System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting input data from a user; means for analyzing the collected data by natural language processing; means for generating an interactive self-care guide based on an analysis result; means for providing the generated self-care guide to the user; and means for determining whether professional psychological assistance is needed according to the analysis result and sending an alert if needed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, a healthy mind and mindset are important for improving overall well-being and work productivity. However, individuals face challenges such as difficulty finding someone to talk to about their mental health or lack of time to do so. Furthermore, companies face a lack of systems for early detection of mental health problems in employees and for coordination with appropriate industrial physicians. The present invention aims to solve these challenges. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means. First, a means for collecting input data from a user is provided. Next, a means for analyzing the collected data using natural language processing is provided. This makes it possible to detect sensitive topics from the data. Next, a means for generating an interactive self-care guide based on the analysis results is provided, and this is provided as a means for providing the guide to the user. Furthermore, the system also includes a means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary. This enables early detection of mental health problems and immediate appropriate response. Furthermore, by providing a means for regularly checking the user's mental health status and suggesting appropriate self-care as needed, the system promotes the user's overall well-being. Finally, a means for generating a report on the user's mental health status and sending it to the company or an industrial physician is provided, allowing companies to efficiently manage the mental health of their employees.

[0006] "User" refers to an individual or employee who uses the System.

[0007] "Input data" includes information provided by users to the system, specifically textual responses and status reports.

[0008] "Means of collection" refers to the functions and methods for incorporating input data from users into the system.

[0009] "Natural language processing" refers to the technology of analyzing user input data and understanding its meaning and intent.

[0010] "Means for analysis" refers to techniques and methods for analyzing collected data and understanding its contents.

[0011] An "interactive self-care guide" refers to guidelines and advice provided to users to support psychological self-care.

[0012] "Means for generating" refers to the functions and methods for creating an interactive self-care guide based on the analysis results.

[0013] The "means for providing" refers to a function for displaying or transmitting the generated interactive self-care guide to the user.

[0014] "Professional psychological assistance" refers to mental health support provided by professionals such as psychological counselors and industrial physicians.

[0015] "Means for making a judgment" refers to the function of determining whether professional psychological assistance is required based on the results of the analysis.

[0016] "Means for sending alerts" refers to the function of sending notifications to experts and relevant parties as needed.

[0017] "Report" refers to a detailed report of a User's mental health status.

[0018] "Transmission means" refers to a function for transmitting the generated report to company managers or industrial physicians. [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 present invention relates to an AI system for supporting a user's mental health, and an embodiment thereof will be described in detail below.

[0041] 1. Collecting user input data

[0042] The terminal displays a mental check question to the user. For example, it provides a question such as "How are you feeling today?" The user inputs a free-form answer to this question. The input data is sent from the terminal to the server.

[0043] 2. Data Analysis

[0044] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. The NLP module identifies topics in the data and determines whether they contain sensitive topics (e.g., "depression," "suicide").

[0045] 3. Providing self-care guidance

[0046] The server generates an appropriate interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, the server will provide the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server will send a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0047] 4. Providing alerts and professional assistance

[0048] If a sensitive topic is detected based on the analysis results, the server determines whether professional psychological assistance is required. If so, the server issues an alert to notify specialists or industrial physicians. The alert includes detailed information indicating that the user is in a critical condition.

[0049] 5. Report Generation

[0050] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results. The generated reports are sent from the server to company managers and industrial physicians. This allows companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0051] To explain with a concrete example, if a user responds, "I'm tired, but I'm fine," the server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The server then sends this guide to the device, which displays it to the user.

[0052] Furthermore, if the user's response contains sensitive content such as "I'm thinking about suicide," the server will send an alert and arrange for professional psychological assistance to be provided. In this way, the present invention is a system that comprehensively supports the user's mental health.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The device displays mental check questions to the user, such as "How are you feeling today?"

[0056] Step 2:

[0057] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[0058] Step 3:

[0059] The terminal sends the user's input data to the server, where the response is sent using a secure communication protocol.

[0060] Step 4:

[0061] The server receives the user's response data sent from the device and temporarily stores the received data.

[0062] Step 5:

[0063] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[0064] Step 6:

[0065] The server generates an interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, it generates a guide such as "Try deep breathing as a relaxation technique." If there are no particular problems, it generates a positive message such as "Have a great day today!"

[0066] Step 7:

[0067] The server then sends the generated self-care guide to the terminal. Because the guide contains important information for the user, it is sent quickly and accurately.

[0068] Step 8:

[0069] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[0070] Step 9:

[0071] The server then reviews the analysis results and determines whether professional psychological assistance is needed. If so, the server generates an alert.

[0072] Step 10:

[0073] The server sends an alert to a specialist or industrial physician, which includes details about the user's mental state.

[0074] Step 11:

[0075] The server periodically generates a report on the user's mental health status based on the user's response history and analytical data.

[0076] Step 12:

[0077] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[0078] This is the specific processing flow of the AI ​​mental health support system "AI." At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[0079] Example 1

[0080] 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."

[0081] Conventional mental health support systems have had problems in that they are unable to properly assess a user's condition and respond quickly. They also lack the functionality to properly issue alerts in serious cases that require specialized assistance. As a result, users' mental health conditions run the risk of continuing to deteriorate, and it is difficult for company managers and industrial physicians to properly understand the status of their employees.

[0082] 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.

[0083] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether a sensitive topic is included based on the analysis results, means for determining whether professional psychological assistance is needed and sending an alert if necessary, and means for generating a report on the user's mental health status and sending it to a company or industrial physician. This makes it possible to quickly and accurately grasp the user's mental health status and provide appropriate self-care and, if necessary, professional assistance.

[0084] "User input data" refers to answers and responses that users input into the system via their terminals.

[0085] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0086] "Interactive self-care guide" means an interactive guide that includes advice and instructions provided to support a user's mental health.

[0087] "Sensitive topics" refer to mental health topics that are of high urgency or seriousness, such as depression or suicide.

[0088] "Professional psychological assistance" refers to specialized help provided by mental health professionals or counselors.

[0089] "Sending an alert" refers to the act of issuing a warning or notification when certain conditions are met.

[0090] "Generating a report" refers to the act of creating a report based on collected and analyzed data.

[0091] "Corporate or industrial physicians" refers to managers and medical professionals in charge of mental health care in the user's work environment.

[0092] "Checking regularly" refers to the act of checking the state of mental health at regular intervals.

[0093] A "prompt" is a document used to input instructions or questions to an AI model.

[0094] The present invention relates to an AI system for supporting a user's mental health. This system collects input data from a user, analyzes it using natural language processing, and generates an interactive self-care guide based on the analysis results. It also has the function of providing professional psychological assistance as needed and generating periodic reports. Detailed embodiments of the system are described below.

[0095] Collecting user input data

[0096] First, the user accesses the system using a terminal. The terminal can be a smartphone, tablet, PC, etc. The terminal displays a mental check question to the user. For example, it may provide a question such as "How are you feeling today?" The user then inputs a free-form answer to this question. This input data is encrypted and sent from the terminal to the server.

[0097] Data analysis

[0098] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. This NLP module is implemented using popular Python libraries such as "spaCy" and "NLTK." The NLP module identifies topics within the data and determines whether they contain sensitive topics (e.g., "depression" or "suicide").

[0099] Creating and providing self-care guides

[0100] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server provides the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server sends a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0101] Alerts and expert assistance

[0102] Based on the analysis results, the server determines whether or not professional psychological assistance is needed. If a sensitive topic is detected, it will determine whether professional psychological assistance is needed and, if so, will issue an alert. This alert will include detailed information indicating that the user is in a critical condition, and will notify specialists or occupational physicians.

[0103] Report generation and delivery

[0104] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. These reports enable companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0105] Examples of concrete examples and prompts

[0106] As a concrete example, consider the case where a user answers "I'm tired, but it's fine." The server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that it's "fine" and generates a positive self-care guide: "Have a great day!" The server then sends this guide to the device, which displays it to the user.

[0107] The following is a specific example of a prompt sentence:

[0108] Prompt: "I'd like to conduct a mental health check on you. I'd like to ask you for some open-ended questions about how I'm feeling today, and then analyze your responses to determine if you need appropriate self-care guidance and professional help."

[0109] Using this prompt, the generative AI model can comprehensively analyze information about the user's mental health and provide appropriate actions.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] The user accesses the device

[0113] Input: A user logs in to a terminal.

[0114] Action: A user accesses the system using a device such as a smartphone, tablet, or PC and logs in.

[0115] Output: The user successfully logs in and is taken to the mental health check question screen.

[0116] Step 2:

[0117] The device displays mental check questions to the user.

[0118] Input: The user's login status.

[0119] What it does: The device displays a pre-defined mental health question (e.g., "How are you feeling today?").

[0120] Output: The question is displayed to the user and an answer can be entered.

[0121] Step 3:

[0122] The user answers the questions and enters them into the terminal

[0123] Input: Mental check question displayed on terminal.

[0124] How it works: The user types a free-form response to a question (e.g., "I'm tired, but I'm OK").

[0125] Output: The user's answer data is input to the terminal.

[0126] Step 4:

[0127] The device collects the user's answers and sends them to the server.

[0128] Input: User response data.

[0129] Operation: The device encrypts and sends the collected response data to the server.

[0130] Output: The server receives the user's answer data.

[0131] Step 5:

[0132] The server receives the user's response data sent from the device.

[0133] Input: Encrypted user response data sent from the device.

[0134] What happens: The server receives the data and stores it in a database.

[0135] Output: User response data stored in a database.

[0136] Step 6:

[0137] The NLP module on the server analyzes the response data.

[0138] Input: User response data stored in the database.

[0139] How it works: A server-based natural language processing (NLP) module analyzes the response data and extracts topics and keywords, using, for example, Python's "spaCy" or "NLTK."

[0140] Output: The topics and keywords extracted as a result of the analysis.

[0141] Step 7:

[0142] The NLP module identifies topics in the data and determines whether they contain sensitive topics.

[0143] Input: Topics and keywords resulting from the analysis.

[0144] How it works: The NLP module evaluates the message to determine if it contains sensitive topics (e.g., "depression," "suicide").

[0145] Output: Evaluation result for the presence or absence of sensitive topics.

[0146] Step 8:

[0147] The server generates a self-care guide based on the analysis results.

[0148] Input: Assessment results for the presence or absence of sensitive topics.

[0149] How it works: The server generates an appropriate self-care guide based on the information (e.g., "Try deep breathing as a way to relax" or "Have a great day!").

[0150] Output: The generated self-care guide.

[0151] Step 9:

[0152] The generated self-care guide is sent from the server to the device.

[0153] Input: The generated self-care guide.

[0154] Operation: The server sends the generated self-care guide to the device.

[0155] Output: The self-care guide is displayed on the user's device.

[0156] Step 10:

[0157] The device displays a self-care guide to the user.

[0158] Input: Self-care guide sent from the server.

[0159] What it does: The device displays a self-care guide to the user, which the user can use to help with mental health care.

[0160] Output: The user reviews and implements the self-care guide.

[0161] Step 11:

[0162] The server determines the necessity based on the analysis results

[0163] Input: Assessment results for the presence or absence of sensitive topics.

[0164] How it works: The server uses the assessment results to determine whether the user needs professional psychological help.

[0165] Output: A decision on whether professional help is needed.

[0166] Step 12:

[0167] Sends alerts when the server deems necessary

[0168] Input: The result of the decision on whether professional assistance is required.

[0169] How it works: The server will alert you if it determines that professional assistance is needed.

[0170] Output: Alert notification to specialists and industrial physicians.

[0171] Step 13:

[0172] The server periodically generates a report on the user's mental health status.

[0173] Input: User's answer history and analysis results.

[0174] How it works: The server generates periodic reports based on this.

[0175] Output: The generated mental health report.

[0176] Step 14:

[0177] The generated report is sent from the server to company managers and industrial physicians.

[0178] Input: Generated mental health report.

[0179] How it works: The server sends the generated report to company management and / or industrial physicians.

[0180] Output: Report received by company management and industrial physician.

[0181] (Application example 1)

[0182] 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."

[0183] In recent years, mental health issues among employees in factories and manufacturing sites have become more serious. In particular, the number of employees suffering from psychological problems due to overwork and excessive stress is increasing. This has led to a significant decline in productivity and an increase in employee turnover. For this reason, there is a need for a system that provides comprehensive support for employee mental health.

[0184] 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.

[0185] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for providing mental health check questions and collecting responses from the user using hardware with a voice recognition function. This allows employees to not only receive mental health checks in real time, but also to receive appropriate self-care or professional support as needed.

[0186] A "user" is an individual employee or user of the system.

[0187] "Input data" refers to answer data to questions in the mental health check entered by the user.

[0188] "Natural language processing" is a technique used to analyze user input data, identifying topics and detecting sensitive topics.

[0189] An "interactive self-care guide" is a means of providing appropriate self-care advice to the user that is generated based on the analysis results.

[0190] An "alert" is a notification that is sent when it is determined that professional psychological assistance is necessary based on the results of the user's analysis.

[0191] "Mental check questions" are questions provided to assess the user's mood or state.

[0192] The "voice recognition function" is a technology that converts a user's voice input into text data.

[0193] "Hardware" refers to all devices and equipment with voice recognition capabilities.

[0194] "Professional psychological assistance" refers to support provided by professionals such as psychological counselors and psychiatrists.

[0195] "Self-care" refers to mental and physical care methods that users can take care of themselves.

[0196] A "report" is a document summarizing the analysis results regarding the user's mental health condition.

[0197] "Corporate and industrial physicians" are institutions and professionals who are responsible for managing and supporting the mental health of employees in factories and organizations.

[0198] The present invention relates to a system for supporting the mental health of employees in factories and manufacturing sites. A specific embodiment of this system will be described below.

[0199] Collecting user input data

[0200] The system periodically presents users with mental check questions. The questions are presented in the form of, for example, "How are you feeling today?" The user inputs the answers using hardware with speech recognition capabilities (e.g., a robot, smart glasses, or a head-mounted display). This automatically collects data about the user's mental state.

[0201] Data analysis

[0202] The collected data is sent to a server and analyzed using a natural language processing (NLP) module. Specifically, the collected text data is categorized by topic and determined to contain sensitive topics. The NLP module includes a function to detect keywords such as "suicide," "depression," and "stress."

[0203] Providing self-care guides

[0204] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server generates advice such as "Try taking deep breaths as a way to relax." On the other hand, if no sensitive topic is detected, the server provides a positive message such as "Have a great day!" The generated self-care guide is sent to the device and displayed to the user.

[0205] Alerts and expert assistance

[0206] Based on the analysis results, the server determines whether professional psychological assistance is needed. If so, the server sends an alert to notify mental health professionals. The alert contains detailed information indicating that the user is in a critical state. This information is used to quickly contact the appropriate professional.

[0207] Mental health status report generation

[0208] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. This allows companies to understand employee mental health trends and take appropriate measures as necessary.

[0209] Specific examples

[0210] For example, if a user responds, "I'm a little tired today, but it's okay," a device with speech recognition capabilities sends this data to the server. The server then analyzes it using an NLP module, and if no sensitive topics are detected, it determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The guide is then sent to the user's device and displayed.

[0211] On the other hand, if the user answers with sensitive content such as "I'm thinking about suicide," the server analyzes this data and determines that the situation is critical. The server then sends out an alert and notifies a mental health professional. In this way, the system provides comprehensive support for the user's mental health.

[0212] Prompt Sentence Examples

[0213] 1. The robot will ask the question, "How are you feeling today?"

[0214] 2. The user responds, "I'm feeling a little stressed."

[0215] 3. The answer is sent to the server and the analysis results are received.

[0216] 4. If no sensitive topics are detected, it will display "Have a nice day!"

[0217] 5. Send alerts to mental health professionals if sensitive topics are detected.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] The user answers mental check questions through a terminal. The terminal uses hardware with speech recognition capabilities (e.g., robots, smart glasses, head-mounted displays) to convert the user's voice input into text data. The input data is the user's response to the question, "How are you feeling today?"

[0221] Input: Voice response to mental check questions

[0222] Output: Conversion result from audio data to text data

[0223] Step 2:

[0224] The terminal sends the collected text data to the server. The server receives the data using HTTP communication (e.g., the requests library). The received data is the text data of the user's responses.

[0225] Input: Converted text data

[0226] Output: Sending text data to the server

[0227] Step 3:

[0228] The server passes the received text data to a natural language processing (NLP) module, which analyzes the text data to identify topics and detect sensitive topics (e.g., "suicide" or "depression"). This analysis generates a score that evaluates the sensitivity of the text.

[0229] Input: Text data of user responses

[0230] Output: Analysis results and sensitivity score

[0231] Step 4:

[0232] The server generates an interactive self-care guide based on the analysis results. Using a generative AI model, it automatically generates messages such as "Try taking a deep breath" or "Have a great day!" The results are saved as text data.

[0233] Input: Analysis results of the NLP module

[0234] Output: Text data of the generated self-care guide

[0235] Step 5:

[0236] The server sends the generated self-care guide to the terminal, which receives the data using HTTP communication and displays the self-care guide to the user.

[0237] Input: Text data of the generated self-care guide

[0238] Output: Display of self-care guide

[0239] Step 6:

[0240] The server uses the analysis results to determine whether professional psychological assistance is needed. If so, the server generates an alert to notify mental health professionals. The alert includes detailed information indicating the user is in a critical condition.

[0241] Input: Analysis results of the NLP module and sensitivity score

[0242] Output: Alert notification data

[0243] Step 7:

[0244] The server periodically generates a report on the user's mental health status. The report includes the user's response history and analysis results, and is sent to the company's management and industrial physician. This allows the company to understand the mental health status of its employees and take measures as necessary.

[0245] Input: User response history and analysis results

[0246] Output: Mental health report

[0247] 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.

[0248] The present invention relates to an AI system for supporting the mental health of users, and in particular to a system that combines an emotion engine that recognizes the user's emotions.

[0249] 1. Collecting user input data

[0250] The terminal displays mental check questions to the user, such as "How are you feeling today?" The user then inputs a free-form response to the question. The input data is sent from the terminal to the server.

[0251] 2. Data Analysis

[0252] The server receives the user's response data from the device. The received data is passed to a natural language processing (NLP) module that includes an emotion engine. The NLP module analyzes the data and identifies emotions (e.g., joy, sadness, fear, anger) contained in the user's text.

[0253] 3. Emotion Recognition by Emotion Engine

[0254] The emotion engine identifies the user's emotions from the analyzed data. For example, in response to the answer "I'm tired, but I'm fine," it recognizes the emotions "fatigue" and "relief." This recognition result is used in subsequent processing.

[0255] 4. Providing self-care guidance

[0256] The server generates an interactive self-care guide based on the analysis results of the NLP and emotion engine. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "Try taking deep breaths" as a way to relax. If the emotion engine recognizes "relief," it will send a positive message such as "Have a great day today!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0257] 5. Providing alerts and professional assistance

[0258] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If it determines that it is necessary, it generates an alert, taking into account the recognition results of the emotion engine. For example, if "sadness" or "fear" is recognized, the server will issue a high-level alert and notify a specialist or industrial physician.

[0259] 6. Report Generation

[0260] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and the emotion recognition results of the emotion engine. The generated report is sent to company managers and industrial physicians. This allows companies to gain a detailed understanding of their employees' mental health status and provide appropriate follow-up care.

[0261] For example, if a user responds, "Recently, work has not been going well and I am feeling very stressed," the server receives this data and analyzes it using the NLP module. At the same time, the emotion engine recognizes emotions such as "stress" and "anxiety." Based on the analysis results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation is serious, an alert will be sent to an expert.

[0262] The present invention provides advanced mental health support that takes into account the user's emotions, thereby improving the user's overall well-being and the working environment in companies.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The device displays mental check questions to the user, such as "How are you feeling today?"

[0266] Step 2:

[0267] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[0268] Step 3:

[0269] The terminal transmits the user's input data to the server, using a secure communication protocol.

[0270] Step 4:

[0271] The server receives the user's response data sent from the device and temporarily stores the received data.

[0272] Step 5:

[0273] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[0274] Step 6:

[0275] The server passes the analysis results of the NLP module to the emotion engine, which recognizes emotions (e.g., joy, sadness, fear, anger) in the data.

[0276] Step 7:

[0277] The server receives the emotion recognition results from the emotion engine and generates an interactive self-care guide based on the analysis results. For example, if the recognized emotion is "fatigue," it generates advice such as "Try deep breathing as a way to relax."

[0278] Step 8:

[0279] The server transmits the generated self-care guide to the terminal. Since the self-care guide contains important information for the user, it is transmitted quickly and accurately.

[0280] Step 9:

[0281] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[0282] Step 10:

[0283] The server then rechecks the analysis results of the emotion engine and NLP module to determine whether professional psychological assistance is needed. If so, it generates an alert.

[0284] Step 11:

[0285] The server sends an alert to a specialist or industrial physician, which contains detailed information about the user's mental state.

[0286] Step 12:

[0287] The server periodically generates a report on the user's mental health status based on the user's response history and emotion recognition results.

[0288] Step 13:

[0289] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[0290] The above is the specific processing flow of the AI ​​mental health support system that combines an emotion engine. At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[0291] Example 2

[0292] 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."

[0293] In recent years, mental stress in the work environment and in daily life has increased, resulting in serious mental health problems. Many companies and individuals require mental health support, but current mental health support systems often have difficulty properly recognizing users' emotions and providing individualized support. Furthermore, there are only a limited number of systems that can respond immediately when specialized psychological assistance is required. Therefore, there is a need for systems that can accurately recognize users' emotions and provide optimal support according to their condition.

[0294] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user input data, means for analyzing the collected data by natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for identifying the user's emotions using a natural language processing module and an emotion engine. This makes it possible to accurately recognize the user's emotions, provide optimal self-care based on them, and immediately respond if professional assistance is needed.

[0295] "User" refers to an individual or group that uses the System.

[0296] "Input data" refers to answers and information provided by users to this system through their terminals.

[0297] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) used by a user to provide input data.

[0298] "Server" refers to a central processing unit for processing, analyzing, and managing input data.

[0299] "Natural language processing (NLP)" refers to technology for analyzing user input data and understanding its meaning.

[0300] "Emotion engine" refers to software for identifying emotions from user input data through natural language processing.

[0301] A "self-care guide" refers to a guide that provides users with advice and activity suggestions aimed at improving and maintaining their mental health.

[0302] "Alert" refers to a warning or notification from the system to inform the user that professional psychological assistance is required.

[0303] "Professional psychological assistance" refers to psychological support and treatment provided by professionals such as psychologists, counselors, and occupational physicians.

[0304] A "report" refers to a document that summarizes information about a user's mental health condition and is provided to relevant parties such as companies and industrial physicians.

[0305] The present invention provides a system for supporting a user's mental health, which utilizes an emotion engine to recognize the user's emotions and provide interactive self-care guidance and professional assistance. The following describes in detail the embodiments of the present invention.

[0306] Collecting user input data

[0307] The device displays mental check questions to the user. An example question is "How are you feeling today?" The user then enters a free-form response. For example, they might enter a response like "I'm a little tired today, but I'm not feeling bad." This input data is encrypted and sent to the server. Protocols such as SSL / TLS are used for security.

[0308] Data analysis

[0309] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from the user's text data. The analysis results are then passed to the emotion engine.

[0310] Emotion recognition by emotion engine

[0311] The emotion engine receives data from the NLP module and performs detailed emotion analysis. For example, it identifies the emotions "fatigue" and "positive" from the user's response "I'm a little tired today, but I'm not feeling bad." The identified emotions are then recorded in the database.

[0312] Providing self-care guides

[0313] The server generates an interactive self-care guide based on the analysis results of the emotion engine. For example, if "fatigue" is recognized, it generates specific advice such as "Try taking deep breaths." The generated self-care guide is sent from the server to the device, which then displays it to the user.

[0314] Alerts and expert assistance

[0315] The server then reviews the analysis results and determines whether professional psychological assistance is required. For example, if "sadness" or "fear" is recognized, it generates a high-urgency alert and notifies a specialist or industrial physician. This alert includes the specific emotion recognition result and recommended measures.

[0316] Report Generation

[0317] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and emotion recognition results and is sent to company managers and industrial physicians. This report allows companies to gain a detailed understanding of their employees' mental health status and take appropriate measures.

[0318] Specific examples

[0319] For example, if a user answers, "Recently, work has not been going well and I am feeling very stressed," the server analyzes this data using the NLP module. At the same time, the emotion engine identifies emotions such as "stress" and "anxiety." Based on these results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation continues to become serious, an alert will also be sent to a specialist.

[0320] Prompt Sentence Examples

[0321] "Tell me how you're feeling today."

[0322] "Have you been feeling stressed lately?"

[0323] "What is your biggest concern right now?"

[0324] The system provides advanced mental health support that takes users' emotions into account, improving their overall well-being and the working environment at companies.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1:

[0327] The user checks the mental health check questions on the terminal. The terminal displays questions such as "How are you feeling today?" to the user. The user answers the questions by entering, for example, "I'm a little tired today, but I'm not feeling bad." The terminal encrypts this input and sends it to the server.

[0328] Input: User's text answer

[0329] Output: Encrypted text data

[0330] Step 2:

[0331] The server receives the encrypted text data. It decrypts the data and passes it to a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to analyze the sentiment of the text. As a result of the analysis, sentiments such as "fatigue" and "positive" are identified.

[0332] Input: Encrypted text data

[0333] Output: Emotion analysis results (fatigue, positive)

[0334] Step 3:

[0335] The server receives the emotion analysis results from the NLP module and passes the data to the emotion engine, which performs detailed emotion analysis to identify emotions more precisely. For example, it recognizes that the emotions "fatigue" and "positive" coexist. The identified emotions are then recorded in a database.

[0336] Input: Sentiment analysis results

[0337] Output: Detailed sentiment analysis results

[0338] Step 4:

[0339] The server generates an interactive self-care guide based on the results of the emotion engine. For example, if "fatigue" is recognized, it generates advice such as "Try taking deep breaths." This self-care guide is then sent to the device.

[0340] Input: Detailed sentiment analysis results

[0341] Output: Self-care guide

[0342] Step 5:

[0343] The device then displays the received self-care guide to the user. For example, it might say, "You look a little tired. Try taking a deep breath!" The user can then take this advice and put it into practice.

[0344] Enter: Self-Care Guide

[0345] Output: Self-care guide displayed to the user

[0346] Step 6:

[0347] The server reviews the analysis results and generates alerts if necessary. For example, if "sadness" or "fear" is consistently recognized, an urgent alert will be sent to an expert. The alert will include the specific emotion recognition result and recommended actions.

[0348] Input: Detailed sentiment analysis results

[0349] Output: Alert (expert notification)

[0350] Step 7:

[0351] The server periodically generates a report on the user's mental health status. This report includes the user's response history and emotion recognition results, and is sent to managers and industrial physicians. This information allows companies to understand the mental health status of their employees and take appropriate measures.

[0352] Input: Detailed sentiment analysis results and answer history

[0353] Output: Mental Health Report

[0354] (Application example 2)

[0355] 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."

[0356] In modern society, providing appropriate support for users' mental health is a crucial issue. Physical stores, in particular, lack the means to understand customers' mental states and provide appropriate product recommendations and services. Therefore, there is a need for a system that supports users' mental health while improving the in-store customer experience.

[0357] 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.

[0358] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, means for proposing individual products and providing service information based on user data collected in a physical commercial facility, and means for recommending a self-care guide or refreshment method in real time based on the detected mental health state. This makes it possible to grasp the user's mental health state in real time and suggest appropriate products and services.

[0359] "Means for collecting input data from users" refers to interfaces and functions for acquiring text data and questionnaire responses that users input into the system.

[0360] "Means for analyzing collected data using natural language processing" refers to a function that uses natural language processing (NLP) technology to analyze input and collected user data and identify emotions and intent.

[0361] The "means for generating an interactive self-care guide based on the analysis results" is a function for generating interactive self-care advice suited to the user based on the results of analysis by natural language processing.

[0362] The "means for providing the generated self-care guide to the user" is a function for notifying the user of the generated self-care guide and displaying it.

[0363] "Means for determining whether professional psychological assistance is required based on the analysis results and sending an alert if necessary" is a function that determines whether the user needs professional psychological assistance based on the results of emotion analysis and sends an alert if necessary.

[0364] "A means of suggesting individual products and providing information about services based on user data collected in physical commercial facilities" is a function that suggests appropriate products and services based on the emotions and state of users who visit physical stores.

[0365] The "means for recommending self-care guides or refreshment methods in real time based on the detected mental health state" is a function that suggests appropriate self-care guides or refreshment methods in real time based on the user's mental health data.

[0366] This invention is a system for understanding the mental health status of users in physical stores in real time and providing appropriate product suggestions and self-care guides.

[0367] 1. System Configuration

[0368] The system consists of a terminal that collects input data from users, a server that analyzes the data and identifies emotions, and a terminal that provides the generated self-care guide to the user.

[0369] 2. Collecting User-Input Data

[0370] When a user visits a store, they use their smartphone to scan a QR code installed in the store. After scanning, the smartphone's browser or a dedicated application opens and a survey screen is displayed. For example, the user can enter a free-form response to a question such as "How are you feeling today?" This input data is sent to the server in real time.

[0371] 3. Data Analysis and Emotion Recognition

[0372] The server passes the received user input data to a natural language processing (NLP) module. This NLP module analyzes the data using, for example, the Hugging Face transformers library, and identifies the emotions contained in the user's text. The analysis results are passed to an emotion engine, which recognizes emotions such as "joy," "sadness," "fear," and "anger."

[0373] 4. Self-care guide and product recommendations

[0374] An interactive self-care guide is generated based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will suggest relaxation. Specifically, it will display messages to the user such as "Try taking deep breaths" or "Use the relaxation space." It will also suggest refreshment items available in the store (e.g., relaxation drinks and aroma products).

[0375] 5. Determining the need for professional assistance and alerting

[0376] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If the emotion engine recognizes "sadness" or "fear," the server issues a high-level alert and notifies experts as necessary, enabling a rapid response.

[0377] 6. Generate scheduled reports

[0378] Data on users' mental health status is periodically aggregated and generated into a report, which is sent to store managers and company executives to serve as a reference for understanding the mental health status of customers.

[0379] Examples of concrete examples and prompts

[0380] For example, if a user answers a questionnaire saying, "I'm very tired and a little stressed," the server receives this data and uses the NLP module to recognize emotions such as "fatigue" and "stress." As a result, the server displays a message to the user such as, "Please use the relaxation space. Try taking a deep breath." It also suggests relaxation drinks for the store.

[0381] Example prompts to input to a generative AI model:

[0382] "Recognize the emotion in the following text: I'm very tired and a little stressed."

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] A user scans a QR code at a physical store. The device (smartphone) displays a survey screen asking, "How are you feeling today?" The user enters a free-form response to this question. The user input (e.g., I'm tired and a little stressed) is sent from the device to the server. Input: User response text, Output: Data sent to the server. The specific operation is that the user scans the QR code, and the browser or application displays the survey screen.

[0386] Step 2:

[0387] The server passes the received user response data to a natural language processing (NLP) module. The NLP module analyzes the sent text data and identifies the emotion contained in the user's text. Input: User response text, Output: Analyzed emotion data (e.g., "fatigue" or "stress"). The specific operation is to send the received text data to the NLP engine and identify the emotion.

[0388] Step 3:

[0389] The server uses the emotion engine to further refine the emotion data analyzed by the NLP module. Specifically, it classifies the answer "I'm tired, but I'm fine" into "fatigue" and "relief." Input: emotion data analyzed by NLP, output: refined emotion data. Specifically, the emotion engine evaluates the analyzed emotion data and identifies more detailed emotions.

[0390] Step 4:

[0391] The server generates an interactive self-care guide based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "try taking deep breaths" as a way to relax. Input: subdivided emotion data, output: self-care guide message. The specific operation is to create a self-care guide message based on the results of the emotion engine.

[0392] Step 5:

[0393] The generated self-care guide is sent to the device and notified to the user. The user can view relaxation methods and self-care suggestions on their smartphone screen. Input: Self-care guide message, Output: Message displayed on the device screen. The specific operation is that the server sends the generated message to the device, and the device displays it.

[0394] Step 6:

[0395] The server then rechecks the analysis results and determines whether professional psychological assistance is required. For example, if the emotion engine recognizes "sadness" or "fear," it determines that professional assistance is required and generates an alert. Input: segmented emotion data, output: alert message. The specific operation is to evaluate the analysis results and generate an alert message if necessary.

[0396] Step 7:

[0397] The server periodically generates reports on the user's mental health status and sends them to the company or industrial physician. The reports are created based on the user's response history and the emotion recognition results of the emotion engine. Input: User's response history and recognition results, Output: Mental health report. The specific operation is to aggregate the data, generate a report, and send it.

[0398] Through these steps, this system can support users' mental health in real time at each store.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Second embodiment]

[0403] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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."

[0415] The present invention relates to an AI system for supporting a user's mental health, and an embodiment thereof will be described in detail below.

[0416] 1. Collecting user input data

[0417] The terminal displays a mental check question to the user. For example, it provides a question such as "How are you feeling today?" The user inputs a free-form answer to this question. The input data is sent from the terminal to the server.

[0418] 2. Data Analysis

[0419] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. The NLP module identifies topics in the data and determines whether they contain sensitive topics (e.g., "depression," "suicide").

[0420] 3. Providing self-care guidance

[0421] The server generates an appropriate interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, the server will provide the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server will send a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0422] 4. Providing alerts and professional assistance

[0423] If a sensitive topic is detected based on the analysis results, the server determines whether professional psychological assistance is required. If so, the server issues an alert to notify specialists or industrial physicians. The alert includes detailed information indicating that the user is in a critical condition.

[0424] 5. Report Generation

[0425] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results. The generated reports are sent from the server to company managers and industrial physicians. This allows companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0426] To explain with a concrete example, if a user responds, "I'm tired, but I'm fine," the server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The server then sends this guide to the device, which displays it to the user.

[0427] Furthermore, if the user's response contains sensitive content such as "I'm thinking about suicide," the server will send an alert and arrange for professional psychological assistance to be provided. In this way, the present invention is a system that comprehensively supports the user's mental health.

[0428] The processing flow will be explained below.

[0429] Step 1:

[0430] The device displays mental check questions to the user, such as "How are you feeling today?"

[0431] Step 2:

[0432] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[0433] Step 3:

[0434] The terminal sends the user's input data to the server, where the response is sent using a secure communication protocol.

[0435] Step 4:

[0436] The server receives the user's response data sent from the device and temporarily stores the received data.

[0437] Step 5:

[0438] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[0439] Step 6:

[0440] The server generates an interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, it generates a guide such as "Try deep breathing as a relaxation technique." If there are no particular problems, it generates a positive message such as "Have a great day today!"

[0441] Step 7:

[0442] The server then sends the generated self-care guide to the terminal. Because the guide contains important information for the user, it is sent quickly and accurately.

[0443] Step 8:

[0444] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[0445] Step 9:

[0446] The server then reviews the analysis results and determines whether professional psychological assistance is needed. If so, the server generates an alert.

[0447] Step 10:

[0448] The server sends an alert to a specialist or industrial physician, which includes details about the user's mental state.

[0449] Step 11:

[0450] The server periodically generates a report on the user's mental health status based on the user's response history and analytical data.

[0451] Step 12:

[0452] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[0453] This is the specific processing flow of the AI ​​mental health support system "AI." At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[0454] Example 1

[0455] 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."

[0456] Conventional mental health support systems have had problems in that they are unable to properly assess a user's condition and respond quickly. They also lack the functionality to properly issue alerts in serious cases that require specialized assistance. As a result, users' mental health conditions run the risk of continuing to deteriorate, and it is difficult for company managers and industrial physicians to properly understand the status of their employees.

[0457] 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.

[0458] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether a sensitive topic is included based on the analysis results, means for determining whether professional psychological assistance is needed and sending an alert if necessary, and means for generating a report on the user's mental health status and sending it to a company or industrial physician. This makes it possible to quickly and accurately grasp the user's mental health status and provide appropriate self-care and, if necessary, professional assistance.

[0459] "User input data" refers to answers and responses that users input into the system via their terminals.

[0460] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0461] "Interactive self-care guide" means an interactive guide that includes advice and instructions provided to support a user's mental health.

[0462] "Sensitive topics" refer to mental health topics that are of high urgency or seriousness, such as depression or suicide.

[0463] "Professional psychological assistance" refers to specialized help provided by mental health professionals or counselors.

[0464] "Sending an alert" refers to the act of issuing a warning or notification when certain conditions are met.

[0465] "Generating a report" refers to the act of creating a report based on collected and analyzed data.

[0466] "Corporate or industrial physicians" refers to managers and medical professionals in charge of mental health care in the user's work environment.

[0467] "Checking regularly" refers to the act of checking the state of mental health at regular intervals.

[0468] A "prompt" is a document used to input instructions or questions to an AI model.

[0469] The present invention relates to an AI system for supporting a user's mental health. This system collects input data from a user, analyzes it using natural language processing, and generates an interactive self-care guide based on the analysis results. It also has the function of providing professional psychological assistance as needed and generating periodic reports. Detailed embodiments of the system are described below.

[0470] Collecting user input data

[0471] First, the user accesses the system using a terminal. The terminal can be a smartphone, tablet, PC, etc. The terminal displays a mental check question to the user. For example, it may provide a question such as "How are you feeling today?" The user then inputs a free-form answer to this question. This input data is encrypted and sent from the terminal to the server.

[0472] Data analysis

[0473] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. This NLP module is implemented using popular Python libraries such as "spaCy" and "NLTK." The NLP module identifies topics within the data and determines whether they contain sensitive topics (e.g., "depression" or "suicide").

[0474] Creating and providing self-care guides

[0475] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server provides the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server sends a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0476] Alerts and expert assistance

[0477] Based on the analysis results, the server determines whether or not professional psychological assistance is needed. If a sensitive topic is detected, it will determine whether professional psychological assistance is needed and, if so, will issue an alert. This alert will include detailed information indicating that the user is in a critical condition, and will notify specialists or occupational physicians.

[0478] Report generation and delivery

[0479] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. These reports enable companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0480] Examples of concrete examples and prompts

[0481] As a concrete example, consider the case where a user answers "I'm tired, but it's fine." The server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that it's "fine" and generates a positive self-care guide: "Have a great day!" The server then sends this guide to the device, which displays it to the user.

[0482] The following is a specific example of a prompt sentence:

[0483] Prompt: "I'd like to conduct a mental health check on you. I'd like to ask you for some open-ended questions about how I'm feeling today, and then analyze your responses to determine if you need appropriate self-care guidance and professional help."

[0484] Using this prompt, the generative AI model can comprehensively analyze information about the user's mental health and provide appropriate actions.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] The user accesses the device

[0488] Input: A user logs in to a terminal.

[0489] Action: A user accesses the system using a device such as a smartphone, tablet, or PC and logs in.

[0490] Output: The user successfully logs in and is taken to the mental health check question screen.

[0491] Step 2:

[0492] The device displays mental check questions to the user.

[0493] Input: The user's login status.

[0494] What it does: The device displays a pre-defined mental health question (e.g., "How are you feeling today?").

[0495] Output: The question is displayed to the user and an answer can be entered.

[0496] Step 3:

[0497] The user answers the questions and enters them into the terminal

[0498] Input: Mental check question displayed on terminal.

[0499] How it works: The user types a free-form response to a question (e.g., "I'm tired, but I'm OK").

[0500] Output: The user's answer data is input to the terminal.

[0501] Step 4:

[0502] The device collects the user's answers and sends them to the server.

[0503] Input: User response data.

[0504] Operation: The device encrypts and sends the collected response data to the server.

[0505] Output: The server receives the user's answer data.

[0506] Step 5:

[0507] The server receives the user's response data sent from the device.

[0508] Input: Encrypted user response data sent from the device.

[0509] What happens: The server receives the data and stores it in a database.

[0510] Output: User response data stored in a database.

[0511] Step 6:

[0512] The NLP module on the server analyzes the response data.

[0513] Input: User response data stored in the database.

[0514] How it works: A server-based natural language processing (NLP) module analyzes the response data and extracts topics and keywords, using, for example, Python's "spaCy" or "NLTK."

[0515] Output: The topics and keywords extracted as a result of the analysis.

[0516] Step 7:

[0517] The NLP module identifies topics in the data and determines whether they contain sensitive topics.

[0518] Input: Topics and keywords resulting from the analysis.

[0519] How it works: The NLP module evaluates the message to determine if it contains sensitive topics (e.g., "depression," "suicide").

[0520] Output: Evaluation result for the presence or absence of sensitive topics.

[0521] Step 8:

[0522] The server generates a self-care guide based on the analysis results.

[0523] Input: Assessment results for the presence or absence of sensitive topics.

[0524] How it works: The server generates an appropriate self-care guide based on the information (e.g., "Try deep breathing as a way to relax" or "Have a great day!").

[0525] Output: The generated self-care guide.

[0526] Step 9:

[0527] The generated self-care guide is sent from the server to the device.

[0528] Input: The generated self-care guide.

[0529] Operation: The server sends the generated self-care guide to the device.

[0530] Output: The self-care guide is displayed on the user's device.

[0531] Step 10:

[0532] The device displays a self-care guide to the user.

[0533] Input: Self-care guide sent from the server.

[0534] What it does: The device displays a self-care guide to the user, which the user can use to help with mental health care.

[0535] Output: The user reviews and implements the self-care guide.

[0536] Step 11:

[0537] The server determines the necessity based on the analysis results

[0538] Input: Assessment results for the presence or absence of sensitive topics.

[0539] How it works: The server uses the assessment results to determine whether the user needs professional psychological help.

[0540] Output: A decision on whether professional help is needed.

[0541] Step 12:

[0542] Sends alerts when the server deems necessary

[0543] Input: The result of the decision on whether professional assistance is required.

[0544] How it works: The server will alert you if it determines that professional assistance is needed.

[0545] Output: Alert notification to specialists and industrial physicians.

[0546] Step 13:

[0547] The server periodically generates a report on the user's mental health status.

[0548] Input: User's answer history and analysis results.

[0549] How it works: The server generates periodic reports based on this.

[0550] Output: The generated mental health report.

[0551] Step 14:

[0552] The generated report is sent from the server to company managers and industrial physicians.

[0553] Input: Generated mental health report.

[0554] How it works: The server sends the generated report to company management and / or industrial physicians.

[0555] Output: Report received by company management and industrial physician.

[0556] (Application example 1)

[0557] 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."

[0558] In recent years, mental health issues among employees in factories and manufacturing sites have become more serious. In particular, the number of employees suffering from psychological problems due to overwork and excessive stress is increasing. This has led to a significant decline in productivity and an increase in employee turnover. For this reason, there is a need for a system that provides comprehensive support for employee mental health.

[0559] 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.

[0560] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for providing mental health check questions and collecting responses from the user using hardware with a voice recognition function. This allows employees to not only receive mental health checks in real time, but also to receive appropriate self-care or professional support as needed.

[0561] A "user" is an individual employee or user of the system.

[0562] "Input data" refers to answer data to questions in the mental health check entered by the user.

[0563] "Natural language processing" is a technique used to analyze user input data, identifying topics and detecting sensitive topics.

[0564] An "interactive self-care guide" is a means of providing appropriate self-care advice to the user that is generated based on the analysis results.

[0565] An "alert" is a notification that is sent when it is determined that professional psychological assistance is necessary based on the results of the user's analysis.

[0566] "Mental check questions" are questions provided to assess the user's mood or state.

[0567] The "voice recognition function" is a technology that converts a user's voice input into text data.

[0568] "Hardware" refers to all devices and equipment with voice recognition capabilities.

[0569] "Professional psychological assistance" refers to support provided by professionals such as psychological counselors and psychiatrists.

[0570] "Self-care" refers to mental and physical care methods that users can take care of themselves.

[0571] A "report" is a document summarizing the analysis results regarding the user's mental health condition.

[0572] "Corporate and industrial physicians" are institutions and professionals who are responsible for managing and supporting the mental health of employees in factories and organizations.

[0573] The present invention relates to a system for supporting the mental health of employees in factories and manufacturing sites. A specific embodiment of this system will be described below.

[0574] Collecting user input data

[0575] The system periodically presents users with mental check questions. The questions are presented in the form of, for example, "How are you feeling today?" The user inputs the answers using hardware with speech recognition capabilities (e.g., a robot, smart glasses, or a head-mounted display). This automatically collects data about the user's mental state.

[0576] Data analysis

[0577] The collected data is sent to a server and analyzed using a natural language processing (NLP) module. Specifically, the collected text data is categorized by topic and determined to contain sensitive topics. The NLP module includes a function to detect keywords such as "suicide," "depression," and "stress."

[0578] Providing self-care guides

[0579] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server generates advice such as "Try taking deep breaths as a way to relax." On the other hand, if no sensitive topic is detected, the server provides a positive message such as "Have a great day!" The generated self-care guide is sent to the device and displayed to the user.

[0580] Alerts and expert assistance

[0581] Based on the analysis results, the server determines whether professional psychological assistance is needed. If so, the server sends an alert to notify mental health professionals. The alert contains detailed information indicating that the user is in a critical state. This information is used to quickly contact the appropriate professional.

[0582] Mental health status report generation

[0583] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. This allows companies to understand employee mental health trends and take appropriate measures as necessary.

[0584] Specific examples

[0585] For example, if a user responds, "I'm a little tired today, but it's okay," a device with speech recognition capabilities sends this data to the server. The server then analyzes it using an NLP module, and if no sensitive topics are detected, it determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The guide is then sent to the user's device and displayed.

[0586] On the other hand, if the user answers with sensitive content such as "I'm thinking about suicide," the server analyzes this data and determines that the situation is critical. The server then sends out an alert and notifies a mental health professional. In this way, the system provides comprehensive support for the user's mental health.

[0587] Prompt Sentence Examples

[0588] 1. The robot will ask the question, "How are you feeling today?"

[0589] 2. The user responds, "I'm feeling a little stressed."

[0590] 3. The answer is sent to the server and the analysis results are received.

[0591] 4. If no sensitive topics are detected, it will display "Have a nice day!"

[0592] 5. Send alerts to mental health professionals if sensitive topics are detected.

[0593] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0594] Step 1:

[0595] The user answers mental check questions through a terminal. The terminal uses hardware with speech recognition capabilities (e.g., robots, smart glasses, head-mounted displays) to convert the user's voice input into text data. The input data is the user's response to the question, "How are you feeling today?"

[0596] Input: Voice response to mental check questions

[0597] Output: Conversion result from audio data to text data

[0598] Step 2:

[0599] The terminal sends the collected text data to the server. The server receives the data using HTTP communication (e.g., the requests library). The received data is the text data of the user's responses.

[0600] Input: Converted text data

[0601] Output: Sending text data to the server

[0602] Step 3:

[0603] The server passes the received text data to a natural language processing (NLP) module, which analyzes the text data to identify topics and detect sensitive topics (e.g., "suicide" or "depression"). This analysis generates a score that evaluates the sensitivity of the text.

[0604] Input: Text data of user responses

[0605] Output: Analysis results and sensitivity score

[0606] Step 4:

[0607] The server generates an interactive self-care guide based on the analysis results. Using a generative AI model, it automatically generates messages such as "Try taking a deep breath" or "Have a great day!" The results are saved as text data.

[0608] Input: Analysis results of the NLP module

[0609] Output: Text data of the generated self-care guide

[0610] Step 5:

[0611] The server sends the generated self-care guide to the terminal, which receives the data using HTTP communication and displays the self-care guide to the user.

[0612] Input: Text data of the generated self-care guide

[0613] Output: Display of self-care guide

[0614] Step 6:

[0615] The server uses the analysis results to determine whether professional psychological assistance is needed. If so, the server generates an alert to notify mental health professionals. The alert includes detailed information indicating the user is in a critical condition.

[0616] Input: Analysis results of the NLP module and sensitivity score

[0617] Output: Alert notification data

[0618] Step 7:

[0619] The server periodically generates a report on the user's mental health status. The report includes the user's response history and analysis results, and is sent to the company's management and industrial physician. This allows the company to understand the mental health status of its employees and take measures as necessary.

[0620] Input: User response history and analysis results

[0621] Output: Mental health report

[0622] 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.

[0623] The present invention relates to an AI system for supporting the mental health of users, and in particular to a system that combines an emotion engine that recognizes the user's emotions.

[0624] 1. Collecting user input data

[0625] The terminal displays mental check questions to the user, such as "How are you feeling today?" The user then inputs a free-form response to the question. The input data is sent from the terminal to the server.

[0626] 2. Data Analysis

[0627] The server receives the user's response data from the device. The received data is passed to a natural language processing (NLP) module that includes an emotion engine. The NLP module analyzes the data and identifies emotions (e.g., joy, sadness, fear, anger) contained in the user's text.

[0628] 3. Emotion Recognition by Emotion Engine

[0629] The emotion engine identifies the user's emotions from the analyzed data. For example, in response to the answer "I'm tired, but I'm fine," it recognizes the emotions "fatigue" and "relief." This recognition result is used in subsequent processing.

[0630] 4. Providing self-care guidance

[0631] The server generates an interactive self-care guide based on the analysis results of the NLP and emotion engine. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "Try taking deep breaths" as a way to relax. If the emotion engine recognizes "relief," it will send a positive message such as "Have a great day today!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0632] 5. Providing alerts and professional assistance

[0633] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If it determines that it is necessary, it generates an alert, taking into account the recognition results of the emotion engine. For example, if "sadness" or "fear" is recognized, the server will issue a high-level alert and notify a specialist or industrial physician.

[0634] 6. Report Generation

[0635] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and the emotion recognition results of the emotion engine. The generated report is sent to company managers and industrial physicians. This allows companies to gain a detailed understanding of their employees' mental health status and provide appropriate follow-up care.

[0636] For example, if a user responds, "Recently, work has not been going well and I am feeling very stressed," the server receives this data and analyzes it using the NLP module. At the same time, the emotion engine recognizes emotions such as "stress" and "anxiety." Based on the analysis results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation is serious, an alert will be sent to an expert.

[0637] The present invention provides advanced mental health support that takes into account the user's emotions, thereby improving the user's overall well-being and the working environment in companies.

[0638] The processing flow will be explained below.

[0639] Step 1:

[0640] The device displays mental check questions to the user, such as "How are you feeling today?"

[0641] Step 2:

[0642] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[0643] Step 3:

[0644] The terminal transmits the user's input data to the server, using a secure communication protocol.

[0645] Step 4:

[0646] The server receives the user's response data sent from the device and temporarily stores the received data.

[0647] Step 5:

[0648] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[0649] Step 6:

[0650] The server passes the analysis results of the NLP module to the emotion engine, which recognizes emotions (e.g., joy, sadness, fear, anger) in the data.

[0651] Step 7:

[0652] The server receives the emotion recognition results from the emotion engine and generates an interactive self-care guide based on the analysis results. For example, if the recognized emotion is "fatigue," it generates advice such as "Try deep breathing as a way to relax."

[0653] Step 8:

[0654] The server transmits the generated self-care guide to the terminal. Since the self-care guide contains important information for the user, it is transmitted quickly and accurately.

[0655] Step 9:

[0656] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[0657] Step 10:

[0658] The server then rechecks the analysis results of the emotion engine and NLP module to determine whether professional psychological assistance is needed. If so, it generates an alert.

[0659] Step 11:

[0660] The server sends an alert to a specialist or industrial physician, which contains detailed information about the user's mental state.

[0661] Step 12:

[0662] The server periodically generates a report on the user's mental health status based on the user's response history and emotion recognition results.

[0663] Step 13:

[0664] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[0665] The above is the specific processing flow of the AI ​​mental health support system that combines an emotion engine. At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[0666] Example 2

[0667] 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."

[0668] In recent years, mental stress in the work environment and in daily life has increased, resulting in serious mental health problems. Many companies and individuals require mental health support, but current mental health support systems often have difficulty properly recognizing users' emotions and providing individualized support. Furthermore, there are only a limited number of systems that can respond immediately when specialized psychological assistance is required. Therefore, there is a need for systems that can accurately recognize users' emotions and provide optimal support according to their condition.

[0669] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user input data, means for analyzing the collected data by natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for identifying the user's emotions using a natural language processing module and an emotion engine. This makes it possible to accurately recognize the user's emotions, provide optimal self-care based on them, and immediately respond if professional assistance is needed.

[0670] "User" refers to an individual or group that uses the System.

[0671] "Input data" refers to answers and information provided by users to this system through their terminals.

[0672] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) used by a user to provide input data.

[0673] "Server" refers to a central processing unit for processing, analyzing, and managing input data.

[0674] "Natural language processing (NLP)" refers to technology for analyzing user input data and understanding its meaning.

[0675] "Emotion engine" refers to software for identifying emotions from user input data through natural language processing.

[0676] A "self-care guide" refers to a guide that provides users with advice and activity suggestions aimed at improving and maintaining their mental health.

[0677] "Alert" refers to a warning or notification from the system to inform the user that professional psychological assistance is required.

[0678] "Professional psychological assistance" refers to psychological support and treatment provided by professionals such as psychologists, counselors, and occupational physicians.

[0679] A "report" refers to a document that summarizes information about a user's mental health condition and is provided to relevant parties such as companies and industrial physicians.

[0680] The present invention provides a system for supporting a user's mental health, which utilizes an emotion engine to recognize the user's emotions and provide interactive self-care guidance and professional assistance. The following describes in detail the embodiments of the present invention.

[0681] Collecting user input data

[0682] The device displays mental check questions to the user. An example question is "How are you feeling today?" The user then enters a free-form response. For example, they might enter a response like "I'm a little tired today, but I'm not feeling bad." This input data is encrypted and sent to the server. Protocols such as SSL / TLS are used for security.

[0683] Data analysis

[0684] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from the user's text data. The analysis results are then passed to the emotion engine.

[0685] Emotion recognition by emotion engine

[0686] The emotion engine receives data from the NLP module and performs detailed emotion analysis. For example, it identifies the emotions "fatigue" and "positive" from the user's response "I'm a little tired today, but I'm not feeling bad." The identified emotions are then recorded in the database.

[0687] Providing self-care guides

[0688] The server generates an interactive self-care guide based on the analysis results of the emotion engine. For example, if "fatigue" is recognized, it generates specific advice such as "Try taking deep breaths." The generated self-care guide is sent from the server to the device, which then displays it to the user.

[0689] Alerts and expert assistance

[0690] The server then reviews the analysis results and determines whether professional psychological assistance is required. For example, if "sadness" or "fear" is recognized, it generates a high-urgency alert and notifies a specialist or industrial physician. This alert includes the specific emotion recognition result and recommended measures.

[0691] Report Generation

[0692] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and emotion recognition results and is sent to company managers and industrial physicians. This report allows companies to gain a detailed understanding of their employees' mental health status and take appropriate measures.

[0693] Specific examples

[0694] For example, if a user answers, "Recently, work has not been going well and I am feeling very stressed," the server analyzes this data using the NLP module. At the same time, the emotion engine identifies emotions such as "stress" and "anxiety." Based on these results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation continues to become serious, an alert will also be sent to a specialist.

[0695] Prompt Sentence Examples

[0696] "Tell me how you're feeling today."

[0697] "Have you been feeling stressed lately?"

[0698] "What is your biggest concern right now?"

[0699] The system provides advanced mental health support that takes users' emotions into account, improving their overall well-being and the working environment at companies.

[0700] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0701] Step 1:

[0702] The user checks the mental health check questions on the terminal. The terminal displays questions such as "How are you feeling today?" to the user. The user answers the questions by entering, for example, "I'm a little tired today, but I'm not feeling bad." The terminal encrypts this input and sends it to the server.

[0703] Input: User's text answer

[0704] Output: Encrypted text data

[0705] Step 2:

[0706] The server receives the encrypted text data. It decrypts the data and passes it to a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to analyze the sentiment of the text. As a result of the analysis, sentiments such as "fatigue" and "positive" are identified.

[0707] Input: Encrypted text data

[0708] Output: Emotion analysis results (fatigue, positive)

[0709] Step 3:

[0710] The server receives the emotion analysis results from the NLP module and passes the data to the emotion engine, which performs detailed emotion analysis to identify emotions more precisely. For example, it recognizes that the emotions "fatigue" and "positive" coexist. The identified emotions are then recorded in a database.

[0711] Input: Sentiment analysis results

[0712] Output: Detailed sentiment analysis results

[0713] Step 4:

[0714] The server generates an interactive self-care guide based on the results of the emotion engine. For example, if "fatigue" is recognized, it generates advice such as "Try taking deep breaths." This self-care guide is then sent to the device.

[0715] Input: Detailed sentiment analysis results

[0716] Output: Self-care guide

[0717] Step 5:

[0718] The device then displays the received self-care guide to the user. For example, it might say, "You look a little tired. Try taking a deep breath!" The user can then take this advice and put it into practice.

[0719] Enter: Self-Care Guide

[0720] Output: Self-care guide displayed to the user

[0721] Step 6:

[0722] The server reviews the analysis results and generates alerts if necessary. For example, if "sadness" or "fear" is consistently recognized, an urgent alert will be sent to an expert. The alert will include the specific emotion recognition result and recommended actions.

[0723] Input: Detailed sentiment analysis results

[0724] Output: Alert (expert notification)

[0725] Step 7:

[0726] The server periodically generates a report on the user's mental health status. This report includes the user's response history and emotion recognition results, and is sent to managers and industrial physicians. This information allows companies to understand the mental health status of their employees and take appropriate measures.

[0727] Input: Detailed sentiment analysis results and answer history

[0728] Output: Mental Health Report

[0729] (Application example 2)

[0730] 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."

[0731] In modern society, providing appropriate support for users' mental health is a crucial issue. Physical stores, in particular, lack the means to understand customers' mental states and provide appropriate product recommendations and services. Therefore, there is a need for a system that supports users' mental health while improving the in-store customer experience.

[0732] 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.

[0733] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, means for proposing individual products and providing service information based on user data collected in a physical commercial facility, and means for recommending a self-care guide or refreshment method in real time based on the detected mental health state. This makes it possible to grasp the user's mental health state in real time and suggest appropriate products and services.

[0734] "Means for collecting input data from users" refers to interfaces and functions for acquiring text data and questionnaire responses that users input into the system.

[0735] "Means for analyzing collected data using natural language processing" refers to a function that uses natural language processing (NLP) technology to analyze input and collected user data and identify emotions and intent.

[0736] The "means for generating an interactive self-care guide based on the analysis results" is a function for generating interactive self-care advice suited to the user based on the results of analysis by natural language processing.

[0737] The "means for providing the generated self-care guide to the user" is a function for notifying the user of the generated self-care guide and displaying it.

[0738] "Means for determining whether professional psychological assistance is required based on the analysis results and sending an alert if necessary" is a function that determines whether the user needs professional psychological assistance based on the results of emotion analysis and sends an alert if necessary.

[0739] "A means of suggesting individual products and providing information about services based on user data collected in physical commercial facilities" is a function that suggests appropriate products and services based on the emotions and state of users who visit physical stores.

[0740] The "means for recommending self-care guides or refreshment methods in real time based on the detected mental health state" is a function that suggests appropriate self-care guides or refreshment methods in real time based on the user's mental health data.

[0741] This invention is a system for understanding the mental health status of users in physical stores in real time and providing appropriate product suggestions and self-care guides.

[0742] 1. System Configuration

[0743] The system consists of a terminal that collects input data from users, a server that analyzes the data and identifies emotions, and a terminal that provides the generated self-care guide to the user.

[0744] 2. Collecting User-Input Data

[0745] When a user visits a store, they use their smartphone to scan a QR code installed in the store. After scanning, the smartphone's browser or a dedicated application opens and a survey screen is displayed. For example, the user can enter a free-form response to a question such as "How are you feeling today?" This input data is sent to the server in real time.

[0746] 3. Data Analysis and Emotion Recognition

[0747] The server passes the received user input data to a natural language processing (NLP) module. This NLP module analyzes the data using, for example, the Hugging Face transformers library, and identifies the emotions contained in the user's text. The analysis results are passed to an emotion engine, which recognizes emotions such as "joy," "sadness," "fear," and "anger."

[0748] 4. Self-care guide and product recommendations

[0749] An interactive self-care guide is generated based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will suggest relaxation. Specifically, it will display messages to the user such as "Try taking deep breaths" or "Use the relaxation space." It will also suggest refreshment items available in the store (e.g., relaxation drinks and aroma products).

[0750] 5. Determining the need for professional assistance and alerting

[0751] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If the emotion engine recognizes "sadness" or "fear," the server issues a high-level alert and notifies experts as necessary, enabling a rapid response.

[0752] 6. Generate scheduled reports

[0753] Data on users' mental health status is periodically aggregated and generated into a report, which is sent to store managers and company executives to serve as a reference for understanding the mental health status of customers.

[0754] Examples of concrete examples and prompts

[0755] For example, if a user answers a questionnaire saying, "I'm very tired and a little stressed," the server receives this data and uses the NLP module to recognize emotions such as "fatigue" and "stress." As a result, the server displays a message to the user such as, "Please use the relaxation space. Try taking a deep breath." It also suggests relaxation drinks for the store.

[0756] Example prompts to input to a generative AI model:

[0757] "Recognize the emotion in the following text: I'm very tired and a little stressed."

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Step 1:

[0760] A user scans a QR code at a physical store. The device (smartphone) displays a survey screen asking, "How are you feeling today?" The user enters a free-form response to this question. The user input (e.g., I'm tired and a little stressed) is sent from the device to the server. Input: User response text, Output: Data sent to the server. The specific operation is that the user scans the QR code, and the browser or application displays the survey screen.

[0761] Step 2:

[0762] The server passes the received user response data to a natural language processing (NLP) module. The NLP module analyzes the sent text data and identifies the emotion contained in the user's text. Input: User response text, Output: Analyzed emotion data (e.g., "fatigue" or "stress"). The specific operation is to send the received text data to the NLP engine and identify the emotion.

[0763] Step 3:

[0764] The server uses the emotion engine to further refine the emotion data analyzed by the NLP module. Specifically, it classifies the answer "I'm tired, but I'm fine" into "fatigue" and "relief." Input: emotion data analyzed by NLP, output: refined emotion data. Specifically, the emotion engine evaluates the analyzed emotion data and identifies more detailed emotions.

[0765] Step 4:

[0766] The server generates an interactive self-care guide based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "try taking deep breaths" as a way to relax. Input: subdivided emotion data, output: self-care guide message. The specific operation is to create a self-care guide message based on the results of the emotion engine.

[0767] Step 5:

[0768] The generated self-care guide is sent to the device and notified to the user. The user can view relaxation methods and self-care suggestions on their smartphone screen. Input: Self-care guide message, Output: Message displayed on the device screen. The specific operation is that the server sends the generated message to the device, and the device displays it.

[0769] Step 6:

[0770] The server then rechecks the analysis results and determines whether professional psychological assistance is required. For example, if the emotion engine recognizes "sadness" or "fear," it determines that professional assistance is required and generates an alert. Input: segmented emotion data, output: alert message. The specific operation is to evaluate the analysis results and generate an alert message if necessary.

[0771] Step 7:

[0772] The server periodically generates reports on the user's mental health status and sends them to the company or industrial physician. The reports are created based on the user's response history and the emotion recognition results of the emotion engine. Input: User's response history and recognition results, Output: Mental health report. The specific operation is to aggregate the data, generate a report, and send it.

[0773] Through these steps, this system can support users' mental health in real time at each store.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] [Third embodiment]

[0778] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0779] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0780] 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).

[0781] 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.

[0782] 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.

[0783] 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).

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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."

[0790] The present invention relates to an AI system for supporting a user's mental health, and an embodiment thereof will be described in detail below.

[0791] 1. Collecting user input data

[0792] The terminal displays a mental check question to the user. For example, it provides a question such as "How are you feeling today?" The user inputs a free-form answer to this question. The input data is sent from the terminal to the server.

[0793] 2. Data Analysis

[0794] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. The NLP module identifies topics in the data and determines whether they contain sensitive topics (e.g., "depression," "suicide").

[0795] 3. Providing self-care guidance

[0796] The server generates an appropriate interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, the server will provide the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server will send a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0797] 4. Providing alerts and professional assistance

[0798] If a sensitive topic is detected based on the analysis results, the server determines whether professional psychological assistance is required. If so, the server issues an alert to notify specialists or industrial physicians. The alert includes detailed information indicating that the user is in a critical condition.

[0799] 5. Report Generation

[0800] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results. The generated reports are sent from the server to company managers and industrial physicians. This allows companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0801] To explain with a concrete example, if a user responds, "I'm tired, but I'm fine," the server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The server then sends this guide to the device, which displays it to the user.

[0802] Furthermore, if the user's response contains sensitive content such as "I'm thinking about suicide," the server will send an alert and arrange for professional psychological assistance to be provided. In this way, the present invention is a system that comprehensively supports the user's mental health.

[0803] The processing flow will be explained below.

[0804] Step 1:

[0805] The device displays mental check questions to the user, such as "How are you feeling today?"

[0806] Step 2:

[0807] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[0808] Step 3:

[0809] The terminal sends the user's input data to the server, where the response is sent using a secure communication protocol.

[0810] Step 4:

[0811] The server receives the user's response data sent from the device and temporarily stores the received data.

[0812] Step 5:

[0813] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[0814] Step 6:

[0815] The server generates an interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, it generates a guide such as "Try deep breathing as a relaxation technique." If there are no particular problems, it generates a positive message such as "Have a great day today!"

[0816] Step 7:

[0817] The server then sends the generated self-care guide to the terminal. Because the guide contains important information for the user, it is sent quickly and accurately.

[0818] Step 8:

[0819] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[0820] Step 9:

[0821] The server then reviews the analysis results and determines whether professional psychological assistance is needed. If so, the server generates an alert.

[0822] Step 10:

[0823] The server sends an alert to a specialist or industrial physician, which includes details about the user's mental state.

[0824] Step 11:

[0825] The server periodically generates a report on the user's mental health status based on the user's response history and analytical data.

[0826] Step 12:

[0827] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[0828] This is the specific processing flow of the AI ​​mental health support system "AI." At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[0829] Example 1

[0830] 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."

[0831] Conventional mental health support systems have had problems in that they are unable to properly assess a user's condition and respond quickly. They also lack the functionality to properly issue alerts in serious cases that require specialized assistance. As a result, users' mental health conditions run the risk of continuing to deteriorate, and it is difficult for company managers and industrial physicians to properly understand the status of their employees.

[0832] 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.

[0833] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether a sensitive topic is included based on the analysis results, means for determining whether professional psychological assistance is needed and sending an alert if necessary, and means for generating a report on the user's mental health status and sending it to a company or industrial physician. This makes it possible to quickly and accurately grasp the user's mental health status and provide appropriate self-care and, if necessary, professional assistance.

[0834] "User input data" refers to answers and responses that users input into the system via their terminals.

[0835] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0836] "Interactive self-care guide" means an interactive guide that includes advice and instructions provided to support a user's mental health.

[0837] "Sensitive topics" refer to mental health topics that are of high urgency or seriousness, such as depression or suicide.

[0838] "Professional psychological assistance" refers to specialized help provided by mental health professionals or counselors.

[0839] "Sending an alert" refers to the act of issuing a warning or notification when certain conditions are met.

[0840] "Generating a report" refers to the act of creating a report based on collected and analyzed data.

[0841] "Corporate or industrial physicians" refers to managers and medical professionals in charge of mental health care in the user's work environment.

[0842] "Checking regularly" refers to the act of checking the state of mental health at regular intervals.

[0843] A "prompt" is a document used to input instructions or questions to an AI model.

[0844] The present invention relates to an AI system for supporting a user's mental health. This system collects input data from a user, analyzes it using natural language processing, and generates an interactive self-care guide based on the analysis results. It also has the function of providing professional psychological assistance as needed and generating periodic reports. Detailed embodiments of the system are described below.

[0845] Collecting user input data

[0846] First, the user accesses the system using a terminal. The terminal can be a smartphone, tablet, PC, etc. The terminal displays a mental check question to the user. For example, it may provide a question such as "How are you feeling today?" The user then inputs a free-form answer to this question. This input data is encrypted and sent from the terminal to the server.

[0847] Data analysis

[0848] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. This NLP module is implemented using popular Python libraries such as "spaCy" and "NLTK." The NLP module identifies topics within the data and determines whether they contain sensitive topics (e.g., "depression" or "suicide").

[0849] Creating and providing self-care guides

[0850] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server provides the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server sends a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[0851] Alerts and expert assistance

[0852] Based on the analysis results, the server determines whether or not professional psychological assistance is needed. If a sensitive topic is detected, it will determine whether professional psychological assistance is needed and, if so, will issue an alert. This alert will include detailed information indicating that the user is in a critical condition, and will notify specialists or occupational physicians.

[0853] Report generation and delivery

[0854] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. These reports enable companies to understand the mental health status of their employees and provide appropriate follow-up care.

[0855] Examples of concrete examples and prompts

[0856] As a concrete example, consider the case where a user answers "I'm tired, but it's fine." The server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that it's "fine" and generates a positive self-care guide: "Have a great day!" The server then sends this guide to the device, which displays it to the user.

[0857] The following is a specific example of a prompt sentence:

[0858] Prompt: "I'd like to conduct a mental health check on you. I'd like to ask you for some open-ended questions about how I'm feeling today, and then analyze your responses to determine if you need appropriate self-care guidance and professional help."

[0859] Using this prompt, the generative AI model can comprehensively analyze information about the user's mental health and provide appropriate actions.

[0860] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0861] Step 1:

[0862] The user accesses the device

[0863] Input: A user logs in to a terminal.

[0864] Action: A user accesses the system using a device such as a smartphone, tablet, or PC and logs in.

[0865] Output: The user successfully logs in and is taken to the mental health check question screen.

[0866] Step 2:

[0867] The device displays mental check questions to the user.

[0868] Input: The user's login status.

[0869] What it does: The device displays a pre-defined mental health question (e.g., "How are you feeling today?").

[0870] Output: The question is displayed to the user and an answer can be entered.

[0871] Step 3:

[0872] The user answers the questions and enters them into the terminal

[0873] Input: Mental check question displayed on terminal.

[0874] How it works: The user types a free-form response to a question (e.g., "I'm tired, but I'm OK").

[0875] Output: The user's answer data is input to the terminal.

[0876] Step 4:

[0877] The device collects the user's answers and sends them to the server.

[0878] Input: User response data.

[0879] Operation: The device encrypts and sends the collected response data to the server.

[0880] Output: The server receives the user's answer data.

[0881] Step 5:

[0882] The server receives the user's response data sent from the device.

[0883] Input: Encrypted user response data sent from the device.

[0884] What happens: The server receives the data and stores it in a database.

[0885] Output: User response data stored in a database.

[0886] Step 6:

[0887] The NLP module on the server analyzes the response data.

[0888] Input: User response data stored in the database.

[0889] How it works: A server-based natural language processing (NLP) module analyzes the response data and extracts topics and keywords, using, for example, Python's "spaCy" or "NLTK."

[0890] Output: The topics and keywords extracted as a result of the analysis.

[0891] Step 7:

[0892] The NLP module identifies topics in the data and determines whether they contain sensitive topics.

[0893] Input: Topics and keywords resulting from the analysis.

[0894] How it works: The NLP module evaluates the message to determine if it contains sensitive topics (e.g., "depression," "suicide").

[0895] Output: Evaluation result for the presence or absence of sensitive topics.

[0896] Step 8:

[0897] The server generates a self-care guide based on the analysis results.

[0898] Input: Assessment results for the presence or absence of sensitive topics.

[0899] How it works: The server generates an appropriate self-care guide based on the information (e.g., "Try deep breathing as a way to relax" or "Have a great day!").

[0900] Output: The generated self-care guide.

[0901] Step 9:

[0902] The generated self-care guide is sent from the server to the device.

[0903] Input: The generated self-care guide.

[0904] Operation: The server sends the generated self-care guide to the device.

[0905] Output: The self-care guide is displayed on the user's device.

[0906] Step 10:

[0907] The device displays a self-care guide to the user.

[0908] Input: Self-care guide sent from the server.

[0909] What it does: The device displays a self-care guide to the user, which the user can use to help with mental health care.

[0910] Output: The user reviews and implements the self-care guide.

[0911] Step 11:

[0912] The server determines the necessity based on the analysis results

[0913] Input: Assessment results for the presence or absence of sensitive topics.

[0914] How it works: The server uses the assessment results to determine whether the user needs professional psychological help.

[0915] Output: A decision on whether professional help is needed.

[0916] Step 12:

[0917] Sends alerts when the server deems necessary

[0918] Input: The result of the decision on whether professional assistance is required.

[0919] How it works: The server will alert you if it determines that professional assistance is needed.

[0920] Output: Alert notification to specialists and industrial physicians.

[0921] Step 13:

[0922] The server periodically generates a report on the user's mental health status.

[0923] Input: User's answer history and analysis results.

[0924] How it works: The server generates periodic reports based on this.

[0925] Output: The generated mental health report.

[0926] Step 14:

[0927] The generated report is sent from the server to company managers and industrial physicians.

[0928] Input: Generated mental health report.

[0929] How it works: The server sends the generated report to company management and / or industrial physicians.

[0930] Output: Report received by company management and industrial physician.

[0931] (Application example 1)

[0932] 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."

[0933] In recent years, mental health issues among employees in factories and manufacturing sites have become more serious. In particular, the number of employees suffering from psychological problems due to overwork and excessive stress is increasing. This has led to a significant decline in productivity and an increase in employee turnover. For this reason, there is a need for a system that provides comprehensive support for employee mental health.

[0934] 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.

[0935] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for providing mental health check questions and collecting responses from the user using hardware with a voice recognition function. This allows employees to not only receive mental health checks in real time, but also to receive appropriate self-care or professional support as needed.

[0936] A "user" is an individual employee or user of the system.

[0937] "Input data" refers to answer data to questions in the mental health check entered by the user.

[0938] "Natural language processing" is a technique used to analyze user input data, identifying topics and detecting sensitive topics.

[0939] An "interactive self-care guide" is a means of providing appropriate self-care advice to the user that is generated based on the analysis results.

[0940] An "alert" is a notification that is sent when it is determined that professional psychological assistance is necessary based on the results of the user's analysis.

[0941] "Mental check questions" are questions provided to assess the user's mood or state.

[0942] The "voice recognition function" is a technology that converts a user's voice input into text data.

[0943] "Hardware" refers to all devices and equipment with voice recognition capabilities.

[0944] "Professional psychological assistance" refers to support provided by professionals such as psychological counselors and psychiatrists.

[0945] "Self-care" refers to mental and physical care methods that users can take care of themselves.

[0946] A "report" is a document summarizing the analysis results regarding the user's mental health condition.

[0947] "Corporate and industrial physicians" are institutions and professionals who are responsible for managing and supporting the mental health of employees in factories and organizations.

[0948] The present invention relates to a system for supporting the mental health of employees in factories and manufacturing sites. A specific embodiment of this system will be described below.

[0949] Collecting user input data

[0950] The system periodically presents users with mental check questions. The questions are presented in the form of, for example, "How are you feeling today?" The user inputs the answers using hardware with speech recognition capabilities (e.g., a robot, smart glasses, or a head-mounted display). This automatically collects data about the user's mental state.

[0951] Data analysis

[0952] The collected data is sent to a server and analyzed using a natural language processing (NLP) module. Specifically, the collected text data is categorized by topic and determined to contain sensitive topics. The NLP module includes a function to detect keywords such as "suicide," "depression," and "stress."

[0953] Providing self-care guides

[0954] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server generates advice such as "Try taking deep breaths as a way to relax." On the other hand, if no sensitive topic is detected, the server provides a positive message such as "Have a great day!" The generated self-care guide is sent to the device and displayed to the user.

[0955] Alerts and expert assistance

[0956] Based on the analysis results, the server determines whether professional psychological assistance is needed. If so, the server sends an alert to notify mental health professionals. The alert contains detailed information indicating that the user is in a critical state. This information is used to quickly contact the appropriate professional.

[0957] Mental health status report generation

[0958] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. This allows companies to understand employee mental health trends and take appropriate measures as necessary.

[0959] Specific examples

[0960] For example, if a user responds, "I'm a little tired today, but it's okay," a device with speech recognition capabilities sends this data to the server. The server then analyzes it using an NLP module, and if no sensitive topics are detected, it determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The guide is then sent to the user's device and displayed.

[0961] On the other hand, if the user answers with sensitive content such as "I'm thinking about suicide," the server analyzes this data and determines that the situation is critical. The server then sends out an alert and notifies a mental health professional. In this way, the system provides comprehensive support for the user's mental health.

[0962] Prompt Sentence Examples

[0963] 1. The robot will ask the question, "How are you feeling today?"

[0964] 2. The user responds, "I'm feeling a little stressed."

[0965] 3. The answer is sent to the server and the analysis results are received.

[0966] 4. If no sensitive topics are detected, it will display "Have a nice day!"

[0967] 5. Send alerts to mental health professionals if sensitive topics are detected.

[0968] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0969] Step 1:

[0970] The user answers mental check questions through a terminal. The terminal uses hardware with speech recognition capabilities (e.g., robots, smart glasses, head-mounted displays) to convert the user's voice input into text data. The input data is the user's response to the question, "How are you feeling today?"

[0971] Input: Voice response to mental check questions

[0972] Output: Conversion result from audio data to text data

[0973] Step 2:

[0974] The terminal sends the collected text data to the server. The server receives the data using HTTP communication (e.g., the requests library). The received data is the text data of the user's responses.

[0975] Input: Converted text data

[0976] Output: Sending text data to the server

[0977] Step 3:

[0978] The server passes the received text data to a natural language processing (NLP) module, which analyzes the text data to identify topics and detect sensitive topics (e.g., "suicide" or "depression"). This analysis generates a score that evaluates the sensitivity of the text.

[0979] Input: Text data of user responses

[0980] Output: Analysis results and sensitivity score

[0981] Step 4:

[0982] The server generates an interactive self-care guide based on the analysis results. Using a generative AI model, it automatically generates messages such as "Try taking a deep breath" or "Have a great day!" The results are saved as text data.

[0983] Input: Analysis results of the NLP module

[0984] Output: Text data of the generated self-care guide

[0985] Step 5:

[0986] The server sends the generated self-care guide to the terminal, which receives the data using HTTP communication and displays the self-care guide to the user.

[0987] Input: Text data of the generated self-care guide

[0988] Output: Display of self-care guide

[0989] Step 6:

[0990] The server uses the analysis results to determine whether professional psychological assistance is needed. If so, the server generates an alert to notify mental health professionals. The alert includes detailed information indicating the user is in a critical condition.

[0991] Input: Analysis results of the NLP module and sensitivity score

[0992] Output: Alert notification data

[0993] Step 7:

[0994] The server periodically generates a report on the user's mental health status. The report includes the user's response history and analysis results, and is sent to the company's management and industrial physician. This allows the company to understand the mental health status of its employees and take measures as necessary.

[0995] Input: User response history and analysis results

[0996] Output: Mental health report

[0997] 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.

[0998] The present invention relates to an AI system for supporting the mental health of users, and in particular to a system that combines an emotion engine that recognizes the user's emotions.

[0999] 1. Collecting user input data

[1000] The terminal displays mental check questions to the user, such as "How are you feeling today?" The user then inputs a free-form response to the question. The input data is sent from the terminal to the server.

[1001] 2. Data Analysis

[1002] The server receives the user's response data from the device. The received data is passed to a natural language processing (NLP) module that includes an emotion engine. The NLP module analyzes the data and identifies emotions (e.g., joy, sadness, fear, anger) contained in the user's text.

[1003] 3. Emotion Recognition by Emotion Engine

[1004] The emotion engine identifies the user's emotions from the analyzed data. For example, in response to the answer "I'm tired, but I'm fine," it recognizes the emotions "fatigue" and "relief." This recognition result is used in subsequent processing.

[1005] 4. Providing self-care guidance

[1006] The server generates an interactive self-care guide based on the analysis results of the NLP and emotion engine. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "Try taking deep breaths" as a way to relax. If the emotion engine recognizes "relief," it will send a positive message such as "Have a great day today!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[1007] 5. Providing alerts and professional assistance

[1008] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If it determines that it is necessary, it generates an alert, taking into account the recognition results of the emotion engine. For example, if "sadness" or "fear" is recognized, the server will issue a high-level alert and notify a specialist or industrial physician.

[1009] 6. Report Generation

[1010] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and the emotion recognition results of the emotion engine. The generated report is sent to company managers and industrial physicians. This allows companies to gain a detailed understanding of their employees' mental health status and provide appropriate follow-up care.

[1011] For example, if a user responds, "Recently, work has not been going well and I am feeling very stressed," the server receives this data and analyzes it using the NLP module. At the same time, the emotion engine recognizes emotions such as "stress" and "anxiety." Based on the analysis results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation is serious, an alert will be sent to an expert.

[1012] The present invention provides advanced mental health support that takes into account the user's emotions, thereby improving the user's overall well-being and the working environment in companies.

[1013] The processing flow will be explained below.

[1014] Step 1:

[1015] The device displays mental check questions to the user, such as "How are you feeling today?"

[1016] Step 2:

[1017] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[1018] Step 3:

[1019] The terminal transmits the user's input data to the server, using a secure communication protocol.

[1020] Step 4:

[1021] The server receives the user's response data sent from the device and temporarily stores the received data.

[1022] Step 5:

[1023] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[1024] Step 6:

[1025] The server passes the analysis results of the NLP module to the emotion engine, which recognizes emotions (e.g., joy, sadness, fear, anger) in the data.

[1026] Step 7:

[1027] The server receives the emotion recognition results from the emotion engine and generates an interactive self-care guide based on the analysis results. For example, if the recognized emotion is "fatigue," it generates advice such as "Try deep breathing as a way to relax."

[1028] Step 8:

[1029] The server transmits the generated self-care guide to the terminal. Since the self-care guide contains important information for the user, it is transmitted quickly and accurately.

[1030] Step 9:

[1031] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[1032] Step 10:

[1033] The server then rechecks the analysis results of the emotion engine and NLP module to determine whether professional psychological assistance is needed. If so, it generates an alert.

[1034] Step 11:

[1035] The server sends an alert to a specialist or industrial physician, which contains detailed information about the user's mental state.

[1036] Step 12:

[1037] The server periodically generates a report on the user's mental health status based on the user's response history and emotion recognition results.

[1038] Step 13:

[1039] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[1040] The above is the specific processing flow of the AI ​​mental health support system that combines an emotion engine. At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[1041] Example 2

[1042] 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."

[1043] In recent years, mental stress in the work environment and in daily life has increased, resulting in serious mental health problems. Many companies and individuals require mental health support, but current mental health support systems often have difficulty properly recognizing users' emotions and providing individualized support. Furthermore, there are only a limited number of systems that can respond immediately when specialized psychological assistance is required. Therefore, there is a need for systems that can accurately recognize users' emotions and provide optimal support according to their condition.

[1044] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user input data, means for analyzing the collected data by natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for identifying the user's emotions using a natural language processing module and an emotion engine. This makes it possible to accurately recognize the user's emotions, provide optimal self-care based on them, and immediately respond if professional assistance is needed.

[1045] "User" refers to an individual or group that uses the System.

[1046] "Input data" refers to answers and information provided by users to this system through their terminals.

[1047] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) used by a user to provide input data.

[1048] "Server" refers to a central processing unit for processing, analyzing, and managing input data.

[1049] "Natural language processing (NLP)" refers to technology for analyzing user input data and understanding its meaning.

[1050] "Emotion engine" refers to software for identifying emotions from user input data through natural language processing.

[1051] A "self-care guide" refers to a guide that provides users with advice and activity suggestions aimed at improving and maintaining their mental health.

[1052] "Alert" refers to a warning or notification from the system to inform the user that professional psychological assistance is required.

[1053] "Professional psychological assistance" refers to psychological support and treatment provided by professionals such as psychologists, counselors, and occupational physicians.

[1054] A "report" refers to a document that summarizes information about a user's mental health condition and is provided to relevant parties such as companies and industrial physicians.

[1055] The present invention provides a system for supporting a user's mental health, which utilizes an emotion engine to recognize the user's emotions and provide interactive self-care guidance and professional assistance. The following describes in detail the embodiments of the present invention.

[1056] Collecting user input data

[1057] The device displays mental check questions to the user. An example question is "How are you feeling today?" The user then enters a free-form response. For example, they might enter a response like "I'm a little tired today, but I'm not feeling bad." This input data is encrypted and sent to the server. Protocols such as SSL / TLS are used for security.

[1058] Data analysis

[1059] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from the user's text data. The analysis results are then passed to the emotion engine.

[1060] Emotion recognition by emotion engine

[1061] The emotion engine receives data from the NLP module and performs detailed emotion analysis. For example, it identifies the emotions "fatigue" and "positive" from the user's response "I'm a little tired today, but I'm not feeling bad." The identified emotions are then recorded in the database.

[1062] Providing self-care guides

[1063] The server generates an interactive self-care guide based on the analysis results of the emotion engine. For example, if "fatigue" is recognized, it generates specific advice such as "Try taking deep breaths." The generated self-care guide is sent from the server to the device, which then displays it to the user.

[1064] Alerts and expert assistance

[1065] The server then reviews the analysis results and determines whether professional psychological assistance is required. For example, if "sadness" or "fear" is recognized, it generates a high-urgency alert and notifies a specialist or industrial physician. This alert includes the specific emotion recognition result and recommended measures.

[1066] Report Generation

[1067] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and emotion recognition results and is sent to company managers and industrial physicians. This report allows companies to gain a detailed understanding of their employees' mental health status and take appropriate measures.

[1068] Specific examples

[1069] For example, if a user answers, "Recently, work has not been going well and I am feeling very stressed," the server analyzes this data using the NLP module. At the same time, the emotion engine identifies emotions such as "stress" and "anxiety." Based on these results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation continues to become serious, an alert will also be sent to a specialist.

[1070] Prompt Sentence Examples

[1071] "Tell me how you're feeling today."

[1072] "Have you been feeling stressed lately?"

[1073] "What is your biggest concern right now?"

[1074] The system provides advanced mental health support that takes users' emotions into account, improving their overall well-being and the working environment at companies.

[1075] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1076] Step 1:

[1077] The user checks the mental health check questions on the terminal. The terminal displays questions such as "How are you feeling today?" to the user. The user answers the questions by entering, for example, "I'm a little tired today, but I'm not feeling bad." The terminal encrypts this input and sends it to the server.

[1078] Input: User's text answer

[1079] Output: Encrypted text data

[1080] Step 2:

[1081] The server receives the encrypted text data. It decrypts the data and passes it to a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to analyze the sentiment of the text. As a result of the analysis, sentiments such as "fatigue" and "positive" are identified.

[1082] Input: Encrypted text data

[1083] Output: Emotion analysis results (fatigue, positive)

[1084] Step 3:

[1085] The server receives the emotion analysis results from the NLP module and passes the data to the emotion engine, which performs detailed emotion analysis to identify emotions more precisely. For example, it recognizes that the emotions "fatigue" and "positive" coexist. The identified emotions are then recorded in a database.

[1086] Input: Sentiment analysis results

[1087] Output: Detailed sentiment analysis results

[1088] Step 4:

[1089] The server generates an interactive self-care guide based on the results of the emotion engine. For example, if "fatigue" is recognized, it generates advice such as "Try taking deep breaths." This self-care guide is then sent to the device.

[1090] Input: Detailed sentiment analysis results

[1091] Output: Self-care guide

[1092] Step 5:

[1093] The device then displays the received self-care guide to the user. For example, it might say, "You look a little tired. Try taking a deep breath!" The user can then take this advice and put it into practice.

[1094] Enter: Self-Care Guide

[1095] Output: Self-care guide displayed to the user

[1096] Step 6:

[1097] The server reviews the analysis results and generates alerts if necessary. For example, if "sadness" or "fear" is consistently recognized, an urgent alert will be sent to an expert. The alert will include the specific emotion recognition result and recommended actions.

[1098] Input: Detailed sentiment analysis results

[1099] Output: Alert (expert notification)

[1100] Step 7:

[1101] The server periodically generates a report on the user's mental health status. This report includes the user's response history and emotion recognition results, and is sent to managers and industrial physicians. This information allows companies to understand the mental health status of their employees and take appropriate measures.

[1102] Input: Detailed sentiment analysis results and answer history

[1103] Output: Mental Health Report

[1104] (Application example 2)

[1105] 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."

[1106] In modern society, providing appropriate support for users' mental health is a crucial issue. Physical stores, in particular, lack the means to understand customers' mental states and provide appropriate product recommendations and services. Therefore, there is a need for a system that supports users' mental health while improving the in-store customer experience.

[1107] 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.

[1108] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, means for proposing individual products and providing service information based on user data collected in a physical commercial facility, and means for recommending a self-care guide or refreshment method in real time based on the detected mental health state. This makes it possible to grasp the user's mental health state in real time and suggest appropriate products and services.

[1109] "Means for collecting input data from users" refers to interfaces and functions for acquiring text data and questionnaire responses that users input into the system.

[1110] "Means for analyzing collected data using natural language processing" refers to a function that uses natural language processing (NLP) technology to analyze input and collected user data and identify emotions and intent.

[1111] The "means for generating an interactive self-care guide based on the analysis results" is a function for generating interactive self-care advice suited to the user based on the results of analysis by natural language processing.

[1112] The "means for providing the generated self-care guide to the user" is a function for notifying the user of the generated self-care guide and displaying it.

[1113] "Means for determining whether professional psychological assistance is required based on the analysis results and sending an alert if necessary" is a function that determines whether the user needs professional psychological assistance based on the results of emotion analysis and sends an alert if necessary.

[1114] "A means of suggesting individual products and providing information about services based on user data collected in physical commercial facilities" is a function that suggests appropriate products and services based on the emotions and state of users who visit physical stores.

[1115] The "means for recommending self-care guides or refreshment methods in real time based on the detected mental health state" is a function that suggests appropriate self-care guides or refreshment methods in real time based on the user's mental health data.

[1116] This invention is a system for understanding the mental health status of users in physical stores in real time and providing appropriate product suggestions and self-care guides.

[1117] 1. System Configuration

[1118] The system consists of a terminal that collects input data from users, a server that analyzes the data and identifies emotions, and a terminal that provides the generated self-care guide to the user.

[1119] 2. Collecting User-Input Data

[1120] When a user visits a store, they use their smartphone to scan a QR code installed in the store. After scanning, the smartphone's browser or a dedicated application opens and a survey screen is displayed. For example, the user can enter a free-form response to a question such as "How are you feeling today?" This input data is sent to the server in real time.

[1121] 3. Data Analysis and Emotion Recognition

[1122] The server passes the received user input data to a natural language processing (NLP) module. This NLP module analyzes the data using, for example, the Hugging Face transformers library, and identifies the emotions contained in the user's text. The analysis results are passed to an emotion engine, which recognizes emotions such as "joy," "sadness," "fear," and "anger."

[1123] 4. Self-care guide and product recommendations

[1124] An interactive self-care guide is generated based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will suggest relaxation. Specifically, it will display messages to the user such as "Try taking deep breaths" or "Use the relaxation space." It will also suggest refreshment items available in the store (e.g., relaxation drinks and aroma products).

[1125] 5. Determining the need for professional assistance and alerting

[1126] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If the emotion engine recognizes "sadness" or "fear," the server issues a high-level alert and notifies experts as necessary, enabling a rapid response.

[1127] 6. Generate scheduled reports

[1128] Data on users' mental health status is periodically aggregated and generated into a report, which is sent to store managers and company executives to serve as a reference for understanding the mental health status of customers.

[1129] Examples of concrete examples and prompts

[1130] For example, if a user answers a questionnaire saying, "I'm very tired and a little stressed," the server receives this data and uses the NLP module to recognize emotions such as "fatigue" and "stress." As a result, the server displays a message to the user such as, "Please use the relaxation space. Try taking a deep breath." It also suggests relaxation drinks for the store.

[1131] Example prompts to input to a generative AI model:

[1132] "Recognize the emotion in the following text: I'm very tired and a little stressed."

[1133] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1134] Step 1:

[1135] A user scans a QR code at a physical store. The device (smartphone) displays a survey screen asking, "How are you feeling today?" The user enters a free-form response to this question. The user input (e.g., I'm tired and a little stressed) is sent from the device to the server. Input: User response text, Output: Data sent to the server. The specific operation is that the user scans the QR code, and the browser or application displays the survey screen.

[1136] Step 2:

[1137] The server passes the received user response data to a natural language processing (NLP) module. The NLP module analyzes the sent text data and identifies the emotion contained in the user's text. Input: User response text, Output: Analyzed emotion data (e.g., "fatigue" or "stress"). The specific operation is to send the received text data to the NLP engine and identify the emotion.

[1138] Step 3:

[1139] The server uses the emotion engine to further refine the emotion data analyzed by the NLP module. Specifically, it classifies the answer "I'm tired, but I'm fine" into "fatigue" and "relief." Input: emotion data analyzed by NLP, output: refined emotion data. Specifically, the emotion engine evaluates the analyzed emotion data and identifies more detailed emotions.

[1140] Step 4:

[1141] The server generates an interactive self-care guide based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "try taking deep breaths" as a way to relax. Input: subdivided emotion data, output: self-care guide message. The specific operation is to create a self-care guide message based on the results of the emotion engine.

[1142] Step 5:

[1143] The generated self-care guide is sent to the device and notified to the user. The user can view relaxation methods and self-care suggestions on their smartphone screen. Input: Self-care guide message, Output: Message displayed on the device screen. The specific operation is that the server sends the generated message to the device, and the device displays it.

[1144] Step 6:

[1145] The server then rechecks the analysis results and determines whether professional psychological assistance is required. For example, if the emotion engine recognizes "sadness" or "fear," it determines that professional assistance is required and generates an alert. Input: segmented emotion data, output: alert message. The specific operation is to evaluate the analysis results and generate an alert message if necessary.

[1146] Step 7:

[1147] The server periodically generates reports on the user's mental health status and sends them to the company or industrial physician. The reports are created based on the user's response history and the emotion recognition results of the emotion engine. Input: User's response history and recognition results, Output: Mental health report. The specific operation is to aggregate the data, generate a report, and send it.

[1148] Through these steps, this system can support users' mental health in real time at each store.

[1149] 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.

[1150] 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.

[1151] 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.

[1152] [Fourth embodiment]

[1153] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1154] 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.

[1155] 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).

[1156] 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.

[1157] 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.

[1158] 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).

[1159] 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.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] 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.

[1164] 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.

[1165] 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."

[1166] The present invention relates to an AI system for supporting a user's mental health, and an embodiment thereof will be described in detail below.

[1167] 1. Collecting user input data

[1168] The terminal displays a mental check question to the user. For example, it provides a question such as "How are you feeling today?" The user inputs a free-form answer to this question. The input data is sent from the terminal to the server.

[1169] 2. Data Analysis

[1170] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. The NLP module identifies topics in the data and determines whether they contain sensitive topics (e.g., "depression," "suicide").

[1171] 3. Providing self-care guidance

[1172] The server generates an appropriate interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, the server will provide the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server will send a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[1173] 4. Providing alerts and professional assistance

[1174] If a sensitive topic is detected based on the analysis results, the server determines whether professional psychological assistance is required. If so, the server issues an alert to notify specialists or industrial physicians. The alert includes detailed information indicating that the user is in a critical condition.

[1175] 5. Report Generation

[1176] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results. The generated reports are sent from the server to company managers and industrial physicians. This allows companies to understand the mental health status of their employees and provide appropriate follow-up care.

[1177] To explain with a concrete example, if a user responds, "I'm tired, but I'm fine," the server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The server then sends this guide to the device, which displays it to the user.

[1178] Furthermore, if the user's response contains sensitive content such as "I'm thinking about suicide," the server will send an alert and arrange for professional psychological assistance to be provided. In this way, the present invention is a system that comprehensively supports the user's mental health.

[1179] The processing flow will be explained below.

[1180] Step 1:

[1181] The device displays mental check questions to the user, such as "How are you feeling today?"

[1182] Step 2:

[1183] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[1184] Step 3:

[1185] The terminal sends the user's input data to the server, where the response is sent using a secure communication protocol.

[1186] Step 4:

[1187] The server receives the user's response data sent from the device and temporarily stores the received data.

[1188] Step 5:

[1189] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[1190] Step 6:

[1191] The server generates an interactive self-care guide based on the analysis results. For example, if a sensitive topic is detected, it generates a guide such as "Try deep breathing as a relaxation technique." If there are no particular problems, it generates a positive message such as "Have a great day today!"

[1192] Step 7:

[1193] The server then sends the generated self-care guide to the terminal. Because the guide contains important information for the user, it is sent quickly and accurately.

[1194] Step 8:

[1195] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[1196] Step 9:

[1197] The server then reviews the analysis results and determines whether professional psychological assistance is needed. If so, the server generates an alert.

[1198] Step 10:

[1199] The server sends an alert to a specialist or industrial physician, which includes details about the user's mental state.

[1200] Step 11:

[1201] The server periodically generates a report on the user's mental health status based on the user's response history and analytical data.

[1202] Step 12:

[1203] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[1204] This is the specific processing flow of the AI ​​mental health support system "AI." At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[1205] Example 1

[1206] 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."

[1207] Conventional mental health support systems have had problems in that they are unable to properly assess a user's condition and respond quickly. They also lack the functionality to properly issue alerts in serious cases that require specialized assistance. As a result, users' mental health conditions run the risk of continuing to deteriorate, and it is difficult for company managers and industrial physicians to properly understand the status of their employees.

[1208] 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.

[1209] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether a sensitive topic is included based on the analysis results, means for determining whether professional psychological assistance is needed and sending an alert if necessary, and means for generating a report on the user's mental health status and sending it to a company or industrial physician. This makes it possible to quickly and accurately grasp the user's mental health status and provide appropriate self-care and, if necessary, professional assistance.

[1210] "User input data" refers to answers and responses that users input into the system via their terminals.

[1211] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[1212] "Interactive self-care guide" means an interactive guide that includes advice and instructions provided to support a user's mental health.

[1213] "Sensitive topics" refer to mental health topics that are of high urgency or seriousness, such as depression or suicide.

[1214] "Professional psychological assistance" refers to specialized help provided by mental health professionals or counselors.

[1215] "Sending an alert" refers to the act of issuing a warning or notification when certain conditions are met.

[1216] "Generating a report" refers to the act of creating a report based on collected and analyzed data.

[1217] "Corporate or industrial physicians" refers to managers and medical professionals in charge of mental health care in the user's work environment.

[1218] "Checking regularly" refers to the act of checking the state of mental health at regular intervals.

[1219] A "prompt" is a document used to input instructions or questions to an AI model.

[1220] The present invention relates to an AI system for supporting a user's mental health. This system collects input data from a user, analyzes it using natural language processing, and generates an interactive self-care guide based on the analysis results. It also has the function of providing professional psychological assistance as needed and generating periodic reports. Detailed embodiments of the system are described below.

[1221] Collecting user input data

[1222] First, the user accesses the system using a terminal. The terminal can be a smartphone, tablet, PC, etc. The terminal displays a mental check question to the user. For example, it may provide a question such as "How are you feeling today?" The user then inputs a free-form answer to this question. This input data is encrypted and sent from the terminal to the server.

[1223] Data analysis

[1224] The server receives the user's response data sent from the device. The received data is analyzed by a natural language processing (NLP) module on the server. This NLP module is implemented using popular Python libraries such as "spaCy" and "NLTK." The NLP module identifies topics within the data and determines whether they contain sensitive topics (e.g., "depression" or "suicide").

[1225] Creating and providing self-care guides

[1226] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server provides the user with advice such as "Try taking deep breaths as a way to relax." If the analysis results show no particular problems, the server sends a positive message such as "Have a great day!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[1227] Alerts and expert assistance

[1228] Based on the analysis results, the server determines whether or not professional psychological assistance is needed. If a sensitive topic is detected, it will determine whether professional psychological assistance is needed and, if so, will issue an alert. This alert will include detailed information indicating that the user is in a critical condition, and will notify specialists or occupational physicians.

[1229] Report generation and delivery

[1230] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. These reports enable companies to understand the mental health status of their employees and provide appropriate follow-up care.

[1231] Examples of concrete examples and prompts

[1232] As a concrete example, consider the case where a user answers "I'm tired, but it's fine." The server receives this data and analyzes it using the NLP module. Since no sensitive topics were detected, the server determines that it's "fine" and generates a positive self-care guide: "Have a great day!" The server then sends this guide to the device, which displays it to the user.

[1233] The following is a specific example of a prompt sentence:

[1234] Prompt: "I'd like to conduct a mental health check on you. I'd like to ask you for some open-ended questions about how I'm feeling today, and then analyze your responses to determine if you need appropriate self-care guidance and professional help."

[1235] Using this prompt, the generative AI model can comprehensively analyze information about the user's mental health and provide appropriate actions.

[1236] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1237] Step 1:

[1238] The user accesses the device

[1239] Input: A user logs in to a terminal.

[1240] Action: A user accesses the system using a device such as a smartphone, tablet, or PC and logs in.

[1241] Output: The user successfully logs in and is taken to the mental health check question screen.

[1242] Step 2:

[1243] The device displays mental check questions to the user.

[1244] Input: The user's login status.

[1245] What it does: The device displays a pre-defined mental health question (e.g., "How are you feeling today?").

[1246] Output: The question is displayed to the user and an answer can be entered.

[1247] Step 3:

[1248] The user answers the questions and enters them into the terminal

[1249] Input: Mental check question displayed on terminal.

[1250] How it works: The user types a free-form response to a question (e.g., "I'm tired, but I'm OK").

[1251] Output: The user's answer data is input to the terminal.

[1252] Step 4:

[1253] The device collects the user's answers and sends them to the server.

[1254] Input: User response data.

[1255] Operation: The device encrypts and sends the collected response data to the server.

[1256] Output: The server receives the user's answer data.

[1257] Step 5:

[1258] The server receives the user's response data sent from the device.

[1259] Input: Encrypted user response data sent from the device.

[1260] What happens: The server receives the data and stores it in a database.

[1261] Output: User response data stored in a database.

[1262] Step 6:

[1263] The NLP module on the server analyzes the response data.

[1264] Input: User response data stored in the database.

[1265] How it works: A server-based natural language processing (NLP) module analyzes the response data and extracts topics and keywords, using, for example, Python's "spaCy" or "NLTK."

[1266] Output: The topics and keywords extracted as a result of the analysis.

[1267] Step 7:

[1268] The NLP module identifies topics in the data and determines whether they contain sensitive topics.

[1269] Input: Topics and keywords resulting from the analysis.

[1270] How it works: The NLP module evaluates the message to determine if it contains sensitive topics (e.g., "depression," "suicide").

[1271] Output: Evaluation result for the presence or absence of sensitive topics.

[1272] Step 8:

[1273] The server generates a self-care guide based on the analysis results.

[1274] Input: Assessment results for the presence or absence of sensitive topics.

[1275] How it works: The server generates an appropriate self-care guide based on the information (e.g., "Try deep breathing as a way to relax" or "Have a great day!").

[1276] Output: The generated self-care guide.

[1277] Step 9:

[1278] The generated self-care guide is sent from the server to the device.

[1279] Input: The generated self-care guide.

[1280] Operation: The server sends the generated self-care guide to the device.

[1281] Output: The self-care guide is displayed on the user's device.

[1282] Step 10:

[1283] The device displays a self-care guide to the user.

[1284] Input: Self-care guide sent from the server.

[1285] What it does: The device displays a self-care guide to the user, which the user can use to help with mental health care.

[1286] Output: The user reviews and implements the self-care guide.

[1287] Step 11:

[1288] The server determines the necessity based on the analysis results

[1289] Input: Assessment results for the presence or absence of sensitive topics.

[1290] How it works: The server uses the assessment results to determine whether the user needs professional psychological help.

[1291] Output: A decision on whether professional help is needed.

[1292] Step 12:

[1293] Sends alerts when the server deems necessary

[1294] Input: The result of the decision on whether professional assistance is required.

[1295] How it works: The server will alert you if it determines that professional assistance is needed.

[1296] Output: Alert notification to specialists and industrial physicians.

[1297] Step 13:

[1298] The server periodically generates a report on the user's mental health status.

[1299] Input: User's answer history and analysis results.

[1300] How it works: The server generates periodic reports based on this.

[1301] Output: The generated mental health report.

[1302] Step 14:

[1303] The generated report is sent from the server to company managers and industrial physicians.

[1304] Input: Generated mental health report.

[1305] How it works: The server sends the generated report to company management and / or industrial physicians.

[1306] Output: Report received by company management and industrial physician.

[1307] (Application example 1)

[1308] 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."

[1309] In recent years, mental health issues among employees in factories and manufacturing sites have become more serious. In particular, the number of employees suffering from psychological problems due to overwork and excessive stress is increasing. This has led to a significant decline in productivity and an increase in employee turnover. For this reason, there is a need for a system that provides comprehensive support for employee mental health.

[1310] 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.

[1311] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for providing mental health check questions and collecting responses from the user using hardware with a voice recognition function. This allows employees to not only receive mental health checks in real time, but also to receive appropriate self-care or professional support as needed.

[1312] A "user" is an individual employee or user of the system.

[1313] "Input data" refers to answer data to questions in the mental health check entered by the user.

[1314] "Natural language processing" is a technique used to analyze user input data, identifying topics and detecting sensitive topics.

[1315] An "interactive self-care guide" is a means of providing appropriate self-care advice to the user that is generated based on the analysis results.

[1316] An "alert" is a notification that is sent when it is determined that professional psychological assistance is necessary based on the results of the user's analysis.

[1317] "Mental check questions" are questions provided to assess the user's mood or state.

[1318] The "voice recognition function" is a technology that converts a user's voice input into text data.

[1319] "Hardware" refers to all devices and equipment with voice recognition capabilities.

[1320] "Professional psychological assistance" refers to support provided by professionals such as psychological counselors and psychiatrists.

[1321] "Self-care" refers to mental and physical care methods that users can take care of themselves.

[1322] A "report" is a document summarizing the analysis results regarding the user's mental health condition.

[1323] "Corporate and industrial physicians" are institutions and professionals who are responsible for managing and supporting the mental health of employees in factories and organizations.

[1324] The present invention relates to a system for supporting the mental health of employees in factories and manufacturing sites. A specific embodiment of this system will be described below.

[1325] Collecting user input data

[1326] The system periodically presents users with mental check questions. The questions are presented in the form of, for example, "How are you feeling today?" The user inputs the answers using hardware with speech recognition capabilities (e.g., a robot, smart glasses, or a head-mounted display). This automatically collects data about the user's mental state.

[1327] Data analysis

[1328] The collected data is sent to a server and analyzed using a natural language processing (NLP) module. Specifically, the collected text data is categorized by topic and determined to contain sensitive topics. The NLP module includes a function to detect keywords such as "suicide," "depression," and "stress."

[1329] Providing self-care guides

[1330] Based on the analysis results, the server generates an appropriate interactive self-care guide. For example, if a sensitive topic is detected, the server generates advice such as "Try taking deep breaths as a way to relax." On the other hand, if no sensitive topic is detected, the server provides a positive message such as "Have a great day!" The generated self-care guide is sent to the device and displayed to the user.

[1331] Alerts and expert assistance

[1332] Based on the analysis results, the server determines whether professional psychological assistance is needed. If so, the server sends an alert to notify mental health professionals. The alert contains detailed information indicating that the user is in a critical state. This information is used to quickly contact the appropriate professional.

[1333] Mental health status report generation

[1334] The server periodically generates reports on the user's mental health status. These reports are created based on the user's response history and analysis results and are sent to company managers and industrial physicians. This allows companies to understand employee mental health trends and take appropriate measures as necessary.

[1335] Specific examples

[1336] For example, if a user responds, "I'm a little tired today, but it's okay," a device with speech recognition capabilities sends this data to the server. The server then analyzes it using an NLP module, and if no sensitive topics are detected, it determines that there is no problem and generates a positive self-care guide, "Have a great day today!" The guide is then sent to the user's device and displayed.

[1337] On the other hand, if the user answers with sensitive content such as "I'm thinking about suicide," the server analyzes this data and determines that the situation is critical. The server then sends out an alert and notifies a mental health professional. In this way, the system provides comprehensive support for the user's mental health.

[1338] Prompt Sentence Examples

[1339] 1. The robot will ask the question, "How are you feeling today?"

[1340] 2. The user responds, "I'm feeling a little stressed."

[1341] 3. The answer is sent to the server and the analysis results are received.

[1342] 4. If no sensitive topics are detected, it will display "Have a nice day!"

[1343] 5. Send alerts to mental health professionals if sensitive topics are detected.

[1344] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1345] Step 1:

[1346] The user answers mental check questions through a terminal. The terminal uses hardware with speech recognition capabilities (e.g., robots, smart glasses, head-mounted displays) to convert the user's voice input into text data. The input data is the user's response to the question, "How are you feeling today?"

[1347] Input: Voice response to mental check questions

[1348] Output: Conversion result from audio data to text data

[1349] Step 2:

[1350] The terminal sends the collected text data to the server. The server receives the data using HTTP communication (e.g., the requests library). The received data is the text data of the user's responses.

[1351] Input: Converted text data

[1352] Output: Sending text data to the server

[1353] Step 3:

[1354] The server passes the received text data to a natural language processing (NLP) module, which analyzes the text data to identify topics and detect sensitive topics (e.g., "suicide" or "depression"). This analysis generates a score that evaluates the sensitivity of the text.

[1355] Input: Text data of user responses

[1356] Output: Analysis results and sensitivity score

[1357] Step 4:

[1358] The server generates an interactive self-care guide based on the analysis results. Using a generative AI model, it automatically generates messages such as "Try taking a deep breath" or "Have a great day!" The results are saved as text data.

[1359] Input: Analysis results of the NLP module

[1360] Output: Text data of the generated self-care guide

[1361] Step 5:

[1362] The server sends the generated self-care guide to the terminal, which receives the data using HTTP communication and displays the self-care guide to the user.

[1363] Input: Text data of the generated self-care guide

[1364] Output: Display of self-care guide

[1365] Step 6:

[1366] The server uses the analysis results to determine whether professional psychological assistance is needed. If so, the server generates an alert to notify mental health professionals. The alert includes detailed information indicating the user is in a critical condition.

[1367] Input: Analysis results of the NLP module and sensitivity score

[1368] Output: Alert notification data

[1369] Step 7:

[1370] The server periodically generates a report on the user's mental health status. The report includes the user's response history and analysis results, and is sent to the company's management and industrial physician. This allows the company to understand the mental health status of its employees and take measures as necessary.

[1371] Input: User response history and analysis results

[1372] Output: Mental health report

[1373] 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.

[1374] The present invention relates to an AI system for supporting the mental health of users, and in particular to a system that combines an emotion engine that recognizes the user's emotions.

[1375] 1. Collecting user input data

[1376] The terminal displays mental check questions to the user, such as "How are you feeling today?" The user then inputs a free-form response to the question. The input data is sent from the terminal to the server.

[1377] 2. Data Analysis

[1378] The server receives the user's response data from the device. The received data is passed to a natural language processing (NLP) module that includes an emotion engine. The NLP module analyzes the data and identifies emotions (e.g., joy, sadness, fear, anger) contained in the user's text.

[1379] 3. Emotion Recognition by Emotion Engine

[1380] The emotion engine identifies the user's emotions from the analyzed data. For example, in response to the answer "I'm tired, but I'm fine," it recognizes the emotions "fatigue" and "relief." This recognition result is used in subsequent processing.

[1381] 4. Providing self-care guidance

[1382] The server generates an interactive self-care guide based on the analysis results of the NLP and emotion engine. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "Try taking deep breaths" as a way to relax. If the emotion engine recognizes "relief," it will send a positive message such as "Have a great day today!" This self-care guide is sent from the server to the device, where it is displayed to the user.

[1383] 5. Providing alerts and professional assistance

[1384] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If it determines that it is necessary, it generates an alert, taking into account the recognition results of the emotion engine. For example, if "sadness" or "fear" is recognized, the server will issue a high-level alert and notify a specialist or industrial physician.

[1385] 6. Report Generation

[1386] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and the emotion recognition results of the emotion engine. The generated report is sent to company managers and industrial physicians. This allows companies to gain a detailed understanding of their employees' mental health status and provide appropriate follow-up care.

[1387] For example, if a user responds, "Recently, work has not been going well and I am feeling very stressed," the server receives this data and analyzes it using the NLP module. At the same time, the emotion engine recognizes emotions such as "stress" and "anxiety." Based on the analysis results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation is serious, an alert will be sent to an expert.

[1388] The present invention provides advanced mental health support that takes into account the user's emotions, thereby improving the user's overall well-being and the working environment in companies.

[1389] The processing flow will be explained below.

[1390] Step 1:

[1391] The device displays mental check questions to the user, such as "How are you feeling today?"

[1392] Step 2:

[1393] The user inputs the answer to the question into the terminal, for example, "I'm tired, but I'm OK."

[1394] Step 3:

[1395] The terminal transmits the user's input data to the server, using a secure communication protocol.

[1396] Step 4:

[1397] The server receives the user's response data sent from the device and temporarily stores the received data.

[1398] Step 5:

[1399] The server then passes the received data to a natural language processing (NLP) module for analysis, which tokenizes words and phrases in the data and detects sensitive topics (e.g., "depression" or "suicide").

[1400] Step 6:

[1401] The server passes the analysis results of the NLP module to the emotion engine, which recognizes emotions (e.g., joy, sadness, fear, anger) in the data.

[1402] Step 7:

[1403] The server receives the emotion recognition results from the emotion engine and generates an interactive self-care guide based on the analysis results. For example, if the recognized emotion is "fatigue," it generates advice such as "Try deep breathing as a way to relax."

[1404] Step 8:

[1405] The server transmits the generated self-care guide to the terminal. Since the self-care guide contains important information for the user, it is transmitted quickly and accurately.

[1406] Step 9:

[1407] The terminal displays the received self-care guide to the user, who can then perform self-care based on the guide.

[1408] Step 10:

[1409] The server then rechecks the analysis results of the emotion engine and NLP module to determine whether professional psychological assistance is needed. If so, it generates an alert.

[1410] Step 11:

[1411] The server sends an alert to a specialist or industrial physician, which contains detailed information about the user's mental state.

[1412] Step 12:

[1413] The server periodically generates a report on the user's mental health status based on the user's response history and emotion recognition results.

[1414] Step 13:

[1415] The server then sends the generated report to company managers and industrial physicians, allowing companies to understand the mental health status of their employees and take appropriate measures.

[1416] The above is the specific processing flow of the AI ​​mental health support system that combines an emotion engine. At each step, the server, device, and user play their respective roles, and the system is designed to ensure that the entire system functions smoothly.

[1417] Example 2

[1418] 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."

[1419] In recent years, mental stress in the work environment and in daily life has increased, resulting in serious mental health problems. Many companies and individuals require mental health support, but current mental health support systems often have difficulty properly recognizing users' emotions and providing individualized support. Furthermore, there are only a limited number of systems that can respond immediately when specialized psychological assistance is required. Therefore, there is a need for systems that can accurately recognize users' emotions and provide optimal support according to their condition.

[1420] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user input data, means for analyzing the collected data by natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, and means for identifying the user's emotions using a natural language processing module and an emotion engine. This makes it possible to accurately recognize the user's emotions, provide optimal self-care based on them, and immediately respond if professional assistance is needed.

[1421] "User" refers to an individual or group that uses the System.

[1422] "Input data" refers to answers and information provided by users to this system through their terminals.

[1423] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) used by a user to provide input data.

[1424] "Server" refers to a central processing unit for processing, analyzing, and managing input data.

[1425] "Natural language processing (NLP)" refers to technology for analyzing user input data and understanding its meaning.

[1426] "Emotion engine" refers to software for identifying emotions from user input data through natural language processing.

[1427] A "self-care guide" refers to a guide that provides users with advice and activity suggestions aimed at improving and maintaining their mental health.

[1428] "Alert" refers to a warning or notification from the system to inform the user that professional psychological assistance is required.

[1429] "Professional psychological assistance" refers to psychological support and treatment provided by professionals such as psychologists, counselors, and occupational physicians.

[1430] A "report" refers to a document that summarizes information about a user's mental health condition and is provided to relevant parties such as companies and industrial physicians.

[1431] The present invention provides a system for supporting a user's mental health, which utilizes an emotion engine to recognize the user's emotions and provide interactive self-care guidance and professional assistance. The following describes in detail the embodiments of the present invention.

[1432] Collecting user input data

[1433] The device displays mental check questions to the user. An example question is "How are you feeling today?" The user then enters a free-form response. For example, they might enter a response like "I'm a little tired today, but I'm not feeling bad." This input data is encrypted and sent to the server. Protocols such as SSL / TLS are used for security.

[1434] Data analysis

[1435] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from the user's text data. The analysis results are then passed to the emotion engine.

[1436] Emotion recognition by emotion engine

[1437] The emotion engine receives data from the NLP module and performs detailed emotion analysis. For example, it identifies the emotions "fatigue" and "positive" from the user's response "I'm a little tired today, but I'm not feeling bad." The identified emotions are then recorded in the database.

[1438] Providing self-care guides

[1439] The server generates an interactive self-care guide based on the analysis results of the emotion engine. For example, if "fatigue" is recognized, it generates specific advice such as "Try taking deep breaths." The generated self-care guide is sent from the server to the device, which then displays it to the user.

[1440] Alerts and expert assistance

[1441] The server then reviews the analysis results and determines whether professional psychological assistance is required. For example, if "sadness" or "fear" is recognized, it generates a high-urgency alert and notifies a specialist or industrial physician. This alert includes the specific emotion recognition result and recommended measures.

[1442] Report Generation

[1443] The server periodically generates a report on the user's mental health status. This report is created based on the user's response history and emotion recognition results and is sent to company managers and industrial physicians. This report allows companies to gain a detailed understanding of their employees' mental health status and take appropriate measures.

[1444] Specific examples

[1445] For example, if a user answers, "Recently, work has not been going well and I am feeling very stressed," the server analyzes this data using the NLP module. At the same time, the emotion engine identifies emotions such as "stress" and "anxiety." Based on these results, the server generates self-care guidance such as "I recommend taking deep breaths and short breaks" and provides it to the user. If the situation continues to become serious, an alert will also be sent to a specialist.

[1446] Prompt Sentence Examples

[1447] "Tell me how you're feeling today."

[1448] "Have you been feeling stressed lately?"

[1449] "What is your biggest concern right now?"

[1450] The system provides advanced mental health support that takes users' emotions into account, improving their overall well-being and the working environment at companies.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user checks the mental health check questions on the terminal. The terminal displays questions such as "How are you feeling today?" to the user. The user answers the questions by entering, for example, "I'm a little tired today, but I'm not feeling bad." The terminal encrypts this input and sends it to the server.

[1454] Input: User's text answer

[1455] Output: Encrypted text data

[1456] Step 2:

[1457] The server receives the encrypted text data. It decrypts the data and passes it to a natural language processing (NLP) module. The NLP module uses a generative AI model (e.g., BERT or GPT-3) to analyze the sentiment of the text. As a result of the analysis, sentiments such as "fatigue" and "positive" are identified.

[1458] Input: Encrypted text data

[1459] Output: Emotion analysis results (fatigue, positive)

[1460] Step 3:

[1461] The server receives the emotion analysis results from the NLP module and passes the data to the emotion engine, which performs detailed emotion analysis to identify emotions more precisely. For example, it recognizes that the emotions "fatigue" and "positive" coexist. The identified emotions are then recorded in a database.

[1462] Input: Sentiment analysis results

[1463] Output: Detailed sentiment analysis results

[1464] Step 4:

[1465] The server generates an interactive self-care guide based on the results of the emotion engine. For example, if "fatigue" is recognized, it generates advice such as "Try taking deep breaths." This self-care guide is then sent to the device.

[1466] Input: Detailed sentiment analysis results

[1467] Output: Self-care guide

[1468] Step 5:

[1469] The device then displays the received self-care guide to the user. For example, it might say, "You look a little tired. Try taking a deep breath!" The user can then take this advice and put it into practice.

[1470] Enter: Self-Care Guide

[1471] Output: Self-care guide displayed to the user

[1472] Step 6:

[1473] The server reviews the analysis results and generates alerts if necessary. For example, if "sadness" or "fear" is consistently recognized, an urgent alert will be sent to an expert. The alert will include the specific emotion recognition result and recommended actions.

[1474] Input: Detailed sentiment analysis results

[1475] Output: Alert (expert notification)

[1476] Step 7:

[1477] The server periodically generates a report on the user's mental health status. This report includes the user's response history and emotion recognition results, and is sent to managers and industrial physicians. This information allows companies to understand the mental health status of their employees and take appropriate measures.

[1478] Input: Detailed sentiment analysis results and answer history

[1479] Output: Mental Health Report

[1480] (Application example 2)

[1481] 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."

[1482] In modern society, providing appropriate support for users' mental health is a crucial issue. Physical stores, in particular, lack the means to understand customers' mental states and provide appropriate product recommendations and services. Therefore, there is a need for a system that supports users' mental health while improving the in-store customer experience.

[1483] 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.

[1484] In this invention, the server includes means for collecting input data from a user, means for analyzing the collected data using natural language processing, means for generating an interactive self-care guide based on the analysis results, means for providing the generated self-care guide to the user, means for determining whether professional psychological assistance is necessary based on the analysis results and sending an alert if necessary, means for proposing individual products and providing service information based on user data collected in a physical commercial facility, and means for recommending a self-care guide or refreshment method in real time based on the detected mental health state. This makes it possible to grasp the user's mental health state in real time and suggest appropriate products and services.

[1485] "Means for collecting input data from users" refers to interfaces and functions for acquiring text data and questionnaire responses that users input into the system.

[1486] "Means for analyzing collected data using natural language processing" refers to a function that uses natural language processing (NLP) technology to analyze input and collected user data and identify emotions and intent.

[1487] The "means for generating an interactive self-care guide based on the analysis results" is a function for generating interactive self-care advice suited to the user based on the results of analysis by natural language processing.

[1488] The "means for providing the generated self-care guide to the user" is a function for notifying the user of the generated self-care guide and displaying it.

[1489] "Means for determining whether professional psychological assistance is required based on the analysis results and sending an alert if necessary" is a function that determines whether the user needs professional psychological assistance based on the results of emotion analysis and sends an alert if necessary.

[1490] "A means of suggesting individual products and providing information about services based on user data collected in physical commercial facilities" is a function that suggests appropriate products and services based on the emotions and state of users who visit physical stores.

[1491] The "means for recommending self-care guides or refreshment methods in real time based on the detected mental health state" is a function that suggests appropriate self-care guides or refreshment methods in real time based on the user's mental health data.

[1492] This invention is a system for understanding the mental health status of users in physical stores in real time and providing appropriate product suggestions and self-care guides.

[1493] 1. System Configuration

[1494] The system consists of a terminal that collects input data from users, a server that analyzes the data and identifies emotions, and a terminal that provides the generated self-care guide to the user.

[1495] 2. Collecting User-Input Data

[1496] When a user visits a store, they use their smartphone to scan a QR code installed in the store. After scanning, the smartphone's browser or a dedicated application opens and a survey screen is displayed. For example, the user can enter a free-form response to a question such as "How are you feeling today?" This input data is sent to the server in real time.

[1497] 3. Data Analysis and Emotion Recognition

[1498] The server passes the received user input data to a natural language processing (NLP) module. This NLP module analyzes the data using, for example, the Hugging Face transformers library, and identifies the emotions contained in the user's text. The analysis results are passed to an emotion engine, which recognizes emotions such as "joy," "sadness," "fear," and "anger."

[1499] 4. Self-care guide and product recommendations

[1500] An interactive self-care guide is generated based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will suggest relaxation. Specifically, it will display messages to the user such as "Try taking deep breaths" or "Use the relaxation space." It will also suggest refreshment items available in the store (e.g., relaxation drinks and aroma products).

[1501] 5. Determining the need for professional assistance and alerting

[1502] The server then rechecks the analysis results and determines whether professional psychological assistance is necessary. If the emotion engine recognizes "sadness" or "fear," the server issues a high-level alert and notifies experts as necessary, enabling a rapid response.

[1503] 6. Generate scheduled reports

[1504] Data on users' mental health status is periodically aggregated and generated into a report, which is sent to store managers and company executives to serve as a reference for understanding the mental health status of customers.

[1505] Examples of concrete examples and prompts

[1506] For example, if a user answers a questionnaire saying, "I'm very tired and a little stressed," the server receives this data and uses the NLP module to recognize emotions such as "fatigue" and "stress." As a result, the server displays a message to the user such as, "Please use the relaxation space. Try taking a deep breath." It also suggests relaxation drinks for the store.

[1507] Example prompts to input to a generative AI model:

[1508] "Recognize the emotion in the following text: I'm very tired and a little stressed."

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1:

[1511] A user scans a QR code at a physical store. The device (smartphone) displays a survey screen asking, "How are you feeling today?" The user enters a free-form response to this question. The user input (e.g., I'm tired and a little stressed) is sent from the device to the server. Input: User response text, Output: Data sent to the server. The specific operation is that the user scans the QR code, and the browser or application displays the survey screen.

[1512] Step 2:

[1513] The server passes the received user response data to a natural language processing (NLP) module. The NLP module analyzes the sent text data and identifies the emotion contained in the user's text. Input: User response text, Output: Analyzed emotion data (e.g., "fatigue" or "stress"). The specific operation is to send the received text data to the NLP engine and identify the emotion.

[1514] Step 3:

[1515] The server uses the emotion engine to further refine the emotion data analyzed by the NLP module. Specifically, it classifies the answer "I'm tired, but I'm fine" into "fatigue" and "relief." Input: emotion data analyzed by NLP, output: refined emotion data. Specifically, the emotion engine evaluates the analyzed emotion data and identifies more detailed emotions.

[1516] Step 4:

[1517] The server generates an interactive self-care guide based on the recognized emotions. For example, if the emotion engine recognizes "fatigue," it will provide advice such as "try taking deep breaths" as a way to relax. Input: subdivided emotion data, output: self-care guide message. The specific operation is to create a self-care guide message based on the results of the emotion engine.

[1518] Step 5:

[1519] The generated self-care guide is sent to the device and notified to the user. The user can view relaxation methods and self-care suggestions on their smartphone screen. Input: Self-care guide message, Output: Message displayed on the device screen. The specific operation is that the server sends the generated message to the device, and the device displays it.

[1520] Step 6:

[1521] The server then rechecks the analysis results and determines whether professional psychological assistance is required. For example, if the emotion engine recognizes "sadness" or "fear," it determines that professional assistance is required and generates an alert. Input: segmented emotion data, output: alert message. The specific operation is to evaluate the analysis results and generate an alert message if necessary.

[1522] Step 7:

[1523] The server periodically generates reports on the user's mental health status and sends them to the company or industrial physician. The reports are created based on the user's response history and the emotion recognition results of the emotion engine. Input: User's response history and recognition results, Output: Mental health report. The specific operation is to aggregate the data, generate a report, and send it.

[1524] Through these steps, this system can support users' mental health in real time at each store.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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).

[1532] 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.

[1533] 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."

[1534] 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.

[1535] 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).

[1536] 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.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] The following is further disclosed regarding the above embodiment.

[1547] (Claim 1)

[1548] means for collecting input data from a user;

[1549] A means for analyzing the collected data using natural language processing;

[1550] means for generating an interactive self-care guide based on the analysis results;

[1551] a means for providing the generated self-care guide to a user;

[1552] Based on the analysis results, a means of determining whether professional psychological assistance is required and sending an alert if necessary;

[1553] A system including:

[1554] (Claim 2)

[1555] 10. The system of claim 1,

[1556] A system that further includes measures to regularly check mental health status and suggest appropriate self-care if necessary.

[1557] (Claim 3)

[1558] 10. The system of claim 1,

[1559] The system further includes means for generating a report on the user's mental health status and sending it to a company or industrial physician.

[1560] "Example 1"

[1561] (Claim 1)

[1562] means for collecting input data from a user;

[1563] A means for analyzing the collected data using natural language processing;

[1564] means for generating an interactive self-care guide based on the analysis results;

[1565] a means for providing the generated self-care guide to a user;

[1566] A means of determining whether sensitive topics are included based on the analysis results; and

[1567] A means to determine whether professional psychological assistance is needed and to send an alert if necessary;

[1568] A means for generating a report on the user's mental health status and sending it to a company or industrial physician;

[1569] A system including:

[1570] (Claim 2)

[1571] 10. The system of claim 1, further comprising means for periodically checking mental health status and suggesting appropriate self-care as needed.

[1572] (Claim 3)

[1573] 10. The system of claim 1, further comprising means for inputting the generated prompt sentence into an AI model to analyze the user's mental health state.

[1574] "Application Example 1"

[1575] (Claim 1)

[1576] means for collecting input data from a user;

[1577] A means for analyzing the collected data using natural language processing;

[1578] means for generating an interactive self-care guide based on the analysis results;

[1579] a means for providing the generated self-care guide to a user;

[1580] Based on the analysis results, a means of determining whether professional psychological assistance is required and sending an alert if necessary;

[1581] A means for providing mental check questions and collecting responses from users using hardware with speech recognition capabilities;

[1582] A system including:

[1583] (Claim 2)

[1584] 10. The system of claim 1, further comprising means for periodically checking mental health status and suggesting appropriate self-care as needed.

[1585] (Claim 3)

[1586] 10. The system of claim 1, further comprising means for generating a report on the user's mental health status and sending the report to a company or industrial physician.

[1587] "Example 2: Combining Emotion Engines"

[1588] (Claim 1)

[1589] means for collecting input data from a user;

[1590] A means for analyzing the collected data using natural language processing;

[1591] means for generating an interactive self-care guide based on the analysis results;

[1592] a means for providing the generated self-care guide to a user;

[1593] Based on the analysis results, a means of determining whether professional psychological assistance is required and sending an alert if necessary;

[1594] means for identifying a user's emotion with a natural language processing module and an emotion engine;

[1595] A system including:

[1596] (Claim 2)

[1597] 10. The system of claim 1, further comprising means for periodically checking mental health status and suggesting appropriate self-care as needed.

[1598] (Claim 3)

[1599] 10. The system of claim 1, further comprising means for generating a report on the user's mental health status and sending the report to a company or industrial physician.

[1600] "Application example 2 when combining emotion engines"

[1601] (Claim 1)

[1602] means for collecting input data from a user;

[1603] A means for analyzing the collected data using natural language processing;

[1604] means for generating an interactive self-care guide based on the analysis results;

[1605] a means for providing the generated self-care guide to a user;

[1606] Based on the analysis results, a means of determining whether professional psychological assistance is required and sending an alert if necessary;

[1607] A means of suggesting individual products and providing information on services based on user data collected in real commercial facilities;

[1608] means for recommending self-care guides or refreshment methods in real time based on the detected mental health state;

[1609] A system including:

[1610] (Claim 2)

[1611] 10. The system of claim 1, further comprising means for periodically checking mental health status and suggesting appropriate self-care as needed.

[1612] (Claim 3)

[1613] 10. The system of claim 1, further comprising means for generating and sending a report on the user's mental health status to a company or occupational physician. [Explanation of symbols]

[1614] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting input data from a user; A means for analyzing the collected data using natural language processing; means for generating an interactive self-care guide based on the analysis results; a means for providing the generated self-care guide to a user; Based on the analysis results, a means of determining whether professional psychological assistance is required and sending an alert if necessary; A system including:

2. 10. The system of claim 1, A system that further includes measures to regularly check mental health status and suggest appropriate self-care if necessary.

3. 10. The system of claim 1, The system further includes means for generating a report on the user's mental health status and sending it to a company or industrial physician.

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