System

Abstract

A system is provided.SOLUTION: A system using artificial intelligence for supporting an employee's mental health, comprising: means for receiving consultation contents from an employee; means for collecting and learning past consultation data and information provided from a specialized institution; means for generating a response using generative artificial intelligence based on the received consultation contents; means for returning the generated response to the employee; and means for recording the consultation contents and the response from the employee.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's workplace, employees often suffer from mental health issues, resulting in decreased productivity and increased turnover. Traditional peer support systems face challenges, such as high barriers to accessing counseling and concerns about confidentiality, making it difficult for employees to seek help. Furthermore, existing counseling systems do not fully utilize past consultation data, making it difficult to generate appropriate responses. Given these circumstances, there is a need for the development of a system that allows employees to seek mental health counseling with peace of mind and provides effective support. [Means for solving the problem]

[0005] The present invention provides a system that uses artificial intelligence to support employee mental health. Specifically, the system includes a means for receiving consultation content from employees, a means for collecting and learning from past consultation data and information provided by specialist institutions, a means for generating a response using a generative artificial intelligence based on the received consultation content, and a means for returning the generated response to the employee. The system also includes a means for recording the consultation content and the response from the employee and continuously training the generative artificial intelligence based on new information. This allows employees to easily seek consultation and receive appropriate and effective responses while ensuring confidentiality, thereby preventing mental health problems from occurring. Furthermore, by including a means for analyzing the employee's input using natural language processing, more accurate responses can be generated.

[0006] "Employee" refers to a staff member who belongs to a company or organization and performs specified duties.

[0007] "Mental health" refers to an individual's state of mind in which they maintain a sense of well-being and are able to manage stress and anxiety appropriately.

[0008] "Artificial intelligence" refers to technology that enables computer systems to mimic human intellectual tasks and automatically learn and make decisions.

[0009] A "system" refers to an integrated structure in which multiple elements interact with each other to perform a specific function.

[0010] "Consultation content" refers to information written by employees about their mental health issues and concerns.

[0011] "Means for receiving" refers to a method or device for acquiring data or information from the outside and incorporating it into the internal environment.

[0012] "Past consultation data" refers to records of consultations previously provided by employees and their responses.

[0013] "Professional organization" refers to an organization or institution with knowledge and experience related to mental health.

[0014] "Generative AI" refers to an AI system that has the ability to generate appropriate responses to employee inquiries.

[0015] "Response" refers to advice or answers provided in response to an employee's inquiry.

[0016] "Recording means" refers to a method or device for storing information or data and managing it for later use.

[0017] "Natural language processing" refers to the technology that allows computers to understand and process human language.

[0018] "Means for analyzing" refers to a method or device that appropriately processes received information and understands its meaning and structure. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The system of the present invention is a system that uses artificial intelligence to support the mental health of employees. This system allows employees to easily consult with the system and receive appropriate advice. Specific forms of the system are described below.

[0041] 1. System Configuration

[0042] The system mainly consists of the following elements:

[0043] Server: Operates and manages the central database and generative AI models.

[0044] Terminal: Provides an interface for employees to input their consultation details.

[0045] User: In this case, an employee.

[0046] 2. Server Roles and Operations

[0047] The server has the following functions:

[0048] Information gathering and learning

[0049] The server periodically collects information from expert institutions and past consultation data, with the aim of training the generative AI model to generate more accurate responses.

[0050] example:

[0051] Collect new mental health research data from professional organizations.

[0052] Receiving and analyzing consultation content

[0053] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing.

[0054] example:

[0055] When a user inputs "I can't sleep because of work stress," the server receives this data and queries a generative AI model for appropriate stress management responses.

[0056] Generating a response

[0057] Generative AI generates appropriate responses based on the analyzed data.

[0058] example:

[0059] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0060] Returning and recording responses

[0061] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0062] example:

[0063] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0064] 3. Roles and Functions of the Terminal

[0065] The terminal provides an interface for employees to input their consultation details.

[0066] Enter the consultation details

[0067] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0068] example:

[0069] An employee types, "I've been so busy at work lately that I don't have time to rest."

[0070] Receiving and displaying responses

[0071] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0072] example:

[0073] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take regular breaks."

[0074] 4. User Interaction

[0075] Users (employees) interact with the system as follows:

[0076] Input of consultation

[0077] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0078] Checking the response

[0079] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[0080] 5. Specific Examples

[0081] Example 1: Stress management consultation

[0082] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[0083] Terminal: Sends the consultation details to the server.

[0084] Server: Analyzes the consultation content and queries the generating AI.

[0085] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[0086] Server: Sends the response back to the user's device.

[0087] Terminal: Display the response.

[0088] Example 2: Consultation about sleep disorders

[0089] User: Employee types, "I've been having trouble sleeping lately."

[0090] Terminal: Sends the consultation details to the server.

[0091] Server: Analyzes the consultation content and queries the generating AI.

[0092] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine."

[0093] Server: Sends the response back to the user's device.

[0094] Terminal: Display the response.

[0095] As described above, the system according to the present invention can quickly and appropriately respond to the mental challenges faced by employees, thereby maintaining the mental health of employees and improving the working environment.

[0096] The processing flow will be explained below.

[0097] Specific explanation of program processing

[0098] Step 1: The user inputs a question from the terminal.

[0099] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[0100] Step 2: The device sends the consultation details to the server

[0101] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[0102] Step 3: The server receives the request.

[0103] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[0104] Step 4: The server analyzes the request

[0105] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[0106] Step 5: The server queries the generated AI

[0107] The server queries the generative AI based on the analysis results, converts the input text into an appropriate format, and provides it as input to the generative AI model (e.g., GPT-4).

[0108] Step 6: The generative AI model generates a response

[0109] The generative AI model generates an appropriate response based on the input text, such as advice like, "To reduce stress, it is effective to set aside time to relax every day."

[0110] Step 7: The server receives the generated response

[0111] The server receives the response from the generated AI and processes it in text format.

[0112] Step 8: The server sends a response to the user's device

[0113] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[0114] Step 9: The terminal displays the response

[0115] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[0116] Step 10: The server records the conversation and response

[0117] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[0118] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In modern society, employee mental health has become an important issue, and many companies are working to support it. However, it is not easy to create an environment where employees can easily seek advice or to provide appropriate, prompt advice. Conventional methods require a great deal of time and effort to collect and analyze consultation content and generate responses, making it difficult to build a system to support employee mental health.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes means for receiving consultation details from employees, means for collecting and learning from past consultation data and information provided by specialized institutions, means for analyzing using natural language processing, means for generating a response using a generative artificial intelligence model based on the analysis results, means for returning the generated response to the employee, and means for recording the consultation details from employees and the responses and saving them as future learning data. This allows employees to easily consult and receive prompt and appropriate advice.

[0124] Below are definitions of important terms contained in the claims.

[0125] An "employee" is an individual who belongs to a company or organization and provides labor on an ongoing basis.

[0126] "Mental health" refers to an individual's psychological and emotional well-being, and is the absence of problems such as stress and anxiety.

[0127] "Artificial intelligence" refers to the technology that enables machines and computers to imitate and perform intelligent human tasks.

[0128] "Means of receiving" refers to a mechanism for obtaining data using electronic communication means.

[0129] "Past consultation data" refers to the consultation content and response history previously collected from employees.

[0130] "Information provided by specialized institutions" refers to the latest information provided by research institutions and experts in mental health and stress management.

[0131] "Learning" refers to the process by which a system uses past data and new information to improve its performance and accuracy.

[0132] "Natural language processing" refers to the technology that allows computers to understand and analyze human language.

[0133] "Means of analysis" refers to the process of evaluating and interpreting input data to understand its meaning and intent.

[0134] A "generative artificial intelligence model" refers to a model that is trained to generate appropriate responses to specific input data.

[0135] "Means for generating a response" refers to a mechanism that automatically creates appropriate advice or answers based on the analysis results.

[0136] "Means of replying" refers to the process of providing the generated response to the original sender (employee).

[0137] "Means for recording" refers to a mechanism for storing received consultations and generated responses in a database for future reference and learning.

[0138] "Future learning data" refers to past data accumulated to improve the system's performance and response accuracy.

[0139] MODE FOR CARRYING OUT THE INVENTION

[0140] System Overview

[0141] This invention is a system that uses artificial intelligence to support the mental health of employees. The system mainly consists of three components: a server, a terminal, and a user. The server operates and manages the central database and generative AI model, and the terminal provides an interface for employees to input their concerns. The user corresponds to the employee.

[0142] Hardware and Software Configuration

[0143] server

[0144] The servers are configured with hardware equipped with powerful processors, ample memory, and fast storage. The software running on these servers includes generative AI models (e.g., GPT-3), natural language processing libraries (e.g., SpaCy and NLTK), and database management systems (e.g., MySQL).

[0145] Terminal

[0146] The terminal is a general computing device such as a PC or smartphone. A web browser and dedicated application are installed on the terminal so that users can input their consultation details, and an interface is provided for communicating with the server.

[0147] Data processing and calculation

[0148] Receiving and analyzing consultation content

[0149] The consultation content entered by the user through the device is sent from the device to the server. After receiving it, the server analyzes it using natural language processing technology. This analysis extracts keywords and context from the input text and understands the intent.

[0150] Generating a response

[0151] Based on the analysis results, the server generates an appropriate response using a generative AI model, which is pre-trained using a large dataset, enabling highly accurate responses.

[0152] Returning and displaying responses

[0153] The generated response is sent back from the server to the user's terminal, which receives the response and displays it to the user, who can then review the displayed response and take any necessary action.

[0154] Data recording and learning

[0155] The server records the consultation details and generated responses in a database, which will be used as training data for the generative AI model in the future, contributing to improving the accuracy of the system.

[0156] Specific examples

[0157] Example 1: Stress management consultation

[0158] User: Type into device, "I'm feeling anxious and stressed at work."

[0159] Terminal: Sends the consultation details to the server.

[0160] Server: Analyzes the content of the consultation and extracts keywords such as "stress" and "anxiety."

[0161] Server: Passes the analysis results to the generative AI model and creates a prompt such as "Please provide advice to reduce stress."

[0162] Generative AI model: Generates appropriate responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[0163] Server: Sends the response back to the user's device.

[0164] Terminal: Display the response and have the user confirm.

[0165] Example 2: Consultation about sleep disorders

[0166] User: Type "I can't sleep lately."

[0167] Terminal: Sends input to the server.

[0168] Server: Analyzes the input and extracts keywords such as "can't sleep."

[0169] Server: Passes the analysis results to the generative AI model and creates a prompt saying, "Please provide advice for the sleep disorder."

[0170] Generative AI model: Generates appropriate responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and to limit caffeine."

[0171] Server: Sends the response back to the user's device.

[0172] Terminal: Display the response and have the user confirm.

[0173] This system is designed to provide prompt and appropriate support for the mental health of users (employees). By using the above-mentioned methods, users can easily consult with the system and quickly receive highly accurate responses from the generative AI model.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The user inputs the content of the consultation through the terminal. For example, the input content is "I can't sleep recently." The input text is stored on the user's terminal.

[0177] Input: The question the user wants to ask (e.g., "I can't sleep lately")

[0178] Output: What you type appears on the terminal

[0179] Specific operation: The user enters the consultation content into the text input field on the device and clicks the "Send" button.

[0180] Step 2:

[0181] The device sends the entered consultation details to the server, and the data may be encrypted.

[0182] Input: The question entered by the user (e.g., "I can't sleep lately")

[0183] Output: Consultation details sent to the server

[0184] Specific operation: The device makes an HTTP POST request and sends the consultation details to the server.

[0185] Step 3:

[0186] The server receives the consultation content from the device and temporarily stores the received data within the server.

[0187] Input: Consultation content sent from the device (e.g., "I can't sleep lately")

[0188] Output: Consultation details saved on the server

[0189] Specific operation: The data received by the server is temporarily stored in memory.

[0190] Step 4:

[0191] The server analyzes the received consultation content using natural language processing (NLP). NLP libraries such as SpaCy and NLTK are used for this analysis. Through the analysis, keywords and context of the consultation content are extracted.

[0192] Input: Received consultation content (e.g., "I can't sleep lately")

[0193] Output: Extracted keywords and contextual information (e.g., stress and anxiety related to "can't sleep")

[0194] What it does: The server uses NLP libraries to perform text analysis and extract keywords and context.

[0195] Step 5:

[0196] The server passes the analysis results to a generative AI model, which generates an appropriate response. The generative AI model has undergone pre-training and creates a prompt sentence based on the analysis results, which is then input into the generative AI model (e.g., GPT-3) to generate a response.

[0197] Input: Analysis results (e.g., keyword "can't sleep")

[0198] Output: Response from the generative AI model (e.g., "To improve the quality of your sleep, you should take time to relax before bed and limit your caffeine intake.")

[0199] Specific operation: The server passes the prompt sentence to the generative AI model and requests it to generate a response.

[0200] Step 6:

[0201] The server generates a response and sends it back to the user's terminal, possibly re-encrypting the data.

[0202] Input: Response from a generative AI model (e.g., "To improve your sleep quality, it's helpful to take some time to relax before bed and limit caffeine consumption.")

[0203] Output: Response sent to the user's device

[0204] Specific operation: The server sends an HTTP response and returns the response data to the terminal.

[0205] Step 7:

[0206] The terminal displays the response received from the server to the user, who then checks it and considers what to do next.

[0207] Input: Response sent by the server (e.g., "To improve your sleep quality, it's helpful to relax before bed and limit caffeine consumption.")

[0208] Output: Response displayed on the terminal

[0209] Specific behavior: Display the response text on the device screen.

[0210] Step 8:

[0211] The server records the consultation details and generated responses in a database, which will be used as learning data for future projects.

[0212] Input: Consultation content and generated response (e.g., Consultation content: "I can't sleep lately," Response: "To improve the quality of my sleep...")

[0213] Output: Consultation details and responses stored in the database

[0214] What happens: The server executes a write operation to the database, storing the data permanently.

[0215] (Application example 1)

[0216] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0217] Today's employees often suffer from stress and mental strain, especially in workplaces such as factories. This can result in reduced work efficiency and health problems. However, there are currently no systems in place that allow employees to seek immediate advice in real time. This creates a need for a system that can respond quickly and appropriately to the mental challenges employees face.

[0218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0219] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for providing an interface through which employees can seek consultation in real time while working via a smart terminal, and means for securely communicating the consultation content and the response. This enables prompt and appropriate responses to mental challenges faced by employees at the workplace.

[0220] An "employee" is a person who works for a company or organization.

[0221] "Mental health" is a state that refers to an individual's psychological or emotional well-being and general sense of well-being.

[0222] "Artificial intelligence" refers to the technology that uses computer systems to imitate human intelligence, as well as the programs and algorithms that make this possible.

[0223] "Consultation content" refers to information that employees input into the system regarding their mental worries and stress.

[0224] The "means for receiving" refers to a method or technology for receiving the consultation content provided by the employee into the system.

[0225] The "means of learning" refers to technology that accumulates and analyzes past consultation data and information provided by specialized institutions to improve the accuracy of the model.

[0226] "Generative AI" is a type of "artificial intelligence" that is a system capable of generating responses based on data received from a user.

[0227] The "means for generating a response" refers to a technique or method for generating appropriate advice or information based on the content of the inquiry from the employee.

[0228] A "response medium" is a technique or method for communicating the generated response to an employee.

[0229] "Means of recording" refers to the technology or methods for saving the employee's consultation and the response thereto.

[0230] "Smart devices" are electronic devices carried or worn by employees, such as smartphones, smart glasses, and head-mounted displays.

[0231] A "real-time consultation interface" is a user interface that allows employees to instantly access the system and input the details of their consultation.

[0232] "Means of communication" refers to the technologies and methods for securely sending and receiving data using the Internet or other communications technologies.

[0233] The system of the present invention is a system that uses artificial intelligence (AI) to support the mental health of employees. This system is composed of the following elements: a server, a terminal, and a user.

[0234] Server Roles and Operations

[0235] The server mainly has the following functions:

[0236] 1. Information gathering and learning:

[0237] The server periodically collects information from specialist institutions and past consultation data, which is used as training data for the generative AI model.

[0238] Example: Collecting new mental health research data from professional organizations.

[0239] 2. Receiving and analyzing consultation content:

[0240] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP).

[0241] For example, if a user enters, "I've been overwhelmed with work lately and it's stressful," the server receives this data and queries a generative AI model for appropriate responses regarding stress management.

[0242] 3. Generate a response:

[0243] Generative AI generates appropriate responses based on the analyzed data.

[0244] Example: A generative AI model might generate a response such as, "To reduce stress, it's effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0245] 4. Returning and Recording Responses:

[0246] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0247] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0248] Device role and operation

[0249] The terminal provides an interface for employees to input the details of their consultation.

[0250] 1. Enter your consultation details:

[0251] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0252] Example: An employee types, "I've been so busy at work lately that I don't have time to rest."

[0253] 2. Receiving and displaying responses:

[0254] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0255] For example, a response such as "Taking breaks helps refresh your mind and body, so make sure to take regular breaks" will be displayed on the device.

[0256] User Interaction

[0257] Users (employees) interact with the system as follows:

[0258] 1. Consultation input:

[0259] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0260] Example prompt: "I've been overwhelmed with work lately and it's been stressful. Can you tell me how to reduce stress in this situation?"

[0261] 2. Check the response:

[0262] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[0263] For example, a possible response would be, "Deep breathing and moderate exercise can help reduce stress. Also, try some relaxation techniques."

[0264] Hardware and software used

[0265] Hardware: Smart devices (smartphones, smart glasses, head-mounted displays, etc.)

[0266] Software: Python program, natural language processing library (spaCy, Transformers), server communication library (requests), generative AI model (OpenAI GPT-4)

[0267] This will enable quick and appropriate responses to the mental challenges employees face at the workplace.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The terminal receives the consultation content from the user (employee). The consultation content entered is saved in text format. For example, the user can enter, "I've been stressed out because I've had too much work lately."

[0271] Step 2:

[0272] The device sends the received consultation content to the server. Specifically, it sends this text data to the server using an HTTP POST request.

[0273] Step 3:

[0274] The server analyzes the received consultation content. First, it uses natural language processing (NLP) technology to convert the consultation content into an easy-to-understand format. This process uses NLP libraries such as spaCy and Transformers. By converting the input text into an easy-to-understand format, keywords and sentiments related to stress and anxiety are extracted.

[0275] Step 4:

[0276] The server queries a generative AI model based on the analyzed data. This generative AI model (e.g., GPT-4) generates an appropriate response based on relevant information. For example, the generative AI model might generate advice such as, "Deep breathing and moderate exercise are effective in reducing stress."

[0277] Step 5:

[0278] The server sends the generated response to the user's terminal, and returns the generated advice to the terminal in text format via an HTTP response.

[0279] Step 6:

[0280] The device displays the response sent back from the server to the user. Specifically, the generated advice is displayed on the display of the smart glasses or smartphone. The user can check the advice and take necessary actions.

[0281] Step 7:

[0282] The server records the consultation and the corresponding response. This record is stored in a database and used as future learning data, allowing the server's generative AI model to be continuously improved.

[0283] Through these steps, users can receive psychological support in real time, and the entire system records the consultation and responses, contributing to improving the accuracy of the generative AI model.

[0284] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0285] The system of the present invention utilizes artificial intelligence to support the mental health of employees, and further combines it with an emotion engine to improve the accuracy and appropriateness of responses. This system allows employees to easily consult with the system and receive appropriate advice according to their emotional state. Specific forms of the system are described below.

[0286] 1. System Configuration

[0287] The system mainly consists of the following elements:

[0288] Server: Operates and manages the central database, generative AI models, and emotion engine.

[0289] Terminal: Provides an interface for employees to input their consultation details.

[0290] User: In this case, an employee.

[0291] 2. Server Roles and Operations

[0292] The server has the following functions:

[0293] Information gathering and learning

[0294] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[0295] example:

[0296] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[0297] Receiving and analyzing consultation content

[0298] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[0299] example:

[0300] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[0301] Generating a response

[0302] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[0303] example:

[0304] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[0305] Returning and recording responses

[0306] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0307] example:

[0308] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0309] 3. Roles and Functions of the Terminal

[0310] The terminal provides an interface for employees to input their consultation details.

[0311] Enter the consultation details

[0312] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0313] example:

[0314] An employee types, "I've been so busy at work lately that I don't have time to rest."

[0315] Receiving and displaying responses

[0316] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0317] example:

[0318] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[0319] 4. User Interaction

[0320] Users (employees) interact with the system as follows:

[0321] Input of consultation

[0322] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0323] Checking the response

[0324] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[0325] 5. Specific Examples

[0326] Example 1: Stress management consultation

[0327] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[0328] Terminal: Sends the consultation details to the server.

[0329] Server: Analyzes the consultation content and queries the generative AI. The emotion engine identifies the user's emotional state as "anxiety."

[0330] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try relaxation techniques. Also, if you are feeling anxious, we recommend consulting a professional counselor."

[0331] Server: Sends the response back to the user's device.

[0332] Terminal: Display the response.

[0333] Example 2: Consultation about sleep disorders

[0334] User: Employee types, "I've been having trouble sleeping lately."

[0335] Terminal: Sends the consultation details to the server.

[0336] Server: Analyzes the consultation content, and the emotion engine identifies the user's emotional state as "stress." It then queries the generation AI.

[0337] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine. Also, it seems like you are feeling stressed, so try some relaxation techniques."

[0338] Server: Sends the response back to the user's device.

[0339] Terminal: Display the response.

[0340] As described above, by combining the emotion engine, the system of the present invention can identify the emotional state of employees and provide more appropriate and personalized advice, thereby maintaining employees' mental health and improving the work environment.

[0341] The processing flow will be explained below.

[0342] Specific explanation of program processing

[0343] Step 1: The user inputs a question from the terminal.

[0344] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[0345] Step 2: The device sends the consultation details to the server

[0346] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[0347] Step 3: The server receives the request.

[0348] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[0349] Step 4: The server analyzes the request

[0350] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[0351] Step 5: The emotion engine recognizes the user's emotion

[0352] An emotion engine built into the server analyzes the text data of the consultation and identifies the user's emotional state, such as "stress," "anxiety," or "sadness."

[0353] Step 6: The server queries the generated AI

[0354] The server queries the generative AI based on the analysis results and the emotion engine results. The input text and emotion data are converted into an appropriate format and provided as input to the generative AI model (e.g., GPT-4).

[0355] Step 7: The generative AI model generates a response

[0356] The generative AI model generates an appropriate response based on the input text and emotional data, such as "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0357] Step 8: The server receives the generated response

[0358] The server receives the response from the generated AI and processes it in text format.

[0359] Step 9: The server sends a response to the user's device

[0360] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[0361] Step 10: The terminal displays the response

[0362] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[0363] Step 11: The server records the conversation and the response.

[0364] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[0365] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice based on their emotional state.

[0366] Example 2

[0367] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] Employee mental health issues have a significant impact on work efficiency and the work environment. Responding promptly and appropriately to stress and anxiety felt by employees is a particularly important issue for companies. However, there is a lack of environments where employees can easily seek advice, and systems that accurately understand their emotional state and provide appropriate advice. This can lead to a decline in employee mental health, which can ultimately lead to a decline in work efficiency and even employee resignation. The purpose of this invention is to solve these issues.

[0369] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the employee, a means for collecting and learning from past consultation data and information provided by specialized institutions, a means for analyzing the received content of the consultation and the emotional state using natural language processing technology, a means for generating a response using a generative artificial intelligence model based on the received content of the consultation and the analysis results, a means for sending the generated response to the employee, and a means for recording the content of the consultation from the employee and the response and using it as future learning data. This makes it possible to accurately grasp the emotional state of the employee and provide prompt and appropriate advice.

[0370] "Employee" refers to an individual working for a business or organization who is particularly required to deal with mental health issues.

[0371] "Consultation content" refers to text information that employees enter into the system, and includes work-related worries and stress in daily life.

[0372] A "means" refers to a device, system, or method used to accomplish a particular purpose.

[0373] "Past consultation data" refers to data including consultation details that employees have previously entered into the system and responses to those consultations.

[0374] "Professional body" refers to an external agency or organization that provides data or research related to mental health or psychology.

[0375] A "generative artificial intelligence model" refers to an artificial intelligence system that automatically generates and analyzes information based on specific algorithms.

[0376] "Natural language processing technology" is a technical field that understands, analyzes, and generates responses to human language, and is used to process text and voice data.

[0377] "Emotional state" refers to the mental state (e.g., anxiety, stress, anger, etc.) of the employee when they enter their consultation details.

[0378] "Response" refers to a message containing advice or a response method that the system generates in response to an employee's inquiry.

[0379] "Recording means" refers to a method or device for storing information for long-term storage and later access.

[0380] "Training data" refers to a collection of past data and new information that the system uses to generate more accurate responses.

[0381] The system of the present invention utilizes artificial intelligence to support the mental health of employees and combines it with an emotion analysis engine to improve the accuracy and appropriateness of responses. Specific embodiments of the system are described below.

[0382] 1. System Configuration

[0383] The system consists of the following main hardware and software:

[0384] Server: Operates and manages the central database, generative AI models (for example, OpenAI's GPT-4 is used as an example of a common generative AI model), and emotion engines (for example, Microsoft's Text Analytics API is an example of a common emotion analysis API).

[0385] Terminal: A device that provides an interface for users, i.e. employees, to input their consultation details. Examples include PCs, smartphones, and tablets.

[0386] User: An employee who uses this system and interacts with the system via a terminal.

[0387] 2. Server Roles and Operations

[0388] Information gathering and learning

[0389] The server periodically collects mental health information provided by specialist institutions, as well as past consultation and emotion data, and uses this data to train the generative AI model and emotion analysis engine, thereby improving the system's response accuracy.

[0390] Example: Download new mental health research data from expert institutions and combine it with historical user consultation data to train a generative AI model.

[0391] Example prompt: "Update your generative AI model with the latest mental health research data."

[0392] Receiving and analyzing consultation content

[0393] The server receives the consultation content sent by the user and analyzes it using natural language processing (NLP) technology, while simultaneously identifying the user's emotional state using an emotion analysis engine.

[0394] Example: If a user types, "I can't sleep because I'm stressed from work," the server receives that data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety or stress).

[0395] Example prompt: "Analyze the following consultation and identify the user's emotional state: 'I can't sleep because of work stress.'"

[0396] Generating a response

[0397] The server uses a generative AI model to generate an appropriate response based on the analysis results, taking into account the emotional state determination results from the emotion analysis engine.

[0398] Example: The generative AI generates a response such as, "To reduce stress, it is important to make time to relax. We also recommend consulting a professional counselor."

[0399] Example prompt: "Generate what advice to offer when the user is feeling anxious."

[0400] Returning and recording responses

[0401] The server returns the generated response to the device, and also records the received consultation content and the generated response in an internal database for future use as learning data.

[0402] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0403] Example prompt: "Send the following response to the user's device and record it on the server: 'To reduce stress, take time to relax.'"

[0404] 3. Roles and Functions of the Terminal

[0405] Enter the consultation details

[0406] Users use the terminal interface to input their concerns and questions, and this information is immediately sent to the server.

[0407] Example: An employee types into a terminal, "I've been busy at work lately and don't have time to refresh myself."

[0408] Example prompt: "Please provide an interface that allows users to enter their concerns."

[0409] Receiving and displaying responses

[0410] The response sent back from the server is displayed on the terminal so that the user can check it.

[0411] Example: A response such as "We recommend you take a short break to relax" appears on the device.

[0412] Example prompt: "Please display the response from the server on the user's terminal."

[0413] 4. User Interaction

[0414] The user inputs the content of the inquiry through the terminal and sends it to the server. The generated response is displayed on the terminal, and the user can check it and take appropriate action.

[0415] Specific examples

[0416] Example 1: Stress management consultation

[0417] User: An employee types into the terminal, "I'm feeling anxious because I'm stressed at work."

[0418] Terminal: Encodes the consultation content into JSON format and sends it to the server.

[0419] Server: The received data is decoded, analyzed using NLP, and the emotion engine identifies the emotional state as "anxiety."

[0420] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to users who are feeling stressed."

[0421] Server: Sends the response to the user's terminal and records it in a database.

[0422] Terminal: Display the response.

[0423] Example 2: Consultation about sleep disorders

[0424] User: An employee types into the terminal, "I haven't been able to sleep lately."

[0425] Terminal: Sends the consultation details to the server.

[0426] Server: Decodes, analyzes using NLP, and the emotion engine identifies the emotional state as "stress."

[0427] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to a user who is experiencing insomnia and stress."

[0428] Server: Sends the response to the user's terminal and records it in a database.

[0429] Terminal: Display the response.

[0430] As described above, the system of the present invention combines an emotion analysis engine and a generative AI model to accurately grasp the emotional state of employees and provide prompt and appropriate advice, thereby maintaining the mental health of employees and improving the work environment.

[0431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0432] Step 1:

[0433] The user inputs the content of the consultation into the terminal interface, which is then processed as text data.

[0434] Specific operation: The user types into the terminal, "I've been busy at work lately and I'm feeling stressed."

[0435] Input: User's concerns (e.g., "I'm busy at work and I'm stressed out")

[0436] Output: The above content will be displayed in the terminal input field.

[0437] Step 2:

[0438] The terminal receives the consultation information entered by the user and formats the data for transmission to the server, where it is encrypted and sent over the Internet.

[0439] Specific operation: The terminal encodes the input consultation content into JSON format and sends it to the server via HTTPS.

[0440] Input: User's consultation content (encrypted text data)

[0441] Output: JSON format data sent to the server

[0442] Step 3:

[0443] The server decodes the consultation content received from the terminal and temporarily stores it in an internal database.

[0444] Specific operation: The server decodes the data received from the terminal and stores it in the "Received Data" table in the database.

[0445] Input: JSON format data sent from the terminal

[0446] Output: Decoded data stored in the "Received Data" table

[0447] Step 4:

[0448] The server applies natural language processing (NLP) technology to the received consultation content and analyzes keywords and themes.

[0449] Specific operation: The server uses an NLP library (e.g., SpaCy or NLTK) to analyze the consultation content and extract keywords such as "stress" and "work."

[0450] Input: Decoded consultation content

[0451] Output: Extracted keywords and themes

[0452] Step 5:

[0453] The server uses a sentiment analysis engine to identify the user's emotional state, using keywords and themes extracted by NLP.

[0454] What it does: The server sends the NLP results to a sentiment analysis engine (e.g., Microsoft's Text Analytics API), which identifies "stress" as the emotion.

[0455] Input: Extracted keywords and themes

[0456] Output: Identified emotional state (e.g., "stressed")

[0457] Step 6:

[0458] The server uses a generative AI model to generate appropriate advice based on the analysis results and emotional state.

[0459] Specific operation: The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "Please provide appropriate advice to a user who is feeling stressed," and generates a response.

[0460] Input: Sentiment analysis engine results and prompt text

[0461] Output: The generated response (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[0462] Step 7:

[0463] The server formats the generated response and prepares it for transmission to the device, where the data is again encrypted and sent over the Internet to the device.

[0464] Specific operation: The server encodes the response data into JSON format and sends it to the terminal via HTTPS.

[0465] Input: Generated response

[0466] Output: JSON format data sent to the terminal

[0467] Step 8:

[0468] The terminal decodes the response data received from the server and displays it on the user interface.

[0469] Specific operation: The terminal analyzes the response data received from the server and displays it to the user.

[0470] Input: JSON format data sent from the server

[0471] Output: The response displayed in the user interface (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[0472] Step 9:

[0473] The server records the received consultation content and the generated response in an internal database and uses it as future learning data.

[0474] Specific operation: The server saves the consultation content and response in the "historical data" table of the database and tags it.

[0475] Input: Consultation content and generated response

[0476] Output: Data stored in the "Historical Data" table

[0477] These are the specific processing steps in a system for supporting the mental health of employees.

[0478] (Application example 2)

[0479] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0480] In today's workplace, employees often experience a lot of stress and anxiety. This can lead to poor mental health, reduced productivity, and a worsening work environment. To solve this problem, a system is needed that employees can easily consult with, understand their emotional state, and provide appropriate advice. However, existing systems lack the ability to analyze emotional states or provide individualized support, making it difficult to effectively support employees' mental health.

[0481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0482] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using generative artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for using an emotion engine to analyze the emotional state of the employee, means for providing the generated response visually and audibly, and means for providing a consultation interface via a smart device and a robot. This makes it possible to more effectively support the mental health of employees and improve the work environment.

[0483] An "employee" is an individual employed by a company or organization whose mental health is to be supported.

[0484] "Mental health" is a state in which an individual maintains a stable psychological state by minimizing mental problems such as stress, anxiety, and depression in their daily lives.

[0485] "Artificial intelligence" refers to technology that learns from large amounts of data and performs pattern recognition and decision-making, including generative AI models and emotion engines.

[0486] "Generative AI" is an AI model that generates appropriate responses and advice based on input data.

[0487] An "emotion engine" is a technology that analyzes and identifies the emotional state of a user based on input data from the user.

[0488] A "smart device" is a smartphone, tablet, or similar electronic device that provides an interface for transferring data between a user and a system.

[0489] A "robot" is a machine that interacts with workers in a factory or other work environment.

[0490] "Natural language processing" is a technology for analyzing, understanding, and generating human language.

[0491] A "problem" is a description of a particular concern or problem that an employee enters into the system.

[0492] A "response" is the advice or answer that the system provides to the employee's inquiry.

[0493] "Recording" is the process by which the system saves employee consultations and responses for subsequent learning and analysis.

[0494] The system of the present invention utilizes artificial intelligence to support employee mental health and further combines an emotion engine to improve the accuracy and appropriateness of responses. Specific embodiments of this system are described below.

[0495] 1. System Configuration

[0496] The system mainly consists of the following elements:

[0497] Server: Operates and manages the central database, generative AI models, and emotion engine.

[0498] Terminal: Provides an interface for employees to input their consultation details. This includes smart devices and robots.

[0499] User: This applies to employees.

[0500] 2. Server Roles and Operations

[0501] The server has the following functions:

[0502] Information gathering and learning

[0503] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[0504] example:

[0505] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[0506] Receiving and analyzing consultation content

[0507] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[0508] example:

[0509] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[0510] Generating a response

[0511] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[0512] example:

[0513] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[0514] Returning and recording responses

[0515] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0516] example:

[0517] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0518] 3. Roles and Functions of the Terminal

[0519] The terminal provides an interface for employees to input their consultation details, and can be a smart device or robot.

[0520] Enter the consultation details

[0521] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0522] example:

[0523] An employee types, "I've been so busy at work lately that I don't have time to rest."

[0524] Receiving and displaying responses

[0525] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0526] example:

[0527] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[0528] 4. User Interaction

[0529] Users (employees) interact with the system as follows:

[0530] Input of consultation

[0531] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0532] Checking the response

[0533] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[0534] 5. Specific Examples

[0535] Example 1: Stress management consultation

[0536] Prompt: "I'm feeling anxious and stressed at work. What can I do?"

[0537] Sample response: "Deep breathing and moderate exercise can help reduce stress. Try relaxation techniques. Also, if you're experiencing anxiety, consider talking to a professional counselor."

[0538] Example 2: Consultation about sleep disorders

[0539] Prompt: "I haven't been able to sleep lately."

[0540] Example response: "To improve the quality of your sleep, you can take some time to relax before bed and avoid caffeine. Also, since you seem to be stressed, try some relaxation techniques."

[0541] The above is a description of the mode for carrying out the invention, which makes it possible to maintain the mental health of employees and improve the working environment.

[0542] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0543] Step 1:

[0544] Input and receive consultation details

[0545] Users input their consultation details via a terminal (smart device or robot). The input data includes text and voice. This input data is immediately sent to the server.

[0546] Input: User's inquiry (e.g., I've been so busy with work lately that I don't have time to rest)

[0547] Output: Received consultation details

[0548] Step 2:

[0549] Analysis of consultation content

[0550] The server analyzes the received consultation content using natural language processing (NLP) technology, which breaks down the content into sentences and recognizes its subject and sentiment.

[0551] Input: Received consultation content

[0552] Output: Analyzed data (themes and sentiment)

[0553] Step 3:

[0554] Emotional state analysis

[0555] The server uses an emotion engine to identify the user's emotional state from the analyzed consultation content, and this identification result is used to generate future responses.

[0556] Input: Parsed data (themes and sentiment)

[0557] Output: Identified emotional state (e.g., anxiety, stress)

[0558] Step 4:

[0559] Generating a response

[0560] The server uses a generative artificial intelligence (generative AI model) to generate appropriate responses based on the identified emotional state and subject matter, which may include specific actions or advice.

[0561] Input: Identified emotional state, subject

[0562] Output: Generated response (e.g., "Deep breathing and moderate exercise can help reduce stress. Try some relaxation techniques.")

[0563] Step 5:

[0564] Returning and displaying responses

[0565] The server generates a response and sends it back to the user's device, which displays the response as voice or text, and the user reviews the response and decides on the next action based on it.

[0566] Input: The generated response

[0567] Output: Display of response (audio or text)

[0568] Step 6:

[0569] Record of consultation content and response

[0570] The server records the employee consultations and responses for future learning and analysis, which will be used to improve the system and collect new data.

[0571] Input: Consultation details, generated response

[0572] Output: Recorded data

[0573] Through these steps, users receive real-time advice to support the mental health of their employees, and the system is continuously learning to improve the accuracy and relevance of its responses.

[0574] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0575] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0576] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0577] [Second embodiment]

[0578] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0579] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0580] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0582] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0584] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0585] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0586] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0587] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0588] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0589] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0590] The system of the present invention is a system that uses artificial intelligence to support the mental health of employees. This system allows employees to easily consult with the system and receive appropriate advice. Specific forms of the system are described below.

[0591] 1. System Configuration

[0592] The system mainly consists of the following elements:

[0593] Server: Operates and manages the central database and generative AI models.

[0594] Terminal: Provides an interface for employees to input their consultation details.

[0595] User: In this case, an employee.

[0596] 2. Server Roles and Operations

[0597] The server has the following functions:

[0598] Information gathering and learning

[0599] The server periodically collects information from expert institutions and past consultation data, with the aim of training the generative AI model to generate more accurate responses.

[0600] example:

[0601] Collect new mental health research data from professional organizations.

[0602] Receiving and analyzing consultation content

[0603] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing.

[0604] example:

[0605] When a user inputs "I can't sleep because of work stress," the server receives this data and queries a generative AI model for appropriate stress management responses.

[0606] Generating a response

[0607] Generative AI generates appropriate responses based on the analyzed data.

[0608] example:

[0609] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0610] Returning and recording responses

[0611] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0612] example:

[0613] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0614] 3. Roles and Functions of the Terminal

[0615] The terminal provides an interface for employees to input their consultation details.

[0616] Enter the consultation details

[0617] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0618] example:

[0619] An employee types, "I've been so busy at work lately that I don't have time to rest."

[0620] Receiving and displaying responses

[0621] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0622] example:

[0623] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take regular breaks."

[0624] 4. User Interaction

[0625] Users (employees) interact with the system as follows:

[0626] Input of consultation

[0627] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0628] Checking the response

[0629] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[0630] 5. Specific Examples

[0631] Example 1: Stress management consultation

[0632] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[0633] Terminal: Sends the consultation details to the server.

[0634] Server: Analyzes the consultation content and queries the generating AI.

[0635] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[0636] Server: Sends the response back to the user's device.

[0637] Terminal: Display the response.

[0638] Example 2: Consultation about sleep disorders

[0639] User: Employee types, "I've been having trouble sleeping lately."

[0640] Terminal: Sends the consultation details to the server.

[0641] Server: Analyzes the consultation content and queries the generating AI.

[0642] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine."

[0643] Server: Sends the response back to the user's device.

[0644] Terminal: Display the response.

[0645] As described above, the system according to the present invention can quickly and appropriately respond to the mental challenges faced by employees, thereby maintaining the mental health of employees and improving the working environment.

[0646] The processing flow will be explained below.

[0647] Specific explanation of program processing

[0648] Step 1: The user inputs a question from the terminal.

[0649] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[0650] Step 2: The device sends the consultation details to the server

[0651] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[0652] Step 3: The server receives the request.

[0653] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[0654] Step 4: The server analyzes the request

[0655] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[0656] Step 5: The server queries the generated AI

[0657] The server queries the generative AI based on the analysis results, converts the input text into an appropriate format, and provides it as input to the generative AI model (e.g., GPT-4).

[0658] Step 6: The generative AI model generates a response

[0659] The generative AI model generates an appropriate response based on the input text, such as advice like, "To reduce stress, it is effective to set aside time to relax every day."

[0660] Step 7: The server receives the generated response

[0661] The server receives the response from the generated AI and processes it in text format.

[0662] Step 8: The server sends a response to the user's device

[0663] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[0664] Step 9: The terminal displays the response

[0665] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[0666] Step 10: The server records the conversation and response

[0667] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[0668] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice.

[0669] Example 1

[0670] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] In modern society, employee mental health has become an important issue, and many companies are working to support it. However, it is not easy to create an environment where employees can easily seek advice or to provide appropriate, prompt advice. Conventional methods require a great deal of time and effort to collect and analyze consultation content and generate responses, making it difficult to build a system to support employee mental health.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0673] In this invention, the server includes means for receiving consultation details from employees, means for collecting and learning from past consultation data and information provided by specialized institutions, means for analyzing using natural language processing, means for generating a response using a generative artificial intelligence model based on the analysis results, means for returning the generated response to the employee, and means for recording the consultation details from employees and the responses and saving them as future learning data. This allows employees to easily consult and receive prompt and appropriate advice.

[0674] Below are definitions of important terms contained in the claims.

[0675] An "employee" is an individual who belongs to a company or organization and provides labor on an ongoing basis.

[0676] "Mental health" refers to an individual's psychological and emotional well-being, and is the absence of problems such as stress and anxiety.

[0677] "Artificial intelligence" refers to the technology that enables machines and computers to imitate and perform intelligent human tasks.

[0678] "Means of receiving" refers to a mechanism for obtaining data using electronic communication means.

[0679] "Past consultation data" refers to the consultation content and response history previously collected from employees.

[0680] "Information provided by specialized institutions" refers to the latest information provided by research institutions and experts in mental health and stress management.

[0681] "Learning" refers to the process by which a system uses past data and new information to improve its performance and accuracy.

[0682] "Natural language processing" refers to the technology that allows computers to understand and analyze human language.

[0683] "Means of analysis" refers to the process of evaluating and interpreting input data to understand its meaning and intent.

[0684] A "generative artificial intelligence model" refers to a model that is trained to generate appropriate responses to specific input data.

[0685] "Means for generating a response" refers to a mechanism that automatically creates appropriate advice or answers based on the analysis results.

[0686] "Means of replying" refers to the process of providing the generated response to the original sender (employee).

[0687] "Means for recording" refers to a mechanism for storing received consultations and generated responses in a database for future reference and learning.

[0688] "Future learning data" refers to past data accumulated to improve the system's performance and response accuracy.

[0689] MODE FOR CARRYING OUT THE INVENTION

[0690] System Overview

[0691] This invention is a system that uses artificial intelligence to support the mental health of employees. The system mainly consists of three components: a server, a terminal, and a user. The server operates and manages the central database and generative AI model, and the terminal provides an interface for employees to input their concerns. The user corresponds to the employee.

[0692] Hardware and Software Configuration

[0693] server

[0694] The servers are configured with hardware equipped with powerful processors, ample memory, and fast storage. The software running on these servers includes generative AI models (e.g., GPT-3), natural language processing libraries (e.g., SpaCy and NLTK), and database management systems (e.g., MySQL).

[0695] Terminal

[0696] The terminal is a general computing device such as a PC or smartphone. A web browser and dedicated application are installed on the terminal so that users can input their consultation details, and an interface is provided for communicating with the server.

[0697] Data processing and calculation

[0698] Receiving and analyzing consultation content

[0699] The consultation content entered by the user through the device is sent from the device to the server. After receiving it, the server analyzes it using natural language processing technology. This analysis extracts keywords and context from the input text and understands the intent.

[0700] Generating a response

[0701] Based on the analysis results, the server generates an appropriate response using a generative AI model, which is pre-trained using a large dataset, enabling highly accurate responses.

[0702] Returning and displaying responses

[0703] The generated response is sent back from the server to the user's terminal, which receives the response and displays it to the user, who can then review the displayed response and take any necessary action.

[0704] Data recording and learning

[0705] The server records the consultation details and generated responses in a database, which will be used as training data for the generative AI model in the future, contributing to improving the accuracy of the system.

[0706] Specific examples

[0707] Example 1: Stress management consultation

[0708] User: Type into device, "I'm feeling anxious and stressed at work."

[0709] Terminal: Sends the consultation details to the server.

[0710] Server: Analyzes the content of the consultation and extracts keywords such as "stress" and "anxiety."

[0711] Server: Passes the analysis results to the generative AI model and creates a prompt such as "Please provide advice to reduce stress."

[0712] Generative AI model: Generates appropriate responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[0713] Server: Sends the response back to the user's device.

[0714] Terminal: Display the response and have the user confirm.

[0715] Example 2: Consultation about sleep disorders

[0716] User: Type "I can't sleep lately."

[0717] Terminal: Sends input to the server.

[0718] Server: Analyzes the input and extracts keywords such as "can't sleep."

[0719] Server: Passes the analysis results to the generative AI model and creates a prompt saying, "Please provide advice for the sleep disorder."

[0720] Generative AI model: Generates appropriate responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and to limit caffeine."

[0721] Server: Sends the response back to the user's device.

[0722] Terminal: Display the response and have the user confirm.

[0723] This system is designed to provide prompt and appropriate support for the mental health of users (employees). By using the above-mentioned methods, users can easily consult with the system and quickly receive highly accurate responses from the generative AI model.

[0724] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0725] Step 1:

[0726] The user inputs the content of the consultation through the terminal. For example, the input content is "I can't sleep recently." The input text is stored on the user's terminal.

[0727] Input: The question the user wants to ask (e.g., "I can't sleep lately")

[0728] Output: What you type appears on the terminal

[0729] Specific operation: The user enters the consultation content into the text input field on the device and clicks the "Send" button.

[0730] Step 2:

[0731] The device sends the entered consultation details to the server, and the data may be encrypted.

[0732] Input: The question entered by the user (e.g., "I can't sleep lately")

[0733] Output: Consultation details sent to the server

[0734] Specific operation: The device makes an HTTP POST request and sends the consultation details to the server.

[0735] Step 3:

[0736] The server receives the consultation content from the device and temporarily stores the received data within the server.

[0737] Input: Consultation content sent from the device (e.g., "I can't sleep lately")

[0738] Output: Consultation details saved on the server

[0739] Specific operation: The data received by the server is temporarily stored in memory.

[0740] Step 4:

[0741] The server analyzes the received consultation content using natural language processing (NLP). NLP libraries such as SpaCy and NLTK are used for this analysis. Through the analysis, keywords and context of the consultation content are extracted.

[0742] Input: Received consultation content (e.g., "I can't sleep lately")

[0743] Output: Extracted keywords and contextual information (e.g., stress and anxiety related to "can't sleep")

[0744] What it does: The server uses NLP libraries to perform text analysis and extract keywords and context.

[0745] Step 5:

[0746] The server passes the analysis results to a generative AI model, which generates an appropriate response. The generative AI model has undergone pre-training and creates a prompt sentence based on the analysis results, which is then input into the generative AI model (e.g., GPT-3) to generate a response.

[0747] Input: Analysis results (e.g., keyword "can't sleep")

[0748] Output: Response from the generative AI model (e.g., "To improve the quality of your sleep, you should take time to relax before bed and limit your caffeine intake.")

[0749] Specific operation: The server passes the prompt sentence to the generative AI model and requests it to generate a response.

[0750] Step 6:

[0751] The server generates a response and sends it back to the user's terminal, possibly re-encrypting the data.

[0752] Input: Response from a generative AI model (e.g., "To improve your sleep quality, it's helpful to take some time to relax before bed and limit caffeine consumption.")

[0753] Output: Response sent to the user's device

[0754] Specific operation: The server sends an HTTP response and returns the response data to the terminal.

[0755] Step 7:

[0756] The terminal displays the response received from the server to the user, who then checks it and considers what to do next.

[0757] Input: Response sent by the server (e.g., "To improve your sleep quality, it's helpful to relax before bed and limit caffeine consumption.")

[0758] Output: Response displayed on the terminal

[0759] Specific behavior: Display the response text on the device screen.

[0760] Step 8:

[0761] The server records the consultation details and generated responses in a database, which will be used as learning data for future projects.

[0762] Input: Consultation content and generated response (e.g., Consultation content: "I can't sleep lately," Response: "To improve the quality of my sleep...")

[0763] Output: Consultation details and responses stored in the database

[0764] What happens: The server executes a write operation to the database, storing the data permanently.

[0765] (Application example 1)

[0766] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0767] Today's employees often suffer from stress and mental strain, especially in workplaces such as factories. This can result in reduced work efficiency and health problems. However, there are currently no systems in place that allow employees to seek immediate advice in real time. This creates a need for a system that can respond quickly and appropriately to the mental challenges employees face.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0769] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for providing an interface through which employees can seek consultation in real time while working via a smart terminal, and means for securely communicating the consultation content and the response. This enables prompt and appropriate responses to mental challenges faced by employees at the workplace.

[0770] An "employee" is a person who works for a company or organization.

[0771] "Mental health" is a state that refers to an individual's psychological or emotional well-being and general sense of well-being.

[0772] "Artificial intelligence" refers to the technology that uses computer systems to imitate human intelligence, as well as the programs and algorithms that make this possible.

[0773] "Consultation content" refers to information that employees input into the system regarding their mental worries and stress.

[0774] The "means for receiving" refers to a method or technology for receiving the consultation content provided by the employee into the system.

[0775] The "means of learning" refers to technology that accumulates and analyzes past consultation data and information provided by specialized institutions to improve the accuracy of the model.

[0776] "Generative AI" is a type of "artificial intelligence" that is a system capable of generating responses based on data received from a user.

[0777] The "means for generating a response" refers to a technique or method for generating appropriate advice or information based on the content of the inquiry from the employee.

[0778] A "response medium" is a technique or method for communicating the generated response to an employee.

[0779] "Means of recording" refers to the technology or methods for saving the employee's consultation and the response thereto.

[0780] "Smart devices" are electronic devices carried or worn by employees, such as smartphones, smart glasses, and head-mounted displays.

[0781] A "real-time consultation interface" is a user interface that allows employees to instantly access the system and input the details of their consultation.

[0782] "Means of communication" refers to the technologies and methods for securely sending and receiving data using the Internet or other communications technologies.

[0783] The system of the present invention is a system that uses artificial intelligence (AI) to support the mental health of employees. This system is composed of the following elements: a server, a terminal, and a user.

[0784] Server Roles and Operations

[0785] The server mainly has the following functions:

[0786] 1. Information gathering and learning:

[0787] The server periodically collects information from specialist institutions and past consultation data, which is used as training data for the generative AI model.

[0788] Example: Collecting new mental health research data from professional organizations.

[0789] 2. Receiving and analyzing consultation content:

[0790] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP).

[0791] For example, if a user enters, "I've been overwhelmed with work lately and it's stressful," the server receives this data and queries a generative AI model for appropriate responses regarding stress management.

[0792] 3. Generate a response:

[0793] Generative AI generates appropriate responses based on the analyzed data.

[0794] Example: A generative AI model might generate a response such as, "To reduce stress, it's effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0795] 4. Returning and Recording Responses:

[0796] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0797] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0798] Device role and operation

[0799] The terminal provides an interface for employees to input the details of their consultation.

[0800] 1. Enter your consultation details:

[0801] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0802] Example: An employee types, "I've been so busy at work lately that I don't have time to rest."

[0803] 2. Receiving and displaying responses:

[0804] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0805] For example, a response such as "Taking breaks helps refresh your mind and body, so make sure to take regular breaks" will be displayed on the device.

[0806] User Interaction

[0807] Users (employees) interact with the system as follows:

[0808] 1. Consultation input:

[0809] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0810] Example prompt: "I've been overwhelmed with work lately and it's been stressful. Can you tell me how to reduce stress in this situation?"

[0811] 2. Check the response:

[0812] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[0813] For example, a possible response would be, "Deep breathing and moderate exercise can help reduce stress. Also, try some relaxation techniques."

[0814] Hardware and software used

[0815] Hardware: Smart devices (smartphones, smart glasses, head-mounted displays, etc.)

[0816] Software: Python program, natural language processing library (spaCy, Transformers), server communication library (requests), generative AI model (OpenAI GPT-4)

[0817] This will enable quick and appropriate responses to the mental challenges employees face at the workplace.

[0818] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0819] Step 1:

[0820] The terminal receives the consultation content from the user (employee). The consultation content entered is saved in text format. For example, the user can enter, "I've been stressed out because I've had too much work lately."

[0821] Step 2:

[0822] The device sends the received consultation content to the server. Specifically, it sends this text data to the server using an HTTP POST request.

[0823] Step 3:

[0824] The server analyzes the received consultation content. First, it uses natural language processing (NLP) technology to convert the consultation content into an easy-to-understand format. This process uses NLP libraries such as spaCy and Transformers. By converting the input text into an easy-to-understand format, keywords and sentiments related to stress and anxiety are extracted.

[0825] Step 4:

[0826] The server queries a generative AI model based on the analyzed data. This generative AI model (e.g., GPT-4) generates an appropriate response based on relevant information. For example, the generative AI model might generate advice such as, "Deep breathing and moderate exercise are effective in reducing stress."

[0827] Step 5:

[0828] The server sends the generated response to the user's terminal, and returns the generated advice to the terminal in text format via an HTTP response.

[0829] Step 6:

[0830] The device displays the response sent back from the server to the user. Specifically, the generated advice is displayed on the display of the smart glasses or smartphone. The user can check the advice and take necessary actions.

[0831] Step 7:

[0832] The server records the consultation and the corresponding response. This record is stored in a database and used as future learning data, allowing the server's generative AI model to be continuously improved.

[0833] Through these steps, users can receive psychological support in real time, and the entire system records the consultation and responses, contributing to improving the accuracy of the generative AI model.

[0834] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0835] The system of the present invention utilizes artificial intelligence to support the mental health of employees, and further combines it with an emotion engine to improve the accuracy and appropriateness of responses. This system allows employees to easily consult with the system and receive appropriate advice according to their emotional state. Specific forms of the system are described below.

[0836] 1. System Configuration

[0837] The system mainly consists of the following elements:

[0838] Server: Operates and manages the central database, generative AI models, and emotion engine.

[0839] Terminal: Provides an interface for employees to input their consultation details.

[0840] User: In this case, an employee.

[0841] 2. Server Roles and Operations

[0842] The server has the following functions:

[0843] Information gathering and learning

[0844] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[0845] example:

[0846] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[0847] Receiving and analyzing consultation content

[0848] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[0849] example:

[0850] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[0851] Generating a response

[0852] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[0853] example:

[0854] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[0855] Returning and recording responses

[0856] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[0857] example:

[0858] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0859] 3. Roles and Functions of the Terminal

[0860] The terminal provides an interface for employees to input their consultation details.

[0861] Enter the consultation details

[0862] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[0863] example:

[0864] An employee types, "I've been so busy at work lately that I don't have time to rest."

[0865] Receiving and displaying responses

[0866] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[0867] example:

[0868] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[0869] 4. User Interaction

[0870] Users (employees) interact with the system as follows:

[0871] Input of consultation

[0872] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[0873] Checking the response

[0874] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[0875] 5. Specific Examples

[0876] Example 1: Stress management consultation

[0877] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[0878] Terminal: Sends the consultation details to the server.

[0879] Server: Analyzes the consultation content and queries the generative AI. The emotion engine identifies the user's emotional state as "anxiety."

[0880] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try relaxation techniques. Also, if you are feeling anxious, we recommend consulting a professional counselor."

[0881] Server: Sends the response back to the user's device.

[0882] Terminal: Display the response.

[0883] Example 2: Consultation about sleep disorders

[0884] User: Employee types, "I've been having trouble sleeping lately."

[0885] Terminal: Sends the consultation details to the server.

[0886] Server: Analyzes the consultation content, and the emotion engine identifies the user's emotional state as "stress." It then queries the generation AI.

[0887] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine. Also, it seems like you are feeling stressed, so try some relaxation techniques."

[0888] Server: Sends the response back to the user's device.

[0889] Terminal: Display the response.

[0890] As described above, by combining the emotion engine, the system of the present invention can identify the emotional state of employees and provide more appropriate and personalized advice, thereby maintaining employees' mental health and improving the work environment.

[0891] The processing flow will be explained below.

[0892] Specific explanation of program processing

[0893] Step 1: The user inputs a question from the terminal.

[0894] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[0895] Step 2: The device sends the consultation details to the server

[0896] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[0897] Step 3: The server receives the request.

[0898] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[0899] Step 4: The server analyzes the request

[0900] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[0901] Step 5: The emotion engine recognizes the user's emotion

[0902] An emotion engine built into the server analyzes the text data of the consultation and identifies the user's emotional state, such as "stress," "anxiety," or "sadness."

[0903] Step 6: The server queries the generated AI

[0904] The server queries the generative AI based on the analysis results and the emotion engine results. The input text and emotion data are converted into an appropriate format and provided as input to the generative AI model (e.g., GPT-4).

[0905] Step 7: The generative AI model generates a response

[0906] The generative AI model generates an appropriate response based on the input text and emotional data, such as "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[0907] Step 8: The server receives the generated response

[0908] The server receives the response from the generated AI and processes it in text format.

[0909] Step 9: The server sends a response to the user's device

[0910] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[0911] Step 10: The terminal displays the response

[0912] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[0913] Step 11: The server records the conversation and the response.

[0914] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[0915] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice based on their emotional state.

[0916] Example 2

[0917] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0918] Employee mental health issues have a significant impact on work efficiency and the work environment. Responding promptly and appropriately to stress and anxiety felt by employees is a particularly important issue for companies. However, there is a lack of environments where employees can easily seek advice, and systems that accurately understand their emotional state and provide appropriate advice. This can lead to a decline in employee mental health, which can ultimately lead to a decline in work efficiency and even employee resignation. The purpose of this invention is to solve these issues.

[0919] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the employee, a means for collecting and learning from past consultation data and information provided by specialized institutions, a means for analyzing the received content of the consultation and the emotional state using natural language processing technology, a means for generating a response using a generative artificial intelligence model based on the received content of the consultation and the analysis results, a means for sending the generated response to the employee, and a means for recording the content of the consultation from the employee and the response and using it as future learning data. This makes it possible to accurately grasp the emotional state of the employee and provide prompt and appropriate advice.

[0920] "Employee" refers to an individual working for a business or organization who is particularly required to deal with mental health issues.

[0921] "Consultation content" refers to text information that employees enter into the system, and includes work-related worries and stress in daily life.

[0922] A "means" refers to a device, system, or method used to accomplish a particular purpose.

[0923] "Past consultation data" refers to data including consultation details that employees have previously entered into the system and responses to those consultations.

[0924] "Professional body" refers to an external agency or organization that provides data or research related to mental health or psychology.

[0925] A "generative artificial intelligence model" refers to an artificial intelligence system that automatically generates and analyzes information based on specific algorithms.

[0926] "Natural language processing technology" is a technical field that understands, analyzes, and generates responses to human language, and is used to process text and voice data.

[0927] "Emotional state" refers to the mental state (e.g., anxiety, stress, anger, etc.) of the employee when they enter their consultation details.

[0928] "Response" refers to a message containing advice or a response method that the system generates in response to an employee's inquiry.

[0929] "Recording means" refers to a method or device for storing information for long-term storage and later access.

[0930] "Training data" refers to a collection of past data and new information that the system uses to generate more accurate responses.

[0931] The system of the present invention utilizes artificial intelligence to support the mental health of employees and combines it with an emotion analysis engine to improve the accuracy and appropriateness of responses. Specific embodiments of the system are described below.

[0932] 1. System Configuration

[0933] The system consists of the following main hardware and software:

[0934] Server: Operates and manages the central database, generative AI models (for example, OpenAI's GPT-4 is used as an example of a common generative AI model), and emotion engines (for example, Microsoft's Text Analytics API is an example of a common emotion analysis API).

[0935] Terminal: A device that provides an interface for users, i.e. employees, to input their consultation details. Examples include PCs, smartphones, and tablets.

[0936] User: An employee who uses this system and interacts with the system via a terminal.

[0937] 2. Server Roles and Operations

[0938] Information gathering and learning

[0939] The server periodically collects mental health information provided by specialist institutions, as well as past consultation and emotion data, and uses this data to train the generative AI model and emotion analysis engine, thereby improving the system's response accuracy.

[0940] Example: Download new mental health research data from expert institutions and combine it with historical user consultation data to train a generative AI model.

[0941] Example prompt: "Update your generative AI model with the latest mental health research data."

[0942] Receiving and analyzing consultation content

[0943] The server receives the consultation content sent by the user and analyzes it using natural language processing (NLP) technology, while simultaneously identifying the user's emotional state using an emotion analysis engine.

[0944] Example: If a user types, "I can't sleep because I'm stressed from work," the server receives that data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety or stress).

[0945] Example prompt: "Analyze the following consultation and identify the user's emotional state: 'I can't sleep because of work stress.'"

[0946] Generating a response

[0947] The server uses a generative AI model to generate an appropriate response based on the analysis results, taking into account the emotional state determination results from the emotion analysis engine.

[0948] Example: The generative AI generates a response such as, "To reduce stress, it is important to make time to relax. We also recommend consulting a professional counselor."

[0949] Example prompt: "Generate what advice to offer when the user is feeling anxious."

[0950] Returning and recording responses

[0951] The server returns the generated response to the device, and also records the received consultation content and the generated response in an internal database for future use as learning data.

[0952] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[0953] Example prompt: "Send the following response to the user's device and record it on the server: 'To reduce stress, take time to relax.'"

[0954] 3. Roles and Functions of the Terminal

[0955] Enter the consultation details

[0956] Users use the terminal interface to input their concerns and questions, and this information is immediately sent to the server.

[0957] Example: An employee types into a terminal, "I've been busy at work lately and don't have time to refresh myself."

[0958] Example prompt: "Please provide an interface that allows users to enter their concerns."

[0959] Receiving and displaying responses

[0960] The response sent back from the server is displayed on the terminal so that the user can check it.

[0961] Example: A response such as "We recommend you take a short break to relax" appears on the device.

[0962] Example prompt: "Please display the response from the server on the user's terminal."

[0963] 4. User Interaction

[0964] The user inputs the content of the inquiry through the terminal and sends it to the server. The generated response is displayed on the terminal, and the user can check it and take appropriate action.

[0965] Specific examples

[0966] Example 1: Stress management consultation

[0967] User: An employee types into the terminal, "I'm feeling anxious because I'm stressed at work."

[0968] Terminal: Encodes the consultation content into JSON format and sends it to the server.

[0969] Server: The received data is decoded, analyzed using NLP, and the emotion engine identifies the emotional state as "anxiety."

[0970] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to users who are feeling stressed."

[0971] Server: Sends the response to the user's terminal and records it in a database.

[0972] Terminal: Display the response.

[0973] Example 2: Consultation about sleep disorders

[0974] User: An employee types into the terminal, "I haven't been able to sleep lately."

[0975] Terminal: Sends the consultation details to the server.

[0976] Server: Decodes, analyzes using NLP, and the emotion engine identifies the emotional state as "stress."

[0977] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to a user who is experiencing insomnia and stress."

[0978] Server: Sends the response to the user's terminal and records it in a database.

[0979] Terminal: Display the response.

[0980] As described above, the system of the present invention combines an emotion analysis engine and a generative AI model to accurately grasp the emotional state of employees and provide prompt and appropriate advice, thereby maintaining the mental health of employees and improving the work environment.

[0981] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0982] Step 1:

[0983] The user inputs the content of the consultation into the terminal interface, which is then processed as text data.

[0984] Specific operation: The user types into the terminal, "I've been busy at work lately and I'm feeling stressed."

[0985] Input: User's concerns (e.g., "I'm busy at work and I'm stressed out")

[0986] Output: The above content will be displayed in the terminal input field.

[0987] Step 2:

[0988] The terminal receives the consultation information entered by the user and formats the data for transmission to the server, where it is encrypted and sent over the Internet.

[0989] Specific operation: The terminal encodes the input consultation content into JSON format and sends it to the server via HTTPS.

[0990] Input: User's consultation content (encrypted text data)

[0991] Output: JSON format data sent to the server

[0992] Step 3:

[0993] The server decodes the consultation content received from the terminal and temporarily stores it in an internal database.

[0994] Specific operation: The server decodes the data received from the terminal and stores it in the "Received Data" table in the database.

[0995] Input: JSON format data sent from the terminal

[0996] Output: Decoded data stored in the "Received Data" table

[0997] Step 4:

[0998] The server applies natural language processing (NLP) technology to the received consultation content and analyzes keywords and themes.

[0999] Specific operation: The server uses an NLP library (e.g., SpaCy or NLTK) to analyze the consultation content and extract keywords such as "stress" and "work."

[1000] Input: Decoded consultation content

[1001] Output: Extracted keywords and themes

[1002] Step 5:

[1003] The server uses a sentiment analysis engine to identify the user's emotional state, using keywords and themes extracted by NLP.

[1004] What it does: The server sends the NLP results to a sentiment analysis engine (e.g., Microsoft's Text Analytics API), which identifies "stress" as the emotion.

[1005] Input: Extracted keywords and themes

[1006] Output: Identified emotional state (e.g., "stressed")

[1007] Step 6:

[1008] The server uses a generative AI model to generate appropriate advice based on the analysis results and emotional state.

[1009] Specific operation: The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "Please provide appropriate advice to a user who is feeling stressed," and generates a response.

[1010] Input: Sentiment analysis engine results and prompt text

[1011] Output: The generated response (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[1012] Step 7:

[1013] The server formats the generated response and prepares it for transmission to the device, where the data is again encrypted and sent over the Internet to the device.

[1014] Specific operation: The server encodes the response data into JSON format and sends it to the terminal via HTTPS.

[1015] Input: Generated response

[1016] Output: JSON format data sent to the terminal

[1017] Step 8:

[1018] The terminal decodes the response data received from the server and displays it on the user interface.

[1019] Specific operation: The terminal analyzes the response data received from the server and displays it to the user.

[1020] Input: JSON format data sent from the server

[1021] Output: The response displayed in the user interface (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[1022] Step 9:

[1023] The server records the received consultation content and the generated response in an internal database and uses it as future learning data.

[1024] Specific operation: The server saves the consultation content and response in the "historical data" table of the database and tags it.

[1025] Input: Consultation content and generated response

[1026] Output: Data stored in the "Historical Data" table

[1027] These are the specific processing steps in a system for supporting the mental health of employees.

[1028] (Application example 2)

[1029] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1030] In today's workplace, employees often experience a lot of stress and anxiety. This can lead to poor mental health, reduced productivity, and a worsening work environment. To solve this problem, a system is needed that employees can easily consult with, understand their emotional state, and provide appropriate advice. However, existing systems lack the ability to analyze emotional states or provide individualized support, making it difficult to effectively support employees' mental health.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1032] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using generative artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for using an emotion engine to analyze the emotional state of the employee, means for providing the generated response visually and audibly, and means for providing a consultation interface via a smart device and a robot. This makes it possible to more effectively support the mental health of employees and improve the work environment.

[1033] An "employee" is an individual employed by a company or organization whose mental health is to be supported.

[1034] "Mental health" is a state in which an individual maintains a stable psychological state by minimizing mental problems such as stress, anxiety, and depression in their daily lives.

[1035] "Artificial intelligence" refers to technology that learns from large amounts of data and performs pattern recognition and decision-making, including generative AI models and emotion engines.

[1036] "Generative AI" is an AI model that generates appropriate responses and advice based on input data.

[1037] An "emotion engine" is a technology that analyzes and identifies the emotional state of a user based on input data from the user.

[1038] A "smart device" is a smartphone, tablet, or similar electronic device that provides an interface for transferring data between a user and a system.

[1039] A "robot" is a machine that interacts with workers in a factory or other work environment.

[1040] "Natural language processing" is a technology for analyzing, understanding, and generating human language.

[1041] A "problem" is a description of a particular concern or problem that an employee enters into the system.

[1042] A "response" is the advice or answer that the system provides to the employee's inquiry.

[1043] "Recording" is the process by which the system saves employee consultations and responses for subsequent learning and analysis.

[1044] The system of the present invention utilizes artificial intelligence to support employee mental health and further combines an emotion engine to improve the accuracy and appropriateness of responses. Specific embodiments of this system are described below.

[1045] 1. System Configuration

[1046] The system mainly consists of the following elements:

[1047] Server: Operates and manages the central database, generative AI models, and emotion engine.

[1048] Terminal: Provides an interface for employees to input their consultation details. This includes smart devices and robots.

[1049] User: This applies to employees.

[1050] 2. Server Roles and Operations

[1051] The server has the following functions:

[1052] Information gathering and learning

[1053] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[1054] example:

[1055] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[1056] Receiving and analyzing consultation content

[1057] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[1058] example:

[1059] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[1060] Generating a response

[1061] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[1062] example:

[1063] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[1064] Returning and recording responses

[1065] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1066] example:

[1067] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1068] 3. Roles and Functions of the Terminal

[1069] The terminal provides an interface for employees to input their consultation details, and can be a smart device or robot.

[1070] Enter the consultation details

[1071] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1072] example:

[1073] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1074] Receiving and displaying responses

[1075] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1076] example:

[1077] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[1078] 4. User Interaction

[1079] Users (employees) interact with the system as follows:

[1080] Input of consultation

[1081] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1082] Checking the response

[1083] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[1084] 5. Specific Examples

[1085] Example 1: Stress management consultation

[1086] Prompt: "I'm feeling anxious and stressed at work. What can I do?"

[1087] Sample response: "Deep breathing and moderate exercise can help reduce stress. Try relaxation techniques. Also, if you're experiencing anxiety, consider talking to a professional counselor."

[1088] Example 2: Consultation about sleep disorders

[1089] Prompt: "I haven't been able to sleep lately."

[1090] Example response: "To improve the quality of your sleep, you can take some time to relax before bed and avoid caffeine. Also, since you seem to be stressed, try some relaxation techniques."

[1091] The above is a description of the mode for carrying out the invention, which makes it possible to maintain the mental health of employees and improve the working environment.

[1092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1093] Step 1:

[1094] Input and receive consultation details

[1095] Users input their consultation details via a terminal (smart device or robot). The input data includes text and voice. This input data is immediately sent to the server.

[1096] Input: User's inquiry (e.g., I've been so busy with work lately that I don't have time to rest)

[1097] Output: Received consultation details

[1098] Step 2:

[1099] Analysis of consultation content

[1100] The server analyzes the received consultation content using natural language processing (NLP) technology, which breaks down the content into sentences and recognizes its subject and sentiment.

[1101] Input: Received consultation content

[1102] Output: Analyzed data (themes and sentiment)

[1103] Step 3:

[1104] Emotional state analysis

[1105] The server uses an emotion engine to identify the user's emotional state from the analyzed consultation content, and this identification result is used to generate future responses.

[1106] Input: Parsed data (themes and sentiment)

[1107] Output: Identified emotional state (e.g., anxiety, stress)

[1108] Step 4:

[1109] Generating a response

[1110] The server uses a generative artificial intelligence (generative AI model) to generate appropriate responses based on the identified emotional state and subject matter, which may include specific actions or advice.

[1111] Input: Identified emotional state, subject

[1112] Output: Generated response (e.g., "Deep breathing and moderate exercise can help reduce stress. Try some relaxation techniques.")

[1113] Step 5:

[1114] Returning and displaying responses

[1115] The server generates a response and sends it back to the user's device, which displays the response as voice or text, and the user reviews the response and decides on the next action based on it.

[1116] Input: The generated response

[1117] Output: Display of response (audio or text)

[1118] Step 6:

[1119] Record of consultation content and response

[1120] The server records the employee consultations and responses for future learning and analysis, which will be used to improve the system and collect new data.

[1121] Input: Consultation details, generated response

[1122] Output: Recorded data

[1123] Through these steps, users receive real-time advice to support the mental health of their employees, and the system is continuously learning to improve the accuracy and relevance of its responses.

[1124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1126] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1127] [Third embodiment]

[1128] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1132] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1136] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1138] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1139] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1140] The system of the present invention is a system that uses artificial intelligence to support the mental health of employees. This system allows employees to easily consult with the system and receive appropriate advice. Specific forms of the system are described below.

[1141] 1. System Configuration

[1142] The system mainly consists of the following elements:

[1143] Server: Operates and manages the central database and generative AI models.

[1144] Terminal: Provides an interface for employees to input their consultation details.

[1145] User: In this case, an employee.

[1146] 2. Server Roles and Operations

[1147] The server has the following functions:

[1148] Information gathering and learning

[1149] The server periodically collects information from expert institutions and past consultation data, with the aim of training the generative AI model to generate more accurate responses.

[1150] example:

[1151] Collect new mental health research data from professional organizations.

[1152] Receiving and analyzing consultation content

[1153] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing.

[1154] example:

[1155] When a user inputs "I can't sleep because of work stress," the server receives this data and queries a generative AI model for appropriate stress management responses.

[1156] Generating a response

[1157] Generative AI generates appropriate responses based on the analyzed data.

[1158] example:

[1159] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[1160] Returning and recording responses

[1161] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1162] example:

[1163] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1164] 3. Roles and Functions of the Terminal

[1165] The terminal provides an interface for employees to input their consultation details.

[1166] Enter the consultation details

[1167] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1168] example:

[1169] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1170] Receiving and displaying responses

[1171] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1172] example:

[1173] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take regular breaks."

[1174] 4. User Interaction

[1175] Users (employees) interact with the system as follows:

[1176] Input of consultation

[1177] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1178] Checking the response

[1179] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[1180] 5. Specific Examples

[1181] Example 1: Stress management consultation

[1182] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[1183] Terminal: Sends the consultation details to the server.

[1184] Server: Analyzes the consultation content and queries the generating AI.

[1185] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[1186] Server: Sends the response back to the user's device.

[1187] Terminal: Display the response.

[1188] Example 2: Consultation about sleep disorders

[1189] User: Employee types, "I've been having trouble sleeping lately."

[1190] Terminal: Sends the consultation details to the server.

[1191] Server: Analyzes the consultation content and queries the generating AI.

[1192] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine."

[1193] Server: Sends the response back to the user's device.

[1194] Terminal: Display the response.

[1195] As described above, the system according to the present invention can quickly and appropriately respond to the mental challenges faced by employees, thereby maintaining the mental health of employees and improving the working environment.

[1196] The processing flow will be explained below.

[1197] Specific explanation of program processing

[1198] Step 1: The user inputs a question from the terminal.

[1199] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[1200] Step 2: The device sends the consultation details to the server

[1201] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[1202] Step 3: The server receives the request.

[1203] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[1204] Step 4: The server analyzes the request

[1205] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[1206] Step 5: The server queries the generated AI

[1207] The server queries the generative AI based on the analysis results, converts the input text into an appropriate format, and provides it as input to the generative AI model (e.g., GPT-4).

[1208] Step 6: The generative AI model generates a response

[1209] The generative AI model generates an appropriate response based on the input text, such as advice like, "To reduce stress, it is effective to set aside time to relax every day."

[1210] Step 7: The server receives the generated response

[1211] The server receives the response from the generated AI and processes it in text format.

[1212] Step 8: The server sends a response to the user's device

[1213] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[1214] Step 9: The terminal displays the response

[1215] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[1216] Step 10: The server records the conversation and response

[1217] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[1218] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice.

[1219] Example 1

[1220] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1221] In modern society, employee mental health has become an important issue, and many companies are working to support it. However, it is not easy to create an environment where employees can easily seek advice or to provide appropriate, prompt advice. Conventional methods require a great deal of time and effort to collect and analyze consultation content and generate responses, making it difficult to build a system to support employee mental health.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1223] In this invention, the server includes means for receiving consultation details from employees, means for collecting and learning from past consultation data and information provided by specialized institutions, means for analyzing using natural language processing, means for generating a response using a generative artificial intelligence model based on the analysis results, means for returning the generated response to the employee, and means for recording the consultation details from employees and the responses and saving them as future learning data. This allows employees to easily consult and receive prompt and appropriate advice.

[1224] Below are definitions of important terms contained in the claims.

[1225] An "employee" is an individual who belongs to a company or organization and provides labor on an ongoing basis.

[1226] "Mental health" refers to an individual's psychological and emotional well-being, and is the absence of problems such as stress and anxiety.

[1227] "Artificial intelligence" refers to the technology that enables machines and computers to imitate and perform intelligent human tasks.

[1228] "Means of receiving" refers to a mechanism for obtaining data using electronic communication means.

[1229] "Past consultation data" refers to the consultation content and response history previously collected from employees.

[1230] "Information provided by specialized institutions" refers to the latest information provided by research institutions and experts in mental health and stress management.

[1231] "Learning" refers to the process by which a system uses past data and new information to improve its performance and accuracy.

[1232] "Natural language processing" refers to the technology that allows computers to understand and analyze human language.

[1233] "Means of analysis" refers to the process of evaluating and interpreting input data to understand its meaning and intent.

[1234] A "generative artificial intelligence model" refers to a model that is trained to generate appropriate responses to specific input data.

[1235] "Means for generating a response" refers to a mechanism that automatically creates appropriate advice or answers based on the analysis results.

[1236] "Means of replying" refers to the process of providing the generated response to the original sender (employee).

[1237] "Means for recording" refers to a mechanism for storing received consultations and generated responses in a database for future reference and learning.

[1238] "Future learning data" refers to past data accumulated to improve the system's performance and response accuracy.

[1239] MODE FOR CARRYING OUT THE INVENTION

[1240] System Overview

[1241] This invention is a system that uses artificial intelligence to support the mental health of employees. The system mainly consists of three components: a server, a terminal, and a user. The server operates and manages the central database and generative AI model, and the terminal provides an interface for employees to input their concerns. The user corresponds to the employee.

[1242] Hardware and Software Configuration

[1243] server

[1244] The servers are configured with hardware equipped with powerful processors, ample memory, and fast storage. The software running on these servers includes generative AI models (e.g., GPT-3), natural language processing libraries (e.g., SpaCy and NLTK), and database management systems (e.g., MySQL).

[1245] Terminal

[1246] The terminal is a general computing device such as a PC or smartphone. A web browser and dedicated application are installed on the terminal so that users can input their consultation details, and an interface is provided for communicating with the server.

[1247] Data processing and calculation

[1248] Receiving and analyzing consultation content

[1249] The consultation content entered by the user through the device is sent from the device to the server. After receiving it, the server analyzes it using natural language processing technology. This analysis extracts keywords and context from the input text and understands the intent.

[1250] Generating a response

[1251] Based on the analysis results, the server generates an appropriate response using a generative AI model, which is pre-trained using a large dataset, enabling highly accurate responses.

[1252] Returning and displaying responses

[1253] The generated response is sent back from the server to the user's terminal, which receives the response and displays it to the user, who can then review the displayed response and take any necessary action.

[1254] Data recording and learning

[1255] The server records the consultation details and generated responses in a database, which will be used as training data for the generative AI model in the future, contributing to improving the accuracy of the system.

[1256] Specific examples

[1257] Example 1: Stress management consultation

[1258] User: Type into device, "I'm feeling anxious and stressed at work."

[1259] Terminal: Sends the consultation details to the server.

[1260] Server: Analyzes the content of the consultation and extracts keywords such as "stress" and "anxiety."

[1261] Server: Passes the analysis results to the generative AI model and creates a prompt such as "Please provide advice to reduce stress."

[1262] Generative AI model: Generates appropriate responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[1263] Server: Sends the response back to the user's device.

[1264] Terminal: Display the response and have the user confirm.

[1265] Example 2: Consultation about sleep disorders

[1266] User: Type "I can't sleep lately."

[1267] Terminal: Sends input to the server.

[1268] Server: Analyzes the input and extracts keywords such as "can't sleep."

[1269] Server: Passes the analysis results to the generative AI model and creates a prompt saying, "Please provide advice for the sleep disorder."

[1270] Generative AI model: Generates appropriate responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and to limit caffeine."

[1271] Server: Sends the response back to the user's device.

[1272] Terminal: Display the response and have the user confirm.

[1273] This system is designed to provide prompt and appropriate support for the mental health of users (employees). By using the above-mentioned methods, users can easily consult with the system and quickly receive highly accurate responses from the generative AI model.

[1274] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1275] Step 1:

[1276] The user inputs the content of the consultation through the terminal. For example, the input content is "I can't sleep recently." The input text is stored on the user's terminal.

[1277] Input: The question the user wants to ask (e.g., "I can't sleep lately")

[1278] Output: What you type appears on the terminal

[1279] Specific operation: The user enters the consultation content into the text input field on the device and clicks the "Send" button.

[1280] Step 2:

[1281] The device sends the entered consultation details to the server, and the data may be encrypted.

[1282] Input: The question entered by the user (e.g., "I can't sleep lately")

[1283] Output: Consultation details sent to the server

[1284] Specific operation: The device makes an HTTP POST request and sends the consultation details to the server.

[1285] Step 3:

[1286] The server receives the consultation content from the device and temporarily stores the received data within the server.

[1287] Input: Consultation content sent from the device (e.g., "I can't sleep lately")

[1288] Output: Consultation details saved on the server

[1289] Specific operation: The data received by the server is temporarily stored in memory.

[1290] Step 4:

[1291] The server analyzes the received consultation content using natural language processing (NLP). NLP libraries such as SpaCy and NLTK are used for this analysis. Through the analysis, keywords and context of the consultation content are extracted.

[1292] Input: Received consultation content (e.g., "I can't sleep lately")

[1293] Output: Extracted keywords and contextual information (e.g., stress and anxiety related to "can't sleep")

[1294] What it does: The server uses NLP libraries to perform text analysis and extract keywords and context.

[1295] Step 5:

[1296] The server passes the analysis results to a generative AI model, which generates an appropriate response. The generative AI model has undergone pre-training and creates a prompt sentence based on the analysis results, which is then input into the generative AI model (e.g., GPT-3) to generate a response.

[1297] Input: Analysis results (e.g., keyword "can't sleep")

[1298] Output: Response from the generative AI model (e.g., "To improve the quality of your sleep, you should take time to relax before bed and limit your caffeine intake.")

[1299] Specific operation: The server passes the prompt sentence to the generative AI model and requests it to generate a response.

[1300] Step 6:

[1301] The server generates a response and sends it back to the user's terminal, possibly re-encrypting the data.

[1302] Input: Response from a generative AI model (e.g., "To improve your sleep quality, it's helpful to take some time to relax before bed and limit caffeine consumption.")

[1303] Output: Response sent to the user's device

[1304] Specific operation: The server sends an HTTP response and returns the response data to the terminal.

[1305] Step 7:

[1306] The terminal displays the response received from the server to the user, who then checks it and considers what to do next.

[1307] Input: Response sent by the server (e.g., "To improve your sleep quality, it's helpful to relax before bed and limit caffeine consumption.")

[1308] Output: Response displayed on the terminal

[1309] Specific behavior: Display the response text on the device screen.

[1310] Step 8:

[1311] The server records the consultation details and generated responses in a database, which will be used as learning data for future projects.

[1312] Input: Consultation content and generated response (e.g., Consultation content: "I can't sleep lately," Response: "To improve the quality of my sleep...")

[1313] Output: Consultation details and responses stored in the database

[1314] What happens: The server executes a write operation to the database, storing the data permanently.

[1315] (Application example 1)

[1316] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1317] Today's employees often suffer from stress and mental strain, especially in workplaces such as factories. This can result in reduced work efficiency and health problems. However, there are currently no systems in place that allow employees to seek immediate advice in real time. This creates a need for a system that can respond quickly and appropriately to the mental challenges employees face.

[1318] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1319] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for providing an interface through which employees can seek consultation in real time while working via a smart terminal, and means for securely communicating the consultation content and the response. This enables prompt and appropriate responses to mental challenges faced by employees at the workplace.

[1320] An "employee" is a person who works for a company or organization.

[1321] "Mental health" is a state that refers to an individual's psychological or emotional well-being and general sense of well-being.

[1322] "Artificial intelligence" refers to the technology that uses computer systems to imitate human intelligence, as well as the programs and algorithms that make this possible.

[1323] "Consultation content" refers to information that employees input into the system regarding their mental worries and stress.

[1324] The "means for receiving" refers to a method or technology for receiving the consultation content provided by the employee into the system.

[1325] The "means of learning" refers to technology that accumulates and analyzes past consultation data and information provided by specialized institutions to improve the accuracy of the model.

[1326] "Generative AI" is a type of "artificial intelligence" that is a system capable of generating responses based on data received from a user.

[1327] The "means for generating a response" refers to a technique or method for generating appropriate advice or information based on the content of the inquiry from the employee.

[1328] A "response medium" is a technique or method for communicating the generated response to an employee.

[1329] "Means of recording" refers to the technology or methods for saving the employee's consultation and the response thereto.

[1330] "Smart devices" are electronic devices carried or worn by employees, such as smartphones, smart glasses, and head-mounted displays.

[1331] A "real-time consultation interface" is a user interface that allows employees to instantly access the system and input the details of their consultation.

[1332] "Means of communication" refers to the technologies and methods for securely sending and receiving data using the Internet or other communications technologies.

[1333] The system of the present invention is a system that uses artificial intelligence (AI) to support the mental health of employees. This system is composed of the following elements: a server, a terminal, and a user.

[1334] Server Roles and Operations

[1335] The server mainly has the following functions:

[1336] 1. Information gathering and learning:

[1337] The server periodically collects information from specialist institutions and past consultation data, which is used as training data for the generative AI model.

[1338] Example: Collecting new mental health research data from professional organizations.

[1339] 2. Receiving and analyzing consultation content:

[1340] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP).

[1341] For example, if a user enters, "I've been overwhelmed with work lately and it's stressful," the server receives this data and queries a generative AI model for appropriate responses regarding stress management.

[1342] 3. Generate a response:

[1343] Generative AI generates appropriate responses based on the analyzed data.

[1344] Example: A generative AI model might generate a response such as, "To reduce stress, it's effective to set aside time to relax every day. Another option is to consult a professional counselor."

[1345] 4. Returning and Recording Responses:

[1346] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1347] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1348] Device role and operation

[1349] The terminal provides an interface for employees to input the details of their consultation.

[1350] 1. Enter your consultation details:

[1351] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1352] Example: An employee types, "I've been so busy at work lately that I don't have time to rest."

[1353] 2. Receiving and displaying responses:

[1354] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1355] For example, a response such as "Taking breaks helps refresh your mind and body, so make sure to take regular breaks" will be displayed on the device.

[1356] User Interaction

[1357] Users (employees) interact with the system as follows:

[1358] 1. Consultation input:

[1359] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1360] Example prompt: "I've been overwhelmed with work lately and it's been stressful. Can you tell me how to reduce stress in this situation?"

[1361] 2. Check the response:

[1362] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[1363] For example, a possible response would be, "Deep breathing and moderate exercise can help reduce stress. Also, try some relaxation techniques."

[1364] Hardware and software used

[1365] Hardware: Smart devices (smartphones, smart glasses, head-mounted displays, etc.)

[1366] Software: Python program, natural language processing library (spaCy, Transformers), server communication library (requests), generative AI model (OpenAI GPT-4)

[1367] This will enable quick and appropriate responses to the mental challenges employees face at the workplace.

[1368] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1369] Step 1:

[1370] The terminal receives the consultation content from the user (employee). The consultation content entered is saved in text format. For example, the user can enter, "I've been stressed out because I've had too much work lately."

[1371] Step 2:

[1372] The device sends the received consultation content to the server. Specifically, it sends this text data to the server using an HTTP POST request.

[1373] Step 3:

[1374] The server analyzes the received consultation content. First, it uses natural language processing (NLP) technology to convert the consultation content into an easy-to-understand format. This process uses NLP libraries such as spaCy and Transformers. By converting the input text into an easy-to-understand format, keywords and sentiments related to stress and anxiety are extracted.

[1375] Step 4:

[1376] The server queries a generative AI model based on the analyzed data. This generative AI model (e.g., GPT-4) generates an appropriate response based on relevant information. For example, the generative AI model might generate advice such as, "Deep breathing and moderate exercise are effective in reducing stress."

[1377] Step 5:

[1378] The server sends the generated response to the user's terminal, and returns the generated advice to the terminal in text format via an HTTP response.

[1379] Step 6:

[1380] The device displays the response sent back from the server to the user. Specifically, the generated advice is displayed on the display of the smart glasses or smartphone. The user can check the advice and take necessary actions.

[1381] Step 7:

[1382] The server records the consultation and the corresponding response. This record is stored in a database and used as future learning data, allowing the server's generative AI model to be continuously improved.

[1383] Through these steps, users can receive psychological support in real time, and the entire system records the consultation and responses, contributing to improving the accuracy of the generative AI model.

[1384] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1385] The system of the present invention utilizes artificial intelligence to support the mental health of employees, and further combines it with an emotion engine to improve the accuracy and appropriateness of responses. This system allows employees to easily consult with the system and receive appropriate advice according to their emotional state. Specific forms of the system are described below.

[1386] 1. System Configuration

[1387] The system mainly consists of the following elements:

[1388] Server: Operates and manages the central database, generative AI models, and emotion engine.

[1389] Terminal: Provides an interface for employees to input their consultation details.

[1390] User: In this case, an employee.

[1391] 2. Server Roles and Operations

[1392] The server has the following functions:

[1393] Information gathering and learning

[1394] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[1395] example:

[1396] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[1397] Receiving and analyzing consultation content

[1398] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[1399] example:

[1400] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[1401] Generating a response

[1402] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[1403] example:

[1404] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[1405] Returning and recording responses

[1406] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1407] example:

[1408] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1409] 3. Roles and Functions of the Terminal

[1410] The terminal provides an interface for employees to input their consultation details.

[1411] Enter the consultation details

[1412] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1413] example:

[1414] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1415] Receiving and displaying responses

[1416] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1417] example:

[1418] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[1419] 4. User Interaction

[1420] Users (employees) interact with the system as follows:

[1421] Input of consultation

[1422] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1423] Checking the response

[1424] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[1425] 5. Specific Examples

[1426] Example 1: Stress management consultation

[1427] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[1428] Terminal: Sends the consultation details to the server.

[1429] Server: Analyzes the consultation content and queries the generative AI. The emotion engine identifies the user's emotional state as "anxiety."

[1430] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try relaxation techniques. Also, if you are feeling anxious, we recommend consulting a professional counselor."

[1431] Server: Sends the response back to the user's device.

[1432] Terminal: Display the response.

[1433] Example 2: Consultation about sleep disorders

[1434] User: Employee types, "I've been having trouble sleeping lately."

[1435] Terminal: Sends the consultation details to the server.

[1436] Server: Analyzes the consultation content, and the emotion engine identifies the user's emotional state as "stress." It then queries the generation AI.

[1437] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine. Also, it seems like you are feeling stressed, so try some relaxation techniques."

[1438] Server: Sends the response back to the user's device.

[1439] Terminal: Display the response.

[1440] As described above, by combining the emotion engine, the system of the present invention can identify the emotional state of employees and provide more appropriate and personalized advice, thereby maintaining employees' mental health and improving the work environment.

[1441] The processing flow will be explained below.

[1442] Specific explanation of program processing

[1443] Step 1: The user inputs a question from the terminal.

[1444] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[1445] Step 2: The device sends the consultation details to the server

[1446] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[1447] Step 3: The server receives the request.

[1448] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[1449] Step 4: The server analyzes the request

[1450] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[1451] Step 5: The emotion engine recognizes the user's emotion

[1452] An emotion engine built into the server analyzes the text data of the consultation and identifies the user's emotional state, such as "stress," "anxiety," or "sadness."

[1453] Step 6: The server queries the generated AI

[1454] The server queries the generative AI based on the analysis results and the emotion engine results. The input text and emotion data are converted into an appropriate format and provided as input to the generative AI model (e.g., GPT-4).

[1455] Step 7: The generative AI model generates a response

[1456] The generative AI model generates an appropriate response based on the input text and emotional data, such as "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[1457] Step 8: The server receives the generated response

[1458] The server receives the response from the generated AI and processes it in text format.

[1459] Step 9: The server sends a response to the user's device

[1460] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[1461] Step 10: The terminal displays the response

[1462] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[1463] Step 11: The server records the conversation and the response.

[1464] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[1465] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice based on their emotional state.

[1466] Example 2

[1467] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1468] Employee mental health issues have a significant impact on work efficiency and the work environment. Responding promptly and appropriately to stress and anxiety felt by employees is a particularly important issue for companies. However, there is a lack of environments where employees can easily seek advice, and systems that accurately understand their emotional state and provide appropriate advice. This can lead to a decline in employee mental health, which can ultimately lead to a decline in work efficiency and even employee resignation. The purpose of this invention is to solve these issues.

[1469] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the employee, a means for collecting and learning from past consultation data and information provided by specialized institutions, a means for analyzing the received content of the consultation and the emotional state using natural language processing technology, a means for generating a response using a generative artificial intelligence model based on the received content of the consultation and the analysis results, a means for sending the generated response to the employee, and a means for recording the content of the consultation from the employee and the response and using it as future learning data. This makes it possible to accurately grasp the emotional state of the employee and provide prompt and appropriate advice.

[1470] "Employee" refers to an individual working for a business or organization who is particularly required to deal with mental health issues.

[1471] "Consultation content" refers to text information that employees enter into the system, and includes work-related worries and stress in daily life.

[1472] A "means" refers to a device, system, or method used to accomplish a particular purpose.

[1473] "Past consultation data" refers to data including consultation details that employees have previously entered into the system and responses to those consultations.

[1474] "Professional body" refers to an external agency or organization that provides data or research related to mental health or psychology.

[1475] A "generative artificial intelligence model" refers to an artificial intelligence system that automatically generates and analyzes information based on specific algorithms.

[1476] "Natural language processing technology" is a technical field that understands, analyzes, and generates responses to human language, and is used to process text and voice data.

[1477] "Emotional state" refers to the mental state (e.g., anxiety, stress, anger, etc.) of the employee when they enter their consultation details.

[1478] "Response" refers to a message containing advice or a response method that the system generates in response to an employee's inquiry.

[1479] "Recording means" refers to a method or device for storing information for long-term storage and later access.

[1480] "Training data" refers to a collection of past data and new information that the system uses to generate more accurate responses.

[1481] The system of the present invention utilizes artificial intelligence to support the mental health of employees and combines it with an emotion analysis engine to improve the accuracy and appropriateness of responses. Specific embodiments of the system are described below.

[1482] 1. System Configuration

[1483] The system consists of the following main hardware and software:

[1484] Server: Operates and manages the central database, generative AI models (for example, OpenAI's GPT-4 is used as an example of a common generative AI model), and emotion engines (for example, Microsoft's Text Analytics API is an example of a common emotion analysis API).

[1485] Terminal: A device that provides an interface for users, i.e. employees, to input their consultation details. Examples include PCs, smartphones, and tablets.

[1486] User: An employee who uses this system and interacts with the system via a terminal.

[1487] 2. Server Roles and Operations

[1488] Information gathering and learning

[1489] The server periodically collects mental health information provided by specialist institutions, as well as past consultation and emotion data, and uses this data to train the generative AI model and emotion analysis engine, thereby improving the system's response accuracy.

[1490] Example: Download new mental health research data from expert institutions and combine it with historical user consultation data to train a generative AI model.

[1491] Example prompt: "Update your generative AI model with the latest mental health research data."

[1492] Receiving and analyzing consultation content

[1493] The server receives the consultation content sent by the user and analyzes it using natural language processing (NLP) technology, while simultaneously identifying the user's emotional state using an emotion analysis engine.

[1494] Example: If a user types, "I can't sleep because I'm stressed from work," the server receives that data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety or stress).

[1495] Example prompt: "Analyze the following consultation and identify the user's emotional state: 'I can't sleep because of work stress.'"

[1496] Generating a response

[1497] The server uses a generative AI model to generate an appropriate response based on the analysis results, taking into account the emotional state determination results from the emotion analysis engine.

[1498] Example: The generative AI generates a response such as, "To reduce stress, it is important to make time to relax. We also recommend consulting a professional counselor."

[1499] Example prompt: "Generate what advice to offer when the user is feeling anxious."

[1500] Returning and recording responses

[1501] The server returns the generated response to the device, and also records the received consultation content and the generated response in an internal database for future use as learning data.

[1502] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1503] Example prompt: "Send the following response to the user's device and record it on the server: 'To reduce stress, take time to relax.'"

[1504] 3. Roles and Functions of the Terminal

[1505] Enter the consultation details

[1506] Users use the terminal interface to input their concerns and questions, and this information is immediately sent to the server.

[1507] Example: An employee types into a terminal, "I've been busy at work lately and don't have time to refresh myself."

[1508] Example prompt: "Please provide an interface that allows users to enter their concerns."

[1509] Receiving and displaying responses

[1510] The response sent back from the server is displayed on the terminal so that the user can check it.

[1511] Example: A response such as "We recommend you take a short break to relax" appears on the device.

[1512] Example prompt: "Please display the response from the server on the user's terminal."

[1513] 4. User Interaction

[1514] The user inputs the content of the inquiry through the terminal and sends it to the server. The generated response is displayed on the terminal, and the user can check it and take appropriate action.

[1515] Specific examples

[1516] Example 1: Stress management consultation

[1517] User: An employee types into the terminal, "I'm feeling anxious because I'm stressed at work."

[1518] Terminal: Encodes the consultation content into JSON format and sends it to the server.

[1519] Server: The received data is decoded, analyzed using NLP, and the emotion engine identifies the emotional state as "anxiety."

[1520] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to users who are feeling stressed."

[1521] Server: Sends the response to the user's terminal and records it in a database.

[1522] Terminal: Display the response.

[1523] Example 2: Consultation about sleep disorders

[1524] User: An employee types into the terminal, "I haven't been able to sleep lately."

[1525] Terminal: Sends the consultation details to the server.

[1526] Server: Decodes, analyzes using NLP, and the emotion engine identifies the emotional state as "stress."

[1527] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to a user who is experiencing insomnia and stress."

[1528] Server: Sends the response to the user's terminal and records it in a database.

[1529] Terminal: Display the response.

[1530] As described above, the system of the present invention combines an emotion analysis engine and a generative AI model to accurately grasp the emotional state of employees and provide prompt and appropriate advice, thereby maintaining the mental health of employees and improving the work environment.

[1531] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] The user inputs the content of the consultation into the terminal interface, which is then processed as text data.

[1534] Specific operation: The user types into the terminal, "I've been busy at work lately and I'm feeling stressed."

[1535] Input: User's concerns (e.g., "I'm busy at work and I'm stressed out")

[1536] Output: The above content will be displayed in the terminal input field.

[1537] Step 2:

[1538] The terminal receives the consultation information entered by the user and formats the data for transmission to the server, where it is encrypted and sent over the Internet.

[1539] Specific operation: The terminal encodes the input consultation content into JSON format and sends it to the server via HTTPS.

[1540] Input: User's consultation content (encrypted text data)

[1541] Output: JSON format data sent to the server

[1542] Step 3:

[1543] The server decodes the consultation content received from the terminal and temporarily stores it in an internal database.

[1544] Specific operation: The server decodes the data received from the terminal and stores it in the "Received Data" table in the database.

[1545] Input: JSON format data sent from the terminal

[1546] Output: Decoded data stored in the "Received Data" table

[1547] Step 4:

[1548] The server applies natural language processing (NLP) technology to the received consultation content and analyzes keywords and themes.

[1549] Specific operation: The server uses an NLP library (e.g., SpaCy or NLTK) to analyze the consultation content and extract keywords such as "stress" and "work."

[1550] Input: Decoded consultation content

[1551] Output: Extracted keywords and themes

[1552] Step 5:

[1553] The server uses a sentiment analysis engine to identify the user's emotional state, using keywords and themes extracted by NLP.

[1554] What it does: The server sends the NLP results to a sentiment analysis engine (e.g., Microsoft's Text Analytics API), which identifies "stress" as the emotion.

[1555] Input: Extracted keywords and themes

[1556] Output: Identified emotional state (e.g., "stressed")

[1557] Step 6:

[1558] The server uses a generative AI model to generate appropriate advice based on the analysis results and emotional state.

[1559] Specific operation: The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "Please provide appropriate advice to a user who is feeling stressed," and generates a response.

[1560] Input: Sentiment analysis engine results and prompt text

[1561] Output: The generated response (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[1562] Step 7:

[1563] The server formats the generated response and prepares it for transmission to the device, where the data is again encrypted and sent over the Internet to the device.

[1564] Specific operation: The server encodes the response data into JSON format and sends it to the terminal via HTTPS.

[1565] Input: Generated response

[1566] Output: JSON format data sent to the terminal

[1567] Step 8:

[1568] The terminal decodes the response data received from the server and displays it on the user interface.

[1569] Specific operation: The terminal analyzes the response data received from the server and displays it to the user.

[1570] Input: JSON format data sent from the server

[1571] Output: The response displayed in the user interface (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[1572] Step 9:

[1573] The server records the received consultation content and the generated response in an internal database and uses it as future learning data.

[1574] Specific operation: The server saves the consultation content and response in the "historical data" table of the database and tags it.

[1575] Input: Consultation content and generated response

[1576] Output: Data stored in the "Historical Data" table

[1577] These are the specific processing steps in a system for supporting the mental health of employees.

[1578] (Application example 2)

[1579] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1580] In today's workplace, employees often experience a lot of stress and anxiety. This can lead to poor mental health, reduced productivity, and a worsening work environment. To solve this problem, a system is needed that employees can easily consult with, understand their emotional state, and provide appropriate advice. However, existing systems lack the ability to analyze emotional states or provide individualized support, making it difficult to effectively support employees' mental health.

[1581] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1582] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using generative artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for using an emotion engine to analyze the emotional state of the employee, means for providing the generated response visually and audibly, and means for providing a consultation interface via a smart device and a robot. This makes it possible to more effectively support the mental health of employees and improve the work environment.

[1583] An "employee" is an individual employed by a company or organization whose mental health is to be supported.

[1584] "Mental health" is a state in which an individual maintains a stable psychological state by minimizing mental problems such as stress, anxiety, and depression in their daily lives.

[1585] "Artificial intelligence" refers to technology that learns from large amounts of data and performs pattern recognition and decision-making, including generative AI models and emotion engines.

[1586] "Generative AI" is an AI model that generates appropriate responses and advice based on input data.

[1587] An "emotion engine" is a technology that analyzes and identifies the emotional state of a user based on input data from the user.

[1588] A "smart device" is a smartphone, tablet, or similar electronic device that provides an interface for transferring data between a user and a system.

[1589] A "robot" is a machine that interacts with workers in a factory or other work environment.

[1590] "Natural language processing" is a technology for analyzing, understanding, and generating human language.

[1591] A "problem" is a description of a particular concern or problem that an employee enters into the system.

[1592] A "response" is the advice or answer that the system provides to the employee's inquiry.

[1593] "Recording" is the process by which the system saves employee consultations and responses for subsequent learning and analysis.

[1594] The system of the present invention utilizes artificial intelligence to support employee mental health and further combines an emotion engine to improve the accuracy and appropriateness of responses. Specific embodiments of this system are described below.

[1595] 1. System Configuration

[1596] The system mainly consists of the following elements:

[1597] Server: Operates and manages the central database, generative AI models, and emotion engine.

[1598] Terminal: Provides an interface for employees to input their consultation details. This includes smart devices and robots.

[1599] User: This applies to employees.

[1600] 2. Server Roles and Operations

[1601] The server has the following functions:

[1602] Information gathering and learning

[1603] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[1604] example:

[1605] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[1606] Receiving and analyzing consultation content

[1607] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[1608] example:

[1609] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[1610] Generating a response

[1611] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[1612] example:

[1613] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[1614] Returning and recording responses

[1615] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1616] example:

[1617] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1618] 3. Roles and Functions of the Terminal

[1619] The terminal provides an interface for employees to input their consultation details, and can be a smart device or robot.

[1620] Enter the consultation details

[1621] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1622] example:

[1623] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1624] Receiving and displaying responses

[1625] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1626] example:

[1627] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[1628] 4. User Interaction

[1629] Users (employees) interact with the system as follows:

[1630] Input of consultation

[1631] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1632] Checking the response

[1633] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[1634] 5. Specific Examples

[1635] Example 1: Stress management consultation

[1636] Prompt: "I'm feeling anxious and stressed at work. What can I do?"

[1637] Sample response: "Deep breathing and moderate exercise can help reduce stress. Try relaxation techniques. Also, if you're experiencing anxiety, consider talking to a professional counselor."

[1638] Example 2: Consultation about sleep disorders

[1639] Prompt: "I haven't been able to sleep lately."

[1640] Example response: "To improve the quality of your sleep, you can take some time to relax before bed and avoid caffeine. Also, since you seem to be stressed, try some relaxation techniques."

[1641] The above is a description of the mode for carrying out the invention, which makes it possible to maintain the mental health of employees and improve the working environment.

[1642] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1643] Step 1:

[1644] Input and receive consultation details

[1645] Users input their consultation details via a terminal (smart device or robot). The input data includes text and voice. This input data is immediately sent to the server.

[1646] Input: User's inquiry (e.g., I've been so busy with work lately that I don't have time to rest)

[1647] Output: Received consultation details

[1648] Step 2:

[1649] Analysis of consultation content

[1650] The server analyzes the received consultation content using natural language processing (NLP) technology, which breaks down the content into sentences and recognizes its subject and sentiment.

[1651] Input: Received consultation content

[1652] Output: Analyzed data (themes and sentiment)

[1653] Step 3:

[1654] Emotional state analysis

[1655] The server uses an emotion engine to identify the user's emotional state from the analyzed consultation content, and this identification result is used to generate future responses.

[1656] Input: Parsed data (themes and sentiment)

[1657] Output: Identified emotional state (e.g., anxiety, stress)

[1658] Step 4:

[1659] Generating a response

[1660] The server uses a generative artificial intelligence (generative AI model) to generate appropriate responses based on the identified emotional state and subject matter, which may include specific actions or advice.

[1661] Input: Identified emotional state, subject

[1662] Output: Generated response (e.g., "Deep breathing and moderate exercise can help reduce stress. Try some relaxation techniques.")

[1663] Step 5:

[1664] Returning and displaying responses

[1665] The server generates a response and sends it back to the user's device, which displays the response as voice or text, and the user reviews the response and decides on the next action based on it.

[1666] Input: The generated response

[1667] Output: Display of response (audio or text)

[1668] Step 6:

[1669] Record of consultation content and response

[1670] The server records the employee consultations and responses for future learning and analysis, which will be used to improve the system and collect new data.

[1671] Input: Consultation details, generated response

[1672] Output: Recorded data

[1673] Through these steps, users receive real-time advice to support the mental health of their employees, and the system is continuously learning to improve the accuracy and relevance of its responses.

[1674] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1675] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1676] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1677] [Fourth embodiment]

[1678] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1679] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1680] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1681] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1682] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1683] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1684] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1685] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1686] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1687] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1688] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1689] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1690] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1691] The system of the present invention is a system that uses artificial intelligence to support the mental health of employees. This system allows employees to easily consult with the system and receive appropriate advice. Specific forms of the system are described below.

[1692] 1. System Configuration

[1693] The system mainly consists of the following elements:

[1694] Server: Operates and manages the central database and generative AI models.

[1695] Terminal: Provides an interface for employees to input their consultation details.

[1696] User: In this case, an employee.

[1697] 2. Server Roles and Operations

[1698] The server has the following functions:

[1699] Information gathering and learning

[1700] The server periodically collects information from expert institutions and past consultation data, with the aim of training the generative AI model to generate more accurate responses.

[1701] example:

[1702] Collect new mental health research data from professional organizations.

[1703] Receiving and analyzing consultation content

[1704] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing.

[1705] example:

[1706] When a user inputs "I can't sleep because of work stress," the server receives this data and queries a generative AI model for appropriate stress management responses.

[1707] Generating a response

[1708] Generative AI generates appropriate responses based on the analyzed data.

[1709] example:

[1710] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[1711] Returning and recording responses

[1712] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1713] example:

[1714] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1715] 3. Roles and Functions of the Terminal

[1716] The terminal provides an interface for employees to input their consultation details.

[1717] Enter the consultation details

[1718] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1719] example:

[1720] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1721] Receiving and displaying responses

[1722] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1723] example:

[1724] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take regular breaks."

[1725] 4. User Interaction

[1726] Users (employees) interact with the system as follows:

[1727] Input of consultation

[1728] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1729] Checking the response

[1730] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[1731] 5. Specific Examples

[1732] Example 1: Stress management consultation

[1733] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[1734] Terminal: Sends the consultation details to the server.

[1735] Server: Analyzes the consultation content and queries the generating AI.

[1736] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[1737] Server: Sends the response back to the user's device.

[1738] Terminal: Display the response.

[1739] Example 2: Consultation about sleep disorders

[1740] User: Employee types, "I've been having trouble sleeping lately."

[1741] Terminal: Sends the consultation details to the server.

[1742] Server: Analyzes the consultation content and queries the generating AI.

[1743] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine."

[1744] Server: Sends the response back to the user's device.

[1745] Terminal: Display the response.

[1746] As described above, the system according to the present invention can quickly and appropriately respond to the mental challenges faced by employees, thereby maintaining the mental health of employees and improving the working environment.

[1747] The processing flow will be explained below.

[1748] Specific explanation of program processing

[1749] Step 1: The user inputs a question from the terminal.

[1750] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[1751] Step 2: The device sends the consultation details to the server

[1752] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[1753] Step 3: The server receives the request.

[1754] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[1755] Step 4: The server analyzes the request

[1756] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[1757] Step 5: The server queries the generated AI

[1758] The server queries the generative AI based on the analysis results, converts the input text into an appropriate format, and provides it as input to the generative AI model (e.g., GPT-4).

[1759] Step 6: The generative AI model generates a response

[1760] The generative AI model generates an appropriate response based on the input text, such as advice like, "To reduce stress, it is effective to set aside time to relax every day."

[1761] Step 7: The server receives the generated response

[1762] The server receives the response from the generated AI and processes it in text format.

[1763] Step 8: The server sends a response to the user's device

[1764] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[1765] Step 9: The terminal displays the response

[1766] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[1767] Step 10: The server records the conversation and response

[1768] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[1769] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice.

[1770] Example 1

[1771] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1772] In modern society, employee mental health has become an important issue, and many companies are working to support it. However, it is not easy to create an environment where employees can easily seek advice or to provide appropriate, prompt advice. Conventional methods require a great deal of time and effort to collect and analyze consultation content and generate responses, making it difficult to build a system to support employee mental health.

[1773] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1774] In this invention, the server includes means for receiving consultation details from employees, means for collecting and learning from past consultation data and information provided by specialized institutions, means for analyzing using natural language processing, means for generating a response using a generative artificial intelligence model based on the analysis results, means for returning the generated response to the employee, and means for recording the consultation details from employees and the responses and saving them as future learning data. This allows employees to easily consult and receive prompt and appropriate advice.

[1775] Below are definitions of important terms contained in the claims.

[1776] An "employee" is an individual who belongs to a company or organization and provides labor on an ongoing basis.

[1777] "Mental health" refers to an individual's psychological and emotional well-being, and is the absence of problems such as stress and anxiety.

[1778] "Artificial intelligence" refers to the technology that enables machines and computers to imitate and perform intelligent human tasks.

[1779] "Means of receiving" refers to a mechanism for obtaining data using electronic communication means.

[1780] "Past consultation data" refers to the consultation content and response history previously collected from employees.

[1781] "Information provided by specialized institutions" refers to the latest information provided by research institutions and experts in mental health and stress management.

[1782] "Learning" refers to the process by which a system uses past data and new information to improve its performance and accuracy.

[1783] "Natural language processing" refers to the technology that allows computers to understand and analyze human language.

[1784] "Means of analysis" refers to the process of evaluating and interpreting input data to understand its meaning and intent.

[1785] A "generative artificial intelligence model" refers to a model that is trained to generate appropriate responses to specific input data.

[1786] "Means for generating a response" refers to a mechanism that automatically creates appropriate advice or answers based on the analysis results.

[1787] "Means of replying" refers to the process of providing the generated response to the original sender (employee).

[1788] "Means for recording" refers to a mechanism for storing received consultations and generated responses in a database for future reference and learning.

[1789] "Future learning data" refers to past data accumulated to improve the system's performance and response accuracy.

[1790] MODE FOR CARRYING OUT THE INVENTION

[1791] System Overview

[1792] This invention is a system that uses artificial intelligence to support the mental health of employees. The system mainly consists of three components: a server, a terminal, and a user. The server operates and manages the central database and generative AI model, and the terminal provides an interface for employees to input their concerns. The user corresponds to the employee.

[1793] Hardware and Software Configuration

[1794] server

[1795] The servers are configured with hardware equipped with powerful processors, ample memory, and fast storage. The software running on these servers includes generative AI models (e.g., GPT-3), natural language processing libraries (e.g., SpaCy and NLTK), and database management systems (e.g., MySQL).

[1796] Terminal

[1797] The terminal is a general computing device such as a PC or smartphone. A web browser and dedicated application are installed on the terminal so that users can input their consultation details, and an interface is provided for communicating with the server.

[1798] Data processing and calculation

[1799] Receiving and analyzing consultation content

[1800] The consultation content entered by the user through the device is sent from the device to the server. After receiving it, the server analyzes it using natural language processing technology. This analysis extracts keywords and context from the input text and understands the intent.

[1801] Generating a response

[1802] Based on the analysis results, the server generates an appropriate response using a generative AI model, which is pre-trained using a large dataset, enabling highly accurate responses.

[1803] Returning and displaying responses

[1804] The generated response is sent back from the server to the user's terminal, which receives the response and displays it to the user, who can then review the displayed response and take any necessary action.

[1805] Data recording and learning

[1806] The server records the consultation details and generated responses in a database, which will be used as training data for the generative AI model in the future, contributing to improving the accuracy of the system.

[1807] Specific examples

[1808] Example 1: Stress management consultation

[1809] User: Type into device, "I'm feeling anxious and stressed at work."

[1810] Terminal: Sends the consultation details to the server.

[1811] Server: Analyzes the content of the consultation and extracts keywords such as "stress" and "anxiety."

[1812] Server: Passes the analysis results to the generative AI model and creates a prompt such as "Please provide advice to reduce stress."

[1813] Generative AI model: Generates appropriate responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try these relaxation techniques."

[1814] Server: Sends the response back to the user's device.

[1815] Terminal: Display the response and have the user confirm.

[1816] Example 2: Consultation about sleep disorders

[1817] User: Type "I can't sleep lately."

[1818] Terminal: Sends input to the server.

[1819] Server: Analyzes the input and extracts keywords such as "can't sleep."

[1820] Server: Passes the analysis results to the generative AI model and creates a prompt saying, "Please provide advice for the sleep disorder."

[1821] Generative AI model: Generates appropriate responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and to limit caffeine."

[1822] Server: Sends the response back to the user's device.

[1823] Terminal: Display the response and have the user confirm.

[1824] This system is designed to provide prompt and appropriate support for the mental health of users (employees). By using the above-mentioned methods, users can easily consult with the system and quickly receive highly accurate responses from the generative AI model.

[1825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1826] Step 1:

[1827] The user inputs the content of the consultation through the terminal. For example, the input content is "I can't sleep recently." The input text is stored on the user's terminal.

[1828] Input: The question the user wants to ask (e.g., "I can't sleep lately")

[1829] Output: What you type appears on the terminal

[1830] Specific operation: The user enters the consultation content into the text input field on the device and clicks the "Send" button.

[1831] Step 2:

[1832] The device sends the entered consultation details to the server, and the data may be encrypted.

[1833] Input: The question entered by the user (e.g., "I can't sleep lately")

[1834] Output: Consultation details sent to the server

[1835] Specific operation: The device makes an HTTP POST request and sends the consultation details to the server.

[1836] Step 3:

[1837] The server receives the consultation content from the device and temporarily stores the received data within the server.

[1838] Input: Consultation content sent from the device (e.g., "I can't sleep lately")

[1839] Output: Consultation details saved on the server

[1840] Specific operation: The data received by the server is temporarily stored in memory.

[1841] Step 4:

[1842] The server analyzes the received consultation content using natural language processing (NLP). NLP libraries such as SpaCy and NLTK are used for this analysis. Through the analysis, keywords and context of the consultation content are extracted.

[1843] Input: Received consultation content (e.g., "I can't sleep lately")

[1844] Output: Extracted keywords and contextual information (e.g., stress and anxiety related to "can't sleep")

[1845] What it does: The server uses NLP libraries to perform text analysis and extract keywords and context.

[1846] Step 5:

[1847] The server passes the analysis results to a generative AI model, which generates an appropriate response. The generative AI model has undergone pre-training and creates a prompt sentence based on the analysis results, which is then input into the generative AI model (e.g., GPT-3) to generate a response.

[1848] Input: Analysis results (e.g., keyword "can't sleep")

[1849] Output: Response from the generative AI model (e.g., "To improve the quality of your sleep, you should take time to relax before bed and limit your caffeine intake.")

[1850] Specific operation: The server passes the prompt sentence to the generative AI model and requests it to generate a response.

[1851] Step 6:

[1852] The server generates a response and sends it back to the user's terminal, possibly re-encrypting the data.

[1853] Input: Response from a generative AI model (e.g., "To improve your sleep quality, it's helpful to take some time to relax before bed and limit caffeine consumption.")

[1854] Output: Response sent to the user's device

[1855] Specific operation: The server sends an HTTP response and returns the response data to the terminal.

[1856] Step 7:

[1857] The terminal displays the response received from the server to the user, who then checks it and considers what to do next.

[1858] Input: Response sent by the server (e.g., "To improve your sleep quality, it's helpful to relax before bed and limit caffeine consumption.")

[1859] Output: Response displayed on the terminal

[1860] Specific behavior: Display the response text on the device screen.

[1861] Step 8:

[1862] The server records the consultation details and generated responses in a database, which will be used as learning data for future projects.

[1863] Input: Consultation content and generated response (e.g., Consultation content: "I can't sleep lately," Response: "To improve the quality of my sleep...")

[1864] Output: Consultation details and responses stored in the database

[1865] What happens: The server executes a write operation to the database, storing the data permanently.

[1866] (Application example 1)

[1867] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1868] Today's employees often suffer from stress and mental strain, especially in workplaces such as factories. This can result in reduced work efficiency and health problems. However, there are currently no systems in place that allow employees to seek immediate advice in real time. This creates a need for a system that can respond quickly and appropriately to the mental challenges employees face.

[1869] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1870] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for providing an interface through which employees can seek consultation in real time while working via a smart terminal, and means for securely communicating the consultation content and the response. This enables prompt and appropriate responses to mental challenges faced by employees at the workplace.

[1871] An "employee" is a person who works for a company or organization.

[1872] "Mental health" is a state that refers to an individual's psychological or emotional well-being and general sense of well-being.

[1873] "Artificial intelligence" refers to the technology that uses computer systems to imitate human intelligence, as well as the programs and algorithms that make this possible.

[1874] "Consultation content" refers to information that employees input into the system regarding their mental worries and stress.

[1875] The "means for receiving" refers to a method or technology for receiving the consultation content provided by the employee into the system.

[1876] The "means of learning" refers to technology that accumulates and analyzes past consultation data and information provided by specialized institutions to improve the accuracy of the model.

[1877] "Generative AI" is a type of "artificial intelligence" that is a system capable of generating responses based on data received from a user.

[1878] The "means for generating a response" refers to a technique or method for generating appropriate advice or information based on the content of the inquiry from the employee.

[1879] A "response medium" is a technique or method for communicating the generated response to an employee.

[1880] "Means of recording" refers to the technology or methods for saving the employee's consultation and the response thereto.

[1881] "Smart devices" are electronic devices carried or worn by employees, such as smartphones, smart glasses, and head-mounted displays.

[1882] A "real-time consultation interface" is a user interface that allows employees to instantly access the system and input the details of their consultation.

[1883] "Means of communication" refers to the technologies and methods for securely sending and receiving data using the Internet or other communications technologies.

[1884] The system of the present invention is a system that uses artificial intelligence (AI) to support the mental health of employees. This system is composed of the following elements: a server, a terminal, and a user.

[1885] Server Roles and Operations

[1886] The server mainly has the following functions:

[1887] 1. Information gathering and learning:

[1888] The server periodically collects information from specialist institutions and past consultation data, which is used as training data for the generative AI model.

[1889] Example: Collecting new mental health research data from professional organizations.

[1890] 2. Receiving and analyzing consultation content:

[1891] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP).

[1892] For example, if a user enters, "I've been overwhelmed with work lately and it's stressful," the server receives this data and queries a generative AI model for appropriate responses regarding stress management.

[1893] 3. Generate a response:

[1894] Generative AI generates appropriate responses based on the analyzed data.

[1895] Example: A generative AI model might generate a response such as, "To reduce stress, it's effective to set aside time to relax every day. Another option is to consult a professional counselor."

[1896] 4. Returning and Recording Responses:

[1897] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1898] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1899] Device role and operation

[1900] The terminal provides an interface for employees to input the details of their consultation.

[1901] 1. Enter your consultation details:

[1902] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1903] Example: An employee types, "I've been so busy at work lately that I don't have time to rest."

[1904] 2. Receiving and displaying responses:

[1905] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1906] For example, a response such as "Taking breaks helps refresh your mind and body, so make sure to take regular breaks" will be displayed on the device.

[1907] User Interaction

[1908] Users (employees) interact with the system as follows:

[1909] 1. Consultation input:

[1910] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1911] Example prompt: "I've been overwhelmed with work lately and it's been stressful. Can you tell me how to reduce stress in this situation?"

[1912] 2. Check the response:

[1913] The generated response is displayed on the terminal so that the user can review it and take any necessary action.

[1914] For example, a possible response would be, "Deep breathing and moderate exercise can help reduce stress. Also, try some relaxation techniques."

[1915] Hardware and software used

[1916] Hardware: Smart devices (smartphones, smart glasses, head-mounted displays, etc.)

[1917] Software: Python program, natural language processing library (spaCy, Transformers), server communication library (requests), generative AI model (OpenAI GPT-4)

[1918] This will enable quick and appropriate responses to the mental challenges employees face at the workplace.

[1919] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1920] Step 1:

[1921] The terminal receives the consultation content from the user (employee). The consultation content entered is saved in text format. For example, the user can enter, "I've been stressed out because I've had too much work lately."

[1922] Step 2:

[1923] The device sends the received consultation content to the server. Specifically, it sends this text data to the server using an HTTP POST request.

[1924] Step 3:

[1925] The server analyzes the received consultation content. First, it uses natural language processing (NLP) technology to convert the consultation content into an easy-to-understand format. This process uses NLP libraries such as spaCy and Transformers. By converting the input text into an easy-to-understand format, keywords and sentiments related to stress and anxiety are extracted.

[1926] Step 4:

[1927] The server queries a generative AI model based on the analyzed data. This generative AI model (e.g., GPT-4) generates an appropriate response based on relevant information. For example, the generative AI model might generate advice such as, "Deep breathing and moderate exercise are effective in reducing stress."

[1928] Step 5:

[1929] The server sends the generated response to the user's terminal, and returns the generated advice to the terminal in text format via an HTTP response.

[1930] Step 6:

[1931] The device displays the response sent back from the server to the user. Specifically, the generated advice is displayed on the display of the smart glasses or smartphone. The user can check the advice and take necessary actions.

[1932] Step 7:

[1933] The server records the consultation and the corresponding response. This record is stored in a database and used as future learning data, allowing the server's generative AI model to be continuously improved.

[1934] Through these steps, users can receive psychological support in real time, and the entire system records the consultation and responses, contributing to improving the accuracy of the generative AI model.

[1935] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1936] The system of the present invention utilizes artificial intelligence to support the mental health of employees, and further combines it with an emotion engine to improve the accuracy and appropriateness of responses. This system allows employees to easily consult with the system and receive appropriate advice according to their emotional state. Specific forms of the system are described below.

[1937] 1. System Configuration

[1938] The system mainly consists of the following elements:

[1939] Server: Operates and manages the central database, generative AI models, and emotion engine.

[1940] Terminal: Provides an interface for employees to input their consultation details.

[1941] User: In this case, an employee.

[1942] 2. Server Roles and Operations

[1943] The server has the following functions:

[1944] Information gathering and learning

[1945] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[1946] example:

[1947] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[1948] Receiving and analyzing consultation content

[1949] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[1950] example:

[1951] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[1952] Generating a response

[1953] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[1954] example:

[1955] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[1956] Returning and recording responses

[1957] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[1958] example:

[1959] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[1960] 3. Roles and Functions of the Terminal

[1961] The terminal provides an interface for employees to input their consultation details.

[1962] Enter the consultation details

[1963] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[1964] example:

[1965] An employee types, "I've been so busy at work lately that I don't have time to rest."

[1966] Receiving and displaying responses

[1967] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[1968] example:

[1969] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[1970] 4. User Interaction

[1971] Users (employees) interact with the system as follows:

[1972] Input of consultation

[1973] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[1974] Checking the response

[1975] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[1976] 5. Specific Examples

[1977] Example 1: Stress management consultation

[1978] User: An employee types into a terminal, "I'm feeling anxious because I'm stressed at work."

[1979] Terminal: Sends the consultation details to the server.

[1980] Server: Analyzes the consultation content and queries the generative AI. The emotion engine identifies the user's emotional state as "anxiety."

[1981] Generative AI: Generates responses such as, "Deep breathing and moderate exercise are effective in reducing stress. Try relaxation techniques. Also, if you are feeling anxious, we recommend consulting a professional counselor."

[1982] Server: Sends the response back to the user's device.

[1983] Terminal: Display the response.

[1984] Example 2: Consultation about sleep disorders

[1985] User: Employee types, "I've been having trouble sleeping lately."

[1986] Terminal: Sends the consultation details to the server.

[1987] Server: Analyzes the consultation content, and the emotion engine identifies the user's emotional state as "stress." It then queries the generation AI.

[1988] Generative AI: Generates responses such as, "To improve the quality of your sleep, it is effective to take time to relax before bed and avoid caffeine. Also, it seems like you are feeling stressed, so try some relaxation techniques."

[1989] Server: Sends the response back to the user's device.

[1990] Terminal: Display the response.

[1991] As described above, by combining the emotion engine, the system of the present invention can identify the emotional state of employees and provide more appropriate and personalized advice, thereby maintaining employees' mental health and improving the work environment.

[1992] The processing flow will be explained below.

[1993] Specific explanation of program processing

[1994] Step 1: The user inputs a question from the terminal.

[1995] The user uses the device interface to input their mental worries and problems, for example, "I can't sleep lately because of work stress."

[1996] Step 2: The device sends the consultation details to the server

[1997] The terminal sends the entered consultation details to the server as text data. Specifically, the text data is sent as a POST request to a specific URL on the server.

[1998] Step 3: The server receives the request.

[1999] The server processes the received POST request, retrieves the consultation content in text format, and then prepares the retrieved text for analysis.

[2000] Step 4: The server analyzes the request

[2001] The server analyzes the received text using natural language processing (NLP) techniques, specifically by dividing the text into tokens and analyzing its meaning.

[2002] Step 5: The emotion engine recognizes the user's emotion

[2003] An emotion engine built into the server analyzes the text data of the consultation and identifies the user's emotional state, such as "stress," "anxiety," or "sadness."

[2004] Step 6: The server queries the generated AI

[2005] The server queries the generative AI based on the analysis results and the emotion engine results. The input text and emotion data are converted into an appropriate format and provided as input to the generative AI model (e.g., GPT-4).

[2006] Step 7: The generative AI model generates a response

[2007] The generative AI model generates an appropriate response based on the input text and emotional data, such as "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor."

[2008] Step 8: The server receives the generated response

[2009] The server receives the response from the generated AI and processes it in text format.

[2010] Step 9: The server sends a response to the user's device

[2011] The server sends the generated response to the user's terminal. Specifically, it returns text data as a response to the user's terminal.

[2012] Step 10: The terminal displays the response

[2013] The terminal displays the received response to the user, who can then receive advice and consider their next course of action.

[2014] Step 11: The server records the conversation and the response.

[2015] The server stores the user's inquiry and the generated response in a database, which can then be used for future data analysis and as training data for generative AI models.

[2016] This series of processes allows employees to easily and quickly seek mental health advice and receive appropriate advice based on their emotional state.

[2017] Example 2

[2018] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2019] Employee mental health issues have a significant impact on work efficiency and the work environment. Responding promptly and appropriately to stress and anxiety felt by employees is a particularly important issue for companies. However, there is a lack of environments where employees can easily seek advice, and systems that accurately understand their emotional state and provide appropriate advice. This can lead to a decline in employee mental health, which can ultimately lead to a decline in work efficiency and even employee resignation. The purpose of this invention is to solve these issues.

[2020] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the employee, a means for collecting and learning from past consultation data and information provided by specialized institutions, a means for analyzing the received content of the consultation and the emotional state using natural language processing technology, a means for generating a response using a generative artificial intelligence model based on the received content of the consultation and the analysis results, a means for sending the generated response to the employee, and a means for recording the content of the consultation from the employee and the response and using it as future learning data. This makes it possible to accurately grasp the emotional state of the employee and provide prompt and appropriate advice.

[2021] "Employee" refers to an individual working for a business or organization who is particularly required to deal with mental health issues.

[2022] "Consultation content" refers to text information that employees enter into the system, and includes work-related worries and stress in daily life.

[2023] A "means" refers to a device, system, or method used to accomplish a particular purpose.

[2024] "Past consultation data" refers to data including consultation details that employees have previously entered into the system and responses to those consultations.

[2025] "Professional body" refers to an external agency or organization that provides data or research related to mental health or psychology.

[2026] A "generative artificial intelligence model" refers to an artificial intelligence system that automatically generates and analyzes information based on specific algorithms.

[2027] "Natural language processing technology" is a technical field that understands, analyzes, and generates responses to human language, and is used to process text and voice data.

[2028] "Emotional state" refers to the mental state (e.g., anxiety, stress, anger, etc.) of the employee when they enter their consultation details.

[2029] "Response" refers to a message containing advice or a response method that the system generates in response to an employee's inquiry.

[2030] "Recording means" refers to a method or device for storing information for long-term storage and later access.

[2031] "Training data" refers to a collection of past data and new information that the system uses to generate more accurate responses.

[2032] The system of the present invention utilizes artificial intelligence to support the mental health of employees and combines it with an emotion analysis engine to improve the accuracy and appropriateness of responses. Specific embodiments of the system are described below.

[2033] 1. System Configuration

[2034] The system consists of the following main hardware and software:

[2035] Server: Operates and manages the central database, generative AI models (for example, OpenAI's GPT-4 is used as an example of a common generative AI model), and emotion engines (for example, Microsoft's Text Analytics API is an example of a common emotion analysis API).

[2036] Terminal: A device that provides an interface for users, i.e. employees, to input their consultation details. Examples include PCs, smartphones, and tablets.

[2037] User: An employee who uses this system and interacts with the system via a terminal.

[2038] 2. Server Roles and Operations

[2039] Information gathering and learning

[2040] The server periodically collects mental health information provided by specialist institutions, as well as past consultation and emotion data, and uses this data to train the generative AI model and emotion analysis engine, thereby improving the system's response accuracy.

[2041] Example: Download new mental health research data from expert institutions and combine it with historical user consultation data to train a generative AI model.

[2042] Example prompt: "Update your generative AI model with the latest mental health research data."

[2043] Receiving and analyzing consultation content

[2044] The server receives the consultation content sent by the user and analyzes it using natural language processing (NLP) technology, while simultaneously identifying the user's emotional state using an emotion analysis engine.

[2045] Example: If a user types, "I can't sleep because I'm stressed from work," the server receives that data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety or stress).

[2046] Example prompt: "Analyze the following consultation and identify the user's emotional state: 'I can't sleep because of work stress.'"

[2047] Generating a response

[2048] The server uses a generative AI model to generate an appropriate response based on the analysis results, taking into account the emotional state determination results from the emotion analysis engine.

[2049] Example: The generative AI generates a response such as, "To reduce stress, it is important to make time to relax. We also recommend consulting a professional counselor."

[2050] Example prompt: "Generate what advice to offer when the user is feeling anxious."

[2051] Returning and recording responses

[2052] The server returns the generated response to the device, and also records the received consultation content and the generated response in an internal database for future use as learning data.

[2053] Example: The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[2054] Example prompt: "Send the following response to the user's device and record it on the server: 'To reduce stress, take time to relax.'"

[2055] 3. Roles and Functions of the Terminal

[2056] Enter the consultation details

[2057] Users use the terminal interface to input their concerns and questions, and this information is immediately sent to the server.

[2058] Example: An employee types into a terminal, "I've been busy at work lately and don't have time to refresh myself."

[2059] Example prompt: "Please provide an interface that allows users to enter their concerns."

[2060] Receiving and displaying responses

[2061] The response sent back from the server is displayed on the terminal so that the user can check it.

[2062] Example: A response such as "We recommend you take a short break to relax" appears on the device.

[2063] Example prompt: "Please display the response from the server on the user's terminal."

[2064] 4. User Interaction

[2065] The user inputs the content of the inquiry through the terminal and sends it to the server. The generated response is displayed on the terminal, and the user can check it and take appropriate action.

[2066] Specific examples

[2067] Example 1: Stress management consultation

[2068] User: An employee types into the terminal, "I'm feeling anxious because I'm stressed at work."

[2069] Terminal: Encodes the consultation content into JSON format and sends it to the server.

[2070] Server: The received data is decoded, analyzed using NLP, and the emotion engine identifies the emotional state as "anxiety."

[2071] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to users who are feeling stressed."

[2072] Server: Sends the response to the user's terminal and records it in a database.

[2073] Terminal: Display the response.

[2074] Example 2: Consultation about sleep disorders

[2075] User: An employee types into the terminal, "I haven't been able to sleep lately."

[2076] Terminal: Sends the consultation details to the server.

[2077] Server: Decodes, analyzes using NLP, and the emotion engine identifies the emotional state as "stress."

[2078] Generative AI model: Generates a response based on the prompt, "Please provide appropriate advice to a user who is experiencing insomnia and stress."

[2079] Server: Sends the response to the user's terminal and records it in a database.

[2080] Terminal: Display the response.

[2081] As described above, the system of the present invention combines an emotion analysis engine and a generative AI model to accurately grasp the emotional state of employees and provide prompt and appropriate advice, thereby maintaining the mental health of employees and improving the work environment.

[2082] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2083] Step 1:

[2084] The user inputs the content of the consultation into the terminal interface, which is then processed as text data.

[2085] Specific operation: The user types into the terminal, "I've been busy at work lately and I'm feeling stressed."

[2086] Input: User's concerns (e.g., "I'm busy at work and I'm stressed out")

[2087] Output: The above content will be displayed in the terminal input field.

[2088] Step 2:

[2089] The terminal receives the consultation information entered by the user and formats the data for transmission to the server, where it is encrypted and sent over the Internet.

[2090] Specific operation: The terminal encodes the input consultation content into JSON format and sends it to the server via HTTPS.

[2091] Input: User's consultation content (encrypted text data)

[2092] Output: JSON format data sent to the server

[2093] Step 3:

[2094] The server decodes the consultation content received from the terminal and temporarily stores it in an internal database.

[2095] Specific operation: The server decodes the data received from the terminal and stores it in the "Received Data" table in the database.

[2096] Input: JSON format data sent from the terminal

[2097] Output: Decoded data stored in the "Received Data" table

[2098] Step 4:

[2099] The server applies natural language processing (NLP) technology to the received consultation content and analyzes keywords and themes.

[2100] Specific operation: The server uses an NLP library (e.g., SpaCy or NLTK) to analyze the consultation content and extract keywords such as "stress" and "work."

[2101] Input: Decoded consultation content

[2102] Output: Extracted keywords and themes

[2103] Step 5:

[2104] The server uses a sentiment analysis engine to identify the user's emotional state, using keywords and themes extracted by NLP.

[2105] What it does: The server sends the NLP results to a sentiment analysis engine (e.g., Microsoft's Text Analytics API), which identifies "stress" as the emotion.

[2106] Input: Extracted keywords and themes

[2107] Output: Identified emotional state (e.g., "stressed")

[2108] Step 6:

[2109] The server uses a generative AI model to generate appropriate advice based on the analysis results and emotional state.

[2110] Specific operation: The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "Please provide appropriate advice to a user who is feeling stressed," and generates a response.

[2111] Input: Sentiment analysis engine results and prompt text

[2112] Output: The generated response (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[2113] Step 7:

[2114] The server formats the generated response and prepares it for transmission to the device, where the data is again encrypted and sent over the Internet to the device.

[2115] Specific operation: The server encodes the response data into JSON format and sends it to the terminal via HTTPS.

[2116] Input: Generated response

[2117] Output: JSON format data sent to the terminal

[2118] Step 8:

[2119] The terminal decodes the response data received from the server and displays it on the user interface.

[2120] Specific operation: The terminal analyzes the response data received from the server and displays it to the user.

[2121] Input: JSON format data sent from the server

[2122] Output: The response displayed in the user interface (e.g., "To reduce stress, it's important to take time to relax. You may also want to consult a professional counselor.")

[2123] Step 9:

[2124] The server records the received consultation content and the generated response in an internal database and uses it as future learning data.

[2125] Specific operation: The server saves the consultation content and response in the "historical data" table of the database and tags it.

[2126] Input: Consultation content and generated response

[2127] Output: Data stored in the "Historical Data" table

[2128] These are the specific processing steps in a system for supporting the mental health of employees.

[2129] (Application example 2)

[2130] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2131] In today's workplace, employees often experience a lot of stress and anxiety. This can lead to poor mental health, reduced productivity, and a worsening work environment. To solve this problem, a system is needed that employees can easily consult with, understand their emotional state, and provide appropriate advice. However, existing systems lack the ability to analyze emotional states or provide individualized support, making it difficult to effectively support employees' mental health.

[2132] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2133] In this invention, the server includes means for receiving consultation content from employees, means for collecting and learning from past consultation data and information provided by specialist institutions, means for generating a response using generative artificial intelligence based on the received consultation content, means for returning the generated response to the employee, means for recording the consultation content from the employee and the response, means for using an emotion engine to analyze the emotional state of the employee, means for providing the generated response visually and audibly, and means for providing a consultation interface via a smart device and a robot. This makes it possible to more effectively support the mental health of employees and improve the work environment.

[2134] An "employee" is an individual employed by a company or organization whose mental health is to be supported.

[2135] "Mental health" is a state in which an individual maintains a stable psychological state by minimizing mental problems such as stress, anxiety, and depression in their daily lives.

[2136] "Artificial intelligence" refers to technology that learns from large amounts of data and performs pattern recognition and decision-making, including generative AI models and emotion engines.

[2137] "Generative AI" is an AI model that generates appropriate responses and advice based on input data.

[2138] An "emotion engine" is a technology that analyzes and identifies the emotional state of a user based on input data from the user.

[2139] A "smart device" is a smartphone, tablet, or similar electronic device that provides an interface for transferring data between a user and a system.

[2140] A "robot" is a machine that interacts with workers in a factory or other work environment.

[2141] "Natural language processing" is a technology for analyzing, understanding, and generating human language.

[2142] A "problem" is a description of a particular concern or problem that an employee enters into the system.

[2143] A "response" is the advice or answer that the system provides to the employee's inquiry.

[2144] "Recording" is the process by which the system saves employee consultations and responses for subsequent learning and analysis.

[2145] The system of the present invention utilizes artificial intelligence to support employee mental health and further combines an emotion engine to improve the accuracy and appropriateness of responses. Specific embodiments of this system are described below.

[2146] 1. System Configuration

[2147] The system mainly consists of the following elements:

[2148] Server: Operates and manages the central database, generative AI models, and emotion engine.

[2149] Terminal: Provides an interface for employees to input their consultation details. This includes smart devices and robots.

[2150] User: This applies to employees.

[2151] 2. Server Roles and Operations

[2152] The server has the following functions:

[2153] Information gathering and learning

[2154] The server periodically collects information from expert institutions, as well as past consultation and emotion data, with the aim of training the generative AI model and emotion engine to generate more accurate responses.

[2155] example:

[2156] It collects new mental health research data from specialized institutions, incorporates emotional data along with past consultation data, and trains AI models and emotion engines.

[2157] Receiving and analyzing consultation content

[2158] The server receives the consultation content sent by the user (employee). The received data is analyzed using natural language processing (NLP) technology. Furthermore, an emotion engine analyzes the user's emotional state.

[2159] example:

[2160] When a user types, "I can't sleep because of work stress," the server receives this data, analyzes it using NLP, and the emotion engine identifies the emotional state (e.g., anxiety, stress).

[2161] Generating a response

[2162] Generative AI generates appropriate responses based on the analyzed data and the results of the emotion engine.

[2163] example:

[2164] The artificial intelligence generates a response such as, "To reduce stress, it is effective to set aside time to relax every day. Another option is to consult a professional counselor. Since you seem to be particularly anxious, we will provide you with more information."

[2165] Returning and recording responses

[2166] The generated response is sent back to the user's device from the server, and the content of the consultation and the response are recorded in the server and used as learning data for future use.

[2167] example:

[2168] The generated response is displayed on the employee's terminal and simultaneously saved on the server.

[2169] 3. Roles and Functions of the Terminal

[2170] The terminal provides an interface for employees to input their consultation details, and can be a smart device or robot.

[2171] Enter the consultation details

[2172] Users use the device interface to input their concerns and questions, which are then immediately sent to the server.

[2173] example:

[2174] An employee types, "I've been so busy at work lately that I don't have time to rest."

[2175] Receiving and displaying responses

[2176] The response sent back from the server is displayed on the terminal, and the user checks it and considers the next action to take.

[2177] example:

[2178] The device will display a response such as, "Taking breaks helps refresh your mind and body, so make sure to take breaks regularly. In particular, we have detected that you are feeling stressed, so please try some relaxation techniques."

[2179] 4. User Interaction

[2180] Users (employees) interact with the system as follows:

[2181] Input of consultation

[2182] Users input their concerns via their terminal and send them to the server, which then immediately begins responding.

[2183] Checking the response

[2184] The generated response is displayed on the terminal, allowing the user to review it and consider any necessary action.

[2185] 5. Specific Examples

[2186] Example 1: Stress management consultation

[2187] Prompt: "I'm feeling anxious and stressed at work. What can I do?"

[2188] Sample response: "Deep breathing and moderate exercise can help reduce stress. Try relaxation techniques. Also, if you're experiencing anxiety, consider talking to a professional counselor."

[2189] Example 2: Consultation about sleep disorders

[2190] Prompt: "I haven't been able to sleep lately."

[2191] Example response: "To improve the quality of your sleep, you can take some time to relax before bed and avoid caffeine. Also, since you seem to be stressed, try some relaxation techniques."

[2192] The above is a description of the mode for carrying out the invention, which makes it possible to maintain the mental health of employees and improve the working environment.

[2193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2194] Step 1:

[2195] Input and receive consultation details

[2196] Users input their consultation details via a terminal (smart device or robot). The input data includes text and voice. This input data is immediately sent to the server.

[2197] Input: User's inquiry (e.g., I've been so busy with work lately that I don't have time to rest)

[2198] Output: Received consultation details

[2199] Step 2:

[2200] Analysis of consultation content

[2201] The server analyzes the received consultation content using natural language processing (NLP) technology, which breaks down the content into sentences and recognizes its subject and sentiment.

[2202] Input: Received consultation content

[2203] Output: Analyzed data (themes and sentiment)

[2204] Step 3:

[2205] Emotional state analysis

[2206] The server uses an emotion engine to identify the user's emotional state from the analyzed consultation content, and this identification result is used to generate future responses.

[2207] Input: Parsed data (themes and sentiment)

[2208] Output: Identified emotional state (e.g., anxiety, stress)

[2209] Step 4:

[2210] Generating a response

[2211] The server uses a generative artificial intelligence (generative AI model) to generate appropriate responses based on the identified emotional state and subject matter, which may include specific actions or advice.

[2212] Input: Identified emotional state, subject

[2213] Output: Generated response (e.g., "Deep breathing and moderate exercise can help reduce stress. Try some relaxation techniques.")

[2214] Step 5:

[2215] Returning and displaying responses

[2216] The server generates a response and sends it back to the user's device, which displays the response as voice or text, and the user reviews the response and decides on the next action based on it.

[2217] Input: The generated response

[2218] Output: Display of response (audio or text)

[2219] Step 6:

[2220] Record of consultation content and response

[2221] The server records the employee consultations and responses for future learning and analysis, which will be used to improve the system and collect new data.

[2222] Input: Consultation details, generated response

[2223] Output: Recorded data

[2224] Through these steps, users receive real-time advice to support the mental health of their employees, and the system is continuously learning to improve the accuracy and relevance of its responses.

[2225] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2226] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2227] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2228] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2229] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2230] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2231] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2232] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2233] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2234] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2235] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2236] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2237] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2238] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2239] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2240] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2241] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2242] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2243] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2244] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2245] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2246] The following is further disclosed regarding the above embodiment.

[2247] (Claim 1)

[2248] An artificial intelligence-based system for supporting the mental health of employees,

[2249] A means for receiving consultation contents from employees;

[2250] A means of collecting and learning from past consultation data and information provided by professional organizations;

[2251] A means for generating a response using artificial intelligence based on the received consultation content;

[2252] a means for returning the generated response to the employee;

[2253] A means of recording employee consultations and responses;

[2254] A system including:

[2255] (Claim 2)

[2256] 10. The system of claim 1,

[2257] Record the details of consultations from employees and their responses,

[2258] The system further includes means for continuously training the generative artificial intelligence based on new information.

[2259] (Claim 3)

[2260] 10. The system of claim 1,

[2261] The system further includes means for analyzing the employee input using natural language processing.

[2262] "Example 1" 【2...

Claims

1. An artificial intelligence-based system for supporting the mental health of employees, A means for receiving consultation contents from employees; A means of collecting and learning from past consultation data and information provided by professional organizations; A means for generating a response using artificial intelligence based on the received consultation content; a means for returning the generated response to the employee; A means of recording employee consultations and responses; A system including:

2. 10. The system of claim 1, Record the details of consultations from employees and their responses, The system further includes means for continuously training the generative artificial intelligence based on new information.

3. 10. The system of claim 1, The system further includes means for analyzing the employee input using natural language processing.

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