.

[0350] 6. The server generates a registration completion message and sends it to the terminal.

[0351] 7. The device displays a registration completion message to the user.

[0352] Initial consultation

[0353] 1. After user registration is complete, the device will display a medical questionnaire form to the user.

[0354] 2. The user selects a category of their problem (for example, work stress) and enters their specific problem.

[0355] 3. The terminal sends the user's input to the server.

[0356] 4. The server sends the received data to the generative artificial intelligence.

[0357] 5. Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[0358] 6. The server sends the constructed dialogue scenario to the terminal.

[0359] Dialogue session

[0360] 1. The device displays a dialogue screen to the user.

[0361] 2. The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[0362] 3. The user enters a specific answer to the question.

[0363] 4. The device sends user input to the emotion engine, which then analyzes the emotions.

[0364] 5. The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[0365] 6. The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[0366] 7. The server sends the generated response to the terminal.

[0367] 8. The device displays a response to the user.

[0368] Generating an action plan

[0369] 1. The server records the contents of the conversation session.

[0370] 2. Generative artificial intelligence generates an optimal action plan for the user based on the conversation content and emotion analysis results.

[0371] 3. The server sends the generated action plan to the terminal.

[0372] 4. The device displays an action plan to the user.

[0373] 5. The user reviews the action plan and approves to proceed to the next step.

[0374] After-sales support

[0375] 1. The server generates reminders to follow up on the progress of the action plan.

[0376] 2. The device periodically sends reminder notifications to the user.

[0377] 3. The user enters the progress of the action plan.

[0378] 4. The terminal sends the user's input results to the server.

[0379] 5. The server sends the input results to the emotion engine for sentiment analysis.

[0380] 6. The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[0381] 7. The server sends any additional dialogue or action plan generated to the terminal.

[0382] 8. The device displays additional dialogue or action plans to the user.

[0383] Specific example

[0384] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[0385] User Registration

[0386] 1. User A enters basic information on the terminal and presses the registration button.

[0387] 2. The entered information is sent to the server and stored in the database.

[0388] 3. A registration completion message is displayed to user A.

[0389] Initial consultation

[0390] 1. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[0391] 2. The input data is sent to the server, and a generative artificial intelligence performs the analysis.

[0392] 3. A dialogue scenario is created and sent to the terminal.

[0393] Dialogue session

[0394] 1. The device displays the question, "What specific problem are you experiencing?"

[0395] 2. User A responded, "My boss doesn't listen to my opinions at all."

[0396] 3. The device sends its response to the emotion engine, which then analyzes the emotion.

[0397] 4. The server sends the results of the emotion engine to the generative artificial intelligence.

[0398] 5. The generative AI generates an emotionally conscious response: "That sounds tough. Do you have an opportunity to talk to your boss about it?"

[0399] 6. The response is sent to the terminal and displayed to User A.

[0400] Generating an action plan

[0401] 1. The server records the conversation content and sentiment analysis results, and generates an action plan such as, "Let's schedule a one-on-one meeting with your boss."

[0402] 2. The action plan is displayed on the device, and User A approves it.

[0403] After-sales support

[0404] 1. The server generates a reminder, and the device sends a notification to user A one week later.

[0405] 2. User A enters the results of the meeting and reports, "I discussed it with my supervisor, but it hasn't improved yet."

[0406] 3. The emotion engine re-analyzes the emotions in the input, and the generative AI generates an additional action plan: "Next time, let's practice expressing your feelings honestly together."

[0407] 4. An additional action plan is sent to the device and displayed to User A.

[0408] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[0409] The following describes the processing flow.

[0410] User Registration

[0411] Step 1:

[0412] The user opens a website or app to access the system.

[0413] Step 2:

[0414] The device displays a new registration form to the user.

[0415] Step 3:

[0416] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[0417] Step 4:

[0418] The terminal sends the entered basic information to the server.

[0419] Step 5:

[0420] The server saves the basic information it receives to the database.

[0421] Step 6:

[0422] The server generates a registration completion message and sends it to the terminal.

[0423] Step 7:

[0424] The device displays a registration completion message to the user.

[0425] Initial consultation

[0426] Step 1:

[0427] After user registration is complete, the device will display a medical questionnaire form to the user.

[0428] Step 2:

[0429] The user selects a category for their problem and enters their specific problem.

[0430] Step 3:

[0431] The terminal sends the user's input to the server.

[0432] Step 4:

[0433] The server sends the received data to a generative artificial intelligence.

[0434] Step 5:

[0435] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0436] Step 6:

[0437] The server sends the constructed dialogue scenario to the terminal.

[0438] Dialogue session

[0439] Step 1:

[0440] The device displays a dialogue screen to the user.

[0441] Step 2:

[0442] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[0443] Step 3:

[0444] The user enters a specific answer to the question.

[0445] Step 4:

[0446] The device sends user input to an emotion engine, which then analyzes the emotions.

[0447] Step 5:

[0448] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[0449] Step 6:

[0450] The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[0451] Step 7:

[0452] The server sends the generated response to the terminal.

[0453] Step 8:

[0454] The device displays a response to the user.

[0455] Generating an action plan

[0456] Step 1:

[0457] The server records the contents of the conversation session.

[0458] Step 2:

[0459] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation and the results of emotion analysis.

[0460] Step 3:

[0461] The server sends the generated action plan to the terminal.

[0462] Step 4:

[0463] The device displays an action plan to the user.

[0464] Step 5:

[0465] The user reviews the action plan and approves to proceed to the next step.

[0466] After-sales support

[0467] Step 1:

[0468] The server generates reminders to follow up on the progress of the action plan.

[0469] Step 2:

[0470] The device periodically sends reminder notifications to the user.

[0471] Step 3:

[0472] The user enters the progress of the action plan.

[0473] Step 4:

[0474] The terminal sends the user's input results to the server.

[0475] Step 5:

[0476] The server sends the input results to the emotion engine, which then performs emotion analysis.

[0477] Step 6:

[0478] The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[0479] Step 7:

[0480] The server sends any additional dialogue or action plans it has generated to the terminal.

[0481] Step 8:

[0482] The device displays additional dialogues or action plans to the user.

[0483] Specific example

[0484] User Registration

[0485] Step 1:

[0486] User A accesses the system and opens a website or app.

[0487] Step 2:

[0488] The terminal displays a new registration form to user A.

[0489] Step 3:

[0490] User A enters basic information such as their name, email address, and password, and then presses the submit button.

[0491] Step 4:

[0492] The terminal sends the entered information to the server.

[0493] Step 5:

[0494] The server saves the received information to the database.

[0495] Step 6:

[0496] The server generates a registration completion message and sends it to the terminal.

[0497] Step 7:

[0498] The device displays a registration completion message to user A.

[0499] Initial consultation

[0500] Step 1:

[0501] After User A completes registration, the terminal displays a medical questionnaire form to User A.

[0502] Step 2:

[0503] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[0504] Step 3:

[0505] The terminal sends the input from user A to the server.

[0506] Step 4:

[0507] The server sends the received data to a generative artificial intelligence.

[0508] Step 5:

[0509] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0510] Step 6:

[0511] The server sends the constructed dialogue scenario to the terminal.

[0512] Dialogue session

[0513] Step 1:

[0514] The terminal displays a dialogue screen to user A.

[0515] Step 2:

[0516] The generative artificial intelligence displays the question, "What specific problems are you experiencing?" based on the dialogue scenario.

[0517] Step 3:

[0518] User A responds, "My boss doesn't listen to my opinions at all."

[0519] Step 4:

[0520] The device sends User A's response to the emotion engine, which then analyzes the emotion.

[0521] Step 5:

[0522] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[0523] Step 6:

[0524] The generative artificial intelligence generates a response that takes into account the sentiment analysis results, such as "That's tough. Do you have a chance to talk to your boss?", and sends it to the server.

[0525] Step 7:

[0526] The server sends the generated response to the terminal.

[0527] Step 8:

[0528] The terminal displays a response to user A.

[0529] Generating an action plan

[0530] Step 1:

[0531] The server records the conversation content and the results of the sentiment analysis.

[0532] Step 2:

[0533] Generative artificial intelligence generates an action plan that says, "Let's schedule a one-on-one meeting with your boss."

[0534] Step 3:

[0535] The server sends the generated action plan to the terminal.

[0536] Step 4:

[0537] The device displays an action plan to user A.

[0538] Step 5:

[0539] User A approves of proceeding to the next step.

[0540] After-sales support

[0541] Step 1:

[0542] The server generates a reminder, and the device sends a notification to user A one week later.

[0543] Step 2:

[0544] User A enters, "I spoke with my supervisor, but the situation hasn't improved yet."

[0545] Step 3:

[0546] The terminal sends the input result of user A to the server.

[0547] Step 4:

[0548] The server sends the input results to the emotion engine for emotion analysis.

[0549] Step 5:

[0550] The analysis results from the emotion engine are sent to the generative artificial intelligence.

[0551] Step 6:

[0552] The generative artificial intelligence generates an additional action plan, "Next, let's practice expressing your feelings honestly," and sends it to the server.

[0553] Step 7:

[0554] The server sends any additional dialogue or action plans it has generated to the terminal.

[0555] Step 8:

[0556] The device displays additional dialogue and action plans to user A.

[0557] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[0558] (Example 2)

[0559] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0560] In modern society, users constantly face a variety of mental and physical health issues, yet there is a lack of systems that provide effective and continuous support for these issues. Furthermore, it is difficult to provide appropriate dialogue and action plans tailored to the user's emotional state. As a result, users have difficulty accurately understanding their situation and receiving concrete advice for improvement. Therefore, there is a need for a system that analyzes the user's emotional state and provides dialogue and action plans based on that analysis.

[0561] The specific processing performed 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 having the user input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving answers to the medical questionnaire form from the user, means for analyzing the received answers to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for conducting a dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means including an emotion engine for analyzing the user's emotions, means for using a generative artificial intelligence that generates a response based on the emotion analysis results, and means for reflecting the emotion analysis results in the dialogue scenario. This makes it possible to provide effective and continuous support to the user regarding their specific concerns, taking into account their emotional state.

[0562] "User" refers to an individual or group that uses the system.

[0563] "Basic information" refers to information such as name, email address, and password that users enter when registering with the system.

[0564] "To remember" refers to saving entered data to storage such as a database.

[0565] A "medical questionnaire form" refers to an input form used to collect information about a user's concerns or problems.

[0566] "Receiving" refers to the server or system acquiring information entered by the user.

[0567] "Analyzing" refers to processing received data and extracting meaning and patterns.

[0568] A "dialogue scenario" refers to a scenario generated based on analysis results to facilitate interaction with the user.

[0569] "Engaging in dialogue" refers to the exchange of information between the user and the system in a question-and-answer format.

[0570] "Recording" refers to saving the content of the conversation and the analysis results to a database or similar system.

[0571] An "action plan" refers to specific measures and action plans to address a user's problems.

[0572] "Following up" means tracking the progress of the action plan and providing additional support as needed.

[0573] An "emotion engine" refers to software that analyzes user input to identify emotions and their emotional state.

[0574] "Generative artificial intelligence" refers to artificial intelligence models that generate responses and scenarios based on user input data and sentiment analysis results.

[0575] A "reminder" refers to a function that notifies users of the progress of action plans and follow-ups.

[0576] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The following describes specific embodiments of the system.

[0577] First, the entire system consists of a terminal that users access, a server that processes data, an emotion engine that performs sentiment analysis, and a generative artificial intelligence system.

[0578] User Registration

[0579] The user accesses the system and opens a website or app. The user's device displays a new registration form, and the user enters and submits basic information (name, email address, password, etc.). The device sends the entered basic information to the server, which stores the information in its database. The server generates a registration completion message, sends it to the device, and displays it to the user.

[0580] Initial consultation

[0581] After user registration is complete, the device automatically displays an initial consultation form. The user enters the category of their problem (e.g., "work stress") and specific details. The device sends the entered data to the server, which then sends that data to a generative artificial intelligence (AI). The AI ​​analyzes the data and generates an appropriate dialogue scenario. The server sends the dialogue scenario to the device, preparing to begin the conversation with the user.

[0582] Dialogue session

[0583] The user's device displays a dialogue screen based on a generated dialogue scenario. Generative artificial intelligence generates appropriate questions and sends them to the device, displaying the questions to the user. The user enters their answers, and these answers are sent from the device to the emotion engine. The emotion engine analyzes the user's emotions and sends the results to the server. The server sends the emotion analysis results to the generative artificial intelligence, which generates an appropriate response. The generated response is sent to the device and displayed to the user.

[0584] Generating an action plan

[0585] The content of the dialogue session is recorded on the server, and a generative artificial intelligence generates an optimal action plan based on the dialogue content and sentiment analysis results. The server sends the generated action plan to the terminal, and the terminal displays the action plan to the user. The user reviews the action plan and approves to proceed to the next step.

[0586] After-sales support

[0587] The server generates periodic reminders to follow up on the progress of the action plan. The device sends a reminder notification to the user, who then enters the progress of the action plan. The entered results are sent to the server and analyzed again by the emotion engine. The analysis results are sent to the generative artificial intelligence, which generates additional dialogues and action plans as needed. These are then sent back from the server to the device and displayed to the user.

[0588] Specific example

[0589] For example, if user A is suffering from "work stress," the following process will occur:

[0590] 1. User A enters basic information and registers it in the system.

[0591] 2. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[0592] 3. The input data is sent to the generative artificial intelligence, and the question "What specific problems are you experiencing?" is generated.

[0593] 4. When User A responds, "My boss doesn't listen to my opinions at all," the emotion engine analyzes that emotion and generates the response, "That's tough. Do you have opportunities to talk to your boss?"

[0594] 5. Based on the dialogue content and emotion analysis results, an action plan is generated and presented to User A: "Let's schedule a one-on-one meeting with your supervisor."

[0595] 6. To follow up on the progress of the action plan, the server generates a reminder and a notification is sent from the terminal. User A inputs the meeting results, which are then re-analyzed to generate new dialogues and action plans.

[0596] Example of a prompt

[0597] The following prompt is input to the generative artificial intelligence:

[0598] "User A is seeking advice about work-related stress, stating, 'My boss doesn't listen to my opinions at all.' Please suggest what questions should be asked next, taking into account the user's emotional state."

[0599] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0600] Step 1: The user accesses the system and opens a website or app.

[0601] The user accesses the system's website or app via the internet. Input here involves the user entering the URL of the website or app, or launching the app. Output is the display of a new registration form.

[0602] Step 2: The device displays the new registration form.

[0603] The device displays the form required for new registration (fields for name, email address, and password). The input is the user's basic information, and the output is the form with the basic information ready to be entered.

[0604] Step 3: The user enters their basic information and presses the submit button.

[0605] The user enters basic information such as their name, email address, and password into the registration form and clicks the submit button. The input is the user's basic information, and the output is the event that sends this basic information.

[0606] Step 4: The terminal sends the entered basic information to the server.

[0607] The terminal sends basic information to the server using the HTTPS protocol. The input is the basic information entered by the user, and the output is the result of the transmission to the server.

[0608] Step 5: The server saves the basic information it received to the database.

[0609] The server records the received basic information in a database (e.g., MySQL®). The input is the transmitted basic information, and the output is the result of saving it to the database. Passwords are hashed (e.g., bcrypt) for security purposes before being stored.

[0610] Step 6: The server generates a registration completion message and sends it to the terminal.

[0611] The server generates a registration completion message and sends it to the terminal. The input is the confirmation result of successful saving, and the output is the generation and sending of the completion message.

[0612] Step 7: The device displays a registration completion message to the user.

[0613] The terminal displays a registration completion message received from the server to the user. The input is the message from the server, and the output is what is displayed to the user.

[0614] Step 8: After user registration is complete, the device will display the medical questionnaire form.

[0615] After user registration is complete, the device displays a medical questionnaire form as the next step. The input is the registration completion event, and the output is the display of the medical questionnaire form.

[0616] Step 9: The user selects a category for their problem and enters their specific problem.

[0617] The user selects a category such as "work stress" in a questionnaire form and enters specific concerns such as "I'm having trouble getting along with my boss at work lately." Input consists of user selections and text input, while output is confirmation of the input and a submission event.

[0618] Step 10: The terminal sends the user's input to the server.

[0619] The terminal sends user input to the server via the HTTPS protocol. The input is the user's responses to a questionnaire, and the output is the result of the transmission to the server.

[0620] Step 11: The server sends the received data to the generative artificial intelligence.

[0621] The server receives the medical interview data and sends it to a generative artificial intelligence (e.g., OpenAI GPT-3®). The input is the medical interview data, and the output is the successful transmission to the generative artificial intelligence.

[0622] Step 12: Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[0623] Generative artificial intelligence analyzes user input data and constructs appropriate dialogue scenarios. For example, it generates questions such as, "Please tell me more about your relationship with your boss." The input is questionnaire data, and the output is a dialogue scenario.

[0624] Step 13: The server sends the constructed dialogue scenario to the terminal.

[0625] The server sends the dialogue scenario received from the generative artificial intelligence to the user's terminal. The input is the dialogue scenario, and the output is the result of the transmission to the terminal.

[0626] Step 14: The device displays a dialogue screen to the user.

[0627] The terminal displays a dialogue screen and initiates a conversation with the user based on the generated dialogue scenario. The input is the dialogue scenario, and the output is the display of the dialogue screen.

[0628] Step 15: The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[0629] A generative artificial intelligence generates appropriate questions, sends them to a terminal, and the terminal displays those questions to the user. The input is the dialogue scenario, and the output is the question displayed to the user.

[0630] Step 16: The user enters a specific answer to the question.

[0631] The user enters a specific answer based on the question. For example, they might enter, "My boss never listens to my opinion." The input is the user's answer, and the output is the entered text.

[0632] Step 17: The device sends user input to the emotion engine, which analyzes the emotions.

[0633] The terminal sends user input to an emotion engine (e.g., IBM Watson® Tone Analyzer) for emotion analysis. The input is the user's text response, and the output is the emotion analysis result.

[0634] Step 18: The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[0635] The server sends the results of the emotion engine's analysis to the generative artificial intelligence. The input is the emotion analysis result, and the output is the result sent to the generative artificial intelligence.

[0636] Step 19: The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[0637] The generative artificial intelligence considers the emotion analysis results and generates an appropriate response. A response such as "That's tough. Do you have a chance to talk to your supervisor?" is generated. The input is the emotion analysis results, and the output is the generated response.

[0638] Step 20: The server sends the generated response to the terminal.

[0639] The server sends the response received from the generative artificial intelligence to the user's terminal. The input is the generated response, and the output is the result of the transmission to the terminal.

[0640] Step 21: The terminal displays a response to the user.

[0641] The terminal displays the generated response to the user. The input is the response message from the server, and the output is the response displayed to the user.

[0642] Step 22: The server records the contents of the conversation session.

[0643] The server records the contents of all dialogue sessions in a database. The input is the dialogue between the user and the generative artificial intelligence, and the output is the success of the recording.

[0644] Step 23: The generative artificial intelligence generates the optimal action plan for the user based on the conversation content and emotion analysis results.

[0645] A generative artificial intelligence generates action plans such as "Let's schedule a one-on-one meeting with your boss" based on recorded dialogue content and emotion analysis results. The input is the dialogue content and emotion analysis results, and the output is the generated action plan.

[0646] Step 24: The server sends the generated action plan to the terminal.

[0647] The server sends the generated action plan to the user's terminal. The input is the generated action plan, and the output is the result of sending it to the terminal.

[0648] Step 25: The device displays the action plan to the user.

[0649] The terminal displays the action plan it received to the user. The input is the action plan from the server, and the output is the action plan displayed to the user.

[0650] Step 26: The user reviews the action plan and approves to proceed to the next step.

[0651] The user reviews the displayed action plan and clicks the approve button. The input is the action plan and its confirmation, and the output is the approval event.

[0652] Step 27: The server generates reminders to follow up on the progress of the action plan.

[0653] The server generates periodic reminder notifications to follow up on the action plan. The input is the action plan and its progress, and the output is the generated reminders.

[0654] Step 28: The device periodically sends reminder notifications to the user.

[0655] The device notifies the user of reminder notifications sent from the server at the appropriate time. The input is the reminder from the server, and the output is the notification to the user.

[0656] Step 29: The user enters the progress of the action plan.

[0657] After the user receives a reminder, they enter the progress of the action plan. The input is the user's status report, and the output is the entered progress details.

[0658] Step 30: The terminal sends the user's input to the server.

[0659] The terminal sends user input to the server. The input is the progress, and the output is the result of the transmission to the server.

[0660] Step 31: The server sends the input results to the emotion engine for sentiment analysis.

[0661] The server sends progress information to the emotion engine, which then performs the latest emotion analysis. The input is the progress information, and the output is the emotion analysis result.

[0662] Step 32: The server sends the results of the emotion engine's analysis to the generative artificial intelligence to generate additional dialogue and action plans.

[0663] The generative artificial intelligence generates new dialogues or action plans as needed, based on the emotion analysis results. The input is the emotion analysis results, and the output is the added dialogue or action plan.

[0664] Step 33: The server sends any additional dialogue or action plan that has been generated to the terminal.

[0665] The server sends the newly generated dialogue and action plan to the terminal. The input is the generated dialogue and action plan, and the output is the result of sending it to the terminal.

[0666] Step 34: The device displays additional dialogue or action plans to the user.

[0667] The terminal displays newly generated dialogues and action plans to the user. The input is the dialogues and action plans from the server, and the output is what is displayed to the user.

[0668] (Application Example 2)

[0669] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0670] Improper management of employees' physical and mental health can lead to fatigue, stress accumulation, and decreased motivation, negatively impacting productivity and safety. To address this challenge, a system is needed that can monitor employees' physical and mental health in real time and provide appropriate support.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0672] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means for performing sentiment analysis of the dialogue content, and means for adjusting the dialogue scenario and action plan considering the sentiment analysis results. This makes it possible to grasp the mental and physical state of employees in real time and provide appropriate support.

[0673] "Basic information" refers to data such as the user's name, email address, and password.

[0674] "To remember" means for a server to store information entered by a user in a database or similar location.

[0675] A "medical questionnaire form" is a sheet of questions used by users to record their physical and mental condition and concerns.

[0676] "Receiving" means that the server obtains input information from the user.

[0677] "To analyze" means to understand and analyze the content based on the received data.

[0678] "Generating" means creating new dialogue scenarios, action plans, and other elements based on the analysis results.

[0679] A "dialogue scenario" refers to a series of questions and answers exchanged between a user and a system.

[0680] "Engaging in dialogue" means that a system exchanges information with a user through conversation.

[0681] An "action plan" is a specific plan for a user to initiate a designated action.

[0682] "Following up" means that the system periodically checks the user's progress and status, and provides advice and support as needed.

[0683] "Emotional analysis" is the process of inferring and analyzing a user's emotional state from their statements and input data.

[0684] "Emotional analysis results" refer to data on emotional states obtained based on emotional analysis.

[0685] "Adjusting" means changing the content of the dialogue scenario or action plan depending on the situation.

[0686] This invention relates to a factory robot that analyzes the physical and mental state of employees in real time within a factory and provides appropriate support. The system collects and analyzes basic information from the user (employee) and provides individualized dialogue and action plans.

[0687] System Program Overview

[0688] User Registration

[0689] The server receives basic information entered by the user (such as name, email address, and password) and stores it in a database. The user then enters this information via a tablet on a factory robot.

[0690] Initial consultation

[0691] Once user registration is complete, the server generates a questionnaire form for the user and displays it on the robot's tablet. The user enters a category (e.g., stress management) and a specific problem (e.g., "I'm exhausted from having too much work"), and this information is sent to the server.

[0692] Dialogue session

[0693] The server uses generative artificial intelligence to analyze the responses to the questionnaire form and construct a dialogue scenario. Once the dialogue scenario is constructed, the robot displays specific questions to the user. The user's responses are analyzed for emotion in real time, and the generative artificial intelligence then generates the next response, taking the emotion analysis results into consideration.

[0694] Generating an action plan

[0695] The server generates an optimal action plan for the user based on the content of the dialogue session and the results of the emotion analysis. This action plan is displayed on the robot's tablet and presented to the user.

[0696] After-sales support

[0697] The server generates reminders to follow up on the progress of the action plan and sends notifications to the user periodically. The user inputs the progress, which is then subjected to sentiment analysis again. The server uses generative artificial intelligence to generate additional dialogues and action plans, which are then provided to the user.

[0698] Hardware and software to use

[0699] This system requires a factory robot (with a tablet), a server, an emotion engine (EmotionEngine), and generative artificial intelligence (ChatbotAI). The server stores basic information from the user and answers to questionnaire forms in a database, and uses generative AI to analyze the data, build dialogue scenarios, and perform emotion analysis. This makes it possible to understand the user's mental and physical state in real time and provide appropriate support.

[0700] Specific example

[0701] Next, I will describe a specific example of this system.

[0702] User registration example

[0703] The user enters basic information on the robot's tablet and presses the registration button. This information is sent to the server and stored in the database. A registration completion message is displayed on the tablet.

[0704] Initial consultation example

[0705] The user selects the "Stress Management" category and enters "I've been really tired lately because I have so much work." This input data is sent to the server, where a generative artificial intelligence analyzes it. A dialogue scenario is then constructed and displayed on the tablet.

[0706] Example prompts for a dialogue session

[0707] User response: "Lately, I feel like my boss doesn't understand me."

[0708] Server response: "That sounds tough. What exactly are the problems you're experiencing?"

[0709] Example of action plan generation

[0710] The server records the conversation (e.g., "My boss doesn't understand me") and generates an action plan such as "Let's practice talking directly to your boss." This is then displayed on the tablet.

[0711] Examples of after-sales support

[0712] The server generates reminders to check progress and sends periodic notifications to the user. The user types, "I spoke with my boss, but it hasn't improved yet." This is then analyzed for sentiment again, generating additional dialogue and action plans, which are displayed on the tablet.

[0713] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0714] Program processing flow

[0715] Step 1: User Registration

[0716] The server stores the basic information (name, email address, etc.) entered by the user in a database.

[0717] Input: The user enters basic information.

[0718] Data processing: Validate the input data.

[0719] Output: Basic information is saved in the database.

[0720] Specific operation: The user enters basic information into the robot's tablet and presses the submit button. The server receives this information and saves it to the database.

[0721] Step 2: Generate the medical questionnaire form

[0722] The server generates a medical questionnaire form based on the stored basic information.

[0723] Input: Basic user information stored on the server.

[0724] Data processing: Generate appropriate questionnaire items based on the user's basic information.

[0725] Output: The generated questionnaire form is displayed on the robot's tablet.

[0726] Specific operation: The server generates a medical questionnaire form based on basic information and sends it to the robot's tablet.

[0727] Step 3: Receiving responses to the medical questionnaire

[0728] The server receives the user's responses to the medical questionnaire form.

[0729] Input: The user enters their answers to the medical questionnaire form.

[0730] Data processing: Receive and validate the entered response data.

[0731] Output: The response data is saved on the server.

[0732] Specific operation: The user enters their answers to a questionnaire form on a tablet and presses the submit button. The server receives this and saves it to the database.

[0733] Step 4: Analysis of the questionnaire form

[0734] The server analyzes the responses to the received medical questionnaire using a generating AI model.

[0735] Input: Data from the medical questionnaire.

[0736] Data processing: Data analysis is performed using a generative AI model.

[0737] Output: Analysis results are generated.

[0738] Specific operation: The server sends the received response to the generating AI model and obtains the analysis result.

[0739] Step 5: Generating dialogue scenarios

[0740] The server generates dialogue scenarios based on the analysis results.

[0741] Input: Analysis results from the medical questionnaire form.

[0742] Data processing: Construct dialogue scenarios using a generative AI model.

[0743] Output: A dialogue scenario is generated.

[0744] Specific operation: The server generates a dialogue scenario using an AI model based on the analysis results and sends it to the tablet.

[0745] Step 6: Conducting a dialogue session

[0746] The server and robot interact with the user based on the generated dialogue scenario.

[0747] Input: Dialogue scenario.

[0748] Data processing: Analyze user responses using an emotion engine.

[0749] Output: A response based on the sentiment analysis results is generated.

[0750] Specific operation: A question from the conversational scenario is displayed on the tablet, and the user enters an answer. The emotion engine analyzes the answer, and the server generates an appropriate response and displays it on the tablet.

[0751] Step 7: Record the conversation

[0752] The server records the contents of the dialogue session.

[0753] Input: Dialogue content data.

[0754] Data processing: Save the conversation content to the database.

[0755] Output: The conversation content is recorded.

[0756] Specific operation: The server saves the results of the dialogue session to the database.

[0757] Step 8: Generate an action plan

[0758] The server generates an action plan based on the interaction.

[0759] Input: Dialogue content data and sentiment analysis results.

[0760] Data processing: Develop action plans using generative AI models.

[0761] Output: An action plan is generated.

[0762] Specific operation: The server generates an action plan using a generative AI model based on the conversation content and sentiment analysis results, and displays it on the tablet.

[0763] Step 9: Present an action plan

[0764] The server presents the generated action plan to the user.

[0765] Input: Action plan.

[0766] Data processing: Display the action plan on the tablet.

[0767] Output: The action plan is displayed on the tablet.

[0768] Specific operation: The server sends the generated action plan to the tablet, and the user confirms it.

[0769] Step 10: Follow up on the progress of the action plan

[0770] The server generates reminders to follow up on the progress of the action plan.

[0771] Input: Action plan and progress data.

[0772] Data processing: Generate a reminder and send it to the tablet.

[0773] Output: A reminder is sent to the user.

[0774] Specific operation: The server monitors the progress, generates a reminder, and sends it to the user's tablet. The user enters the progress, which is then analyzed for sentiment again.

[0775] Step 11: Generating additional dialogues and action plans

[0776] The server generates additional dialogue and action plans based on the sentiment analysis results.

[0777] Input: Progress data and sentiment analysis results.

[0778] Data processing: Generative AI models are used to build additional dialogues and action plans.

[0779] Output: Additional dialogues and action plans are generated.

[0780] Specific operation: The server uses a generative AI model based on progress data and sentiment analysis results to generate additional dialogue and action plans, which are then displayed on the tablet.

[0781] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0782] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0783] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0784] [Second Embodiment]

[0785] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0786] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0787] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0788] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0789] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0790] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0791] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0792] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0793] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0794] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0795] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0796] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0797] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system's processing is described below in natural language.

[0798] System Program Overview

[0799] 1. User Registration

[0800] In order for a user to use the system, they must first register.

[0801] The terminal prompts the user to enter basic information (name, email address, password, etc.).

[0802] The terminal sends the entered basic information to the server.

[0803] The server saves the user's basic information to a database and sends a registration completion notification to the device.

[0804] The device displays a registration completion message to the user.

[0805] 2. Initial consultation

[0806] After user registration is complete, the device will display a medical questionnaire form to the user.

[0807] The user selects a category of their problem (for example, work stress) and enters their specific problem.

[0808] The terminal sends user input to the server.

[0809] The server receives the input data and passes it to the generative artificial intelligence for analysis.

[0810] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0811] 3. Dialogue Session

[0812] The server sends the generated dialogue scenario to the terminal.

[0813] The device displays a dialogue screen and initiates a conversation with the generative artificial intelligence for the user.

[0814] The terminal sends user input to the server, which then uses generative artificial intelligence to generate an appropriate response.

[0815] The response is sent to the terminal and displayed to the user.

[0816] 4. Generating an action plan

[0817] The server records and analyzes the information obtained through the dialogue session.

[0818] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation.

[0819] The server sends the generated action plan to the terminal and displays it to the user.

[0820] The user reviews the action plan and proceeds to the next step.

[0821] 5. After-sales support

[0822] The server generates periodic reminders to follow up on the progress of the action plan.

[0823] The device sends a reminder notification to the user, and the user enters the progress of the action plan.

[0824] The server receives the input results, a generative artificial intelligence analyzes them, and proposes additional dialogue or action plans as needed.

[0825] Specific example

[0826] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[0827] User Registration

[0828] User A enters basic information on the terminal and presses the registration button.

[0829] The entered information is sent to the server and stored in the database.

[0830] A registration completion message is displayed to user A.

[0831] Initial consultation

[0832] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[0833] The input data is sent to the server, where a generative artificial intelligence performs the analysis.

[0834] A dialogue scenario is created and sent to the terminal.

[0835] Dialogue session

[0836] Based on the dialogue scenario sent from the server, the terminal displays the question, "What exactly is the problem?"

[0837] User A responded, "My boss doesn't listen to my opinions at all."

[0838] The generative artificial intelligence analyzed the message and generated the response, "That sounds tough. Do you have an opportunity to talk to your supervisor?"

[0839] The response is displayed to user A.

[0840] Generating an action plan

[0841] The server records the conversation, and a generative artificial intelligence generates an action plan: "Let's schedule a one-on-one meeting with your boss."

[0842] The action plan is displayed on the device, and User A accepts it.

[0843] After-sales support

[0844] The server generates a reminder based on the action plan, and the device sends a notification one week later.

[0845] User A entered the results of the meeting and reported, "I discussed it with my supervisor, but it hasn't improved yet."

[0846] The generative artificial intelligence re-analyzed the text and suggested, "Next, let's practice expressing your feelings honestly together."

[0847] In this way, the system addresses users' concerns step by step, providing attentive support and continuous follow-up.

[0848] The following describes the processing flow.

[0849] User Registration

[0850] Step 1:

[0851] The user opens a website or app to access the system.

[0852] Step 2:

[0853] The device displays a new registration form to the user.

[0854] Step 3:

[0855] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[0856] Step 4:

[0857] The terminal sends the entered basic information to the server.

[0858] Step 5:

[0859] The server saves the basic information it receives to the database.

[0860] Step 6:

[0861] The server generates a registration completion message and sends it to the terminal.

[0862] Step 7:

[0863] The device displays a registration completion message to the user.

[0864] Initial consultation

[0865] Step 1:

[0866] After user registration is complete, the device will display a medical questionnaire form to the user.

[0867] Step 2:

[0868] The user selects a category for their problem and enters their specific problem.

[0869] Step 3:

[0870] The terminal sends the user's input to the server.

[0871] Step 4:

[0872] The server sends the received data to a generative artificial intelligence.

[0873] Step 5:

[0874] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[0875] Step 6:

[0876] The server sends the constructed dialogue scenario to the terminal.

[0877] Dialogue session

[0878] Step 1:

[0879] The device displays a dialogue screen to the user.

[0880] Step 2:

[0881] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[0882] Step 3:

[0883] The user enters a specific answer to the question.

[0884] Step 4:

[0885] The terminal sends user input to the server.

[0886] Step 5:

[0887] The server sends the user's input data to a generative artificial intelligence system and generates the following response.

[0888] Step 6:

[0889] The generative artificial intelligence analyzes the data, generates an appropriate response, and sends it to the server.

[0890] Step 7:

[0891] The server sends the generated response to the terminal.

[0892] Step 8:

[0893] The device displays a response to the user.

[0894] Generating an action plan

[0895] Step 1:

[0896] The server records the contents of the conversation session.

[0897] Step 2:

[0898] Generative artificial intelligence analyzes the conversation content and generates the optimal action plan for the user.

[0899] Step 3:

[0900] The server sends the generated action plan to the terminal.

[0901] Step 4:

[0902] The device displays an action plan to the user.

[0903] Step 5:

[0904] The user reviews the action plan and approves to proceed to the next step.

[0905] After-sales support

[0906] Step 1:

[0907] The server generates reminders to follow up on the progress of the action plan.

[0908] Step 2:

[0909] The device periodically sends reminder notifications to the user.

[0910] Step 3:

[0911] The user enters the progress of the action plan.

[0912] Step 4:

[0913] The terminal sends the user's input results to the server.

[0914] Step 5:

[0915] The server sends the input results to a generative artificial intelligence system for analysis.

[0916] Step 6:

[0917] Generative artificial intelligence analyzes the progress and generates additional dialogues and action plans as needed.

[0918] Step 7:

[0919] The server sends any additional dialogue or action plans it has generated to the terminal.

[0920] Step 8:

[0921] The device displays additional dialogues or action plans to the user.

[0922] As described above, the "LifeBridge" system provides gradual and continuous support for users' physical and mental health concerns.

[0923] (Example 1)

[0924] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0925] Conventional lifestyle platform systems have not provided sufficient, empathetic support for users' physical and mental health concerns, particularly inadequate, continuous follow-up. As a result, effective solutions to the problems users face may not be provided, potentially leading to decreased user satisfaction. Furthermore, the inability to effectively utilize artificial intelligence in dialogue and action plan generation could result in inconsistent support for users.

[0926] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0927] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's answers to the medical questionnaire form, means for analyzing the received answers to the medical questionnaire form using generative artificial intelligence, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the content of the dialogue, means for generating an action plan based on the content of the dialogue, means for presenting the generated action plan to the user, means for generating and sending periodic reminders to the user to follow up on the progress of the action plan, and means for receiving progress information from the user, re-analyzing it, and proposing additional dialogues or action plans as necessary. This enables empathetic support and continuous follow-up for the user's physical and mental health concerns, thereby improving user satisfaction.

[0928] "Basic information" refers to personal information necessary to start using the system, such as the user's name, email address, and password.

[0929] A "database" is a digital storage device used to store and manage information such as a user's basic information, conversation history, and action plans.

[0930] A "medical questionnaire form" is a question-based input screen used to gain a detailed understanding of a user's physical and mental health concerns.

[0931] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to analyze input data and generate appropriate dialogue scenarios and action plans.

[0932] A "dialogue scenario" refers to the pre-conversation flow and questions created by generative artificial intelligence to facilitate smooth interaction with the user.

[0933] An "action plan" refers to specific measures and action plans proposed by a generative artificial intelligence system to resolve the user's mental and physical problems.

[0934] A "reminder" is a notification or message that is sent periodically to a user to check and input the progress of their action plan.

[0935] "Follow-up" refers to a series of activities and actions taken to check the progress of the action plan and provide additional support as needed.

[0936] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system requires a server, terminals, and a generative AI model to be implemented.

[0937] The system operates as follows: First, the user enters basic information using a terminal. The terminal has a function to display a form for entering information such as name, email address, and password. The terminal then sends the entered basic information to the server. The server saves the received basic information to a database and sends a registration completion notification to the terminal. The terminal displays this notification to the user.

[0938] Once user registration is complete, the device displays a questionnaire form to the user. This questionnaire includes areas where the user can select a category related to their mental or physical health concerns and enter their specific concerns. For example, the user might select the category "work stress" and enter, "Recently, my relationship with my boss hasn't been going well." The device then sends this input data to the server.

[0939] The server passes the received data to a generative artificial intelligence model. This generative AI model analyzes the input data and constructs an appropriate dialogue scenario. The generated dialogue scenario is sent from the server to the terminal and displayed to the user on the terminal.

[0940] Once a dialogue session begins, the server generates questions based on a pre-defined dialogue scenario. The user's input through the terminal is sent to the server, where a generative artificial intelligence analyzes it and generates an appropriate response. The generated response is displayed on the terminal, and the user responds to it. This dialogue process continues.

[0941] The entire content of the conversation session is recorded on the server. Based on the conversation, the server uses generative artificial intelligence to generate an appropriate action plan. For example, an action plan such as "Let's schedule a one-on-one meeting with your boss" is generated and displayed on the terminal. The user can review this action plan and proceed to the next step.

[0942] To track the progress of the action plan, the server generates periodic reminders. These reminder notifications are sent to the device, and the user receives them. The user then inputs the progress of the action plan according to the reminder and sends it from the device to the server. The server re-analyzes this progress information and, if necessary, uses generative artificial intelligence to suggest additional dialogues or action plans.

[0943] For example, if user A is suffering from work-related stress, the following dialogue may take place:

[0944] User A: "Lately, my relationship with my boss hasn't been going well."

[0945] Generative AI: "That sounds tough. Do you have a chance to talk to your supervisor about it?"

[0946] In this way, the system approaches users' problems step by step, providing attentive support and continuous follow-up.

[0947] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0948] Step 1:

[0949] User registration (entering basic information)

[0950] The user enters basic information using a device. The device displays a form for entering basic information such as name, email address, and password. This form includes required fields for name, email address, and password. The entered data is sent from the device to the server.

[0951] Input: Name, email address, password

[0952] Output: Basic information is sent to the server.

[0953] Step 2:

[0954] Basic information saving

[0955] The server receives basic information sent from the terminal. The received basic information is stored in the database. The server notifies the terminal when the saving is complete.

[0956] Input: Basic Information

[0957] Output: Basic information is saved to the database, and a registration completion notification is sent to the device.

[0958] Step 3:

[0959] Display of registration completion message

[0960] The device receives a registration completion notification sent from the server. The device displays a registration completion message to the user. This allows the user to confirm that registration was successful.

[0961] Input: Registration completion notification

[0962] Output: A registration complete message is displayed to the user.

[0963] Step 4:

[0964] Display of the medical questionnaire form

[0965] After user registration is complete, the device displays a questionnaire form to the user. This questionnaire form includes a category selection field for mental and physical health concerns and a text box for entering specific concerns.

[0966] Input: None (A questionnaire form is displayed as a prompt)

[0967] Output: The medical questionnaire form is displayed to the user.

[0968] Step 5:

[0969] Enter category and problem

[0970] The user selects a category of their problem in a questionnaire form and enters their specific problem. For example, the user might select the category "Work Stress" and enter, "Recently, my relationship with my boss hasn't been going well." This information is sent from the device to the server.

[0971] Input: Category of problem and specific problem

[0972] Output: Category and problem information is sent to the server.

[0973] Step 6:

[0974] Data Analysis

[0975] The server provides the data from the received questionnaire form to a generative artificial intelligence model. The generative AI model analyzes the data and generates an appropriate dialogue scenario. For example, it might take a prompt such as "The user is suffering from work-related stress" and generate a scenario such as "Please tell me more."

[0976] Input: User's problem data

[0977] Output: Dialogue scenario generated by generative artificial intelligence

[0978] Step 7:

[0979] Sending and displaying dialogue scenarios

[0980] The server sends a generated dialogue scenario to the terminal. The terminal displays the dialogue scenario to the user and begins the dialogue. At this time, a question such as "What specific problems are you experiencing?" is presented.

[0981] Input: Dialogue scenario

[0982] Output: The dialogue scenario is displayed to the user.

[0983] Step 8:

[0984] Submitting user input

[0985] The user enters a response according to the dialogue scenario. For example, they might enter a response such as, "My boss doesn't listen to my opinion at all." This input is sent from the terminal to the server.

[0986] Input: User's response

[0987] Output: The answer is sent to the server.

[0988] Step 9:

[0989] Response generation

[0990] The server passes the user's response to a generative artificial intelligence model, which then generates an appropriate response. For example, a response such as, "That's tough. Do you have a chance to talk to your supervisor?" might be generated.

[0991] Input: User response data

[0992] Output: Generated response

[0993] Step 10:

[0994] Display of response

[0995] The server sends the generated response to the terminal, which then displays it to the user. The response acts as a trigger for the user to enter the next answer.

[0996] Input: Generated response

[0997] Output: Response is lowered to the user.

[0998] Step 11:

[0999] Record of the conversation

[1000] The content of the dialogue session is recorded on the server. This includes all questions and answers. The recorded data is used to generate an action plan.

[1001] Input: Content of the dialogue session

[1002] Output: The conversation content is saved to the database.

[1003] Step 12:

[1004] Generating an action plan

[1005] The server passes the conversation content to a generative artificial intelligence model, which then generates an appropriate action plan. For example, a specific action plan such as "Let's schedule a one-on-one meeting with your boss" might be generated.

[1006] Input: Dialogue content data

[1007] Output: Generated action plan

[1008] Step 13:

[1009] Sending and displaying action plans

[1010] The generated action plan is sent from the server to the terminal and displayed to the user. The user can review the action plan and proceed to the next step.

[1011] Input: Action Plan

[1012] Output: The action plan is displayed to the user.

[1013] Step 14:

[1014] Creating and sending reminders

[1015] To follow up on the progress of the action plan, the server generates periodic reminders. These reminders are sent to the device and notified to the user.

[1016] Input: Action plan execution schedule

[1017] Output: A reminder is sent to the user.

[1018] Step 15:

[1019] Inputting progress and reanalysis

[1020] The user enters the progress of their action plan via their device, following a reminder. The entered progress information is sent to the server and re-analyzed by generative artificial intelligence. Additional dialogues and action plans are suggested as needed.

[1021] Input: User progress information

[1022] Output: Additional dialogue or action plan

[1023] This series of steps enables the system to provide attentive support and ongoing follow-up for users' concerns.

[1024] (Application Example 1)

[1025] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1026] In modern society, users often experience mental and physical stress and anxieties, which can prevent them from receiving professional support. However, conventional mental health care systems lack concrete action plans and follow-up to effectively and sustainably resolve users' problems. In particular, there is a need for a method that provides appropriate dialogue sessions for each user's individual concerns and automatically follows up on their progress.

[1027] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1028] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for notifying a smart device of a reminder, means for analyzing the medical questionnaire data and dialogue data with a generative artificial intelligence system, means for generating prompt sentences, means for presenting the generated action plan to the user, and means for following up on the progress of the action plan. As a result, the user can receive prompt and appropriate support for their physical and mental health concerns, and continuous follow-up is also possible.

[1029] "Basic information" refers to information necessary to identify a user, such as the user's name, email address, and password.

[1030] "To store" means to save received data in an information management device.

[1031] A "medical questionnaire form" is a questionnaire used to collect information about a user's mental and physical health concerns and stress levels.

[1032] "Analyzing" means processing received data to extract useful information.

[1033] A "dialogue scenario" is a plan of a series of questions and answers used when interacting with a user.

[1034] "Generative artificial intelligence" is an advanced computational technique that generates optimal responses and scenarios based on large amounts of data.

[1035] An "action plan" is a set of specific guidelines for actions taken to solve a user's problem.

[1036] A "smart device" is a highly functional portable device capable of connecting to the internet and launching applications.

[1037] A "reminder" is a notification that prompts a user to take a specific action or confirm something.

[1038] A "prompt" is the input text for a generative artificial intelligence system, and a response is generated based on it.

[1039] "Follow-up" is the process of evaluating the progress of the established action plan and taking additional measures as needed.

[1040] This invention is a mental health care system that supports users with their physical and mental health concerns. The system inputs and records the user's basic information, generates a questionnaire form, generates and executes dialogue scenarios using generative artificial intelligence (AI) based on that form, and has the function of following up on the progress of the action plan. The implementation method of this system is described in detail below.

[1041] System Configuration

[1042] 1. Hardware and software

[1043] The system consists of a smartphone and a server. The smartphone has a mental health care application installed, which allows users to input basic information and answer questionnaires. The server is a cloud platform equipped with a database, generative artificial intelligence (e.g., OpenAI's GPT-4), a notification system, and other features.

[1044] 2. Data processing and data calculation

[1045] User registration:

[1046] When a user enters basic information through a smartphone application, that information is sent to a server and stored in a database. The server generates a registration completion notification and sends it to the user's smartphone to inform them of the completion.

[1047] Generating and analyzing medical questionnaire forms:

[1048] The server generates a questionnaire form based on stored basic information and presents it to the user. When the user answers the questionnaire form, the data is sent to the server. The server uses generative AI to analyze the answers and generate a personalized dialogue scenario.

[1049] Dialogue session:

[1050] Based on the dialogue scenario sent from the server, the smartphone application displays a dialogue screen and begins interacting with the user. The user's input is sent back to the server, where a generative AI generates an appropriate response, which is then sent and displayed on the smartphone.

[1051] Generating and presenting an action plan:

[1052] Based on the information gathered through the dialogue session, the server uses generative AI to generate a user-specific action plan. This generated action plan is then presented to the user via a smartphone application.

[1053] After-sales support:

[1054] The server generates periodic reminders and sends notifications to the user's smartphone. The user inputs the progress of their action plan, and this data is sent to the server. If necessary, a generative AI re-analyzes the data and suggests additional dialogue or action plans.

[1055] Specific example

[1056] For example, if user A is suffering from work-related stress, the following prompt message will be generated.

[1057] Example of a prompt:

[1058] Specific problems related to work stress include:

[1059] Lately, I've been having trouble with my boss at work. Specifically, my boss completely ignores my opinions.

[1060] Please provide advice on this issue.

[1061] Based on this prompt, the generative AI analyzes and responds, providing user A with interactive support. Ultimately, user A is given actionable plans (e.g., scheduling a one-on-one meeting with their supervisor) and follow-up support based on those plans.

[1062] This allows users to receive immediate and appropriate feedback on their concerns, ensuring continuous support.

[1063] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1064] Step 1:

[1065] User Registration

[1066] The user launches the smartphone application and enters basic information (name, email address, password). The device sends this input data to the server. The server stores the received information in its database and generates a registration completion notification. The server sends the registration completion notification to the device, and the device displays a completion message to the user.

[1067] Input: Name, email address, password

[1068] Data processing: Save input data to a database.

[1069] Output: Registration complete notification

[1070] Step 2:

[1071] Generate and receive questionnaire forms.

[1072] The server generates a questionnaire form based on stored basic information. This questionnaire form includes questions for the user to input the category of their concern and specific problems. The questionnaire form is sent to the terminal, and the user fills it out. The answers entered by the user are sent to the server via the terminal.

[1073] Input: User's basic information

[1074] Data processing: Generating a medical questionnaire form

[1075] Output: Response data from the medical questionnaire

[1076] Step 3:

[1077] Analysis of response data

[1078] The server passes the responses from the received questionnaire form to a generative artificial intelligence (AI) system for analysis. The AI ​​then generates an appropriate dialogue scenario based on the response data. This scenario is specifically designed to address the user's concerns.

[1079] Input: Response data from the medical questionnaire

[1080] Data processing: Analysis using generative artificial intelligence

[1081] Output: Dialogue Scenario

[1082] Step 4:

[1083] Executing a dialogue session

[1084] The server sends the generated dialogue scenario to the terminal. The terminal displays a dialogue screen based on the dialogue scenario and begins interacting with the user. User input is sent to the server through the terminal, and the server uses generative artificial intelligence to generate an appropriate response, which is then sent to the terminal for display.

[1085] Input: Dialogue scenario, user dialogue input

[1086] Data processing: Response generation using generative artificial intelligence.

[1087] Output: Response to the user

[1088] Step 5:

[1089] Generating an action plan

[1090] Based on the information obtained through the dialogue session, the server uses generative artificial intelligence to generate a user-specific action plan. The generated action plan is sent to the terminal and presented to the user.

[1091] Input: Dialogue content

[1092] Data processing: Generation of action plans using generative artificial intelligence.

[1093] Output: Action Plan

[1094] Step 6:

[1095] After-sales support

[1096] The server generates periodic reminders to follow up on the progress of the action plan. These reminders are sent to the device, and the user enters the progress. The entered data is sent to the server, where generative artificial intelligence re-analyzes it and suggests additional dialogue or action plans as needed.

[1097] Input: Action plan, user progress input

[1098] Data processing: Reminder generation and reanalysis using generative artificial intelligence.

[1099] Output: Reminder notifications and additional interaction / action plans

[1100] Through these steps, users can receive prompt and appropriate support for their physical and mental health concerns, and ongoing follow-up is also possible.

[1101] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1102] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The system's processing is described below in natural language.

[1103] System Program Overview

[1104] User Registration

[1105] 1. The user accesses the system and opens a website or app.

[1106] 2. The device displays a new registration form to the user.

[1107] 3. The user enters basic information such as their name, email address, and password, and then presses the submit button.

[1108] 4. The terminal sends the entered basic information to the server.

[1109] 5. The server saves the basic information it receives to the database.

[1110] 6. The server generates a registration completion message and sends it to the terminal.

[1111] 7. The device displays a registration completion message to the user.

[1112] Initial consultation

[1113] 1. After user registration is complete, the device will display a medical questionnaire form to the user.

[1114] 2. The user selects a category of their problem (for example, work stress) and enters their specific problem.

[1115] 3. The terminal sends the user's input to the server.

[1116] 4. The server sends the received data to the generative artificial intelligence.

[1117] 5. Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[1118] 6. The server sends the constructed dialogue scenario to the terminal.

[1119] Dialogue session

[1120] 1. The device displays a dialogue screen to the user.

[1121] 2. The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[1122] 3. The user enters a specific answer to the question.

[1123] 4. The device sends user input to the emotion engine, which then analyzes the emotions.

[1124] 5. The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1125] 6. The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[1126] 7. The server sends the generated response to the terminal.

[1127] 8. The device displays a response to the user.

[1128] Generating an action plan

[1129] 1. The server records the contents of the conversation session.

[1130] 2. Generative artificial intelligence generates an optimal action plan for the user based on the conversation content and emotion analysis results.

[1131] 3. The server sends the generated action plan to the terminal.

[1132] 4. The device displays an action plan to the user.

[1133] 5. The user reviews the action plan and approves to proceed to the next step.

[1134] After-sales support

[1135] 1. The server generates reminders to follow up on the progress of the action plan.

[1136] 2. The device periodically sends reminder notifications to the user.

[1137] 3. The user enters the progress of the action plan.

[1138] 4. The terminal sends the user's input results to the server.

[1139] 5. The server sends the input results to the emotion engine for sentiment analysis.

[1140] 6. The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[1141] 7. The server sends any additional dialogue or action plan generated to the terminal.

[1142] 8. The device displays additional dialogue or action plans to the user.

[1143] Specific example

[1144] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[1145] User Registration

[1146] 1. User A enters basic information on the terminal and presses the registration button.

[1147] 2. The entered information is sent to the server and stored in the database.

[1148] 3. A registration completion message is displayed to user A.

[1149] Initial consultation

[1150] 1. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[1151] 2. The input data is sent to the server, and a generative artificial intelligence performs the analysis.

[1152] 3. A dialogue scenario is created and sent to the terminal.

[1153] Dialogue session

[1154] 1. The device displays the question, "What specific problem are you experiencing?"

[1155] 2. User A responded, "My boss doesn't listen to my opinions at all."

[1156] 3. The device sends its response to the emotion engine, which then analyzes the emotion.

[1157] 4. The server sends the results of the emotion engine to the generative artificial intelligence.

[1158] 5. The generative AI generates an emotionally conscious response: "That sounds tough. Do you have an opportunity to talk to your boss about it?"

[1159] 6. The response is sent to the terminal and displayed to User A.

[1160] Generating an action plan

[1161] 1. The server records the conversation content and sentiment analysis results, and generates an action plan such as, "Let's schedule a one-on-one meeting with your boss."

[1162] 2. The action plan is displayed on the device, and User A approves it.

[1163] After-sales support

[1164] 1. The server generates a reminder, and the device sends a notification to user A one week later.

[1165] 2. User A enters the results of the meeting and reports, "I discussed it with my supervisor, but it hasn't improved yet."

[1166] 3. The emotion engine re-analyzes the emotions in the input, and the generative AI generates an additional action plan: "Next time, let's practice expressing your feelings honestly together."

[1167] 4. An additional action plan is sent to the device and displayed to User A.

[1168] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[1169] The following describes the processing flow.

[1170] User Registration

[1171] Step 1:

[1172] The user opens a website or app to access the system.

[1173] Step 2:

[1174] The device displays a new registration form to the user.

[1175] Step 3:

[1176] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[1177] Step 4:

[1178] The terminal sends the entered basic information to the server.

[1179] Step 5:

[1180] The server saves the basic information it receives to the database.

[1181] Step 6:

[1182] The server generates a registration completion message and sends it to the terminal.

[1183] Step 7:

[1184] The device displays a registration completion message to the user.

[1185] Initial consultation

[1186] Step 1:

[1187] After user registration is complete, the device will display a medical questionnaire form to the user.

[1188] Step 2:

[1189] The user selects a category for their problem and enters their specific problem.

[1190] Step 3:

[1191] The terminal sends the user's input to the server.

[1192] Step 4:

[1193] The server sends the received data to a generative artificial intelligence.

[1194] Step 5:

[1195] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[1196] Step 6:

[1197] The server sends the constructed dialogue scenario to the terminal.

[1198] Dialogue session

[1199] Step 1:

[1200] The device displays a dialogue screen to the user.

[1201] Step 2:

[1202] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[1203] Step 3:

[1204] The user enters a specific answer to the question.

[1205] Step 4:

[1206] The device sends user input to an emotion engine, which then analyzes the emotions.

[1207] Step 5:

[1208] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1209] Step 6:

[1210] The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[1211] Step 7:

[1212] The server sends the generated response to the terminal.

[1213] Step 8:

[1214] The device displays a response to the user.

[1215] Generating an action plan

[1216] Step 1:

[1217] The server records the contents of the conversation session.

[1218] Step 2:

[1219] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation and the results of emotion analysis.

[1220] Step 3:

[1221] The server sends the generated action plan to the terminal.

[1222] Step 4:

[1223] The device displays an action plan to the user.

[1224] Step 5:

[1225] The user reviews the action plan and approves to proceed to the next step.

[1226] After-sales support

[1227] Step 1:

[1228] The server generates reminders to follow up on the progress of the action plan.

[1229] Step 2:

[1230] The device periodically sends reminder notifications to the user.

[1231] Step 3:

[1232] The user enters the progress of the action plan.

[1233] Step 4:

[1234] The terminal sends the user's input results to the server.

[1235] Step 5:

[1236] The server sends the input results to the emotion engine, which then performs emotion analysis.

[1237] Step 6:

[1238] The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[1239] Step 7:

[1240] The server sends any additional dialogue or action plans it has generated to the terminal.

[1241] Step 8:

[1242] The device displays additional dialogues or action plans to the user.

[1243] Specific example

[1244] User Registration

[1245] Step 1:

[1246] User A accesses the system and opens a website or app.

[1247] Step 2:

[1248] The terminal displays a new registration form to user A.

[1249] Step 3:

[1250] User A enters basic information such as their name, email address, and password, and then presses the submit button.

[1251] Step 4:

[1252] The terminal sends the entered information to the server.

[1253] Step 5:

[1254] The server saves the received information to the database.

[1255] Step 6:

[1256] The server generates a registration completion message and sends it to the terminal.

[1257] Step 7:

[1258] The device displays a registration completion message to user A.

[1259] Initial consultation

[1260] Step 1:

[1261] After User A completes registration, the terminal displays a medical questionnaire form to User A.

[1262] Step 2:

[1263] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[1264] Step 3:

[1265] The terminal sends the input from user A to the server.

[1266] Step 4:

[1267] The server sends the received data to a generative artificial intelligence.

[1268] Step 5:

[1269] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[1270] Step 6:

[1271] The server sends the constructed dialogue scenario to the terminal.

[1272] Dialogue session

[1273] Step 1:

[1274] The terminal displays a dialogue screen to user A.

[1275] Step 2:

[1276] The generative artificial intelligence displays the question, "What specific problems are you experiencing?" based on the dialogue scenario.

[1277] Step 3:

[1278] User A responds, "My boss doesn't listen to my opinions at all."

[1279] Step 4:

[1280] The device sends User A's response to the emotion engine, which then analyzes the emotion.

[1281] Step 5:

[1282] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1283] Step 6:

[1284] The generative artificial intelligence generates a response that takes into account the sentiment analysis results, such as "That's tough. Do you have a chance to talk to your boss?", and sends it to the server.

[1285] Step 7:

[1286] The server sends the generated response to the terminal.

[1287] Step 8:

[1288] The terminal displays a response to user A.

[1289] Generating an action plan

[1290] Step 1:

[1291] The server records the conversation content and the results of the sentiment analysis.

[1292] Step 2:

[1293] Generative artificial intelligence generates an action plan that says, "Let's schedule a one-on-one meeting with your boss."

[1294] Step 3:

[1295] The server sends the generated action plan to the terminal.

[1296] Step 4:

[1297] The device displays an action plan to user A.

[1298] Step 5:

[1299] User A approves of proceeding to the next step.

[1300] After-sales support

[1301] Step 1:

[1302] The server generates a reminder, and the device sends a notification to user A one week later.

[1303] Step 2:

[1304] User A enters, "I spoke with my supervisor, but the situation hasn't improved yet."

[1305] Step 3:

[1306] The terminal sends the input result of user A to the server.

[1307] Step 4:

[1308] The server sends the input results to the emotion engine for emotion analysis.

[1309] Step 5:

[1310] The analysis results from the emotion engine are sent to the generative artificial intelligence.

[1311] Step 6:

[1312] The generative artificial intelligence generates an additional action plan, "Next, let's practice expressing your feelings honestly," and sends it to the server.

[1313] Step 7:

[1314] The server sends any additional dialogue or action plans it has generated to the terminal.

[1315] Step 8:

[1316] The device displays additional dialogue and action plans to user A.

[1317] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[1318] (Example 2)

[1319] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1320] In modern society, users constantly face a variety of mental and physical health issues, yet there is a lack of systems that provide effective and continuous support for these issues. Furthermore, it is difficult to provide appropriate dialogue and action plans tailored to the user's emotional state. As a result, users have difficulty accurately understanding their situation and receiving concrete advice for improvement. Therefore, there is a need for a system that analyzes the user's emotional state and provides dialogue and action plans based on that analysis.

[1321] The specific processing performed 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 having the user input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving answers to the medical questionnaire form from the user, means for analyzing the received answers to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for conducting a dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means including an emotion engine for analyzing the user's emotions, means for using a generative artificial intelligence that generates a response based on the emotion analysis results, and means for reflecting the emotion analysis results in the dialogue scenario. This makes it possible to provide effective and continuous support to the user regarding their specific concerns, taking into account their emotional state.

[1322] "User" refers to an individual or group that uses the system.

[1323] "Basic information" refers to information such as name, email address, and password that users enter when registering with the system.

[1324] "To remember" refers to saving entered data to storage such as a database.

[1325] A "medical questionnaire form" refers to an input form used to collect information about a user's concerns or problems.

[1326] "Receiving" refers to the server or system acquiring information entered by the user.

[1327] "Analyzing" refers to processing received data and extracting meaning and patterns.

[1328] A "dialogue scenario" refers to a scenario generated based on analysis results to facilitate interaction with the user.

[1329] "Engaging in dialogue" refers to the exchange of information between the user and the system in a question-and-answer format.

[1330] "Recording" refers to saving the content of the conversation and the analysis results to a database or similar system.

[1331] An "action plan" refers to specific measures and action plans to address a user's problems.

[1332] "Following up" means tracking the progress of the action plan and providing additional support as needed.

[1333] An "emotion engine" refers to software that analyzes user input to identify emotions and their emotional state.

[1334] "Generative artificial intelligence" refers to artificial intelligence models that generate responses and scenarios based on user input data and sentiment analysis results.

[1335] A "reminder" refers to a function that notifies users of the progress of action plans and follow-ups.

[1336] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The following describes specific embodiments of the system.

[1337] First, the entire system consists of a terminal that users access, a server that processes data, an emotion engine that performs sentiment analysis, and a generative artificial intelligence system.

[1338] User Registration

[1339] The user accesses the system and opens a website or app. The user's device displays a new registration form, and the user enters and submits basic information (name, email address, password, etc.). The device sends the entered basic information to the server, which stores the information in its database. The server generates a registration completion message, sends it to the device, and displays it to the user.

[1340] Initial consultation

[1341] After user registration is complete, the device automatically displays an initial consultation form. The user enters the category of their problem (e.g., "work stress") and specific details. The device sends the entered data to the server, which then sends that data to a generative artificial intelligence (AI). The AI ​​analyzes the data and generates an appropriate dialogue scenario. The server sends the dialogue scenario to the device, preparing to begin the conversation with the user.

[1342] Dialogue session

[1343] The user's device displays a dialogue screen based on a generated dialogue scenario. Generative artificial intelligence generates appropriate questions and sends them to the device, displaying the questions to the user. The user enters their answers, and these answers are sent from the device to the emotion engine. The emotion engine analyzes the user's emotions and sends the results to the server. The server sends the emotion analysis results to the generative artificial intelligence, which generates an appropriate response. The generated response is sent to the device and displayed to the user.

[1344] Generating an action plan

[1345] The content of the dialogue session is recorded on the server, and a generative artificial intelligence generates an optimal action plan based on the dialogue content and sentiment analysis results. The server sends the generated action plan to the terminal, and the terminal displays the action plan to the user. The user reviews the action plan and approves to proceed to the next step.

[1346] After-sales support

[1347] The server generates periodic reminders to follow up on the progress of the action plan. The device sends a reminder notification to the user, who then enters the progress of the action plan. The entered results are sent to the server and analyzed again by the emotion engine. The analysis results are sent to the generative artificial intelligence, which generates additional dialogues and action plans as needed. These are then sent back from the server to the device and displayed to the user.

[1348] Specific example

[1349] For example, if user A is suffering from "work stress," the following process will occur:

[1350] 1. User A enters basic information and registers it in the system.

[1351] 2. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[1352] 3. The input data is sent to the generative artificial intelligence, and the question "What specific problems are you experiencing?" is generated.

[1353] 4. When User A responds, "My boss doesn't listen to my opinions at all," the emotion engine analyzes that emotion and generates the response, "That's tough. Do you have opportunities to talk to your boss?"

[1354] 5. Based on the dialogue content and emotion analysis results, an action plan is generated and presented to User A: "Let's schedule a one-on-one meeting with your supervisor."

[1355] 6. To follow up on the progress of the action plan, the server generates a reminder and a notification is sent from the terminal. User A inputs the meeting results, which are then re-analyzed to generate new dialogues and action plans.

[1356] Example of a prompt

[1357] The following prompt is input to the generative artificial intelligence:

[1358] "User A is seeking advice about work-related stress, stating, 'My boss doesn't listen to my opinions at all.' Please suggest what questions should be asked next, taking into account the user's emotional state."

[1359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1360] Step 1: The user accesses the system and opens a website or app.

[1361] The user accesses the system's website or app via the internet. Input here involves the user entering the URL of the website or app, or launching the app. Output is the display of a new registration form.

[1362] Step 2: The device displays the new registration form.

[1363] The device displays the form required for new registration (fields for name, email address, and password). The input is the user's basic information, and the output is the form with the basic information ready to be entered.

[1364] Step 3: The user enters their basic information and presses the submit button.

[1365] The user enters basic information such as their name, email address, and password into the registration form and clicks the submit button. The input is the user's basic information, and the output is the event that sends this basic information.

[1366] Step 4: The terminal sends the entered basic information to the server.

[1367] The terminal sends basic information to the server using the HTTPS protocol. The input is the basic information entered by the user, and the output is the result of the transmission to the server.

[1368] Step 5: The server saves the basic information it received to the database.

[1369] The server records the received basic information in a database (e.g., MySQL). The input is the transmitted basic information, and the output is the result of saving it to the database. Passwords are hashed (e.g., bcrypt) for security purposes before being stored.

[1370] Step 6: The server generates a registration completion message and sends it to the terminal.

[1371] The server generates a registration completion message and sends it to the terminal. The input is the confirmation result of successful saving, and the output is the generation and sending of the completion message.

[1372] Step 7: The device displays a registration completion message to the user.

[1373] The terminal displays a registration completion message received from the server to the user. The input is the message from the server, and the output is what is displayed to the user.

[1374] Step 8: After user registration is complete, the device will display the medical questionnaire form.

[1375] After user registration is complete, the device displays a medical questionnaire form as the next step. The input is the registration completion event, and the output is the display of the medical questionnaire form.

[1376] Step 9: The user selects a category for their problem and enters their specific problem.

[1377] The user selects a category such as "work stress" in a questionnaire form and enters specific concerns such as "I'm having trouble getting along with my boss at work lately." Input consists of user selections and text input, while output is confirmation of the input and a submission event.

[1378] Step 10: The terminal sends the user's input to the server.

[1379] The terminal sends user input to the server via the HTTPS protocol. The input is the user's responses to a questionnaire, and the output is the result of the transmission to the server.

[1380] Step 11: The server sends the received data to the generative artificial intelligence.

[1381] The server receives the medical interview data and sends it to a generative artificial intelligence (e.g., OpenAI GPT-3). The input is the medical interview data, and the output is a successful transmission to the generative AI.

[1382] Step 12: Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[1383] Generative artificial intelligence analyzes user input data and constructs appropriate dialogue scenarios. For example, it generates questions such as, "Please tell me more about your relationship with your boss." The input is questionnaire data, and the output is a dialogue scenario.

[1384] Step 13: The server sends the constructed dialogue scenario to the terminal.

[1385] The server sends the dialogue scenario received from the generative artificial intelligence to the user's terminal. The input is the dialogue scenario, and the output is the result of the transmission to the terminal.

[1386] Step 14: The device displays a dialogue screen to the user.

[1387] The terminal displays a dialogue screen and initiates a conversation with the user based on the generated dialogue scenario. The input is the dialogue scenario, and the output is the display of the dialogue screen.

[1388] Step 15: The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[1389] A generative artificial intelligence generates appropriate questions, sends them to a terminal, and the terminal displays those questions to the user. The input is the dialogue scenario, and the output is the question displayed to the user.

[1390] Step 16: The user enters a specific answer to the question.

[1391] The user enters a specific answer based on the question. For example, they might enter, "My boss never listens to my opinion." The input is the user's answer, and the output is the entered text.

[1392] Step 17: The device sends user input to the emotion engine, which analyzes the emotions.

[1393] The terminal sends user input to an emotion engine (e.g., IBM Watson Tone Analyzer) for emotion analysis. The input is the user's text response, and the output is the emotion analysis result.

[1394] Step 18: The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1395] The server sends the results of the emotion engine's analysis to the generative artificial intelligence. The input is the emotion analysis result, and the output is the result sent to the generative artificial intelligence.

[1396] Step 19: The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[1397] The generative artificial intelligence considers the emotion analysis results and generates an appropriate response. A response such as "That's tough. Do you have a chance to talk to your supervisor?" is generated. The input is the emotion analysis results, and the output is the generated response.

[1398] Step 20: The server sends the generated response to the terminal.

[1399] The server sends the response received from the generative artificial intelligence to the user's terminal. The input is the generated response, and the output is the result of the transmission to the terminal.

[1400] Step 21: The terminal displays a response to the user.

[1401] The terminal displays the generated response to the user. The input is the response message from the server, and the output is the response displayed to the user.

[1402] Step 22: The server records the contents of the conversation session.

[1403] The server records the contents of all dialogue sessions in a database. The input is the dialogue between the user and the generative artificial intelligence, and the output is the success of the recording.

[1404] Step 23: The generative artificial intelligence generates the optimal action plan for the user based on the conversation content and emotion analysis results.

[1405] A generative artificial intelligence generates action plans such as "Let's schedule a one-on-one meeting with your boss" based on recorded dialogue content and emotion analysis results. The input is the dialogue content and emotion analysis results, and the output is the generated action plan.

[1406] Step 24: The server sends the generated action plan to the terminal.

[1407] The server sends the generated action plan to the user's terminal. The input is the generated action plan, and the output is the result of sending it to the terminal.

[1408] Step 25: The device displays the action plan to the user.

[1409] The terminal displays the action plan it received to the user. The input is the action plan from the server, and the output is the action plan displayed to the user.

[1410] Step 26: The user reviews the action plan and approves to proceed to the next step.

[1411] The user reviews the displayed action plan and clicks the approve button. The input is the action plan and its confirmation, and the output is the approval event.

[1412] Step 27: The server generates reminders to follow up on the progress of the action plan.

[1413] The server generates periodic reminder notifications to follow up on the action plan. The input is the action plan and its progress, and the output is the generated reminders.

[1414] Step 28: The device periodically sends reminder notifications to the user.

[1415] The device notifies the user of reminder notifications sent from the server at the appropriate time. The input is the reminder from the server, and the output is the notification to the user.

[1416] Step 29: The user enters the progress of the action plan.

[1417] After the user receives a reminder, they enter the progress of the action plan. The input is the user's status report, and the output is the entered progress details.

[1418] Step 30: The terminal sends the user's input to the server.

[1419] The terminal sends user input to the server. The input is the progress, and the output is the result of the transmission to the server.

[1420] Step 31: The server sends the input results to the emotion engine for sentiment analysis.

[1421] The server sends progress information to the emotion engine, which then performs the latest emotion analysis. The input is the progress information, and the output is the emotion analysis result.

[1422] Step 32: The server sends the results of the emotion engine's analysis to the generative artificial intelligence to generate additional dialogue and action plans.

[1423] The generative artificial intelligence generates new dialogues or action plans as needed, based on the emotion analysis results. The input is the emotion analysis results, and the output is the added dialogue or action plan.

[1424] Step 33: The server sends any additional dialogue or action plan that has been generated to the terminal.

[1425] The server sends the newly generated dialogue and action plan to the terminal. The input is the generated dialogue and action plan, and the output is the result of sending it to the terminal.

[1426] Step 34: The device displays additional dialogue or action plans to the user.

[1427] The terminal displays newly generated dialogues and action plans to the user. The input is the dialogues and action plans from the server, and the output is what is displayed to the user.

[1428] (Application Example 2)

[1429] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1430] Improper management of employees' physical and mental health can lead to fatigue, stress accumulation, and decreased motivation, negatively impacting productivity and safety. To address this challenge, a system is needed that can monitor employees' physical and mental health in real time and provide appropriate support.

[1431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1432] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means for performing sentiment analysis of the dialogue content, and means for adjusting the dialogue scenario and action plan considering the sentiment analysis results. This makes it possible to grasp the mental and physical state of employees in real time and provide appropriate support.

[1433] "Basic information" refers to data such as the user's name, email address, and password.

[1434] "To remember" means for a server to store information entered by a user in a database or similar location.

[1435] A "medical questionnaire form" is a sheet of questions used by users to record their physical and mental condition and concerns.

[1436] "Receiving" means that the server obtains input information from the user.

[1437] "To analyze" means to understand and analyze the content based on the received data.

[1438] "Generating" means creating new dialogue scenarios, action plans, and other elements based on the analysis results.

[1439] A "dialogue scenario" refers to a series of questions and answers exchanged between a user and a system.

[1440] "Engaging in dialogue" means that a system exchanges information with a user through conversation.

[1441] An "action plan" is a specific plan for a user to initiate a designated action.

[1442] "Following up" means that the system periodically checks the user's progress and status, and provides advice and support as needed.

[1443] "Emotional analysis" is the process of inferring and analyzing a user's emotional state from their statements and input data.

[1444] "Emotional analysis results" refer to data on emotional states obtained based on emotional analysis.

[1445] "Adjusting" means changing the content of the dialogue scenario or action plan depending on the situation.

[1446] This invention relates to a factory robot that analyzes the physical and mental state of employees in real time within a factory and provides appropriate support. The system collects and analyzes basic information from the user (employee) and provides individualized dialogue and action plans.

[1447] System Program Overview

[1448] User Registration

[1449] The server receives basic information entered by the user (such as name, email address, and password) and stores it in a database. The user then enters this information via a tablet on a factory robot.

[1450] Initial consultation

[1451] Once user registration is complete, the server generates a questionnaire form for the user and displays it on the robot's tablet. The user enters a category (e.g., stress management) and a specific problem (e.g., "I'm exhausted from having too much work"), and this information is sent to the server.

[1452] Dialogue session

[1453] The server uses generative artificial intelligence to analyze the responses to the questionnaire form and construct a dialogue scenario. Once the dialogue scenario is constructed, the robot displays specific questions to the user. The user's responses are analyzed for emotion in real time, and the generative artificial intelligence then generates the next response, taking the emotion analysis results into consideration.

[1454] Generating an action plan

[1455] The server generates an optimal action plan for the user based on the content of the dialogue session and the results of the emotion analysis. This action plan is displayed on the robot's tablet and presented to the user.

[1456] After-sales support

[1457] The server generates reminders to follow up on the progress of the action plan and sends notifications to the user periodically. The user inputs the progress, which is then subjected to sentiment analysis again. The server uses generative artificial intelligence to generate additional dialogues and action plans, which are then provided to the user.

[1458] Hardware and software to use

[1459] This system requires a factory robot (with a tablet), a server, an emotion engine (EmotionEngine), and generative artificial intelligence (ChatbotAI). The server stores basic information from the user and answers to questionnaire forms in a database, and uses generative AI to analyze the data, build dialogue scenarios, and perform emotion analysis. This makes it possible to understand the user's mental and physical state in real time and provide appropriate support.

[1460] Specific example

[1461] Next, I will describe a specific example of this system.

[1462] User registration example

[1463] The user enters basic information on the robot's tablet and presses the registration button. This information is sent to the server and stored in the database. A registration completion message is displayed on the tablet.

[1464] Initial consultation example

[1465] The user selects the "Stress Management" category and enters "I've been really tired lately because I have so much work." This input data is sent to the server, where a generative artificial intelligence analyzes it. A dialogue scenario is then constructed and displayed on the tablet.

[1466] Example prompts for a dialogue session

[1467] User response: "Lately, I feel like my boss doesn't understand me."

[1468] Server response: "That sounds tough. What exactly are the problems you're experiencing?"

[1469] Example of action plan generation

[1470] The server records the conversation (e.g., "My boss doesn't understand me") and generates an action plan such as "Let's practice talking directly to your boss." This is then displayed on the tablet.

[1471] Examples of after-sales support

[1472] The server generates reminders to check progress and sends periodic notifications to the user. The user types, "I spoke with my boss, but it hasn't improved yet." This is then analyzed for sentiment again, generating additional dialogue and action plans, which are displayed on the tablet.

[1473] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1474] Program processing flow

[1475] Step 1: User Registration

[1476] The server stores the basic information (name, email address, etc.) entered by the user in a database.

[1477] Input: The user enters basic information.

[1478] Data processing: Validate the input data.

[1479] Output: Basic information is saved in the database.

[1480] Specific operation: The user enters basic information into the robot's tablet and presses the submit button. The server receives this information and saves it to the database.

[1481] Step 2: Generate the medical questionnaire form

[1482] The server generates a medical questionnaire form based on the stored basic information.

[1483] Input: Basic user information stored on the server.

[1484] Data processing: Generate appropriate questionnaire items based on the user's basic information.

[1485] Output: The generated questionnaire form is displayed on the robot's tablet.

[1486] Specific operation: The server generates a medical questionnaire form based on basic information and sends it to the robot's tablet.

[1487] Step 3: Receiving responses to the medical questionnaire

[1488] The server receives the user's responses to the medical questionnaire form.

[1489] Input: The user enters their answers to the medical questionnaire form.

[1490] Data processing: Receive and validate the entered response data.

[1491] Output: The response data is saved on the server.

[1492] Specific operation: The user enters their answers to a questionnaire form on a tablet and presses the submit button. The server receives this and saves it to the database.

[1493] Step 4: Analysis of the questionnaire form

[1494] The server analyzes the responses to the received medical questionnaire using a generating AI model.

[1495] Input: Data from the medical questionnaire.

[1496] Data processing: Data analysis is performed using a generative AI model.

[1497] Output: Analysis results are generated.

[1498] Specific operation: The server sends the received response to the generating AI model and obtains the analysis result.

[1499] Step 5: Generating dialogue scenarios

[1500] The server generates dialogue scenarios based on the analysis results.

[1501] Input: Analysis results from the medical questionnaire form.

[1502] Data processing: Construct dialogue scenarios using a generative AI model.

[1503] Output: A dialogue scenario is generated.

[1504] Specific operation: The server generates a dialogue scenario using an AI model based on the analysis results and sends it to the tablet.

[1505] Step 6: Conducting a dialogue session

[1506] The server and robot interact with the user based on the generated dialogue scenario.

[1507] Input: Dialogue scenario.

[1508] Data processing: Analyze user responses using an emotion engine.

[1509] Output: A response based on the sentiment analysis results is generated.

[1510] Specific operation: A question from the conversational scenario is displayed on the tablet, and the user enters an answer. The emotion engine analyzes the answer, and the server generates an appropriate response and displays it on the tablet.

[1511] Step 7: Record the conversation

[1512] The server records the contents of the dialogue session.

[1513] Input: Dialogue content data.

[1514] Data processing: Save the conversation content to the database.

[1515] Output: The conversation content is recorded.

[1516] Specific operation: The server saves the results of the dialogue session to the database.

[1517] Step 8: Generate an action plan

[1518] The server generates an action plan based on the interaction.

[1519] Input: Dialogue content data and sentiment analysis results.

[1520] Data processing: Develop action plans using generative AI models.

[1521] Output: An action plan is generated.

[1522] Specific operation: The server generates an action plan using a generative AI model based on the conversation content and sentiment analysis results, and displays it on the tablet.

[1523] Step 9: Present an action plan

[1524] The server presents the generated action plan to the user.

[1525] Input: Action plan.

[1526] Data processing: Display the action plan on the tablet.

[1527] Output: The action plan is displayed on the tablet.

[1528] Specific operation: The server sends the generated action plan to the tablet, and the user confirms it.

[1529] Step 10: Follow up on the progress of the action plan

[1530] The server generates reminders to follow up on the progress of the action plan.

[1531] Input: Action plan and progress data.

[1532] Data processing: Generate a reminder and send it to the tablet.

[1533] Output: A reminder is sent to the user.

[1534] Specific operation: The server monitors the progress, generates a reminder, and sends it to the user's tablet. The user enters the progress, which is then analyzed for sentiment again.

[1535] Step 11: Generating additional dialogues and action plans

[1536] The server generates additional dialogue and action plans based on the sentiment analysis results.

[1537] Input: Progress data and sentiment analysis results.

[1538] Data processing: Generative AI models are used to build additional dialogues and action plans.

[1539] Output: Additional dialogues and action plans are generated.

[1540] Specific operation: The server uses a generative AI model based on progress data and sentiment analysis results to generate additional dialogue and action plans, which are then displayed on the tablet.

[1541] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1542] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1543] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1544] [Third Embodiment]

[1545] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1546] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1547] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1548] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1549] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1550] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1551] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1552] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1553] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1554] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1555] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1556] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1557] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system's processing is described below in natural language.

[1558] System Program Overview

[1559] 1. User Registration

[1560] In order for a user to use the system, they must first register.

[1561] The terminal prompts the user to enter basic information (name, email address, password, etc.).

[1562] The terminal sends the entered basic information to the server.

[1563] The server saves the user's basic information to a database and sends a registration completion notification to the device.

[1564] The device displays a registration completion message to the user.

[1565] 2. Initial consultation

[1566] After user registration is complete, the device will display a medical questionnaire form to the user.

[1567] The user selects a category of their problem (for example, work stress) and enters their specific problem.

[1568] The terminal sends user input to the server.

[1569] The server receives the input data and passes it to the generative artificial intelligence for analysis.

[1570] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[1571] 3. Dialogue Session

[1572] The server sends the generated dialogue scenario to the terminal.

[1573] The device displays a dialogue screen and initiates a conversation with the generative artificial intelligence for the user.

[1574] The terminal sends user input to the server, which then uses generative artificial intelligence to generate an appropriate response.

[1575] The response is sent to the terminal and displayed to the user.

[1576] 4. Generating an action plan

[1577] The server records and analyzes the information obtained through the dialogue session.

[1578] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation.

[1579] The server sends the generated action plan to the terminal and displays it to the user.

[1580] The user reviews the action plan and proceeds to the next step.

[1581] 5. After-sales support

[1582] The server generates periodic reminders to follow up on the progress of the action plan.

[1583] The device sends a reminder notification to the user, and the user enters the progress of the action plan.

[1584] The server receives the input results, a generative artificial intelligence analyzes them, and proposes additional dialogue or action plans as needed.

[1585] Specific example

[1586] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[1587] User Registration

[1588] User A enters basic information on the terminal and presses the registration button.

[1589] The entered information is sent to the server and stored in the database.

[1590] A registration completion message is displayed to user A.

[1591] Initial consultation

[1592] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[1593] The input data is sent to the server, where a generative artificial intelligence performs the analysis.

[1594] A dialogue scenario is created and sent to the terminal.

[1595] Dialogue session

[1596] Based on the dialogue scenario sent from the server, the terminal displays the question, "What exactly is the problem?"

[1597] User A responded, "My boss doesn't listen to my opinions at all."

[1598] The generative artificial intelligence analyzed the message and generated the response, "That sounds tough. Do you have an opportunity to talk to your supervisor?"

[1599] The response is displayed to user A.

[1600] Generating an action plan

[1601] The server records the conversation, and a generative artificial intelligence generates an action plan: "Let's schedule a one-on-one meeting with your boss."

[1602] The action plan is displayed on the device, and User A accepts it.

[1603] After-sales support

[1604] The server generates a reminder based on the action plan, and the device sends a notification one week later.

[1605] User A entered the results of the meeting and reported, "I discussed it with my supervisor, but it hasn't improved yet."

[1606] The generative artificial intelligence re-analyzed the text and suggested, "Next, let's practice expressing your feelings honestly together."

[1607] In this way, the system addresses users' concerns step by step, providing attentive support and continuous follow-up.

[1608] The following describes the processing flow.

[1609] User Registration

[1610] Step 1:

[1611] The user opens a website or app to access the system.

[1612] Step 2:

[1613] The device displays a new registration form to the user.

[1614] Step 3:

[1615] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[1616] Step 4:

[1617] The terminal sends the entered basic information to the server.

[1618] Step 5:

[1619] The server saves the basic information it receives to the database.

[1620] Step 6:

[1621] The server generates a registration completion message and sends it to the terminal.

[1622] Step 7:

[1623] The device displays a registration completion message to the user.

[1624] Initial consultation

[1625] Step 1:

[1626] After user registration is complete, the device will display a medical questionnaire form to the user.

[1627] Step 2:

[1628] The user selects a category for their problem and enters their specific problem.

[1629] Step 3:

[1630] The terminal sends the user's input to the server.

[1631] Step 4:

[1632] The server sends the received data to a generative artificial intelligence.

[1633] Step 5:

[1634] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[1635] Step 6:

[1636] The server sends the constructed dialogue scenario to the terminal.

[1637] Dialogue session

[1638] Step 1:

[1639] The device displays a dialogue screen to the user.

[1640] Step 2:

[1641] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[1642] Step 3:

[1643] The user enters a specific answer to the question.

[1644] Step 4:

[1645] The terminal sends user input to the server.

[1646] Step 5:

[1647] The server sends the user's input data to a generative artificial intelligence system and generates the following response.

[1648] Step 6:

[1649] The generative artificial intelligence analyzes the data, generates an appropriate response, and sends it to the server.

[1650] Step 7:

[1651] The server sends the generated response to the terminal.

[1652] Step 8:

[1653] The device displays a response to the user.

[1654] Generating an action plan

[1655] Step 1:

[1656] The server records the contents of the conversation session.

[1657] Step 2:

[1658] Generative artificial intelligence analyzes the conversation content and generates the optimal action plan for the user.

[1659] Step 3:

[1660] The server sends the generated action plan to the terminal.

[1661] Step 4:

[1662] The device displays an action plan to the user.

[1663] Step 5:

[1664] The user reviews the action plan and approves to proceed to the next step.

[1665] After-sales support

[1666] Step 1:

[1667] The server generates reminders to follow up on the progress of the action plan.

[1668] Step 2:

[1669] The device periodically sends reminder notifications to the user.

[1670] Step 3:

[1671] The user enters the progress of the action plan.

[1672] Step 4:

[1673] The terminal sends the user's input results to the server.

[1674] Step 5:

[1675] The server sends the input results to a generative artificial intelligence system for analysis.

[1676] Step 6:

[1677] Generative artificial intelligence analyzes the progress and generates additional dialogues and action plans as needed.

[1678] Step 7:

[1679] The server sends any additional dialogue or action plans it has generated to the terminal.

[1680] Step 8:

[1681] The device displays additional dialogues or action plans to the user.

[1682] As described above, the "LifeBridge" system provides gradual and continuous support for users' physical and mental health concerns.

[1683] (Example 1)

[1684] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1685] Conventional lifestyle platform systems have not provided sufficient, empathetic support for users' physical and mental health concerns, particularly inadequate, continuous follow-up. As a result, effective solutions to the problems users face may not be provided, potentially leading to decreased user satisfaction. Furthermore, the inability to effectively utilize artificial intelligence in dialogue and action plan generation could result in inconsistent support for users.

[1686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1687] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's answers to the medical questionnaire form, means for analyzing the received answers to the medical questionnaire form using generative artificial intelligence, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the content of the dialogue, means for generating an action plan based on the content of the dialogue, means for presenting the generated action plan to the user, means for generating and sending periodic reminders to the user to follow up on the progress of the action plan, and means for receiving progress information from the user, re-analyzing it, and proposing additional dialogues or action plans as necessary. This enables empathetic support and continuous follow-up for the user's physical and mental health concerns, thereby improving user satisfaction.

[1688] "Basic information" refers to personal information necessary to start using the system, such as the user's name, email address, and password.

[1689] A "database" is a digital storage device used to store and manage information such as a user's basic information, conversation history, and action plans.

[1690] A "medical questionnaire form" is a question-based input screen used to gain a detailed understanding of a user's physical and mental health concerns.

[1691] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to analyze input data and generate appropriate dialogue scenarios and action plans.

[1692] A "dialogue scenario" refers to the pre-conversation flow and questions created by generative artificial intelligence to facilitate smooth interaction with the user.

[1693] An "action plan" refers to specific measures and action plans proposed by a generative artificial intelligence system to resolve the user's mental and physical problems.

[1694] A "reminder" is a notification or message that is sent periodically to a user to check and input the progress of their action plan.

[1695] "Follow-up" refers to a series of activities and actions taken to check the progress of the action plan and provide additional support as needed.

[1696] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system requires a server, terminals, and a generative AI model to be implemented.

[1697] The system operates as follows: First, the user enters basic information using a terminal. The terminal has a function to display a form for entering information such as name, email address, and password. The terminal then sends the entered basic information to the server. The server saves the received basic information to a database and sends a registration completion notification to the terminal. The terminal displays this notification to the user.

[1698] Once user registration is complete, the device displays a questionnaire form to the user. This questionnaire includes areas where the user can select a category related to their mental or physical health concerns and enter their specific concerns. For example, the user might select the category "work stress" and enter, "Recently, my relationship with my boss hasn't been going well." The device then sends this input data to the server.

[1699] The server passes the received data to a generative artificial intelligence model. This generative AI model analyzes the input data and constructs an appropriate dialogue scenario. The generated dialogue scenario is sent from the server to the terminal and displayed to the user on the terminal.

[1700] Once a dialogue session begins, the server generates questions based on a pre-defined dialogue scenario. The user's input through the terminal is sent to the server, where a generative artificial intelligence analyzes it and generates an appropriate response. The generated response is displayed on the terminal, and the user responds to it. This dialogue process continues.

[1701] The entire content of the conversation session is recorded on the server. Based on the conversation, the server uses generative artificial intelligence to generate an appropriate action plan. For example, an action plan such as "Let's schedule a one-on-one meeting with your boss" is generated and displayed on the terminal. The user can review this action plan and proceed to the next step.

[1702] To track the progress of the action plan, the server generates periodic reminders. These reminder notifications are sent to the device, and the user receives them. The user then inputs the progress of the action plan according to the reminder and sends it from the device to the server. The server re-analyzes this progress information and, if necessary, uses generative artificial intelligence to suggest additional dialogues or action plans.

[1703] For example, if user A is suffering from work-related stress, the following dialogue may take place:

[1704] User A: "Lately, my relationship with my boss hasn't been going well."

[1705] Generative AI: "That sounds tough. Do you have a chance to talk to your supervisor about it?"

[1706] In this way, the system approaches users' problems step by step, providing attentive support and continuous follow-up.

[1707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1708] Step 1:

[1709] User registration (entering basic information)

[1710] The user enters basic information using a device. The device displays a form for entering basic information such as name, email address, and password. This form includes required fields for name, email address, and password. The entered data is sent from the device to the server.

[1711] Input: Name, email address, password

[1712] Output: Basic information is sent to the server.

[1713] Step 2:

[1714] Basic information saving

[1715] The server receives basic information sent from the terminal. The received basic information is stored in the database. The server notifies the terminal when the saving is complete.

[1716] Input: Basic Information

[1717] Output: Basic information is saved to the database, and a registration completion notification is sent to the device.

[1718] Step 3:

[1719] Display of registration completion message

[1720] The device receives a registration completion notification sent from the server. The device displays a registration completion message to the user. This allows the user to confirm that registration was successful.

[1721] Input: Registration completion notification

[1722] Output: A registration complete message is displayed to the user.

[1723] Step 4:

[1724] Display of the medical questionnaire form

[1725] After user registration is complete, the device displays a questionnaire form to the user. This questionnaire form includes a category selection field for mental and physical health concerns and a text box for entering specific concerns.

[1726] Input: None (A questionnaire form is displayed as a prompt)

[1727] Output: The medical questionnaire form is displayed to the user.

[1728] Step 5:

[1729] Enter category and problem

[1730] The user selects a category of their problem in a questionnaire form and enters their specific problem. For example, the user might select the category "Work Stress" and enter, "Recently, my relationship with my boss hasn't been going well." This information is sent from the device to the server.

[1731] Input: Category of problem and specific problem

[1732] Output: Category and problem information is sent to the server.

[1733] Step 6:

[1734] Data Analysis

[1735] The server provides the data from the received questionnaire form to a generative artificial intelligence model. The generative AI model analyzes the data and generates an appropriate dialogue scenario. For example, it might take a prompt such as "The user is suffering from work-related stress" and generate a scenario such as "Please tell me more."

[1736] Input: User's problem data

[1737] Output: Dialogue scenario generated by generative artificial intelligence

[1738] Step 7:

[1739] Sending and displaying dialogue scenarios

[1740] The server sends a generated dialogue scenario to the terminal. The terminal displays the dialogue scenario to the user and begins the dialogue. At this time, a question such as "What specific problems are you experiencing?" is presented.

[1741] Input: Dialogue scenario

[1742] Output: The dialogue scenario is displayed to the user.

[1743] Step 8:

[1744] Submitting user input

[1745] The user enters a response according to the dialogue scenario. For example, they might enter a response such as, "My boss doesn't listen to my opinion at all." This input is sent from the terminal to the server.

[1746] Input: User's response

[1747] Output: The answer is sent to the server.

[1748] Step 9:

[1749] Response generation

[1750] The server passes the user's response to a generative artificial intelligence model, which then generates an appropriate response. For example, a response such as, "That's tough. Do you have a chance to talk to your supervisor?" might be generated.

[1751] Input: User response data

[1752] Output: Generated response

[1753] Step 10:

[1754] Display of response

[1755] The server sends the generated response to the terminal, which then displays it to the user. The response acts as a trigger for the user to enter the next answer.

[1756] Input: Generated response

[1757] Output: Response is lowered to the user.

[1758] Step 11:

[1759] Record of the conversation

[1760] The content of the dialogue session is recorded on the server. This includes all questions and answers. The recorded data is used to generate an action plan.

[1761] Input: Content of the dialogue session

[1762] Output: The conversation content is saved to the database.

[1763] Step 12:

[1764] Generating an action plan

[1765] The server passes the conversation content to a generative artificial intelligence model, which then generates an appropriate action plan. For example, a specific action plan such as "Let's schedule a one-on-one meeting with your boss" might be generated.

[1766] Input: Dialogue content data

[1767] Output: Generated action plan

[1768] Step 13:

[1769] Sending and displaying action plans

[1770] The generated action plan is sent from the server to the terminal and displayed to the user. The user can review the action plan and proceed to the next step.

[1771] Input: Action Plan

[1772] Output: The action plan is displayed to the user.

[1773] Step 14:

[1774] Creating and sending reminders

[1775] To follow up on the progress of the action plan, the server generates periodic reminders. These reminders are sent to the device and notified to the user.

[1776] Input: Action plan execution schedule

[1777] Output: A reminder is sent to the user.

[1778] Step 15:

[1779] Inputting progress and reanalysis

[1780] The user enters the progress of their action plan via their device, following a reminder. The entered progress information is sent to the server and re-analyzed by generative artificial intelligence. Additional dialogues and action plans are suggested as needed.

[1781] Input: User progress information

[1782] Output: Additional dialogue or action plan

[1783] This series of steps enables the system to provide attentive support and ongoing follow-up for users' concerns.

[1784] (Application Example 1)

[1785] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1786] In modern society, users often experience mental and physical stress and anxieties, which can prevent them from receiving professional support. However, conventional mental health care systems lack concrete action plans and follow-up to effectively and sustainably resolve users' problems. In particular, there is a need for a method that provides appropriate dialogue sessions for each user's individual concerns and automatically follows up on their progress.

[1787] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1788] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for notifying a smart device of a reminder, means for analyzing the medical questionnaire data and dialogue data with a generative artificial intelligence system, means for generating prompt sentences, means for presenting the generated action plan to the user, and means for following up on the progress of the action plan. As a result, the user can receive prompt and appropriate support for their physical and mental health concerns, and continuous follow-up is also possible.

[1789] "Basic information" refers to information necessary to identify a user, such as the user's name, email address, and password.

[1790] "To store" means to save received data in an information management device.

[1791] A "medical questionnaire form" is a questionnaire used to collect information about a user's mental and physical health concerns and stress levels.

[1792] "Analyzing" means processing received data to extract useful information.

[1793] A "dialogue scenario" is a plan of a series of questions and answers used when interacting with a user.

[1794] "Generative artificial intelligence" is an advanced computational technique that generates optimal responses and scenarios based on large amounts of data.

[1795] An "action plan" is a set of specific guidelines for actions taken to solve a user's problem.

[1796] A "smart device" is a highly functional portable device capable of connecting to the internet and launching applications.

[1797] A "reminder" is a notification that prompts a user to take a specific action or confirm something.

[1798] A "prompt" is the input text for a generative artificial intelligence system, and a response is generated based on it.

[1799] "Follow-up" is the process of evaluating the progress of the established action plan and taking additional measures as needed.

[1800] This invention is a mental health care system that supports users with their physical and mental health concerns. The system inputs and records the user's basic information, generates a questionnaire form, generates and executes dialogue scenarios using generative artificial intelligence (AI) based on that form, and has the function of following up on the progress of the action plan. The implementation method of this system is described in detail below.

[1801] System Configuration

[1802] 1. Hardware and software

[1803] The system consists of a smartphone and a server. The smartphone has a mental health care application installed, which allows users to input basic information and answer questionnaires. The server is a cloud platform equipped with a database, generative artificial intelligence (e.g., OpenAI's GPT-4), a notification system, and other features.

[1804] 2. Data processing and data calculation

[1805] User registration:

[1806] When a user enters basic information through a smartphone application, that information is sent to a server and stored in a database. The server generates a registration completion notification and sends it to the user's smartphone to inform them of the completion.

[1807] Generating and analyzing medical questionnaire forms:

[1808] The server generates a questionnaire form based on stored basic information and presents it to the user. When the user answers the questionnaire form, the data is sent to the server. The server uses generative AI to analyze the answers and generate a personalized dialogue scenario.

[1809] Dialogue session:

[1810] Based on the dialogue scenario sent from the server, the smartphone application displays a dialogue screen and begins interacting with the user. The user's input is sent back to the server, where a generative AI generates an appropriate response, which is then sent and displayed on the smartphone.

[1811] Generating and presenting an action plan:

[1812] Based on the information gathered through the dialogue session, the server uses generative AI to generate a user-specific action plan. This generated action plan is then presented to the user via a smartphone application.

[1813] After-sales support:

[1814] The server generates periodic reminders and sends notifications to the user's smartphone. The user inputs the progress of their action plan, and this data is sent to the server. If necessary, a generative AI re-analyzes the data and suggests additional dialogue or action plans.

[1815] Specific example

[1816] For example, if user A is suffering from work-related stress, the following prompt message will be generated.

[1817] Example of a prompt:

[1818] Specific problems related to work stress include:

[1819] Lately, I've been having trouble with my boss at work. Specifically, my boss completely ignores my opinions.

[1820] Please provide advice on this issue.

[1821] Based on this prompt, the generative AI analyzes and responds, providing user A with interactive support. Ultimately, user A is given actionable plans (e.g., scheduling a one-on-one meeting with their supervisor) and follow-up support based on those plans.

[1822] This allows users to receive immediate and appropriate feedback on their concerns, ensuring continuous support.

[1823] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1824] Step 1:

[1825] User Registration

[1826] The user launches the smartphone application and enters basic information (name, email address, password). The device sends this input data to the server. The server stores the received information in its database and generates a registration completion notification. The server sends the registration completion notification to the device, and the device displays a completion message to the user.

[1827] Input: Name, email address, password

[1828] Data processing: Save input data to a database.

[1829] Output: Registration complete notification

[1830] Step 2:

[1831] Generate and receive questionnaire forms.

[1832] The server generates a questionnaire form based on stored basic information. This questionnaire form includes questions for the user to input the category of their concern and specific problems. The questionnaire form is sent to the terminal, and the user fills it out. The answers entered by the user are sent to the server via the terminal.

[1833] Input: User's basic information

[1834] Data processing: Generating a medical questionnaire form

[1835] Output: Response data from the medical questionnaire

[1836] Step 3:

[1837] Analysis of response data

[1838] The server passes the responses from the received questionnaire form to a generative artificial intelligence (AI) system for analysis. The AI ​​then generates an appropriate dialogue scenario based on the response data. This scenario is specifically designed to address the user's concerns.

[1839] Input: Response data from the medical questionnaire

[1840] Data processing: Analysis using generative artificial intelligence

[1841] Output: Dialogue Scenario

[1842] Step 4:

[1843] Executing a dialogue session

[1844] The server sends the generated dialogue scenario to the terminal. The terminal displays a dialogue screen based on the dialogue scenario and begins interacting with the user. User input is sent to the server through the terminal, and the server uses generative artificial intelligence to generate an appropriate response, which is then sent to the terminal for display.

[1845] Input: Dialogue scenario, user dialogue input

[1846] Data processing: Response generation using generative artificial intelligence.

[1847] Output: Response to the user

[1848] Step 5:

[1849] Generating an action plan

[1850] Based on the information obtained through the dialogue session, the server uses generative artificial intelligence to generate a user-specific action plan. The generated action plan is sent to the terminal and presented to the user.

[1851] Input: Dialogue content

[1852] Data processing: Generation of action plans using generative artificial intelligence.

[1853] Output: Action Plan

[1854] Step 6:

[1855] After-sales support

[1856] The server generates periodic reminders to follow up on the progress of the action plan. These reminders are sent to the device, and the user enters the progress. The entered data is sent to the server, where generative artificial intelligence re-analyzes it and suggests additional dialogue or action plans as needed.

[1857] Input: Action plan, user progress input

[1858] Data processing: Reminder generation and reanalysis using generative artificial intelligence.

[1859] Output: Reminder notifications and additional interaction / action plans

[1860] Through these steps, users can receive prompt and appropriate support for their physical and mental health concerns, and ongoing follow-up is also possible.

[1861] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1862] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The system's processing is described below in natural language.

[1863] System Program Overview

[1864] User Registration

[1865] 1. The user accesses the system and opens a website or app.

[1866] 2. The device displays a new registration form to the user.

[1867] 3. The user enters basic information such as their name, email address, and password, and then presses the submit button.

[1868] 4. The terminal sends the entered basic information to the server.

[1869] 5. The server saves the basic information it receives to the database.

[1870] 6. The server generates a registration completion message and sends it to the terminal.

[1871] 7. The device displays a registration completion message to the user.

[1872] Initial consultation

[1873] 1. After user registration is complete, the device will display a medical questionnaire form to the user.

[1874] 2. The user selects a category of their problem (for example, work stress) and enters their specific problem.

[1875] 3. The terminal sends the user's input to the server.

[1876] 4. The server sends the received data to the generative artificial intelligence.

[1877] 5. Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[1878] 6. The server sends the constructed dialogue scenario to the terminal.

[1879] Dialogue session

[1880] 1. The device displays a dialogue screen to the user.

[1881] 2. The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[1882] 3. The user enters a specific answer to the question.

[1883] 4. The device sends user input to the emotion engine, which then analyzes the emotions.

[1884] 5. The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1885] 6. The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[1886] 7. The server sends the generated response to the terminal.

[1887] 8. The device displays a response to the user.

[1888] Generating an action plan

[1889] 1. The server records the contents of the conversation session.

[1890] 2. Generative artificial intelligence generates an optimal action plan for the user based on the conversation content and emotion analysis results.

[1891] 3. The server sends the generated action plan to the terminal.

[1892] 4. The device displays an action plan to the user.

[1893] 5. The user reviews the action plan and approves to proceed to the next step.

[1894] After-sales support

[1895] 1. The server generates reminders to follow up on the progress of the action plan.

[1896] 2. The device periodically sends reminder notifications to the user.

[1897] 3. The user enters the progress of the action plan.

[1898] 4. The terminal sends the user's input results to the server.

[1899] 5. The server sends the input results to the emotion engine for sentiment analysis.

[1900] 6. The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[1901] 7. The server sends any additional dialogue or action plan generated to the terminal.

[1902] 8. The device displays additional dialogue or action plans to the user.

[1903] Specific example

[1904] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[1905] User Registration

[1906] 1. User A enters basic information on the terminal and presses the registration button.

[1907] 2. The entered information is sent to the server and stored in the database.

[1908] 3. A registration completion message is displayed to user A.

[1909] Initial consultation

[1910] 1. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[1911] 2. The input data is sent to the server, and a generative artificial intelligence performs the analysis.

[1912] 3. A dialogue scenario is created and sent to the terminal.

[1913] Dialogue session

[1914] 1. The device displays the question, "What specific problem are you experiencing?"

[1915] 2. User A responded, "My boss doesn't listen to my opinions at all."

[1916] 3. The device sends its response to the emotion engine, which then analyzes the emotion.

[1917] 4. The server sends the results of the emotion engine to the generative artificial intelligence.

[1918] 5. The generative AI generates an emotionally conscious response: "That sounds tough. Do you have an opportunity to talk to your boss about it?"

[1919] 6. The response is sent to the terminal and displayed to User A.

[1920] Generating an action plan

[1921] 1. The server records the conversation content and sentiment analysis results, and generates an action plan such as, "Let's schedule a one-on-one meeting with your boss."

[1922] 2. The action plan is displayed on the device, and User A approves it.

[1923] After-sales support

[1924] 1. The server generates a reminder, and the device sends a notification to user A one week later.

[1925] 2. User A enters the results of the meeting and reports, "I discussed it with my supervisor, but it hasn't improved yet."

[1926] 3. The emotion engine re-analyzes the emotions in the input, and the generative AI generates an additional action plan: "Next time, let's practice expressing your feelings honestly together."

[1927] 4. An additional action plan is sent to the device and displayed to User A.

[1928] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[1929] The following describes the processing flow.

[1930] User Registration

[1931] Step 1:

[1932] The user opens a website or app to access the system.

[1933] Step 2:

[1934] The device displays a new registration form to the user.

[1935] Step 3:

[1936] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[1937] Step 4:

[1938] The terminal sends the entered basic information to the server.

[1939] Step 5:

[1940] The server saves the basic information it receives to the database.

[1941] Step 6:

[1942] The server generates a registration completion message and sends it to the terminal.

[1943] Step 7:

[1944] The device displays a registration completion message to the user.

[1945] Initial consultation

[1946] Step 1:

[1947] After user registration is complete, the device will display a medical questionnaire form to the user.

[1948] Step 2:

[1949] The user selects a category for their problem and enters their specific problem.

[1950] Step 3:

[1951] The terminal sends the user's input to the server.

[1952] Step 4:

[1953] The server sends the received data to a generative artificial intelligence.

[1954] Step 5:

[1955] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[1956] Step 6:

[1957] The server sends the constructed dialogue scenario to the terminal.

[1958] Dialogue session

[1959] Step 1:

[1960] The device displays a dialogue screen to the user.

[1961] Step 2:

[1962] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[1963] Step 3:

[1964] The user enters a specific answer to the question.

[1965] Step 4:

[1966] The device sends user input to an emotion engine, which then analyzes the emotions.

[1967] Step 5:

[1968] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[1969] Step 6:

[1970] The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[1971] Step 7:

[1972] The server sends the generated response to the terminal.

[1973] Step 8:

[1974] The device displays a response to the user.

[1975] Generating an action plan

[1976] Step 1:

[1977] The server records the contents of the conversation session.

[1978] Step 2:

[1979] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation and the results of emotion analysis.

[1980] Step 3:

[1981] The server sends the generated action plan to the terminal.

[1982] Step 4:

[1983] The device displays an action plan to the user.

[1984] Step 5:

[1985] The user reviews the action plan and approves to proceed to the next step.

[1986] After-sales support

[1987] Step 1:

[1988] The server generates reminders to follow up on the progress of the action plan.

[1989] Step 2:

[1990] The device periodically sends reminder notifications to the user.

[1991] Step 3:

[1992] The user enters the progress of the action plan.

[1993] Step 4:

[1994] The terminal sends the user's input results to the server.

[1995] Step 5:

[1996] The server sends the input results to the emotion engine, which then performs emotion analysis.

[1997] Step 6:

[1998] The analysis results from the emotion engine are sent to the generative artificial intelligence system to generate additional dialogues and action plans.

[1999] Step 7:

[2000] The server sends any additional dialogue or action plans it has generated to the terminal.

[2001] Step 8:

[2002] The device displays additional dialogues or action plans to the user.

[2003] Specific example

[2004] User Registration

[2005] Step 1:

[2006] User A accesses the system and opens a website or app.

[2007] Step 2:

[2008] The terminal displays a new registration form to user A.

[2009] Step 3:

[2010] User A enters basic information such as their name, email address, and password, and then presses the submit button.

[2011] Step 4:

[2012] The terminal sends the entered information to the server.

[2013] Step 5:

[2014] The server saves the received information to the database.

[2015] Step 6:

[2016] The server generates a registration completion message and sends it to the terminal.

[2017] Step 7:

[2018] The device displays a registration completion message to user A.

[2019] Initial consultation

[2020] Step 1:

[2021] After User A completes registration, the terminal displays a medical questionnaire form to User A.

[2022] Step 2:

[2023] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[2024] Step 3:

[2025] The terminal sends the input from user A to the server.

[2026] Step 4:

[2027] The server sends the received data to a generative artificial intelligence.

[2028] Step 5:

[2029] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[2030] Step 6:

[2031] The server sends the constructed dialogue scenario to the terminal.

[2032] Dialogue session

[2033] Step 1:

[2034] The terminal displays a dialogue screen to user A.

[2035] Step 2:

[2036] The generative artificial intelligence displays the question, "What specific problems are you experiencing?" based on the dialogue scenario.

[2037] Step 3:

[2038] User A responds, "My boss doesn't listen to my opinions at all."

[2039] Step 4:

[2040] The device sends User A's response to the emotion engine, which then analyzes the emotion.

[2041] Step 5:

[2042] The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[2043] Step 6:

[2044] The generative artificial intelligence generates a response that takes into account the sentiment analysis results, such as "That's tough. Do you have a chance to talk to your boss?", and sends it to the server.

[2045] Step 7:

[2046] The server sends the generated response to the terminal.

[2047] Step 8:

[2048] The terminal displays a response to user A.

[2049] Generating an action plan

[2050] Step 1:

[2051] The server records the conversation content and the results of the sentiment analysis.

[2052] Step 2:

[2053] Generative artificial intelligence generates an action plan that says, "Let's schedule a one-on-one meeting with your boss."

[2054] Step 3:

[2055] The server sends the generated action plan to the terminal.

[2056] Step 4:

[2057] The device displays an action plan to user A.

[2058] Step 5:

[2059] User A approves of proceeding to the next step.

[2060] After-sales support

[2061] Step 1:

[2062] The server generates a reminder, and the device sends a notification to user A one week later.

[2063] Step 2:

[2064] User A enters, "I spoke with my supervisor, but the situation hasn't improved yet."

[2065] Step 3:

[2066] The terminal sends the input result of user A to the server.

[2067] Step 4:

[2068] The server sends the input results to the emotion engine for emotion analysis.

[2069] Step 5:

[2070] The analysis results from the emotion engine are sent to the generative artificial intelligence.

[2071] Step 6:

[2072] The generative artificial intelligence generates an additional action plan, "Next, let's practice expressing your feelings honestly," and sends it to the server.

[2073] Step 7:

[2074] The server sends any additional dialogue or action plans it has generated to the terminal.

[2075] Step 8:

[2076] The device displays additional dialogue and action plans to user A.

[2077] In this way, the system responds to users' concerns in a step-by-step and continuous manner, and uses an emotion engine to provide empathetic support based on the user's emotional state.

[2078] (Example 2)

[2079] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[2080] In modern society, users constantly face a variety of mental and physical health issues, yet there is a lack of systems that provide effective and continuous support for these issues. Furthermore, it is difficult to provide appropriate dialogue and action plans tailored to the user's emotional state. As a result, users have difficulty accurately understanding their situation and receiving concrete advice for improvement. Therefore, there is a need for a system that analyzes the user's emotional state and provides dialogue and action plans based on that analysis.

[2081] The specific processing performed 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 having the user input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving answers to the medical questionnaire form from the user, means for analyzing the received answers to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for conducting a dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means including an emotion engine for analyzing the user's emotions, means for using a generative artificial intelligence that generates a response based on the emotion analysis results, and means for reflecting the emotion analysis results in the dialogue scenario. This makes it possible to provide effective and continuous support to the user regarding their specific concerns, taking into account their emotional state.

[2082] "User" refers to an individual or group that uses the system.

[2083] "Basic information" refers to information such as name, email address, and password that users enter when registering with the system.

[2084] "To remember" refers to saving entered data to storage such as a database.

[2085] A "medical questionnaire form" refers to an input form used to collect information about a user's concerns or problems.

[2086] "Receiving" refers to the server or system acquiring information entered by the user.

[2087] "Analyzing" refers to processing received data and extracting meaning and patterns.

[2088] A "dialogue scenario" refers to a scenario generated based on analysis results to facilitate interaction with the user.

[2089] "Engaging in dialogue" refers to the exchange of information between the user and the system in a question-and-answer format.

[2090] "Recording" refers to saving the content of the conversation and the analysis results to a database or similar system.

[2091] An "action plan" refers to specific measures and action plans to address a user's problems.

[2092] "Following up" means tracking the progress of the action plan and providing additional support as needed.

[2093] An "emotion engine" refers to software that analyzes user input to identify emotions and their emotional state.

[2094] "Generative artificial intelligence" refers to artificial intelligence models that generate responses and scenarios based on user input data and sentiment analysis results.

[2095] A "reminder" refers to a function that notifies users of the progress of action plans and follow-ups.

[2096] This invention combines an emotional engine with a lifestyle platform system that provides empathetic support for users' mental and physical concerns. The following describes specific embodiments of the system.

[2097] First, the entire system consists of a terminal that users access, a server that processes data, an emotion engine that performs sentiment analysis, and a generative artificial intelligence system.

[2098] User Registration

[2099] The user accesses the system and opens a website or app. The user's device displays a new registration form, and the user enters and submits basic information (name, email address, password, etc.). The device sends the entered basic information to the server, which stores the information in its database. The server generates a registration completion message, sends it to the device, and displays it to the user.

[2100] Initial consultation

[2101] After user registration is complete, the device automatically displays an initial consultation form. The user enters the category of their problem (e.g., "work stress") and specific details. The device sends the entered data to the server, which then sends that data to a generative artificial intelligence (AI). The AI ​​analyzes the data and generates an appropriate dialogue scenario. The server sends the dialogue scenario to the device, preparing to begin the conversation with the user.

[2102] Dialogue session

[2103] The user's device displays a dialogue screen based on a generated dialogue scenario. Generative artificial intelligence generates appropriate questions and sends them to the device, displaying the questions to the user. The user enters their answers, and these answers are sent from the device to the emotion engine. The emotion engine analyzes the user's emotions and sends the results to the server. The server sends the emotion analysis results to the generative artificial intelligence, which generates an appropriate response. The generated response is sent to the device and displayed to the user.

[2104] Generating an action plan

[2105] The content of the dialogue session is recorded on the server, and a generative artificial intelligence generates an optimal action plan based on the dialogue content and sentiment analysis results. The server sends the generated action plan to the terminal, and the terminal displays the action plan to the user. The user reviews the action plan and approves to proceed to the next step.

[2106] After-sales support

[2107] The server generates periodic reminders to follow up on the progress of the action plan. The device sends a reminder notification to the user, who then enters the progress of the action plan. The entered results are sent to the server and analyzed again by the emotion engine. The analysis results are sent to the generative artificial intelligence, which generates additional dialogues and action plans as needed. These are then sent back from the server to the device and displayed to the user.

[2108] Specific example

[2109] For example, if user A is suffering from "work stress," the following process will occur:

[2110] 1. User A enters basic information and registers it in the system.

[2111] 2. User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[2112] 3. The input data is sent to the generative artificial intelligence, and the question "What specific problems are you experiencing?" is generated.

[2113] 4. When User A responds, "My boss doesn't listen to my opinions at all," the emotion engine analyzes that emotion and generates the response, "That's tough. Do you have opportunities to talk to your boss?"

[2114] 5. Based on the dialogue content and emotion analysis results, an action plan is generated and presented to User A: "Let's schedule a one-on-one meeting with your supervisor."

[2115] 6. To follow up on the progress of the action plan, the server generates a reminder and a notification is sent from the terminal. User A inputs the meeting results, which are then re-analyzed to generate new dialogues and action plans.

[2116] Example of a prompt

[2117] The following prompt is input to the generative artificial intelligence:

[2118] "User A is seeking advice about work-related stress, stating, 'My boss doesn't listen to my opinions at all.' Please suggest what questions should be asked next, taking into account the user's emotional state."

[2119] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2120] Step 1: The user accesses the system and opens a website or app.

[2121] The user accesses the system's website or app via the internet. Input here involves the user entering the URL of the website or app, or launching the app. Output is the display of a new registration form.

[2122] Step 2: The device displays the new registration form.

[2123] The device displays the form required for new registration (fields for name, email address, and password). The input is the user's basic information, and the output is the form with the basic information ready to be entered.

[2124] Step 3: The user enters their basic information and presses the submit button.

[2125] The user enters basic information such as their name, email address, and password into the registration form and clicks the submit button. The input is the user's basic information, and the output is the event that sends this basic information.

[2126] Step 4: The terminal sends the entered basic information to the server.

[2127] The terminal sends basic information to the server using the HTTPS protocol. The input is the basic information entered by the user, and the output is the result of the transmission to the server.

[2128] Step 5: The server saves the basic information it received to the database.

[2129] The server records the received basic information in a database (e.g., MySQL). The input is the transmitted basic information, and the output is the result of saving it to the database. Passwords are hashed (e.g., bcrypt) for security purposes before being stored.

[2130] Step 6: The server generates a registration completion message and sends it to the terminal.

[2131] The server generates a registration completion message and sends it to the terminal. The input is the confirmation result of successful saving, and the output is the generation and sending of the completion message.

[2132] Step 7: The device displays a registration completion message to the user.

[2133] The terminal displays a registration completion message received from the server to the user. The input is the message from the server, and the output is what is displayed to the user.

[2134] Step 8: After user registration is complete, the device will display the medical questionnaire form.

[2135] After user registration is complete, the device displays a medical questionnaire form as the next step. The input is the registration completion event, and the output is the display of the medical questionnaire form.

[2136] Step 9: The user selects a category for their problem and enters their specific problem.

[2137] The user selects a category such as "work stress" in a questionnaire form and enters specific concerns such as "I'm having trouble getting along with my boss at work lately." Input consists of user selections and text input, while output is confirmation of the input and a submission event.

[2138] Step 10: The terminal sends the user's input to the server.

[2139] The terminal sends user input to the server via the HTTPS protocol. The input is the user's responses to a questionnaire, and the output is the result of the transmission to the server.

[2140] Step 11: The server sends the received data to the generative artificial intelligence.

[2141] The server receives the medical interview data and sends it to a generative artificial intelligence (e.g., OpenAI GPT-3). The input is the medical interview data, and the output is a successful transmission to the generative AI.

[2142] Step 12: Generative artificial intelligence analyzes the input data and constructs a dialogue scenario.

[2143] Generative artificial intelligence analyzes user input data and constructs appropriate dialogue scenarios. For example, it generates questions such as, "Please tell me more about your relationship with your boss." The input is questionnaire data, and the output is a dialogue scenario.

[2144] Step 13: The server sends the constructed dialogue scenario to the terminal.

[2145] The server sends the dialogue scenario received from the generative artificial intelligence to the user's terminal. The input is the dialogue scenario, and the output is the result of the transmission to the terminal.

[2146] Step 14: The device displays a dialogue screen to the user.

[2147] The terminal displays a dialogue screen and initiates a conversation with the user based on the generated dialogue scenario. The input is the dialogue scenario, and the output is the display of the dialogue screen.

[2148] Step 15: The generative artificial intelligence displays questions to the user based on the dialogue scenario.

[2149] A generative artificial intelligence generates appropriate questions, sends them to a terminal, and the terminal displays those questions to the user. The input is the dialogue scenario, and the output is the question displayed to the user.

[2150] Step 16: The user enters a specific answer to the question.

[2151] The user enters a specific answer based on the question. For example, they might enter, "My boss never listens to my opinion." The input is the user's answer, and the output is the entered text.

[2152] Step 17: The device sends user input to the emotion engine, which analyzes the emotions.

[2153] The terminal sends user input to an emotion engine (e.g., IBM Watson Tone Analyzer) for emotion analysis. The input is the user's text response, and the output is the emotion analysis result.

[2154] Step 18: The server sends the analysis results from the emotion engine to the generative artificial intelligence.

[2155] The server sends the results of the emotion engine's analysis to the generative artificial intelligence. The input is the emotion analysis result, and the output is the result sent to the generative artificial intelligence.

[2156] Step 19: The generative artificial intelligence generates a response that takes the emotion analysis results into account and sends it to the server.

[2157] The generative artificial intelligence considers the emotion analysis results and generates an appropriate response. A response such as "That's tough. Do you have a chance to talk to your supervisor?" is generated. The input is the emotion analysis results, and the output is the generated response.

[2158] Step 20: The server sends the generated response to the terminal.

[2159] The server sends the response received from the generative artificial intelligence to the user's terminal. The input is the generated response, and the output is the result of the transmission to the terminal.

[2160] Step 21: The terminal displays a response to the user.

[2161] The terminal displays the generated response to the user. The input is the response message from the server, and the output is the response displayed to the user.

[2162] Step 22: The server records the contents of the conversation session.

[2163] The server records the contents of all dialogue sessions in a database. The input is the dialogue between the user and the generative artificial intelligence, and the output is the success of the recording.

[2164] Step 23: The generative artificial intelligence generates the optimal action plan for the user based on the conversation content and emotion analysis results.

[2165] A generative artificial intelligence generates action plans such as "Let's schedule a one-on-one meeting with your boss" based on recorded dialogue content and emotion analysis results. The input is the dialogue content and emotion analysis results, and the output is the generated action plan.

[2166] Step 24: The server sends the generated action plan to the terminal.

[2167] The server sends the generated action plan to the user's terminal. The input is the generated action plan, and the output is the result of sending it to the terminal.

[2168] Step 25: The device displays the action plan to the user.

[2169] The terminal displays the action plan it received to the user. The input is the action plan from the server, and the output is the action plan displayed to the user.

[2170] Step 26: The user reviews the action plan and approves to proceed to the next step.

[2171] The user reviews the displayed action plan and clicks the approve button. The input is the action plan and its confirmation, and the output is the approval event.

[2172] Step 27: The server generates reminders to follow up on the progress of the action plan.

[2173] The server generates periodic reminder notifications to follow up on the action plan. The input is the action plan and its progress, and the output is the generated reminders.

[2174] Step 28: The device periodically sends reminder notifications to the user.

[2175] The device notifies the user of reminder notifications sent from the server at the appropriate time. The input is the reminder from the server, and the output is the notification to the user.

[2176] Step 29: The user enters the progress of the action plan.

[2177] After the user receives a reminder, they enter the progress of the action plan. The input is the user's status report, and the output is the entered progress details.

[2178] Step 30: The terminal sends the user's input to the server.

[2179] The terminal sends user input to the server. The input is the progress, and the output is the result of the transmission to the server.

[2180] Step 31: The server sends the input results to the emotion engine for sentiment analysis.

[2181] The server sends progress information to the emotion engine, which then performs the latest emotion analysis. The input is the progress information, and the output is the emotion analysis result.

[2182] Step 32: The server sends the results of the emotion engine's analysis to the generative artificial intelligence to generate additional dialogue and action plans.

[2183] The generative artificial intelligence generates new dialogues or action plans as needed, based on the emotion analysis results. The input is the emotion analysis results, and the output is the added dialogue or action plan.

[2184] Step 33: The server sends any additional dialogue or action plan that has been generated to the terminal.

[2185] The server sends the newly generated dialogue and action plan to the terminal. The input is the generated dialogue and action plan, and the output is the result of sending it to the terminal.

[2186] Step 34: The device displays additional dialogue or action plans to the user.

[2187] The terminal displays newly generated dialogues and action plans to the user. The input is the dialogues and action plans from the server, and the output is what is displayed to the user.

[2188] (Application Example 2)

[2189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[2190] Improper management of employees' physical and mental health can lead to fatigue, stress accumulation, and decreased motivation, negatively impacting productivity and safety. To address this challenge, a system is needed that can monitor employees' physical and mental health in real time and provide appropriate support.

[2191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2192] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for presenting the generated action plan to the user, means for following up on the progress of the action plan, means for performing sentiment analysis of the dialogue content, and means for adjusting the dialogue scenario and action plan considering the sentiment analysis results. This makes it possible to grasp the mental and physical state of employees in real time and provide appropriate support.

[2193] "Basic information" refers to data such as the user's name, email address, and password.

[2194] "To remember" means for a server to store information entered by a user in a database or similar location.

[2195] A "medical questionnaire form" is a sheet of questions used by users to record their physical and mental condition and concerns.

[2196] "Receiving" means that the server obtains input information from the user.

[2197] "To analyze" means to understand and analyze the content based on the received data.

[2198] "Generating" means creating new dialogue scenarios, action plans, and other elements based on the analysis results.

[2199] A "dialogue scenario" refers to a series of questions and answers exchanged between a user and a system.

[2200] "Engaging in dialogue" means that a system exchanges information with a user through conversation.

[2201] An "action plan" is a specific plan for a user to initiate a designated action.

[2202] "Following up" means that the system periodically checks the user's progress and status, and provides advice and support as needed.

[2203] "Emotional analysis" is the process of inferring and analyzing a user's emotional state from their statements and input data.

[2204] "Emotional analysis results" refer to data on emotional states obtained based on emotional analysis.

[2205] "Adjusting" means changing the content of the dialogue scenario or action plan depending on the situation.

[2206] This invention relates to a factory robot that analyzes the physical and mental state of employees in real time within a factory and provides appropriate support. The system collects and analyzes basic information from the user (employee) and provides individualized dialogue and action plans.

[2207] System Program Overview

[2208] User Registration

[2209] The server receives basic information entered by the user (such as name, email address, and password) and stores it in a database. The user then enters this information via a tablet on a factory robot.

[2210] Initial consultation

[2211] Once user registration is complete, the server generates a questionnaire form for the user and displays it on the robot's tablet. The user enters a category (e.g., stress management) and a specific problem (e.g., "I'm exhausted from having too much work"), and this information is sent to the server.

[2212] Dialogue session

[2213] The server uses generative artificial intelligence to analyze the responses to the questionnaire form and construct a dialogue scenario. Once the dialogue scenario is constructed, the robot displays specific questions to the user. The user's responses are analyzed for emotion in real time, and the generative artificial intelligence then generates the next response, taking the emotion analysis results into consideration.

[2214] Generating an action plan

[2215] The server generates an optimal action plan for the user based on the content of the dialogue session and the results of the emotion analysis. This action plan is displayed on the robot's tablet and presented to the user.

[2216] After-sales support

[2217] The server generates reminders to follow up on the progress of the action plan and sends notifications to the user periodically. The user inputs the progress, which is then subjected to sentiment analysis again. The server uses generative artificial intelligence to generate additional dialogues and action plans, which are then provided to the user.

[2218] Hardware and software to use

[2219] This system requires a factory robot (with a tablet), a server, an emotion engine (EmotionEngine), and generative artificial intelligence (ChatbotAI). The server stores basic information from the user and answers to questionnaire forms in a database, and uses generative AI to analyze the data, build dialogue scenarios, and perform emotion analysis. This makes it possible to understand the user's mental and physical state in real time and provide appropriate support.

[2220] Specific example

[2221] Next, I will describe a specific example of this system.

[2222] User registration example

[2223] The user enters basic information on the robot's tablet and presses the registration button. This information is sent to the server and stored in the database. A registration completion message is displayed on the tablet.

[2224] Initial consultation example

[2225] The user selects the "Stress Management" category and enters "I've been really tired lately because I have so much work." This input data is sent to the server, where a generative artificial intelligence analyzes it. A dialogue scenario is then constructed and displayed on the tablet.

[2226] Example prompts for a dialogue session

[2227] User response: "Lately, I feel like my boss doesn't understand me."

[2228] Server response: "That sounds tough. What exactly are the problems you're experiencing?"

[2229] Example of action plan generation

[2230] The server records the conversation (e.g., "My boss doesn't understand me") and generates an action plan such as "Let's practice talking directly to your boss." This is then displayed on the tablet.

[2231] Examples of after-sales support

[2232] The server generates reminders to check progress and sends periodic notifications to the user. The user types, "I spoke with my boss, but it hasn't improved yet." This is then analyzed for sentiment again, generating additional dialogue and action plans, which are displayed on the tablet.

[2233] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2234] Program processing flow

[2235] Step 1: User Registration

[2236] The server stores the basic information (name, email address, etc.) entered by the user in a database.

[2237] Input: The user enters basic information.

[2238] Data processing: Validate the input data.

[2239] Output: Basic information is saved in the database.

[2240] Specific operation: The user enters basic information into the robot's tablet and presses the submit button. The server receives this information and saves it to the database.

[2241] Step 2: Generate the medical questionnaire form

[2242] The server generates a medical questionnaire form based on the stored basic information.

[2243] Input: Basic user information stored on the server.

[2244] Data processing: Generate appropriate questionnaire items based on the user's basic information.

[2245] Output: The generated questionnaire form is displayed on the robot's tablet.

[2246] Specific operation: The server generates a medical questionnaire form based on basic information and sends it to the robot's tablet.

[2247] Step 3: Receiving responses to the medical questionnaire

[2248] The server receives the user's responses to the medical questionnaire form.

[2249] Input: The user enters their answers to the medical questionnaire form.

[2250] Data processing: Receive and validate the entered response data.

[2251] Output: The response data is saved on the server.

[2252] Specific operation: The user enters their answers to a questionnaire form on a tablet and presses the submit button. The server receives this and saves it to the database.

[2253] Step 4: Analysis of the questionnaire form

[2254] The server analyzes the responses to the received medical questionnaire using a generating AI model.

[2255] Input: Data from the medical questionnaire.

[2256] Data processing: Data analysis is performed using a generative AI model.

[2257] Output: Analysis results are generated.

[2258] Specific operation: The server sends the received response to the generating AI model and obtains the analysis result.

[2259] Step 5: Generating dialogue scenarios

[2260] The server generates dialogue scenarios based on the analysis results.

[2261] Input: Analysis results from the medical questionnaire form.

[2262] Data processing: Construct dialogue scenarios using a generative AI model.

[2263] Output: A dialogue scenario is generated.

[2264] Specific operation: The server generates a dialogue scenario using an AI model based on the analysis results and sends it to the tablet.

[2265] Step 6: Conducting a dialogue session

[2266] The server and robot interact with the user based on the generated dialogue scenario.

[2267] Input: Dialogue scenario.

[2268] Data processing: Analyze user responses using an emotion engine.

[2269] Output: A response based on the sentiment analysis results is generated.

[2270] Specific operation: A question from the conversational scenario is displayed on the tablet, and the user enters an answer. The emotion engine analyzes the answer, and the server generates an appropriate response and displays it on the tablet.

[2271] Step 7: Record the conversation

[2272] The server records the contents of the dialogue session.

[2273] Input: Dialogue content data.

[2274] Data processing: Save the conversation content to the database.

[2275] Output: The conversation content is recorded.

[2276] Specific operation: The server saves the results of the dialogue session to the database.

[2277] Step 8: Generate an action plan

[2278] The server generates an action plan based on the interaction.

[2279] Input: Dialogue content data and sentiment analysis results.

[2280] Data processing: Develop action plans using generative AI models.

[2281] Output: An action plan is generated.

[2282] Specific operation: The server generates an action plan using a generative AI model based on the conversation content and sentiment analysis results, and displays it on the tablet.

[2283] Step 9: Present an action plan

[2284] The server presents the generated action plan to the user.

[2285] Input: Action plan.

[2286] Data processing: Display the action plan on the tablet.

[2287] Output: The action plan is displayed on the tablet.

[2288] Specific operation: The server sends the generated action plan to the tablet, and the user confirms it.

[2289] Step 10: Follow up on the progress of the action plan

[2290] The server generates reminders to follow up on the progress of the action plan.

[2291] Input: Action plan and progress data.

[2292] Data processing: Generate a reminder and send it to the tablet.

[2293] Output: A reminder is sent to the user.

[2294] Specific operation: The server monitors the progress, generates a reminder, and sends it to the user's tablet. The user enters the progress, which is then analyzed for sentiment again.

[2295] Step 11: Generating additional dialogues and action plans

[2296] The server generates additional dialogue and action plans based on the sentiment analysis results.

[2297] Input: Progress data and sentiment analysis results.

[2298] Data processing: Generative AI models are used to build additional dialogues and action plans.

[2299] Output: Additional dialogues and action plans are generated.

[2300] Specific operation: The server uses a generative AI model based on progress data and sentiment analysis results to generate additional dialogue and action plans, which are then displayed on the tablet.

[2301] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2302] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2303] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[2304] [Fourth Embodiment]

[2305] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2306] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2307] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2308] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[2309] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[2310] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[2311] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[2312] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[2313] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[2314] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2315] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2316] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[2317] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2318] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system's processing is described below in natural language.

[2319] System Program Overview

[2320] 1. User Registration

[2321] In order for a user to use the system, they must first register.

[2322] The terminal prompts the user to enter basic information (name, email address, password, etc.).

[2323] The terminal sends the entered basic information to the server.

[2324] The server saves the user's basic information to a database and sends a registration completion notification to the device.

[2325] The device displays a registration completion message to the user.

[2326] 2. Initial consultation

[2327] After user registration is complete, the device will display a medical questionnaire form to the user.

[2328] The user selects a category of their problem (for example, work stress) and enters their specific problem.

[2329] The terminal sends user input to the server.

[2330] The server receives the input data and passes it to the generative artificial intelligence for analysis.

[2331] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[2332] 3. Dialogue Session

[2333] The server sends the generated dialogue scenario to the terminal.

[2334] The device displays a dialogue screen and initiates a conversation with the generative artificial intelligence for the user.

[2335] The terminal sends user input to the server, which then uses generative artificial intelligence to generate an appropriate response.

[2336] The response is sent to the terminal and displayed to the user.

[2337] 4. Generating an action plan

[2338] The server records and analyzes the information obtained through the dialogue session.

[2339] Generative artificial intelligence generates the optimal action plan for the user based on the content of the conversation.

[2340] The server sends the generated action plan to the terminal and displays it to the user.

[2341] The user reviews the action plan and proceeds to the next step.

[2342] 5. After-sales support

[2343] The server generates periodic reminders to follow up on the progress of the action plan.

[2344] The device sends a reminder notification to the user, and the user enters the progress of the action plan.

[2345] The server receives the input results, a generative artificial intelligence analyzes them, and proposes additional dialogue or action plans as needed.

[2346] Specific example

[2347] For example, let's explain specific ways to handle the situation when User A is suffering from work-related stress.

[2348] User Registration

[2349] User A enters basic information on the terminal and presses the registration button.

[2350] The entered information is sent to the server and stored in the database.

[2351] A registration completion message is displayed to user A.

[2352] Initial consultation

[2353] User A selects the category "Work Stress" and enters "Recently, my relationship with my boss at work hasn't been going well."

[2354] The input data is sent to the server, where a generative artificial intelligence performs the analysis.

[2355] A dialogue scenario is created and sent to the terminal.

[2356] Dialogue session

[2357] Based on the dialogue scenario sent from the server, the terminal displays the question, "What exactly is the problem?"

[2358] User A responded, "My boss doesn't listen to my opinions at all."

[2359] The generative artificial intelligence analyzed the message and generated the response, "That sounds tough. Do you have an opportunity to talk to your supervisor?"

[2360] The response is displayed to user A.

[2361] Generating an action plan

[2362] The server records the conversation, and a generative artificial intelligence generates an action plan: "Let's schedule a one-on-one meeting with your boss."

[2363] The action plan is displayed on the device, and User A accepts it.

[2364] After-sales support

[2365] The server generates a reminder based on the action plan, and the device sends a notification one week later.

[2366] User A entered the results of the meeting and reported, "I discussed it with my supervisor, but it hasn't improved yet."

[2367] The generative artificial intelligence re-analyzed the text and suggested, "Next, let's practice expressing your feelings honestly together."

[2368] In this way, the system addresses users' concerns step by step, providing attentive support and continuous follow-up.

[2369] The following describes the processing flow.

[2370] User Registration

[2371] Step 1:

[2372] The user opens a website or app to access the system.

[2373] Step 2:

[2374] The device displays a new registration form to the user.

[2375] Step 3:

[2376] The user enters basic information such as their name, email address, and password, and then presses the submit button.

[2377] Step 4:

[2378] The terminal sends the entered basic information to the server.

[2379] Step 5:

[2380] The server saves the basic information it receives to the database.

[2381] Step 6:

[2382] The server generates a registration completion message and sends it to the terminal.

[2383] Step 7:

[2384] The device displays a registration completion message to the user.

[2385] Initial consultation

[2386] Step 1:

[2387] After user registration is complete, the device will display a medical questionnaire form to the user.

[2388] Step 2:

[2389] The user selects a category for their problem and enters their specific problem.

[2390] Step 3:

[2391] The terminal sends the user's input to the server.

[2392] Step 4:

[2393] The server sends the received data to a generative artificial intelligence.

[2394] Step 5:

[2395] Generative artificial intelligence analyzes input data and constructs dialogue scenarios.

[2396] Step 6:

[2397] The server sends the constructed dialogue scenario to the terminal.

[2398] Dialogue session

[2399] Step 1:

[2400] The device displays a dialogue screen to the user.

[2401] Step 2:

[2402] The generative artificial intelligence displays questions to the user based on a dialogue scenario.

[2403] Step 3:

[2404] The user enters a specific answer to the question.

[2405] Step 4:

[2406] The terminal sends user input to the server.

[2407] Step 5:

[2408] The server sends the user's input data to a generative artificial intelligence system and generates the following response.

[2409] Step 6:

[2410] The generative artificial intelligence analyzes the data, generates an appropriate response, and sends it to the server.

[2411] Step 7:

[2412] The server sends the generated response to the terminal.

[2413] Step 8:

[2414] The device displays a response to the user.

[2415] Generating an action plan

[2416] Step 1:

[2417] The server records the contents of the conversation session.

[2418] Step 2:

[2419] Generative artificial intelligence analyzes the conversation content and generates the optimal action plan for the user.

[2420] Step 3:

[2421] The server sends the generated action plan to the terminal.

[2422] Step 4:

[2423] The device displays an action plan to the user.

[2424] Step 5:

[2425] The user reviews the action plan and approves to proceed to the next step.

[2426] After-sales support

[2427] Step 1:

[2428] The server generates reminders to follow up on the progress of the action plan.

[2429] Step 2:

[2430] The device periodically sends reminder notifications to the user.

[2431] Step 3:

[2432] The user enters the progress of the action plan.

[2433] Step 4:

[2434] The terminal sends the user's input results to the server.

[2435] Step 5:

[2436] The server sends the input results to a generative artificial intelligence system for analysis.

[2437] Step 6:

[2438] Generative artificial intelligence analyzes the progress and generates additional dialogues and action plans as needed.

[2439] Step 7:

[2440] The server sends any additional dialogue or action plans it has generated to the terminal.

[2441] Step 8:

[2442] The device displays additional dialogues or action plans to the user.

[2443] As described above, the "LifeBridge" system provides gradual and continuous support for users' physical and mental health concerns.

[2444] (Example 1)

[2445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2446] Conventional lifestyle platform systems have not provided sufficient, empathetic support for users' physical and mental health concerns, particularly inadequate, continuous follow-up. As a result, effective solutions to the problems users face may not be provided, potentially leading to decreased user satisfaction. Furthermore, the inability to effectively utilize artificial intelligence in dialogue and action plan generation could result in inconsistent support for users.

[2447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[2448] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's answers to the medical questionnaire form, means for analyzing the received answers to the medical questionnaire form using generative artificial intelligence, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the content of the dialogue, means for generating an action plan based on the content of the dialogue, means for presenting the generated action plan to the user, means for generating and sending periodic reminders to the user to follow up on the progress of the action plan, and means for receiving progress information from the user, re-analyzing it, and proposing additional dialogues or action plans as necessary. This enables empathetic support and continuous follow-up for the user's physical and mental health concerns, thereby improving user satisfaction.

[2449] "Basic information" refers to personal information necessary to start using the system, such as the user's name, email address, and password.

[2450] A "database" is a digital storage device used to store and manage information such as a user's basic information, conversation history, and action plans.

[2451] A "medical questionnaire form" is a question-based input screen used to gain a detailed understanding of a user's physical and mental health concerns.

[2452] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to analyze input data and generate appropriate dialogue scenarios and action plans.

[2453] A "dialogue scenario" refers to the pre-conversation flow and questions created by generative artificial intelligence to facilitate smooth interaction with the user.

[2454] An "action plan" refers to specific measures and action plans proposed by a generative artificial intelligence system to resolve the user's mental and physical problems.

[2455] A "reminder" is a notification or message that is sent periodically to a user to check and input the progress of their action plan.

[2456] "Follow-up" refers to a series of activities and actions taken to check the progress of the action plan and provide additional support as needed.

[2457] This invention is a lifestyle platform system that provides empathetic support for users' physical and mental health concerns. The system requires a server, terminals, and a generative AI model to be implemented.

[2458] The system operates as follows: First, the user enters basic information using a terminal. The terminal has a function to display a form for entering information such as name, email address, and password. The terminal then sends the entered basic information to the server. The server saves the received basic information to a database and sends a registration completion notification to the terminal. The terminal displays this notification to the user.

[2459] Once user registration is complete, the device displays a questionnaire form to the user. This questionnaire includes areas where the user can select a category related to their mental or physical health concerns and enter their specific concerns. For example, the user might select the category "work stress" and enter, "Recently, my relationship with my boss hasn't been going well." The device then sends this input data to the server.

[2460] The server passes the received data to a generative artificial intelligence model. This generative AI model analyzes the input data and constructs an appropriate dialogue scenario. The generated dialogue scenario is sent from the server to the terminal and displayed to the user on the terminal.

[2461] Once a dialogue session begins, the server generates questions based on a pre-defined dialogue scenario. The user's input through the terminal is sent to the server, where a generative artificial intelligence analyzes it and generates an appropriate response. The generated response is displayed on the terminal, and the user responds to it. This dialogue process continues.

[2462] The entire content of the conversation session is recorded on the server. Based on the conversation, the server uses generative artificial intelligence to generate an appropriate action plan. For example, an action plan such as "Let's schedule a one-on-one meeting with your boss" is generated and displayed on the terminal. The user can review this action plan and proceed to the next step.

[2463] To track the progress of the action plan, the server generates periodic reminders. These reminder notifications are sent to the device, and the user receives them. The user then inputs the progress of the action plan according to the reminder and sends it from the device to the server. The server re-analyzes this progress information and, if necessary, uses generative artificial intelligence to suggest additional dialogues or action plans.

[2464] For example, if user A is suffering from work-related stress, the following dialogue may take place:

[2465] User A: "Lately, my relationship with my boss hasn't been going well."

[2466] Generative AI: "That sounds tough. Do you have a chance to talk to your supervisor about it?"

[2467] In this way, the system approaches users' problems step by step, providing attentive support and continuous follow-up.

[2468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2469] Step 1:

[2470] User registration (entering basic information)

[2471] The user enters basic information using a device. The device displays a form for entering basic information such as name, email address, and password. This form includes required fields for name, email address, and password. The entered data is sent from the device to the server.

[2472] Input: Name, email address, password

[2473] Output: Basic information is sent to the server.

[2474] Step 2:

[2475] Basic information saving

[2476] The server receives basic information sent from the terminal. The received basic information is stored in the database. The server notifies the terminal when the saving is complete.

[2477] Input: Basic Information

[2478] Output: Basic information is saved to the database, and a registration completion notification is sent to the device.

[2479] Step 3:

[2480] Display of registration completion message

[2481] The device receives a registration completion notification sent from the server. The device displays a registration completion message to the user. This allows the user to confirm that registration was successful.

[2482] Input: Registration completion notification

[2483] Output: A registration complete message is displayed to the user.

[2484] Step 4:

[2485] Display of the medical questionnaire form

[2486] After user registration is complete, the device displays a questionnaire form to the user. This questionnaire form includes a category selection field for mental and physical health concerns and a text box for entering specific concerns.

[2487] Input: None (A questionnaire form is displayed as a prompt)

[2488] Output: The medical questionnaire form is displayed to the user.

[2489] Step 5:

[2490] Enter category and problem

[2491] The user selects a category of their problem in a questionnaire form and enters their specific problem. For example, the user might select the category "Work Stress" and enter, "Recently, my relationship with my boss hasn't been going well." This information is sent from the device to the server.

[2492] Input: Category of problem and specific problem

[2493] Output: Category and problem information is sent to the server.

[2494] Step 6:

[2495] Data Analysis

[2496] The server provides the data from the received questionnaire form to a generative artificial intelligence model. The generative AI model analyzes the data and generates an appropriate dialogue scenario. For example, it might take a prompt such as "The user is suffering from work-related stress" and generate a scenario such as "Please tell me more."

[2497] Input: User's problem data

[2498] Output: Dialogue scenario generated by generative artificial intelligence

[2499] Step 7:

[2500] Sending and displaying dialogue scenarios

[2501] The server sends a generated dialogue scenario to the terminal. The terminal displays the dialogue scenario to the user and begins the dialogue. At this time, a question such as "What specific problems are you experiencing?" is presented.

[2502] Input: Dialogue scenario

[2503] Output: The dialogue scenario is displayed to the user.

[2504] Step 8:

[2505] Submitting user input

[2506] The user enters a response according to the dialogue scenario. For example, they might enter a response such as, "My boss doesn't listen to my opinion at all." This input is sent from the terminal to the server.

[2507] Input: User's response

[2508] Output: The answer is sent to the server.

[2509] Step 9:

[2510] Response generation

[2511] The server passes the user's response to a generative artificial intelligence model, which then generates an appropriate response. For example, a response such as, "That's tough. Do you have a chance to talk to your supervisor?" might be generated.

[2512] Input: User response data

[2513] Output: Generated response

[2514] Step 10:

[2515] Display of response

[2516] The server sends the generated response to the terminal, which then displays it to the user. The response acts as a trigger for the user to enter the next answer.

[2517] Input: Generated response

[2518] Output: Response is lowered to the user.

[2519] Step 11:

[2520] Record of the conversation

[2521] The content of the dialogue session is recorded on the server. This includes all questions and answers. The recorded data is used to generate an action plan.

[2522] Input: Content of the dialogue session

[2523] Output: The conversation content is saved to the database.

[2524] Step 12:

[2525] Generating an action plan

[2526] The server passes the conversation content to a generative artificial intelligence model, which then generates an appropriate action plan. For example, a specific action plan such as "Let's schedule a one-on-one meeting with your boss" might be generated.

[2527] Input: Dialogue content data

[2528] Output: Generated action plan

[2529] Step 13:

[2530] Sending and displaying action plans

[2531] The generated action plan is sent from the server to the terminal and displayed to the user. The user can review the action plan and proceed to the next step.

[2532] Input: Action Plan

[2533] Output: The action plan is displayed to the user.

[2534] Step 14:

[2535] Creating and sending reminders

[2536] To follow up on the progress of the action plan, the server generates periodic reminders. These reminders are sent to the device and notified to the user.

[2537] Input: Action plan execution schedule

[2538] Output: A reminder is sent to the user.

[2539] Step 15:

[2540] Inputting progress and reanalysis

[2541] The user enters the progress of their action plan via their device, following a reminder. The entered progress information is sent to the server and re-analyzed by generative artificial intelligence. Additional dialogues and action plans are suggested as needed.

[2542] Input: User progress information

[2543] Output: Additional dialogue or action plan

[2544] This series of steps enables the system to provide attentive support and ongoing follow-up for users' concerns.

[2545] (Application Example 1)

[2546] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2547] In modern society, users often experience mental and physical stress and anxieties, which can prevent them from receiving professional support. However, conventional mental health care systems lack concrete action plans and follow-up to effectively and sustainably resolve users' problems. In particular, there is a need for a method that provides appropriate dialogue sessions for each user's individual concerns and automatically follows up on their progress.

[2548] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[2549] In this invention, the server includes means for prompting the user to input basic information, means for storing the input basic information, means for generating a medical questionnaire form based on the stored basic information, means for receiving the user's response to the medical questionnaire form, means for analyzing the received response to the medical questionnaire form, means for generating a dialogue scenario based on the analysis results, means for engaging in dialogue with the user based on the generated dialogue scenario, means for storing the dialogue content, means for generating an action plan based on the dialogue content, means for notifying a smart device of a reminder, means for analyzing the medical questionnaire data and dialogue data with a generative artificial intelligence system, means for generating prompt sentences, means for presenting the generated action plan to the user, and means for following up on the progress of the action plan. As a result, the user can receive prompt and appropriate support for their physical and mental health concerns, and continuous follow-up is also possible.

[2550] "Basic information" refers to information necessary to identify a user, such as the user's name, email address, and password.

[2551] "To store" means to save received data in an information management device.

[2552] A "medical questionnaire form" is a questionnaire used to collect information about a user's mental and physical health concerns and stress levels.

[2553] "Analyzing" means processing received data to extract useful information.

[2554] A "dialogue scenario" is a plan of a series of questions and answers used when interacting with a user.

[2555] "Generative artificial intelligence" is an advanced computational technique that generates optimal responses and scenarios based on large amounts of data.

[2556] An "action plan" is a set of specific guidelines for actions taken to solve a user's problem.

[2557] A "smart device" is a highly functional portable device capable of connect...

Claims

1. A means of getting users to input basic information, A means for storing the basic information that has been entered, A means for generating a medical questionnaire form based on stored basic information, A means of receiving responses from users via a medical questionnaire form, A means of analyzing the responses to the received medical questionnaire form, A means for generating a dialogue scenario based on the analysis results, A means of conducting a dialogue with the user based on a generated dialogue scenario, A means of memorizing the content of the dialogue, A means of generating an action plan based on the content of the dialogue, A means of presenting the generated action plan to the user, Means for following up on the progress of the action plan, A system that includes this.

2. A method of using generative artificial intelligence to analyze responses from user questionnaire forms, The system according to claim 1, characterized by including means for constructing a dialogue scenario using generative artificial intelligence.

3. A means to generate and send regular reminders to users to follow up on the progress of the action plan, The system according to claim 1, characterized by including means for conducting follow-up dialogue using generative artificial intelligence.

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