System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing a user interface for employees to enter their distress or stress; means for encrypting and transmitting the entered information to a server; means for executing a generative AI model at the server to decrypt and analyze the encrypted information; means for storing the analysis results in a database; and means for identifying high-risk employees and notifying them of specialized support.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's workplace, employees often find it difficult to talk about their stress and worries with their superiors, colleagues, or family members. Furthermore, if employee stress and mental health issues are left unaddressed, it can lead to serious risks, such as a decline in productivity across the company, scandals, and harassment. Therefore, there is a need for a system that can efficiently support employee mental health while protecting privacy. [Means for solving the problem]

[0005] The present invention is a system that provides a user interface for employees to input their worries and stress, encrypts the input data, and sends it to a server. On the server side, the encrypted data is decrypted and analyzed using a generative AI model. The analysis results are stored in a database, and if high-risk employees are identified, they are notified of access to specialized support. Furthermore, the system includes functions to evaluate employee stress levels, identify potential risks, and evaluate the soundness of corporate governance overall. It uses encryption technology to respect privacy, providing an environment that employees can use with peace of mind.

[0006] "Employee" refers to an individual staff member working for a company or organization.

[0007] "User interface" refers to an interface that allows a user to interact with a device or system. Specifically, it includes input methods such as chat.

[0008] "Data encryption" is a technology that converts data into a form that is not easily understandable by third parties and is used to protect employee privacy.

[0009] "Server" refers to a computer system that provides data processing and storage functions.

[0010] "Decryption" refers to the process of restoring encrypted data to its original state.

[0011] A "generative AI model" is a model that uses artificial intelligence to analyze data and detect specific patterns or risks.

[0012] "Analysis results" refers to the conclusions or evaluations obtained after analyzing data using generative AI models, etc.

[0013] "Database" refers to a system for systematically storing and managing data.

[0014] "High-risk employees" are employees who are assessed as having high mental health or stress levels based on the analysis results.

[0015] "Professional support" refers to assistance from professionals such as psychological counselling or medical institutions.

[0016] "Notification" refers to a message that the system sends to inform the user of a particular event or situation.

[0017] "Stress level" refers to an indicator that evaluates the degree of stress felt by employees.

[0018] "Potential risks" refer to situations or conditions that have not yet clearly surfaced but could become problems in the future if left unchecked.

[0019] "Sound governance" refers to an indicator that evaluates whether a company or organization is being run properly.

[0020] "Encryption technology" refers to technology that converts data into a format that cannot be understood by third parties in order to ensure data security. [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] The present invention is a system that supports employee mental health, focusing in particular on utilizing generative AI models to analyze employees' daily interactions and assess stress levels and potential risks.

[0043] System Overview

[0044] This system mainly consists of the following components:

[0045] 1. User Interface

[0046] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[0047] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0048] 2. Encryption and Transmission

[0049] The terminal encrypts the input data and transmits it to the server.

[0050] This protects the privacy of employees.

[0051] 3. Data reception and analysis on the server

[0052] The server receives and decrypts the encrypted data.

[0053] A generative AI model is used to analyze the decoded data.

[0054] A generative AI model assesses stress levels and potential risks.

[0055] 4. Save to database

[0056] The server stores the analysis results in a database.

[0057] The stored data is later used to identify high-risk employees.

[0058] 5. Professional Support and Notifications

[0059] The server identifies potentially high-risk employees and directs them to appropriate professional support.

[0060] For example, this may include psychological counseling or contacting a medical institution.

[0061] Specific examples of program processing

[0062] Initial Setup

[0063] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[0064] Entering and Submitting Data

[0065] Users input their worries and stresses into a chat-style interface, which is then encrypted on the device and securely sent to the server.

[0066] Receiving and analyzing data

[0067] The server receives the encrypted data, decrypts it, and analyzes it using a generative AI model to assess stress levels. The analysis results are then automatically stored in a database.

[0068] Identifying and notifying high-risk employees

[0069] The server then identifies high-risk employees based on the results stored in the database, and provides them with information on appropriate counseling services and medical institutions, allowing for specific measures to be taken to support the mental health of employees.

[0070] Specific examples

[0071] When Employee A types a workplace concern into a chat window, saying, "My recent project hasn't been going well, and my relationship with my boss is deteriorating," the data is encrypted on the user's device and sent to the server. After receiving the data, the server decrypts it and uses a generative AI model to evaluate the stress level as "high." Based on this, the evaluation results stored in the database are sent to the user's device as a guide to a professional counseling service.

[0072] In this way, the system aims to improve the mental health of employees and support the overall well-being of the company.

[0073] ---

[0074] The above is an example of a specific form for implementing the present invention, detailing the operations at each step and showing how the entire system interacts to support employee mental health.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user uses the user interface to input their worries and stress in a chat format. An example of input might be, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[0078] Step 2:

[0079] The terminal receives the data entered by the user and encrypts it to ensure security, and then transmits the encrypted data over the network to the server.

[0080] Step 3:

[0081] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[0082] Step 4:

[0083] The server then provides the decrypted data to a generative AI model for analysis, which assesses stress levels and identifies potential risks based on the input data.

[0084] Step 5:

[0085] The server stores the analysis results, including stress levels and potential risks, in a database that also includes past analysis results, allowing for long-term trend analysis.

[0086] Step 6:

[0087] The server periodically scans the data stored in the database to identify high-risk employees, and if a high-risk employee is identified, appropriate action is taken.

[0088] Step 7:

[0089] The server then sends notifications to high-risk employees, directing them to professional counseling services and medical institutions. Notifications are sent to user terminals, and employees can follow the instructions to receive professional support.

[0090] Step 8:

[0091] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[0092] Example 1

[0093] 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."

[0094] In recent working environments, mental health issues among employees have been increasing. However, many companies lack systems to properly address this issue, making it difficult to detect employee stress and distress early and provide appropriate support. Furthermore, in many cases, privacy protection measures for safely handling employee data are not adequately ensured. Therefore, there is a need for a system that effectively supports employee mental health while simultaneously ensuring data security.

[0095] 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.

[0096] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for installing an SSL / TLS certificate on a user terminal and creating an encryption key to ensure secure transmission and reception of employee data; means for providing the input data to the generative AI model as prompt text to evaluate employee stress levels and potential risks; and means for automatically sending emails and notifications to high-risk employees based on the evaluation results. This makes it possible to effectively support employee mental health while ensuring data security and privacy protection.

[0097] A "user interface" is the operating environment on a device screen such as a computer or smartphone where employees can enter their worries and stress.

[0098] "Encryption" is a technology that converts employee input data into a format that cannot be understood by others, thereby maintaining confidentiality.

[0099] A "server" is a computer system that receives data sent from terminals on a network and stores and analyzes the data.

[0100] A "generative AI model" is an artificial intelligence model that analyzes data received from employees and assesses stress levels and potential risks.

[0101] A "database" is a data management system that stores analytical results and other related data for easy retrieval and use at a later time.

[0102] "High-risk employees" are employees who are assessed as experiencing very high levels of stress and distress based on the analysis results.

[0103] "Professional support" refers to support services such as psychological counseling and referrals to medical institutions provided to employees with mental health issues.

[0104] An "SSL / TLS certificate" is an electronic certificate that protects the security and privacy of communications when sending and receiving data over the Internet.

[0105] An "encryption key" is a string of characters used in the encryption and decryption process, and is a means of protecting the confidentiality of data.

[0106] A "prompt sentence" is a form of text data that is input into a generative AI model and serves as material for the model's analysis.

[0107] "Notifications" are messages or announcements sent from the server to employees to inform them of analysis results and appropriate support.

[0108] This invention is a system that supports employee mental health. Specifically, it uses a generative AI model to analyze employees' daily conversations and evaluate their stress levels and potential risks. Below, we explain the program processing of this system in natural language.

[0109] System Configuration

[0110] The system mainly consists of the following components:

[0111] 1. User Interface

[0112] User terminal: Provides a chat-style interface for employees to input their worries and stress. For example, an employee might input, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0113] 2. Encryption and Transmission

[0114] Terminal: The entered data is encrypted and sent to the server. This protects employee privacy. Install an SSL / TLS certificate and create an encryption key to ensure the security of data communication.

[0115] 3. Data Reception and Decryption

[0116] Server: Receives the encrypted data and decrypts it, converting it into a format that can be analyzed.

[0117] 4. Analysis using generative AI models

[0118] Server: The decrypted data is input into a generative AI model for analysis. This generative AI model is used to assess an employee's stress level and potential risks. For example, given the input data "My recent project has not been going well, and my relationship with my boss has deteriorated," the model might assess the stress level as "high."

[0119] 5. Saving to the database

[0120] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[0121] 6. Identifying and notifying high-risk employees

[0122] Server: Identifies high-risk employees based on information from the database. These employees are then notified of appropriate support and counseling services. For example, identified high-risk employees are notified of psychological counseling or contact information for medical institutions.

[0123] Specific examples

[0124] Initial Setup

[0125] Device: An IT administrator installs an SSL / TLS certificate on a new user device and generates encryption keys, allowing the device to send and receive data securely.

[0126] Data entry and encryption

[0127] User: Employee B types in the chat interface, "There are a lot of disagreements within the team and I'm feeling stressed."

[0128] Terminal: The input data is encrypted using AES-256 and ready to go.

[0129] Sending data

[0130] On your device: Encrypted data is sent to the server using HTTPS, which ensures data security.

[0131] Receiving and Decrypting Data

[0132] Server: The server receives the encrypted data and decrypts it using the stored encryption key.

[0133] Analysis using generative AI models

[0134] Server: The decrypted data is input into the generative AI model, which analyzes the content, "There are many differences of opinion within the team, and I am feeling stressed." The generative AI model analyzes the stress level and evaluates it as "medium."

[0135] Identifying and notifying high-risk employees

[0136] Server: Identifies employees from the database whose stress level is rated as "high" and prepares information related to those employees.

[0137] Server: Sends email notifications to high-risk employees, providing guidance such as, "Your stress level has been assessed as high, so we recommend psychological counseling." This allows for prompt measures to be taken to address employees' mental health.

[0138] Examples of prompt statements

[0139] The following prompts are fed into the generative AI model to assess stress levels:

[0140] Rate your stress level if someone typed in, "My recent project hasn't been going well and my relationship with my boss has deteriorated."

[0141] This system can effectively support the mental health of employees and improve the overall well-being of the company.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] Initial Setup

[0145] Device: Initialize the user device, install an SSL / TLS certificate, and create an encryption key. This configuration ensures secure data transmission from the device to the server.

[0146] Specific operation: An IT administrator installs an SSL / TLS certificate on a new user device and generates an encryption key. At this step, the device is ready for secure data transmission and reception.

[0147] Input: None

[0148] Output: SSL / TLS certificate installed, encryption key generated

[0149] Step 2:

[0150] Data entry and encryption

[0151] User: Enters worries and stress on the chat interface. For example, the user enters, "My recent project has not been going well, and my relationship with my boss has deteriorated."

[0152] Terminal: Input data is encrypted using the AES-256 method and prepared for secure transmission to the server.

[0153] Specific operation: The user enters text into the input field and clicks the send button. The device encrypts the input data.

[0154] Input: User text input such as "My recent projects have been going poorly and my relationship with my boss has been strained."

[0155] Output: Encrypted data

[0156] Step 3:

[0157] Sending data

[0158] Terminal: Encrypted data is sent to the server using SSL / TLS, which reduces the risk of data being intercepted by a third party.

[0159] Specific operation: The device sends data to the server via HTTPS.

[0160] Input: Encrypted data

[0161] Output: Encrypted data sent to the server

[0162] Step 4:

[0163] Receiving and Decrypting Data

[0164] Server: Receives the encrypted data and decrypts it using a stored encryption key, converting the decrypted data into an analyzable format.

[0165] What happens: The server receives the data and decrypts it using the encryption key.

[0166] Input: Encrypted data

[0167] Output: Decoded text data

[0168] Step 5:

[0169] Analysis using generative AI models

[0170] Server: The decrypted data is fed into a generative AI model to assess stress levels and potential risks. For example, a statement like "My recent project hasn't been going well, and my relationship with my boss has deteriorated" would be evaluated as a "high" stress level.

[0171] Specific operation: The server inputs the decoded data as a prompt sentence into the generative AI model, and the model performs analysis.

[0172] Input: Text data: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0173] Output: Stress level assessment result (e.g. "High")

[0174] Step 6:

[0175] Saving to a database

[0176] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[0177] Specific operation: Save the stress level assessment results in a MySQL database.

[0178] Input: Stress level assessment result

[0179] Output: Saved evaluation results

[0180] Step 7:

[0181] Identifying high-risk employees

[0182] Server: Identifies high-risk employees based on information from the database.

[0183] What happens: The server runs an SQL query to extract employees with a "high" stress level.

[0184] Input: Evaluation results stored in the database

[0185] Output: List of high-risk employees

[0186] Step 8:

[0187] Notifications and Support Information

[0188] Server: Notify high-risk employees and direct them to appropriate support and counseling services.

[0189] What happens: The server automatically sends an email to the employee with a message such as, "We recommend that you seek psychological counseling."

[0190] Input: List of high-risk employees

[0191] Output: Notifications and support information

[0192] (Application example 1)

[0193] 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."

[0194] Employee mental health issues have a significant impact on the productivity and atmosphere of the entire workplace. High stress levels are often seen, especially in brick-and-mortar stores, which negatively impact employee performance and health. Furthermore, the lack of an environment in which employees can easily seek advice about their mental health makes it difficult to detect and address problems early. Therefore, there is a need for a system that can quickly and appropriately assess employee concerns and stress and provide the necessary support.

[0195] 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.

[0196] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for store employees to input their mental health status from their smartphones; and means for evaluating stress levels using the generative AI model and notifying them of appropriate support. This makes it possible to quickly evaluate the stress and worries that store employees experience on a daily basis and provide them with appropriate counseling services or guidance to medical institutions.

[0197] A "user interface" is a system component that provides a screen and operating means for employees to input their worries and stress.

[0198] "Encryption" is a technology that converts input data so that it cannot be deciphered by third parties.

[0199] A "server" is a computer system that receives and analyzes data sent from a user interface.

[0200] "Decryption" is a technique for restoring encrypted data to its original state.

[0201] A "generative AI model" is an artificial intelligence model that analyzes input data and assesses stress levels and risks based on that data.

[0202] "Analysis" is the process of analyzing the content of input data using a generative AI model and deriving evaluation results.

[0203] A "database" is an information management system that stores analysis results and allows them to be referenced later.

[0204] "High-risk employees" are employees who are assessed as having high stress levels through analysis by the generative AI model.

[0205] "Professional support" refers to services to support employees' mental health, such as psychological counseling and referrals to medical institutions.

[0206] A "physical store" is a physical location where sales or services are provided.

[0207] A "smartphone" is a mobile device that has the functionality of a mobile phone and the ability to run a variety of applications.

[0208] "Stress level" is a numerical value or assessment result that evaluates the degree of stress felt by an employee.

[0209] "Counseling services" are services in which professional counselors provide advice to employees about their worries and stress.

[0210] A "prompt sentence" is a form of text data input into a generative AI model and is an instruction sentence for analysis.

[0211] The present invention is a system for supporting the mental health of employees, and is particularly focused on employees in brick-and-mortar stores. A specific embodiment of this system will be described below.

[0212] System configuration

[0213] 1. User Interface

[0214] Users (employees) use an application installed on their smartphones to input their worries and stress levels. The user interface is in chat format, making it easy for employees to operate.

[0215] 2. Data Encryption and Transmission

[0216] The device (smartphone) uses encryption technology to safely transmit the entered data to the server. This encryption technology is designed to prevent third parties from stealing or tampering with the data.

[0217] 3. Data reception and analysis on the server

[0218] The server automatically decrypts the encrypted data upon receiving it. It then analyzes the data using a generative AI model to assess the employee's stress level and potential risks. The generative AI model generates prompts based on the employee's input data and performs the analysis.

[0219] 4. Saving to the database

[0220] The server stores the analysis results in a database that can be used for future reference, enabling continuous monitoring of employees' mental health.

[0221] 5. Professional Support and Notifications

[0222] The server identifies high-risk employees based on the analysis results in the database, and these employees are notified via their smartphones about professional support (such as psychological counseling or referrals to medical institutions).

[0223] Specific examples of processing

[0224] Initial Setup

[0225] Users install the application on their smartphone and perform basic configuration, which includes generating encryption keys to ensure secure data transmission.

[0226] Entering and Submitting Data

[0227] When an employee types in "My recent project hasn't gone well and my relationship with my boss is also deteriorating," the data is encrypted on the smartphone and sent to a server.

[0228] Receiving and analyzing data

[0229] When the server receives the data, it decrypts it. The decrypted data is then used to analyze the input data using a generative AI model. The generative AI model is given a prompt like this:

[0230] Stress Assessment: My recent project has been going poorly and my relationship with my boss has deteriorated.

[0231] Identifying and notifying high-risk employees

[0232] If the stress level is assessed as "high" based on the analysis results of the generative AI model, the result is stored in a database. The server then uses this result to identify high-risk employees and sends a notification to their smartphones directing them to appropriate counseling services.

[0233] In this way, a system can be built to support the mental health of employees in brick-and-mortar stores. The hardware used includes smartphones and servers, and the software includes encryption technology, database management systems, and generative AI models.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] Initial Setup

[0237] A user installs an application on their smartphone and performs basic configuration. Specifically, the user launches the application and enters their account information. The application generates an encryption key, which ensures secure data transmission. The input is the user's personal information and a request to generate an encryption key, and the output is the generated encryption key.

[0238] Step 2:

[0239] Entering data

[0240] Users (employees) input their worries and stress through a smartphone application. Specifically, they type "My recent project hasn't been going well, and my relationship with my boss has deteriorated" into a chat-style interface. The input is text data about the employee's worries and stress, and the output is the input data saved in the application.

[0241] Step 3:

[0242] Data encryption

[0243] The terminal (smartphone) encrypts the input data. Specifically, it encrypts the input text data using an encryption key to generate encrypted data. The input is the employee's input data and the encryption key, and the output is the encrypted data. Encrypted data is important for protecting privacy.

[0244] Step 4:

[0245] Sending data

[0246] The terminal sends the encrypted data to the server. Specifically, it uses an HTTP request to send the encrypted data to the specified server address. The input is the encrypted data and the server address information, and the output is the sending status. If successful, the server receives the encrypted data.

[0247] Step 5:

[0248] Receiving and Decrypting Data

[0249] The server receives the encrypted data sent from the terminal and decrypts it. Specifically, it uses a decryption key to return the received data to the original text data. The input is the encrypted data and the decryption key, and the output is the original text data. This allows the server to obtain the correct input information.

[0250] Step 6:

[0251] Data analysis

[0252] The server uses a generative AI model to analyze the decoded data. Specifically, it generates a prompt for stress assessment based on the input text data and inputs that prompt into the generative AI model. The input is the decoded text data and the generated prompt, and the output is the stress level assessment result. An example of a prompt is "Stress assessment: My recent project has not been going well, and my relationship with my boss has deteriorated."

[0253] Step 7:

[0254] Saving analysis results

[0255] The server stores the analysis results from the generative AI model in a database. Specifically, it stores the stress level assessment results and employee identification information in the database. The input is the stress level assessment results and employee identification information, and the output is the analysis results stored in the database.

[0256] Step 8:

[0257] Identifying and notifying high-risk employees

[0258] The server identifies high-risk employees based on the analysis results in the database. Specifically, it extracts employees who are assessed as having high stress levels and generates a guide to professional support. The input is the stress level assessment results in the database, and the output is a notification directing them to a professional counseling service. This notification is delivered to the employee's smartphone.

[0259] Through the above steps, a system for supporting employee mental health is concretely implemented.

[0260] 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.

[0261] This invention is a system that combines a generative AI model and an emotion engine, and provides the function of analyzing employees' daily conversations to assess their stress levels and potential risks. Furthermore, by using the emotion engine to recognize the user's emotions and incorporating the results into the analysis results, more accurate mental health support is realized.

[0262] System Overview

[0263] This system mainly consists of the following components:

[0264] 1. User Interface

[0265] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[0266] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0267] 2. Encryption and Transmission

[0268] The terminal encrypts the input data and transmits it to the server.

[0269] This protects the privacy of employees.

[0270] 3. Data reception and analysis on the server

[0271] The server receives and decrypts the encrypted data.

[0272] The decoded data is provided to a generative AI model for analysis.

[0273] The generative AI model assesses stress levels based on input data and identifies potential risks.

[0274] 4. Use of Emotion Engine

[0275] The server uses an emotion engine to analyze the user's emotions in real time.

[0276] The analysis results of the emotion engine are also incorporated into the analysis results of the generative AI model.

[0277] This allows for detailed assessment according to changes in emotions and situations.

[0278] 5. Save to database

[0279] The server stores the analysis results from both the generative AI model and the emotion engine in a database.

[0280] The stored data is used to identify high-risk employees, along with long-term trends.

[0281] 6. Professional Support and Notifications

[0282] The server identifies high-risk employees based on the results stored in a database.

[0283] If high-risk employees are identified, they will be notified and directed to appropriate professional support (e.g., counseling services or medical referrals).

[0284] Specific examples of program processing

[0285] Initial Setup

[0286] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[0287] Entering and Submitting Data

[0288] The user enters their worries and stresses into a chat-style interface. This data is encrypted on the device and securely sent to the server. For example, a user might enter, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0289] Receiving and analyzing data

[0290] The server receives the encrypted data, decrypts it, and feeds it into a generative AI model to assess stress levels and potential risks.

[0291] Emotion Engine Analysis

[0292] The server uses an emotion engine to analyze the user's emotions in real time. For example, it can recognize emotions such as "anxiety" or "stress" from the user's text input. The results of the emotion engine are also incorporated into the analysis of the generative AI model, resulting in a more accurate evaluation.

[0293] Database storage and identification of high-risk employees

[0294] The server stores the analysis results of the generative AI model and emotion engine in a database. Based on the results stored in the database, the server identifies high-risk employees. For example, identified employees may be judged as needing counseling services.

[0295] Professional support and notifications

[0296] The server notifies high-risk employees of professional counseling services and medical institutions. The notification is sent to the user's terminal, and employees can follow the instructions to receive professional support. For example, an employee who is recognized as being under high stress will receive a notification directing them to professional counseling.

[0297] In this way, the system combines a generative AI model with an emotion engine to improve employee mental health and support the overall well-being of the company.

[0298] The processing flow will be explained below.

[0299] Step 1:

[0300] A user inputs worries and stress in a chat format using a user interface. For example, the user inputs, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[0301] Step 2:

[0302] The terminal encrypts the input data, thereby protecting the user's privacy, and then sends the encrypted data to the server.

[0303] Step 3:

[0304] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[0305] Step 4:

[0306] The server provides the decrypted data to a generative AI model, which assesses stress levels and identifies potential risks based on the input data.

[0307] Step 5:

[0308] The server uses an emotion engine to analyze the user's emotions in real time, for example recognizing the emotions of "anxiety" and "stress" from the user's text input.

[0309] Step 6:

[0310] The server incorporates the emotion results recognized by the emotion engine into the analysis results of the generative AI model, enabling detailed evaluation according to emotional changes and situations.

[0311] Step 7:

[0312] The server stores the results of both the generative AI model and the emotion engine in a database, which is also used to analyze long-term trends.

[0313] Step 8:

[0314] The server periodically scans the results stored in the database to identify high-risk employees, who are deemed to require appropriate action.

[0315] Step 9:

[0316] The server notifies high-risk employees of professional counseling services and medical referrals, and the notification is sent to the user's device.

[0317] Step 10:

[0318] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[0319] Through these steps, the system combines a generative AI model and an emotion engine to comprehensively assess employees' stress and mental health and provide appropriate support.

[0320] Example 2

[0321] 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."

[0322] Managing and supporting employee mental health is an important issue for modern companies. However, systems for efficiently and accurately identifying employee concerns and stress levels and implementing appropriate measures are not yet fully in place. Traditional methods often rely on employees' self-reporting or interviews, which can effectively assess stress and potentially overlook potential risks. Furthermore, a lack of technology to protect data privacy and capture emotional changes in real time makes it difficult to accurately assess employee health.

[0323] 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.

[0324] In this invention, the server includes means for providing a user interface for employees to input their worries and stress, means for encrypting the input data and sending it to the server, means for running a generative AI model that decrypts and analyzes the encrypted data on the server side, means for analyzing the user's emotions in real time using an emotion engine on the server side, and means for storing the analysis results of the generative AI model and the emotion engine in a database. This makes it possible to accurately evaluate employee stress levels and changes in emotions and provide appropriate support while protecting privacy.

[0325] "User interface" refers to the interactive screen and operating means through which system users input their worries and stress.

[0326] "Encryption" refers to the technology of converting input data into a form that cannot be deciphered by third parties, and is a method of protecting the privacy and security of data.

[0327] "Server" refers to the central processing unit that analyzes the data received, runs the generative AI model, uses the emotion engine, stores data, and notifies users.

[0328] "Decryption" refers to the process of returning encrypted data to its original form, making it available as analyzable data.

[0329] "Generative AI model" refers to an artificial intelligence model that assesses employee stress levels and potential risks based on received data.

[0330] An "emotion engine" refers to software that analyzes emotions from user input and evaluates emotional trends and changes.

[0331] "Database" refers to an information storage system that stores the analysis results of the generative AI model and emotion engine, and enables reference and analysis as needed.

[0332] "High-risk employees" are those who are deemed to be in particular need of mental health support based on the analysis results of the generative AI model and emotion engine.

[0333] "Specialized support" refers to appropriate assistance provided to employees identified as at high risk, such as counseling services or referrals to medical facilities.

[0334] "Stress level" refers to the quantitative assessment of the degree of stress experienced by employees.

[0335] "Privacy" refers to the state in which employees' personal information and data are protected and not shared with third parties without approval.

[0336] "Real-time" refers to the instant analysis and evaluation of user behavior and emotions on the spot.

[0337] This invention relates to a system that analyzes employees' daily conversations to assess their stress levels and potential risks. The purpose of this invention is to effectively support employees' mental health by combining a generative AI model and an emotion engine.

[0338] System Configuration

[0339] User Interface

[0340] The terminal provides a chat-style user interface for employees to input their worries and stress. Specifically, the user interface includes a text box and a send button, allowing employees to easily input their daily stress and worries.

[0341] Data encryption

[0342] The terminal encrypts the input data using encryption technology such as AES (Advanced Encryption Standard) before sending it, thereby protecting the privacy of user data.

[0343] Sending and Receiving Data

[0344] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS, and the server receives the data and decrypts it using a private key.

[0345] Analysis of generative AI models

[0346] The server then provides the decoded data to a generative AI model, which uses natural language processing (NLP) to assess the stress level and potential risks from the input text. Specifically, the generative AI model calculates a stress score and returns the result to the server.

[0347] Emotion Engine Analysis

[0348] In addition to the analysis results of the generative AI model, the server analyzes the user's emotions in real time using an emotion engine. The emotion engine detects emotions such as "anxiety" or "stress" from the input text and integrates the results into the analysis results of the generative AI model.

[0349] Saving analysis results

[0350] The server stores the results of the generative AI model and emotion engine analysis in a database, which includes the employee's ID, input text, stress score, and detected emotion.

[0351] Identifying high-risk employees

[0352] The server analyzes the results stored in the database and identifies high-risk employees. Specifically, if an employee's stress score or emotion analysis results exceed a certain threshold, they are deemed high-risk.

[0353] Professional Support Notification

[0354] The server then sends notifications to employees identified as high-risk with information on how to access professional support, including counseling services and medical facilities.

[0355] Specific examples

[0356] 1. User Interface

[0357] The user types into a chat-style interface, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0358] 2. Encryption and Transmission

[0359] The device encrypts the input text and sends it to the server using HTTPS.

[0360] 3. Data Reception and Decryption

[0361] The server receives the data sent from the terminal and decrypts the encrypted data using the private key.

[0362] 4. Analysis of generative AI models

[0363] The server inputs the decoded text into a generative AI model, which analyzes it as "high stress level."

[0364] 5. Use of Emotion Engine

[0365] The server uses an emotion engine to detect emotions such as "anxiety" or "stress" from the text.

[0366] 6. Saving the analysis results

[0367] The server stores the results of the generative AI model and emotion engine in a database.

[0368] 7. Identifying high-risk employees

[0369] The server analyzes the stored data and identifies employees as high risk if their stress score is high.

[0370] 8. Professional Support Notification

[0371] The server sends notifications to terminals for professional counseling for high-risk employees.

[0372] Prompt Sentence Examples

[0373] 1. Stress Level Assessment Prompt

[0374] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0375] Prompt: "Please rate the user's stress level based on this text."

[0376] 2. Emotion Recognition Prompts

[0377] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0378] Prompt: "Identify the user's emotion from this text."

[0379] In this way, the present invention provides a system that provides advanced support for the mental health of employees, thereby improving the overall health of the company.

[0380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] The user inputs their worries and stress into a chat-style interface on the device. The input is in text format, for example, "My recent project has not been going well, and my relationship with my boss has deteriorated." This text is input as data.

[0383] Step 2:

[0384] The terminal uses AES encryption to encrypt the text data entered by the user, producing encrypted data that is then output. This encryption ensures the privacy of the data.

[0385] Step 3:

[0386] The terminal sends the encrypted data to the server using the HTTPS protocol, in this process the encrypted data is sent as input and received on the server side.

[0387] Step 4:

[0388] The server decrypts the encrypted data received via the HTTPS protocol using the private key. This process results in the decrypted data, which is the output.

[0389] Step 5:

[0390] The server provides the decrypted text data to the generative AI model, which evaluates the employee's stress level from the text data and calculates a stress score. The input is the text data and the output is the stress score. For example, the stress score is calculated as "85."

[0391] Step 6:

[0392] The server analyzes the user's emotions in real time using an emotion engine in parallel with the analysis results of the generative AI model. The emotion engine identifies emotions such as "anxiety" or "stress" from the text data and outputs them. The input is the text data, and the output is the detected emotion. For example, "anxiety" is detected.

[0393] Step 7:

[0394] The server combines the results of the generative AI model and the emotion engine analysis and stores them in a database, including employee IDs, input text, stress scores, and detected emotions, enabling long-term trend analysis and risk assessment.

[0395] Step 8:

[0396] The server analyzes the data stored in the database and identifies high-risk employees. Specifically, if the stress score or emotion analysis results exceed a certain threshold, the employee is deemed high-risk. For example, a stress score of 85 exceeds the threshold and is deemed high-risk.

[0397] Step 9:

[0398] The server then notifies employees identified as high-risk of professional counseling services or medical consultations. The notification is sent to the device, where the user can view and confirm the content of the notification. For example, a notification saying "Professional counseling is required" is sent.

[0399] Through these steps, the system is able to assess employees' mental health with high accuracy and provide the necessary support in a timely manner.

[0400] (Application example 2)

[0401] 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."

[0402] Employee mental health issues have a significant impact on the productivity and health of an entire company. However, even if employees are experiencing mental health-related worries or stress, it is difficult to accurately assess their mental health through self-reporting alone. There is also a lack of effective means to quickly identify employees who need professional support and provide appropriate support. There is a need for a system that can assess employee mental health in real time and quickly identify potential risks.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0404] In this invention, the server includes: a means for providing a user interface through which employees input their worries and stress; a means for encrypting the input data and sending it to the server; a means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; a means for storing the analysis results in a database; a means for identifying high-risk employees and notifying them of professional support; a means for analyzing user emotions in real time using an emotion engine and incorporating the results into the analysis data; and a means for assessing stress levels and potential risks using a robot for interacting with employees. This makes it possible to analyze employee emotions in real time and accurately assess stress levels and potential risks. It also makes it possible to provide employees with mental health support in real time, identify high-risk employees early, and notify them of appropriate professional support.

[0405] A "user interface" is an interface that allows a user to perform operations and input data, and is typically in the form of a chat.

[0406] "Encryption" is the technique of converting data into a special code to protect it and ensure its safety from unauthorized access.

[0407] A "server" is a computer system that provides specific services or data over a network.

[0408] "Decryption" is a technique for restoring encrypted data to its original state.

[0409] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and generate specific results or predictions.

[0410] A "database" is a system that systematically organizes and stores data, and allows for efficient access and management.

[0411] "High-risk employees" are employees who have been identified as having high stress levels and potential health risks.

[0412] "Specialized support" refers to specialized services to support mental health, such as counseling services and referrals to medical institutions.

[0413] An "emotion engine" is an algorithm for analyzing and recognizing user emotions from text and voice data.

[0414] A "robot" is a mechanical device that performs physical tasks or interactions autonomously or remotely.

[0415] "Stress level" is an indicator that evaluates the degree of stress felt by employees.

[0416] "Potential risks" are risks that have not materialized at present but may cause problems in the future.

[0417] "Real-time" refers to the immediate processing and provision of data and information without delay.

[0418] The present invention provides a system for assessing and supporting the mental health of employees. This system analyzes data on worries and stress entered by employees, identifies high-risk employees, and provides appropriate support.

[0419] Hardware and Software Configuration

[0420] A server, a terminal and a robot are used.

[0421] The server has the processing power to run the generative AI model and emotion engine.

[0422] A terminal is a device that provides a user interface for employees to input data, and includes a smartphone or tablet.

[0423] The robot is a device that interacts with employees and assesses their stress levels and emotions in real time.

[0424] System processing overview

[0425] Users (employees) input their worries and stress into a chat-style interface. This input data is encrypted on the device and sent to the server. The server receives and decrypts the encrypted data. The decrypted data is analyzed by a generative AI model to evaluate stress levels and potential risks. An emotion engine is used for the analysis, analyzing the employee's emotions in real time and reflecting them in the analysis results. These results are stored in a database. Based on the stored results, the server identifies high-risk employees and notifies them of appropriate professional support (such as counseling services or referrals to medical institutions).

[0426] Specific operation example

[0427] Initial Setup: Use the terminal to set up encryption of user-entered data.

[0428] Data Entry: User types, "My recent project hasn't been going well and I'm feeling the pressure."

[0429] Data transmission: The encrypted data is sent to the server.

[0430] Data Decryption and Analysis: The server decrypts the data and uses an emotion engine to analyze emotions such as "anxiety" or "pressure." The generative AI model evaluates the stress level and determines "Stress Level: High."

[0431] Data storage: The analysis results are stored in a database. For example, it may be stored as "Employee A: High stress level, high risk."

[0432] Professional support: High-stress employees are notified that they may be advised to seek counseling services.

[0433] Technology used

[0434] Generative AI model: A text analysis model using the OpenAI API.

[0435] Emotion Engine: An emotion recognition engine that also uses the OpenAI API.

[0436] Cryptography: Data protection is achieved using the cryptography library.

[0437] Prompt Sentence Examples

[0438] "Recent projects have been going poorly and the employee is feeling the pressure. Please assess this employee's stress level and identify potential risks."

[0439] In this way, the system can support the mental health of employees and improve the overall well-being of the company.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Program processing steps

[0442] Step 1:

[0443] Initial Setup

[0444] Input: Initialization program for user terminal

[0445] Processing: The user device is configured to support encryption. An encryption key is generated within the device, and settings are made to send data securely. For example, the key is generated using Fernet, a Python cryptography library.

[0446] Output: Generate and store encryption keys

[0447] Step 2:

[0448] Entering data

[0449] Input: User-input worries and stress in a chat-style interface (e.g., "My latest project hasn't gone well, and I'm feeling the pressure").

[0450] Processing: The user interface receives the employee's input and encrypts the input text using the secret key generated during the initial setup.

[0451] Output: Encrypted worry and stress data

[0452] Step 3:

[0453] Sending data

[0454] Input: Encrypted worries and stress data

[0455] Processing: The user terminal sends the encrypted data to the server via network communication (e.g., socket communication).

[0456] Output: Encrypted data sent to the server side

[0457] Step 4:

[0458] Receiving and Decrypting Data

[0459] Input: Encrypted worries and stress data

[0460] Processing: The server decrypts the received encrypted data using the same encryption key (secret key) that was used on the user's device.

[0461] Output: Decoded worry and stress data

[0462] Step 5:

[0463] Analysis using generative AI models

[0464] Input: Decoded worry and stress data

[0465] Processing: The server uses the generative AI model to analyze the input data. Specifically, it uses OpenAI's API to perform prompt-based analysis and assess the employee's stress level. Example prompt: "Recent projects have not gone well, and the employee is feeling pressured. Please assess this employee's stress level and identify potential risks."

[0466] Output: Stress level and potential risk assessment results

[0467] Step 6:

[0468] Real-time analysis by emotion engine

[0469] Input: Decoded worry and stress data

[0470] Processing: The server uses an emotion engine to analyze emotions from the input text in real time. Specifically, it recognizes emotions such as "anxiety" or "pressure" from the user's text input and incorporates the results into the analysis results of the generative AI model.

[0471] Output: Final analysis data including sentiment analysis results

[0472] Step 7:

[0473] Saving to a database

[0474] Input: Final analysis data including sentiment analysis results

[0475] Processing: The server stores the results of both the generative AI model and the emotion engine in a database, which is used for long-term trend analysis and to identify high-risk employees.

[0476] Output: Employee mental health assessment data stored in a database

[0477] Step 8:

[0478] Identifying and notifying high-risk employees

[0479] Input: Employee mental health assessment data stored in a database

[0480] Processing: The server analyzes the data in the database to identify high-risk employees and notify them of appropriate professional support (such as counseling services or referrals to medical facilities).

[0481] Output: Support notification for high-risk employees

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] In the smart glasses 214, 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.

[0497] 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."

[0498] The present invention is a system that supports employee mental health, focusing in particular on utilizing generative AI models to analyze employees' daily interactions and assess stress levels and potential risks.

[0499] System Overview

[0500] This system mainly consists of the following components:

[0501] 1. User Interface

[0502] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[0503] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0504] 2. Encryption and Transmission

[0505] The terminal encrypts the input data and transmits it to the server.

[0506] This protects the privacy of employees.

[0507] 3. Data reception and analysis on the server

[0508] The server receives and decrypts the encrypted data.

[0509] A generative AI model is used to analyze the decoded data.

[0510] A generative AI model assesses stress levels and potential risks.

[0511] 4. Save to database

[0512] The server stores the analysis results in a database.

[0513] The stored data is later used to identify high-risk employees.

[0514] 5. Professional Support and Notifications

[0515] The server identifies potentially high-risk employees and directs them to appropriate professional support.

[0516] For example, this may include psychological counseling or contacting a medical institution.

[0517] Specific examples of program processing

[0518] Initial Setup

[0519] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[0520] Entering and Submitting Data

[0521] Users input their worries and stresses into a chat-style interface, which is then encrypted on the device and securely sent to the server.

[0522] Receiving and analyzing data

[0523] The server receives the encrypted data, decrypts it, and analyzes it using a generative AI model to assess stress levels. The analysis results are then automatically stored in a database.

[0524] Identifying and notifying high-risk employees

[0525] The server then identifies high-risk employees based on the results stored in the database, and provides them with information on appropriate counseling services and medical institutions, allowing for specific measures to be taken to support the mental health of employees.

[0526] Specific examples

[0527] When Employee A types a workplace concern into a chat window, saying, "My recent project hasn't been going well, and my relationship with my boss is deteriorating," the data is encrypted on the user's device and sent to the server. After receiving the data, the server decrypts it and uses a generative AI model to evaluate the stress level as "high." Based on this, the evaluation results stored in the database are sent to the user's device as a guide to a professional counseling service.

[0528] In this way, the system aims to improve the mental health of employees and support the overall well-being of the company.

[0529] ---

[0530] The above is an example of a specific form for implementing the present invention, detailing the operations at each step and showing how the entire system interacts to support employee mental health.

[0531] The processing flow will be explained below.

[0532] Step 1:

[0533] The user uses the user interface to input their worries and stress in a chat format. An example of input might be, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[0534] Step 2:

[0535] The terminal receives the data entered by the user and encrypts it to ensure security, and then transmits the encrypted data over the network to the server.

[0536] Step 3:

[0537] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[0538] Step 4:

[0539] The server then provides the decrypted data to a generative AI model for analysis, which assesses stress levels and identifies potential risks based on the input data.

[0540] Step 5:

[0541] The server stores the analysis results, including stress levels and potential risks, in a database that also includes past analysis results, allowing for long-term trend analysis.

[0542] Step 6:

[0543] The server periodically scans the data stored in the database to identify high-risk employees, and if a high-risk employee is identified, appropriate action is taken.

[0544] Step 7:

[0545] The server then sends notifications to high-risk employees, directing them to professional counseling services and medical institutions. Notifications are sent to user terminals, and employees can follow the instructions to receive professional support.

[0546] Step 8:

[0547] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[0548] Example 1

[0549] 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."

[0550] In recent working environments, mental health issues among employees have been increasing. However, many companies lack systems to properly address this issue, making it difficult to detect employee stress and distress early and provide appropriate support. Furthermore, in many cases, privacy protection measures for safely handling employee data are not adequately ensured. Therefore, there is a need for a system that effectively supports employee mental health while simultaneously ensuring data security.

[0551] 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.

[0552] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for installing an SSL / TLS certificate on a user terminal and creating an encryption key to ensure secure transmission and reception of employee data; means for providing the input data to the generative AI model as prompt text to evaluate employee stress levels and potential risks; and means for automatically sending emails and notifications to high-risk employees based on the evaluation results. This makes it possible to effectively support employee mental health while ensuring data security and privacy protection.

[0553] A "user interface" is the operating environment on a device screen such as a computer or smartphone where employees can enter their worries and stress.

[0554] "Encryption" is a technology that converts employee input data into a format that cannot be understood by others, thereby maintaining confidentiality.

[0555] A "server" is a computer system that receives data sent from terminals on a network and stores and analyzes the data.

[0556] A "generative AI model" is an artificial intelligence model that analyzes data received from employees and assesses stress levels and potential risks.

[0557] A "database" is a data management system that stores analytical results and other related data for easy retrieval and use at a later time.

[0558] "High-risk employees" are employees who are assessed as experiencing very high levels of stress and distress based on the analysis results.

[0559] "Professional support" refers to support services such as psychological counseling and referrals to medical institutions provided to employees with mental health issues.

[0560] An "SSL / TLS certificate" is an electronic certificate that protects the security and privacy of communications when sending and receiving data over the Internet.

[0561] An "encryption key" is a string of characters used in the encryption and decryption process, and is a means of protecting the confidentiality of data.

[0562] A "prompt sentence" is a form of text data that is input into a generative AI model and serves as material for the model's analysis.

[0563] "Notifications" are messages or announcements sent from the server to employees to inform them of analysis results and appropriate support.

[0564] This invention is a system that supports employee mental health. Specifically, it uses a generative AI model to analyze employees' daily conversations and evaluate their stress levels and potential risks. Below, we explain the program processing of this system in natural language.

[0565] System Configuration

[0566] The system mainly consists of the following components:

[0567] 1. User Interface

[0568] User terminal: Provides a chat-style interface for employees to input their worries and stress. For example, an employee might input, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0569] 2. Encryption and Transmission

[0570] Terminal: The entered data is encrypted and sent to the server. This protects employee privacy. Install an SSL / TLS certificate and create an encryption key to ensure the security of data communication.

[0571] 3. Data Reception and Decryption

[0572] Server: Receives the encrypted data and decrypts it, converting it into a format that can be analyzed.

[0573] 4. Analysis using generative AI models

[0574] Server: The decrypted data is input into a generative AI model for analysis. This generative AI model is used to assess an employee's stress level and potential risks. For example, given the input data "My recent project has not been going well, and my relationship with my boss has deteriorated," the model might assess the stress level as "high."

[0575] 5. Saving to the database

[0576] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[0577] 6. Identifying and notifying high-risk employees

[0578] Server: Identifies high-risk employees based on information from the database. These employees are then notified of appropriate support and counseling services. For example, identified high-risk employees are notified of psychological counseling or contact information for medical institutions.

[0579] Specific examples

[0580] Initial Setup

[0581] Device: An IT administrator installs an SSL / TLS certificate on a new user device and generates encryption keys, allowing the device to send and receive data securely.

[0582] Data entry and encryption

[0583] User: Employee B types in the chat interface, "There are a lot of disagreements within the team and I'm feeling stressed."

[0584] Terminal: The input data is encrypted using AES-256 and ready to go.

[0585] Sending data

[0586] On your device: Encrypted data is sent to the server using HTTPS, which ensures data security.

[0587] Receiving and Decrypting Data

[0588] Server: The server receives the encrypted data and decrypts it using the stored encryption key.

[0589] Analysis using generative AI models

[0590] Server: The decrypted data is input into the generative AI model, which analyzes the content, "There are many differences of opinion within the team, and I am feeling stressed." The generative AI model analyzes the stress level and evaluates it as "medium."

[0591] Identifying and notifying high-risk employees

[0592] Server: Identifies employees from the database whose stress level is rated as "high" and prepares information related to those employees.

[0593] Server: Sends email notifications to high-risk employees, providing guidance such as, "Your stress level has been assessed as high, so we recommend psychological counseling." This allows for prompt measures to be taken to address employees' mental health.

[0594] Examples of prompt statements

[0595] The following prompts are fed into the generative AI model to assess stress levels:

[0596] Rate your stress level if someone typed in, "My recent project hasn't been going well and my relationship with my boss has deteriorated."

[0597] This system can effectively support the mental health of employees and improve the overall well-being of the company.

[0598] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0599] Step 1:

[0600] Initial Setup

[0601] Device: Initialize the user device, install an SSL / TLS certificate, and create an encryption key. This configuration ensures secure data transmission from the device to the server.

[0602] Specific operation: An IT administrator installs an SSL / TLS certificate on a new user device and generates an encryption key. At this step, the device is ready for secure data transmission and reception.

[0603] Input: None

[0604] Output: SSL / TLS certificate installed, encryption key generated

[0605] Step 2:

[0606] Data entry and encryption

[0607] User: Enters worries and stress on the chat interface. For example, the user enters, "My recent project has not been going well, and my relationship with my boss has deteriorated."

[0608] Terminal: Input data is encrypted using the AES-256 method and prepared for secure transmission to the server.

[0609] Specific operation: The user enters text into the input field and clicks the send button. The device encrypts the input data.

[0610] Input: User text input such as "My recent projects have been going poorly and my relationship with my boss has been strained."

[0611] Output: Encrypted data

[0612] Step 3:

[0613] Sending data

[0614] Terminal: Encrypted data is sent to the server using SSL / TLS, which reduces the risk of data being intercepted by a third party.

[0615] Specific operation: The device sends data to the server via HTTPS.

[0616] Input: Encrypted data

[0617] Output: Encrypted data sent to the server

[0618] Step 4:

[0619] Receiving and Decrypting Data

[0620] Server: Receives the encrypted data and decrypts it using a stored encryption key, converting the decrypted data into an analyzable format.

[0621] What happens: The server receives the data and decrypts it using the encryption key.

[0622] Input: Encrypted data

[0623] Output: Decoded text data

[0624] Step 5:

[0625] Analysis using generative AI models

[0626] Server: The decrypted data is fed into a generative AI model to assess stress levels and potential risks. For example, a statement like "My recent project hasn't been going well, and my relationship with my boss has deteriorated" would be evaluated as a "high" stress level.

[0627] Specific operation: The server inputs the decoded data as a prompt sentence into the generative AI model, and the model performs analysis.

[0628] Input: Text data: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0629] Output: Stress level assessment result (e.g. "High")

[0630] Step 6:

[0631] Saving to a database

[0632] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[0633] Specific operation: Save the stress level assessment results in a MySQL database.

[0634] Input: Stress level assessment result

[0635] Output: Saved evaluation results

[0636] Step 7:

[0637] Identifying high-risk employees

[0638] Server: Identifies high-risk employees based on information from the database.

[0639] What happens: The server runs an SQL query to extract employees with a "high" stress level.

[0640] Input: Evaluation results stored in the database

[0641] Output: List of high-risk employees

[0642] Step 8:

[0643] Notifications and Support Information

[0644] Server: Notify high-risk employees and direct them to appropriate support and counseling services.

[0645] What happens: The server automatically sends an email to the employee with a message such as, "We recommend that you seek psychological counseling."

[0646] Input: List of high-risk employees

[0647] Output: Notifications and support information

[0648] (Application example 1)

[0649] 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."

[0650] Employee mental health issues have a significant impact on the productivity and atmosphere of the entire workplace. High stress levels are often seen, especially in brick-and-mortar stores, which negatively impact employee performance and health. Furthermore, the lack of an environment in which employees can easily seek advice about their mental health makes it difficult to detect and address problems early. Therefore, there is a need for a system that can quickly and appropriately assess employee concerns and stress and provide the necessary support.

[0651] 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.

[0652] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for store employees to input their mental health status from their smartphones; and means for evaluating stress levels using the generative AI model and notifying them of appropriate support. This makes it possible to quickly evaluate the stress and worries that store employees experience on a daily basis and provide them with appropriate counseling services or guidance to medical institutions.

[0653] A "user interface" is a system component that provides a screen and operating means for employees to input their worries and stress.

[0654] "Encryption" is a technology that converts input data so that it cannot be deciphered by third parties.

[0655] A "server" is a computer system that receives and analyzes data sent from a user interface.

[0656] "Decryption" is a technique for restoring encrypted data to its original state.

[0657] A "generative AI model" is an artificial intelligence model that analyzes input data and assesses stress levels and risks based on that data.

[0658] "Analysis" is the process of analyzing the content of input data using a generative AI model and deriving evaluation results.

[0659] A "database" is an information management system that stores analysis results and allows them to be referenced later.

[0660] "High-risk employees" are employees who are assessed as having high stress levels through analysis by the generative AI model.

[0661] "Professional support" refers to services to support employees' mental health, such as psychological counseling and referrals to medical institutions.

[0662] A "physical store" is a physical location where sales or services are provided.

[0663] A "smartphone" is a mobile device that has the functionality of a mobile phone and the ability to run a variety of applications.

[0664] "Stress level" is a numerical value or assessment result that evaluates the degree of stress felt by an employee.

[0665] "Counseling services" are services in which professional counselors provide advice to employees about their worries and stress.

[0666] A "prompt sentence" is a form of text data input into a generative AI model and is an instruction sentence for analysis.

[0667] The present invention is a system for supporting the mental health of employees, and is particularly focused on employees in brick-and-mortar stores. A specific embodiment of this system will be described below.

[0668] System configuration

[0669] 1. User Interface

[0670] Users (employees) use an application installed on their smartphones to input their worries and stress levels. The user interface is in chat format, making it easy for employees to operate.

[0671] 2. Data Encryption and Transmission

[0672] The device (smartphone) uses encryption technology to safely transmit the entered data to the server. This encryption technology is designed to prevent third parties from stealing or tampering with the data.

[0673] 3. Data reception and analysis on the server

[0674] The server automatically decrypts the encrypted data upon receiving it. It then analyzes the data using a generative AI model to assess the employee's stress level and potential risks. The generative AI model generates prompts based on the employee's input data and performs the analysis.

[0675] 4. Saving to the database

[0676] The server stores the analysis results in a database that can be used for future reference, enabling continuous monitoring of employees' mental health.

[0677] 5. Professional Support and Notifications

[0678] The server identifies high-risk employees based on the analysis results in the database, and these employees are notified via their smartphones about professional support (such as psychological counseling or referrals to medical institutions).

[0679] Specific examples of processing

[0680] Initial Setup

[0681] Users install the application on their smartphone and perform basic configuration, which includes generating encryption keys to ensure secure data transmission.

[0682] Entering and Submitting Data

[0683] When an employee types in "My recent project hasn't gone well and my relationship with my boss is also deteriorating," the data is encrypted on the smartphone and sent to a server.

[0684] Receiving and analyzing data

[0685] When the server receives the data, it decrypts it. The decrypted data is then used to analyze the input data using a generative AI model. The generative AI model is given a prompt like this:

[0686] Stress Assessment: My recent project has been going poorly and my relationship with my boss has deteriorated.

[0687] Identifying and notifying high-risk employees

[0688] If the stress level is assessed as "high" based on the analysis results of the generative AI model, the result is stored in a database. The server then uses this result to identify high-risk employees and sends a notification to their smartphones directing them to appropriate counseling services.

[0689] In this way, a system can be built to support the mental health of employees in brick-and-mortar stores. The hardware used includes smartphones and servers, and the software includes encryption technology, database management systems, and generative AI models.

[0690] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0691] Step 1:

[0692] Initial Setup

[0693] A user installs an application on their smartphone and performs basic configuration. Specifically, the user launches the application and enters their account information. The application generates an encryption key, which ensures secure data transmission. The input is the user's personal information and a request to generate an encryption key, and the output is the generated encryption key.

[0694] Step 2:

[0695] Entering data

[0696] Users (employees) input their worries and stress through a smartphone application. Specifically, they type "My recent project hasn't been going well, and my relationship with my boss has deteriorated" into a chat-style interface. The input is text data about the employee's worries and stress, and the output is the input data saved in the application.

[0697] Step 3:

[0698] Data encryption

[0699] The terminal (smartphone) encrypts the input data. Specifically, it encrypts the input text data using an encryption key to generate encrypted data. The input is the employee's input data and the encryption key, and the output is the encrypted data. Encrypted data is important for protecting privacy.

[0700] Step 4:

[0701] Sending data

[0702] The terminal sends the encrypted data to the server. Specifically, it uses an HTTP request to send the encrypted data to the specified server address. The input is the encrypted data and the server address information, and the output is the sending status. If successful, the server receives the encrypted data.

[0703] Step 5:

[0704] Receiving and Decrypting Data

[0705] The server receives the encrypted data sent from the terminal and decrypts it. Specifically, it uses a decryption key to return the received data to the original text data. The input is the encrypted data and the decryption key, and the output is the original text data. This allows the server to obtain the correct input information.

[0706] Step 6:

[0707] Data analysis

[0708] The server uses a generative AI model to analyze the decoded data. Specifically, it generates a prompt for stress assessment based on the input text data and inputs that prompt into the generative AI model. The input is the decoded text data and the generated prompt, and the output is the stress level assessment result. An example of a prompt is "Stress assessment: My recent project has not been going well, and my relationship with my boss has deteriorated."

[0709] Step 7:

[0710] Saving analysis results

[0711] The server stores the analysis results from the generative AI model in a database. Specifically, it stores the stress level assessment results and employee identification information in the database. The input is the stress level assessment results and employee identification information, and the output is the analysis results stored in the database.

[0712] Step 8:

[0713] Identifying and notifying high-risk employees

[0714] The server identifies high-risk employees based on the analysis results in the database. Specifically, it extracts employees who are assessed as having high stress levels and generates a guide to professional support. The input is the stress level assessment results in the database, and the output is a notification directing them to a professional counseling service. This notification is delivered to the employee's smartphone.

[0715] Through the above steps, a system for supporting employee mental health is concretely implemented.

[0716] 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.

[0717] This invention is a system that combines a generative AI model and an emotion engine, and provides the function of analyzing employees' daily conversations to assess their stress levels and potential risks. Furthermore, by using the emotion engine to recognize the user's emotions and incorporating the results into the analysis results, more accurate mental health support is realized.

[0718] System Overview

[0719] This system mainly consists of the following components:

[0720] 1. User Interface

[0721] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[0722] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0723] 2. Encryption and Transmission

[0724] The terminal encrypts the input data and transmits it to the server.

[0725] This protects the privacy of employees.

[0726] 3. Data reception and analysis on the server

[0727] The server receives and decrypts the encrypted data.

[0728] The decoded data is provided to a generative AI model for analysis.

[0729] The generative AI model assesses stress levels based on input data and identifies potential risks.

[0730] 4. Use of Emotion Engine

[0731] The server uses an emotion engine to analyze the user's emotions in real time.

[0732] The analysis results of the emotion engine are also incorporated into the analysis results of the generative AI model.

[0733] This allows for detailed assessment according to changes in emotions and situations.

[0734] 5. Save to database

[0735] The server stores the analysis results from both the generative AI model and the emotion engine in a database.

[0736] The stored data is used to identify high-risk employees, along with long-term trends.

[0737] 6. Professional Support and Notifications

[0738] The server identifies high-risk employees based on the results stored in a database.

[0739] If high-risk employees are identified, they will be notified and directed to appropriate professional support (e.g., counseling services or medical referrals).

[0740] Specific examples of program processing

[0741] Initial Setup

[0742] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[0743] Entering and Submitting Data

[0744] The user enters their worries and stresses into a chat-style interface. This data is encrypted on the device and securely sent to the server. For example, a user might enter, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0745] Receiving and analyzing data

[0746] The server receives the encrypted data, decrypts it, and feeds it into a generative AI model to assess stress levels and potential risks.

[0747] Emotion Engine Analysis

[0748] The server uses an emotion engine to analyze the user's emotions in real time. For example, it can recognize emotions such as "anxiety" or "stress" from the user's text input. The results of the emotion engine are also incorporated into the analysis of the generative AI model, resulting in a more accurate evaluation.

[0749] Database storage and identification of high-risk employees

[0750] The server stores the analysis results of the generative AI model and emotion engine in a database. Based on the results stored in the database, the server identifies high-risk employees. For example, identified employees may be judged as needing counseling services.

[0751] Professional support and notifications

[0752] The server notifies high-risk employees of professional counseling services and medical institutions. The notification is sent to the user's terminal, and employees can follow the instructions to receive professional support. For example, an employee who is recognized as being under high stress will receive a notification directing them to professional counseling.

[0753] In this way, the system combines a generative AI model with an emotion engine to improve employee mental health and support the overall well-being of the company.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] A user inputs worries and stress in a chat format using a user interface. For example, the user inputs, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[0757] Step 2:

[0758] The terminal encrypts the input data, thereby protecting the user's privacy, and then sends the encrypted data to the server.

[0759] Step 3:

[0760] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[0761] Step 4:

[0762] The server provides the decrypted data to a generative AI model, which assesses stress levels and identifies potential risks based on the input data.

[0763] Step 5:

[0764] The server uses an emotion engine to analyze the user's emotions in real time, for example recognizing the emotions of "anxiety" and "stress" from the user's text input.

[0765] Step 6:

[0766] The server incorporates the emotion results recognized by the emotion engine into the analysis results of the generative AI model, enabling detailed evaluation according to emotional changes and situations.

[0767] Step 7:

[0768] The server stores the results of both the generative AI model and the emotion engine in a database, which is also used to analyze long-term trends.

[0769] Step 8:

[0770] The server periodically scans the results stored in the database to identify high-risk employees, who are deemed to require appropriate action.

[0771] Step 9:

[0772] The server notifies high-risk employees of professional counseling services and medical referrals, and the notification is sent to the user's device.

[0773] Step 10:

[0774] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[0775] Through these steps, the system combines a generative AI model and an emotion engine to comprehensively assess employees' stress and mental health and provide appropriate support.

[0776] Example 2

[0777] 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."

[0778] Managing and supporting employee mental health is an important issue for modern companies. However, systems for efficiently and accurately identifying employee concerns and stress levels and implementing appropriate measures are not yet fully in place. Traditional methods often rely on employees' self-reporting or interviews, which can effectively assess stress and potentially overlook potential risks. Furthermore, a lack of technology to protect data privacy and capture emotional changes in real time makes it difficult to accurately assess employee health.

[0779] 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.

[0780] In this invention, the server includes means for providing a user interface for employees to input their worries and stress, means for encrypting the input data and sending it to the server, means for running a generative AI model that decrypts and analyzes the encrypted data on the server side, means for analyzing the user's emotions in real time using an emotion engine on the server side, and means for storing the analysis results of the generative AI model and the emotion engine in a database. This makes it possible to accurately evaluate employee stress levels and changes in emotions and provide appropriate support while protecting privacy.

[0781] "User interface" refers to the interactive screen and operating means through which system users input their worries and stress.

[0782] "Encryption" refers to the technology of converting input data into a form that cannot be deciphered by third parties, and is a method of protecting the privacy and security of data.

[0783] "Server" refers to the central processing unit that analyzes the data received, runs the generative AI model, uses the emotion engine, stores data, and notifies users.

[0784] "Decryption" refers to the process of returning encrypted data to its original form, making it available as analyzable data.

[0785] "Generative AI model" refers to an artificial intelligence model that assesses employee stress levels and potential risks based on received data.

[0786] An "emotion engine" refers to software that analyzes emotions from user input and evaluates emotional trends and changes.

[0787] "Database" refers to an information storage system that stores the analysis results of the generative AI model and emotion engine, and enables reference and analysis as needed.

[0788] "High-risk employees" are those who are deemed to be in particular need of mental health support based on the analysis results of the generative AI model and emotion engine.

[0789] "Specialized support" refers to appropriate assistance provided to employees identified as at high risk, such as counseling services or referrals to medical facilities.

[0790] "Stress level" refers to the quantitative assessment of the degree of stress experienced by employees.

[0791] "Privacy" refers to the state in which employees' personal information and data are protected and not shared with third parties without approval.

[0792] "Real-time" refers to the instant analysis and evaluation of user behavior and emotions on the spot.

[0793] This invention relates to a system that analyzes employees' daily conversations to assess their stress levels and potential risks. The purpose of this invention is to effectively support employees' mental health by combining a generative AI model and an emotion engine.

[0794] System Configuration

[0795] User Interface

[0796] The terminal provides a chat-style user interface for employees to input their worries and stress. Specifically, the user interface includes a text box and a send button, allowing employees to easily input their daily stress and worries.

[0797] Data encryption

[0798] The terminal encrypts the input data using encryption technology such as AES (Advanced Encryption Standard) before transmitting it, thereby protecting the privacy of user data.

[0799] Sending and Receiving Data

[0800] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS, and the server receives the data and decrypts it using a private key.

[0801] Analysis of generative AI models

[0802] The server then provides the decoded data to a generative AI model, which uses natural language processing (NLP) to assess the stress level and potential risks from the input text. Specifically, the generative AI model calculates a stress score and returns the result to the server.

[0803] Emotion Engine Analysis

[0804] In addition to the analysis results of the generative AI model, the server analyzes the user's emotions in real time using an emotion engine. The emotion engine detects emotions such as "anxiety" or "stress" from the input text and integrates the results into the analysis results of the generative AI model.

[0805] Saving analysis results

[0806] The server stores the results of the generative AI model and emotion engine analysis in a database, which includes the employee's ID, input text, stress score, and detected emotion.

[0807] Identifying high-risk employees

[0808] The server analyzes the results stored in the database and identifies high-risk employees. Specifically, if an employee's stress score or emotion analysis results exceed a certain threshold, they are deemed high-risk.

[0809] Professional Support Notification

[0810] The server then sends notifications to employees identified as high-risk with information on how to access professional support, including counseling services and medical facilities.

[0811] Specific examples

[0812] 1. User Interface

[0813] The user types into a chat-style interface, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[0814] 2. Encryption and Transmission

[0815] The device encrypts the input text and sends it to the server using HTTPS.

[0816] 3. Data Reception and Decryption

[0817] The server receives the data sent from the terminal and decrypts the encrypted data using the private key.

[0818] 4. Analysis of generative AI models

[0819] The server inputs the decoded text into a generative AI model, which analyzes it as "high stress level."

[0820] 5. Use of Emotion Engine

[0821] The server uses an emotion engine to detect emotions such as "anxiety" or "stress" from the text.

[0822] 6. Saving the analysis results

[0823] The server stores the results of the generative AI model and emotion engine in a database.

[0824] 7. Identifying high-risk employees

[0825] The server analyzes the stored data and identifies employees as high risk if their stress score is high.

[0826] 8. Professional Support Notification

[0827] The server sends notifications to terminals for professional counseling for high-risk employees.

[0828] Prompt Sentence Examples

[0829] 1. Stress Level Assessment Prompt

[0830] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0831] Prompt: "Please rate the user's stress level based on this text."

[0832] 2. Emotion Recognition Prompts

[0833] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0834] Prompt: "Identify the user's emotion from this text."

[0835] In this way, the present invention provides a system that provides advanced support for the mental health of employees, thereby improving the overall health of the company.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] The user inputs their worries and stress into a chat-style interface on the device. The input is in text format, for example, "My recent project has not been going well, and my relationship with my boss has deteriorated." This text is input as data.

[0839] Step 2:

[0840] The terminal uses AES encryption to encrypt the text data entered by the user, producing encrypted data that is then output. This encryption ensures the privacy of the data.

[0841] Step 3:

[0842] The terminal sends the encrypted data to the server using the HTTPS protocol, in this process the encrypted data is sent as input and received on the server side.

[0843] Step 4:

[0844] The server decrypts the encrypted data received via the HTTPS protocol using the private key. This process results in the decrypted data, which is the output.

[0845] Step 5:

[0846] The server provides the decrypted text data to the generative AI model, which evaluates the employee's stress level from the text data and calculates a stress score. The input is the text data and the output is the stress score. For example, the stress score is calculated as "85."

[0847] Step 6:

[0848] The server analyzes the user's emotions in real time using an emotion engine in parallel with the analysis results of the generative AI model. The emotion engine identifies emotions such as "anxiety" or "stress" from the text data and outputs them. The input is the text data, and the output is the detected emotion. For example, "anxiety" is detected.

[0849] Step 7:

[0850] The server combines the results of the generative AI model and the emotion engine analysis and stores them in a database, including employee IDs, input text, stress scores, and detected emotions, enabling long-term trend analysis and risk assessment.

[0851] Step 8:

[0852] The server analyzes the data stored in the database and identifies high-risk employees. Specifically, if the stress score or emotion analysis results exceed a certain threshold, the employee is deemed high-risk. For example, a stress score of 85 exceeds the threshold and is deemed high-risk.

[0853] Step 9:

[0854] The server then notifies employees identified as high-risk of professional counseling services or medical consultations. The notification is sent to the device, where the user can view and confirm the content of the notification. For example, a notification saying "Professional counseling is required" is sent.

[0855] Through these steps, the system is able to assess employees' mental health with high accuracy and provide the necessary support in a timely manner.

[0856] (Application example 2)

[0857] 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."

[0858] Employee mental health issues have a significant impact on the productivity and health of an entire company. However, even if employees are experiencing mental health-related worries or stress, it is difficult to accurately assess their mental health through self-reporting alone. There is also a lack of effective means to quickly identify employees who need professional support and provide appropriate support. There is a need for a system that can assess employee mental health in real time and quickly identify potential risks.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0860] In this invention, the server includes: a means for providing a user interface through which employees input their worries and stress; a means for encrypting the input data and sending it to the server; a means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; a means for storing the analysis results in a database; a means for identifying high-risk employees and notifying them of professional support; a means for analyzing user emotions in real time using an emotion engine and incorporating the results into the analysis data; and a means for assessing stress levels and potential risks using a robot for interacting with employees. This makes it possible to analyze employee emotions in real time and accurately assess stress levels and potential risks. It also makes it possible to provide employees with mental health support in real time, identify high-risk employees early, and notify them of appropriate professional support.

[0861] A "user interface" is an interface that allows a user to perform operations and input data, and is typically in the form of a chat.

[0862] "Encryption" is the technique of converting data into a special code to protect it and ensure its safety from unauthorized access.

[0863] A "server" is a computer system that provides specific services or data over a network.

[0864] "Decryption" is a technique for restoring encrypted data to its original state.

[0865] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and generate specific results or predictions.

[0866] A "database" is a system that systematically organizes and stores data, and allows for efficient access and management.

[0867] "High-risk employees" are employees who have been identified as having high stress levels and potential health risks.

[0868] "Specialized support" refers to specialized services to support mental health, such as counseling services and referrals to medical institutions.

[0869] An "emotion engine" is an algorithm for analyzing and recognizing user emotions from text and voice data.

[0870] A "robot" is a mechanical device that performs physical tasks or interactions autonomously or remotely.

[0871] "Stress level" is an indicator that evaluates the degree of stress felt by employees.

[0872] "Potential risks" are risks that have not materialized at present but may cause problems in the future.

[0873] "Real-time" refers to the immediate processing and provision of data and information without delay.

[0874] The present invention provides a system for assessing and supporting the mental health of employees. This system analyzes data on worries and stress entered by employees, identifies high-risk employees, and provides appropriate support.

[0875] Hardware and Software Configuration

[0876] A server, a terminal and a robot are used.

[0877] The server has the processing power to run the generative AI model and emotion engine.

[0878] A terminal is a device that provides a user interface for employees to input data, and includes a smartphone or tablet.

[0879] The robot is a device that interacts with employees and assesses their stress levels and emotions in real time.

[0880] System processing overview

[0881] Users (employees) input their worries and stress into a chat-style interface. This input data is encrypted on the device and sent to the server. The server receives and decrypts the encrypted data. The decrypted data is analyzed by a generative AI model to evaluate stress levels and potential risks. An emotion engine is used for the analysis, analyzing the employee's emotions in real time and reflecting them in the analysis results. These results are stored in a database. Based on the stored results, the server identifies high-risk employees and notifies them of appropriate professional support (such as counseling services or referrals to medical institutions).

[0882] Specific operation example

[0883] Initial Setup: Use the terminal to set up encryption of user-entered data.

[0884] Data Entry: User types, "My recent project hasn't been going well and I'm feeling the pressure."

[0885] Data transmission: The encrypted data is sent to the server.

[0886] Data Decryption and Analysis: The server decrypts the data and uses an emotion engine to analyze emotions such as "anxiety" or "pressure." The generative AI model evaluates the stress level and determines "Stress Level: High."

[0887] Data storage: The analysis results are stored in a database. For example, it may be stored as "Employee A: High stress level, high risk."

[0888] Professional support: High-stress employees are notified that they may be advised to seek counseling services.

[0889] Technology used

[0890] Generative AI model: A text analysis model using the OpenAI API.

[0891] Emotion Engine: An emotion recognition engine that also uses the OpenAI API.

[0892] Cryptography: Data protection is achieved using the cryptography library.

[0893] Prompt Sentence Examples

[0894] "Recent projects have been going poorly and the employee is feeling the pressure. Please assess this employee's stress level and identify potential risks."

[0895] In this way, the system can support the mental health of employees and improve the overall well-being of the company.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Program processing steps

[0898] Step 1:

[0899] Initial Setup

[0900] Input: Initialization program for user terminal

[0901] Processing: The user device is configured to support encryption. An encryption key is generated within the device, and settings are made to send data securely. For example, the key is generated using Fernet, a Python cryptography library.

[0902] Output: Generate and store encryption keys

[0903] Step 2:

[0904] Entering data

[0905] Input: User-input worries and stress in a chat-style interface (e.g., "My latest project hasn't gone well, and I'm feeling the pressure").

[0906] Processing: The user interface receives the employee's input and encrypts the input text using the secret key generated during the initial setup.

[0907] Output: Encrypted worry and stress data

[0908] Step 3:

[0909] Sending data

[0910] Input: Encrypted worries and stress data

[0911] Processing: The user terminal sends the encrypted data to the server via network communication (e.g., socket communication).

[0912] Output: Encrypted data sent to the server side

[0913] Step 4:

[0914] Receiving and Decrypting Data

[0915] Input: Encrypted worries and stress data

[0916] Processing: The server decrypts the received encrypted data using the same encryption key (secret key) that was used on the user's device.

[0917] Output: Decoded worry and stress data

[0918] Step 5:

[0919] Analysis using generative AI models

[0920] Input: Decoded worry and stress data

[0921] Processing: The server uses the generative AI model to analyze the input data. Specifically, it uses OpenAI's API to perform prompt-based analysis and assess the employee's stress level. Example prompt: "Recent projects have not gone well, and the employee is feeling pressured. Please assess this employee's stress level and identify potential risks."

[0922] Output: Stress level and potential risk assessment results

[0923] Step 6:

[0924] Real-time analysis by emotion engine

[0925] Input: Decoded worry and stress data

[0926] Processing: The server uses an emotion engine to analyze emotions from the input text in real time. Specifically, it recognizes emotions such as "anxiety" or "pressure" from the user's text input and incorporates the results into the analysis results of the generative AI model.

[0927] Output: Final analysis data including sentiment analysis results

[0928] Step 7:

[0929] Saving to a database

[0930] Input: Final analysis data including sentiment analysis results

[0931] Processing: The server stores the results of both the generative AI model and the emotion engine in a database, which is used for long-term trend analysis and to identify high-risk employees.

[0932] Output: Employee mental health assessment data stored in a database

[0933] Step 8:

[0934] Identifying and notifying high-risk employees

[0935] Input: Employee mental health assessment data stored in a database

[0936] Processing: The server analyzes the data in the database to identify high-risk employees and notify them of appropriate professional support (such as counseling services or referrals to medical facilities).

[0937] Output: Support notification for high-risk employees

[0938] 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.

[0939] 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.

[0940] 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.

[0941] [Third embodiment]

[0942] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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).

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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."

[0954] The present invention is a system that supports employee mental health, focusing in particular on utilizing generative AI models to analyze employees' daily interactions and assess stress levels and potential risks.

[0955] System Overview

[0956] This system mainly consists of the following components:

[0957] 1. User Interface

[0958] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[0959] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[0960] 2. Encryption and Transmission

[0961] The terminal encrypts the input data and transmits it to the server.

[0962] This protects the privacy of employees.

[0963] 3. Data reception and analysis on the server

[0964] The server receives and decrypts the encrypted data.

[0965] A generative AI model is used to analyze the decoded data.

[0966] A generative AI model assesses stress levels and potential risks.

[0967] 4. Save to database

[0968] The server stores the analysis results in a database.

[0969] The stored data is later used to identify high-risk employees.

[0970] 5. Professional Support and Notifications

[0971] The server identifies potentially high-risk employees and directs them to appropriate professional support.

[0972] For example, this may include psychological counseling or contacting a medical institution.

[0973] Specific examples of program processing

[0974] Initial Setup

[0975] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[0976] Entering and Submitting Data

[0977] Users input their worries and stresses into a chat-style interface, which is then encrypted on the device and securely sent to the server.

[0978] Receiving and analyzing data

[0979] The server receives the encrypted data, decrypts it, and analyzes it using a generative AI model to assess stress levels. The analysis results are then automatically stored in a database.

[0980] Identifying and notifying high-risk employees

[0981] The server then identifies high-risk employees based on the results stored in the database, and provides them with information on appropriate counseling services and medical institutions, allowing for specific measures to be taken to support the mental health of employees.

[0982] Specific examples

[0983] When Employee A types a workplace concern into a chat window, saying, "My recent project hasn't been going well, and my relationship with my boss is deteriorating," the data is encrypted on the user's device and sent to the server. After receiving the data, the server decrypts it and uses a generative AI model to evaluate the stress level as "high." Based on this, the evaluation results stored in the database are sent to the user's device as a guide to a professional counseling service.

[0984] In this way, the system aims to improve the mental health of employees and support the overall well-being of the company.

[0985] ---

[0986] The above is an example of a specific form for implementing the present invention, detailing the operations at each step and showing how the entire system interacts to support employee mental health.

[0987] The processing flow will be explained below.

[0988] Step 1:

[0989] The user uses the user interface to input their worries and stress in a chat format. An example of input might be, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[0990] Step 2:

[0991] The terminal receives the data entered by the user and encrypts it to ensure security, and then transmits the encrypted data over the network to the server.

[0992] Step 3:

[0993] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[0994] Step 4:

[0995] The server then provides the decrypted data to a generative AI model for analysis, which assesses stress levels and identifies potential risks based on the input data.

[0996] Step 5:

[0997] The server stores the analysis results, including stress levels and potential risks, in a database that also includes past analysis results, allowing for long-term trend analysis.

[0998] Step 6:

[0999] The server periodically scans the data stored in the database to identify high-risk employees, and if a high-risk employee is identified, appropriate action is taken.

[1000] Step 7:

[1001] The server then sends notifications to high-risk employees, directing them to professional counseling services and medical institutions. Notifications are sent to user terminals, and employees can follow the instructions to receive professional support.

[1002] Step 8:

[1003] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[1004] Example 1

[1005] 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."

[1006] In recent working environments, mental health issues among employees have been increasing. However, many companies lack systems to properly address this issue, making it difficult to detect employee stress and distress early and provide appropriate support. Furthermore, in many cases, privacy protection measures for safely handling employee data are not adequately ensured. Therefore, there is a need for a system that effectively supports employee mental health while simultaneously ensuring data security.

[1007] 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.

[1008] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for installing an SSL / TLS certificate on a user terminal and creating an encryption key to ensure secure transmission and reception of employee data; means for providing the input data to the generative AI model as prompt text to evaluate employee stress levels and potential risks; and means for automatically sending emails and notifications to high-risk employees based on the evaluation results. This makes it possible to effectively support employee mental health while ensuring data security and privacy protection.

[1009] A "user interface" is the operating environment on a device screen such as a computer or smartphone where employees can enter their worries and stress.

[1010] "Encryption" is a technology that converts employee input data into a format that cannot be understood by others, thereby maintaining confidentiality.

[1011] A "server" is a computer system that receives data sent from terminals on a network and stores and analyzes the data.

[1012] A "generative AI model" is an artificial intelligence model that analyzes data received from employees and assesses stress levels and potential risks.

[1013] A "database" is a data management system that stores analytical results and other related data for easy retrieval and use at a later time.

[1014] "High-risk employees" are employees who are assessed as experiencing very high levels of stress and distress based on the analysis results.

[1015] "Professional support" refers to support services such as psychological counseling and referrals to medical institutions provided to employees with mental health issues.

[1016] An "SSL / TLS certificate" is an electronic certificate that protects the security and privacy of communications when sending and receiving data over the Internet.

[1017] An "encryption key" is a string of characters used in the encryption and decryption process, and is a means of protecting the confidentiality of data.

[1018] A "prompt sentence" is a form of text data that is input into a generative AI model and serves as material for the model's analysis.

[1019] "Notifications" are messages or announcements sent from the server to employees to inform them of analysis results and appropriate support.

[1020] This invention is a system that supports employee mental health. Specifically, it uses a generative AI model to analyze employees' daily conversations and evaluate their stress levels and potential risks. Below, we explain the program processing of this system in natural language.

[1021] System Configuration

[1022] The system mainly consists of the following components:

[1023] 1. User Interface

[1024] User terminal: Provides a chat-style interface for employees to input their worries and stress. For example, an employee might input, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1025] 2. Encryption and Transmission

[1026] Terminal: The entered data is encrypted and sent to the server. This protects employee privacy. Install an SSL / TLS certificate and create an encryption key to ensure the security of data communication.

[1027] 3. Data Reception and Decryption

[1028] Server: Receives the encrypted data and decrypts it, converting it into a format that can be analyzed.

[1029] 4. Analysis using generative AI models

[1030] Server: The decrypted data is input into a generative AI model for analysis. This generative AI model is used to assess an employee's stress level and potential risks. For example, given the input data "My recent project has not been going well, and my relationship with my boss has deteriorated," the model might assess the stress level as "high."

[1031] 5. Saving to the database

[1032] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[1033] 6. Identifying and notifying high-risk employees

[1034] Server: Identifies high-risk employees based on information from the database. These employees are then notified of appropriate support and counseling services. For example, identified high-risk employees are notified of psychological counseling or contact information for medical institutions.

[1035] Specific examples

[1036] Initial Setup

[1037] Device: An IT administrator installs an SSL / TLS certificate on a new user device and generates encryption keys, allowing the device to send and receive data securely.

[1038] Data entry and encryption

[1039] User: Employee B types in the chat interface, "There are a lot of disagreements within the team and I'm feeling stressed."

[1040] Terminal: The input data is encrypted using AES-256 and ready to go.

[1041] Sending data

[1042] On your device: Encrypted data is sent to the server using HTTPS, which ensures data security.

[1043] Receiving and Decrypting Data

[1044] Server: The server receives the encrypted data and decrypts it using the stored encryption key.

[1045] Analysis using generative AI models

[1046] Server: The decrypted data is input into the generative AI model, which analyzes the content, "There are many differences of opinion within the team, and I am feeling stressed." The generative AI model analyzes the stress level and evaluates it as "medium."

[1047] Identifying and notifying high-risk employees

[1048] Server: Identifies employees from the database whose stress level is rated as "high" and prepares information related to those employees.

[1049] Server: Sends email notifications to high-risk employees, providing guidance such as, "Your stress level has been assessed as high, so we recommend psychological counseling." This allows for prompt measures to be taken to address employees' mental health.

[1050] Examples of prompt statements

[1051] The following prompts are fed into the generative AI model to assess stress levels:

[1052] Rate your stress level if someone typed in, "My recent project hasn't been going well and my relationship with my boss has deteriorated."

[1053] This system can effectively support the mental health of employees and improve the overall well-being of the company.

[1054] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] Initial Setup

[1057] Device: Initialize the user device, install an SSL / TLS certificate, and create an encryption key. This configuration ensures secure data transmission from the device to the server.

[1058] Specific operation: An IT administrator installs an SSL / TLS certificate on a new user device and generates an encryption key. At this step, the device is ready for secure data transmission and reception.

[1059] Input: None

[1060] Output: SSL / TLS certificate installed, encryption key generated

[1061] Step 2:

[1062] Data entry and encryption

[1063] User: Enters worries and stress on the chat interface. For example, the user enters, "My recent project has not been going well, and my relationship with my boss has deteriorated."

[1064] Terminal: Input data is encrypted using the AES-256 method and prepared for secure transmission to the server.

[1065] Specific operation: The user enters text into the input field and clicks the send button. The device encrypts the input data.

[1066] Input: User text input such as "My recent projects have been going poorly and my relationship with my boss has been strained."

[1067] Output: Encrypted data

[1068] Step 3:

[1069] Sending data

[1070] Terminal: Encrypted data is sent to the server using SSL / TLS, which reduces the risk of data being intercepted by a third party.

[1071] Specific operation: The device sends data to the server via HTTPS.

[1072] Input: Encrypted data

[1073] Output: Encrypted data sent to the server

[1074] Step 4:

[1075] Receiving and Decrypting Data

[1076] Server: Receives the encrypted data and decrypts it using a stored encryption key, converting the decrypted data into an analyzable format.

[1077] What happens: The server receives the data and decrypts it using the encryption key.

[1078] Input: Encrypted data

[1079] Output: Decoded text data

[1080] Step 5:

[1081] Analysis using generative AI models

[1082] Server: The decrypted data is fed into a generative AI model to assess stress levels and potential risks. For example, a statement like "My recent project hasn't been going well, and my relationship with my boss has deteriorated" would be evaluated as a "high" stress level.

[1083] Specific operation: The server inputs the decoded data as a prompt sentence into the generative AI model, and the model performs analysis.

[1084] Input: Text data: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1085] Output: Stress level assessment result (e.g. "High")

[1086] Step 6:

[1087] Saving to a database

[1088] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[1089] Specific operation: Save the stress level assessment results in a MySQL database.

[1090] Input: Stress level assessment result

[1091] Output: Saved evaluation results

[1092] Step 7:

[1093] Identifying high-risk employees

[1094] Server: Identifies high-risk employees based on information from the database.

[1095] What happens: The server runs an SQL query to extract employees with a "high" stress level.

[1096] Input: Evaluation results stored in the database

[1097] Output: List of high-risk employees

[1098] Step 8:

[1099] Notifications and Support Information

[1100] Server: Notify high-risk employees and direct them to appropriate support and counseling services.

[1101] What happens: The server automatically sends an email to the employee with a message such as, "We recommend that you seek psychological counseling."

[1102] Input: List of high-risk employees

[1103] Output: Notifications and support information

[1104] (Application example 1)

[1105] 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."

[1106] Employee mental health issues have a significant impact on the productivity and atmosphere of the entire workplace. High stress levels are often seen, especially in brick-and-mortar stores, which negatively impact employee performance and health. Furthermore, the lack of an environment in which employees can easily seek advice about their mental health makes it difficult to detect and address problems early. Therefore, there is a need for a system that can quickly and appropriately assess employee concerns and stress and provide the necessary support.

[1107] 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.

[1108] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for store employees to input their mental health status from their smartphones; and means for evaluating stress levels using the generative AI model and notifying them of appropriate support. This makes it possible to quickly evaluate the stress and worries that store employees experience on a daily basis and provide them with appropriate counseling services or guidance to medical institutions.

[1109] A "user interface" is a system component that provides a screen and operating means for employees to input their worries and stress.

[1110] "Encryption" is a technology that converts input data so that it cannot be deciphered by third parties.

[1111] A "server" is a computer system that receives and analyzes data sent from a user interface.

[1112] "Decryption" is a technique for restoring encrypted data to its original state.

[1113] A "generative AI model" is an artificial intelligence model that analyzes input data and assesses stress levels and risks based on that data.

[1114] "Analysis" is the process of analyzing the content of input data using a generative AI model and deriving evaluation results.

[1115] A "database" is an information management system that stores analysis results and allows them to be referenced later.

[1116] "High-risk employees" are employees who are assessed as having high stress levels through analysis by the generative AI model.

[1117] "Professional support" refers to services to support employees' mental health, such as psychological counseling and referrals to medical institutions.

[1118] A "physical store" is a physical location where sales or services are provided.

[1119] A "smartphone" is a mobile device that has the functionality of a mobile phone and the ability to run a variety of applications.

[1120] "Stress level" is a numerical value or assessment result that evaluates the degree of stress felt by an employee.

[1121] "Counseling services" are services in which professional counselors provide advice to employees about their worries and stress.

[1122] A "prompt sentence" is a form of text data input into a generative AI model and is an instruction sentence for analysis.

[1123] The present invention is a system for supporting the mental health of employees, and is particularly focused on employees in brick-and-mortar stores. A specific embodiment of this system will be described below.

[1124] System configuration

[1125] 1. User Interface

[1126] Users (employees) use an application installed on their smartphones to input their worries and stress levels. The user interface is in chat format, making it easy for employees to operate.

[1127] 2. Data Encryption and Transmission

[1128] The device (smartphone) uses encryption technology to safely transmit the entered data to the server. This encryption technology is designed to prevent third parties from stealing or tampering with the data.

[1129] 3. Data reception and analysis on the server

[1130] The server automatically decrypts the encrypted data upon receiving it. It then analyzes the data using a generative AI model to assess the employee's stress level and potential risks. The generative AI model generates prompts based on the employee's input data and performs the analysis.

[1131] 4. Saving to the database

[1132] The server stores the analysis results in a database that can be used for future reference, enabling continuous monitoring of employees' mental health.

[1133] 5. Professional Support and Notifications

[1134] The server identifies high-risk employees based on the analysis results in the database, and these employees are notified via their smartphones about professional support (such as psychological counseling or referrals to medical institutions).

[1135] Specific examples of processing

[1136] Initial Setup

[1137] Users install the application on their smartphone and perform basic configuration, which includes generating encryption keys to ensure secure data transmission.

[1138] Entering and Submitting Data

[1139] When an employee types in "My recent project hasn't gone well and my relationship with my boss is also deteriorating," the data is encrypted on the smartphone and sent to a server.

[1140] Receiving and analyzing data

[1141] When the server receives the data, it decrypts it. The decrypted data is then used to analyze the input data using a generative AI model. The generative AI model is given a prompt like this:

[1142] Stress Assessment: My recent project has been going poorly and my relationship with my boss has deteriorated.

[1143] Identifying and notifying high-risk employees

[1144] If the stress level is assessed as "high" based on the analysis results of the generative AI model, the result is stored in a database. The server then uses this result to identify high-risk employees and sends a notification to their smartphones directing them to appropriate counseling services.

[1145] In this way, a system can be built to support the mental health of employees in brick-and-mortar stores. The hardware used includes smartphones and servers, and the software includes encryption technology, database management systems, and generative AI models.

[1146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1147] Step 1:

[1148] Initial Setup

[1149] A user installs an application on their smartphone and performs basic configuration. Specifically, the user launches the application and enters their account information. The application generates an encryption key, which ensures secure data transmission. The input is the user's personal information and a request to generate an encryption key, and the output is the generated encryption key.

[1150] Step 2:

[1151] Entering data

[1152] Users (employees) input their worries and stress through a smartphone application. Specifically, they type "My recent project hasn't been going well, and my relationship with my boss has deteriorated" into a chat-style interface. The input is text data about the employee's worries and stress, and the output is the input data saved in the application.

[1153] Step 3:

[1154] Data encryption

[1155] The terminal (smartphone) encrypts the input data. Specifically, it encrypts the input text data using an encryption key to generate encrypted data. The input is the employee's input data and the encryption key, and the output is the encrypted data. Encrypted data is important for protecting privacy.

[1156] Step 4:

[1157] Sending data

[1158] The terminal sends the encrypted data to the server. Specifically, it uses an HTTP request to send the encrypted data to the specified server address. The input is the encrypted data and the server address information, and the output is the sending status. If successful, the server receives the encrypted data.

[1159] Step 5:

[1160] Receiving and Decrypting Data

[1161] The server receives the encrypted data sent from the terminal and decrypts it. Specifically, it uses a decryption key to return the received data to the original text data. The input is the encrypted data and the decryption key, and the output is the original text data. This allows the server to obtain the correct input information.

[1162] Step 6:

[1163] Data analysis

[1164] The server uses a generative AI model to analyze the decoded data. Specifically, it generates a prompt for stress assessment based on the input text data and inputs that prompt into the generative AI model. The input is the decoded text data and the generated prompt, and the output is the stress level assessment result. An example of a prompt is "Stress assessment: My recent project has not been going well, and my relationship with my boss has deteriorated."

[1165] Step 7:

[1166] Saving analysis results

[1167] The server stores the analysis results from the generative AI model in a database. Specifically, it stores the stress level assessment results and employee identification information in the database. The input is the stress level assessment results and employee identification information, and the output is the analysis results stored in the database.

[1168] Step 8:

[1169] Identifying and notifying high-risk employees

[1170] The server identifies high-risk employees based on the analysis results in the database. Specifically, it extracts employees who are assessed as having high stress levels and generates a guide to professional support. The input is the stress level assessment results in the database, and the output is a notification directing them to a professional counseling service. This notification is delivered to the employee's smartphone.

[1171] Through the above steps, a system for supporting employee mental health is concretely implemented.

[1172] 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.

[1173] This invention is a system that combines a generative AI model and an emotion engine, and provides the function of analyzing employees' daily conversations to assess their stress levels and potential risks. Furthermore, by using the emotion engine to recognize the user's emotions and incorporating the results into the analysis results, more accurate mental health support is realized.

[1174] System Overview

[1175] This system mainly consists of the following components:

[1176] 1. User Interface

[1177] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[1178] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1179] 2. Encryption and Transmission

[1180] The terminal encrypts the input data and transmits it to the server.

[1181] This protects the privacy of employees.

[1182] 3. Data reception and analysis on the server

[1183] The server receives and decrypts the encrypted data.

[1184] The decoded data is provided to a generative AI model for analysis.

[1185] The generative AI model assesses stress levels based on input data and identifies potential risks.

[1186] 4. Use of Emotion Engine

[1187] The server uses an emotion engine to analyze the user's emotions in real time.

[1188] The analysis results of the emotion engine are also incorporated into the analysis results of the generative AI model.

[1189] This allows for detailed assessment according to changes in emotions and situations.

[1190] 5. Save to database

[1191] The server stores the analysis results from both the generative AI model and the emotion engine in a database.

[1192] The stored data is used to identify high-risk employees, along with long-term trends.

[1193] 6. Professional Support and Notifications

[1194] The server identifies high-risk employees based on the results stored in a database.

[1195] If high-risk employees are identified, they will be notified and directed to appropriate professional support (e.g., counseling services or medical referrals).

[1196] Specific examples of program processing

[1197] Initial Setup

[1198] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[1199] Entering and Submitting Data

[1200] The user enters their worries and stresses into a chat-style interface. This data is encrypted on the device and securely sent to the server. For example, a user might enter, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1201] Receiving and analyzing data

[1202] The server receives the encrypted data, decrypts it, and feeds it into a generative AI model to assess stress levels and potential risks.

[1203] Emotion Engine Analysis

[1204] The server uses an emotion engine to analyze the user's emotions in real time. For example, it can recognize emotions such as "anxiety" or "stress" from the user's text input. The results of the emotion engine are also incorporated into the analysis of the generative AI model, resulting in a more accurate evaluation.

[1205] Database storage and identification of high-risk employees

[1206] The server stores the analysis results of the generative AI model and emotion engine in a database. Based on the results stored in the database, the server identifies high-risk employees. For example, identified employees may be judged as needing counseling services.

[1207] Professional support and notifications

[1208] The server notifies high-risk employees of professional counseling services and medical institutions. The notification is sent to the user's terminal, and employees can follow the instructions to receive professional support. For example, an employee who is recognized as being under high stress will receive a notification directing them to professional counseling.

[1209] In this way, the system combines a generative AI model with an emotion engine to improve employee mental health and support the overall well-being of the company.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] A user inputs worries and stress in a chat format using a user interface. For example, the user inputs, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[1213] Step 2:

[1214] The terminal encrypts the input data, thereby protecting the user's privacy, and then sends the encrypted data to the server.

[1215] Step 3:

[1216] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[1217] Step 4:

[1218] The server provides the decrypted data to a generative AI model, which assesses stress levels and identifies potential risks based on the input data.

[1219] Step 5:

[1220] The server uses an emotion engine to analyze the user's emotions in real time, for example recognizing the emotions of "anxiety" and "stress" from the user's text input.

[1221] Step 6:

[1222] The server incorporates the emotion results recognized by the emotion engine into the analysis results of the generative AI model, enabling detailed evaluation according to emotional changes and situations.

[1223] Step 7:

[1224] The server stores the results of both the generative AI model and the emotion engine in a database, which is also used to analyze long-term trends.

[1225] Step 8:

[1226] The server periodically scans the results stored in the database to identify high-risk employees, who are deemed to require appropriate action.

[1227] Step 9:

[1228] The server notifies high-risk employees of professional counseling services and medical referrals, and the notification is sent to the user's device.

[1229] Step 10:

[1230] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[1231] Through these steps, the system combines a generative AI model and an emotion engine to comprehensively assess employees' stress and mental health and provide appropriate support.

[1232] Example 2

[1233] 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."

[1234] Managing and supporting employee mental health is an important issue for modern companies. However, systems for efficiently and accurately identifying employee concerns and stress levels and implementing appropriate measures are not yet fully in place. Traditional methods often rely on employees' self-reporting or interviews, which can effectively assess stress and potentially overlook potential risks. Furthermore, a lack of technology to protect data privacy and capture emotional changes in real time makes it difficult to accurately assess employee health.

[1235] 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.

[1236] In this invention, the server includes means for providing a user interface for employees to input their worries and stress, means for encrypting the input data and sending it to the server, means for running a generative AI model that decrypts and analyzes the encrypted data on the server side, means for analyzing the user's emotions in real time using an emotion engine on the server side, and means for storing the analysis results of the generative AI model and the emotion engine in a database. This makes it possible to accurately evaluate employee stress levels and changes in emotions and provide appropriate support while protecting privacy.

[1237] "User interface" refers to the interactive screen and operating means through which system users input their worries and stress.

[1238] "Encryption" refers to the technology of converting input data into a form that cannot be deciphered by third parties, and is a method of protecting the privacy and security of data.

[1239] "Server" refers to the central processing unit that analyzes the data received, runs the generative AI model, uses the emotion engine, stores data, and notifies users.

[1240] "Decryption" refers to the process of returning encrypted data to its original form, making it available as analyzable data.

[1241] "Generative AI model" refers to an artificial intelligence model that assesses employee stress levels and potential risks based on received data.

[1242] An "emotion engine" refers to software that analyzes emotions from user input and evaluates emotional trends and changes.

[1243] "Database" refers to an information storage system that stores the analysis results of the generative AI model and emotion engine, and enables reference and analysis as needed.

[1244] "High-risk employees" are those who are deemed to be in particular need of mental health support based on the analysis results of the generative AI model and emotion engine.

[1245] "Specialized support" refers to appropriate assistance provided to employees identified as at high risk, such as counseling services or referrals to medical facilities.

[1246] "Stress level" refers to the quantitative assessment of the degree of stress experienced by employees.

[1247] "Privacy" refers to the state in which employees' personal information and data are protected and not shared with third parties without approval.

[1248] "Real-time" refers to the instant analysis and evaluation of user behavior and emotions on the spot.

[1249] This invention relates to a system that analyzes employees' daily conversations to assess their stress levels and potential risks. The purpose of this invention is to effectively support employees' mental health by combining a generative AI model and an emotion engine.

[1250] System Configuration

[1251] User Interface

[1252] The terminal provides a chat-style user interface for employees to input their worries and stress. Specifically, the user interface includes a text box and a send button, allowing employees to easily input their daily stress and worries.

[1253] Data encryption

[1254] The terminal encrypts the input data using encryption technology such as AES (Advanced Encryption Standard) before sending it, thereby protecting the privacy of user data.

[1255] Sending and Receiving Data

[1256] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS, and the server receives the data and decrypts it using a private key.

[1257] Analysis of generative AI models

[1258] The server then provides the decoded data to a generative AI model, which uses natural language processing (NLP) to assess the stress level and potential risks from the input text. Specifically, the generative AI model calculates a stress score and returns the result to the server.

[1259] Emotion Engine Analysis

[1260] In addition to the analysis results of the generative AI model, the server analyzes the user's emotions in real time using an emotion engine. The emotion engine detects emotions such as "anxiety" or "stress" from the input text and integrates the results into the analysis results of the generative AI model.

[1261] Saving analysis results

[1262] The server stores the results of the generative AI model and emotion engine analysis in a database, which includes the employee's ID, input text, stress score, and detected emotion.

[1263] Identifying high-risk employees

[1264] The server analyzes the results stored in the database and identifies high-risk employees. Specifically, if an employee's stress score or emotion analysis results exceed a certain threshold, they are deemed high-risk.

[1265] Professional Support Notification

[1266] The server then sends notifications to employees identified as high-risk with information on how to access professional support, including counseling services and medical facilities.

[1267] Specific examples

[1268] 1. User Interface

[1269] The user types into a chat-style interface, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1270] 2. Encryption and Transmission

[1271] The device encrypts the input text and sends it to the server using HTTPS.

[1272] 3. Data Reception and Decryption

[1273] The server receives the data sent from the terminal and decrypts the encrypted data using the private key.

[1274] 4. Analysis of generative AI models

[1275] The server inputs the decoded text into a generative AI model, which analyzes it as "high stress level."

[1276] 5. Use of Emotion Engine

[1277] The server uses an emotion engine to detect emotions such as "anxiety" or "stress" from the text.

[1278] 6. Saving the analysis results

[1279] The server stores the results of the generative AI model and emotion engine in a database.

[1280] 7. Identifying high-risk employees

[1281] The server analyzes the stored data and identifies employees as high risk if their stress score is high.

[1282] 8. Professional Support Notification

[1283] The server sends notifications to terminals for professional counseling for high-risk employees.

[1284] Prompt Sentence Examples

[1285] 1. Stress Level Assessment Prompt

[1286] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1287] Prompt: "Please rate the user's stress level based on this text."

[1288] 2. Emotion Recognition Prompts

[1289] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1290] Prompt: "Identify the user's emotion from this text."

[1291] In this way, the present invention provides a system that provides advanced support for the mental health of employees, thereby improving the overall health of the company.

[1292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1293] Step 1:

[1294] The user inputs their worries and stress into a chat-style interface on the device. The input is in text format, for example, "My recent project has not been going well, and my relationship with my boss has deteriorated." This text is input as data.

[1295] Step 2:

[1296] The terminal uses AES encryption to encrypt the text data entered by the user, producing encrypted data that is then output. This encryption ensures the privacy of the data.

[1297] Step 3:

[1298] The terminal sends the encrypted data to the server using the HTTPS protocol, in this process the encrypted data is sent as input and received on the server side.

[1299] Step 4:

[1300] The server decrypts the encrypted data received via the HTTPS protocol using the private key. This process results in the decrypted data, which is the output.

[1301] Step 5:

[1302] The server provides the decrypted text data to the generative AI model, which evaluates the employee's stress level from the text data and calculates a stress score. The input is the text data and the output is the stress score. For example, the stress score is calculated as "85."

[1303] Step 6:

[1304] The server analyzes the user's emotions in real time using an emotion engine in parallel with the analysis results of the generative AI model. The emotion engine identifies emotions such as "anxiety" or "stress" from the text data and outputs them. The input is the text data, and the output is the detected emotion. For example, "anxiety" is detected.

[1305] Step 7:

[1306] The server combines the results of the generative AI model and the emotion engine analysis and stores them in a database, including employee IDs, input text, stress scores, and detected emotions, enabling long-term trend analysis and risk assessment.

[1307] Step 8:

[1308] The server analyzes the data stored in the database and identifies high-risk employees. Specifically, if the stress score or emotion analysis results exceed a certain threshold, the employee is deemed high-risk. For example, a stress score of 85 exceeds the threshold and is deemed high-risk.

[1309] Step 9:

[1310] The server then notifies employees identified as high-risk of professional counseling services or medical consultations. The notification is sent to the device, where the user can view and confirm the content of the notification. For example, a notification saying "Professional counseling is required" is sent.

[1311] Through these steps, the system is able to assess employees' mental health with high accuracy and provide the necessary support in a timely manner.

[1312] (Application example 2)

[1313] 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."

[1314] Employee mental health issues have a significant impact on the productivity and health of an entire company. However, even if employees are experiencing mental health-related worries or stress, it is difficult to accurately assess their mental health through self-reporting alone. There is also a lack of effective means to quickly identify employees who need professional support and provide appropriate support. There is a need for a system that can assess employee mental health in real time and quickly identify potential risks.

[1315] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1316] In this invention, the server includes: a means for providing a user interface through which employees input their worries and stress; a means for encrypting the input data and sending it to the server; a means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; a means for storing the analysis results in a database; a means for identifying high-risk employees and notifying them of professional support; a means for analyzing user emotions in real time using an emotion engine and incorporating the results into the analysis data; and a means for assessing stress levels and potential risks using a robot for interacting with employees. This makes it possible to analyze employee emotions in real time and accurately assess stress levels and potential risks. It also makes it possible to provide employees with mental health support in real time, identify high-risk employees early, and notify them of appropriate professional support.

[1317] A "user interface" is an interface that allows a user to perform operations and input data, and is typically in the form of a chat.

[1318] "Encryption" is the technique of converting data into a special code to protect it and ensure its safety from unauthorized access.

[1319] A "server" is a computer system that provides specific services or data over a network.

[1320] "Decryption" is a technique for restoring encrypted data to its original state.

[1321] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and generate specific results or predictions.

[1322] A "database" is a system that systematically organizes and stores data, and allows for efficient access and management.

[1323] "High-risk employees" are employees who have been identified as having high stress levels and potential health risks.

[1324] "Specialized support" refers to specialized services to support mental health, such as counseling services and referrals to medical institutions.

[1325] An "emotion engine" is an algorithm for analyzing and recognizing user emotions from text and voice data.

[1326] A "robot" is a mechanical device that performs physical tasks or interactions autonomously or remotely.

[1327] "Stress level" is an indicator that evaluates the degree of stress felt by employees.

[1328] "Potential risks" are risks that have not materialized at present but may cause problems in the future.

[1329] "Real-time" refers to the immediate processing and provision of data and information without delay.

[1330] The present invention provides a system for assessing and supporting the mental health of employees. This system analyzes data on worries and stress entered by employees, identifies high-risk employees, and provides appropriate support.

[1331] Hardware and Software Configuration

[1332] A server, a terminal and a robot are used.

[1333] The server has the processing power to run the generative AI model and emotion engine.

[1334] A terminal is a device that provides a user interface for employees to input data, and includes a smartphone or tablet.

[1335] The robot is a device that interacts with employees and assesses their stress levels and emotions in real time.

[1336] System processing overview

[1337] Users (employees) input their worries and stress into a chat-style interface. This input data is encrypted on the device and sent to the server. The server receives and decrypts the encrypted data. The decrypted data is analyzed by a generative AI model to evaluate stress levels and potential risks. An emotion engine is used for the analysis, analyzing the employee's emotions in real time and reflecting them in the analysis results. These results are stored in a database. Based on the stored results, the server identifies high-risk employees and notifies them of appropriate professional support (such as counseling services or referrals to medical institutions).

[1338] Specific operation example

[1339] Initial Setup: Use the terminal to set up encryption of user-entered data.

[1340] Data Entry: User types, "My recent project hasn't been going well and I'm feeling the pressure."

[1341] Data transmission: The encrypted data is sent to the server.

[1342] Data Decryption and Analysis: The server decrypts the data and uses an emotion engine to analyze emotions such as "anxiety" or "pressure." The generative AI model evaluates the stress level and determines "Stress Level: High."

[1343] Data storage: The analysis results are stored in a database. For example, it may be stored as "Employee A: High stress level, high risk."

[1344] Professional support: High-stress employees are notified that they may be advised to seek counseling services.

[1345] Technology used

[1346] Generative AI model: A text analysis model using the OpenAI API.

[1347] Emotion Engine: An emotion recognition engine that also uses the OpenAI API.

[1348] Cryptography: Data protection is achieved using the cryptography library.

[1349] Prompt Sentence Examples

[1350] "Recent projects have been going poorly and the employee is feeling the pressure. Please assess this employee's stress level and identify potential risks."

[1351] In this way, the system can support the mental health of employees and improve the overall well-being of the company.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Program processing steps

[1354] Step 1:

[1355] Initial Setup

[1356] Input: Initialization program for user terminal

[1357] Processing: The user device is configured to support encryption. An encryption key is generated within the device, and settings are made to send data securely. For example, the key is generated using Fernet, a Python cryptography library.

[1358] Output: Generate and store encryption keys

[1359] Step 2:

[1360] Entering data

[1361] Input: User-input worries and stress in a chat-style interface (e.g., "My latest project hasn't gone well, and I'm feeling the pressure").

[1362] Processing: The user interface receives the employee's input and encrypts the input text using the secret key generated during the initial setup.

[1363] Output: Encrypted worry and stress data

[1364] Step 3:

[1365] Sending data

[1366] Input: Encrypted worries and stress data

[1367] Processing: The user terminal sends the encrypted data to the server via network communication (e.g., socket communication).

[1368] Output: Encrypted data sent to the server side

[1369] Step 4:

[1370] Receiving and Decrypting Data

[1371] Input: Encrypted worries and stress data

[1372] Processing: The server decrypts the received encrypted data using the same encryption key (secret key) that was used on the user's device.

[1373] Output: Decoded worry and stress data

[1374] Step 5:

[1375] Analysis using generative AI models

[1376] Input: Decoded worry and stress data

[1377] Processing: The server uses the generative AI model to analyze the input data. Specifically, it uses OpenAI's API to perform prompt-based analysis and assess the employee's stress level. Example prompt: "Recent projects have not gone well, and the employee is feeling pressured. Please assess this employee's stress level and identify potential risks."

[1378] Output: Stress level and potential risk assessment results

[1379] Step 6:

[1380] Real-time analysis by emotion engine

[1381] Input: Decoded worry and stress data

[1382] Processing: The server uses an emotion engine to analyze emotions from the input text in real time. Specifically, it recognizes emotions such as "anxiety" or "pressure" from the user's text input and incorporates the results into the analysis results of the generative AI model.

[1383] Output: Final analysis data including sentiment analysis results

[1384] Step 7:

[1385] Saving to a database

[1386] Input: Final analysis data including sentiment analysis results

[1387] Processing: The server stores the results of both the generative AI model and the emotion engine in a database, which is used for long-term trend analysis and to identify high-risk employees.

[1388] Output: Employee mental health assessment data stored in a database

[1389] Step 8:

[1390] Identifying and notifying high-risk employees

[1391] Input: Employee mental health assessment data stored in a database

[1392] Processing: The server analyzes the data in the database to identify high-risk employees and notify them of appropriate professional support (such as counseling services or referrals to medical facilities).

[1393] Output: Support notification for high-risk employees

[1394] 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.

[1395] 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.

[1396] 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.

[1397] [Fourth embodiment]

[1398] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1399] 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.

[1400] 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).

[1401] 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.

[1402] 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.

[1403] 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).

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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."

[1411] The present invention is a system that supports employee mental health, focusing in particular on utilizing generative AI models to analyze employees' daily interactions and assess stress levels and potential risks.

[1412] System Overview

[1413] This system mainly consists of the following components:

[1414] 1. User Interface

[1415] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[1416] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1417] 2. Encryption and Transmission

[1418] The terminal encrypts the input data and transmits it to the server.

[1419] This protects the privacy of employees.

[1420] 3. Data reception and analysis on the server

[1421] The server receives and decrypts the encrypted data.

[1422] A generative AI model is used to analyze the decoded data.

[1423] A generative AI model assesses stress levels and potential risks.

[1424] 4. Save to database

[1425] The server stores the analysis results in a database.

[1426] The stored data is later used to identify high-risk employees.

[1427] 5. Professional Support and Notifications

[1428] The server identifies potentially high-risk employees and directs them to appropriate professional support.

[1429] For example, this may include psychological counseling or contacting a medical institution.

[1430] Specific examples of program processing

[1431] Initial Setup

[1432] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[1433] Entering and Submitting Data

[1434] Users input their worries and stresses into a chat-style interface, which is then encrypted on the device and securely sent to the server.

[1435] Receiving and analyzing data

[1436] The server receives the encrypted data, decrypts it, and analyzes it using a generative AI model to assess stress levels. The analysis results are then automatically stored in a database.

[1437] Identifying and notifying high-risk employees

[1438] The server then identifies high-risk employees based on the results stored in the database, and provides them with information on appropriate counseling services and medical institutions, allowing for specific measures to be taken to support the mental health of employees.

[1439] Specific examples

[1440] When Employee A types a workplace concern into a chat window, saying, "My recent project hasn't been going well, and my relationship with my boss is deteriorating," the data is encrypted on the user's device and sent to the server. After receiving the data, the server decrypts it and uses a generative AI model to evaluate the stress level as "high." Based on this, the evaluation results stored in the database are sent to the user's device as a guide to a professional counseling service.

[1441] In this way, the system aims to improve the mental health of employees and support the overall well-being of the company.

[1442] ---

[1443] The above is an example of a specific form for implementing the present invention, detailing the operations at each step and showing how the entire system interacts to support employee mental health.

[1444] The processing flow will be explained below.

[1445] Step 1:

[1446] The user uses the user interface to input their worries and stress in a chat format. An example of input might be, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[1447] Step 2:

[1448] The terminal receives the data entered by the user and encrypts it to ensure security, and then transmits the encrypted data over the network to the server.

[1449] Step 3:

[1450] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[1451] Step 4:

[1452] The server then provides the decrypted data to a generative AI model for analysis, which assesses stress levels and identifies potential risks based on the input data.

[1453] Step 5:

[1454] The server stores the analysis results, including stress levels and potential risks, in a database that also includes past analysis results, allowing for long-term trend analysis.

[1455] Step 6:

[1456] The server periodically scans the data stored in the database to identify high-risk employees, and if a high-risk employee is identified, appropriate action is taken.

[1457] Step 7:

[1458] The server then sends notifications to high-risk employees, directing them to professional counseling services and medical institutions. Notifications are sent to user terminals, and employees can follow the instructions to receive professional support.

[1459] Step 8:

[1460] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[1461] Example 1

[1462] 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."

[1463] In recent working environments, mental health issues among employees have been increasing. However, many companies lack systems to properly address this issue, making it difficult to detect employee stress and distress early and provide appropriate support. Furthermore, in many cases, privacy protection measures for safely handling employee data are not adequately ensured. Therefore, there is a need for a system that effectively supports employee mental health while simultaneously ensuring data security.

[1464] 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.

[1465] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for installing an SSL / TLS certificate on a user terminal and creating an encryption key to ensure secure transmission and reception of employee data; means for providing the input data to the generative AI model as prompt text to evaluate employee stress levels and potential risks; and means for automatically sending emails and notifications to high-risk employees based on the evaluation results. This makes it possible to effectively support employee mental health while ensuring data security and privacy protection.

[1466] A "user interface" is the operating environment on a device screen such as a computer or smartphone where employees can enter their worries and stress.

[1467] "Encryption" is a technology that converts employee input data into a format that cannot be understood by others, thereby maintaining confidentiality.

[1468] A "server" is a computer system that receives data sent from terminals on a network and stores and analyzes the data.

[1469] A "generative AI model" is an artificial intelligence model that analyzes data received from employees and assesses stress levels and potential risks.

[1470] A "database" is a data management system that stores analytical results and other related data for easy retrieval and use at a later time.

[1471] "High-risk employees" are employees who are assessed as experiencing very high levels of stress and distress based on the analysis results.

[1472] "Professional support" refers to support services such as psychological counseling and referrals to medical institutions provided to employees with mental health issues.

[1473] An "SSL / TLS certificate" is an electronic certificate that protects the security and privacy of communications when sending and receiving data over the Internet.

[1474] An "encryption key" is a string of characters used in the encryption and decryption process, and is a means of protecting the confidentiality of data.

[1475] A "prompt sentence" is a form of text data that is input into a generative AI model and serves as material for the model's analysis.

[1476] "Notifications" are messages or announcements sent from the server to employees to inform them of analysis results and appropriate support.

[1477] This invention is a system that supports employee mental health. Specifically, it uses a generative AI model to analyze employees' daily conversations and evaluate their stress levels and potential risks. Below, we explain the program processing of this system in natural language.

[1478] System Configuration

[1479] The system mainly consists of the following components:

[1480] 1. User Interface

[1481] User terminal: Provides a chat-style interface for employees to input their worries and stress. For example, an employee might input, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1482] 2. Encryption and Transmission

[1483] Terminal: The entered data is encrypted and sent to the server. This protects employee privacy. Install an SSL / TLS certificate and create an encryption key to ensure the security of data communication.

[1484] 3. Data Reception and Decryption

[1485] Server: Receives the encrypted data and decrypts it, converting it into a format that can be analyzed.

[1486] 4. Analysis using generative AI models

[1487] Server: The decrypted data is input into a generative AI model for analysis. This generative AI model is used to assess an employee's stress level and potential risks. For example, given the input data "My recent project has not been going well, and my relationship with my boss has deteriorated," the model might assess the stress level as "high."

[1488] 5. Saving to the database

[1489] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[1490] 6. Identifying and notifying high-risk employees

[1491] Server: Identifies high-risk employees based on information from the database. These employees are then notified of appropriate support and counseling services. For example, identified high-risk employees are notified of psychological counseling or contact information for medical institutions.

[1492] Specific examples

[1493] Initial Setup

[1494] Device: An IT administrator installs an SSL / TLS certificate on a new user device and generates encryption keys, allowing the device to send and receive data securely.

[1495] Data entry and encryption

[1496] User: Employee B types in the chat interface, "There are a lot of disagreements within the team and I'm feeling stressed."

[1497] Terminal: The input data is encrypted using AES-256 and ready to go.

[1498] Sending data

[1499] On your device: Encrypted data is sent to the server using HTTPS, which ensures data security.

[1500] Receiving and Decrypting Data

[1501] Server: The server receives the encrypted data and decrypts it using the stored encryption key.

[1502] Analysis using generative AI models

[1503] Server: The decrypted data is input into the generative AI model, which analyzes the content, "There are many differences of opinion within the team, and I am feeling stressed." The generative AI model analyzes the stress level and evaluates it as "medium."

[1504] Identifying and notifying high-risk employees

[1505] Server: Identifies employees from the database whose stress level is rated as "high" and prepares information related to those employees.

[1506] Server: Sends email notifications to high-risk employees, providing guidance such as, "Your stress level has been assessed as high, so we recommend psychological counseling." This allows for prompt measures to be taken to address employees' mental health.

[1507] Examples of prompt statements

[1508] The following prompts are fed into the generative AI model to assess stress levels:

[1509] Rate your stress level if someone typed in, "My recent project hasn't been going well and my relationship with my boss has deteriorated."

[1510] This system can effectively support the mental health of employees and improve the overall well-being of the company.

[1511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1512] Step 1:

[1513] Initial Setup

[1514] Device: Initialize the user device, install an SSL / TLS certificate, and create an encryption key. This configuration ensures secure data transmission from the device to the server.

[1515] Specific operation: An IT administrator installs an SSL / TLS certificate on a new user device and generates an encryption key. At this step, the device is ready for secure data transmission and reception.

[1516] Input: None

[1517] Output: SSL / TLS certificate installed, encryption key generated

[1518] Step 2:

[1519] Data entry and encryption

[1520] User: Enters worries and stress on the chat interface. For example, the user enters, "My recent project has not been going well, and my relationship with my boss has deteriorated."

[1521] Terminal: Input data is encrypted using the AES-256 method and prepared for secure transmission to the server.

[1522] Specific operation: The user enters text into the input field and clicks the send button. The device encrypts the input data.

[1523] Input: User text input such as "My recent projects have been going poorly and my relationship with my boss has been strained."

[1524] Output: Encrypted data

[1525] Step 3:

[1526] Sending data

[1527] Terminal: Encrypted data is sent to the server using SSL / TLS, which reduces the risk of data being intercepted by a third party.

[1528] Specific operation: The device sends data to the server via HTTPS.

[1529] Input: Encrypted data

[1530] Output: Encrypted data sent to the server

[1531] Step 4:

[1532] Receiving and Decrypting Data

[1533] Server: Receives the encrypted data and decrypts it using a stored encryption key, converting the decrypted data into an analyzable format.

[1534] What happens: The server receives the data and decrypts it using the encryption key.

[1535] Input: Encrypted data

[1536] Output: Decoded text data

[1537] Step 5:

[1538] Analysis using generative AI models

[1539] Server: The decrypted data is fed into a generative AI model to assess stress levels and potential risks. For example, a statement like "My recent project hasn't been going well, and my relationship with my boss has deteriorated" would be evaluated as a "high" stress level.

[1540] Specific operation: The server inputs the decoded data as a prompt sentence into the generative AI model, and the model performs analysis.

[1541] Input: Text data: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1542] Output: Stress level assessment result (e.g. "High")

[1543] Step 6:

[1544] Saving to a database

[1545] Server: Stores the analysis results in a database, which is later used to identify high-risk employees.

[1546] Specific operation: Save the stress level assessment results in a MySQL database.

[1547] Input: Stress level assessment result

[1548] Output: Saved evaluation results

[1549] Step 7:

[1550] Identifying high-risk employees

[1551] Server: Identifies high-risk employees based on information from the database.

[1552] What happens: The server runs an SQL query to extract employees with a "high" stress level.

[1553] Input: Evaluation results stored in the database

[1554] Output: List of high-risk employees

[1555] Step 8:

[1556] Notifications and Support Information

[1557] Server: Notify high-risk employees and direct them to appropriate support and counseling services.

[1558] What happens: The server automatically sends an email to the employee with a message such as, "We recommend that you seek psychological counseling."

[1559] Input: List of high-risk employees

[1560] Output: Notifications and support information

[1561] (Application example 1)

[1562] 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."

[1563] Employee mental health issues have a significant impact on the productivity and atmosphere of the entire workplace. High stress levels are often seen, especially in brick-and-mortar stores, which negatively impact employee performance and health. Furthermore, the lack of an environment in which employees can easily seek advice about their mental health makes it difficult to detect and address problems early. Therefore, there is a need for a system that can quickly and appropriately assess employee concerns and stress and provide the necessary support.

[1564] 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.

[1565] In this invention, the server includes: means for providing a user interface for employees to input their worries and stress; means for encrypting the input data and sending it to the server; means for decrypting the encrypted data on the server side and running a generative AI model that analyzes it; means for saving the analysis results in a database; means for identifying high-risk employees and notifying them of professional support; means for store employees to input their mental health status from their smartphones; and means for evaluating stress levels using the generative AI model and notifying them of appropriate support. This makes it possible to quickly evaluate the stress and worries that store employees experience on a daily basis and provide them with appropriate counseling services or guidance to medical institutions.

[1566] A "user interface" is a system component that provides a screen and operating means for employees to input their worries and stress.

[1567] "Encryption" is a technology that converts input data so that it cannot be deciphered by third parties.

[1568] A "server" is a computer system that receives and analyzes data sent from a user interface.

[1569] "Decryption" is a technique for restoring encrypted data to its original state.

[1570] A "generative AI model" is an artificial intelligence model that analyzes input data and assesses stress levels and risks based on that data.

[1571] "Analysis" is the process of analyzing the content of input data using a generative AI model and deriving evaluation results.

[1572] A "database" is an information management system that stores analysis results and allows them to be referenced later.

[1573] "High-risk employees" are employees who are assessed as having high stress levels through analysis by the generative AI model.

[1574] "Professional support" refers to services to support employees' mental health, such as psychological counseling and referrals to medical institutions.

[1575] A "physical store" is a physical location where sales or services are provided.

[1576] A "smartphone" is a mobile device that has the functionality of a mobile phone and the ability to run a variety of applications.

[1577] "Stress level" is a numerical value or assessment result that evaluates the degree of stress felt by an employee.

[1578] "Counseling services" are services in which professional counselors provide advice to employees about their worries and stress.

[1579] A "prompt sentence" is a form of text data input into a generative AI model and is an instruction sentence for analysis.

[1580] The present invention is a system for supporting the mental health of employees, and is particularly focused on employees in brick-and-mortar stores. A specific embodiment of this system will be described below.

[1581] System configuration

[1582] 1. User Interface

[1583] Users (employees) use an application installed on their smartphones to input their worries and stress levels. The user interface is in chat format, making it easy for employees to operate.

[1584] 2. Data Encryption and Transmission

[1585] The device (smartphone) uses encryption technology to safely transmit the entered data to the server. This encryption technology is designed to prevent third parties from stealing or tampering with the data.

[1586] 3. Data reception and analysis on the server

[1587] The server automatically decrypts the encrypted data upon receiving it. It then analyzes the data using a generative AI model to assess the employee's stress level and potential risks. The generative AI model generates prompts based on the employee's input data and performs the analysis.

[1588] 4. Saving to the database

[1589] The server stores the analysis results in a database that can be used for future reference, enabling continuous monitoring of employees' mental health.

[1590] 5. Professional Support and Notifications

[1591] The server identifies high-risk employees based on the analysis results in the database, and these employees are notified via their smartphones about professional support (such as psychological counseling or referrals to medical institutions).

[1592] Specific examples of processing

[1593] Initial Setup

[1594] Users install the application on their smartphone and perform basic configuration, which includes generating encryption keys to ensure secure data transmission.

[1595] Entering and Submitting Data

[1596] When an employee types in "My recent project hasn't gone well and my relationship with my boss is also deteriorating," the data is encrypted on the smartphone and sent to a server.

[1597] Receiving and analyzing data

[1598] When the server receives the data, it decrypts it. The decrypted data is then used to analyze the input data using a generative AI model. The generative AI model is given a prompt like this:

[1599] Stress Assessment: My recent project has been going poorly and my relationship with my boss has deteriorated.

[1600] Identifying and notifying high-risk employees

[1601] If the stress level is assessed as "high" based on the analysis results of the generative AI model, the result is stored in a database. The server then uses this result to identify high-risk employees and sends a notification to their smartphones directing them to appropriate counseling services.

[1602] In this way, a system can be built to support the mental health of employees in brick-and-mortar stores. The hardware used includes smartphones and servers, and the software includes encryption technology, database management systems, and generative AI models.

[1603] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1604] Step 1:

[1605] Initial Setup

[1606] A user installs an application on their smartphone and performs basic configuration. Specifically, the user launches the application and enters their account information. The application generates an encryption key, which ensures secure data transmission. The input is the user's personal information and a request to generate an encryption key, and the output is the generated encryption key.

[1607] Step 2:

[1608] Entering data

[1609] Users (employees) input their worries and stress through a smartphone application. Specifically, they type "My recent project hasn't been going well, and my relationship with my boss has deteriorated" into a chat-style interface. The input is text data about the employee's worries and stress, and the output is the input data saved in the application.

[1610] Step 3:

[1611] Data encryption

[1612] The terminal (smartphone) encrypts the input data. Specifically, it encrypts the input text data using an encryption key to generate encrypted data. The input is the employee's input data and the encryption key, and the output is the encrypted data. Encrypted data is important for protecting privacy.

[1613] Step 4:

[1614] Sending data

[1615] The terminal sends the encrypted data to the server. Specifically, it uses an HTTP request to send the encrypted data to the specified server address. The input is the encrypted data and the server address information, and the output is the sending status. If successful, the server receives the encrypted data.

[1616] Step 5:

[1617] Receiving and Decrypting Data

[1618] The server receives the encrypted data sent from the terminal and decrypts it. Specifically, it uses a decryption key to return the received data to the original text data. The input is the encrypted data and the decryption key, and the output is the original text data. This allows the server to obtain the correct input information.

[1619] Step 6:

[1620] Data analysis

[1621] The server uses a generative AI model to analyze the decoded data. Specifically, it generates a prompt for stress assessment based on the input text data and inputs that prompt into the generative AI model. The input is the decoded text data and the generated prompt, and the output is the stress level assessment result. An example of a prompt is "Stress assessment: My recent project has not been going well, and my relationship with my boss has deteriorated."

[1622] Step 7:

[1623] Saving analysis results

[1624] The server stores the analysis results from the generative AI model in a database. Specifically, it stores the stress level assessment results and employee identification information in the database. The input is the stress level assessment results and employee identification information, and the output is the analysis results stored in the database.

[1625] Step 8:

[1626] Identifying and notifying high-risk employees

[1627] The server identifies high-risk employees based on the analysis results in the database. Specifically, it extracts employees who are assessed as having high stress levels and generates a guide to professional support. The input is the stress level assessment results in the database, and the output is a notification directing them to a professional counseling service. This notification is delivered to the employee's smartphone.

[1628] Through the above steps, a system for supporting employee mental health is concretely implemented.

[1629] 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.

[1630] This invention is a system that combines a generative AI model and an emotion engine, and provides the function of analyzing employees' daily conversations to assess their stress levels and potential risks. Furthermore, by using the emotion engine to recognize the user's emotions and incorporating the results into the analysis results, more accurate mental health support is realized.

[1631] System Overview

[1632] This system mainly consists of the following components:

[1633] 1. User Interface

[1634] It provides a chat-style interface for employees to input their worries and stress on user terminals.

[1635] For example, an employee might type, "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1636] 2. Encryption and Transmission

[1637] The terminal encrypts the input data and transmits it to the server.

[1638] This protects the privacy of employees.

[1639] 3. Data reception and analysis on the server

[1640] The server receives and decrypts the encrypted data.

[1641] The decoded data is provided to a generative AI model for analysis.

[1642] The generative AI model assesses stress levels based on input data and identifies potential risks.

[1643] 4. Use of Emotion Engine

[1644] The server uses an emotion engine to analyze the user's emotions in real time.

[1645] The analysis results of the emotion engine are also incorporated into the analysis results of the generative AI model.

[1646] This allows for detailed assessment according to changes in emotions and situations.

[1647] 5. Save to database

[1648] The server stores the analysis results from both the generative AI model and the emotion engine in a database.

[1649] The stored data is used to identify high-risk employees, along with long-term trends.

[1650] 6. Professional Support and Notifications

[1651] The server identifies high-risk employees based on the results stored in a database.

[1652] If high-risk employees are identified, they will be notified and directed to appropriate professional support (e.g., counseling services or medical referrals).

[1653] Specific examples of program processing

[1654] Initial Setup

[1655] Initialize the user terminal and set up encryption to safely transmit data entered by the user, thereby ensuring the security of the entire system.

[1656] Entering and Submitting Data

[1657] The user enters their worries and stresses into a chat-style interface. This data is encrypted on the device and securely sent to the server. For example, a user might enter, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1658] Receiving and analyzing data

[1659] The server receives the encrypted data, decrypts it, and feeds it into a generative AI model to assess stress levels and potential risks.

[1660] Emotion Engine Analysis

[1661] The server uses an emotion engine to analyze the user's emotions in real time. For example, it can recognize emotions such as "anxiety" or "stress" from the user's text input. The results of the emotion engine are also incorporated into the analysis of the generative AI model, resulting in a more accurate evaluation.

[1662] Database storage and identification of high-risk employees

[1663] The server stores the analysis results of the generative AI model and emotion engine in a database. Based on the results stored in the database, the server identifies high-risk employees. For example, identified employees may be judged as needing counseling services.

[1664] Professional support and notifications

[1665] The server notifies high-risk employees of professional counseling services and medical institutions. The notification is sent to the user's terminal, and employees can follow the instructions to receive professional support. For example, an employee who is recognized as being under high stress will receive a notification directing them to professional counseling.

[1666] In this way, the system combines a generative AI model with an emotion engine to improve employee mental health and support the overall well-being of the company.

[1667] The processing flow will be explained below.

[1668] Step 1:

[1669] A user inputs worries and stress in a chat format using a user interface. For example, the user inputs, "My recent project has not been going well, and my relationship with my boss has also deteriorated."

[1670] Step 2:

[1671] The terminal encrypts the input data, thereby protecting the user's privacy, and then sends the encrypted data to the server.

[1672] Step 3:

[1673] The server receives the encrypted data sent from the terminal and decrypts it to return it to the original text.

[1674] Step 4:

[1675] The server provides the decrypted data to a generative AI model, which assesses stress levels and identifies potential risks based on the input data.

[1676] Step 5:

[1677] The server uses an emotion engine to analyze the user's emotions in real time, for example recognizing the emotions of "anxiety" and "stress" from the user's text input.

[1678] Step 6:

[1679] The server incorporates the emotion results recognized by the emotion engine into the analysis results of the generative AI model, enabling detailed evaluation according to emotional changes and situations.

[1680] Step 7:

[1681] The server stores the results of both the generative AI model and the emotion engine in a database, which is also used to analyze long-term trends.

[1682] Step 8:

[1683] The server periodically scans the results stored in the database to identify high-risk employees, who are deemed to require appropriate action.

[1684] Step 9:

[1685] The server notifies high-risk employees of professional counseling services and medical referrals, and the notification is sent to the user's device.

[1686] Step 10:

[1687] Users can receive information about professional counseling services and medical institutions and make appointments or consultations as needed, thereby supporting the mental health of employees.

[1688] Through these steps, the system combines a generative AI model and an emotion engine to comprehensively assess employees' stress and mental health and provide appropriate support.

[1689] Example 2

[1690] 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."

[1691] Managing and supporting employee mental health is an important issue for modern companies. However, systems for efficiently and accurately identifying employee concerns and stress levels and implementing appropriate measures are not yet fully in place. Traditional methods often rely on employees' self-reporting or interviews, which can effectively assess stress and potentially overlook potential risks. Furthermore, a lack of technology to protect data privacy and capture emotional changes in real time makes it difficult to accurately assess employee health.

[1692] 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.

[1693] In this invention, the server includes means for providing a user interface for employees to input their worries and stress, means for encrypting the input data and sending it to the server, means for running a generative AI model that decrypts and analyzes the encrypted data on the server side, means for analyzing the user's emotions in real time using an emotion engine on the server side, and means for storing the analysis results of the generative AI model and the emotion engine in a database. This makes it possible to accurately evaluate employee stress levels and changes in emotions and provide appropriate support while protecting privacy.

[1694] "User interface" refers to the interactive screen and operating means through which system users input their worries and stress.

[1695] "Encryption" refers to the technology of converting input data into a form that cannot be deciphered by third parties, and is a method of protecting the privacy and security of data.

[1696] "Server" refers to the central processing unit that analyzes the data received, runs the generative AI model, uses the emotion engine, stores data, and notifies users.

[1697] "Decryption" refers to the process of returning encrypted data to its original form, making it available as analyzable data.

[1698] "Generative AI model" refers to an artificial intelligence model that assesses employee stress levels and potential risks based on received data.

[1699] An "emotion engine" refers to software that analyzes emotions from user input and evaluates emotional trends and changes.

[1700] "Database" refers to an information storage system that stores the analysis results of the generative AI model and emotion engine, and enables reference and analysis as needed.

[1701] "High-risk employees" are those who are deemed to be in particular need of mental health support based on the analysis results of the generative AI model and emotion engine.

[1702] "Specialized support" refers to appropriate assistance provided to employees identified as at high risk, such as counseling services or referrals to medical facilities.

[1703] "Stress level" refers to the quantitative assessment of the degree of stress experienced by employees.

[1704] "Privacy" refers to the state in which employees' personal information and data are protected and not shared with third parties without approval.

[1705] "Real-time" refers to the instant analysis and evaluation of user behavior and emotions on the spot.

[1706] This invention relates to a system that analyzes employees' daily conversations to assess their stress levels and potential risks. The purpose of this invention is to effectively support employees' mental health by combining a generative AI model and an emotion engine.

[1707] System Configuration

[1708] User Interface

[1709] The terminal provides a chat-style user interface for employees to input their worries and stress. Specifically, the user interface includes a text box and a send button, allowing employees to easily input their daily stress and worries.

[1710] Data encryption

[1711] The terminal encrypts the input data using encryption technology such as AES (Advanced Encryption Standard) before sending it, thereby protecting the privacy of user data.

[1712] Sending and Receiving Data

[1713] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS, and the server receives the data and decrypts it using a private key.

[1714] Analysis of generative AI models

[1715] The server then provides the decoded data to a generative AI model, which uses natural language processing (NLP) to assess the stress level and potential risks from the input text. Specifically, the generative AI model calculates a stress score and returns the result to the server.

[1716] Emotion Engine Analysis

[1717] In addition to the analysis results of the generative AI model, the server analyzes the user's emotions in real time using an emotion engine. The emotion engine detects emotions such as "anxiety" or "stress" from the input text and integrates the results into the analysis results of the generative AI model.

[1718] Saving analysis results

[1719] The server stores the results of the generative AI model and emotion engine analysis in a database, which includes the employee's ID, input text, stress score, and detected emotion.

[1720] Identifying high-risk employees

[1721] The server analyzes the results stored in the database and identifies high-risk employees. Specifically, if an employee's stress score or emotion analysis results exceed a certain threshold, they are deemed high-risk.

[1722] Professional Support Notification

[1723] The server then sends notifications to employees identified as high-risk with information on how to access professional support, including counseling services and medical facilities.

[1724] Specific examples

[1725] 1. User Interface

[1726] The user types into a chat-style interface, "My recent project hasn't been going well, and my relationship with my boss has deteriorated."

[1727] 2. Encryption and Transmission

[1728] The device encrypts the input text and sends it to the server using HTTPS.

[1729] 3. Data Reception and Decryption

[1730] The server receives the data sent from the terminal and decrypts the encrypted data using the private key.

[1731] 4. Analysis of generative AI models

[1732] The server inputs the decoded text into a generative AI model, which analyzes it as "high stress level."

[1733] 5. Use of Emotion Engine

[1734] The server uses an emotion engine to detect emotions such as "anxiety" or "stress" from the text.

[1735] 6. Saving the analysis results

[1736] The server stores the results of the generative AI model and emotion engine in a database.

[1737] 7. Identifying high-risk employees

[1738] The server analyzes the stored data and identifies employees as high risk if their stress score is high.

[1739] 8. Professional Support Notification

[1740] The server sends notifications to terminals for professional counseling for high-risk employees.

[1741] Prompt Sentence Examples

[1742] 1. Stress Level Assessment Prompt

[1743] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1744] Prompt: "Please rate the user's stress level based on this text."

[1745] 2. Emotion Recognition Prompts

[1746] User input: "My recent projects haven't been going well, and my relationship with my boss has deteriorated."

[1747] Prompt: "Identify the user's emotion from this text."

[1748] In this way, the present invention provides a system that provides advanced support for the mental health of employees, thereby improving the overall health of the company.

[1749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1750] Step 1:

[1751] The user inputs their worries and stress into a chat-style interface on the device. The input is in text format, for example, "My recent project has not been going well, and my relationship with my boss has deteriorated." This text is input as data.

[1752] Step 2:

[1753] The terminal uses AES encryption to encrypt the text data entered by the user, producing encrypted data that is then output. This encryption ensures the privacy of the data.

[1754] Step 3:

[1755] The terminal sends the encrypted data to the server using the HTTPS protocol, in this process the encrypted data is sent as input and received on the server side.

[1756] Step 4:

[1757] The server decrypts the encrypted data received via the HTTPS protocol using the private key. This process results in the decrypted data, which is the output.

[1758] Step 5:

[1759] The server provides the decrypted text data to the generative AI model, which evaluates the employee's stress level from the text data and calculates a stress score. The input is the text data and the output is the stress score. For example, the stress score is calculated as "85."

[1760] Step 6:

[1761] The server analyzes the user's emotions in real time using an emotion engine in parallel with the analysis results of the generative AI model. The emotion engine identifies emotions such as "anxiety" or "stress" from the text data and outputs them. The input is the text data, and the output is the detected emotion. For example, "anxiety" is detected.

[1762] Step 7:

[1763] The server combines the results of the generative AI model and the emotion engine analysis and stores them in a database, including employee IDs, input text, stress scores, and detected emotions, enabling long-term trend analysis and risk assessment.

[1764] Step 8:

[1765] The server analyzes the data stored in the database and identifies high-risk employees. Specifically, if the stress score or emotion analysis results exceed a certain threshold, the employee is deemed high-risk. For example, a stress score of 85 exceeds the threshold and is deemed high-risk.

[1766] Step 9:

[1767] The server then notifies employees identified as high-risk of professional counseling services or medical consultations. The notification is sent to the device, where the user can view and confirm the content of the notification. For example, a notification saying "Professional counseling is required" is sent.

[1768] Through these steps, the system is able to assess employees' mental health with high accuracy and provide the necessary support in a timely manner.

[1769] (Application example 2)

[1770] 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."

[1771] Employee mental health issues have a significant impact on the productivity and health of an entire company. However, even if employees are experiencing mental health-related worries or stress, it is difficult to accurately assess their mental health through self-reporting alone. There is also a lack of effective means to quickly identify employees who need professional support and provide appropriate support. There is a need for a system that can assess employee mental health in real time and quickly identify potential risks.

[1772] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1773] In this invention, the server includes: a means for providing a user interface through which employees input their worries and stress; a means for encrypting the input data and sending it to the server; a means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; a means for storing the analysis results in a database; a means for identifying high-risk employees and notifying them of professional support; a means for analyzing user emotions in real time using an emotion engine and incorporating the results into the analysis data; and a means for assessing stress levels and potential risks using a robot for interacting with employees. This makes it possible to analyze employee emotions in real time and accurately assess stress levels and potential risks. It also makes it possible to provide employees with mental health support in real time, identify high-risk employees early, and notify them of appropriate professional support.

[1774] A "user interface" is an interface that allows a user to perform operations and input data, and is typically in the form of a chat.

[1775] "Encryption" is the technique of converting data into a special code to protect it and ensure its safety from unauthorized access.

[1776] A "server" is a computer system that provides specific services or data over a network.

[1777] "Decryption" is a technique for restoring encrypted data to its original state.

[1778] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and generate specific results or predictions.

[1779] A "database" is a system that systematically organizes and stores data, and allows for efficient access and management.

[1780] "High-risk employees" are employees who have been identified as having high stress levels and potential health risks.

[1781] "Specialized support" refers to specialized services to support mental health, such as counseling services and referrals to medical institutions.

[1782] An "emotion engine" is an algorithm for analyzing and recognizing user emotions from text and voice data.

[1783] A "robot" is a mechanical device that performs physical tasks or interactions autonomously or remotely.

[1784] "Stress level" is an indicator that evaluates the degree of stress felt by employees.

[1785] "Potential risks" are risks that have not materialized at present but may cause problems in the future.

[1786] "Real-time" refers to the immediate processing and provision of data and information without delay.

[1787] The present invention provides a system for assessing and supporting the mental health of employees. This system analyzes data on worries and stress entered by employees, identifies high-risk employees, and provides appropriate support.

[1788] Hardware and Software Configuration

[1789] A server, a terminal and a robot are used.

[1790] The server has the processing power to run the generative AI model and emotion engine.

[1791] A terminal is a device that provides a user interface for employees to input data, and includes a smartphone or tablet.

[1792] The robot is a device that interacts with employees and assesses their stress levels and emotions in real time.

[1793] System processing overview

[1794] Users (employees) input their worries and stress into a chat-style interface. This input data is encrypted on the device and sent to the server. The server receives and decrypts the encrypted data. The decrypted data is analyzed by a generative AI model to evaluate stress levels and potential risks. An emotion engine is used for the analysis, analyzing the employee's emotions in real time and reflecting them in the analysis results. These results are stored in a database. Based on the stored results, the server identifies high-risk employees and notifies them of appropriate professional support (such as counseling services or referrals to medical institutions).

[1795] Specific operation example

[1796] Initial Setup: Use the terminal to set up encryption of user-entered data.

[1797] Data Entry: User types, "My recent project hasn't been going well and I'm feeling the pressure."

[1798] Data transmission: The encrypted data is sent to the server.

[1799] Data Decryption and Analysis: The server decrypts the data and uses an emotion engine to analyze emotions such as "anxiety" or "pressure." The generative AI model evaluates the stress level and determines "Stress Level: High."

[1800] Data storage: The analysis results are stored in a database. For example, it may be stored as "Employee A: High stress level, high risk."

[1801] Professional support: High-stress employees are notified that they may be advised to seek counseling services.

[1802] Technology used

[1803] Generative AI model: A text analysis model using the OpenAI API.

[1804] Emotion Engine: An emotion recognition engine that also uses the OpenAI API.

[1805] Cryptography: Data protection is achieved using the cryptography library.

[1806] Prompt Sentence Examples

[1807] "Recent projects have been going poorly and the employee is feeling the pressure. Please assess this employee's stress level and identify potential risks."

[1808] In this way, the system can support the mental health of employees and improve the overall well-being of the company.

[1809] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1810] Program processing steps

[1811] Step 1:

[1812] Initial Setup

[1813] Input: Initialization program for user terminal

[1814] Processing: The user device is configured to support encryption. An encryption key is generated within the device, and settings are made to send data securely. For example, the key is generated using Fernet, a Python cryptography library.

[1815] Output: Generate and store encryption keys

[1816] Step 2:

[1817] Entering data

[1818] Input: User-input worries and stress in a chat-style interface (e.g., "My latest project hasn't gone well, and I'm feeling the pressure").

[1819] Processing: The user interface receives the employee's input and encrypts the input text using the secret key generated during the initial setup.

[1820] Output: Encrypted worry and stress data

[1821] Step 3:

[1822] Sending data

[1823] Input: Encrypted worries and stress data

[1824] Processing: The user terminal sends the encrypted data to the server via network communication (e.g., socket communication).

[1825] Output: Encrypted data sent to the server side

[1826] Step 4:

[1827] Receiving and Decrypting Data

[1828] Input: Encrypted worries and stress data

[1829] Processing: The server decrypts the received encrypted data using the same encryption key (secret key) that was used on the user's device.

[1830] Output: Decoded worry and stress data

[1831] Step 5:

[1832] Analysis using generative AI models

[1833] Input: Decoded worry and stress data

[1834] Processing: The server uses the generative AI model to analyze the input data. Specifically, it uses OpenAI's API to perform prompt-based analysis and assess the employee's stress level. Example prompt: "Recent projects have not gone well, and the employee is feeling pressured. Please assess this employee's stress level and identify potential risks."

[1835] Output: Stress level and potential risk assessment results

[1836] Step 6:

[1837] Real-time analysis by emotion engine

[1838] Input: Decoded worry and stress data

[1839] Processing: The server uses an emotion engine to analyze emotions from the input text in real time. Specifically, it recognizes emotions such as "anxiety" or "pressure" from the user's text input and incorporates the results into the analysis results of the generative AI model.

[1840] Output: Final analysis data including sentiment analysis results

[1841] Step 7:

[1842] Saving to a database

[1843] Input: Final analysis data including sentiment analysis results

[1844] Processing: The server stores the results of both the generative AI model and the emotion engine in a database, which is used for long-term trend analysis and to identify high-risk employees.

[1845] Output: Employee mental health assessment data stored in a database

[1846] Step 8:

[1847] Identifying and notifying high-risk employees

[1848] Input: Employee mental health assessment data stored in a database

[1849] Processing: The server analyzes the data in the database to identify high-risk employees and notify them of appropriate professional support (such as counseling services or referrals to medical facilities).

[1850] Output: Support notification for high-risk employees

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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).

[1858] 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.

[1859] 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."

[1860] 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.

[1861] 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).

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] The following is further disclosed regarding the above embodiment.

[1873] (Claim 1)

[1874] A means for providing a user interface for employees to input their worries and stress;

[1875] means for encrypting input data and transmitting the data to a server;

[1876] A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and

[1877] a means for storing the analysis results in a database;

[1878] A system that includes a means to identify high-risk employees and notify them for specialized support.

[1879] (Claim 2)

[1880] A means of assessing employee stress levels and identifying potential risks;

[1881] A means of communicating referrals to appropriate counseling services based on that assessment; and

[1882] 10. The system of claim 1, further comprising:

[1883] (Claim 3)

[1884] measures to use encryption technology to protect employee data and respect privacy;

[1885] A means of evaluating the soundness of governance across the entire company based on the analysis results;

[1886] 10. The system of claim 1, comprising:

[1887] "Example 1"

[1888] (Claim 1)

[1889] A means for providing a user interface for employees to input their worries and stress;

[1890] means for encrypting input data and transmitting the data to a server;

[1891] A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and

[1892] a means for storing the analysis results in a database;

[1893] A means to identify high-risk employees and notify them of specialized support;

[1894] To ensure the safe transmission and reception of employee data, a method is provided to install an SSL / TLS certificate on user devices and create encryption keys.

[1895] A means to provide input data as prompts to the generative AI model to assess employee stress levels and potential risks;

[1896] A means of automatically sending emails and notifications to high-risk employees based on the assessment results

[1897] A system including:

[1898] (Claim 2)

[1899] A means of assessing employee stress levels and identifying potential risks;

[1900] A means of communicating referrals to appropriate counseling services based on that assessment; and

[1901] 10. The system of claim 1, further comprising:

[1902] (Claim 3)

[1903] measures to use encryption technology to protect employee data and respect privacy;

[1904] A means of evaluating the soundness of governance across the entire company based on the analysis results;

[1905] Employee data is encrypted and transmitted using SSL / TLS certificates to ensure security,

[1906] 10. The system of claim 1, comprising:

[1907] "Application Example 1"

[1908] (Claim 1)

[1909] A means for providing a user interface for employees to input their worries and stress;

[1910] means for encrypting input data and transmitting the data to a server;

[1911] A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and

[1912] a means for storing the analysis results in a database;

[1913] A means to identify high-risk employees and notify them of specialized support;

[1914] A way for store employees to input their mental health status via smartphone,

[1915] A generative AI model assesses stress levels and provides appropriate support.

[1916] A system including:

[1917] (Claim 2)

[1918] A means of assessing employee stress levels and identifying potential risks;

[1919] A means of communicating referrals to appropriate counseling services based on that assessment; and

[1920] A means for inputting the prompt sentence into a generative AI model to perform analysis;

[1921] 10. The system of claim 1, further comprising:

[1922] (Claim 3)

[1923] measures to use encryption technology to protect employee data and respect privacy;

[1924] A means of evaluating the soundness of governance across the entire company based on the analysis results;

[1925] A means for managing employee mental health using a smartphone;

[1926] 10. The system of claim 1, comprising:

[1927] "Example 2: Combining Emotion Engines"

[1928] (Claim 1)

[1929] A means for providing a user interface for employees to input their worries and stress;

[1930] means for encrypting input data and transmitting the data to a server;

[1931] A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and

[1932] A means for analyzing user emotions in real time using an emotion engine on the server side;

[1933] A means of storing the analysis results of the generative AI model and emotion engine in a database;

[1934] A system that includes a means to identify high-risk employees and notify them for specialized support.

[1935] (Claim 2)

[1936] A means of assessing employee stress levels and identifying potential risks;

[1937] A means of communicating referrals to appropriate counseling services based on that assessment; and

[1938] 10. The system of claim 1, further comprising:

[1939] (Claim 3)

[1940] measures to use encryption technology to protect employee data and respect privacy;

[1941] A means to identify high-risk employees based on the results of generative AI models and emotion engine analysis; and

[1942] A means of evaluating the soundness of governance across the entire company based on the analysis results;

[1943] 10. The system of claim 1, comprising:

[1944] "Application example 2 when combining emotion engines"

[1945] (Claim 1)

[1946] A means for providing a user interface for employees to input their worries and stress;

[1947] means for encrypting input data and transmitting the data to a server;

[1948] A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and

[1949] a means for storing the analysis results in a database;

[1950] A means to identify high-risk employees and notify them of specialized support;

[1951] A means for analyzing user emotions in real time using an emotion engine and incorporating the results into the analysis data;

[1952] A means for assessing stress levels and potential risks using a robot to interact with employees;

[1953] A system including:

[1954] (Claim 2)

[1955] A means of assessing employee stress levels and identifying potential risks;

[1956] A means of communicating referrals to appropriate counseling services based on that assessment; and

[1957] A method to support the mental health of employees using robots that patrol the factory,

[1958] 10. The system of claim 1, further comprising:

[1959] (Claim 3)

[1960] measures to use encryption technology to protect employee data and respect privacy;

[1961] A means of evaluating the soundness of governance across the entire company based on the analysis results;

[1962] Using robots to interact with employees in real time and assess their stress levels;

[1963] 10. The system of claim 1, comprising: [Explanation of symbols]

[1964] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for providing a user interface for employees to input their worries and stress; means for encrypting input data and transmitting the data to a server; A means for running a generative AI model that decrypts and analyzes the encrypted data on the server side; and a means for storing the analysis results in a database; A system that includes a means to identify high-risk employees and notify them for specialized support.

2. A means of assessing employee stress levels and identifying potential risks; A means of communicating referrals to appropriate counseling services based on that assessment; and The system of claim 1 further comprising:

3. measures to use encryption technology to protect employee data and respect privacy; A means of evaluating the soundness of governance across the entire company based on the analysis results; The system of claim 1 , comprising:

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