System

Abstract

A system is provided.SOLUTION: A system comprising: a AI counselor; means for receiving and analyzing text data entered by a user and generating an appropriate response; means for analyzing the interaction to detect a cognitive distortion of the user; and means for generating and providing the user with advice and / or tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortion.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] Currently, the number of people suffering from mental disorders is rapidly increasing worldwide, significantly impacting productivity and employee happiness, particularly in companies. Despite many companies conducting stress checks and employing industrial physicians, many people with high stress levels remain silent about their mental health problems due to the negative social stigma surrounding mental disorders. The existence of these hidden high-stress individuals delays the prevention and treatment of mental illness, resulting in enormous losses for companies. Therefore, as a preventative measure against mental disorders, there is a need for a system that provides an environment where users can easily seek advice and that can appropriately manage and improve their mental health. [Means for solving the problem]

[0005] To address these challenges, the present invention provides a system equipped with an AI counselor. This system includes a means for receiving and analyzing text data entered by a user and generating appropriate responses. It also includes a means for analyzing dialogue data to detect cognitive distortions in the user and, based on the results, generating advice and tasks based on cognitive behavioral therapy and providing them to the user. The system also includes a means for receiving and analyzing user feedback to improve the system, a means for anonymizing and compiling and displaying the mental health status of all employees to company managers, a means for generating an AI counselor profile that can be freely customized by the user, and a means for deleting dialogue data at the user's request. This provides an environment in which users feel comfortable discussing their mental health issues, improving their mental health by correcting cognitive distortions, and preventing mental illness in hidden, high-stress individuals.

[0006] "AI Counselor" is a virtual counselor system that uses artificial intelligence to provide mental care through dialogue with users.

[0007] "Text data" refers to the string of characters entered by the user, which forms part of the dialogue with the AI ​​counselor.

[0008] "Analysis" refers to the process by which the system analyzes text data and dialogue data to extract information and detect user emotions and cognitive distortions.

[0009] "Response" or "means for generating a response" refers to a function that provides an appropriate reply or advice to the user based on the analysis results.

[0010] "Dialogue data" is a collection of text data exchanged between the user and the AI ​​counselor.

[0011] "Cognitive distortion" refers to biases or misperceptions in a user's thinking, which can lead to stress and psychological disorders.

[0012] "Cognitive behavioral therapy" is a type of psychotherapy that corrects users' cognitive distortions and leads them to positive thought patterns.

[0013] "Advice and tasks" are specific instructions and advice for actions that users can take to improve their own mental health.

[0014] "Feedback" refers to users providing their opinions and impressions regarding the effectiveness and usability of the system.

[0015] "Corporate administrator" refers to the individual or department responsible for managing the mental health of employees within a company.

[0016] "Anonymization" is the act of processing data so that individual users cannot be identified.

[0017] "Customization" refers to the ability of users to change the AI ​​counselor's profile and settings according to their own preferences.

[0018] A "profile" is a set of information that defines an AI counselor's personality, speaking style, appearance, etc., and users can use this information to customize their AI counselor.

[0019] "Deletion" is the act of completely removing specific interaction data within the system when requested by the user.

[0020] "Mental care" refers to support and efforts to maintain and improve users' psychological health. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. Furthermore, it receives user feedback and anonymizes, aggregates, and displays the mental health status of all employees to company managers.

[0043] Program processing overview

[0044] To realize this system, the following program processing is performed.

[0045] 1. The user launches the app

[0046] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[0047] Terminal: Sends a login request to the server.

[0048] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[0049] Terminal: Presents the main screen to the user.

[0050] 2. Start a conversation with an AI counselor

[0051] User: Enters "I'm tired from work today" in the text box and presses the send button.

[0052] Terminal: Sends text data entered by the user to the server.

[0053] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[0054] Server: Generates and sends the response to the device.

[0055] Terminal: Displays the AI ​​counselor's response message to the user.

[0056] 3. Analyze users' cognitive distortions

[0057] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[0058] Terminal: Periodically transmits collected interaction data to the server.

[0059] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[0060] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[0061] Terminal: Displays the visual data of the analysis results to the user.

[0062] 4. Provide cognitive behavioral therapy support

[0063] Server: Generates individually customized cognitive behavioral therapy approaches and specific advice based on the results of cognitive distortion analysis.

[0064] Server: Sends generated advice and exercises to the device.

[0065] Device: Displays advice and exercise instructions to the user.

[0066] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[0067] 5. Feedback and Improvement

[0068] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[0069] Device: Sends collected feedback data to the server.

[0070] Server: We store your feedback in a database, analyze it and use it to improve our services.

[0071] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[0072] 6. Provision of enterprise features

[0073] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[0074] Server: Sends the generated dashboard data to the enterprise administrator portal.

[0075] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[0076] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[0077] Specific examples

[0078] As a specific example, suppose a user types, "Recently, things haven't been going well at work and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you are feeling specifically." The user then continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice such as, "Try writing down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0079] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent mental illness in people with hidden high levels of stress.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] A user launches the app on their smartphone or computer and logs in with their account information, including their email address and password.

[0083] Step 2:

[0084] The terminal sends a login request from the user to the server, where the login information is encrypted using a security protocol.

[0085] Step 3:

[0086] The server analyzes the received login information and authenticates the user by comparing it with the account information in the database. If authentication is successful, a login session is established.

[0087] Step 4:

[0088] The server sends the main screen data to the device, which includes an interface for starting a conversation with the AI ​​counselor.

[0089] Step 5:

[0090] The device displays the main screen to the user, where the user enters "I'm tired from work today" in the text box and presses the send button.

[0091] Step 6:

[0092] The terminal transmits the user's input text data to the server, where the text data is packaged in an appropriate format.

[0093] Step 7:

[0094] The server's text analysis engine receives the input text and performs an analysis, which evaluates the user's emotions and stress level.

[0095] Step 8:

[0096] The server uses the generative AI model to generate an appropriate response. For example, if the input text is "I'm tired from work today," it generates the response "Tell me specifically what kind of work you did."

[0097] Step 9:

[0098] The server generates a response and sends it to the terminal. The response message is sent in text format.

[0099] Step 10:

[0100] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[0101] Step 11:

[0102] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[0103] Step 12:

[0104] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[0105] Step 13:

[0106] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[0107] Step 14:

[0108] The device displays the analysis results visual data to the user, who then sees the message "There is a tendency for generalization" on the dashboard.

[0109] Step 15:

[0110] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[0111] Step 16:

[0112] The device displays advice to the user, who then follows the advice and lists their successes.

[0113] Step 17:

[0114] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[0115] Step 18:

[0116] The device sends the collected feedback data to the server, which can be used to improve the service.

[0117] Step 19:

[0118] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[0119] Step 20:

[0120] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[0121] Step 21:

[0122] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[0123] Step 22:

[0124] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[0125] Example 1

[0126] 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."

[0127] Conventional mental health support systems lack an easy-to-use environment for users, do not provide real-time feedback, and make it difficult for corporate managers to grasp the mental health status of all employees. Furthermore, they lack the means to properly detect cognitive distortions and provide individually customized cognitive behavioral therapy approaches. To solve these issues, it is necessary to provide a system that is both useful and easy to use for both users and companies.

[0128] 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.

[0129] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response using a generative model; means for analyzing dialogue data to detect cognitive distortions in the user; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; and means for periodically collecting the user's dialogue data and generating the analysis results as visual data. This allows users to easily understand their own mental health status and receive appropriate support. Corporate managers can also anonymize and understand the mental health status of their employees and take appropriate measures.

[0130] The "AI Counselor" is an artificial intelligence system that analyzes text data entered by users and generates appropriate responses using a generative model.

[0131] "Text data" refers to character information that a user inputs through an application.

[0132] A "generative model" is a machine learning model used to generate appropriate responses based on input data.

[0133] "Dialogue data" refers to the text-based communication between the user and the AI ​​counselor.

[0134] "Cognitive distortions" are irrational thinking patterns and misperceptions that users have, which can have a negative impact on their mental health.

[0135] "Cognitive behavioral therapy" is a type of psychotherapy that corrects cognitive distortions and encourages more adaptive behavior.

[0136] "Visual data" refers to graphs and charts that display analysis results in a visually easy-to-understand manner.

[0137] "Feedback" refers to information that users can input about their experience and thoughts on using the system.

[0138] A "dashboard" is a visual display screen designed to allow administrators to grasp system usage and analysis results at a glance.

[0139] A "corporate administrator" is a person within a company who is responsible for overseeing and managing the mental health of employees.

[0140] "Anonymization" is the process of processing data so that individual users' information cannot be identified.

[0141] "Customization" refers to the ability of users to tailor the system's features and profiles to suit their own preferences and needs.

[0142] "Profile" means your account data, including your personal information and settings.

[0143] "Periodic collection" refers to collecting data at regular intervals, with the aim of continuously reflecting updated information from users.

[0144] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. It also receives user feedback and anonymizes and aggregates the mental health status of all employees for corporate managers.

[0145] The specific steps to implement this are shown below.

[0146] Program processing overview

[0147] 1. Launch the app and log in

[0148] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and authenticates the account information in a database. If authentication is successful, a login session is established, and main screen data is generated and sent to the device. The device displays the main screen data received from the server to the user.

[0149] 2. Start a conversation with an AI counselor

[0150] The user enters a message in the text box and presses the send button. The device sends the text data entered by the user to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model (e.g., OpenAI's GPT-3). The server then sends the generated response to the device, which displays the response message to the user.

[0151] 3. Analysis of cognitive distortions

[0152] Through daily interactions, users enter detailed information about stressful or difficult situations. The device periodically transmits the collected dialogue data to the server. The server inputs the dialogue data into a cognitive distortion analysis module and uses natural language processing technology to detect patterns of cognitive distortion from the user's text. The server generates the results of the cognitive distortion analysis as visual data and reflects this in each user's profile along with their dialogue history. The server then transmits the analysis results, including the visual data, to the device, which then displays the visual data of the cognitive distortion analysis results to the user.

[0153] 4. Providing cognitive behavioral therapy advice

[0154] Based on the results of the cognitive distortion analysis, the server generates an individually customized cognitive behavioral therapy approach and specific advice. Specific exercises and ways of thinking are suggested using the generative AI model and existing CBT manuals. The server then sends the generated advice and exercises to the device, which then displays the advice and exercise instructions to the user. The user follows the advice provided, reassessing their way of thinking and behavior, and engaging in the exercises. This may include writing a diary or trying relaxation techniques.

[0155] 5. Collecting feedback and improving our services

[0156] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device then sends the feedback data from the user to the server. The server stores the received feedback in a database and analyzes the feedback data to help improve the service. The server then generates the feedback results in the form of a dashboard for company administrators and sends it to the administrator portal.

[0157] 6. Provision of enterprise features

[0158] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this data. The generated dashboard data is sent to a portal for company administrators. Company administrators log in to the portal and check the dashboard to understand employee stress levels and the effectiveness of mental health care. Based on the obtained data, company administrators can take specific actions to strengthen and improve employee mental health measures.

[0159] Specific examples

[0160] When a user types, "Recently, things haven't been going well at work and I'm feeling stressed," the server analyzes the input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." If the user continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me," the server detects the cognitive distortion known as "generalization." The server displays the message "You have a tendency to generalize" on the user's dashboard, and further generates advice, "Try writing down three successful experiences," which is displayed on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0161] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1:

[0164] Launching the app and logging in

[0165] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and performs authentication by checking the account information against a database. It receives the account information as input and outputs the authentication result. If authentication is successful, the server establishes a login session, generates main screen data, and sends it to the device. The device displays the main screen data received from the server to the user.

[0166] Step 2:

[0167] Start a conversation with an AI counselor

[0168] The user enters a message in the text box and presses the send button. The device sends the text data to the server. The server inputs the entered text data into a text analysis engine and performs text analysis. It receives the user's message as input and outputs the analysis results. It passes the analysis results to a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. It outputs a response message and sends it to the device. The device displays the response message received from the server to the user.

[0169] Step 3:

[0170] Analysis of cognitive distortions

[0171] The user enters detailed information about stressful or difficult situations through daily interactions. The device periodically sends the collected interaction data to the server. The server inputs the interaction data into a cognitive distortion analysis module, which uses natural language processing technology to detect cognitive distortion patterns from the user's text. The server receives the interaction data as input and outputs distortion patterns and analysis results. The server generates visual data of the cognitive distortion analysis results and sends it to the device. The device displays the visual data and informs the user of the cognitive distortion analysis results.

[0172] Step 4:

[0173] Providing cognitive behavioral therapy advice

[0174] The server generates an individually customized cognitive behavioral therapy approach and specific advice based on the results of the cognitive distortion analysis. It uses the analysis results as input and outputs advice and exercise content. The generated advice and exercise content are sent to the device. The device displays the advice and exercise instructions to the user. The user follows the advice provided, revises their way of thinking and behavior, and engages in the exercises.

[0175] Step 5:

[0176] Collecting feedback and improving our services

[0177] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device sends the feedback data from the user to the server. The server stores the feedback data in a database, analyzes it, and uses it to improve the service. It collects the feedback data as input and outputs the analysis results. The server also generates the feedback results in dashboard format for company administrators and sends them to the administrator portal.

[0178] Step 6:

[0179] Providing enterprise features

[0180] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this. It receives usage and measurement data as input and outputs dashboard data. The generated dashboard data is sent to a portal for company administrators. The company administrator logs in to the portal and checks the dashboard. Based on the obtained data, the company administrator takes specific actions to strengthen and improve employee mental health measures.

[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 modern society, many people suffer from mental health issues due to work and life stress. In the workplace in particular, employees often experience stress and mental disorders, which can result in decreased productivity and increased risk of employee turnover. There is a need for a system that can effectively monitor employees' mental health in real time and provide appropriate responses. However, conventional systems have difficulty analyzing mental health status in real time or responding immediately on-site. Furthermore, corporate managers have limited means of understanding the mental health status of their entire workforce. This poses a challenge, making it difficult to intervene early to prevent mental health problems.

[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 receiving and analyzing text data entered by a user and generating an appropriate response; means for analyzing the dialogue data to detect the user's cognitive distortions; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; means for analyzing the user's statements and actions in real time using speech recognition and natural language processing to detect the user's mental health status; means for proposing countermeasures based on the detected mental health status; and means for displaying the feedback results as visual data. This enables real-time analysis of mental health status and immediate response. It also makes it easier for corporate managers to grasp the mental health status of their entire workforce, enabling early intervention and appropriate support.

[0186] "AI Counselor" is a system that uses artificial intelligence to analyze users' text data and generate appropriate responses and advice.

[0187] "User cognitive distortions" refer to cognitive biases such as biased thinking, misunderstandings, and overgeneralizations held by users.

[0188] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct cognitive distortions and improve behavior and thinking.

[0189] "Speech recognition" is a technology that analyzes voice data and converts it into text data.

[0190] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[0191] "Real-time analysis" is the process of instantly analyzing user input and providing immediate feedback on the results.

[0192] "Mental health status" refers to the mental and psychological well-being of a user.

[0193] "Responses" are specific measures or advice suggested based on the detected mental health condition.

[0194] "Feedback results" refers to reaction data such as user evaluations and impressions, which are analyzed and used to improve the system.

[0195] "Visual data" is graphical information used to visually display analysis results and feedback.

[0196] This invention is a system equipped with an AI counselor that provides real-time mental health support and offers appropriate solutions to users. The system works in conjunction with smart glasses, a server, and other devices.

[0197] The server receives and analyzes the text data entered by the user and generates an appropriate response. It uses speech recognition technology (e.g., Google Speech Recognition API) to convert the user's speech into text data. It then analyzes the user's statements and actions using speech recognition and natural language processing technology (e.g., OpenAI's generative AI model) to detect their mental health status in real time. Based on the detected cognitive distortions and mental health status, it generates and provides advice and tasks based on cognitive behavioral therapy to the user.

[0198] The server proposes countermeasures based on the detected mental health status and displays the feedback results as visual data, which can be displayed on the user's smart glasses or other display devices using graphical display technologies such as Matplotlib.

[0199] For example, if a user says through smart glasses, "I've been feeling tired lately because of stress at work," the speech recognition technology converts this into text data. The server inputs the prompt sentence into the generative AI model and analyzes the speech:

[0200] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[0201] Based on the analysis results, the server generates specific advice such as, "This person is experiencing significant work-related stress. We recommend talking to them to relieve stress and encouraging them to take a short break." The generated advice is provided to the user in real time.

[0202] The server can also aggregate and display anonymized mental health status data for all employees to company administrators, allowing them to grasp the overall picture of mental health status and take necessary measures.

[0203] This system overcomes the difficulties of real-time monitoring and immediate response that existed in conventional mental health support systems, making it possible to efficiently manage and improve the mental health status of employees.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] A user wears smart glasses and speaks, which is collected as voice data by the terminal via the smart glasses' microphone.

[0207] Input: User's voice data

[0208] Output: Audio data sent to the device

[0209] Specific operation: The user says, "I've been feeling tired lately because of stress at work." The microphone in the smart glasses recognizes the voice and sends the voice data to the device.

[0210] Step 2:

[0211] The device receives the voice data and converts it into text data using voice recognition technology (e.g., Google Speech Recognition API).

[0212] Input: Audio data

[0213] Output: Text data

[0214] Specific operation: The device recognizes "I've been feeling tired lately because of work stress" and converts this into "text data."

[0215] Step 3:

[0216] The terminal transmits the converted text data to the server.

[0217] Input: Text data

[0218] Output: Send text data to the server

[0219] Specific operation: The terminal transfers the generated text data to the server.

[0220] Step 4:

[0221] The server receives the text data and analyzes the text using natural language processing techniques (e.g., OpenAI's generative AI model).

[0222] Input: Text data

[0223] Output: Analysis results

[0224] Specific operation: The server analyzes the "text data" and understands the information "I've been feeling tired lately because of work stress." Then, it analyzes it using the following prompt sentence for the generative AI model:

[0225] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[0226] Step 5:

[0227] Based on the analysis results, the server detects cognitive distortions and signs of stress and generates appropriate countermeasures and advice.

[0228] Input: Analysis results

[0229] Output: Countermeasures, advice

[0230] Specific action: The server generates advice such as "We recommend talking to the person to relieve stress and encouraging them to take a short break."

[0231] Step 6:

[0232] The server sends the generated advice and countermeasures to the terminal.

[0233] Input: Measures, advice

[0234] Output: Send advice and solutions to your device

[0235] Specific operation: The server transfers the generated advice to the terminal.

[0236] Step 7:

[0237] The device then displays the received advice and solutions to the user in real time, and these are presented to the user through the smart glasses display.

[0238] Input: Measures, advice

[0239] Output: Shown to the user

[0240] Specific action: The device displays the advice "We recommend talking to someone to relieve stress or taking a short break" on the smart glasses display.

[0241] Step 8:

[0242] The user inputs the result of the advice received as feedback and transmits it to the terminal.

[0243] Input: Feedback data

[0244] Output: Send feedback data to the terminal

[0245] Specific operation: The user inputs "The advice was helpful" through the smart glasses, and the device sends this to the server.

[0246] Step 9:

[0247] The server collects feedback data from users and uses it to improve the system and measure its effectiveness.

[0248] Input: Feedback data

[0249] Output: analysis results, improvement suggestions

[0250] Specific operation: The server analyzes the feedback data and identifies areas for improvement in the system. It also generates visual data and sends it to the company administrator's portal to display the aggregated results of mental health status to the company administrator.

[0251] 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.

[0252] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts the response, providing more personalized mental care. This system analyzes the user's dialogue data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts responses and advice based on the recognition results.

[0253] Program processing overview

[0254] To realize this system, the following program processing is performed.

[0255] 1. The user launches the app

[0256] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[0257] Terminal: Sends a login request to the server.

[0258] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[0259] Terminal: Presents the main screen to the user.

[0260] 2. Start a conversation with an AI counselor

[0261] User: Enters "I'm tired from work today" in the text box and presses the send button.

[0262] Terminal: Sends text data entered by the user to the server.

[0263] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[0264] Server: Generates and sends the response to the device.

[0265] Terminal: Displays the AI ​​counselor's response message to the user.

[0266] 3. Recognize user emotions

[0267] Server: The emotion engine recognizes the user's emotion from the input text. For example, it recognizes the feeling of fatigue from the text "I'm tired."

[0268] Server: Sends the recognized emotion data to the device in an appropriate format and displays the user's emotional state.

[0269] 4. Analyze users' cognitive distortions

[0270] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[0271] Terminal: Periodically transmits collected interaction data to the server.

[0272] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[0273] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[0274] Terminal: Displays the visual data of the analysis results to the user.

[0275] 5. Provide cognitive behavioral therapy support

[0276] Server: Using the cognitive distortion analysis results and emotion engine data together, it generates individually customized cognitive behavioral therapy approaches and specific advice.

[0277] Server: Sends generated advice and exercises to the device.

[0278] Device: Displays advice and exercise instructions to the user.

[0279] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[0280] 6. Feedback and Improvement

[0281] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[0282] Device: Sends collected feedback data to the server.

[0283] Server: We store your feedback in a database, analyze it and use it to improve our services.

[0284] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[0285] 7. Provision of enterprise features

[0286] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[0287] Server: Sends the generated dashboard data to the enterprise administrator portal.

[0288] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[0289] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[0290] Specific examples

[0291] As a specific example, suppose a user types, "Work hasn't been going well lately, and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine recognizes a high level of stress from the text, "I'm feeling stressed," and adapts the response accordingly. The user then types, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice, such as, "Write down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0292] In this way, this system takes into consideration the user's emotions and provides an environment in which it is easy to seek advice about mental health issues. By correcting cognitive distortions, this system can improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[0293] The processing flow will be explained below.

[0294] Step 1:

[0295] The user taps the app on their smartphone or computer to launch it, then enters their account information (email address and password) to log in.

[0296] Step 2:

[0297] The terminal sends a login request from the user to the server. The login information is encrypted using the SSL / TLS protocol.

[0298] Step 3:

[0299] The server parses the received login information and matches it with the account information in its database. If successful, a login session is established.

[0300] Step 4:

[0301] The server sends the main screen data to the device, which displays an interface for starting a conversation with the AI ​​counselor.

[0302] Step 5:

[0303] The device displays the main screen to the user, who enters "I'm tired from work today" in the text box and presses the send button.

[0304] Step 6:

[0305] The terminal sends the user's input text data to the server, which packages the text data in an appropriate format.

[0306] Step 7:

[0307] The server's text analysis engine receives the input text and analyzes it, extracting keywords and sentiments from the text.

[0308] Step 8:

[0309] The server's emotion engine recognizes the user's emotional state based on the extracted keywords and emotions. For example, it recognizes fatigue from the text "tired."

[0310] Step 9:

[0311] The server uses the generative AI model to generate an appropriate response based on the emotion recognition results, such as "Tell me specifically what kind of work you did."

[0312] Step 10:

[0313] The server sends the generated response to the terminal. The response message is sent in text format.

[0314] Step 11:

[0315] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[0316] Step 12:

[0317] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[0318] Step 13:

[0319] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[0320] Step 14:

[0321] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[0322] Step 15:

[0323] The device displays the analysis results visual data to the user, who then sees a message on the dashboard saying, "You have a tendency toward generalization."

[0324] Step 16:

[0325] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[0326] Step 17:

[0327] The device displays advice to the user, who can then follow the advice and list their successes, thereby recognizing their positive aspects.

[0328] Step 18:

[0329] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[0330] Step 19:

[0331] The device sends the collected feedback data to the server, which can be used to improve the service.

[0332] Step 20:

[0333] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[0334] Step 21:

[0335] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[0336] Step 22:

[0337] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[0338] Step 23:

[0339] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[0340] Example 2

[0341] 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."

[0342] Conventional mental health systems were able to generate appropriate responses based on user input data, but they had difficulty recognizing individual emotional states and adjusting responses accordingly. They also had limited functionality for detecting cognitive distortions and providing appropriate cognitive behavioral therapy (CBT) advice. Furthermore, there were insufficient means for companies to provide data that would allow them to effectively understand employees' mental health conditions and take appropriate measures. To resolve these issues, a system that provides more accurate and personalized mental health care is needed.

[0343] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0344] In this invention, the server includes means for receiving and analyzing text data entered by a user and generating an appropriate response, means for analyzing dialogue data to detect cognitive distortions in the user, means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user, means for recognizing emotions from the input text, and means for adjusting the response and advice based on the recognized emotional data. This enables highly accurate and personalized mental care, responses to cognitive distortions, and the provision of data to companies.

[0345] "User" refers to an individual who uses a system or application.

[0346] "Text data" refers to textual information that a user inputs into a system.

[0347] A "server" refers to a computer system that performs processes such as analyzing data and generating responses.

[0348] "Analysis" refers to the process of analyzing input data and identifying its content and characteristics.

[0349] "Response" refers to a reply message generated by the system in response to a user's input.

[0350] "Dialogue data" refers to the history and content of messages exchanged between a user and the system.

[0351] "Cognitive distortion" refers to biases or misunderstandings in a user's thinking or perception.

[0352] "Cognitive behavioral therapy (CBT)" refers to psychological therapy aimed at correcting cognitive distortions and promoting adaptive behavior.

[0353] "Emotion" refers to the user's psychological state or mood.

[0354] "Emotion engine" refers to a system component for recognizing and analyzing emotions from user text data.

[0355] "Feedback" refers to the opinions and impressions provided by users after using the system.

[0356] A "corporate administrator" refers to a person in charge of managing the entire system and evaluating mental health within a company.

[0357] A "dashboard" refers to an interface that visualizes and displays data such as system usage and effectiveness measurements.

[0358] "Customization" refers to the ability for users to change system settings and profiles to suit their own needs and preferences.

[0359] "Profile" refers to a data set of information and settings related to a user.

[0360] "Advice" refers to guidance or suggestions provided to the user by the system.

[0361] A "task" refers to a specific task or exercise that a user must perform.

[0362] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts responses accordingly, providing more personalized mental care. The system analyzes user interaction data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. It also has a function that aggregates employee mental health status and provides it in the form of a dashboard for corporate managers.

[0363] Specific program description

[0364] Hardware and software used

[0365] Hardware: smartphones, PCs, servers

[0366] Software: Text analysis engines, sentiment engines, generative AI models (e.g., GPT-3), database management systems, dashboard generation tools

[0367] Details of data processing and calculation

[0368] 1. Analyzing user input data

[0369] A user launches the app on their smartphone or PC and logs in with their account information. The device sends a login request to the server, which then authenticates the user. If authentication is successful, the server sends the main screen to the device, which then displays the main screen to the user. The user then enters dialogue text, which the device then sends to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model.

[0370] 2. Emotion recognition

[0371] The server uses an emotion engine to recognize the user's emotions from the input text. For example, the phrase "tired" identifies fatigue or stress. The recognized emotion data is sent to the terminal in an appropriate format, and the user's emotional state is displayed.

[0372] 3. Analysis of cognitive distortions

[0373] The system collects daily user interaction data and periodically sends it to a server. The server then inputs the interaction data into a cognitive distortion analysis module, which uses data mining techniques to detect the user's cognitive distortions. The detection results are generated as visual data, sent to the device, and displayed to the user.

[0374] 4. Support for cognitive behavioral therapy (CBT)

[0375] The server combines the results of the cognitive distortion analysis with data from the emotion engine to generate individually customized CBT advice and tasks. The generated advice and exercises are sent to the user's device and displayed. The user follows the advice, changes their thoughts and behavior, and engages in the exercises.

[0376] 5. Gathering feedback and improving the system

[0377] Users enter their feedback into a survey form within the app, and the device sends the data to the server. The server stores the feedback in a database, analyzes it, and uses it to improve the service. The feedback results are also provided to company administrators in the form of a dashboard, allowing them to measure the effectiveness of the service.

[0378] 6. Enterprise Features

[0379] The server aggregates usage and effectiveness data for each company and generates a dashboard. This dashboard data is sent to a portal for company administrators, who can analyze employee stress levels and the effectiveness of mental care. Based on the data obtained, company administrators can take action to strengthen and improve employee mental health measures.

[0380] Examples of concrete examples and prompts

[0381] For example, if a user enters "Recently, things haven't been going well at work and I'm feeling stressed," the server will analyze this input text and respond with, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine will recognize a high level of stress from the text "I'm feeling stressed," and tailor the response accordingly.

[0382] Example prompt sentence:

[0383] "Recently, things haven't been going well at work and I'm feeling stressed."

[0384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0385] Step 1:

[0386] The user launches the app.

[0387] Users tap the app on their smartphone or computer to launch it and display the login screen.

[0388] Input: Login request (user ID and password)

[0389] Output: A login request is sent to the server.

[0390] Step 2:

[0391] The terminal sends a login request to the server.

[0392] When the login button is pressed, the terminal transmits the user's authentication information to the server.

[0393] Input: User authentication information (user ID and password)

[0394] Output: The authentication information reaches the server.

[0395] Step 3:

[0396] The server authenticates the user.

[0397] The server checks the received authentication information against a database and generates an authentication result.

[0398] Input: Authentication information that reaches the server

[0399] Output: Authentication result (success or failure)

[0400] Step 4:

[0401] The server establishes a login session and sends the main screen to the terminal.

[0402] If the authentication is successful, the server starts a session for the user, generates main screen data, and sends it to the terminal.

[0403] Input: Authentication success result

[0404] Output: Main screen data

[0405] Step 5:

[0406] The terminal displays the main screen to the user.

[0407] The terminal displays the received main screen data on the user interface.

[0408] Input: Main screen data

[0409] Output: Main screen display

[0410] Step 6:

[0411] The user begins a conversation with the AI ​​counselor.

[0412] The user enters the dialogue text in the text box and presses the send button.

[0413] Input: User dialogue text (e.g., "I'm tired from work today.")

[0414] Output: The dialogue text is sent from the terminal to the server.

[0415] Step 7:

[0416] The server analyzes the dialogue text.

[0417] The server's text analysis engine breaks down the dialogue text and recognizes context and sentiment.

[0418] Input: Dialogue text

[0419] Output: Analysis results (context and sentiment data)

[0420] Step 8:

[0421] The server generates an appropriate response using a generative AI model.

[0422] The server uses a generative AI model (e.g., GPT-3) to generate an appropriate response based on the analysis results.

[0423] Input: Analysis results (context and emotion data)

[0424] Output: Response message

[0425] Step 9:

[0426] The server sends the generated response to the terminal.

[0427] The server sends the generated response message to the terminal.

[0428] Input: Response message

[0429] Output: The response message arrives at the terminal.

[0430] Step 10:

[0431] The terminal displays the response message to the user.

[0432] The terminal displays the received response message on the user interface.

[0433] Input: Response message

[0434] Output: Display of response message

[0435] Step 11:

[0436] The server recognizes emotions from the input text.

[0437] The server's emotion engine analyzes the input text and identifies the user's emotional state.

[0438] Input: Dialogue text

[0439] Output: Emotion data (e.g., fatigue, stress)

[0440] Step 12:

[0441] The server transmits the emotion data to the terminal.

[0442] The server transmits the recognized emotion data to the terminal.

[0443] Input: Emotion data

[0444] Output: Emotion data arrives at the terminal.

[0445] Step 13:

[0446] The terminal displays the emotional state to the user.

[0447] The device visualizes the received emotion data and displays it on a user interface.

[0448] Input: Emotion data

[0449] Output: Display of emotional state

[0450] (Application example 2)

[0451] 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."

[0452] Conventional mental healthcare systems have difficulty recognizing users' emotions in real time and providing appropriate advice, and have faced particular challenges in terms of insufficient on-site immediate response and continuous monitoring functions. Furthermore, seamless data collection and analysis are required, but current systems have technical limitations. There is a particular need to improve the quality of mental health support in high-stress workplaces, such as security staff.

[0453] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically displaying questions about mental health and transmitting data of the user's responses to the server, means for recognizing the user's emotions in real time through conversation and facial expression recognition and providing appropriate relaxation methods and mental care advice when a high-stress state is detected, and means for collecting user feedback and analyzing the data to improve the quality of the service. This makes it possible to recognize the user's emotions in real time and provide appropriate, individually customized mental care.

[0454] "AI Counselor" is an artificial intelligence system that analyzes text data entered by the user and generates appropriate responses.

[0455] "Cognitive distortions" refer to incorrect cognitive patterns in a user's thinking, and the aim is to detect and correct these.

[0456] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct users' cognitive distortions and promote appropriate behavior.

[0457] "Smart glasses" are a type of electronic device worn by a user that can provide visual information and perform data collection and analysis.

[0458] "Mental health" refers to an individual's mental health status and aims to provide support to maintain or improve it.

[0459] "Relaxation methods" refer to techniques and activities that reduce stress and promote mental and physical relaxation in users.

[0460] "Feedback" refers to opinions and impressions provided by users after using the system, and collecting and analyzing this information will help improve the system.

[0461] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state in real time from their conversation and facial expressions.

[0462] The present invention relates to a mental health support system using smart glasses designed for security staff. When a user wears the smart glasses, the system periodically displays questions about mental health and transmits the user's responses to a server in real time. Specific embodiments for implementing the present invention are described below.

[0463] First, an application installed on the smart glasses periodically asks the user simple questions about their mental health, such as, "Please rate your current mood on a scale of 1 to 10." Once the user enters their information, the data is sent to a server via a wireless network.

[0464] The server runs an AI counselor to analyze the received data. This AI counselor uses a generative AI model to analyze the input data and generate appropriate responses and advice. The generated data is fed back to the smart glasses in real time.

[0465] In particular, the system is equipped with an emotion recognition engine that uses speech and facial expression recognition technology to recognize the user's emotions in real time. For example, if a user types "I'm tired," the emotion recognition engine will detect a high level of stress and provide immediate advice such as "Try taking a few minutes of rest and taking some deep breaths."

[0466] Furthermore, the server periodically analyzes this dialogue data and runs an algorithm to detect cognitive distortions. Based on the detected cognitive distortions, cognitive behavioral therapy (CBT) advice and tasks are generated. For example, if the cognitive distortion of "holism" is detected, the user is given specific advice such as "Try writing down three successful experiences."

[0467] The collected feedback data is analyzed to improve the quality of the service. Users can enter feedback on advice provided within the app, such as "This advice made me feel better," and this data is sent back to the server for analysis, which helps improve the service.

[0468] In a specific example of an embodiment, the following prompt may be presented:

[0469] "Rate your current mood from 1 to 10"

[0470] "Tell me about the most stressful thing that happened to you recently."

[0471] "Tell me about the most exhausting moment at work today."

[0472] The system of the present invention includes smart glasses, an AI counselor, an emotion recognition engine, and a cognitive behavioral therapy (CBT) module, and can provide personalized and appropriate mental health care to users in real time. In this way, high-quality mental health care support is realized while taking into account the user's emotional state.

[0473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0474] Step 1:

[0475] A user puts on smart glasses and launches a mental health check application. The device prompts the user, saying, "Please rate your current mood on a scale of 1 to 10." The user then inputs the rating score. This input data (rating score) is stored on the device and transmitted to a server via a wireless network.

[0476] Step 2:

[0477] The server analyzes the received data (evaluation score) and uses a generative AI model to determine the user's state. The system evaluates the user's current mental state based on the input data. For example, if the evaluation score is low, it will determine that the user is under high stress. Based on the analysis results, it generates an appropriate response or advice. This response data is sent to the device.

[0478] Step 3:

[0479] The device displays the response data received from the server to the user. For example, it displays advice such as "Take a few minutes break and try taking deep breaths." The user follows the displayed advice. The device collects how the user responded to the provided advice (e.g., thoughts and feedback).

[0480] Step 4:

[0481] The server uses speech and facial expression recognition technology to analyze the user's emotions in real time. For example, if the user types "I'm tired," the server uses an emotion recognition engine to detect a high level of stress. Based on the analysis results, it immediately generates appropriate countermeasures (e.g., relaxation techniques) and sends them to the device.

[0482] Step 5:

[0483] The device receives the solution from the server and displays it to the user. For example, a specific relaxation method such as "Take deep breaths repeatedly for one minute" is displayed. The user follows the solution and inputs the results and impressions into the device, which are then sent back to the server.

[0484] Step 6:

[0485] The server continuously collects dialogue data and performs data analysis to detect cognitive distortions. Based on the collected data, a cognitive distortion analysis algorithm is run to identify the cognitive distortions the user has. Based on the results of this analysis, cognitive behavioral therapy (CBT) advice and tasks are generated and sent to the device.

[0486] Step 7:

[0487] The device displays the generated CBT advice and tasks to the user. For example, a specific task such as "Write down three successful experiences" is presented. The user performs this task and inputs the results, which are then sent to the server via the device.

[0488] Step 8:

[0489] The server collects and analyzes user feedback and performs data analysis to improve the quality of the system. Based on the feedback, the server tunes the generative AI model and emotion recognition engine. This process improves the accuracy and usefulness of the advice and tasks provided.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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."

[0506] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. Furthermore, it receives user feedback and anonymizes, aggregates, and displays the mental health status of all employees to company managers.

[0507] Program processing overview

[0508] To realize this system, the following program processing is performed.

[0509] 1. The user launches the app

[0510] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[0511] Terminal: Sends a login request to the server.

[0512] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[0513] Terminal: Presents the main screen to the user.

[0514] 2. Start a conversation with an AI counselor

[0515] User: Enters "I'm tired from work today" in the text box and presses the send button.

[0516] Terminal: Sends text data entered by the user to the server.

[0517] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[0518] Server: Generates and sends the response to the device.

[0519] Terminal: Displays the AI ​​counselor's response message to the user.

[0520] 3. Analyze users' cognitive distortions

[0521] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[0522] Terminal: Periodically transmits collected interaction data to the server.

[0523] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[0524] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[0525] Terminal: Displays the visual data of the analysis results to the user.

[0526] 4. Provide cognitive behavioral therapy support

[0527] Server: Generates individually customized cognitive behavioral therapy approaches and specific advice based on the results of cognitive distortion analysis.

[0528] Server: Sends generated advice and exercises to the device.

[0529] Device: Displays advice and exercise instructions to the user.

[0530] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[0531] 5. Feedback and Improvement

[0532] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[0533] Device: Sends collected feedback data to the server.

[0534] Server: We store your feedback in a database, analyze it and use it to improve our services.

[0535] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[0536] 6. Provision of enterprise features

[0537] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[0538] Server: Sends the generated dashboard data to the enterprise administrator portal.

[0539] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[0540] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[0541] Specific examples

[0542] As a specific example, suppose a user types, "Recently, things haven't been going well at work and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you are feeling specifically." The user then continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice such as, "Try writing down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0543] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent mental illness in people with hidden high levels of stress.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] A user launches the app on their smartphone or computer and logs in with their account information, including their email address and password.

[0547] Step 2:

[0548] The terminal sends a login request from the user to the server, where the login information is encrypted using a security protocol.

[0549] Step 3:

[0550] The server analyzes the received login information and authenticates the user by comparing it with the account information in the database. If authentication is successful, a login session is established.

[0551] Step 4:

[0552] The server sends the main screen data to the device, which includes an interface for starting a conversation with the AI ​​counselor.

[0553] Step 5:

[0554] The device displays the main screen to the user, where the user enters "I'm tired from work today" in the text box and presses the send button.

[0555] Step 6:

[0556] The terminal transmits the user's input text data to the server, where the text data is packaged in an appropriate format.

[0557] Step 7:

[0558] The server's text analysis engine receives the input text and performs an analysis, which evaluates the user's emotions and stress level.

[0559] Step 8:

[0560] The server uses the generative AI model to generate an appropriate response. For example, if the input text is "I'm tired from work today," it generates the response "Tell me specifically what kind of work you did."

[0561] Step 9:

[0562] The server generates a response and sends it to the terminal. The response message is sent in text format.

[0563] Step 10:

[0564] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[0565] Step 11:

[0566] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[0567] Step 12:

[0568] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[0569] Step 13:

[0570] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[0571] Step 14:

[0572] The device displays the analysis results visual data to the user, who then sees the message "There is a tendency for generalization" on the dashboard.

[0573] Step 15:

[0574] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[0575] Step 16:

[0576] The device displays advice to the user, who then follows the advice and lists their successes.

[0577] Step 17:

[0578] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[0579] Step 18:

[0580] The device sends the collected feedback data to the server, which can be used to improve the service.

[0581] Step 19:

[0582] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[0583] Step 20:

[0584] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[0585] Step 21:

[0586] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[0587] Step 22:

[0588] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[0589] Example 1

[0590] 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."

[0591] Conventional mental health support systems lack an easy-to-use environment for users, do not provide real-time feedback, and make it difficult for corporate managers to grasp the mental health status of all employees. Furthermore, they lack the means to properly detect cognitive distortions and provide individually customized cognitive behavioral therapy approaches. To solve these issues, it is necessary to provide a system that is both useful and easy to use for both users and companies.

[0592] 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.

[0593] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response using a generative model; means for analyzing dialogue data to detect cognitive distortions in the user; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; and means for periodically collecting the user's dialogue data and generating the analysis results as visual data. This allows users to easily understand their own mental health status and receive appropriate support. Corporate managers can also anonymize and understand the mental health status of their employees and take appropriate measures.

[0594] The "AI Counselor" is an artificial intelligence system that analyzes text data entered by users and generates appropriate responses using a generative model.

[0595] "Text data" refers to character information that a user inputs through an application.

[0596] A "generative model" is a machine learning model used to generate appropriate responses based on input data.

[0597] "Dialogue data" refers to the text-based communication between the user and the AI ​​counselor.

[0598] "Cognitive distortions" are irrational thinking patterns and misperceptions that users have, which can have a negative impact on their mental health.

[0599] "Cognitive behavioral therapy" is a type of psychotherapy that corrects cognitive distortions and encourages more adaptive behavior.

[0600] "Visual data" refers to graphs and charts that display analysis results in a visually easy-to-understand manner.

[0601] "Feedback" refers to information that users can input about their experience and thoughts on using the system.

[0602] A "dashboard" is a visual display screen designed to allow administrators to grasp system usage and analysis results at a glance.

[0603] A "corporate administrator" is a person within a company who is responsible for overseeing and managing the mental health of employees.

[0604] "Anonymization" is the process of processing data so that individual users' information cannot be identified.

[0605] "Customization" refers to the ability of users to tailor the system's features and profiles to suit their own preferences and needs.

[0606] "Profile" means your account data, including your personal information and settings.

[0607] "Periodic collection" refers to collecting data at regular intervals, with the aim of continuously reflecting updated information from users.

[0608] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. It also receives user feedback and anonymizes and aggregates the mental health status of all employees for corporate managers.

[0609] The specific steps to implement this are shown below.

[0610] Program processing overview

[0611] 1. Launch the app and log in

[0612] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and authenticates the account information in a database. If authentication is successful, a login session is established, and main screen data is generated and sent to the device. The device displays the main screen data received from the server to the user.

[0613] 2. Start a conversation with an AI counselor

[0614] The user enters a message in the text box and presses the send button. The device sends the text data entered by the user to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model (e.g., OpenAI's GPT-3). The server then sends the generated response to the device, which displays the response message to the user.

[0615] 3. Analysis of cognitive distortions

[0616] Through daily interactions, users enter detailed information about stressful or difficult situations. The device periodically transmits the collected dialogue data to the server. The server inputs the dialogue data into a cognitive distortion analysis module and uses natural language processing technology to detect patterns of cognitive distortion from the user's text. The server generates the results of the cognitive distortion analysis as visual data and reflects this in each user's profile along with their dialogue history. The server then transmits the analysis results, including the visual data, to the device, which then displays the visual data of the cognitive distortion analysis results to the user.

[0617] 4. Providing cognitive behavioral therapy advice

[0618] Based on the results of the cognitive distortion analysis, the server generates an individually customized cognitive behavioral therapy approach and specific advice. Specific exercises and ways of thinking are suggested using the generative AI model and existing CBT manuals. The server then sends the generated advice and exercises to the device, which then displays the advice and exercise instructions to the user. The user follows the advice provided, reassessing their way of thinking and behavior, and engaging in the exercises. This may include writing a diary or trying relaxation techniques.

[0619] 5. Collecting feedback and improving our services

[0620] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device then sends the feedback data from the user to the server. The server stores the received feedback in a database and analyzes the feedback data to help improve the service. The server then generates the feedback results in the form of a dashboard for company administrators and sends it to the administrator portal.

[0621] 6. Provision of enterprise features

[0622] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this data. The generated dashboard data is sent to a portal for company administrators. Company administrators log in to the portal and check the dashboard to understand employee stress levels and the effectiveness of mental health care. Based on the obtained data, company administrators can take specific actions to strengthen and improve employee mental health measures.

[0623] Specific examples

[0624] When a user types, "Recently, things haven't been going well at work and I'm feeling stressed," the server analyzes the input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." If the user continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me," the server detects the cognitive distortion known as "generalization." The server displays the message "You have a tendency to generalize" on the user's dashboard, and further generates advice, "Try writing down three successful experiences," which is displayed on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0625] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[0626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0627] Step 1:

[0628] Launching the app and logging in

[0629] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and performs authentication by checking the account information against a database. It receives the account information as input and outputs the authentication result. If authentication is successful, the server establishes a login session, generates main screen data, and sends it to the device. The device displays the main screen data received from the server to the user.

[0630] Step 2:

[0631] Start a conversation with an AI counselor

[0632] The user enters a message in the text box and presses the send button. The device sends the text data to the server. The server inputs the entered text data into a text analysis engine and performs text analysis. It receives the user's message as input and outputs the analysis results. It passes the analysis results to a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. It outputs a response message and sends it to the device. The device displays the response message received from the server to the user.

[0633] Step 3:

[0634] Analysis of cognitive distortions

[0635] The user enters detailed information about stressful or difficult situations through daily interactions. The device periodically sends the collected interaction data to the server. The server inputs the interaction data into a cognitive distortion analysis module, which uses natural language processing technology to detect cognitive distortion patterns from the user's text. The server receives the interaction data as input and outputs distortion patterns and analysis results. The server generates visual data of the cognitive distortion analysis results and sends it to the device. The device displays the visual data and informs the user of the cognitive distortion analysis results.

[0636] Step 4:

[0637] Providing cognitive behavioral therapy advice

[0638] The server generates an individually customized cognitive behavioral therapy approach and specific advice based on the results of the cognitive distortion analysis. It uses the analysis results as input and outputs advice and exercise content. The generated advice and exercise content are sent to the device. The device displays the advice and exercise instructions to the user. The user follows the advice provided, revises their way of thinking and behavior, and engages in the exercises.

[0639] Step 5:

[0640] Collecting feedback and improving our services

[0641] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device sends the feedback data from the user to the server. The server stores the feedback data in a database, analyzes it, and uses it to improve the service. It collects the feedback data as input and outputs the analysis results. The server also generates the feedback results in dashboard format for company administrators and sends them to the administrator portal.

[0642] Step 6:

[0643] Providing enterprise features

[0644] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this. It receives usage and measurement data as input and outputs dashboard data. The generated dashboard data is sent to a portal for company administrators. The company administrator logs in to the portal and checks the dashboard. Based on the obtained data, the company administrator takes specific actions to strengthen and improve employee mental health measures.

[0645] (Application example 1)

[0646] 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."

[0647] In modern society, many people suffer from mental health issues due to work and life stress. In the workplace in particular, employees often experience stress and mental disorders, which can result in decreased productivity and increased risk of employee turnover. There is a need for a system that can effectively monitor employees' mental health in real time and provide appropriate responses. However, conventional systems have difficulty analyzing mental health status in real time or responding immediately on-site. Furthermore, corporate managers have limited means of understanding the mental health status of their entire workforce. This poses a challenge, making it difficult to intervene early to prevent mental health problems.

[0648] 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.

[0649] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response; means for analyzing the dialogue data to detect the user's cognitive distortions; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; means for analyzing the user's statements and actions in real time using speech recognition and natural language processing to detect the user's mental health status; means for proposing countermeasures based on the detected mental health status; and means for displaying the feedback results as visual data. This enables real-time analysis of mental health status and immediate response. It also makes it easier for corporate managers to grasp the mental health status of their entire workforce, enabling early intervention and appropriate support.

[0650] "AI Counselor" is a system that uses artificial intelligence to analyze users' text data and generate appropriate responses and advice.

[0651] "User cognitive distortions" refer to cognitive biases such as biased thinking, misunderstandings, and overgeneralizations held by users.

[0652] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct cognitive distortions and improve behavior and thinking.

[0653] "Speech recognition" is a technology that analyzes voice data and converts it into text data.

[0654] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[0655] "Real-time analysis" is the process of instantly analyzing user input and providing immediate feedback on the results.

[0656] "Mental health status" refers to the mental and psychological well-being of a user.

[0657] "Responses" are specific measures or advice suggested based on the detected mental health condition.

[0658] "Feedback results" refers to reaction data such as user evaluations and impressions, which are analyzed and used to improve the system.

[0659] "Visual data" is graphical information used to visually display analysis results and feedback.

[0660] This invention is a system equipped with an AI counselor that provides real-time mental health support and offers appropriate solutions to users. The system works in conjunction with smart glasses, a server, and other devices.

[0661] The server receives and analyzes the text data entered by the user and generates an appropriate response. It uses speech recognition technology (e.g., Google Speech Recognition API) to convert the user's speech into text data. It then analyzes the user's statements and actions using speech recognition and natural language processing technology (e.g., OpenAI's generative AI model) to detect their mental health status in real time. Based on the detected cognitive distortions and mental health status, it generates and provides advice and tasks based on cognitive behavioral therapy to the user.

[0662] The server proposes countermeasures based on the detected mental health status and displays the feedback results as visual data, which can be displayed on the user's smart glasses or other display devices using graphical display technologies such as Matplotlib.

[0663] For example, if a user says through smart glasses, "I've been feeling tired lately because of stress at work," the speech recognition technology converts this into text data. The server inputs the prompt sentence into the generative AI model and analyzes the speech:

[0664] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[0665] Based on the analysis results, the server generates specific advice such as, "This person is experiencing significant work-related stress. We recommend talking to them to relieve stress and encouraging them to take a short break." The generated advice is provided to the user in real time.

[0666] The server can also aggregate and display anonymized mental health status data for all employees to company administrators, allowing them to grasp the overall picture of mental health status and take necessary measures.

[0667] This system overcomes the difficulties of real-time monitoring and immediate response that existed in conventional mental health support systems, making it possible to efficiently manage and improve the mental health status of employees.

[0668] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0669] Step 1:

[0670] A user wears smart glasses and speaks, which is collected as voice data by the terminal via the smart glasses' microphone.

[0671] Input: User's voice data

[0672] Output: Audio data sent to the device

[0673] Specific operation: The user says, "I've been feeling tired lately because of stress at work." The microphone in the smart glasses recognizes the voice and sends the voice data to the device.

[0674] Step 2:

[0675] The device receives the voice data and converts it into text data using voice recognition technology (e.g., Google Speech Recognition API).

[0676] Input: Audio data

[0677] Output: Text data

[0678] Specific operation: The device recognizes "I've been feeling tired lately because of work stress" and converts this into "text data."

[0679] Step 3:

[0680] The terminal transmits the converted text data to the server.

[0681] Input: Text data

[0682] Output: Send text data to the server

[0683] Specific operation: The terminal transfers the generated text data to the server.

[0684] Step 4:

[0685] The server receives the text data and analyzes the text using natural language processing techniques (e.g., OpenAI's generative AI model).

[0686] Input: Text data

[0687] Output: Analysis results

[0688] Specific operation: The server analyzes the "text data" and understands the information "I've been feeling tired lately because of work stress." Then, it analyzes it using the following prompt sentence for the generative AI model:

[0689] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[0690] Step 5:

[0691] Based on the analysis results, the server detects cognitive distortions and signs of stress and generates appropriate countermeasures and advice.

[0692] Input: Analysis results

[0693] Output: Countermeasures, advice

[0694] Specific action: The server generates advice such as "We recommend talking to the person to relieve stress and encouraging them to take a short break."

[0695] Step 6:

[0696] The server sends the generated advice and countermeasures to the terminal.

[0697] Input: Measures, advice

[0698] Output: Send advice and solutions to your device

[0699] Specific operation: The server transfers the generated advice to the terminal.

[0700] Step 7:

[0701] The device then displays the received advice and solutions to the user in real time, and these are presented to the user through the smart glasses display.

[0702] Input: Measures, advice

[0703] Output: Shown to the user

[0704] Specific action: The device displays the advice "We recommend talking to someone to relieve stress or taking a short break" on the smart glasses display.

[0705] Step 8:

[0706] The user inputs the result of the advice received as feedback and transmits it to the terminal.

[0707] Input: Feedback data

[0708] Output: Send feedback data to the terminal

[0709] Specific operation: The user inputs "The advice was helpful" through the smart glasses, and the device sends this to the server.

[0710] Step 9:

[0711] The server collects feedback data from users and uses it to improve the system and measure its effectiveness.

[0712] Input: Feedback data

[0713] Output: analysis results, improvement suggestions

[0714] Specific operation: The server analyzes the feedback data and identifies areas for improvement in the system. It also generates visual data and sends it to the company administrator's portal to display the aggregated results of mental health status to the company administrator.

[0715] 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.

[0716] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts the response, providing more personalized mental care. This system analyzes the user's dialogue data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts responses and advice based on the recognition results.

[0717] Program processing overview

[0718] To realize this system, the following program processing is performed.

[0719] 1. The user launches the app

[0720] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[0721] Terminal: Sends a login request to the server.

[0722] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[0723] Terminal: Presents the main screen to the user.

[0724] 2. Start a conversation with an AI counselor

[0725] User: Enters "I'm tired from work today" in the text box and presses the send button.

[0726] Terminal: Sends text data entered by the user to the server.

[0727] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[0728] Server: Generates and sends the response to the device.

[0729] Terminal: Displays the AI ​​counselor's response message to the user.

[0730] 3. Recognize user emotions

[0731] Server: The emotion engine recognizes the user's emotion from the input text. For example, it recognizes the feeling of fatigue from the text "I'm tired."

[0732] Server: Sends the recognized emotion data to the device in an appropriate format and displays the user's emotional state.

[0733] 4. Analyze users' cognitive distortions

[0734] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[0735] Terminal: Periodically transmits collected interaction data to the server.

[0736] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[0737] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[0738] Terminal: Displays the visual data of the analysis results to the user.

[0739] 5. Provide cognitive behavioral therapy support

[0740] Server: Using the cognitive distortion analysis results and emotion engine data together, it generates individually customized cognitive behavioral therapy approaches and specific advice.

[0741] Server: Sends generated advice and exercises to the device.

[0742] Device: Displays advice and exercise instructions to the user.

[0743] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[0744] 6. Feedback and Improvement

[0745] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[0746] Device: Sends collected feedback data to the server.

[0747] Server: We store your feedback in a database, analyze it and use it to improve our services.

[0748] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[0749] 7. Provision of enterprise features

[0750] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[0751] Server: Sends the generated dashboard data to the enterprise administrator portal.

[0752] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[0753] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[0754] Specific examples

[0755] As a specific example, suppose a user types, "Work hasn't been going well lately, and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine recognizes a high level of stress from the text, "I'm feeling stressed," and adapts the response accordingly. The user then types, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice, such as, "Write down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[0756] In this way, this system takes into consideration the user's emotions and provides an environment in which it is easy to seek advice about mental health issues. By correcting cognitive distortions, this system can improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] The user taps the app on their smartphone or computer to launch it, then enters their account information (email address and password) to log in.

[0760] Step 2:

[0761] The terminal sends a login request from the user to the server. The login information is encrypted using the SSL / TLS protocol.

[0762] Step 3:

[0763] The server parses the received login information and matches it with the account information in its database. If successful, a login session is established.

[0764] Step 4:

[0765] The server sends the main screen data to the device, which displays an interface for starting a conversation with the AI ​​counselor.

[0766] Step 5:

[0767] The device displays the main screen to the user, who enters "I'm tired from work today" in the text box and presses the send button.

[0768] Step 6:

[0769] The terminal sends the user's input text data to the server, which packages the text data in an appropriate format.

[0770] Step 7:

[0771] The server's text analysis engine receives the input text and analyzes it, extracting keywords and sentiments from the text.

[0772] Step 8:

[0773] The server's emotion engine recognizes the user's emotional state based on the extracted keywords and emotions. For example, it recognizes fatigue from the text "tired."

[0774] Step 9:

[0775] The server uses the generative AI model to generate an appropriate response based on the emotion recognition results, such as "Tell me specifically what kind of work you did."

[0776] Step 10:

[0777] The server sends the generated response to the terminal. The response message is sent in text format.

[0778] Step 11:

[0779] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[0780] Step 12:

[0781] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[0782] Step 13:

[0783] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[0784] Step 14:

[0785] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[0786] Step 15:

[0787] The device displays the analysis results visual data to the user, who then sees a message on the dashboard saying, "You have a tendency toward generalization."

[0788] Step 16:

[0789] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[0790] Step 17:

[0791] The device displays advice to the user, who can then follow the advice and list their successes, thereby recognizing their positive aspects.

[0792] Step 18:

[0793] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[0794] Step 19:

[0795] The device sends the collected feedback data to the server, which can be used to improve the service.

[0796] Step 20:

[0797] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[0798] Step 21:

[0799] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[0800] Step 22:

[0801] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[0802] Step 23:

[0803] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[0804] Example 2

[0805] 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."

[0806] Conventional mental health systems were able to generate appropriate responses based on user input data, but they had difficulty recognizing individual emotional states and adjusting responses accordingly. They also had limited functionality for detecting cognitive distortions and providing appropriate cognitive behavioral therapy (CBT) advice. Furthermore, there were insufficient means for companies to provide data that would allow them to effectively understand employees' mental health conditions and take appropriate measures. To resolve these issues, a system that provides more accurate and personalized mental health care is needed.

[0807] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0808] In this invention, the server includes means for receiving and analyzing text data entered by a user and generating an appropriate response, means for analyzing dialogue data to detect cognitive distortions in the user, means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user, means for recognizing emotions from the input text, and means for adjusting the response and advice based on the recognized emotional data. This enables highly accurate and personalized mental care, responses to cognitive distortions, and the provision of data to companies.

[0809] "User" refers to an individual who uses a system or application.

[0810] "Text data" refers to textual information that a user inputs into a system.

[0811] A "server" refers to a computer system that performs processes such as analyzing data and generating responses.

[0812] "Analysis" refers to the process of analyzing input data and identifying its content and characteristics.

[0813] "Response" refers to a reply message generated by the system in response to a user's input.

[0814] "Dialogue data" refers to the history and content of messages exchanged between a user and the system.

[0815] "Cognitive distortion" refers to biases or misunderstandings in a user's thinking or perception.

[0816] "Cognitive behavioral therapy (CBT)" refers to psychological therapy aimed at correcting cognitive distortions and promoting adaptive behavior.

[0817] "Emotion" refers to the user's psychological state or mood.

[0818] "Emotion engine" refers to a system component for recognizing and analyzing emotions from user text data.

[0819] "Feedback" refers to the opinions and impressions provided by users after using the system.

[0820] A "corporate administrator" refers to a person in charge of managing the entire system and evaluating mental health within a company.

[0821] A "dashboard" refers to an interface that visualizes and displays data such as system usage and effectiveness measurements.

[0822] "Customization" refers to the ability for users to change system settings and profiles to suit their own needs and preferences.

[0823] "Profile" refers to a data set of information and settings related to a user.

[0824] "Advice" refers to guidance or suggestions provided to the user by the system.

[0825] A "task" refers to a specific task or exercise that a user must perform.

[0826] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts responses accordingly, providing more personalized mental care. The system analyzes user interaction data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. It also has a function that aggregates employee mental health status and provides it in the form of a dashboard for corporate managers.

[0827] Specific program description

[0828] Hardware and software used

[0829] Hardware: smartphones, PCs, servers

[0830] Software: Text analysis engines, sentiment engines, generative AI models (e.g., GPT-3), database management systems, dashboard generation tools

[0831] Details of data processing and calculation

[0832] 1. Analyzing user input data

[0833] A user launches the app on their smartphone or PC and logs in with their account information. The device sends a login request to the server, which then authenticates the user. If authentication is successful, the server sends the main screen to the device, which then displays the main screen to the user. The user then enters dialogue text, which the device then sends to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model.

[0834] 2. Emotion recognition

[0835] The server uses an emotion engine to recognize the user's emotions from the input text. For example, the phrase "tired" identifies fatigue or stress. The recognized emotion data is sent to the terminal in an appropriate format, and the user's emotional state is displayed.

[0836] 3. Analysis of cognitive distortions

[0837] The system collects daily user interaction data and periodically sends it to a server. The server then inputs the interaction data into a cognitive distortion analysis module, which uses data mining techniques to detect the user's cognitive distortions. The detection results are generated as visual data, sent to the device, and displayed to the user.

[0838] 4. Support for cognitive behavioral therapy (CBT)

[0839] The server combines the results of the cognitive distortion analysis with data from the emotion engine to generate individually customized CBT advice and tasks. The generated advice and exercises are sent to the user's device and displayed. The user follows the advice, changes their thoughts and behavior, and engages in the exercises.

[0840] 5. Gathering feedback and improving the system

[0841] Users enter their feedback into a survey form within the app, and the device sends the data to the server. The server stores the feedback in a database, analyzes it, and uses it to improve the service. The feedback results are also provided to company administrators in the form of a dashboard, allowing them to measure the effectiveness of the service.

[0842] 6. Enterprise Features

[0843] The server aggregates usage and effectiveness data for each company and generates a dashboard. This dashboard data is sent to a portal for company administrators, who can analyze employee stress levels and the effectiveness of mental care. Based on the data obtained, company administrators can take action to strengthen and improve employee mental health measures.

[0844] Examples of concrete examples and prompts

[0845] For example, if a user enters "Recently, things haven't been going well at work and I'm feeling stressed," the server will analyze this input text and respond with, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine will recognize a high level of stress from the text "I'm feeling stressed," and tailor the response accordingly.

[0846] Example prompt sentence:

[0847] "Recently, things haven't been going well at work and I'm feeling stressed."

[0848] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0849] Step 1:

[0850] The user launches the app.

[0851] Users tap the app on their smartphone or computer to launch it and display the login screen.

[0852] Input: Login request (user ID and password)

[0853] Output: A login request is sent to the server.

[0854] Step 2:

[0855] The terminal sends a login request to the server.

[0856] When the login button is pressed, the terminal transmits the user's authentication information to the server.

[0857] Input: User authentication information (user ID and password)

[0858] Output: The authentication information reaches the server.

[0859] Step 3:

[0860] The server authenticates the user.

[0861] The server checks the received authentication information against a database and generates an authentication result.

[0862] Input: Authentication information that reaches the server

[0863] Output: Authentication result (success or failure)

[0864] Step 4:

[0865] The server establishes a login session and sends the main screen to the terminal.

[0866] If the authentication is successful, the server starts a session for the user, generates main screen data, and sends it to the terminal.

[0867] Input: Authentication success result

[0868] Output: Main screen data

[0869] Step 5:

[0870] The terminal displays the main screen to the user.

[0871] The terminal displays the received main screen data on the user interface.

[0872] Input: Main screen data

[0873] Output: Main screen display

[0874] Step 6:

[0875] The user begins a conversation with the AI ​​counselor.

[0876] The user enters the dialogue text in the text box and presses the send button.

[0877] Input: User dialogue text (e.g., "I'm tired from work today.")

[0878] Output: The dialogue text is sent from the terminal to the server.

[0879] Step 7:

[0880] The server analyzes the dialogue text.

[0881] The server's text analysis engine breaks down the dialogue text and recognizes context and sentiment.

[0882] Input: Dialogue text

[0883] Output: Analysis results (context and sentiment data)

[0884] Step 8:

[0885] The server generates an appropriate response using a generative AI model.

[0886] The server uses a generative AI model (e.g., GPT-3) to generate an appropriate response based on the analysis results.

[0887] Input: Analysis results (context and emotion data)

[0888] Output: Response message

[0889] Step 9:

[0890] The server sends the generated response to the terminal.

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

[0892] Input: Response message

[0893] Output: The response message arrives at the terminal.

[0894] Step 10:

[0895] The terminal displays the response message to the user.

[0896] The terminal displays the received response message on the user interface.

[0897] Input: Response message

[0898] Output: Display of response message

[0899] Step 11:

[0900] The server recognizes emotions from the input text.

[0901] The server's emotion engine analyzes the input text and identifies the user's emotional state.

[0902] Input: Dialogue text

[0903] Output: Emotion data (e.g., fatigue, stress)

[0904] Step 12:

[0905] The server transmits the emotion data to the terminal.

[0906] The server transmits the recognized emotion data to the terminal.

[0907] Input: Emotion data

[0908] Output: Emotion data arrives at the terminal.

[0909] Step 13:

[0910] The terminal displays the emotional state to the user.

[0911] The device visualizes the received emotion data and displays it on a user interface.

[0912] Input: Emotion data

[0913] Output: Display of emotional state

[0914] (Application example 2)

[0915] 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."

[0916] Conventional mental healthcare systems have difficulty recognizing users' emotions in real time and providing appropriate advice, and have faced particular challenges in terms of insufficient on-site immediate response and continuous monitoring functions. Furthermore, seamless data collection and analysis are required, but current systems have technical limitations. There is a particular need to improve the quality of mental health support in high-stress workplaces, such as security staff.

[0917] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically displaying questions about mental health and transmitting data of the user's responses to the server, means for recognizing the user's emotions in real time through conversation and facial expression recognition and providing appropriate relaxation methods and mental care advice when a high-stress state is detected, and means for collecting user feedback and analyzing the data to improve the quality of the service. This makes it possible to recognize the user's emotions in real time and provide appropriate, individually customized mental care.

[0918] "AI Counselor" is an artificial intelligence system that analyzes text data entered by the user and generates appropriate responses.

[0919] "Cognitive distortions" refer to incorrect cognitive patterns in a user's thinking, and the aim is to detect and correct these.

[0920] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct users' cognitive distortions and promote appropriate behavior.

[0921] "Smart glasses" are a type of electronic device worn by a user that can provide visual information and perform data collection and analysis.

[0922] "Mental health" refers to an individual's mental health status and aims to provide support to maintain or improve it.

[0923] "Relaxation methods" refer to techniques and activities that reduce stress and promote mental and physical relaxation in users.

[0924] "Feedback" refers to opinions and impressions provided by users after using the system, and collecting and analyzing this information will help improve the system.

[0925] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state in real time from their conversation and facial expressions.

[0926] The present invention relates to a mental health support system using smart glasses designed for security staff. When a user wears the smart glasses, the system periodically displays questions about mental health and transmits the user's responses to a server in real time. Specific embodiments for implementing the present invention are described below.

[0927] First, an application installed on the smart glasses periodically asks the user simple questions about their mental health, such as, "Please rate your current mood on a scale of 1 to 10." Once the user enters their information, the data is sent to a server via a wireless network.

[0928] The server runs an AI counselor to analyze the received data. This AI counselor uses a generative AI model to analyze the input data and generate appropriate responses and advice. The generated data is fed back to the smart glasses in real time.

[0929] In particular, the system is equipped with an emotion recognition engine that uses speech and facial expression recognition technology to recognize the user's emotions in real time. For example, if a user types "I'm tired," the emotion recognition engine will detect a high level of stress and provide immediate advice such as "Try taking a few minutes of rest and taking some deep breaths."

[0930] Furthermore, the server periodically analyzes this dialogue data and runs an algorithm to detect cognitive distortions. Based on the detected cognitive distortions, cognitive behavioral therapy (CBT) advice and tasks are generated. For example, if the cognitive distortion of "holism" is detected, the user is given specific advice such as "Try writing down three successful experiences."

[0931] The collected feedback data is analyzed to improve the quality of the service. Users can enter feedback on advice provided within the app, such as "This advice made me feel better," and this data is sent back to the server for analysis, which helps improve the service.

[0932] In a specific example of an embodiment, the following prompt may be presented:

[0933] "Rate your current mood from 1 to 10"

[0934] "Tell me about the most stressful thing that happened to you recently."

[0935] "Tell me about the most exhausting moment at work today."

[0936] The system of the present invention includes smart glasses, an AI counselor, an emotion recognition engine, and a cognitive behavioral therapy (CBT) module, and can provide personalized and appropriate mental health care to users in real time. In this way, high-quality mental health care support is realized while taking into account the user's emotional state.

[0937] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0938] Step 1:

[0939] A user puts on smart glasses and launches a mental health check application. The device prompts the user, saying, "Please rate your current mood on a scale of 1 to 10." The user then inputs the rating score. This input data (rating score) is stored on the device and transmitted to a server via a wireless network.

[0940] Step 2:

[0941] The server analyzes the received data (evaluation score) and uses a generative AI model to determine the user's state. The system evaluates the user's current mental state based on the input data. For example, if the evaluation score is low, it will determine that the user is under high stress. Based on the analysis results, it generates an appropriate response or advice. This response data is sent to the device.

[0942] Step 3:

[0943] The device displays the response data received from the server to the user. For example, it displays advice such as "Take a few minutes break and try taking deep breaths." The user follows the displayed advice. The device collects how the user responded to the provided advice (e.g., thoughts and feedback).

[0944] Step 4:

[0945] The server uses speech and facial expression recognition technology to analyze the user's emotions in real time. For example, if the user types "I'm tired," the server uses an emotion recognition engine to detect a high level of stress. Based on the analysis results, it immediately generates appropriate countermeasures (e.g., relaxation techniques) and sends them to the device.

[0946] Step 5:

[0947] The device receives the solution from the server and displays it to the user. For example, a specific relaxation method such as "Take deep breaths repeatedly for one minute" is displayed. The user follows the solution and inputs the results and impressions into the device, which are then sent back to the server.

[0948] Step 6:

[0949] The server continuously collects dialogue data and performs data analysis to detect cognitive distortions. Based on the collected data, a cognitive distortion analysis algorithm is run to identify the cognitive distortions the user has. Based on the results of this analysis, cognitive behavioral therapy (CBT) advice and tasks are generated and sent to the device.

[0950] Step 7:

[0951] The device displays the generated CBT advice and tasks to the user. For example, a specific task such as "Write down three successful experiences" is presented. The user performs this task and inputs the results, which are then sent to the server via the device.

[0952] Step 8:

[0953] The server collects and analyzes user feedback and performs data analysis to improve the quality of the system. Based on the feedback, the server tunes the generative AI model and emotion recognition engine. This process improves the accuracy and usefulness of the advice and tasks provided.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] [Third embodiment]

[0958] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0959] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0960] 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).

[0961] 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.

[0962] 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.

[0963] 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).

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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."

[0970] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. Furthermore, it receives user feedback and anonymizes, aggregates, and displays the mental health status of all employees to company managers.

[0971] Program processing overview

[0972] To realize this system, the following program processing is performed.

[0973] 1. The user launches the app

[0974] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[0975] Terminal: Sends a login request to the server.

[0976] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[0977] Terminal: Presents the main screen to the user.

[0978] 2. Start a conversation with an AI counselor

[0979] User: Enters "I'm tired from work today" in the text box and presses the send button.

[0980] Terminal: Sends text data entered by the user to the server.

[0981] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[0982] Server: Generates and sends the response to the device.

[0983] Terminal: Displays the AI ​​counselor's response message to the user.

[0984] 3. Analyze users' cognitive distortions

[0985] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[0986] Terminal: Periodically transmits collected interaction data to the server.

[0987] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[0988] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[0989] Terminal: Displays the visual data of the analysis results to the user.

[0990] 4. Provide cognitive behavioral therapy support

[0991] Server: Generates individually customized cognitive behavioral therapy approaches and specific advice based on the results of cognitive distortion analysis.

[0992] Server: Sends generated advice and exercises to the device.

[0993] Device: Displays advice and exercise instructions to the user.

[0994] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[0995] 5. Feedback and Improvement

[0996] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[0997] Device: Sends collected feedback data to the server.

[0998] Server: We store your feedback in a database, analyze it and use it to improve our services.

[0999] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[1000] 6. Provision of enterprise features

[1001] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[1002] Server: Sends the generated dashboard data to the enterprise administrator portal.

[1003] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[1004] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[1005] Specific examples

[1006] As a specific example, suppose a user types, "Recently, things haven't been going well at work and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you are feeling specifically." The user then continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice such as, "Try writing down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1007] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent mental illness in people with hidden high levels of stress.

[1008] The processing flow will be explained below.

[1009] Step 1:

[1010] A user launches the app on their smartphone or computer and logs in with their account information, including their email address and password.

[1011] Step 2:

[1012] The terminal sends a login request from the user to the server, where the login information is encrypted using a security protocol.

[1013] Step 3:

[1014] The server analyzes the received login information and authenticates the user by comparing it with the account information in the database. If authentication is successful, a login session is established.

[1015] Step 4:

[1016] The server sends the main screen data to the device, which includes an interface for starting a conversation with the AI ​​counselor.

[1017] Step 5:

[1018] The device displays the main screen to the user, where the user enters "I'm tired from work today" in the text box and presses the send button.

[1019] Step 6:

[1020] The terminal transmits the user's input text data to the server, where the text data is packaged in an appropriate format.

[1021] Step 7:

[1022] The server's text analysis engine receives the input text and performs an analysis, which evaluates the user's emotions and stress level.

[1023] Step 8:

[1024] The server uses the generative AI model to generate an appropriate response. For example, if the input text is "I'm tired from work today," it generates the response "Tell me specifically what kind of work you did."

[1025] Step 9:

[1026] The server generates a response and sends it to the terminal. The response message is sent in text format.

[1027] Step 10:

[1028] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[1029] Step 11:

[1030] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[1031] Step 12:

[1032] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[1033] Step 13:

[1034] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[1035] Step 14:

[1036] The device displays the analysis results visual data to the user, who then sees the message "There is a tendency for generalization" on the dashboard.

[1037] Step 15:

[1038] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[1039] Step 16:

[1040] The device displays advice to the user, who then follows the advice and lists their successes.

[1041] Step 17:

[1042] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[1043] Step 18:

[1044] The device sends the collected feedback data to the server, which can be used to improve the service.

[1045] Step 19:

[1046] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[1047] Step 20:

[1048] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[1049] Step 21:

[1050] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[1051] Step 22:

[1052] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[1053] Example 1

[1054] 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."

[1055] Conventional mental health support systems lack an easy-to-use environment for users, do not provide real-time feedback, and make it difficult for corporate managers to grasp the mental health status of all employees. Furthermore, they lack the means to properly detect cognitive distortions and provide individually customized cognitive behavioral therapy approaches. To solve these issues, it is necessary to provide a system that is both useful and easy to use for both users and companies.

[1056] 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.

[1057] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response using a generative model; means for analyzing dialogue data to detect cognitive distortions in the user; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; and means for periodically collecting the user's dialogue data and generating the analysis results as visual data. This allows users to easily understand their own mental health status and receive appropriate support. Corporate managers can also anonymize and understand the mental health status of their employees and take appropriate measures.

[1058] The "AI Counselor" is an artificial intelligence system that analyzes text data entered by users and generates appropriate responses using a generative model.

[1059] "Text data" refers to character information that a user inputs through an application.

[1060] A "generative model" is a machine learning model used to generate appropriate responses based on input data.

[1061] "Dialogue data" refers to the text-based communication between the user and the AI ​​counselor.

[1062] "Cognitive distortions" are irrational thinking patterns and misperceptions that users have, which can have a negative impact on their mental health.

[1063] "Cognitive behavioral therapy" is a type of psychotherapy that corrects cognitive distortions and encourages more adaptive behavior.

[1064] "Visual data" refers to graphs and charts that display analysis results in a visually easy-to-understand manner.

[1065] "Feedback" refers to information that users can input about their experience and thoughts on using the system.

[1066] A "dashboard" is a visual display screen designed to allow administrators to grasp system usage and analysis results at a glance.

[1067] A "corporate administrator" is a person within a company who is responsible for overseeing and managing the mental health of employees.

[1068] "Anonymization" is the process of processing data so that individual users' information cannot be identified.

[1069] "Customization" refers to the ability of users to tailor the system's features and profiles to suit their own preferences and needs.

[1070] "Profile" means your account data, including your personal information and settings.

[1071] "Periodic collection" refers to collecting data at regular intervals, with the aim of continuously reflecting updated information from users.

[1072] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. It also receives user feedback and anonymizes and aggregates the mental health status of all employees for corporate managers.

[1073] The specific steps to implement this are shown below.

[1074] Program processing overview

[1075] 1. Launch the app and log in

[1076] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and authenticates the account information in a database. If authentication is successful, a login session is established, and main screen data is generated and sent to the device. The device displays the main screen data received from the server to the user.

[1077] 2. Start a conversation with an AI counselor

[1078] The user enters a message in the text box and presses the send button. The device sends the text data entered by the user to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model (e.g., OpenAI's GPT-3). The server then sends the generated response to the device, which displays the response message to the user.

[1079] 3. Analysis of cognitive distortions

[1080] Through daily interactions, users enter detailed information about stressful or difficult situations. The device periodically transmits the collected dialogue data to the server. The server inputs the dialogue data into a cognitive distortion analysis module and uses natural language processing technology to detect patterns of cognitive distortion from the user's text. The server generates the results of the cognitive distortion analysis as visual data and reflects this in each user's profile along with their dialogue history. The server then transmits the analysis results, including the visual data, to the device, which then displays the visual data of the cognitive distortion analysis results to the user.

[1081] 4. Providing cognitive behavioral therapy advice

[1082] Based on the results of the cognitive distortion analysis, the server generates an individually customized cognitive behavioral therapy approach and specific advice. Specific exercises and ways of thinking are suggested using the generative AI model and existing CBT manuals. The server then sends the generated advice and exercises to the device, which then displays the advice and exercise instructions to the user. The user follows the advice provided, reassessing their way of thinking and behavior, and engaging in the exercises. This may include writing a diary or trying relaxation techniques.

[1083] 5. Collecting feedback and improving our services

[1084] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device then sends the feedback data from the user to the server. The server stores the received feedback in a database and analyzes the feedback data to help improve the service. The server then generates the feedback results in the form of a dashboard for company administrators and sends it to the administrator portal.

[1085] 6. Provision of enterprise features

[1086] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this data. The generated dashboard data is sent to a portal for company administrators. Company administrators log in to the portal and check the dashboard to understand employee stress levels and the effectiveness of mental health care. Based on the obtained data, company administrators can take specific actions to strengthen and improve employee mental health measures.

[1087] Specific examples

[1088] When a user types, "Recently, things haven't been going well at work and I'm feeling stressed," the server analyzes the input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." If the user continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me," the server detects the cognitive distortion known as "generalization." The server displays the message "You have a tendency to generalize" on the user's dashboard, and further generates advice, "Try writing down three successful experiences," which is displayed on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1089] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[1090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1091] Step 1:

[1092] Launching the app and logging in

[1093] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and performs authentication by checking the account information against a database. It receives the account information as input and outputs the authentication result. If authentication is successful, the server establishes a login session, generates main screen data, and sends it to the device. The device displays the main screen data received from the server to the user.

[1094] Step 2:

[1095] Start a conversation with an AI counselor

[1096] The user enters a message in the text box and presses the send button. The device sends the text data to the server. The server inputs the entered text data into a text analysis engine and performs text analysis. It receives the user's message as input and outputs the analysis results. It passes the analysis results to a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. It outputs a response message and sends it to the device. The device displays the response message received from the server to the user.

[1097] Step 3:

[1098] Analysis of cognitive distortions

[1099] The user enters detailed information about stressful or difficult situations through daily interactions. The device periodically sends the collected interaction data to the server. The server inputs the interaction data into a cognitive distortion analysis module, which uses natural language processing technology to detect cognitive distortion patterns from the user's text. The server receives the interaction data as input and outputs distortion patterns and analysis results. The server generates visual data of the cognitive distortion analysis results and sends it to the device. The device displays the visual data and informs the user of the cognitive distortion analysis results.

[1100] Step 4:

[1101] Providing cognitive behavioral therapy advice

[1102] The server generates an individually customized cognitive behavioral therapy approach and specific advice based on the results of the cognitive distortion analysis. It uses the analysis results as input and outputs advice and exercise content. The generated advice and exercise content are sent to the device. The device displays the advice and exercise instructions to the user. The user follows the advice provided, revises their way of thinking and behavior, and engages in the exercises.

[1103] Step 5:

[1104] Collecting feedback and improving our services

[1105] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device sends the feedback data from the user to the server. The server stores the feedback data in a database, analyzes it, and uses it to improve the service. It collects the feedback data as input and outputs the analysis results. The server also generates the feedback results in dashboard format for company administrators and sends them to the administrator portal.

[1106] Step 6:

[1107] Providing enterprise features

[1108] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this. It receives usage and measurement data as input and outputs dashboard data. The generated dashboard data is sent to a portal for company administrators. The company administrator logs in to the portal and checks the dashboard. Based on the obtained data, the company administrator takes specific actions to strengthen and improve employee mental health measures.

[1109] (Application example 1)

[1110] 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."

[1111] In modern society, many people suffer from mental health issues due to work and life stress. In the workplace in particular, employees often experience stress and mental disorders, which can result in decreased productivity and increased risk of employee turnover. There is a need for a system that can effectively monitor employees' mental health in real time and provide appropriate responses. However, conventional systems have difficulty analyzing mental health status in real time or responding immediately on-site. Furthermore, corporate managers have limited means of understanding the mental health status of their entire workforce. This poses a challenge, making it difficult to intervene early to prevent mental health problems.

[1112] 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.

[1113] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response; means for analyzing the dialogue data to detect the user's cognitive distortions; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; means for analyzing the user's statements and actions in real time using speech recognition and natural language processing to detect the user's mental health status; means for proposing countermeasures based on the detected mental health status; and means for displaying the feedback results as visual data. This enables real-time analysis of mental health status and immediate response. It also makes it easier for corporate managers to grasp the mental health status of their entire workforce, enabling early intervention and appropriate support.

[1114] "AI Counselor" is a system that uses artificial intelligence to analyze users' text data and generate appropriate responses and advice.

[1115] "User cognitive distortions" refer to cognitive biases such as biased thinking, misunderstandings, and overgeneralizations held by users.

[1116] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct cognitive distortions and improve behavior and thinking.

[1117] "Speech recognition" is a technology that analyzes voice data and converts it into text data.

[1118] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[1119] "Real-time analysis" is the process of instantly analyzing user input and providing immediate feedback on the results.

[1120] "Mental health status" refers to the mental and psychological well-being of a user.

[1121] "Responses" are specific measures or advice suggested based on the detected mental health condition.

[1122] "Feedback results" refers to reaction data such as user evaluations and impressions, which are analyzed and used to improve the system.

[1123] "Visual data" is graphical information used to visually display analysis results and feedback.

[1124] This invention is a system equipped with an AI counselor that provides real-time mental health support and offers appropriate solutions to users. The system works in conjunction with smart glasses, a server, and other devices.

[1125] The server receives and analyzes the text data entered by the user and generates an appropriate response. It uses speech recognition technology (e.g., Google Speech Recognition API) to convert the user's speech into text data. It then analyzes the user's statements and actions using speech recognition and natural language processing technology (e.g., OpenAI's generative AI model) to detect their mental health status in real time. Based on the detected cognitive distortions and mental health status, it generates and provides advice and tasks based on cognitive behavioral therapy to the user.

[1126] The server proposes countermeasures based on the detected mental health status and displays the feedback results as visual data, which can be displayed on the user's smart glasses or other display devices using graphical display technologies such as Matplotlib.

[1127] For example, if a user says through smart glasses, "I've been feeling tired lately because of stress at work," the speech recognition technology converts this into text data. The server inputs the prompt sentence into the generative AI model and analyzes the speech:

[1128] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[1129] Based on the analysis results, the server generates specific advice such as, "This person is experiencing significant work-related stress. We recommend talking to them to relieve stress and encouraging them to take a short break." The generated advice is provided to the user in real time.

[1130] The server can also aggregate and display anonymized mental health status data for all employees to company administrators, allowing them to grasp the overall picture of mental health status and take necessary measures.

[1131] This system overcomes the difficulties of real-time monitoring and immediate response that existed in conventional mental health support systems, making it possible to efficiently manage and improve the mental health status of employees.

[1132] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1133] Step 1:

[1134] A user wears smart glasses and speaks, which is collected as voice data by the terminal via the smart glasses' microphone.

[1135] Input: User's voice data

[1136] Output: Audio data sent to the device

[1137] Specific operation: The user says, "I've been feeling tired lately because of stress at work." The microphone in the smart glasses recognizes the voice and sends the voice data to the device.

[1138] Step 2:

[1139] The device receives the voice data and converts it into text data using voice recognition technology (e.g., Google Speech Recognition API).

[1140] Input: Audio data

[1141] Output: Text data

[1142] Specific operation: The device recognizes "I've been feeling tired lately because of work stress" and converts this into "text data."

[1143] Step 3:

[1144] The terminal transmits the converted text data to the server.

[1145] Input: Text data

[1146] Output: Send text data to the server

[1147] Specific operation: The terminal transfers the generated text data to the server.

[1148] Step 4:

[1149] The server receives the text data and analyzes the text using natural language processing techniques (e.g., OpenAI's generative AI model).

[1150] Input: Text data

[1151] Output: Analysis results

[1152] Specific operation: The server analyzes the "text data" and understands the information "I've been feeling tired lately because of work stress." Then, it analyzes it using the following prompt sentence for the generative AI model:

[1153] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[1154] Step 5:

[1155] Based on the analysis results, the server detects cognitive distortions and signs of stress and generates appropriate countermeasures and advice.

[1156] Input: Analysis results

[1157] Output: Countermeasures, advice

[1158] Specific action: The server generates advice such as "We recommend talking to the person to relieve stress and encouraging them to take a short break."

[1159] Step 6:

[1160] The server sends the generated advice and countermeasures to the terminal.

[1161] Input: Measures, advice

[1162] Output: Send advice and solutions to your device

[1163] Specific operation: The server transfers the generated advice to the terminal.

[1164] Step 7:

[1165] The device then displays the received advice and solutions to the user in real time, and these are presented to the user through the smart glasses display.

[1166] Input: Measures, advice

[1167] Output: Shown to the user

[1168] Specific action: The device displays the advice "We recommend talking to someone to relieve stress or taking a short break" on the smart glasses display.

[1169] Step 8:

[1170] The user inputs the result of the advice received as feedback and transmits it to the terminal.

[1171] Input: Feedback data

[1172] Output: Send feedback data to the terminal

[1173] Specific operation: The user inputs "The advice was helpful" through the smart glasses, and the device sends this to the server.

[1174] Step 9:

[1175] The server collects feedback data from users and uses it to improve the system and measure its effectiveness.

[1176] Input: Feedback data

[1177] Output: analysis results, improvement suggestions

[1178] Specific operation: The server analyzes the feedback data and identifies areas for improvement in the system. It also generates visual data and sends it to the company administrator's portal to display the aggregated results of mental health status to the company administrator.

[1179] 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.

[1180] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts the response, providing more personalized mental care. This system analyzes the user's dialogue data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts responses and advice based on the recognition results.

[1181] Program processing overview

[1182] To realize this system, the following program processing is performed.

[1183] 1. The user launches the app

[1184] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[1185] Terminal: Sends a login request to the server.

[1186] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[1187] Terminal: Presents the main screen to the user.

[1188] 2. Start a conversation with an AI counselor

[1189] User: Enters "I'm tired from work today" in the text box and presses the send button.

[1190] Terminal: Sends text data entered by the user to the server.

[1191] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[1192] Server: Generates and sends the response to the device.

[1193] Terminal: Displays the AI ​​counselor's response message to the user.

[1194] 3. Recognize user emotions

[1195] Server: The emotion engine recognizes the user's emotion from the input text. For example, it recognizes the feeling of fatigue from the text "I'm tired."

[1196] Server: Sends the recognized emotion data to the device in an appropriate format and displays the user's emotional state.

[1197] 4. Analyze users' cognitive distortions

[1198] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[1199] Terminal: Periodically transmits collected interaction data to the server.

[1200] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[1201] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[1202] Terminal: Displays the visual data of the analysis results to the user.

[1203] 5. Provide cognitive behavioral therapy support

[1204] Server: Using the cognitive distortion analysis results and emotion engine data together, it generates individually customized cognitive behavioral therapy approaches and specific advice.

[1205] Server: Sends generated advice and exercises to the device.

[1206] Device: Displays advice and exercise instructions to the user.

[1207] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[1208] 6. Feedback and Improvement

[1209] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[1210] Device: Sends collected feedback data to the server.

[1211] Server: We store your feedback in a database, analyze it and use it to improve our services.

[1212] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[1213] 7. Provision of enterprise features

[1214] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[1215] Server: Sends the generated dashboard data to the enterprise administrator portal.

[1216] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[1217] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[1218] Specific examples

[1219] As a specific example, suppose a user types, "Work hasn't been going well lately, and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine recognizes a high level of stress from the text, "I'm feeling stressed," and adapts the response accordingly. The user then types, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice, such as, "Write down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1220] In this way, this system takes into consideration the user's emotions and provides an environment in which it is easy to seek advice about mental health issues. By correcting cognitive distortions, this system can improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[1221] The processing flow will be explained below.

[1222] Step 1:

[1223] The user taps the app on their smartphone or computer to launch it, then enters their account information (email address and password) to log in.

[1224] Step 2:

[1225] The terminal sends a login request from the user to the server. The login information is encrypted using the SSL / TLS protocol.

[1226] Step 3:

[1227] The server parses the received login information and matches it with the account information in its database. If successful, a login session is established.

[1228] Step 4:

[1229] The server sends the main screen data to the device, which displays an interface for starting a conversation with the AI ​​counselor.

[1230] Step 5:

[1231] The device displays the main screen to the user, who enters "I'm tired from work today" in the text box and presses the send button.

[1232] Step 6:

[1233] The terminal sends the user's input text data to the server, which packages the text data in an appropriate format.

[1234] Step 7:

[1235] The server's text analysis engine receives the input text and analyzes it, extracting keywords and sentiments from the text.

[1236] Step 8:

[1237] The server's emotion engine recognizes the user's emotional state based on the extracted keywords and emotions. For example, it recognizes fatigue from the text "tired."

[1238] Step 9:

[1239] The server uses the generative AI model to generate an appropriate response based on the emotion recognition results, such as "Tell me specifically what kind of work you did."

[1240] Step 10:

[1241] The server sends the generated response to the terminal. The response message is sent in text format.

[1242] Step 11:

[1243] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[1244] Step 12:

[1245] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[1246] Step 13:

[1247] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[1248] Step 14:

[1249] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[1250] Step 15:

[1251] The device displays the analysis results visual data to the user, who then sees a message on the dashboard saying, "You have a tendency toward generalization."

[1252] Step 16:

[1253] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[1254] Step 17:

[1255] The device displays advice to the user, who can then follow the advice and list their successes, thereby recognizing their positive aspects.

[1256] Step 18:

[1257] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[1258] Step 19:

[1259] The device sends the collected feedback data to the server, which can be used to improve the service.

[1260] Step 20:

[1261] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[1262] Step 21:

[1263] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[1264] Step 22:

[1265] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[1266] Step 23:

[1267] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[1268] Example 2

[1269] 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."

[1270] Conventional mental health systems were able to generate appropriate responses based on user input data, but they had difficulty recognizing individual emotional states and adjusting responses accordingly. They also had limited functionality for detecting cognitive distortions and providing appropriate cognitive behavioral therapy (CBT) advice. Furthermore, there were insufficient means for companies to provide data that would allow them to effectively understand employees' mental health conditions and take appropriate measures. To resolve these issues, a system that provides more accurate and personalized mental health care is needed.

[1271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1272] In this invention, the server includes means for receiving and analyzing text data entered by a user and generating an appropriate response, means for analyzing dialogue data to detect cognitive distortions in the user, means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user, means for recognizing emotions from the input text, and means for adjusting the response and advice based on the recognized emotional data. This enables highly accurate and personalized mental care, responses to cognitive distortions, and the provision of data to companies.

[1273] "User" refers to an individual who uses a system or application.

[1274] "Text data" refers to textual information that a user inputs into a system.

[1275] A "server" refers to a computer system that performs processes such as analyzing data and generating responses.

[1276] "Analysis" refers to the process of analyzing input data and identifying its content and characteristics.

[1277] "Response" refers to a reply message generated by the system in response to a user's input.

[1278] "Dialogue data" refers to the history and content of messages exchanged between a user and the system.

[1279] "Cognitive distortion" refers to biases or misunderstandings in a user's thinking or perception.

[1280] "Cognitive behavioral therapy (CBT)" refers to psychological therapy aimed at correcting cognitive distortions and promoting adaptive behavior.

[1281] "Emotion" refers to the user's psychological state or mood.

[1282] "Emotion engine" refers to a system component for recognizing and analyzing emotions from user text data.

[1283] "Feedback" refers to the opinions and impressions provided by users after using the system.

[1284] A "corporate administrator" refers to a person in charge of managing the entire system and evaluating mental health within a company.

[1285] A "dashboard" refers to an interface that visualizes and displays data such as system usage and effectiveness measurements.

[1286] "Customization" refers to the ability for users to change system settings and profiles to suit their own needs and preferences.

[1287] "Profile" refers to a data set of information and settings related to a user.

[1288] "Advice" refers to guidance or suggestions provided to the user by the system.

[1289] A "task" refers to a specific task or exercise that a user must perform.

[1290] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts responses accordingly, providing more personalized mental care. The system analyzes user interaction data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. It also has a function that aggregates employee mental health status and provides it in the form of a dashboard for corporate managers.

[1291] Specific program description

[1292] Hardware and software used

[1293] Hardware: smartphones, PCs, servers

[1294] Software: Text analysis engines, sentiment engines, generative AI models (e.g., GPT-3), database management systems, dashboard generation tools

[1295] Details of data processing and calculation

[1296] 1. Analyzing user input data

[1297] A user launches the app on their smartphone or PC and logs in with their account information. The device sends a login request to the server, which then authenticates the user. If authentication is successful, the server sends the main screen to the device, which then displays the main screen to the user. The user then enters dialogue text, which the device then sends to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model.

[1298] 2. Emotion recognition

[1299] The server uses an emotion engine to recognize the user's emotions from the input text. For example, the phrase "tired" identifies fatigue or stress. The recognized emotion data is sent to the terminal in an appropriate format, and the user's emotional state is displayed.

[1300] 3. Analysis of cognitive distortions

[1301] The system collects daily user interaction data and periodically sends it to a server. The server then inputs the interaction data into a cognitive distortion analysis module, which uses data mining techniques to detect the user's cognitive distortions. The detection results are generated as visual data, sent to the device, and displayed to the user.

[1302] 4. Support for cognitive behavioral therapy (CBT)

[1303] The server combines the results of the cognitive distortion analysis with data from the emotion engine to generate individually customized CBT advice and tasks. The generated advice and exercises are sent to the user's device and displayed. The user follows the advice, changes their thoughts and behavior, and engages in the exercises.

[1304] 5. Gathering feedback and improving the system

[1305] Users enter their feedback into a survey form within the app, and the device sends the data to the server. The server stores the feedback in a database, analyzes it, and uses it to improve the service. The feedback results are also provided to company administrators in the form of a dashboard, allowing them to measure the effectiveness of the service.

[1306] 6. Enterprise Features

[1307] The server aggregates usage and effectiveness data for each company and generates a dashboard. This dashboard data is sent to a portal for company administrators, who can analyze employee stress levels and the effectiveness of mental care. Based on the data obtained, company administrators can take action to strengthen and improve employee mental health measures.

[1308] Examples of concrete examples and prompts

[1309] For example, if a user enters "Recently, things haven't been going well at work and I'm feeling stressed," the server will analyze this input text and respond with, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine will recognize a high level of stress from the text "I'm feeling stressed," and tailor the response accordingly.

[1310] Example prompt sentence:

[1311] "Recently, things haven't been going well at work and I'm feeling stressed."

[1312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1313] Step 1:

[1314] The user launches the app.

[1315] Users tap the app on their smartphone or computer to launch it and display the login screen.

[1316] Input: Login request (user ID and password)

[1317] Output: A login request is sent to the server.

[1318] Step 2:

[1319] The terminal sends a login request to the server.

[1320] When the login button is pressed, the terminal transmits the user's authentication information to the server.

[1321] Input: User authentication information (user ID and password)

[1322] Output: The authentication information reaches the server.

[1323] Step 3:

[1324] The server authenticates the user.

[1325] The server checks the received authentication information against a database and generates an authentication result.

[1326] Input: Authentication information that reaches the server

[1327] Output: Authentication result (success or failure)

[1328] Step 4:

[1329] The server establishes a login session and sends the main screen to the terminal.

[1330] If the authentication is successful, the server starts a session for the user, generates main screen data, and sends it to the terminal.

[1331] Input: Authentication success result

[1332] Output: Main screen data

[1333] Step 5:

[1334] The terminal displays the main screen to the user.

[1335] The terminal displays the received main screen data on the user interface.

[1336] Input: Main screen data

[1337] Output: Main screen display

[1338] Step 6:

[1339] The user begins a conversation with the AI ​​counselor.

[1340] The user enters the dialogue text in the text box and presses the send button.

[1341] Input: User dialogue text (e.g., "I'm tired from work today.")

[1342] Output: The dialogue text is sent from the terminal to the server.

[1343] Step 7:

[1344] The server analyzes the dialogue text.

[1345] The server's text analysis engine breaks down the dialogue text and recognizes context and sentiment.

[1346] Input: Dialogue text

[1347] Output: Analysis results (context and sentiment data)

[1348] Step 8:

[1349] The server generates an appropriate response using a generative AI model.

[1350] The server uses a generative AI model (e.g., GPT-3) to generate an appropriate response based on the analysis results.

[1351] Input: Analysis results (context and emotion data)

[1352] Output: Response message

[1353] Step 9:

[1354] The server sends the generated response to the terminal.

[1355] The server sends the generated response message to the terminal.

[1356] Input: Response message

[1357] Output: The response message arrives at the terminal.

[1358] Step 10:

[1359] The terminal displays the response message to the user.

[1360] The terminal displays the received response message on the user interface.

[1361] Input: Response message

[1362] Output: Display of response message

[1363] Step 11:

[1364] The server recognizes emotions from the input text.

[1365] The server's emotion engine analyzes the input text and identifies the user's emotional state.

[1366] Input: Dialogue text

[1367] Output: Emotion data (e.g., fatigue, stress)

[1368] Step 12:

[1369] The server transmits the emotion data to the terminal.

[1370] The server transmits the recognized emotion data to the terminal.

[1371] Input: Emotion data

[1372] Output: Emotion data arrives at the terminal.

[1373] Step 13:

[1374] The terminal displays the emotional state to the user.

[1375] The device visualizes the received emotion data and displays it on a user interface.

[1376] Input: Emotion data

[1377] Output: Display of emotional state

[1378] (Application example 2)

[1379] 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."

[1380] Conventional mental healthcare systems have difficulty recognizing users' emotions in real time and providing appropriate advice, and have faced particular challenges in terms of insufficient on-site immediate response and continuous monitoring functions. Furthermore, seamless data collection and analysis are required, but current systems have technical limitations. There is a particular need to improve the quality of mental health support in high-stress workplaces, such as security staff.

[1381] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically displaying questions about mental health and transmitting data of the user's responses to the server, means for recognizing the user's emotions in real time through conversation and facial expression recognition and providing appropriate relaxation methods and mental care advice when a high-stress state is detected, and means for collecting user feedback and analyzing the data to improve the quality of the service. This makes it possible to recognize the user's emotions in real time and provide appropriate, individually customized mental care.

[1382] "AI Counselor" is an artificial intelligence system that analyzes text data entered by the user and generates appropriate responses.

[1383] "Cognitive distortions" refer to incorrect cognitive patterns in a user's thinking, and the aim is to detect and correct these.

[1384] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct users' cognitive distortions and promote appropriate behavior.

[1385] "Smart glasses" are a type of electronic device worn by a user that can provide visual information and perform data collection and analysis.

[1386] "Mental health" refers to an individual's mental health status and aims to provide support to maintain or improve it.

[1387] "Relaxation methods" refer to techniques and activities that reduce stress and promote mental and physical relaxation in users.

[1388] "Feedback" refers to opinions and impressions provided by users after using the system, and collecting and analyzing this information will help improve the system.

[1389] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state in real time from their conversation and facial expressions.

[1390] The present invention relates to a mental health support system using smart glasses designed for security staff. When a user wears the smart glasses, the system periodically displays questions about mental health and transmits the user's responses to a server in real time. Specific embodiments for implementing the present invention are described below.

[1391] First, an application installed on the smart glasses periodically asks the user simple questions about their mental health, such as, "Please rate your current mood on a scale of 1 to 10." Once the user enters their information, the data is sent to a server via a wireless network.

[1392] The server runs an AI counselor to analyze the received data. This AI counselor uses a generative AI model to analyze the input data and generate appropriate responses and advice. The generated data is fed back to the smart glasses in real time.

[1393] In particular, the system is equipped with an emotion recognition engine that uses speech and facial expression recognition technology to recognize the user's emotions in real time. For example, if a user types "I'm tired," the emotion recognition engine will detect a high level of stress and provide immediate advice such as "Try taking a few minutes of rest and taking some deep breaths."

[1394] Furthermore, the server periodically analyzes this dialogue data and runs an algorithm to detect cognitive distortions. Based on the detected cognitive distortions, cognitive behavioral therapy (CBT) advice and tasks are generated. For example, if the cognitive distortion of "holism" is detected, the user is given specific advice such as "Try writing down three successful experiences."

[1395] The collected feedback data is analyzed to improve the quality of the service. Users can enter feedback on advice provided within the app, such as "This advice made me feel better," and this data is sent back to the server for analysis, which helps improve the service.

[1396] In a specific example of an embodiment, the following prompt may be presented:

[1397] "Rate your current mood from 1 to 10"

[1398] "Tell me about the most stressful thing that happened to you recently."

[1399] "Tell me about the most exhausting moment at work today."

[1400] The system of the present invention includes smart glasses, an AI counselor, an emotion recognition engine, and a cognitive behavioral therapy (CBT) module, and can provide personalized and appropriate mental health care to users in real time. In this way, high-quality mental health care support is realized while taking into account the user's emotional state.

[1401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1402] Step 1:

[1403] A user puts on smart glasses and launches a mental health check application. The device prompts the user, saying, "Please rate your current mood on a scale of 1 to 10." The user then inputs the rating score. This input data (rating score) is stored on the device and transmitted to a server via a wireless network.

[1404] Step 2:

[1405] The server analyzes the received data (evaluation score) and uses a generative AI model to determine the user's state. The system evaluates the user's current mental state based on the input data. For example, if the evaluation score is low, it will determine that the user is under high stress. Based on the analysis results, it generates an appropriate response or advice. This response data is sent to the device.

[1406] Step 3:

[1407] The device displays the response data received from the server to the user. For example, it displays advice such as "Take a few minutes break and try taking deep breaths." The user follows the displayed advice. The device collects how the user responded to the provided advice (e.g., thoughts and feedback).

[1408] Step 4:

[1409] The server uses speech and facial expression recognition technology to analyze the user's emotions in real time. For example, if the user types "I'm tired," the server uses an emotion recognition engine to detect a high level of stress. Based on the analysis results, it immediately generates appropriate countermeasures (e.g., relaxation techniques) and sends them to the device.

[1410] Step 5:

[1411] The device receives the solution from the server and displays it to the user. For example, a specific relaxation method such as "Take deep breaths repeatedly for one minute" is displayed. The user follows the solution and inputs the results and impressions into the device, which are then sent back to the server.

[1412] Step 6:

[1413] The server continuously collects dialogue data and performs data analysis to detect cognitive distortions. Based on the collected data, a cognitive distortion analysis algorithm is run to identify the cognitive distortions the user has. Based on the results of this analysis, cognitive behavioral therapy (CBT) advice and tasks are generated and sent to the device.

[1414] Step 7:

[1415] The device displays the generated CBT advice and tasks to the user. For example, a specific task such as "Write down three successful experiences" is presented. The user performs this task and inputs the results, which are then sent to the server via the device.

[1416] Step 8:

[1417] The server collects and analyzes user feedback and performs data analysis to improve the quality of the system. Based on the feedback, the server tunes the generative AI model and emotion recognition engine. This process improves the accuracy and usefulness of the advice and tasks provided.

[1418] 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.

[1419] 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.

[1420] 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.

[1421] [Fourth embodiment]

[1422] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1423] 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.

[1424] 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).

[1425] 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.

[1426] 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.

[1427] 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).

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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."

[1435] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. Furthermore, it receives user feedback and anonymizes, aggregates, and displays the mental health status of all employees to company managers.

[1436] Program processing overview

[1437] To realize this system, the following program processing is performed.

[1438] 1. The user launches the app

[1439] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[1440] Terminal: Sends a login request to the server.

[1441] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[1442] Terminal: Presents the main screen to the user.

[1443] 2. Start a conversation with an AI counselor

[1444] User: Enters "I'm tired from work today" in the text box and presses the send button.

[1445] Terminal: Sends text data entered by the user to the server.

[1446] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[1447] Server: Generates and sends the response to the device.

[1448] Terminal: Displays the AI ​​counselor's response message to the user.

[1449] 3. Analyze users' cognitive distortions

[1450] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[1451] Terminal: Periodically transmits collected interaction data to the server.

[1452] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[1453] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[1454] Terminal: Displays the visual data of the analysis results to the user.

[1455] 4. Provide cognitive behavioral therapy support

[1456] Server: Generates individually customized cognitive behavioral therapy approaches and specific advice based on the results of cognitive distortion analysis.

[1457] Server: Sends generated advice and exercises to the device.

[1458] Device: Displays advice and exercise instructions to the user.

[1459] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[1460] 5. Feedback and Improvement

[1461] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[1462] Device: Sends collected feedback data to the server.

[1463] Server: We store your feedback in a database, analyze it and use it to improve our services.

[1464] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[1465] 6. Provision of enterprise features

[1466] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[1467] Server: Sends the generated dashboard data to the enterprise administrator portal.

[1468] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[1469] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[1470] Specific examples

[1471] As a specific example, suppose a user types, "Recently, things haven't been going well at work and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you are feeling specifically." The user then continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice such as, "Try writing down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1472] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent mental illness in people with hidden high levels of stress.

[1473] The processing flow will be explained below.

[1474] Step 1:

[1475] A user launches the app on their smartphone or computer and logs in with their account information, including their email address and password.

[1476] Step 2:

[1477] The terminal sends a login request from the user to the server, where the login information is encrypted using a security protocol.

[1478] Step 3:

[1479] The server analyzes the received login information and authenticates the user by comparing it with the account information in the database. If authentication is successful, a login session is established.

[1480] Step 4:

[1481] The server sends the main screen data to the device, which includes an interface for starting a conversation with the AI ​​counselor.

[1482] Step 5:

[1483] The device displays the main screen to the user, where the user enters "I'm tired from work today" in the text box and presses the send button.

[1484] Step 6:

[1485] The terminal transmits the user's input text data to the server, where the text data is packaged in an appropriate format.

[1486] Step 7:

[1487] The server's text analysis engine receives the input text and performs an analysis, which evaluates the user's emotions and stress level.

[1488] Step 8:

[1489] The server uses the generative AI model to generate an appropriate response. For example, if the input text is "I'm tired from work today," it generates the response "Tell me specifically what kind of work you did."

[1490] Step 9:

[1491] The server generates a response and sends it to the terminal. The response message is sent in text format.

[1492] Step 10:

[1493] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[1494] Step 11:

[1495] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[1496] Step 12:

[1497] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[1498] Step 13:

[1499] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[1500] Step 14:

[1501] The device displays the analysis results visual data to the user, who then sees the message "There is a tendency for generalization" on the dashboard.

[1502] Step 15:

[1503] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[1504] Step 16:

[1505] The device displays advice to the user, who then follows the advice and lists their successes.

[1506] Step 17:

[1507] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[1508] Step 18:

[1509] The device sends the collected feedback data to the server, which can be used to improve the service.

[1510] Step 19:

[1511] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[1512] Step 20:

[1513] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[1514] Step 21:

[1515] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[1516] Step 22:

[1517] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[1518] Example 1

[1519] 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."

[1520] Conventional mental health support systems lack an easy-to-use environment for users, do not provide real-time feedback, and make it difficult for corporate managers to grasp the mental health status of all employees. Furthermore, they lack the means to properly detect cognitive distortions and provide individually customized cognitive behavioral therapy approaches. To solve these issues, it is necessary to provide a system that is both useful and easy to use for both users and companies.

[1521] 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.

[1522] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response using a generative model; means for analyzing dialogue data to detect cognitive distortions in the user; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; and means for periodically collecting the user's dialogue data and generating the analysis results as visual data. This allows users to easily understand their own mental health status and receive appropriate support. Corporate managers can also anonymize and understand the mental health status of their employees and take appropriate measures.

[1523] The "AI Counselor" is an artificial intelligence system that analyzes text data entered by users and generates appropriate responses using a generative model.

[1524] "Text data" refers to character information that a user inputs through an application.

[1525] A "generative model" is a machine learning model used to generate appropriate responses based on input data.

[1526] "Dialogue data" refers to the text-based communication between the user and the AI ​​counselor.

[1527] "Cognitive distortions" are irrational thinking patterns and misperceptions that users have, which can have a negative impact on their mental health.

[1528] "Cognitive behavioral therapy" is a type of psychotherapy that corrects cognitive distortions and encourages more adaptive behavior.

[1529] "Visual data" refers to graphs and charts that display analysis results in a visually easy-to-understand manner.

[1530] "Feedback" refers to information that users can input about their experience and thoughts on using the system.

[1531] A "dashboard" is a visual display screen designed to allow administrators to grasp system usage and analysis results at a glance.

[1532] A "corporate administrator" is a person within a company who is responsible for overseeing and managing the mental health of employees.

[1533] "Anonymization" is the process of processing data so that individual users' information cannot be identified.

[1534] "Customization" refers to the ability of users to tailor the system's features and profiles to suit their own preferences and needs.

[1535] "Profile" means your account data, including your personal information and settings.

[1536] "Periodic collection" refers to collecting data at regular intervals, with the aim of continuously reflecting updated information from users.

[1537] This invention is a system equipped with an AI counselor to support users' mental health. The system analyzes text data entered by users, detects cognitive distortions, and provides appropriate cognitive behavioral therapy (CBT) advice and tasks based on the results. It also receives user feedback and anonymizes and aggregates the mental health status of all employees for corporate managers.

[1538] The specific steps to implement this are shown below.

[1539] Program processing overview

[1540] 1. Launch the app and log in

[1541] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and authenticates the account information in a database. If authentication is successful, a login session is established, and main screen data is generated and sent to the device. The device displays the main screen data received from the server to the user.

[1542] 2. Start a conversation with an AI counselor

[1543] The user enters a message in the text box and presses the send button. The device sends the text data entered by the user to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model (e.g., OpenAI's GPT-3). The server then sends the generated response to the device, which displays the response message to the user.

[1544] 3. Analysis of cognitive distortions

[1545] Through daily interactions, users enter detailed information about stressful or difficult situations. The device periodically transmits the collected dialogue data to the server. The server inputs the dialogue data into a cognitive distortion analysis module and uses natural language processing technology to detect patterns of cognitive distortion from the user's text. The server generates the results of the cognitive distortion analysis as visual data and reflects this in each user's profile along with their dialogue history. The server then transmits the analysis results, including the visual data, to the device, which then displays the visual data of the cognitive distortion analysis results to the user.

[1546] 4. Providing cognitive behavioral therapy advice

[1547] Based on the results of the cognitive distortion analysis, the server generates an individually customized cognitive behavioral therapy approach and specific advice. Specific exercises and ways of thinking are suggested using the generative AI model and existing CBT manuals. The server then sends the generated advice and exercises to the device, which then displays the advice and exercise instructions to the user. The user follows the advice provided, reassessing their way of thinking and behavior, and engaging in the exercises. This may include writing a diary or trying relaxation techniques.

[1548] 5. Collecting feedback and improving our services

[1549] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device then sends the feedback data from the user to the server. The server stores the received feedback in a database and analyzes the feedback data to help improve the service. The server then generates the feedback results in the form of a dashboard for company administrators and sends it to the administrator portal.

[1550] 6. Provision of enterprise features

[1551] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this data. The generated dashboard data is sent to a portal for company administrators. Company administrators log in to the portal and check the dashboard to understand employee stress levels and the effectiveness of mental health care. Based on the obtained data, company administrators can take specific actions to strengthen and improve employee mental health measures.

[1552] Specific examples

[1553] When a user types, "Recently, things haven't been going well at work and I'm feeling stressed," the server analyzes the input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." If the user continues by typing, "I feel like I'm making a lot of mistakes and causing trouble for those around me," the server detects the cognitive distortion known as "generalization." The server displays the message "You have a tendency to generalize" on the user's dashboard, and further generates advice, "Try writing down three successful experiences," which is displayed on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1554] In this way, this system provides an environment where users feel comfortable discussing their mental health issues, and by correcting cognitive distortions, it is possible to improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[1555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1556] Step 1:

[1557] Launching the app and logging in

[1558] The user launches a dedicated app on their smartphone or PC and is taken to the login screen. The device receives the account information (username, password) entered by the user and sends a login request to the server. The server receives the login request and performs authentication by checking the account information against a database. It receives the account information as input and outputs the authentication result. If authentication is successful, the server establishes a login session, generates main screen data, and sends it to the device. The device displays the main screen data received from the server to the user.

[1559] Step 2:

[1560] Start a conversation with an AI counselor

[1561] The user enters a message in the text box and presses the send button. The device sends the text data to the server. The server inputs the entered text data into a text analysis engine and performs text analysis. It receives the user's message as input and outputs the analysis results. It passes the analysis results to a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. It outputs a response message and sends it to the device. The device displays the response message received from the server to the user.

[1562] Step 3:

[1563] Analysis of cognitive distortions

[1564] The user enters detailed information about stressful or difficult situations through daily interactions. The device periodically sends the collected interaction data to the server. The server inputs the interaction data into a cognitive distortion analysis module, which uses natural language processing technology to detect cognitive distortion patterns from the user's text. The server receives the interaction data as input and outputs distortion patterns and analysis results. The server generates visual data of the cognitive distortion analysis results and sends it to the device. The device displays the visual data and informs the user of the cognitive distortion analysis results.

[1565] Step 4:

[1566] Providing cognitive behavioral therapy advice

[1567] The server generates an individually customized cognitive behavioral therapy approach and specific advice based on the results of the cognitive distortion analysis. It uses the analysis results as input and outputs advice and exercise content. The generated advice and exercise content are sent to the device. The device displays the advice and exercise instructions to the user. The user follows the advice provided, revises their way of thinking and behavior, and engages in the exercises.

[1568] Step 5:

[1569] Collecting feedback and improving our services

[1570] Users enter their thoughts and opinions into the feedback form within the app and press the send button. The device sends the feedback data from the user to the server. The server stores the feedback data in a database, analyzes it, and uses it to improve the service. It collects the feedback data as input and outputs the analysis results. The server also generates the feedback results in dashboard format for company administrators and sends them to the administrator portal.

[1571] Step 6:

[1572] Providing enterprise features

[1573] The server aggregates usage and effectiveness measurement data for each company and generates a dashboard based on this. It receives usage and measurement data as input and outputs dashboard data. The generated dashboard data is sent to a portal for company administrators. The company administrator logs in to the portal and checks the dashboard. Based on the obtained data, the company administrator takes specific actions to strengthen and improve employee mental health measures.

[1574] (Application example 1)

[1575] 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."

[1576] In modern society, many people suffer from mental health issues due to work and life stress. In the workplace in particular, employees often experience stress and mental disorders, which can result in decreased productivity and increased risk of employee turnover. There is a need for a system that can effectively monitor employees' mental health in real time and provide appropriate responses. However, conventional systems have difficulty analyzing mental health status in real time or responding immediately on-site. Furthermore, corporate managers have limited means of understanding the mental health status of their entire workforce. This poses a challenge, making it difficult to intervene early to prevent mental health problems.

[1577] 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.

[1578] In this invention, the server includes: means for receiving and analyzing text data entered by a user and generating an appropriate response; means for analyzing the dialogue data to detect the user's cognitive distortions; means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user; means for analyzing the user's statements and actions in real time using speech recognition and natural language processing to detect the user's mental health status; means for proposing countermeasures based on the detected mental health status; and means for displaying the feedback results as visual data. This enables real-time analysis of mental health status and immediate response. It also makes it easier for corporate managers to grasp the mental health status of their entire workforce, enabling early intervention and appropriate support.

[1579] "AI Counselor" is a system that uses artificial intelligence to analyze users' text data and generate appropriate responses and advice.

[1580] "User cognitive distortions" refer to cognitive biases such as biased thinking, misunderstandings, and overgeneralizations held by users.

[1581] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct cognitive distortions and improve behavior and thinking.

[1582] "Speech recognition" is a technology that analyzes voice data and converts it into text data.

[1583] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[1584] "Real-time analysis" is the process of instantly analyzing user input and providing immediate feedback on the results.

[1585] "Mental health status" refers to the mental and psychological well-being of a user.

[1586] "Responses" are specific measures or advice suggested based on the detected mental health condition.

[1587] "Feedback results" refers to reaction data such as user evaluations and impressions, which are analyzed and used to improve the system.

[1588] "Visual data" is graphical information used to visually display analysis results and feedback.

[1589] This invention is a system equipped with an AI counselor that provides real-time mental health support and offers appropriate solutions to users. The system works in conjunction with smart glasses, a server, and other devices.

[1590] The server receives and analyzes the text data entered by the user and generates an appropriate response. It uses speech recognition technology (e.g., Google Speech Recognition API) to convert the user's speech into text data. It then analyzes the user's statements and actions using speech recognition and natural language processing technology (e.g., OpenAI's generative AI model) to detect their mental health status in real time. Based on the detected cognitive distortions and mental health status, it generates and provides advice and tasks based on cognitive behavioral therapy to the user.

[1591] The server proposes countermeasures based on the detected mental health status and displays the feedback results as visual data, which can be displayed on the user's smart glasses or other display devices using graphical display technologies such as Matplotlib.

[1592] For example, if a user says through smart glasses, "I've been feeling tired lately because of stress at work," the speech recognition technology converts this into text data. The server inputs the prompt sentence into the generative AI model and analyzes the speech:

[1593] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[1594] Based on the analysis results, the server generates specific advice such as, "This person is experiencing significant work-related stress. We recommend talking to them to relieve stress and encouraging them to take a short break." The generated advice is provided to the user in real time.

[1595] The server can also aggregate and display anonymized mental health status data for all employees to company administrators, allowing them to grasp the overall picture of mental health status and take necessary measures.

[1596] This system overcomes the difficulties of real-time monitoring and immediate response that existed in conventional mental health support systems, making it possible to efficiently manage and improve the mental health status of employees.

[1597] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1598] Step 1:

[1599] A user wears smart glasses and speaks, which is collected as voice data by the terminal via the smart glasses' microphone.

[1600] Input: User's voice data

[1601] Output: Audio data sent to the device

[1602] Specific operation: The user says, "I've been feeling tired lately because of stress at work." The microphone in the smart glasses recognizes the voice and sends the voice data to the device.

[1603] Step 2:

[1604] The device receives the voice data and converts it into text data using voice recognition technology (e.g., Google Speech Recognition API).

[1605] Input: Audio data

[1606] Output: Text data

[1607] Specific operation: The device recognizes "I've been feeling tired lately because of work stress" and converts this into "text data."

[1608] Step 3:

[1609] The terminal transmits the converted text data to the server.

[1610] Input: Text data

[1611] Output: Send text data to the server

[1612] Specific operation: The terminal transfers the generated text data to the server.

[1613] Step 4:

[1614] The server receives the text data and analyzes the text using natural language processing techniques (e.g., OpenAI's generative AI model).

[1615] Input: Text data

[1616] Output: Analysis results

[1617] Specific operation: The server analyzes the "text data" and understands the information "I've been feeling tired lately because of work stress." Then, it analyzes it using the following prompt sentence for the generative AI model:

[1618] Below is an excerpt from a conversation heard by security staff: "I've been feeling tired lately due to work stress." Analyze this to assess the subject's condition and provide appropriate countermeasures.

[1619] Step 5:

[1620] Based on the analysis results, the server detects cognitive distortions and signs of stress and generates appropriate countermeasures and advice.

[1621] Input: Analysis results

[1622] Output: Countermeasures, advice

[1623] Specific action: The server generates advice such as "We recommend talking to the person to relieve stress and encouraging them to take a short break."

[1624] Step 6:

[1625] The server sends the generated advice and countermeasures to the terminal.

[1626] Input: Measures, advice

[1627] Output: Send advice and solutions to your device

[1628] Specific operation: The server transfers the generated advice to the terminal.

[1629] Step 7:

[1630] The device then displays the received advice and solutions to the user in real time, and these are presented to the user through the smart glasses display.

[1631] Input: Measures, advice

[1632] Output: Shown to the user

[1633] Specific action: The device displays the advice "We recommend talking to someone to relieve stress or taking a short break" on the smart glasses display.

[1634] Step 8:

[1635] The user inputs the result of the advice received as feedback and transmits it to the terminal.

[1636] Input: Feedback data

[1637] Output: Send feedback data to the terminal

[1638] Specific operation: The user inputs "The advice was helpful" through the smart glasses, and the device sends this to the server.

[1639] Step 9:

[1640] The server collects feedback data from users and uses it to improve the system and measure its effectiveness.

[1641] Input: Feedback data

[1642] Output: analysis results, improvement suggestions

[1643] Specific operation: The server analyzes the feedback data and identifies areas for improvement in the system. It also generates visual data and sends it to the company administrator's portal to display the aggregated results of mental health status to the company administrator.

[1644] 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.

[1645] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts the response, providing more personalized mental care. This system analyzes the user's dialogue data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts responses and advice based on the recognition results.

[1646] Program processing overview

[1647] To realize this system, the following program processing is performed.

[1648] 1. The user launches the app

[1649] User: Tap to launch the app on their smartphone or computer and log in with their account information.

[1650] Terminal: Sends a login request to the server.

[1651] Server: Authenticates the user, establishes a login session, and sends the main screen to the terminal.

[1652] Terminal: Presents the main screen to the user.

[1653] 2. Start a conversation with an AI counselor

[1654] User: Enters "I'm tired from work today" in the text box and presses the send button.

[1655] Terminal: Sends text data entered by the user to the server.

[1656] Server: Analyzes the input text with a text analytics engine and generates an appropriate response using a generative AI model.

[1657] Server: Generates and sends the response to the device.

[1658] Terminal: Displays the AI ​​counselor's response message to the user.

[1659] 3. Recognize user emotions

[1660] Server: The emotion engine recognizes the user's emotion from the input text. For example, it recognizes the feeling of fatigue from the text "I'm tired."

[1661] Server: Sends the recognized emotion data to the device in an appropriate format and displays the user's emotional state.

[1662] 4. Analyze users' cognitive distortions

[1663] Users: Maintain daily interactions and provide details about stressful or difficult situations.

[1664] Terminal: Periodically transmits collected interaction data to the server.

[1665] Server: Inputs the dialogue data into the cognitive distortion analysis module to detect the user's cognitive distortions.

[1666] Server: Generates the results of cognitive distortion analysis as visual data and sends it to the device.

[1667] Terminal: Displays the visual data of the analysis results to the user.

[1668] 5. Provide cognitive behavioral therapy support

[1669] Server: Using the cognitive distortion analysis results and emotion engine data together, it generates individually customized cognitive behavioral therapy approaches and specific advice.

[1670] Server: Sends generated advice and exercises to the device.

[1671] Device: Displays advice and exercise instructions to the user.

[1672] User: Based on the advice, they change their thoughts and behaviors and engage in the exercises.

[1673] 6. Feedback and Improvement

[1674] Users: Enter their feedback in the in-app survey form and submit comments such as, "This advice made me feel better."

[1675] Device: Sends collected feedback data to the server.

[1676] Server: We store your feedback in a database, analyze it and use it to improve our services.

[1677] Server: Provides the feedback results in a dashboard format to company administrators to measure effectiveness.

[1678] 7. Provision of enterprise features

[1679] Server: Aggregates usage and effectiveness measurement data for each company and generates a dashboard.

[1680] Server: Sends the generated dashboard data to the enterprise administrator portal.

[1681] Device (Company Administrator): Company administrators can view the dashboard and analyze employee stress levels and the effectiveness of mental care.

[1682] Company managers: Use the data obtained to take action to strengthen and improve employee mental health measures.

[1683] Specific examples

[1684] As a specific example, suppose a user types, "Work hasn't been going well lately, and I'm feeling stressed." The server analyzes this input text and responds, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine recognizes a high level of stress from the text, "I'm feeling stressed," and adapts the response accordingly. The user then types, "I feel like I'm making a lot of mistakes and causing trouble for those around me." The server detects the cognitive distortion known as "generalization" and displays the message "You tend to generalize" on the user's dashboard. The server also generates advice, such as, "Write down three successful experiences," and displays it on the device. By following this advice and making a list of successful experiences, the user regains confidence.

[1685] In this way, this system takes into consideration the user's emotions and provides an environment in which it is easy to seek advice about mental health issues. By correcting cognitive distortions, this system can improve mental health and prevent hidden mental illnesses in people with high levels of stress.

[1686] The processing flow will be explained below.

[1687] Step 1:

[1688] The user taps the app on their smartphone or computer to launch it, then enters their account information (email address and password) to log in.

[1689] Step 2:

[1690] The terminal sends a login request from the user to the server. The login information is encrypted using the SSL / TLS protocol.

[1691] Step 3:

[1692] The server parses the received login information and matches it with the account information in its database. If successful, a login session is established.

[1693] Step 4:

[1694] The server sends the main screen data to the device, which displays an interface for starting a conversation with the AI ​​counselor.

[1695] Step 5:

[1696] The device displays the main screen to the user, who enters "I'm tired from work today" in the text box and presses the send button.

[1697] Step 6:

[1698] The terminal sends the user's input text data to the server, which packages the text data in an appropriate format.

[1699] Step 7:

[1700] The server's text analysis engine receives the input text and analyzes it, extracting keywords and sentiments from the text.

[1701] Step 8:

[1702] The server's emotion engine recognizes the user's emotional state based on the extracted keywords and emotions. For example, it recognizes fatigue from the text "tired."

[1703] Step 9:

[1704] The server uses the generative AI model to generate an appropriate response based on the emotion recognition results, such as "Tell me specifically what kind of work you did."

[1705] Step 10:

[1706] The server sends the generated response to the terminal. The response message is sent in text format.

[1707] Step 11:

[1708] The terminal displays the response message received from the server to the user, allowing the user to check the response from the server.

[1709] Step 12:

[1710] The user again types into the text box and continues the dialogue, this time typing "I feel like I make a lot of mistakes."

[1711] Step 13:

[1712] The device sends the input text back to the server, which analyzes it and detects the cognitive distortion known as "totalization."

[1713] Step 14:

[1714] The server generates the results of the cognitive distortion analysis as visual data and sends the data to the terminal to be displayed on the user's dashboard.

[1715] Step 15:

[1716] The device displays the analysis results visual data to the user, who then sees a message on the dashboard saying, "You have a tendency toward generalization."

[1717] Step 16:

[1718] Based on the analysis results, the server generates advice such as "Write down three successful experiences" as part of cognitive behavioral therapy and sends it to the device.

[1719] Step 17:

[1720] The device displays advice to the user, who can then follow the advice and list their successes, thereby recognizing their positive aspects.

[1721] Step 18:

[1722] Users can enter and submit feedback about the effectiveness of the advice and their impressions through a survey form within the app.

[1723] Step 19:

[1724] The device sends the collected feedback data to the server, which can be used to improve the service.

[1725] Step 20:

[1726] The server stores the feedback data in a database and analyzes it, which helps determine the next service updates and improvements.

[1727] Step 21:

[1728] As a function for businesses, the server compiles usage and effectiveness measurement data for each company and generates a dashboard.

[1729] Step 22:

[1730] The server generates dashboard data and sends it to a portal for company administrators, who can use it to understand the mental health status of their employees.

[1731] Step 23:

[1732] The device (company administrator) checks the dashboard and analyzes employee stress levels and the effectiveness of mental care. Based on the data obtained, the company administrator can take action to strengthen and improve employee mental health measures.

[1733] Example 2

[1734] 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."

[1735] Conventional mental health systems were able to generate appropriate responses based on user input data, but they had difficulty recognizing individual emotional states and adjusting responses accordingly. They also had limited functionality for detecting cognitive distortions and providing appropriate cognitive behavioral therapy (CBT) advice. Furthermore, there were insufficient means for companies to provide data that would allow them to effectively understand employees' mental health conditions and take appropriate measures. To resolve these issues, a system that provides more accurate and personalized mental health care is needed.

[1736] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1737] In this invention, the server includes means for receiving and analyzing text data entered by a user and generating an appropriate response, means for analyzing dialogue data to detect cognitive distortions in the user, means for generating advice and tasks in accordance with cognitive behavioral therapy based on the detected cognitive distortions and providing them to the user, means for recognizing emotions from the input text, and means for adjusting the response and advice based on the recognized emotional data. This enables highly accurate and personalized mental care, responses to cognitive distortions, and the provision of data to companies.

[1738] "User" refers to an individual who uses a system or application.

[1739] "Text data" refers to textual information that a user inputs into a system.

[1740] A "server" refers to a computer system that performs processes such as analyzing data and generating responses.

[1741] "Analysis" refers to the process of analyzing input data and identifying its content and characteristics.

[1742] "Response" refers to a reply message generated by the system in response to a user's input.

[1743] "Dialogue data" refers to the history and content of messages exchanged between a user and the system.

[1744] "Cognitive distortion" refers to biases or misunderstandings in a user's thinking or perception.

[1745] "Cognitive behavioral therapy (CBT)" refers to psychological therapy aimed at correcting cognitive distortions and promoting adaptive behavior.

[1746] "Emotion" refers to the user's psychological state or mood.

[1747] "Emotion engine" refers to a system component for recognizing and analyzing emotions from user text data.

[1748] "Feedback" refers to the opinions and impressions provided by users after using the system.

[1749] A "corporate administrator" refers to a person in charge of managing the entire system and evaluating mental health within a company.

[1750] A "dashboard" refers to an interface that visualizes and displays data such as system usage and effectiveness measurements.

[1751] "Customization" refers to the ability for users to change system settings and profiles to suit their own needs and preferences.

[1752] "Profile" refers to a data set of information and settings related to a user.

[1753] "Advice" refers to guidance or suggestions provided to the user by the system.

[1754] A "task" refers to a specific task or exercise that a user must perform.

[1755] This invention is a system equipped with an AI counselor that analyzes user input data and generates appropriate responses. It also combines an emotion engine that recognizes the user's emotions and adjusts responses accordingly, providing more personalized mental care. The system analyzes user interaction data, detects cognitive distortions, and generates cognitive behavioral therapy (CBT) advice. It also has a function that aggregates employee mental health status and provides it in the form of a dashboard for corporate managers.

[1756] Specific program description

[1757] Hardware and software used

[1758] Hardware: smartphones, PCs, servers

[1759] Software: Text analysis engines, sentiment engines, generative AI models (e.g., GPT-3), database management systems, dashboard generation tools

[1760] Details of data processing and calculation

[1761] 1. Analyzing user input data

[1762] A user launches the app on their smartphone or PC and logs in with their account information. The device sends a login request to the server, which then authenticates the user. If authentication is successful, the server sends the main screen to the device, which then displays the main screen to the user. The user then enters dialogue text, which the device then sends to the server. The server uses a text analysis engine to analyze the input text and generates an appropriate response using a generative AI model.

[1763] 2. Emotion recognition

[1764] The server uses an emotion engine to recognize the user's emotions from the input text. For example, the phrase "tired" identifies fatigue or stress. The recognized emotion data is sent to the terminal in an appropriate format, and the user's emotional state is displayed.

[1765] 3. Analysis of cognitive distortions

[1766] The system collects daily user interaction data and periodically sends it to a server. The server then inputs the interaction data into a cognitive distortion analysis module, which uses data mining techniques to detect the user's cognitive distortions. The detection results are generated as visual data, sent to the device, and displayed to the user.

[1767] 4. Support for cognitive behavioral therapy (CBT)

[1768] The server combines the results of the cognitive distortion analysis with data from the emotion engine to generate individually customized CBT advice and tasks. The generated advice and exercises are sent to the user's device and displayed. The user follows the advice, changes their thoughts and behavior, and engages in the exercises.

[1769] 5. Gathering feedback and improving the system

[1770] Users enter their feedback into a survey form within the app, and the device sends the data to the server. The server stores the feedback in a database, analyzes it, and uses it to improve the service. The feedback results are also provided to company administrators in the form of a dashboard, allowing them to measure the effectiveness of the service.

[1771] 6. Enterprise Features

[1772] The server aggregates usage and effectiveness data for each company and generates a dashboard. This dashboard data is sent to a portal for company administrators, who can analyze employee stress levels and the effectiveness of mental care. Based on the data obtained, company administrators can take action to strengthen and improve employee mental health measures.

[1773] Examples of concrete examples and prompts

[1774] For example, if a user enters "Recently, things haven't been going well at work and I'm feeling stressed," the server will analyze this input text and respond with, "Please tell me in detail what kind of stress you're feeling specifically." The emotion engine will recognize a high level of stress from the text "I'm feeling stressed," and tailor the response accordingly.

[1775] Example prompt sentence:

[1776] "Recently, things haven't been going well at work and I'm feeling stressed."

[1777] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1778] Step 1:

[1779] The user launches the app.

[1780] Users tap the app on their smartphone or computer to launch it and display the login screen.

[1781] Input: Login request (user ID and password)

[1782] Output: A login request is sent to the server.

[1783] Step 2:

[1784] The terminal sends a login request to the server.

[1785] When the login button is pressed, the terminal transmits the user's authentication information to the server.

[1786] Input: User authentication information (user ID and password)

[1787] Output: The authentication information reaches the server.

[1788] Step 3:

[1789] The server authenticates the user.

[1790] The server checks the received authentication information against a database and generates an authentication result.

[1791] Input: Authentication information that reaches the server

[1792] Output: Authentication result (success or failure)

[1793] Step 4:

[1794] The server establishes a login session and sends the main screen to the terminal.

[1795] If the authentication is successful, the server starts a session for the user, generates main screen data, and sends it to the terminal.

[1796] Input: Authentication success result

[1797] Output: Main screen data

[1798] Step 5:

[1799] The terminal displays the main screen to the user.

[1800] The terminal displays the received main screen data on the user interface.

[1801] Input: Main screen data

[1802] Output: Main screen display

[1803] Step 6:

[1804] The user begins a conversation with the AI ​​counselor.

[1805] The user enters the dialogue text in the text box and presses the send button.

[1806] Input: User dialogue text (e.g., "I'm tired from work today.")

[1807] Output: The dialogue text is sent from the terminal to the server.

[1808] Step 7:

[1809] The server analyzes the dialogue text.

[1810] The server's text analysis engine breaks down the dialogue text and recognizes context and sentiment.

[1811] Input: Dialogue text

[1812] Output: Analysis results (context and sentiment data)

[1813] Step 8:

[1814] The server generates an appropriate response using a generative AI model.

[1815] The server uses a generative AI model (e.g., GPT-3) to generate an appropriate response based on the analysis results.

[1816] Input: Analysis results (context and emotion data)

[1817] Output: Response message

[1818] Step 9:

[1819] The server sends the generated response to the terminal.

[1820] The server sends the generated response message to the terminal.

[1821] Input: Response message

[1822] Output: The response message arrives at the terminal.

[1823] Step 10:

[1824] The terminal displays the response message to the user.

[1825] The terminal displays the received response message on the user interface.

[1826] Input: Response message

[1827] Output: Display of response message

[1828] Step 11:

[1829] The server recognizes emotions from the input text.

[1830] The server's emotion engine analyzes the input text and identifies the user's emotional state.

[1831] Input: Dialogue text

[1832] Output: Emotion data (e.g., fatigue, stress)

[1833] Step 12:

[1834] The server transmits the emotion data to the terminal.

[1835] The server transmits the recognized emotion data to the terminal.

[1836] Input: Emotion data

[1837] Output: Emotion data arrives at the terminal.

[1838] Step 13:

[1839] The terminal displays the emotional state to the user.

[1840] The device visualizes the received emotion data and displays it on a user interface.

[1841] Input: Emotion data

[1842] Output: Display of emotional state

[1843] (Application example 2)

[1844] 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."

[1845] Conventional mental healthcare systems have difficulty recognizing users' emotions in real time and providing appropriate advice, and have faced particular challenges in terms of insufficient on-site immediate response and continuous monitoring functions. Furthermore, seamless data collection and analysis are required, but current systems have technical limitations. There is a particular need to improve the quality of mental health support in high-stress workplaces, such as security staff.

[1846] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically displaying questions about mental health and transmitting data of the user's responses to the server, means for recognizing the user's emotions in real time through conversation and facial expression recognition and providing appropriate relaxation methods and mental care advice when a high-stress state is detected, and means for collecting user feedback and analyzing the data to improve the quality of the service. This makes it possible to recognize the user's emotions in real time and provide appropriate, individually customized mental care.

[1847] "AI Counselor" is an artificial intelligence system that analyzes text data entered by the user and generates appropriate responses.

[1848] "Cognitive distortions" refer to incorrect cognitive patterns in a user's thinking, and the aim is to detect and correct these.

[1849] "Cognitive behavioral therapy" is a type of psychotherapy that aims to correct users' cognitive distortions and promote appropriate behavior.

[1850] "Smart glasses" are a type of electronic device worn by a user that can provide visual information and perform data collection and analysis.

[1851] "Mental health" refers to an individual's mental health status and aims to provide support to maintain or improve it.

[1852] "Relaxation methods" refer to techniques and activities that reduce stress and promote mental and physical relaxation in users.

[1853] "Feedback" refers to opinions and impressions provided by users after using the system, and collecting and analyzing this information will help improve the system.

[1854] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state in real time from their conversation and facial expressions.

[1855] The present invention relates to a mental health support system using smart glasses designed for security staff. When a user wears the smart glasses, the system periodically displays questions about mental health and transmits the user's responses to a server in real time. Specific embodiments for implementing the present invention are described below.

[1856] First, an application installed on the smart glasses periodically asks the user simple questions about their mental health, such as, "Please rate your current mood on a scale of 1 to 10." Once the user enters their information, the data is sent to a server via a wireless network.

[1857] The server runs an AI counselor to analyze the received data. This AI counselor uses a generative AI model to analyze the input data and generate appropriate responses and advice. The generated data is fed back to the smart glasses in real time.

[1858] In particular, the system is equipped with an emotion recognition engine that uses speech and facial expression recognition technology to recognize the user's emotions in real time. For example, if a user types "I'm tired," the emotion recognition engine will detect a high level of stress and provide immediate advice such as "Try taking a few minutes of rest and taking some deep breaths."

[1859] Furthermore, the server periodically analyzes this dialogue data and runs an algorithm to detect cognitive distortions. Based on the detected cognitive distortions, cognitive behavioral therapy (CBT) advice and tasks are generated. For example, if the cognitive distortion of "holism" is detected, the user is given specific advice such as "Try writing down three successful experiences."

[1860] The collected feedback data is analyzed to improve the quality of the service. Users can enter feedback on advice provided within the app, such as "This advice made me feel better," and this data is sent back to the server for analysis, which helps improve the service.

[1861] In a specific example of an embodiment, the following prompt may be presented:

[1862] "Rate your current mood from 1 to 10"

[1863] "Tell me about the most stressful thing that happened to you recently."

[1864] "Tell me about the most exhausting moment at work today."

[1865] The system of the present invention includes smart glasses, an AI counselor, an emotion recognition engine, and a cognitive behavioral therapy (CBT) module, and can provide personalized and appropriate mental health care to users in real time. In this way, high-quality mental health care support is realized while taking into account the user's emotional state.

[1866] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1867] Step 1:

[1868] A user puts on smart glasses and launches a mental health check application. The device prompts the user, saying, "Please rate your current mood on a scale of 1 to 10." The user then inputs the rating score. This input data (rating score) is stored on the device and transmitted to a server via a wireless network.

[1869] Step 2:

[1870] The server analyzes the received data (evaluation score) and uses a generative AI model to determine the user's state. The system evaluates the user's current mental state based on the input data. For example, if the evaluation score is low, it will determine that the user is under high stress. Based on the analysis results, it generates an appropriate response or advice. This response data is sent to the device.

[1871] Step 3:

[1872] The device displays the response data received from the server to the user. For example, it displays advice such as "Take a few minutes break and try taking deep breaths." The user follows the displayed advice. The device collects how the user responded to the provided advice (e.g., thoughts and feedback).

[1873] Step 4:

[1874] The server uses speech and facial expression recognition technology to analyze the user's emotions in real time. For example, if the user types "I'm tired," the server uses an emotion recognition engine to detect a high level of stress. Based on the analysis results, it immediately generates appropriate countermeasures (e.g., relaxation techniques) and sends them to the device.

[1875] Step 5:

[1876] The device receives the solution from the server and displays it to the user. For example, a specific relaxation method such as "Take deep breaths repeatedly for one minute" is displayed. The user follows the solution and inputs the results and impressions into the device, which are then sent back to the server.

[1877] Step 6:

[1878] The server continuously collects dialogue data and performs data analysis to detect cognitive distortions. Based on the collected data, a cognitive distortion analysis algorithm is run to identify the cognitive distortions the user has. Based on the results of this analysis, cognitive behavioral therapy (CBT) advice and tasks are generated and sent to the device.

[1879] Step 7:

[1880] The device displays the generated CBT advice and tasks to the user. For example, a specific task such as "Write down three successful experiences" is presented. The user performs this task and inputs the results, which are then sent to the server via the device.

[1881] Step 8:

[1882] The server collects and analyzes user feedback and performs data analysis to improve the quality of the system. Based on the feedback, the server tunes the generative AI model and emotion recognition engine. This process improves the accuracy and usefulness of the advice and tasks provided.

[1883] 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.

[1884] 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.

[1885] 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.

[1886] 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.

[1887] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1888] 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.

[1889] 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).

[1890] 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.

[1891] 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."

[1892] 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.

[1893] 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).

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] The following is further disclosed regarding the above embodiment.

[1905] (Claim 1)

[1906] Equipped with an AI counselor, it receives and analyzes text data entered by the user and generates an appropriate response;

[1907] a means for analyzing the dialogue data to detect cognitive distortions of the user;

[1908] A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user;

[1909] A system including:

[1910] (Claim 2)

[1911] a means for receiving user feedback and analyzing the feedback data to help improve the system;

[1912] A means to aggregate and display anonymized mental health status of all employees to company administrators;

[1913] 10. The system of claim 1, further comprising:

[1914] (Claim 3)

[1915] A means for users to create and manage their own customizable AI counselor profiles;

[1916] means for deleting the interaction data in response to a user request;

[1917] 10. The system of claim 1, further comprising:

[1918] "Example 1"

[1919] (Claim 1)

[1920] A device equipped with an AI counselor, which receives and analyzes text data entered by the user and generates an appropriate response using a generative model;

[1921] a means for analyzing the dialogue data to detect cognitive distortions of the user;

[1922] A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user;

[1923] A means for periodically collecting user interaction data and generating analysis results as visual data;

[1924] A system including:

[1925] (Claim 2)

[1926] a means for receiving user feedback and analyzing the feedback data to help improve the system;

[1927] A means to aggregate and display anonymized mental health status of all employees to company administrators;

[1928] A means to display the feedback results in a dashboard format;

[1929] 10. The system of claim 1, further comprising:

[1930] (Claim 3)

[1931] A means for users to create and manage their own customizable AI counselor profiles;

[1932] means for deleting the interaction data in response to a user request;

[1933] means for providing the generated visual data to a user;

[1934] 10. The system of claim 1, further comprising:

[1935] "Application Example 1"

[1936] (Claim 1)

[1937] Equipped with an AI counselor, it receives and analyzes text data entered by the user and generates an appropriate response;

[1938] a means for analyzing the dialogue data to detect cognitive distortions of the user;

[1939] A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user;

[1940] A means of detecting mental health status by analyzing users' statements and actions in real time using voice recognition and natural language processing;

[1941] a means of suggesting responses based on detected mental health conditions; and

[1942] a means for displaying the feedback results as visual data;

[1943] A system including:

[1944] (Claim 2)

[1945] a means for receiving user feedback and analyzing the feedback data to help improve the system;

[1946] A means to aggregate and display anonymized mental health status of all employees to company administrators;

[1947] 10. The system of claim 1, further comprising:

[1948] (Claim 3)

[1949] A means for users to create and manage their own customizable AI counselor profiles;

[1950] means for deleting the interaction data in response to a user request;

[1951] 10. The system of claim 1, further comprising:

[1952] "Example 2: Combining Emotion Engines"

[1953] (Claim 1)

[1954] means for receiving and parsing text data entered by a user and generating an appropriate response;

[1955] a means for analyzing the dialogue data to detect cognitive distortions of the user;

[1956] A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user;

[1957] a means for recognizing emotions from input text;

[1958] a means for tailoring responses or advice based on the recognized emotion data;

[1959] A system including:

[1960] (Claim 2)

[1961] a means for receiving user feedback and analyzing the feedback data to help improve the system;

[1962] A means to aggregate and display anonymized mental health status of all employees to company administrators;

[1963] 10. The system of claim 1, further comprising means for providing the interaction data and emotion recognition data to an enterprise administrator.

[1964] (Claim 3)

[1965] A means for users to create and manage their own customizable AI counselor profiles;

[1966] means for deleting the interaction data in response to a user request;

[1967] A means for providing a personalized mental care program based on the user's emotion recognition result;

[1968] 10. The system of claim 1, further comprising:

[1969] "Application example 2 when combining emotion engines"

[1970] (Claim 1)

[1971] Equipped with an AI counselor, it receives and analyzes text data entered by the user and generates an appropriate response;

[1972] a means for analyzing the dialogue data to detect cognitive distortions of the user;

[1973] A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user;

[1974] means for periodically displaying mental health questions using the smart glasses and transmitting user responses to a server;

[1975] Through conversation and facial expression recognition, it will recognize the user's emotions in real time, and when it detects a high-stress state, it will provide appropriate relaxation methods and mental care advice.

[1976] a means of collecting user feedback and analyzing that data to improve the quality of the service;

[1977] A system including:

[1978] (Claim 2)

[1979] 10. The system of claim 1, further comprising means for periodically checking the user's mental health status using the smart glasses and transmitting the data to a server.

[1980] (Claim 3)

[1981] 10. The system of claim 1, further comprising: means for providing advice generated based on the emotion recognition results to the user in real time. [Explanation of symbols]

[1982] 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. Equipped with an AI counselor, it receives and analyzes text data entered by the user and generates an appropriate response; a means for analyzing the dialogue data to detect cognitive distortions of the user; A means for generating advice and tasks according to cognitive behavioral therapy based on the detected cognitive distortions and providing the advice and tasks to the user; A system including:

2. a means for receiving user feedback and analyzing the feedback data to help improve the system; A means to aggregate and display anonymized mental health status of all employees to company administrators; The system of claim 1 further comprising:

3. A means for users to create and manage their own customizable AI counselor profiles; means for deleting the interaction data in response to a user request; The system of claim 1 further comprising:

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