System

Abstract

A system is provided.SOLUTION: A system comprising: inputting means for a user to anonymously input consultation contents; analyzing means for analyzing and categorizing the consultation contents received from the inputting means; generating and AI means for generating a solution based on the consultation contents classified by the analyzing means; presenting means for presenting the solution generated by the generating and AI means to the user; and collecting means for collecting feedback of the user received through the presenting means and improving accuracy of the generating and AI means.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 educational environment, problems such as bullying, moral harassment, power harassment, and sexual harassment are occurring frequently. These problems are particularly prevalent in educational settings such as schools, cram schools, and sports clubs, where hierarchical relationships and rigid human relationships make it difficult for self-cleansing mechanisms to function when problems arise. Furthermore, teachers, coaches, students, and parents often find it difficult to obtain external support, and the communication costs of verbalizing the problems are enormous, leaving those involved mentally exhausted. This often delays the discovery of problems and makes them difficult to resolve. [Means for solving the problem]

[0005] The present invention provides the following means to solve the above problems. An input means is provided for users to anonymously input consultation content, and an analysis means is used to analyze and categorize the consultation content received from the input means. Furthermore, a generation AI means is provided to generate solutions based on the consultation content classified by the analysis means, and a presentation means is provided to present the solutions generated by the generation AI means to the user. The system also includes a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means. In this way, the present invention realizes an anonymous consultation service that supports the early detection and resolution of ethical issues in educational settings, reducing communication costs for those involved and preventing the issues from worsening.

[0006] The term "user" refers to a party who uses the system of the present invention to anonymously input the details of a problem and obtain a solution.

[0007] "Input means" refers to an interface that allows a user to anonymously input the details of a consultation.

[0008] The "analysis means" refers to a system for analyzing the consultation content received from the input means and classifying the consultation content into appropriate categories.

[0009] "Generative AI means" refers to a module that uses artificial intelligence to generate optimal solutions based on the consultation content classified by the analytical means.

[0010] "Presentation means" refers to an interface that displays the solution generated by the generation AI means to the user so that the user can confirm it.

[0011] "Collection means" refers to a system for collecting user feedback received via the presentation means and using it to improve the accuracy of the generation AI means.

[0012] "Authentication means" refers to a mechanism for performing the necessary authentication while preserving the anonymity of the user.

[0013] "Database" refers to a storage device for storing feedback information collected by a collection means and for use in subsequent solution generation. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0036] First, the user downloads the application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password. This prevents duplicate registrations while maintaining anonymity. Once registration is complete, the user is allowed to log in to the system using authentication methods.

[0037] After logging in, users use the input method to enter the details of their consultation. Specifically, they can freely describe the problems or worries they are facing in the consultation form within the application. For example, they could enter something like, "I'm having trouble with bullying in my class and I don't know how to deal with it."

[0038] Once the consultation content is entered, the device sends this information to the server. The server passes the received consultation content to the analysis means, which uses natural language processing to tokenize the consultation content and classify it into an appropriate category (bullying, moral harassment, power harassment, sexual harassment, etc.). Next, based on the classification by the analysis means, the generation AI means is activated and performs a detailed analysis of the problem. The generation AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0039] The generated solutions are formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with detailed solutions including specific guidelines and expert advice, allowing the user to review the solutions and take actual action according to the guidelines.

[0040] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0041] The following cases are specific examples:

[0042] Example 1:

[0043] User: A, a second-year junior high school student

[0044] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0045] Person A opens the application and inputs the details of his or her problem. This information is sent to the server, and the analysis means classifies it as a case of bullying. The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." Person A checks the presented solution and puts it into action. He or she later provides feedback, contributing to improving the accuracy of the generation AI.

[0046] Example 2:

[0047] User: Mother of B, a fourth-grader

[0048] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0049] B's mother enters the details of her consultation into the application and sends them to the server. The analysis means classifies it as a case of moral harassment, and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0050] As described above, the present invention provides a system for supporting problem solving in educational settings and reducing the mental burden on those involved.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0054] Step 2:

[0055] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0056] Step 3:

[0057] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0058] Step 4:

[0059] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0060] Step 5:

[0061] The device sends the input consultation details to the server, which then passes the received consultation details to an analysis means, tokenizes them using natural language processing technology, and classifies them into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.).

[0062] Step 6:

[0063] The server operates the generation AI means based on the consultation content classified by the analysis means. The generation AI means identifies the root cause of the problem and the scope of its impact, and generates an optimal solution.

[0064] Step 7:

[0065] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0066] Step 8:

[0067] The device receives the solutions from the server and displays them on the screen. The user can then review the solutions and take the necessary action. For example, in the case of bullying, a specific action plan such as "Talk to a trusted teacher or counselor" is displayed.

[0068] Step 9:

[0069] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0070] Step 10:

[0071] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method and is used for generating solutions in the future.

[0072] Example 1

[0073] 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."

[0074] In traditional educational environments, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment were difficult to resolve quickly due to a lack of concrete measures for those involved to take appropriate action. Furthermore, there was no system that could ensure the anonymity of those seeking advice while proposing appropriate solutions, so those seeking advice could not feel safe. Furthermore, there was also a lack of a way to utilize collected feedback to improve the accuracy of the system.

[0075] 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.

[0076] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details classified by the analysis means, presentation means for presenting the solutions generated by the generation AI means to the user, collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means, authentication means for authenticating users and controlling access to the system, and communication means for encrypting the transmission and reception of the consultation details. This allows the user to input the consultation details with peace of mind while maintaining anonymity, and the system can quickly generate and present appropriate solutions and then improve the accuracy of the system based on the collected feedback.

[0077] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0078] The "analysis means" is a mechanism for analyzing the consultation content received from the input means and classifying it into an appropriate category.

[0079] The "generative AI means" is an artificial intelligence system that generates solutions based on the consultation content classified by the analysis means.

[0080] The "presentation means" is an interface for visually or audibly presenting the solution generated by the generation AI means to the user.

[0081] The "collection means" is a mechanism for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0082] "Authentication means" is a mechanism for authenticating users and controlling access to the system.

[0083] The "communication means" is a communication protocol for encrypting the sending and receiving of consultation contents.

[0084] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0085] First, the user downloads the system's application from the app store on their smartphone or tablet. When the user launches the application, a registration screen appears the first time they use it. On the registration screen, the user enters their username, email address, and password to create an account. This registration information is stored in a database, which prevents duplicate registrations while maintaining the user's anonymity.

[0086] Next, the user logs in by entering their registered email address and password. The server verifies this information, and if authentication is successful, the user is allowed to use the system. Secure communication is performed using SSL / TLS to prevent unauthorized access.

[0087] After logging in, users can tap the "Consult" button in the application and freely describe the problems or concerns they are facing in the consultation form. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0088] When a user enters the details of their consultation and taps the "Send" button, the device sends this information to the server. The communication is encrypted and uses a communication method to prevent information leaks.

[0089] The server uses a natural language processing library (e.g., SpaCy) to tokenize the received consultation content and classify it into an appropriate category. This results in categories such as "bullying," "moral harassment," "power harassment," and "sexual harassment." For example, it may be classified into the category "bullying."

[0090] Based on the classification results, the server uses a generative AI model (e.g., OpenAI's GPT-4) to perform a detailed analysis of the problem. The generative AI identifies the root cause and scope of the problem and creates a specific solution. For example, in the case of bullying, it generates "how to inform a trusted teacher or counselor and specific action steps."

[0091] The generated solutions are presented on the device in a user-friendly format, including specific guidelines and expert advice, such as "how to talk to a teacher" or "what to do if you see bullying."

[0092] The user checks the proposed solutions and implements them if necessary. For example, they follow the proposed solutions and consult with a trusted teacher. After implementing the solutions, the user also enters the results and experience as feedback. The application provides a feedback form, which the user can fill out and submit.

[0093] The server receives user feedback and stores it in a database. The collected feedback is used as training data for the generative AI model, helping to improve the accuracy of the system.

[0094] The following cases are specific examples:

[0095] Example 1:

[0096] User: 2nd year junior high school student

[0097] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0098] The user opens the application and inputs the details of their problem. This information is sent to the server, and the analysis means classifies it as "bullying." The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." The user checks the presented solutions and puts them into action. They then provide feedback, contributing to improving the accuracy of the generation AI.

[0099] Example 2:

[0100] User: Parent of an elementary school student

[0101] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0102] Parents enter their concerns into the application and send them to the server. The analysis means classifies the issue as "moral harassment," and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." Parents confirm the solutions and implement them. Feedback after implementation is provided through the application, which will help further improve the system.

[0103] Example prompt sentence:

[0104] "Please tell me what to do if I see a friend being bullied in class."

[0105] "I want to know how to deal with teachers who are strict with discipline."

[0106] As described above, this system efficiently analyzes user inquiries and utilizes generative AI models to provide specific solutions, thereby supporting problem-solving in educational settings.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] Users download the system's application from the app store on their smartphone or tablet and launch the app. On first use, they create an account by entering their username, email address, and password on the registration screen that appears. The information entered is sent from the device to the server and stored in a database. This prevents duplicate registrations while maintaining the user's anonymity.

[0110] Step 2:

[0111] Users log in using their registered email address and password. The server receives this authentication information and compares it with information in the database. If it matches, the user is allowed to use the system and an authentication token is issued. To prevent unauthorized access, all communication is encrypted with SSL / TLS. This authentication token is used to ensure the user's ongoing session.

[0112] Step 3:

[0113] After logging in, users tap the "Consult" button on the home screen of the application. A consultation form will appear, and the user can freely describe the problems or worries they are facing. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0114] Step 4:

[0115] When the user enters the consultation content and taps the "Send" button, the device sends the entered consultation content to the server. This communication is also encrypted to prevent information leaks. The input at this stage is the consultation content entered by the user, and the output is sent to the server as encrypted consultation content.

[0116] Step 5:

[0117] The server tokenizes the received consultation content using a natural language processing library (e.g., SpaCy). The tokenized data is further analyzed and classified into appropriate categories such as bullying, moral harassment, power harassment, and sexual harassment. The input is the received consultation content, and the output is the categorized consultation content.

[0118] Step 6:

[0119] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate a specific solution to the consultation content. For example, if the issue is classified as "bullying," it will generate "ways to consult with a trusted teacher or counselor" and "specific action steps." The input is the categorized consultation content, and the output is the generated solution.

[0120] Step 7:

[0121] The server formats the generated solution into a user-friendly format. The formatted solution is sent to the terminal through the presentation means and presented to the user. Specifically, it is visually displayed to the user in the application interface. The generated solution is used as input, and the formatted solution is presented as output.

[0122] Step 8:

[0123] The user checks the proposed solution and implements it if necessary, for example, by consulting a trusted teacher. After implementing the solution, the user also inputs the results and experience as feedback.

[0124] Step 9:

[0125] The feedback entered by the user is sent from the terminal to the server, which receives it and stores it in a database. The input is the user's feedback, and the output is stored in the database.

[0126] Step 10:

[0127] The server uses the collected feedback as learning data for the generative AI model, helping to improve its accuracy. This makes it possible to provide even more accurate solutions for future consultations. The input is the feedback stored in the database, and the output is an improved AI model.

[0128] (Application example 1)

[0129] 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."

[0130] Ethical issues such as harassment, bullying, and whistle-blowing in the workplace are serious challenges for companies. These issues are difficult to address individually, so there is a need for a system that can effectively collect information while ensuring anonymity and provide appropriate solutions.

[0131] 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.

[0132] In this invention, the server includes an input means for users to anonymously input consultation details, an analysis means for analyzing and categorizing the consultation details received from the input means, a generation AI means for generating solutions based on the consultation details categorized by the analysis means, a presentation means for presenting the solutions generated by the generation AI means to users, a means for corporate employees to anonymously consult and report issues such as harassment, bullying, and whistleblowing that they face in the workplace, and a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means. This makes it possible to resolve ethical issues in the workplace anonymously and effectively.

[0133] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0134] The "analysis means" is a technology for analyzing the consultation content received from the input means and classifying it into categories.

[0135] The "generative AI means" is an artificial intelligence technology for generating solutions based on the consultation content classified by the analytical means.

[0136] The "presentation means" is an interface for presenting the solution generated by the generation AI means to the user.

[0137] The "collection means" is a technology for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0138] "System" refers to the entire computer system including input means, analysis means, generation AI means, presentation means, and collection means.

[0139] "Authentication means" is a technology for ensuring the anonymity of users and an interface for verifying the identity of users.

[0140] "Feedback" is information that provides results and opinions after a user implements a solution.

[0141] "Database" refers to a data storage technology for storing feedback collected by collection means and utilizing it in subsequent solution generation.

[0142] "A means to anonymously consult and report issues such as harassment, bullying, and whistle-blowing that are faced in the workplace" is a technology that allows corporate employees to anonymously report and consult on ethical issues that arise in the workplace to a system.

[0143] "A system for corporate employees to anonymously seek advice on problems" refers to the entire system that allows employees working within a company to anonymously seek advice on problems that arise in the workplace.

[0144] This invention relates to a system that allows corporate employees to anonymously seek advice and report issues they face in the workplace, such as harassment, bullying, whistle-blowing, etc. A specific form for realizing this system is described below.

[0145] First, the user downloads the smartphone application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password, which prevents duplicate registrations while maintaining anonymity. Once registration is complete, authentication measures allow login to the system.

[0146] After logging in, the user uses the input means to enter the content of their consultation. Specifically, they can freely write down the problems or worries they are facing at work in the consultation form within the application. For example, they can enter content such as "How should I deal with power harassment from my boss?"

[0147] The input consultation content is sent from the device to the server. The server uses an analytical means to analyze the received consultation content and classify it into categories (harassment, bullying, whistleblowing, etc.). The analytical means uses natural language processing technology to tokenize the consultation content and classify it into the appropriate category. Next, the generative AI means operates based on the classification by the analytical means and performs a detailed analysis of the problem. The generative AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0148] The generated solution is formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with a detailed solution including specific guidelines and expert advice, allowing the user to review the provided solution and take actual action according to the guidelines.

[0149] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0150] The following cases are specific examples:

[0151] Example 1:

[0152] If an employee is experiencing power harassment at work, they open the application and enter the details of their complaint. This information is sent to the server, and the analysis means classifies it as a case of power harassment. The generation AI means provides "ways to deal with power harassment," such as "ways to avoid talking to your superiors," "ways to collect evidence," and "ways to officially report to the human resources department." The employee checks the proposed solutions and puts them into action.

[0153] Specific prompt examples:

[0154] How to deal with power harassment from your boss

[0155] "I want to know how to solve bullying in the workplace."

[0156] The system allows for the anonymous and effective resolution of ethical issues in the workplace, and is an advanced tool to ensure employee safety and security.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] Users download the smartphone application and go through a simple registration process when they use it for the first time.

[0160] Input: Username, Email Address, Password

[0161] Output: User ID (unique identifier)

[0162] What happens: A user opens the application and enters their username, email address, and password, which are then sent to the server, which generates a unique user ID for the user and completes the registration.

[0163] Step 2:

[0164] A user logs into the system using authentication methods.

[0165] Input: Email address, password

[0166] Output: Login session (authentication token)

[0167] How it works: The user enters their email address and password into the application and the authentication process begins. The server verifies this information and, if authentication is successful, creates a login session and returns an authentication token.

[0168] Step 3:

[0169] The user inputs the consultation content using the input means.

[0170] Input: Consultation content (text format)

[0171] Output: Consultation data object

[0172] Specific operation: The user enters the consultation details into the form in the application and presses the send button. This information is sent from the terminal to the server as a consultation data object.

[0173] Step 4:

[0174] The server analyzes the consultation contents received by the server using an analysis means and classifies them into categories.

[0175] Input: Consultation data object

[0176] Output: Category information (bullying, harassment, whistleblowing, etc.)

[0177] Specific operation: The server analyzes the received consultation data using natural language processing technology and tokenizes the consultation content. The analysis means classifies the consultation content into appropriate categories based on this data.

[0178] Step 5:

[0179] Based on the consultation content classified by the analysis means, the generation AI means generates a solution.

[0180] Input: Category information, consultation data object

[0181] Output: Solution data object

[0182] Specific operation: Based on the categorical information generated by the analytical means, the generative AI model generates an optimal solution, which is formatted as a solution data object.

[0183] Step 6:

[0184] The solution generated by the generation AI means is presented to the user through the presentation means.

[0185] Input: Solution data object

[0186] Output: The solution displayed in the user interface

[0187] Specific Actions: Solution data objects are transformed into a user-friendly format and displayed to the user through presentation means within the application, allowing the user to view and understand them.

[0188] Step 7:

[0189] The user implements the provided solution and inputs the results and feedback into the system through a collection means.

[0190] Input: Feedback data (text format, feedback content)

[0191] Output: Feedback data object

[0192] Specific operation: The user implements the provided solution and inputs the results and impressions into the system through the collection means. The feedback data is sent to the server as a feedback data object.

[0193] Step 8:

[0194] The server stores the feedback collected by the collection means in a database to improve the accuracy of the generation AI means.

[0195] Input: Feedback data object

[0196] Output: Updated generative AI model

[0197] How it works: The server stores the received feedback in a database and uses it as training data to retrain the generative AI model, enabling it to provide more accurate solutions in the future.

[0198] 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.

[0199] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. The system incorporates an emotion engine that recognizes and analyzes users' emotions, thereby improving the accuracy and applicability of solutions.

[0200] First, the user downloads the application and goes through a simple registration process the first time they use it. They then use an input method to anonymously enter their concerns in free-form. For example, they could write something like, "There's bullying in my class and I don't know what to do about it."

[0201] Next, the input consultation content is sent from the terminal to the server. The server passes the received consultation content to an analysis means, which uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. At this point, the emotion engine incorporated in the present invention is activated and recognizes the user's emotions from the consultation content. For example, the emotion engine may detect from the input content that the user is feeling emotions such as "anxiety" or "fear."

[0202] The AI ​​generator then generates an optimal solution based on the consultation content classified by the analysis unit and the recognized emotional data. The generated solution is tailored to the user's emotional state and provides specific and appropriate advice. For example, if the user is feeling "fear" about a bullying issue, the generated solution would include how to seek psychological help.

[0203] The generated solutions are formatted on the server and sent to the device, which then displays them on the screen in a user-friendly format. The user can then review the solutions and implement them. For example, in the case of a bullying problem, the solution suggests "talking to a trusted teacher" and "taking a break or finding ways to relax."

[0204] After implementing a solution, the user enters the results and experience into the terminal through a feedback input form. The terminal then sends the entered feedback to the server and stores it in a database via the collection means. In particular, since the feedback includes emotion data collected by the emotion engine of the present invention, it contributes to improving the accuracy of the generation AI means in subsequent solution generation.

[0205] The following cases are specific examples:

[0206] Example 1:

[0207] User: A, a second-year junior high school student

[0208] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0209] Person A opens the application and enters the details of his or her problem. This information is sent to the server, where the analysis means classifies it as a case of bullying. At the same time, the emotion engine detects that Person A is feeling "helpless." The generation AI means analyzes "how to respond when witnessing bullying" and generates "how to report it to a trusted teacher or counselor" and "actions to increase self-esteem." Person A checks the proposed solutions and implements them. Later, feedback is provided, contributing to improving the accuracy of the system.

[0210] Example 2:

[0211] User: Mother of B, a fourth-grader

[0212] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0213] B's mother opens the application, inputs the details of her consultation, and sends them to the server. The analysis means classifies it as a case of moral harassment, and the emotion engine detects emotions such as "concern." The generative AI means generates "methods of communication between teachers and parents" and "methods of formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0214] As described above, this invention provides a system that supports problem solving in educational settings and reduces the mental burden on those involved. By incorporating an emotion engine, the applicability and accuracy of solutions are improved, enabling support that is more suited to the user's needs.

[0215] The processing flow will be explained below.

[0216] Step 1:

[0217] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0218] Step 2:

[0219] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0220] Step 3:

[0221] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0222] Step 4:

[0223] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0224] Step 5:

[0225] The device sends the input consultation content to the server. The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.). During this classification process, an emotion engine is activated and recognizes the user's emotions from the consultation content.

[0226] Step 6:

[0227] The server operates the generative AI means based on the consultation content and emotion data analyzed by the emotion engine. The generative AI means identifies the root cause of the problem and the extent of its impact, and generates an optimal solution that takes the user's emotions into consideration.

[0228] Step 7:

[0229] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0230] Step 8:

[0231] The device displays the solutions received from the server on the screen. The user can then review the solutions and take the necessary action. For example, in the case of a bullying issue, the device displays "ways to calm down" along with the action to "consult a trusted teacher or counselor."

[0232] Step 9:

[0233] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0234] Step 10:

[0235] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method. In particular, the feedback includes emotional data collected by the emotion engine, which further improves the accuracy of solution generation in future.

[0236] Example 2

[0237] 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."

[0238] Ethical issues such as bullying, moral harassment, power harassment, and sexual harassment, which are difficult to resolve in traditional educational environments, can have serious psychological and health effects if left unaddressed. Users are also required to provide their concerns anonymously, which protects their privacy. However, traditional systems often lack the means to resolve these issues efficiently and accurately. In particular, there has been no system that can properly recognize the emotions in the content of a consultation and provide appropriate solutions based on this.

[0239] 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.

[0240] In this invention, the server includes an input / output device for users to anonymously input consultation details, an analysis device for analyzing and categorizing the consultation details received from the input / output device, a device using natural language processing technology for tokenizing the consultation details classified by the analysis device and classifying them into appropriate categories, an emotion recognition device for recognizing users' emotions based on the natural language processing technology, a generation algorithm device for generating solutions based on the user's emotion data recognized by the emotion recognition device, a presentation device for presenting the solutions generated by the generation algorithm device to users, and a collection device for collecting user feedback received via the presentation device and improving the accuracy of the generation algorithm device. This enables the generation and provision of appropriate solutions that take user emotions into consideration. Furthermore, the collected feedback data can be used to improve the accuracy of the system, contributing to improved accuracy in future solution generation.

[0241] A "user" is an individual who accesses the system, anonymously enters a request, and reviews and implements the solutions provided.

[0242] The "input / output device" is a device that allows users to anonymously input the details of their inquiries and displays solutions from the server.

[0243] The "analysis device" is a device that analyzes the consultation content received from the input / output device and performs tokenization and category classification using natural language processing technology.

[0244] "Natural language processing technology" is a technology for understanding and analyzing human language, tokenizing the content of consultations, and classifying them into appropriate categories.

[0245] An "emotion recognition device" is a device that uses natural language processing technology to recognize emotions from the content of a user's consultation and generates emotion data.

[0246] The "generative algorithm device" is a device that includes an artificial intelligence model for generating optimal solutions based on the emotion data recognized by the emotion recognition device.

[0247] A "presentation device" is a device for presenting to a user a solution generated by a generation algorithm device.

[0248] A "collection device" is a device that collects user feedback received via a presentation device and uses it to improve the accuracy of the generation algorithm device.

[0249] "Feedback" is information that a user records and sends to the system the results and experiences of implementing a provided solution.

[0250] MODE FOR CARRYING OUT THE INVENTION

[0251] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. This system allows users to anonymously input their consultation details and provides optimal solutions based on the details of the consultation.

[0252] User usage of the application

[0253] Users first download the system's application from an application store. When using the system for the first time, they register an account by entering basic information, which ensures the user's anonymity.

[0254] Enter the consultation details

[0255] Users tap "New Consultation" on the main screen of the application and enter the content of their consultation in free-form. For example, they could write specific details such as "I'm having trouble with bullying in my class and I don't know how to deal with it." Once they've finished entering their information, they tap the "Send" button.

[0256] Data transmission and reception

[0257] The device encrypts the consultation content entered by the user and sends it to the server using the HTTPS protocol. The server then passes the received consultation content to an analysis method (for example, using natural language processing libraries such as SpaCy or NLTK).

[0258] Data analysis and emotion recognition

[0259] The server uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. Based on this, an emotion engine (e.g., emotion recognition models BERT or GPT-3) is activated to recognize the user's emotions. Specifically, it generates emotion tags such as "anxiety" or "fear" from the text.

[0260] Solution Generation

[0261] The server inputs the analyzed consultation content and detected emotion tags as prompts into a generative AI model (e.g., OpenAI's GPT-3), which then generates an optimal solution.

[0262] Examples of prompts include:

[0263] Consultation content: Bullying is an issue in class and I don't know how to deal with it.

[0264] Emotion: Fear

[0265] The generated solutions are formatted at the server and sent to the terminal.

[0266] View and implement solutions

[0267] The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, specific actions such as "Consult a trusted teacher" are displayed on the screen.

[0268] Collecting feedback

[0269] After implementing the solution, the user enters the results and experience as feedback into the application, which is then sent by tapping the "Feedback" button.

[0270] The device sends the feedback information to the server. This communication is also encrypted using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation. Emotional data in particular is useful for model training, contributing to improving the accuracy of future solutions generated by the generative AI model.

[0271] The above is a specific embodiment of this system. The present invention aims to provide a more appropriate and practical solution by taking into account the user's emotions.

[0272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0273] Step 1: User downloads and registers the application

[0274] How it works: A user downloads the system's application from an application store. When using it for the first time, they register an account by entering basic information such as their name, age, and school name. The information they enter is hashed to ensure anonymity.

[0275] Input: User's basic information (name, age, school name, etc.).

[0276] Output: The registered user account.

[0277] Step 2: User inputs the content of the consultation

[0278] Operation: The user selects "New Consultation" from the main screen of the application, enters the consultation details in free text format, and presses the "Send" button to send the data.

[0279] Input: The user's problem (e.g., "I'm having trouble with bullying in my class and I don't know what to do about it.").

[0280] Output: Consultation details saved on the device.

[0281] Step 3: Send data from the device to the server

[0282] How it works: The device encrypts the consultation information entered by the user and sends it to the server using the HTTPS protocol. The data is encrypted to ensure the security of the communication.

[0283] Input: User's consultation (encrypted).

[0284] Output: The consultation details sent to the server.

[0285] Step 4: Data analysis and emotion recognition by the server

[0286] How it works: The server passes the received consultation content to an analysis tool (e.g., a natural language processing library like SpaCy or NLTK), tokenizes the content, and classifies it into appropriate categories. It then uses an emotion recognition model (e.g., BERT or GPT-3) to recognize the user's emotions.

[0287] Input: Received consultation content.

[0288] Output: Tokenized data, emotion tags (e.g., "anxiety", "fear").

[0289] Step 5: Generative AI model generates solutions

[0290] How it works: The server inputs the analyzed consultation content and the recognized emotion tag as a prompt sentence into a generative AI model (e.g., OpenAI's GPT-3). Based on this, the generative AI model generates an optimal solution.

[0291] Input: Prompt statement (e.g., "Contact: Bullying in class is an issue and I don't know what to do. Emotion: Fear").

[0292] Output: Optimal solution (in text format).

[0293] Step 6: Formatting and sending the solution on the server

[0294] How it works: The solution generated by the generative AI model is formatted in an appropriate format (e.g., JSON or XML) and sent to the device.

[0295] Input: The generated solution.

[0296] Output: A formatted solution.

[0297] Step 7: User confirms and implements the solution

[0298] Operation: The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, a specific action such as "Consult a trusted teacher" is displayed on the screen.

[0299] Input: A formatted solution.

[0300] Output: A specific action to be taken.

[0301] Step 8: Collecting user feedback and sending it to the server

[0302] How it works: After the user implements a solution, they input their results and experience as feedback into the application. The input feedback is sent by tapping the "Feedback" button. The device encrypts the feedback information and sends it to the server using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation.

[0303] Input: User feedback.

[0304] Output: Feedback data stored in a database.

[0305] The above is a description of each processing step and its specific operation of this system. By taking into consideration the user's feelings and providing appropriate solutions quickly, it helps prevent and resolve ethical issues.

[0306] (Application example 2)

[0307] 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."

[0308] In today's educational environment, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment frequently occur, often causing mental stress. Conventional problem-solving methods have not provided effective support because they are unable to properly understand the client's emotions and provide applicable solutions. For this reason, there is a need to provide an environment where users can seek advice anonymously, as well as incorporate functions to recognize and analyze emotions in order to provide more specific and appropriate solutions.

[0309] 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.

[0310] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details categorized by the analysis means, emotion analysis means for recognizing and analyzing the user's emotions when generating solutions using the emotion analysis means, and means for incorporating emotion data recognized by the emotion analysis means into the solution generation process, thereby making it possible to provide highly applicable solutions that take the user's emotions into consideration.

[0311] "User" refers to an individual who uses the system to input their inquiry and receive a solution.

[0312] "Anonymity" refers to a state in which a user's personal information or name is not revealed, with the aim of protecting the user's privacy.

[0313] "Input means" refers to the method or device by which a user enters the details of their consultation. For example, this includes a smartphone or computer input form.

[0314] "Analysis means" refers to the technology or algorithm used to analyze the received consultation content and classify it into an appropriate category.

[0315] "Generative AI means" refers to artificial intelligence technologies and algorithms that generate optimal solutions based on the consultation content classified by analytical means.

[0316] "Emotion analysis means" refers to the technology and algorithms used to recognize and analyze the user's emotions from the consultation content entered.

[0317] "Presentation Means" refers to a method or device for visually or audibly presenting the solution generated by the Generative AI Means to the user.

[0318] "Collection Method" refers to the technology or algorithm used to collect feedback provided by users on proposed solutions and store it in the system.

[0319] "Authentication methods" refers to techniques and methods for ensuring and guaranteeing the anonymity of users.

[0320] "Database" refers to an electronic information storage system for storing collected feedback and sentiment data and utilizing it for subsequent solution generation.

[0321] A "prompt" refers to the input format that a generative AI model uses to generate a solution and get the optimal output.

[0322] The embodiment of this invention provides a system for anonymously consulting and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. In particular, by incorporating a function to analyze the user's emotions, the system provides more appropriate solutions.

[0323] Hardware and software used

[0324] Hardware: Smartphone (iOS and Android compatible)

[0325] Software: Python, Hugging Face Transformers library, requests library

[0326] System configuration

[0327] User device:

[0328] Users access the system using a smartphone, enter their concerns anonymously through the application, and receive solutions.

[0329] server

[0330] The server includes the following components:

[0331] 1. Input means: A means for receiving the consultation content input by the user.

[0332] 2. Analysis method: The received consultation content is analyzed using natural language processing technology and classified into appropriate categories.

[0333] 3. Emotion analysis method: A method for recognizing and analyzing the user's emotions from the analyzed consultation content. It uses the Hugging Face Transformers library.

[0334] 4. Generative AI method: Based on the user's consultation content and recognized emotion data, the optimal solution is generated using a generative AI model.

[0335] 5. Presentation means: A means for presenting the generated solution to the user.

[0336] 6. Collection methods: User-provided feedback is collected and used to improve the accuracy of the generative AI methods.

[0337] Data Flow

[0338] 1. Input of consultation content: The user inputs the consultation content into the application and sends it to the server.

[0339] 2. Analysis of consultation content: The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories.

[0340] 3. Emotion analysis: The analyzed data is input into the emotion analysis means to recognize and analyze the user's emotions.

[0341] 4. Solution generation: The generative AI means generates a solution based on the data obtained by the analysis means and the sentiment analysis means.

[0342] 5. Presentation of the solution: The generated solution is formatted and sent to the user's terminal through a presentation means.

[0343] 6. Feedback collection: The user implements the proposed solution and provides feedback through the application. The collection mechanism sends this feedback to the server and stores it in the database.

[0344] Specific examples

[0345] Example 1:

[0346] If a second-year junior high school student, A, sees a friend being bullied in class but doesn't know what to do, the emotion analysis means will detect A's "helplessness." The generative AI means will suggest ways to deal with bullying, such as consulting a trusted teacher or taking actions to improve self-esteem.

[0347] Example prompt sentence:

[0348] Input: My classmate is being bullied and I don't know what to do about it.

[0349] Emotion: helplessness

[0350] Output formats:

[0351] Suggestion 1: Talk to a trusted teacher or counselor

[0352] Suggestion 2: Actions to improve self-esteem

[0353] Example 2:

[0354] If the mother of a fourth-grader named B says that her son comes home crying every day because he is being strict with a teacher at school, the emotion analysis means will detect the concern. The AI ​​generation means will generate and suggest ways to communicate with the teacher and provide formal feedback to the school.

[0355] Example prompt sentence:

[0356] My son comes home crying every day because he has a strict teacher at school. What should I do?

[0357] Emotion: Concern

[0358] Output formats:

[0359] Suggestion 1: How to communicate with teachers

[0360] Suggestion 2: Formal feedback methods for schools

[0361] In this way, the present invention realizes a system that supports problem solving in educational settings and provides specific and appropriate advice according to the user's emotions.

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] The user anonymously inputs the details of the consultation using a device (smartphone), and the input details are sent to the server.

[0365] Input: User's problem (e.g., "My friend is being bullied in class")

[0366] Output: The consultation content is sent to the server

[0367] Step 2:

[0368] The server passes the received consultation content to an analysis means, where it tokenizes it using natural language processing technology and classifies it into appropriate categories.

[0369] Input: Consultation content received by the server

[0370] Data processing: tokenization and categorization using natural language processing

[0371] Output: Consultation contents categorized

[0372] Step 3:

[0373] The analyzed consultation content is input into the emotion analysis means, and the user's emotions are recognized and analyzed.

[0374] Input: Consultation content categorized

[0375] Data Computation: Sentiment Analysis Using Hugging Face's Transformers Library

[0376] Output: Recognized emotion data (e.g., "helplessness")

[0377] Step 4:

[0378] Based on the data obtained by the analysis means and the sentiment analysis means, the generation AI means generates the optimal solution.

[0379] Input: Categorized consultation content and recognized emotion data

[0380] Data calculation: Enter a prompt into the generative AI model to generate the optimal solution

[0381] Output: Generated solutions (e.g., "How to talk to a trusted teacher" or "What to do to improve self-esteem")

[0382] Step 5:

[0383] The generated solution is formatted and sent to the user's terminal via a presentation means.

[0384] Input: Generated solution

[0385] Data processing: formatting the solution

[0386] Output: The solution sent to the user's device

[0387] Step 6:

[0388] The user reviews and implements the proposed solution, then provides feedback through the application.

[0389] Input: User feedback (e.g., "I felt relieved after implementing the suggested solution")

[0390] Data Processing: Feedback Data Collection and Storage

[0391] Output: The collected feedback data is stored on the server.

[0392] Step 7:

[0393] The collection method uses the provided feedback to improve the accuracy of the generative AI method, and the feedback and emotion data are stored in a database and used for subsequent solution generation.

[0394] Input: Collected feedback and sentiment data

[0395] Data processing: Used as training data for generative AI models

[0396] Output: Improved accuracy in solution generation from next time onwards

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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. 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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."

[0413] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0414] First, the user downloads the application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password. This prevents duplicate registrations while maintaining anonymity. Once registration is complete, the user is allowed to log in to the system using authentication methods.

[0415] After logging in, users use the input method to enter the details of their consultation. Specifically, they can freely describe the problems or worries they are facing in the consultation form within the application. For example, they could enter something like, "I'm having trouble with bullying in my class and I don't know how to deal with it."

[0416] Once the consultation content is entered, the device sends this information to the server. The server passes the received consultation content to the analysis means, which uses natural language processing to tokenize the consultation content and classify it into an appropriate category (bullying, moral harassment, power harassment, sexual harassment, etc.). Next, based on the classification by the analysis means, the generation AI means is activated and performs a detailed analysis of the problem. The generation AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0417] The generated solutions are formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with detailed solutions including specific guidelines and expert advice, allowing the user to review the solutions and take actual action according to the guidelines.

[0418] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0419] The following cases are specific examples:

[0420] Example 1:

[0421] User: A, a second-year junior high school student

[0422] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0423] Person A opens the application and inputs the details of his or her problem. This information is sent to the server, and the analysis means classifies it as a case of bullying. The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." Person A checks the presented solution and puts it into action. He or she later provides feedback, contributing to improving the accuracy of the generation AI.

[0424] Example 2:

[0425] User: Mother of B, a fourth-grader

[0426] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0427] B's mother enters the details of her consultation into the application and sends them to the server. The analysis means classifies it as a case of moral harassment, and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0428] As described above, the present invention provides a system for supporting problem solving in educational settings and reducing the mental burden on those involved.

[0429] The processing flow will be explained below.

[0430] Step 1:

[0431] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0432] Step 2:

[0433] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0434] Step 3:

[0435] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0436] Step 4:

[0437] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0438] Step 5:

[0439] The device sends the input consultation details to the server, which then passes the received consultation details to an analysis means, tokenizes them using natural language processing technology, and classifies them into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.).

[0440] Step 6:

[0441] The server operates the generation AI means based on the consultation content classified by the analysis means. The generation AI means identifies the root cause of the problem and the scope of its impact, and generates an optimal solution.

[0442] Step 7:

[0443] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0444] Step 8:

[0445] The device receives the solutions from the server and displays them on the screen. The user can then review the solutions and take the necessary action. For example, in the case of bullying, a specific action plan such as "Talk to a trusted teacher or counselor" is displayed.

[0446] Step 9:

[0447] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0448] Step 10:

[0449] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method and is used for generating solutions in the future.

[0450] Example 1

[0451] 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."

[0452] In traditional educational environments, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment were difficult to resolve quickly due to a lack of concrete measures for those involved to take appropriate action. Furthermore, there was no system that could ensure the anonymity of those seeking advice while proposing appropriate solutions, so those seeking advice could not feel safe. Furthermore, there was also a lack of a way to utilize collected feedback to improve the accuracy of the system.

[0453] 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.

[0454] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details classified by the analysis means, presentation means for presenting the solutions generated by the generation AI means to the user, collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means, authentication means for authenticating users and controlling access to the system, and communication means for encrypting the transmission and reception of the consultation details. This allows the user to input the consultation details with peace of mind while maintaining anonymity, and the system can quickly generate and present appropriate solutions and then improve the accuracy of the system based on the collected feedback.

[0455] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0456] The "analysis means" is a mechanism for analyzing the consultation content received from the input means and classifying it into an appropriate category.

[0457] The "generative AI means" is an artificial intelligence system that generates solutions based on the consultation content classified by the analysis means.

[0458] The "presentation means" is an interface for visually or audibly presenting the solution generated by the generation AI means to the user.

[0459] The "collection means" is a mechanism for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0460] "Authentication means" is a mechanism for authenticating users and controlling access to the system.

[0461] The "communication means" is a communication protocol for encrypting the sending and receiving of consultation contents.

[0462] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0463] First, the user downloads the system's application from the app store on their smartphone or tablet. When the user launches the application, a registration screen appears the first time they use it. On the registration screen, the user enters their username, email address, and password to create an account. This registration information is stored in a database, which prevents duplicate registrations while maintaining the user's anonymity.

[0464] Next, the user logs in by entering their registered email address and password. The server verifies this information, and if authentication is successful, the user is allowed to use the system. Secure communication is performed using SSL / TLS to prevent unauthorized access.

[0465] After logging in, users can tap the "Consult" button in the application and freely describe the problems or worries they are facing in the consultation form. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0466] When a user enters the details of their consultation and taps the "Send" button, the device sends this information to the server. The communication is encrypted and uses a communication method to prevent information leaks.

[0467] The server uses a natural language processing library (e.g., SpaCy) to tokenize the received consultation content and classify it into an appropriate category. This results in categories such as "bullying," "moral harassment," "power harassment," and "sexual harassment." For example, it may be classified into the category "bullying."

[0468] Based on the classification results, the server uses a generative AI model (e.g., OpenAI's GPT-4) to perform a detailed analysis of the problem. The generative AI identifies the root cause and scope of the problem and creates a specific solution. For example, in the case of bullying, it generates "how to inform a trusted teacher or counselor and specific action steps."

[0469] The generated solutions are presented on the device in a user-friendly format, including specific guidelines and expert advice, such as "how to talk to a teacher" or "what to do if you see bullying."

[0470] The user checks the proposed solutions and implements them if necessary. For example, they follow the proposed solutions and consult with a trusted teacher. After implementing the solutions, the user also enters the results and experience as feedback. The application provides a feedback form, which the user can fill out and submit.

[0471] The server receives user feedback and stores it in a database. The collected feedback is used as training data for the generative AI model, helping to improve the accuracy of the system.

[0472] The following cases are specific examples:

[0473] Example 1:

[0474] User: 2nd year junior high school student

[0475] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0476] The user opens the application and inputs the details of their problem. This information is sent to the server, and the analysis means classifies it as "bullying." The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." The user checks the presented solutions and puts them into action. They then provide feedback, contributing to improving the accuracy of the generation AI.

[0477] Example 2:

[0478] User: Parent of an elementary school student

[0479] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0480] Parents enter their concerns into the application and send them to the server. The analysis means classifies the issue as "moral harassment," and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." Parents confirm the solutions and implement them. Feedback after implementation is provided through the application, which will help further improve the system.

[0481] Example prompt sentence:

[0482] "Please tell me what to do if I see a friend being bullied in class."

[0483] "I want to know how to deal with teachers who are strict with discipline."

[0484] As described above, this system efficiently analyzes user inquiries and utilizes generative AI models to provide specific solutions, thereby supporting problem-solving in educational settings.

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

[0486] Step 1:

[0487] Users download the system's application from the app store on their smartphone or tablet and launch the app. On first use, they create an account by entering their username, email address, and password on the registration screen that appears. The information entered is sent from the device to the server and stored in a database. This prevents duplicate registrations while maintaining the user's anonymity.

[0488] Step 2:

[0489] Users log in using their registered email address and password. The server receives this authentication information and compares it with information in the database. If it matches, the user is allowed to use the system and an authentication token is issued. To prevent unauthorized access, all communication is encrypted with SSL / TLS. This authentication token is used to ensure the user's ongoing session.

[0490] Step 3:

[0491] After logging in, users tap the "Consult" button on the home screen of the application. A consultation form will appear, and the user can freely describe the problems or worries they are facing. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0492] Step 4:

[0493] When the user enters the consultation content and taps the "Send" button, the device sends the entered consultation content to the server. This communication is also encrypted to prevent information leaks. The input at this stage is the consultation content entered by the user, and the output is sent to the server as encrypted consultation content.

[0494] Step 5:

[0495] The server tokenizes the received consultation content using a natural language processing library (e.g., SpaCy). The tokenized data is further analyzed and classified into appropriate categories such as bullying, moral harassment, power harassment, and sexual harassment. The input is the received consultation content, and the output is the categorized consultation content.

[0496] Step 6:

[0497] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate a specific solution to the consultation content. For example, if the issue is classified as "bullying," it will generate "ways to consult with a trusted teacher or counselor" and "specific action steps." The input is the categorized consultation content, and the output is the generated solution.

[0498] Step 7:

[0499] The server formats the generated solution into a user-friendly format. The formatted solution is sent to the terminal through the presentation means and presented to the user. Specifically, it is visually displayed to the user in the application interface. The generated solution is used as input, and the formatted solution is presented as output.

[0500] Step 8:

[0501] The user checks the proposed solution and implements it if necessary, for example, by consulting a trusted teacher. After implementing the solution, the user also inputs the results and experience as feedback.

[0502] Step 9:

[0503] The feedback entered by the user is sent from the terminal to the server, which receives it and stores it in a database. The input is the user's feedback, and the output is stored in the database.

[0504] Step 10:

[0505] The server uses the collected feedback as training data for the generative AI model, helping to improve its accuracy. This makes it possible to provide even more accurate solutions for future consultations. The input is the feedback stored in the database, and the output is an improved AI model.

[0506] (Application example 1)

[0507] 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."

[0508] Ethical issues such as harassment, bullying, and whistle-blowing in the workplace are serious challenges for companies. These issues are difficult to address individually, so there is a need for a system that can effectively collect information while ensuring anonymity and provide appropriate solutions.

[0509] 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.

[0510] In this invention, the server includes an input means for users to anonymously input consultation details, an analysis means for analyzing and categorizing the consultation details received from the input means, a generation AI means for generating solutions based on the consultation details categorized by the analysis means, a presentation means for presenting the solutions generated by the generation AI means to users, a means for corporate employees to anonymously consult and report issues such as harassment, bullying, and whistleblowing that they face in the workplace, and a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means. This makes it possible to resolve ethical issues in the workplace anonymously and effectively.

[0511] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0512] The "analysis means" is a technology for analyzing the consultation content received from the input means and classifying it into categories.

[0513] The "generative AI means" is an artificial intelligence technology for generating solutions based on the consultation content classified by the analytical means.

[0514] The "presentation means" is an interface for presenting the solution generated by the generation AI means to the user.

[0515] The "collection means" is a technology for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0516] "System" refers to the entire computer system including input means, analysis means, generation AI means, presentation means, and collection means.

[0517] "Authentication means" is a technology for ensuring the anonymity of users and an interface for verifying the identity of users.

[0518] "Feedback" is information that provides results and opinions after a user implements a solution.

[0519] "Database" refers to a data storage technology for storing feedback collected by collection means and utilizing it in subsequent solution generation.

[0520] "A means to anonymously consult and report issues such as harassment, bullying, and whistle-blowing that are faced in the workplace" is a technology that allows corporate employees to anonymously report and consult on ethical issues that arise in the workplace to a system.

[0521] "A system for corporate employees to anonymously seek advice on problems" refers to the entire system that allows employees working within a company to anonymously seek advice on problems that arise in the workplace.

[0522] This invention relates to a system that allows corporate employees to anonymously seek advice and report on issues such as harassment, bullying, whistle-blowing, etc. that they face in the workplace. A specific form for realizing this system is described below.

[0523] First, the user downloads the smartphone application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password, which prevents duplicate registrations while maintaining anonymity. Once registration is complete, authentication measures allow login to the system.

[0524] After logging in, the user uses the input means to enter the content of their consultation. Specifically, they can freely write down the problems or worries they are facing at work in the consultation form within the application. For example, they can enter content such as "How should I deal with power harassment from my boss?"

[0525] The input consultation content is sent from the device to the server. The server uses an analytical means to analyze the received consultation content and classify it into categories (harassment, bullying, whistleblowing, etc.). The analytical means uses natural language processing technology to tokenize the consultation content and classify it into the appropriate category. Next, the generative AI means operates based on the classification by the analytical means and performs a detailed analysis of the problem. The generative AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0526] The generated solution is formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with a detailed solution including specific guidelines and expert advice, allowing the user to review the provided solution and take actual action according to the guidelines.

[0527] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0528] The following cases are specific examples:

[0529] Example 1:

[0530] If an employee is experiencing power harassment at work, they open the application and enter the details of their complaint. This information is sent to the server, and the analysis means classifies it as a case of power harassment. The generation AI means provides "ways to deal with power harassment," such as "ways to avoid talking to your boss," "ways to collect evidence," and "ways to officially report to the human resources department." The employee checks the proposed solutions and puts them into action.

[0531] Specific prompt examples:

[0532] How to deal with power harassment from your boss

[0533] "I want to know how to solve bullying in the workplace."

[0534] The system allows for the anonymous and effective resolution of ethical issues in the workplace, and is an advanced tool to ensure employee safety and security.

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] Users download the smartphone application and go through a simple registration process when they use it for the first time.

[0538] Input: Username, Email Address, Password

[0539] Output: User ID (unique identifier)

[0540] What happens: A user opens the application and enters their username, email address, and password, which are then sent to the server, which generates a unique user ID for the user and completes the registration.

[0541] Step 2:

[0542] A user logs into the system using authentication methods.

[0543] Input: Email address, password

[0544] Output: Login session (authentication token)

[0545] How it works: The user enters their email address and password into the application and the authentication process begins. The server verifies this information and, if authentication is successful, creates a login session and returns an authentication token.

[0546] Step 3:

[0547] The user inputs the consultation content using the input means.

[0548] Input: Consultation content (text format)

[0549] Output: Consultation data object

[0550] Specific operation: The user enters the consultation details into the form in the application and presses the send button. This information is sent from the terminal to the server as a consultation data object.

[0551] Step 4:

[0552] The server analyzes the consultation contents received by the server using an analysis means and classifies them into categories.

[0553] Input: Consultation data object

[0554] Output: Category information (bullying, harassment, whistleblowing, etc.)

[0555] Specific operation: The server analyzes the received consultation data using natural language processing technology and tokenizes the consultation content. The analysis means classifies the consultation content into appropriate categories based on this data.

[0556] Step 5:

[0557] Based on the consultation content classified by the analysis means, the generation AI means generates a solution.

[0558] Input: Category information, consultation data object

[0559] Output: Solution data object

[0560] Specific operation: Based on the categorical information generated by the analytical means, the generative AI model generates an optimal solution, which is formatted as a solution data object.

[0561] Step 6:

[0562] The solution generated by the generation AI means is presented to the user through the presentation means.

[0563] Input: Solution data object

[0564] Output: The solution displayed in the user interface

[0565] Specific Actions: Solution data objects are transformed into a user-friendly format and displayed to the user through presentation means within the application, allowing the user to view and understand them.

[0566] Step 7:

[0567] The user implements the provided solution and inputs the results and feedback into the system through a collection means.

[0568] Input: Feedback data (text format, feedback content)

[0569] Output: Feedback data object

[0570] Specific operation: The user implements the provided solution and inputs the results and impressions into the system through the collection means. The feedback data is sent to the server as a feedback data object.

[0571] Step 8:

[0572] The server stores the feedback collected by the collection means in a database to improve the accuracy of the generation AI means.

[0573] Input: Feedback data object

[0574] Output: Updated generative AI model

[0575] How it works: The server stores the received feedback in a database and uses it as training data to retrain the generative AI model, enabling it to provide more accurate solutions in the future.

[0576] 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.

[0577] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. The system incorporates an emotion engine that recognizes and analyzes users' emotions, thereby improving the accuracy and applicability of solutions.

[0578] First, the user downloads the application and goes through a simple registration process the first time they use it. They then use an input method to anonymously enter their concerns in free-form. For example, they could write something like, "There's bullying in my class and I don't know what to do about it."

[0579] Next, the input consultation content is sent from the terminal to the server. The server passes the received consultation content to an analysis means, which uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. At this point, the emotion engine incorporated in the present invention is activated and recognizes the user's emotions from the consultation content. For example, the emotion engine may detect from the input content that the user is feeling emotions such as "anxiety" or "fear."

[0580] The AI ​​generator then generates an optimal solution based on the consultation content classified by the analysis unit and the recognized emotional data. The generated solution is tailored to the user's emotional state and provides specific and appropriate advice. For example, if the user is feeling "fear" about a bullying issue, the generated solution would include how to seek psychological help.

[0581] The generated solutions are formatted on the server and sent to the device, which then displays them on the screen in a user-friendly format. The user can then review the solutions and implement them. For example, in the case of a bullying problem, the solution suggests "talking to a trusted teacher" and "taking a break or finding ways to relax."

[0582] After implementing a solution, the user enters the results and experience into the terminal through a feedback input form. The terminal then sends the entered feedback to the server and stores it in a database via the collection means. In particular, since the feedback includes emotion data collected by the emotion engine of the present invention, it contributes to improving the accuracy of the generation AI means in subsequent solution generation.

[0583] The following cases are specific examples:

[0584] Example 1:

[0585] User: A, a second-year junior high school student

[0586] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0587] Person A opens the application and enters the details of his or her problem. This information is sent to the server, where the analysis means classifies it as a case of bullying. At the same time, the emotion engine detects that Person A is feeling "helpless." The generation AI means analyzes "how to respond when witnessing bullying" and generates "how to report it to a trusted teacher or counselor" and "actions to increase self-esteem." Person A checks the proposed solutions and implements them. Later, feedback is provided, contributing to improving the accuracy of the system.

[0588] Example 2:

[0589] User: Mother of B, a fourth-grader

[0590] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0591] B's mother opens the application, inputs the details of her consultation, and sends them to the server. The analysis means classifies it as a case of moral harassment, and the emotion engine detects emotions such as "concern." The generative AI means generates "methods of communication between teachers and parents" and "methods of formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0592] As described above, the present invention provides a system that supports problem solving in educational settings and reduces the mental burden on those involved. The incorporation of an emotion engine improves the applicability and accuracy of solutions, enabling support that is more suited to the user's needs.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0596] Step 2:

[0597] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0598] Step 3:

[0599] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0600] Step 4:

[0601] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0602] Step 5:

[0603] The device sends the input consultation content to the server. The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.). During this classification process, an emotion engine is activated and recognizes the user's emotions from the consultation content.

[0604] Step 6:

[0605] The server operates the generative AI means based on the consultation content and emotion data analyzed by the emotion engine. The generative AI means identifies the root cause of the problem and the extent of its impact, and generates an optimal solution that takes the user's emotions into consideration.

[0606] Step 7:

[0607] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0608] Step 8:

[0609] The device displays the solutions received from the server on the screen. The user can then review the solutions and take the necessary action. For example, in the case of a bullying issue, the device displays "ways to calm down" along with the action to "consult a trusted teacher or counselor."

[0610] Step 9:

[0611] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0612] Step 10:

[0613] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method. In particular, the feedback includes emotional data collected by the emotion engine, which further improves the accuracy of solution generation in future.

[0614] Example 2

[0615] 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."

[0616] Ethical issues such as bullying, moral harassment, power harassment, and sexual harassment, which are difficult to resolve in traditional educational environments, can have serious psychological and health effects if left unaddressed. Users are also required to provide their concerns anonymously, which protects their privacy. However, traditional systems often lack the means to resolve these issues efficiently and accurately. In particular, there has been no system that can properly recognize the emotions in the content of a consultation and provide appropriate solutions based on this.

[0617] 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.

[0618] In this invention, the server includes an input / output device for users to anonymously input consultation details, an analysis device for analyzing and categorizing the consultation details received from the input / output device, a device using natural language processing technology for tokenizing the consultation details classified by the analysis device and classifying them into appropriate categories, an emotion recognition device for recognizing users' emotions based on the natural language processing technology, a generation algorithm device for generating solutions based on the user's emotion data recognized by the emotion recognition device, a presentation device for presenting the solutions generated by the generation algorithm device to users, and a collection device for collecting user feedback received via the presentation device and improving the accuracy of the generation algorithm device. This enables the generation and provision of appropriate solutions that take user emotions into consideration. Furthermore, the collected feedback data can be used to improve the accuracy of the system, contributing to improved accuracy in future solution generation.

[0619] A "user" is an individual who accesses the system, anonymously enters a request, and reviews and implements the solutions provided.

[0620] The "input / output device" is a device that allows users to anonymously input the details of their inquiries and displays solutions from the server.

[0621] The "analysis device" is a device that analyzes the consultation content received from the input / output device and performs tokenization and category classification using natural language processing technology.

[0622] "Natural language processing technology" is a technology for understanding and analyzing human language, tokenizing the content of consultations, and classifying them into appropriate categories.

[0623] An "emotion recognition device" is a device that uses natural language processing technology to recognize emotions from the content of a user's consultation and generates emotion data.

[0624] The "generative algorithm device" is a device that includes an artificial intelligence model for generating optimal solutions based on the emotion data recognized by the emotion recognition device.

[0625] A "presentation device" is a device for presenting to a user a solution generated by a generation algorithm device.

[0626] A "collection device" is a device that collects user feedback received via a presentation device and uses it to improve the accuracy of the generation algorithm device.

[0627] "Feedback" is information that a user records and sends to the system the results and experiences of implementing a provided solution.

[0628] MODE FOR CARRYING OUT THE INVENTION

[0629] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. This system allows users to anonymously input their consultation details and provides optimal solutions based on the details of the consultation.

[0630] User usage of the application

[0631] Users first download the system's application from an application store. When using the system for the first time, they register an account by entering basic information, which ensures the user's anonymity.

[0632] Enter the consultation details

[0633] Users tap "New Consultation" on the main screen of the application and enter the content of their consultation in free-form. For example, they could write specific details such as "I'm having trouble with bullying in my class and I don't know how to deal with it." Once they've finished entering their information, they tap the "Send" button.

[0634] Data transmission and reception

[0635] The device encrypts the consultation content entered by the user and sends it to the server using the HTTPS protocol. The server then passes the received consultation content to an analysis method (for example, using natural language processing libraries such as SpaCy or NLTK).

[0636] Data analysis and emotion recognition

[0637] The server uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. Based on this, an emotion engine (e.g., emotion recognition models BERT or GPT-3) is activated to recognize the user's emotions. Specifically, it generates emotion tags such as "anxiety" or "fear" from the text.

[0638] Solution Generation

[0639] The server inputs the analyzed consultation content and detected emotion tags as prompts into a generative AI model (e.g., OpenAI's GPT-3), which then generates an optimal solution.

[0640] Examples of prompts include:

[0641] Consultation content: Bullying is an issue in class and I don't know how to deal with it.

[0642] Emotion: Fear

[0643] The generated solutions are formatted at the server and sent to the terminal.

[0644] View and implement solutions

[0645] The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, specific actions such as "Consult a trusted teacher" are displayed on the screen.

[0646] Collecting feedback

[0647] After implementing the solution, the user enters the results and experience as feedback into the application, which is then sent by tapping the "Feedback" button.

[0648] The device sends the feedback information to the server. This communication is also encrypted using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation. Emotional data in particular is useful for model training, contributing to improving the accuracy of future solutions generated by the generative AI model.

[0649] The above is a specific embodiment of this system. The present invention aims to provide a more appropriate and practical solution by taking into account the user's emotions.

[0650] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0651] Step 1: User downloads and registers the application

[0652] How it works: A user downloads the system's application from an application store. When using it for the first time, they register an account by entering basic information such as their name, age, and school name. The information they enter is hashed to ensure anonymity.

[0653] Input: User's basic information (name, age, school name, etc.).

[0654] Output: The registered user account.

[0655] Step 2: User inputs the content of the consultation

[0656] Operation: The user selects "New Consultation" from the main screen of the application, enters the consultation details in free text format, and presses the "Send" button to send the data.

[0657] Input: The user's problem (e.g., "I'm having trouble with bullying in my class and I don't know what to do about it.").

[0658] Output: Consultation details saved on the device.

[0659] Step 3: Send data from the device to the server

[0660] How it works: The device encrypts the consultation information entered by the user and sends it to the server using the HTTPS protocol. The data is encrypted to ensure the security of the communication.

[0661] Input: User's consultation (encrypted).

[0662] Output: The consultation details sent to the server.

[0663] Step 4: Data analysis and emotion recognition by the server

[0664] How it works: The server passes the received consultation content to an analysis tool (e.g., a natural language processing library like SpaCy or NLTK), tokenizes the content, and classifies it into appropriate categories. It then uses an emotion recognition model (e.g., BERT or GPT-3) to recognize the user's emotions.

[0665] Input: Received consultation content.

[0666] Output: Tokenized data, emotion tags (e.g., "anxiety", "fear").

[0667] Step 5: Generative AI model generates solutions

[0668] How it works: The server inputs the analyzed consultation content and the recognized emotion tag as a prompt sentence into a generative AI model (e.g., OpenAI's GPT-3). Based on this, the generative AI model generates an optimal solution.

[0669] Input: Prompt statement (e.g., "Contact: Bullying in class is an issue and I don't know what to do. Emotion: Fear").

[0670] Output: Optimal solution (in text format).

[0671] Step 6: Formatting and sending the solution on the server

[0672] How it works: The solution generated by the generative AI model is formatted in an appropriate format (e.g., JSON or XML) and sent to the device.

[0673] Input: The generated solution.

[0674] Output: A formatted solution.

[0675] Step 7: User confirms and implements the solution

[0676] Operation: The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, a specific action such as "Consult a trusted teacher" is displayed on the screen.

[0677] Input: A formatted solution.

[0678] Output: A specific action to be taken.

[0679] Step 8: Collecting user feedback and sending it to the server

[0680] How it works: After the user implements a solution, they input their results and experience as feedback into the application. The input feedback is sent by tapping the "Feedback" button. The device encrypts the feedback information and sends it to the server using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation.

[0681] Input: User feedback.

[0682] Output: Feedback data stored in a database.

[0683] The above is a description of each processing step and its specific operation of this system. By taking into consideration the user's feelings and providing appropriate solutions quickly, it helps prevent and resolve ethical issues.

[0684] (Application example 2)

[0685] 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."

[0686] In today's educational environment, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment frequently occur, often causing mental stress. Conventional problem-solving methods have not provided effective support because they are unable to properly understand the client's emotions and provide applicable solutions. For this reason, there is a need to provide an environment where users can seek advice anonymously, as well as incorporate functions to recognize and analyze emotions in order to provide more specific and appropriate solutions.

[0687] 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.

[0688] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details categorized by the analysis means, emotion analysis means for recognizing and analyzing the user's emotions when generating solutions by the emotion analysis means, and means for incorporating emotion data recognized by the emotion analysis means into the solution generation process, thereby making it possible to provide highly applicable solutions that take the user's emotions into consideration.

[0689] "User" refers to an individual who uses the system to input their inquiry and receive a solution.

[0690] "Anonymity" refers to a state in which a user's personal information or name is not revealed, with the aim of protecting the user's privacy.

[0691] "Input means" refers to the method or device by which a user enters the details of their consultation. For example, this includes a smartphone or computer input form.

[0692] "Analysis means" refers to the technology or algorithm used to analyze the received consultation content and classify it into an appropriate category.

[0693] "Generative AI means" refers to artificial intelligence technologies and algorithms that generate optimal solutions based on the consultation content classified by analytical means.

[0694] "Emotion analysis means" refers to the technology and algorithms used to recognize and analyze the user's emotions from the consultation content entered.

[0695] "Presentation Means" refers to a method or device for visually or audibly presenting the solution generated by the Generative AI Means to the user.

[0696] "Collection Method" refers to the technology or algorithm used to collect feedback provided by users on proposed solutions and store it in the system.

[0697] "Authentication methods" refers to techniques and methods for ensuring and guaranteeing the anonymity of users.

[0698] "Database" refers to an electronic information storage system for storing collected feedback and sentiment data and utilizing it for subsequent solution generation.

[0699] A "prompt" refers to the input format that a generative AI model uses to generate a solution and get the optimal output.

[0700] The embodiment of this invention provides a system for anonymously consulting and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. In particular, by incorporating a function to analyze the user's emotions, the system provides more appropriate solutions.

[0701] Hardware and software used

[0702] Hardware: Smartphone (iOS and Android compatible)

[0703] Software: Python, Hugging Face Transformers library, requests library

[0704] System configuration

[0705] User device:

[0706] Users access the system using a smartphone, enter their concerns anonymously through the application, and receive solutions.

[0707] server

[0708] The server includes the following components:

[0709] 1. Input means: A means for receiving the consultation content input by the user.

[0710] 2. Analysis method: The received consultation content is analyzed using natural language processing technology and classified into appropriate categories.

[0711] 3. Emotion analysis method: A method for recognizing and analyzing the user's emotions from the analyzed consultation content. It uses the Hugging Face Transformers library.

[0712] 4. Generative AI method: Based on the user's consultation content and recognized emotion data, the optimal solution is generated using a generative AI model.

[0713] 5. Presentation means: A means for presenting the generated solution to the user.

[0714] 6. Collection methods: User-provided feedback is collected and used to improve the accuracy of the generative AI methods.

[0715] Data Flow

[0716] 1. Input of consultation content: The user inputs the consultation content into the application and sends it to the server.

[0717] 2. Analysis of consultation content: The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories.

[0718] 3. Emotion analysis: The analyzed data is input into the emotion analysis means to recognize and analyze the user's emotions.

[0719] 4. Solution generation: The generative AI means generates a solution based on the data obtained by the analysis means and the sentiment analysis means.

[0720] 5. Presentation of the solution: The generated solution is formatted and sent to the user's terminal through a presentation means.

[0721] 6. Feedback collection: The user implements the proposed solution and provides feedback through the application. The collection mechanism sends this feedback to the server and stores it in the database.

[0722] Specific examples

[0723] Example 1:

[0724] If a second-year junior high school student, A, sees a friend being bullied in class but doesn't know what to do, the emotion analysis means will detect A's "helplessness." The generative AI means will suggest ways to deal with bullying, such as consulting a trusted teacher or taking actions to improve self-esteem.

[0725] Example prompt sentence:

[0726] Input: My classmate is being bullied and I don't know what to do about it.

[0727] Emotion: helplessness

[0728] Output formats:

[0729] Suggestion 1: Talk to a trusted teacher or counselor

[0730] Suggestion 2: Actions to improve self-esteem

[0731] Example 2:

[0732] If the mother of a fourth-grader named B says that her son comes home crying every day because he is being strict with a teacher at school, the emotion analysis means will detect the concern. The AI ​​generation means will generate and suggest ways to communicate with the teacher and provide formal feedback to the school.

[0733] Example prompt sentence:

[0734] My son comes home crying every day because he has a strict teacher at school. What should I do?

[0735] Emotion: Concern

[0736] Output formats:

[0737] Suggestion 1: How to communicate with teachers

[0738] Suggestion 2: Formal feedback methods for schools

[0739] In this way, the present invention realizes a system that supports problem solving in educational settings and provides specific and appropriate advice according to the user's emotions.

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] The user anonymously inputs the details of the consultation using a device (smartphone), and the input details are sent to the server.

[0743] Input: User's problem (e.g., "My friend is being bullied in class")

[0744] Output: The consultation content is sent to the server

[0745] Step 2:

[0746] The server passes the received consultation content to an analysis means, where it tokenizes it using natural language processing technology and classifies it into appropriate categories.

[0747] Input: Consultation content received by the server

[0748] Data processing: tokenization and categorization using natural language processing

[0749] Output: Consultation contents categorized

[0750] Step 3:

[0751] The analyzed consultation content is input into the emotion analysis means, and the user's emotions are recognized and analyzed.

[0752] Input: Consultation content categorized

[0753] Data Computation: Sentiment Analysis Using Hugging Face's Transformers Library

[0754] Output: Recognized emotion data (e.g., "helplessness")

[0755] Step 4:

[0756] Based on the data obtained by the analysis means and the sentiment analysis means, the generation AI means generates the optimal solution.

[0757] Input: Categorized consultation content and recognized emotion data

[0758] Data calculation: Enter a prompt into the generative AI model to generate the optimal solution

[0759] Output: Generated solutions (e.g., "How to talk to a trusted teacher" or "What to do to improve self-esteem")

[0760] Step 5:

[0761] The generated solution is formatted and sent to the user's terminal via a presentation means.

[0762] Input: Generated solution

[0763] Data processing: formatting the solution

[0764] Output: The solution sent to the user's device

[0765] Step 6:

[0766] The user reviews and implements the proposed solution, then provides feedback through the application.

[0767] Input: User feedback (e.g., "I felt relieved after implementing the suggested solution")

[0768] Data Processing: Feedback Data Collection and Storage

[0769] Output: The collected feedback data is stored on the server.

[0770] Step 7:

[0771] The collection method uses the provided feedback to improve the accuracy of the generative AI method, and the feedback and emotion data are stored in a database and used for subsequent solution generation.

[0772] Input: Collected feedback and sentiment data

[0773] Data processing: Used as training data for generative AI models

[0774] Output: Improved accuracy in solution generation from next time onwards

[0775] 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.

[0776] 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.

[0777] 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.

[0778] [Third embodiment]

[0779] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0780] 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.

[0781] 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).

[0782] 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.

[0783] 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.

[0784] 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).

[0785] 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. 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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."

[0791] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0792] First, the user downloads the application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password. This prevents duplicate registrations while maintaining anonymity. Once registration is complete, the user is allowed to log in to the system using authentication methods.

[0793] After logging in, users use the input method to enter the details of their consultation. Specifically, they can freely describe the problems or worries they are facing in the consultation form within the application. For example, they could enter something like, "I'm having trouble with bullying in my class and I don't know how to deal with it."

[0794] Once the consultation content is entered, the device sends this information to the server. The server passes the received consultation content to the analysis means, which uses natural language processing to tokenize the consultation content and classify it into an appropriate category (bullying, moral harassment, power harassment, sexual harassment, etc.). Next, based on the classification by the analysis means, the generation AI means is activated and performs a detailed analysis of the problem. The generation AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0795] The generated solutions are formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with detailed solutions including specific guidelines and expert advice, allowing the user to review the solutions and take actual action according to the guidelines.

[0796] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0797] The following cases are specific examples:

[0798] Example 1:

[0799] User: A, a second-year junior high school student

[0800] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0801] Person A opens the application and inputs the details of his or her problem. This information is sent to the server, and the analysis means classifies it as a case of bullying. The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." Person A checks the presented solution and puts it into action. He or she later provides feedback, contributing to improving the accuracy of the generation AI.

[0802] Example 2:

[0803] User: Mother of B, a fourth-grader

[0804] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0805] B's mother enters the details of her consultation into the application and sends them to the server. The analysis means classifies it as a case of moral harassment, and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0806] As described above, the present invention provides a system for supporting problem solving in educational settings and reducing the mental burden on those involved.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0810] Step 2:

[0811] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0812] Step 3:

[0813] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0814] Step 4:

[0815] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0816] Step 5:

[0817] The device sends the input consultation details to the server, which then passes the received consultation details to an analysis means, tokenizes them using natural language processing technology, and classifies them into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.).

[0818] Step 6:

[0819] The server operates the generation AI means based on the consultation content classified by the analysis means. The generation AI means identifies the root cause of the problem and the scope of its impact, and generates an optimal solution.

[0820] Step 7:

[0821] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0822] Step 8:

[0823] The device receives the solutions from the server and displays them on the screen. The user can then review the solutions and take the necessary action. For example, in the case of bullying, a specific action plan such as "Talk to a trusted teacher or counselor" is displayed.

[0824] Step 9:

[0825] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0826] Step 10:

[0827] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method and is used for generating solutions in the future.

[0828] Example 1

[0829] 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."

[0830] In traditional educational environments, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment were difficult to resolve quickly due to a lack of concrete measures for those involved to take appropriate action. Furthermore, there was no system that could ensure the anonymity of those seeking advice while proposing appropriate solutions, so those seeking advice could not feel safe. Furthermore, there was also a lack of a way to utilize collected feedback to improve the accuracy of the system.

[0831] 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.

[0832] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details classified by the analysis means, presentation means for presenting the solutions generated by the generation AI means to the user, collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means, authentication means for authenticating users and controlling access to the system, and communication means for encrypting the transmission and reception of the consultation details. This allows the user to input the consultation details with peace of mind while maintaining anonymity, and the system can quickly generate and present appropriate solutions and then improve the accuracy of the system based on the collected feedback.

[0833] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0834] The "analysis means" is a mechanism for analyzing the consultation content received from the input means and classifying it into an appropriate category.

[0835] The "generative AI means" is an artificial intelligence system that generates solutions based on the consultation content classified by the analysis means.

[0836] The "presentation means" is an interface for visually or audibly presenting the solution generated by the generation AI means to the user.

[0837] The "collection means" is a mechanism for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0838] "Authentication means" is a mechanism for authenticating users and controlling access to the system.

[0839] The "communication means" is a communication protocol for encrypting the sending and receiving of consultation contents.

[0840] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[0841] First, the user downloads the system's application from the app store on their smartphone or tablet. When the user launches the application, a registration screen appears the first time they use it. On the registration screen, the user enters their username, email address, and password to create an account. This registration information is stored in a database, which prevents duplicate registrations while maintaining the user's anonymity.

[0842] Next, the user logs in by entering their registered email address and password. The server verifies this information, and if authentication is successful, the user is allowed to use the system. Secure communication is performed using SSL / TLS to prevent unauthorized access.

[0843] After logging in, users can tap the "Consult" button in the application and freely describe the problems or worries they are facing in the consultation form. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0844] When a user enters the details of their consultation and taps the "Send" button, the device sends this information to the server. The communication is encrypted and uses a communication method to prevent information leaks.

[0845] The server uses a natural language processing library (e.g., SpaCy) to tokenize the received consultation content and classify it into an appropriate category. This results in categories such as "bullying," "moral harassment," "power harassment," and "sexual harassment." For example, it may be classified into the category "bullying."

[0846] Based on the classification results, the server uses a generative AI model (e.g., OpenAI's GPT-4) to perform a detailed analysis of the problem. The generative AI identifies the root cause and scope of the problem and creates a specific solution. For example, in the case of bullying, it generates "how to inform a trusted teacher or counselor and specific action steps."

[0847] The generated solutions are presented on the device in a user-friendly format, including specific guidelines and expert advice, such as "how to talk to a teacher" or "what to do if you see bullying."

[0848] The user checks the proposed solutions and implements them if necessary. For example, they follow the proposed solutions and consult with a trusted teacher. After implementing the solutions, the user also enters the results and experience as feedback. The application provides a feedback form, which the user can fill out and submit.

[0849] The server receives user feedback and stores it in a database. The collected feedback is used as training data for the generative AI model, helping to improve the accuracy of the system.

[0850] The following cases are specific examples:

[0851] Example 1:

[0852] User: 2nd year junior high school student

[0853] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0854] The user opens the application and inputs the details of their problem. This information is sent to the server, and the analysis means classifies it as "bullying." The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." The user checks the presented solutions and puts them into action. They then provide feedback, contributing to improving the accuracy of the generation AI.

[0855] Example 2:

[0856] User: Parent of an elementary school student

[0857] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0858] Parents enter their concerns into the application and send them to the server. The analysis means classifies the issue as "moral harassment," and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." Parents confirm the solutions and implement them. Feedback after implementation is provided through the application, which will help further improve the system.

[0859] Example prompt sentence:

[0860] "Please tell me what to do if I see a friend being bullied in class."

[0861] "I want to know how to deal with teachers who are strict with discipline."

[0862] As described above, this system efficiently analyzes user inquiries and utilizes generative AI models to provide specific solutions, thereby supporting problem-solving in educational settings.

[0863] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0864] Step 1:

[0865] Users download the system's application from the app store on their smartphone or tablet and launch the app. On first use, they create an account by entering their username, email address, and password on the registration screen that appears. The information entered is sent from the device to the server and stored in a database. This prevents duplicate registrations while maintaining the user's anonymity.

[0866] Step 2:

[0867] Users log in using their registered email address and password. The server receives this authentication information and compares it with information in the database. If it matches, the user is allowed to use the system and an authentication token is issued. To prevent unauthorized access, all communication is encrypted with SSL / TLS. This authentication token is used to ensure the user's ongoing session.

[0868] Step 3:

[0869] After logging in, users tap the "Consult" button on the home screen of the application. A consultation form will appear, and the user can freely describe the problems or worries they are facing. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[0870] Step 4:

[0871] When the user enters the consultation content and taps the "Send" button, the device sends the entered consultation content to the server. This communication is also encrypted to prevent information leaks. The input at this stage is the consultation content entered by the user, and the output is sent to the server as encrypted consultation content.

[0872] Step 5:

[0873] The server tokenizes the received consultation content using a natural language processing library (e.g., SpaCy). The tokenized data is further analyzed and classified into appropriate categories such as bullying, moral harassment, power harassment, and sexual harassment. The input is the received consultation content, and the output is the categorized consultation content.

[0874] Step 6:

[0875] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate a specific solution to the consultation content. For example, if the issue is classified as "bullying," it will generate "ways to consult with a trusted teacher or counselor" and "specific action steps." The input is the categorized consultation content, and the output is the generated solution.

[0876] Step 7:

[0877] The server formats the generated solution into a user-friendly format. The formatted solution is sent to the terminal through the presentation means and presented to the user. Specifically, it is visually displayed to the user in the application interface. The generated solution is used as input, and the formatted solution is presented as output.

[0878] Step 8:

[0879] The user checks the proposed solution and implements it if necessary, for example, by consulting a trusted teacher. After implementing the solution, the user also inputs the results and experience as feedback.

[0880] Step 9:

[0881] The feedback entered by the user is sent from the terminal to the server, which receives it and stores it in a database. The input is the user's feedback, and the output is stored in the database.

[0882] Step 10:

[0883] The server uses the collected feedback as training data for the generative AI model, helping to improve its accuracy. This makes it possible to provide even more accurate solutions for future consultations. The input is the feedback stored in the database, and the output is an improved AI model.

[0884] (Application example 1)

[0885] 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."

[0886] Ethical issues such as harassment, bullying, and whistle-blowing in the workplace are serious challenges for companies. These issues are difficult to address individually, so there is a need for a system that can effectively collect information while ensuring anonymity and provide appropriate solutions.

[0887] 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.

[0888] In this invention, the server includes an input means for users to anonymously input consultation details, an analysis means for analyzing and categorizing the consultation details received from the input means, a generation AI means for generating solutions based on the consultation details categorized by the analysis means, a presentation means for presenting the solutions generated by the generation AI means to users, a means for corporate employees to anonymously consult and report issues such as harassment, bullying, and whistleblowing that they face in the workplace, and a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means. This makes it possible to resolve ethical issues in the workplace anonymously and effectively.

[0889] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[0890] The "analysis means" is a technology for analyzing the consultation content received from the input means and classifying it into categories.

[0891] The "generative AI means" is an artificial intelligence technology for generating solutions based on the consultation content classified by the analytical means.

[0892] The "presentation means" is an interface for presenting the solution generated by the generation AI means to the user.

[0893] The "collection means" is a technology for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[0894] "System" refers to the entire computer system including input means, analysis means, generation AI means, presentation means, and collection means.

[0895] "Authentication means" is a technology for ensuring the anonymity of users and an interface for verifying the identity of users.

[0896] "Feedback" is information that provides results and opinions after a user implements a solution.

[0897] "Database" refers to a data storage technology for storing feedback collected by collection means and utilizing it in subsequent solution generation.

[0898] "A means to anonymously consult and report issues such as harassment, bullying, and whistle-blowing that are faced in the workplace" is a technology that allows corporate employees to anonymously report and consult on ethical issues that arise in the workplace to a system.

[0899] "A system for corporate employees to anonymously seek advice on problems" refers to the entire system that allows employees working within a company to anonymously seek advice on problems that arise in the workplace.

[0900] This invention relates to a system that allows corporate employees to anonymously seek advice and report on issues such as harassment, bullying, whistle-blowing, etc. that they face in the workplace. A specific form for realizing this system is described below.

[0901] First, the user downloads the smartphone application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password, which prevents duplicate registrations while maintaining anonymity. Once registration is complete, authentication measures allow login to the system.

[0902] After logging in, the user uses the input means to enter the content of their consultation. Specifically, they can freely write down the problems or worries they are facing at work in the consultation form within the application. For example, they can enter content such as "How should I deal with power harassment from my boss?"

[0903] The input consultation content is sent from the device to the server. The server uses an analytical means to analyze the received consultation content and classify it into categories (harassment, bullying, whistleblowing, etc.). The analytical means uses natural language processing technology to tokenize the consultation content and classify it into the appropriate category. Next, the generative AI means operates based on the classification by the analytical means and performs a detailed analysis of the problem. The generative AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[0904] The generated solution is formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with a detailed solution including specific guidelines and expert advice, allowing the user to review the provided solution and take actual action according to the guidelines.

[0905] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[0906] The following cases are specific examples:

[0907] Example 1:

[0908] If an employee is experiencing power harassment at work, they open the application and enter the details of their complaint. This information is sent to the server, and the analysis means classifies it as a case of power harassment. The generation AI means provides "ways to deal with power harassment," such as "ways to avoid talking to your boss," "ways to collect evidence," and "ways to officially report to the human resources department." The employee checks the proposed solutions and puts them into action.

[0909] Specific prompt examples:

[0910] How to deal with power harassment from your boss

[0911] "I want to know how to solve bullying in the workplace."

[0912] The system allows for the anonymous and effective resolution of ethical issues in the workplace, and is an advanced tool to ensure employee safety and security.

[0913] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0914] Step 1:

[0915] Users download the smartphone application and go through a simple registration process when they use it for the first time.

[0916] Input: Username, Email Address, Password

[0917] Output: User ID (unique identifier)

[0918] What happens: A user opens the application and enters their username, email address, and password, which are then sent to the server, which generates a unique user ID for the user and completes the registration.

[0919] Step 2:

[0920] A user logs into the system using authentication methods.

[0921] Input: Email address, password

[0922] Output: Login session (authentication token)

[0923] How it works: The user enters their email address and password into the application and the authentication process begins. The server verifies this information and, if authentication is successful, creates a login session and returns an authentication token.

[0924] Step 3:

[0925] The user inputs the consultation content using the input means.

[0926] Input: Consultation content (text format)

[0927] Output: Consultation data object

[0928] Specific operation: The user enters the consultation details into the form in the application and presses the send button. This information is sent from the terminal to the server as a consultation data object.

[0929] Step 4:

[0930] The server analyzes the consultation contents received by the server using an analysis means and classifies them into categories.

[0931] Input: Consultation data object

[0932] Output: Category information (bullying, harassment, whistleblowing, etc.)

[0933] Specific operation: The server analyzes the received consultation data using natural language processing technology and tokenizes the consultation content. The analysis means classifies the consultation content into appropriate categories based on this data.

[0934] Step 5:

[0935] Based on the consultation content classified by the analysis means, the generation AI means generates a solution.

[0936] Input: Category information, consultation data object

[0937] Output: Solution data object

[0938] Specific operation: Based on the categorical information generated by the analytical means, the generative AI model generates an optimal solution, which is formatted as a solution data object.

[0939] Step 6:

[0940] The solution generated by the generation AI means is presented to the user through the presentation means.

[0941] Input: Solution data object

[0942] Output: The solution displayed in the user interface

[0943] Specific Actions: Solution data objects are transformed into a user-friendly format and displayed to the user through presentation means within the application, allowing the user to view and understand them.

[0944] Step 7:

[0945] The user implements the provided solution and inputs the results and feedback into the system through a collection means.

[0946] Input: Feedback data (text format, feedback content)

[0947] Output: Feedback data object

[0948] Specific operation: The user implements the provided solution and inputs the results and impressions into the system through the collection means. The feedback data is sent to the server as a feedback data object.

[0949] Step 8:

[0950] The server stores the feedback collected by the collection means in a database to improve the accuracy of the generation AI means.

[0951] Input: Feedback data object

[0952] Output: Updated generative AI model

[0953] How it works: The server stores the received feedback in a database and uses it as training data to retrain the generative AI model, enabling it to provide more accurate solutions in the future.

[0954] 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.

[0955] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. The system incorporates an emotion engine that recognizes and analyzes users' emotions, thereby improving the accuracy and applicability of solutions.

[0956] First, the user downloads the application and goes through a simple registration process the first time they use it. They then use an input method to anonymously enter their concerns in free-form. For example, they could write something like, "There's bullying in my class and I don't know what to do about it."

[0957] Next, the input consultation content is sent from the terminal to the server. The server passes the received consultation content to an analysis means, which uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. At this point, the emotion engine incorporated in the present invention is activated and recognizes the user's emotions from the consultation content. For example, the emotion engine may detect from the input content that the user is feeling emotions such as "anxiety" or "fear."

[0958] The AI ​​generator then generates an optimal solution based on the consultation content classified by the analysis unit and the recognized emotional data. The generated solution is tailored to the user's emotional state and provides specific and appropriate advice. For example, if the user is feeling "fear" about a bullying issue, the generated solution would include how to seek psychological help.

[0959] The generated solutions are formatted on the server and sent to the device, which then displays them on the screen in a user-friendly format. The user can then review the solutions and implement them. For example, in the case of a bullying problem, the solution suggests "talking to a trusted teacher" and "taking a break or finding ways to relax."

[0960] After implementing a solution, the user enters the results and experience into the terminal through a feedback input form. The terminal then sends the entered feedback to the server and stores it in a database via the collection means. In particular, since the feedback includes emotion data collected by the emotion engine of the present invention, it contributes to improving the accuracy of the generation AI means in subsequent solution generation.

[0961] The following cases are specific examples:

[0962] Example 1:

[0963] User: A, a second-year junior high school student

[0964] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[0965] Person A opens the application and enters the details of his or her problem. This information is sent to the server, where the analysis means classifies it as a case of bullying. At the same time, the emotion engine detects that Person A is feeling "helpless." The generation AI means analyzes "how to respond when witnessing bullying" and generates "how to report it to a trusted teacher or counselor" and "actions to increase self-esteem." Person A checks the proposed solutions and implements them. Later, feedback is provided, contributing to improving the accuracy of the system.

[0966] Example 2:

[0967] User: Mother of B, a fourth-grader

[0968] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[0969] B's mother opens the application, inputs the details of her consultation, and sends them to the server. The analysis means classifies it as a case of moral harassment, and the emotion engine detects emotions such as "concern." The generative AI means generates "methods of communication between teachers and parents" and "methods of formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[0970] As described above, the present invention provides a system that supports problem solving in educational settings and reduces the mental burden on those involved. The incorporation of an emotion engine improves the applicability and accuracy of solutions, enabling support that is more suited to the user's needs.

[0971] The processing flow will be explained below.

[0972] Step 1:

[0973] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[0974] Step 2:

[0975] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[0976] Step 3:

[0977] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[0978] Step 4:

[0979] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[0980] Step 5:

[0981] The device sends the input consultation content to the server. The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.). During this classification process, an emotion engine is activated and recognizes the user's emotions from the consultation content.

[0982] Step 6:

[0983] The server operates the generative AI means based on the consultation content and emotion data analyzed by the emotion engine. The generative AI means identifies the root cause of the problem and the extent of its impact, and generates an optimal solution that takes the user's emotions into consideration.

[0984] Step 7:

[0985] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[0986] Step 8:

[0987] The device displays the solutions received from the server on the screen. The user can then review the solutions and take the necessary action. For example, in the case of a bullying issue, the device displays "ways to calm down" along with the action to "consult a trusted teacher or counselor."

[0988] Step 9:

[0989] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[0990] Step 10:

[0991] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method. In particular, the feedback includes emotional data collected by the emotion engine, which further improves the accuracy of solution generation in future.

[0992] Example 2

[0993] 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."

[0994] Ethical issues such as bullying, moral harassment, power harassment, and sexual harassment, which are difficult to resolve in traditional educational environments, can have serious psychological and health effects if left unaddressed. Users are also required to provide their concerns anonymously, which protects their privacy. However, traditional systems often lack the means to resolve these issues efficiently and accurately. In particular, there has been no system that can properly recognize the emotions in the content of a consultation and provide appropriate solutions based on this.

[0995] 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.

[0996] In this invention, the server includes an input / output device for users to anonymously input consultation details, an analysis device for analyzing and categorizing the consultation details received from the input / output device, a device using natural language processing technology for tokenizing the consultation details classified by the analysis device and classifying them into appropriate categories, an emotion recognition device for recognizing users' emotions based on the natural language processing technology, a generation algorithm device for generating solutions based on the user's emotion data recognized by the emotion recognition device, a presentation device for presenting the solutions generated by the generation algorithm device to users, and a collection device for collecting user feedback received via the presentation device and improving the accuracy of the generation algorithm device. This enables the generation and provision of appropriate solutions that take user emotions into consideration. Furthermore, the collected feedback data can be used to improve the accuracy of the system, contributing to improved accuracy in future solution generation.

[0997] A "user" is an individual who accesses the system, anonymously enters a request, and reviews and implements the solutions provided.

[0998] The "input / output device" is a device that allows users to anonymously input the details of their inquiries and displays solutions from the server.

[0999] The "analysis device" is a device that analyzes the consultation content received from the input / output device and performs tokenization and category classification using natural language processing technology.

[1000] "Natural language processing technology" is a technology for understanding and analyzing human language, tokenizing the content of consultations, and classifying them into appropriate categories.

[1001] An "emotion recognition device" is a device that uses natural language processing technology to recognize emotions from the content of a user's consultation and generates emotion data.

[1002] The "generative algorithm device" is a device that includes an artificial intelligence model for generating optimal solutions based on the emotion data recognized by the emotion recognition device.

[1003] A "presentation device" is a device for presenting to a user a solution generated by a generation algorithm device.

[1004] A "collection device" is a device that collects user feedback received via a presentation device and uses it to improve the accuracy of the generation algorithm device.

[1005] "Feedback" is information that a user records and sends to the system the results and experiences of implementing a provided solution.

[1006] MODE FOR CARRYING OUT THE INVENTION

[1007] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. This system allows users to anonymously input their consultation details and provides optimal solutions based on the details of the consultation.

[1008] User usage of the application

[1009] Users first download the system's application from an application store. When using the system for the first time, they register an account by entering basic information, which ensures the user's anonymity.

[1010] Enter the consultation details

[1011] Users tap "New Consultation" on the main screen of the application and enter the content of their consultation in free-form. For example, they could write specific details such as "I'm having trouble with bullying in my class and I don't know how to deal with it." Once they've finished entering their information, they tap the "Send" button.

[1012] Data transmission and reception

[1013] The device encrypts the consultation content entered by the user and sends it to the server using the HTTPS protocol. The server then passes the received consultation content to an analysis method (for example, using natural language processing libraries such as SpaCy or NLTK).

[1014] Data analysis and emotion recognition

[1015] The server uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. Based on this, an emotion engine (e.g., emotion recognition models BERT or GPT-3) is activated to recognize the user's emotions. Specifically, it generates emotion tags such as "anxiety" or "fear" from the text.

[1016] Solution Generation

[1017] The server inputs the analyzed consultation content and detected emotion tags as prompts into a generative AI model (e.g., OpenAI's GPT-3), which then generates an optimal solution.

[1018] Examples of prompts include:

[1019] Consultation content: Bullying is an issue in class and I don't know how to deal with it.

[1020] Emotion: Fear

[1021] The generated solutions are formatted at the server and sent to the terminal.

[1022] View and implement solutions

[1023] The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, specific actions such as "Consult a trusted teacher" are displayed on the screen.

[1024] Gathering feedback

[1025] After implementing the solution, the user enters the results and experience as feedback into the application, which is then sent by tapping the "Feedback" button.

[1026] The device sends the feedback information to the server. This communication is also encrypted using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation. Emotional data in particular is useful for model training, contributing to improving the accuracy of future solutions generated by the generative AI model.

[1027] The above is a specific embodiment of this system. The present invention aims to provide a more appropriate and practical solution by taking into account the user's emotions.

[1028] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1029] Step 1: User downloads and registers the application

[1030] How it works: A user downloads the system's application from an application store. When using it for the first time, they register an account by entering basic information such as their name, age, and school name. The information they enter is hashed to ensure anonymity.

[1031] Input: User's basic information (name, age, school name, etc.).

[1032] Output: The registered user account.

[1033] Step 2: User inputs the content of the consultation

[1034] Operation: The user selects "New Consultation" from the main screen of the application, enters the consultation details in free text format, and presses the "Send" button to send the data.

[1035] Input: The user's problem (e.g., "I'm having trouble with bullying in my class and I don't know what to do about it.").

[1036] Output: Consultation details saved on the device.

[1037] Step 3: Send data from the device to the server

[1038] How it works: The device encrypts the consultation information entered by the user and sends it to the server using the HTTPS protocol. The data is encrypted to ensure the security of the communication.

[1039] Input: User's consultation (encrypted).

[1040] Output: The consultation details sent to the server.

[1041] Step 4: Data analysis and emotion recognition by the server

[1042] How it works: The server passes the received consultation content to an analysis tool (e.g., a natural language processing library like SpaCy or NLTK), tokenizes the content, and classifies it into appropriate categories. It then uses an emotion recognition model (e.g., BERT or GPT-3) to recognize the user's emotions.

[1043] Input: Received consultation content.

[1044] Output: Tokenized data, emotion tags (e.g., "anxiety", "fear").

[1045] Step 5: Generative AI model generates solutions

[1046] How it works: The server inputs the analyzed consultation content and the recognized emotion tag as a prompt sentence into a generative AI model (e.g., OpenAI's GPT-3). Based on this, the generative AI model generates an optimal solution.

[1047] Input: Prompt statement (e.g., "Contact: Bullying in class is an issue and I don't know what to do. Emotion: Fear").

[1048] Output: Optimal solution (in text format).

[1049] Step 6: Formatting and sending the solution on the server

[1050] How it works: The solution generated by the generative AI model is formatted in an appropriate format (e.g., JSON or XML) and sent to the device.

[1051] Input: The generated solution.

[1052] Output: A formatted solution.

[1053] Step 7: User confirms and implements the solution

[1054] Operation: The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, a specific action such as "Consult a trusted teacher" is displayed on the screen.

[1055] Input: A formatted solution.

[1056] Output: A specific action to be taken.

[1057] Step 8: Collecting user feedback and sending it to the server

[1058] How it works: After the user implements a solution, they input their results and experience as feedback into the application. The input feedback is sent by tapping the "Feedback" button. The device encrypts the feedback information and sends it to the server using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation.

[1059] Input: User feedback.

[1060] Output: Feedback data stored in a database.

[1061] The above is a description of each processing step and its specific operation of this system. By taking into consideration the user's feelings and providing appropriate solutions quickly, it helps prevent and resolve ethical issues.

[1062] (Application example 2)

[1063] 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."

[1064] In today's educational environment, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment frequently occur, often causing mental stress. Conventional problem-solving methods have not provided effective support because they are unable to properly understand the client's emotions and provide applicable solutions. For this reason, there is a need to provide an environment where users can seek advice anonymously, as well as incorporate functions to recognize and analyze emotions in order to provide more specific and appropriate solutions.

[1065] 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.

[1066] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details categorized by the analysis means, emotion analysis means for recognizing and analyzing the user's emotions when generating solutions by the emotion analysis means, and means for incorporating emotion data recognized by the emotion analysis means into the solution generation process, thereby making it possible to provide highly applicable solutions that take the user's emotions into consideration.

[1067] "User" refers to an individual who uses the system to input their inquiry and receive a solution.

[1068] "Anonymity" refers to a state in which a user's personal information or name is not revealed, with the aim of protecting the user's privacy.

[1069] "Input means" refers to the method or device by which a user enters the details of their consultation. For example, this includes a smartphone or computer input form.

[1070] "Analysis means" refers to the technology or algorithm used to analyze the received consultation content and classify it into an appropriate category.

[1071] "Generative AI means" refers to artificial intelligence technologies and algorithms that generate optimal solutions based on the consultation content classified by analytical means.

[1072] "Emotion analysis means" refers to the technology and algorithms used to recognize and analyze the user's emotions from the consultation content entered.

[1073] "Presentation Means" refers to a method or device for visually or audibly presenting the solution generated by the Generative AI Means to the user.

[1074] "Collection Method" refers to the technology or algorithm used to collect feedback provided by users on proposed solutions and store it in the system.

[1075] "Authentication methods" refers to techniques and methods for ensuring and guaranteeing the anonymity of users.

[1076] "Database" refers to an electronic information storage system for storing collected feedback and sentiment data and utilizing it for subsequent solution generation.

[1077] A "prompt" refers to the input format that a generative AI model uses to generate a solution and get the optimal output.

[1078] The embodiment of this invention provides a system for anonymously consulting and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. In particular, by incorporating a function to analyze the user's emotions, the system provides more appropriate solutions.

[1079] Hardware and software used

[1080] Hardware: Smartphone (iOS and Android compatible)

[1081] Software: Python, Hugging Face Transformers library, requests library

[1082] System configuration

[1083] User device:

[1084] Users access the system using a smartphone, enter their concerns anonymously through the application, and receive solutions.

[1085] server

[1086] The server includes the following components:

[1087] 1. Input means: A means for receiving the consultation content input by the user.

[1088] 2. Analysis method: The received consultation content is analyzed using natural language processing technology and classified into appropriate categories.

[1089] 3. Emotion analysis method: A method for recognizing and analyzing the user's emotions from the analyzed consultation content. It uses the Hugging Face Transformers library.

[1090] 4. Generative AI method: Based on the user's consultation content and recognized emotion data, the optimal solution is generated using a generative AI model.

[1091] 5. Presentation means: A means for presenting the generated solution to the user.

[1092] 6. Collection methods: User-provided feedback is collected and used to improve the accuracy of the generative AI methods.

[1093] Data Flow

[1094] 1. Input of consultation content: The user inputs the consultation content into the application and sends it to the server.

[1095] 2. Analysis of consultation content: The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories.

[1096] 3. Emotion analysis: The analyzed data is input into the emotion analysis means to recognize and analyze the user's emotions.

[1097] 4. Solution generation: The generative AI means generates a solution based on the data obtained by the analysis means and the sentiment analysis means.

[1098] 5. Presentation of the solution: The generated solution is formatted and sent to the user's terminal through a presentation means.

[1099] 6. Feedback collection: The user implements the proposed solution and provides feedback through the application. The collection mechanism sends this feedback to the server and stores it in the database.

[1100] Specific examples

[1101] Example 1:

[1102] If a second-year junior high school student, A, sees a friend being bullied in class but doesn't know what to do, the emotion analysis means will detect A's "helplessness." The generative AI means will suggest ways to deal with bullying, such as consulting a trusted teacher or taking actions to improve self-esteem.

[1103] Example prompt sentence:

[1104] Input: My classmate is being bullied and I don't know what to do about it.

[1105] Emotion: helplessness

[1106] Output formats:

[1107] Suggestion 1: Talk to a trusted teacher or counselor

[1108] Suggestion 2: Actions to improve self-esteem

[1109] Example 2:

[1110] If the mother of a fourth-grader named B says that her son comes home crying every day because he is being strict with a teacher at school, the emotion analysis means will detect the concern. The AI ​​generation means will generate and suggest ways to communicate with the teacher and provide formal feedback to the school.

[1111] Example prompt sentence:

[1112] My son comes home crying every day because he has a strict teacher at school. What should I do?

[1113] Emotion: Concern

[1114] Output formats:

[1115] Suggestion 1: How to communicate with teachers

[1116] Suggestion 2: Formal feedback methods for schools

[1117] In this way, the present invention realizes a system that supports problem solving in educational settings and provides specific and appropriate advice according to the user's emotions.

[1118] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1119] Step 1:

[1120] The user anonymously inputs the details of the consultation using a device (smartphone), and the input details are sent to the server.

[1121] Input: User's problem (e.g., "My friend is being bullied in class")

[1122] Output: The consultation content is sent to the server

[1123] Step 2:

[1124] The server passes the received consultation content to an analysis means, where it tokenizes it using natural language processing technology and classifies it into appropriate categories.

[1125] Input: Consultation content received by the server

[1126] Data processing: tokenization and categorization using natural language processing

[1127] Output: Consultation contents categorized

[1128] Step 3:

[1129] The analyzed consultation content is input into the emotion analysis means, and the user's emotions are recognized and analyzed.

[1130] Input: Consultation content categorized

[1131] Data Computation: Sentiment Analysis Using Hugging Face's Transformers Library

[1132] Output: Recognized emotion data (e.g., "helplessness")

[1133] Step 4:

[1134] Based on the data obtained by the analysis means and the sentiment analysis means, the generation AI means generates the optimal solution.

[1135] Input: Categorized consultation content and recognized emotion data

[1136] Data calculation: Enter a prompt into the generative AI model to generate the optimal solution

[1137] Output: Generated solutions (e.g., "How to talk to a trusted teacher" or "What to do to improve self-esteem")

[1138] Step 5:

[1139] The generated solution is formatted and sent to the user's terminal via a presentation means.

[1140] Input: Generated solution

[1141] Data processing: formatting the solution

[1142] Output: The solution sent to the user's device

[1143] Step 6:

[1144] The user reviews and implements the proposed solution, then provides feedback through the application.

[1145] Input: User feedback (e.g., "I felt relieved after implementing the suggested solution")

[1146] Data Processing: Feedback Data Collection and Storage

[1147] Output: The collected feedback data is stored on the server.

[1148] Step 7:

[1149] The collection method uses the provided feedback to improve the accuracy of the generative AI method, and the feedback and emotion data are stored in a database and used for subsequent solution generation.

[1150] Input: Collected feedback and sentiment data

[1151] Data processing: Used as training data for generative AI models

[1152] Output: Improved accuracy in solution generation from next time onwards

[1153] 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.

[1154] 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.

[1155] 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.

[1156] [Fourth embodiment]

[1157] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1158] 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.

[1159] 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).

[1160] 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.

[1161] 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.

[1162] 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).

[1163] 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. 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.

[1164] 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.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] 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."

[1170] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[1171] First, the user downloads the application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password. This prevents duplicate registrations while maintaining anonymity. Once registration is complete, the user is allowed to log in to the system using authentication methods.

[1172] After logging in, users use the input method to enter the details of their consultation. Specifically, they can freely describe the problems or worries they are facing in the consultation form within the application. For example, they could enter something like, "I'm having trouble with bullying in my class and I don't know how to deal with it."

[1173] Once the consultation content is entered, the device sends this information to the server. The server passes the received consultation content to the analysis means, which uses natural language processing to tokenize the consultation content and classify it into an appropriate category (bullying, moral harassment, power harassment, sexual harassment, etc.). Next, based on the classification by the analysis means, the generation AI means is activated and performs a detailed analysis of the problem. The generation AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[1174] The generated solutions are formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with detailed solutions including specific guidelines and expert advice, allowing the user to review the solutions and take actual action according to the guidelines.

[1175] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[1176] The following cases are specific examples:

[1177] Example 1:

[1178] User: A, a second-year junior high school student

[1179] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[1180] Person A opens the application and inputs the details of his or her problem. This information is sent to the server, and the analysis means classifies it as a case of bullying. The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." Person A checks the presented solution and puts it into action. He or she later provides feedback, contributing to improving the accuracy of the generation AI.

[1181] Example 2:

[1182] User: Mother of B, a fourth-grader

[1183] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[1184] B's mother enters the details of her consultation into the application and sends them to the server. The analysis means classifies it as a case of moral harassment, and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[1185] As described above, the present invention provides a system for supporting problem solving in educational settings and reducing the mental burden on those involved.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[1189] Step 2:

[1190] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[1191] Step 3:

[1192] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[1193] Step 4:

[1194] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[1195] Step 5:

[1196] The device sends the input consultation details to the server, which then passes the received consultation details to an analysis means, tokenizes them using natural language processing technology, and classifies them into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.).

[1197] Step 6:

[1198] The server operates the generation AI means based on the consultation content classified by the analysis means. The generation AI means identifies the root cause of the problem and the scope of its impact, and generates an optimal solution.

[1199] Step 7:

[1200] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[1201] Step 8:

[1202] The device receives the solutions from the server and displays them on the screen. The user can then review the solutions and take the necessary action. For example, in the case of bullying, a specific action plan such as "Talk to a trusted teacher or counselor" is displayed.

[1203] Step 9:

[1204] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[1205] Step 10:

[1206] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method and is used for generating solutions in the future.

[1207] Example 1

[1208] 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."

[1209] In traditional educational environments, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment were difficult to resolve quickly due to a lack of concrete measures for those involved to take appropriate action. Furthermore, there was no system that could ensure the anonymity of those seeking advice while proposing appropriate solutions, so those seeking advice could not feel safe. Furthermore, there was also a lack of a way to utilize collected feedback to improve the accuracy of the system.

[1210] 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.

[1211] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details classified by the analysis means, presentation means for presenting the solutions generated by the generation AI means to the user, collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means, authentication means for authenticating users and controlling access to the system, and communication means for encrypting the transmission and reception of the consultation details. This allows the user to input the consultation details with peace of mind while maintaining anonymity, and the system can quickly generate and present appropriate solutions and then improve the accuracy of the system based on the collected feedback.

[1212] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[1213] The "analysis means" is a mechanism for analyzing the consultation content received from the input means and classifying it into an appropriate category.

[1214] The "generative AI means" is an artificial intelligence system that generates solutions based on the consultation content classified by the analysis means.

[1215] The "presentation means" is an interface for visually or audibly presenting the solution generated by the generation AI means to the user.

[1216] The "collection means" is a mechanism for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[1217] "Authentication means" is a mechanism for authenticating users and controlling access to the system.

[1218] The "communication means" is a communication protocol for encrypting the sending and receiving of consultation contents.

[1219] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. A specific form for effectively operating this system will be described.

[1220] First, the user downloads the system's application from the app store on their smartphone or tablet. When the user launches the application, a registration screen appears the first time they use it. On the registration screen, the user enters their username, email address, and password to create an account. This registration information is stored in a database, which prevents duplicate registrations while maintaining the user's anonymity.

[1221] Next, the user logs in by entering their registered email address and password. The server verifies this information, and if authentication is successful, the user is allowed to use the system. Secure communication is performed using SSL / TLS to prevent unauthorized access.

[1222] After logging in, users can tap the "Consult" button in the application and freely describe the problems or worries they are facing in the consultation form. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[1223] When a user enters the details of their consultation and taps the "Send" button, the device sends this information to the server. The communication is encrypted and uses a communication method to prevent information leaks.

[1224] The server uses a natural language processing library (e.g., SpaCy) to tokenize the received consultation content and classify it into an appropriate category. This results in categories such as "bullying," "moral harassment," "power harassment," and "sexual harassment." For example, it may be classified into the category "bullying."

[1225] Based on the classification results, the server uses a generative AI model (e.g., OpenAI's GPT-4) to perform a detailed analysis of the problem. The generative AI identifies the root cause and scope of the problem and creates a specific solution. For example, in the case of bullying, it generates "how to inform a trusted teacher or counselor and specific action steps."

[1226] The generated solutions are presented on the device in a user-friendly format, including specific guidelines and expert advice, such as "how to talk to a teacher" or "what to do if you see bullying."

[1227] The user checks the proposed solutions and implements them if necessary. For example, they follow the proposed solutions and consult with a trusted teacher. After implementing the solutions, the user also enters the results and experience as feedback. The application provides a feedback form, which the user can fill out and submit.

[1228] The server receives user feedback and stores it in a database. The collected feedback is used as training data for the generative AI model, helping to improve the accuracy of the system.

[1229] The following cases are specific examples:

[1230] Example 1:

[1231] User: 2nd year junior high school student

[1232] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[1233] The user opens the application and inputs the details of their problem. This information is sent to the server, and the analysis means classifies it as "bullying." The generation AI means provides "how to deal with witnessing bullying," including "how to report it to a trusted teacher or counselor and specific action steps." The user checks the presented solutions and puts them into action. They then provide feedback, contributing to improving the accuracy of the generation AI.

[1234] Example 2:

[1235] User: Parent of an elementary school student

[1236] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[1237] Parents enter their concerns into the application and send them to the server. The analysis means classifies the issue as "moral harassment," and the generation AI means presents specific solutions, such as "methods of communication between teachers and parents and methods of providing formal feedback to the school." Parents confirm the solutions and implement them. Feedback after implementation is provided through the application, which will help further improve the system.

[1238] Example prompt sentence:

[1239] "Please tell me what to do if I see a friend being bullied in class."

[1240] "I want to know how to deal with teachers who are strict with discipline."

[1241] As described above, this system efficiently analyzes user inquiries and utilizes generative AI models to provide specific solutions, thereby supporting problem-solving in educational settings.

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] Users download the system's application from the app store on their smartphone or tablet and launch the app. On first use, they create an account by entering their username, email address, and password on the registration screen that appears. The information entered is sent from the device to the server and stored in a database. This prevents duplicate registrations while maintaining the user's anonymity.

[1245] Step 2:

[1246] Users log in using their registered email address and password. The server receives this authentication information and compares it with information in the database. If it matches, the user is allowed to use the system and an authentication token is issued. To prevent unauthorized access, all communication is encrypted with SSL / TLS. This authentication token is used to ensure the user's ongoing session.

[1247] Step 3:

[1248] After logging in, users tap the "Consult" button on the home screen of the application. A consultation form will appear, and the user can freely describe the problems or worries they are facing. For example, they could write, "I saw my friend being bullied in class, but I don't know what to do."

[1249] Step 4:

[1250] When the user enters the consultation content and taps the "Send" button, the device sends the entered consultation content to the server. This communication is also encrypted to prevent information leaks. The input at this stage is the consultation content entered by the user, and the output is sent to the server as encrypted consultation content.

[1251] Step 5:

[1252] The server tokenizes the received consultation content using a natural language processing library (e.g., SpaCy). The tokenized data is further analyzed and classified into appropriate categories such as bullying, moral harassment, power harassment, and sexual harassment. The input is the received consultation content, and the output is the categorized consultation content.

[1253] Step 6:

[1254] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate a specific solution to the consultation content. For example, if the issue is classified as "bullying," it will generate "ways to consult with a trusted teacher or counselor" and "specific action steps." The input is the categorized consultation content, and the output is the generated solution.

[1255] Step 7:

[1256] The server formats the generated solution into a user-friendly format. The formatted solution is sent to the terminal through the presentation means and presented to the user. Specifically, it is visually displayed to the user in the application interface. The generated solution is used as input, and the formatted solution is presented as output.

[1257] Step 8:

[1258] The user checks the proposed solution and implements it if necessary, for example, by consulting a trusted teacher. After implementing the solution, the user also inputs the results and experience as feedback.

[1259] Step 9:

[1260] The feedback entered by the user is sent from the terminal to the server, which receives it and stores it in a database. The input is the user's feedback, and the output is stored in the database.

[1261] Step 10:

[1262] The server uses the collected feedback as training data for the generative AI model, helping to improve its accuracy. This makes it possible to provide even more accurate solutions for future consultations. The input is the feedback stored in the database, and the output is an improved AI model.

[1263] (Application example 1)

[1264] 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."

[1265] Ethical issues such as harassment, bullying, and whistle-blowing in the workplace are serious challenges for companies. These issues are difficult to address individually, so there is a need for a system that can effectively collect information while ensuring anonymity and provide appropriate solutions.

[1266] 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.

[1267] In this invention, the server includes an input means for users to anonymously input consultation details, an analysis means for analyzing and categorizing the consultation details received from the input means, a generation AI means for generating solutions based on the consultation details categorized by the analysis means, a presentation means for presenting the solutions generated by the generation AI means to users, a means for corporate employees to anonymously consult and report issues such as harassment, bullying, and whistleblowing that they face in the workplace, and a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means. This makes it possible to resolve ethical issues in the workplace anonymously and effectively.

[1268] The "input means" is an interface that allows a user to anonymously input the content of a consultation.

[1269] The "analysis means" is a technology for analyzing the consultation content received from the input means and classifying it into categories.

[1270] The "generative AI means" is an artificial intelligence technology for generating solutions based on the consultation content classified by the analytical means.

[1271] The "presentation means" is an interface for presenting the solution generated by the generation AI means to the user.

[1272] The "collection means" is a technology for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means.

[1273] "System" refers to the entire computer system including input means, analysis means, generation AI means, presentation means, and collection means.

[1274] "Authentication means" is a technology for ensuring the anonymity of users and an interface for verifying the identity of users.

[1275] "Feedback" is information that provides results and opinions after a user implements a solution.

[1276] "Database" refers to a data storage technology for storing feedback collected by collection means and utilizing it in subsequent solution generation.

[1277] "A means to anonymously consult and report issues such as harassment, bullying, and whistle-blowing that are faced in the workplace" is a technology that allows corporate employees to anonymously report and consult on ethical issues that arise in the workplace to a system.

[1278] "A system for corporate employees to anonymously seek advice on problems" refers to the entire system that allows employees working within a company to anonymously seek advice on problems that arise in the workplace.

[1279] This invention relates to a system that allows corporate employees to anonymously seek advice and report on issues such as harassment, bullying, whistle-blowing, etc. that they face in the workplace. A specific form for realizing this system is described below.

[1280] First, the user downloads the smartphone application and goes through a simple registration process the first time they use it. Registration requires minimal information, such as a username, email address, and password, which prevents duplicate registrations while maintaining anonymity. Once registration is complete, authentication measures allow login to the system.

[1281] After logging in, the user uses the input means to enter the content of their consultation. Specifically, they can freely write down the problems or worries they are facing at work in the consultation form within the application. For example, they can enter content such as "How should I deal with power harassment from my boss?"

[1282] The input consultation content is sent from the device to the server. The server uses an analytical means to analyze the received consultation content and classify it into categories (harassment, bullying, whistleblowing, etc.). The analytical means uses natural language processing technology to tokenize the consultation content and classify it into the appropriate category. Next, the generative AI means operates based on the classification by the analytical means and performs a detailed analysis of the problem. The generative AI identifies the root cause of the problem and the scope of its impact, and generates the optimal solution.

[1283] The generated solution is formatted and displayed to the user in a user-friendly format through a presentation tool, which provides the user with a detailed solution including specific guidelines and expert advice, allowing the user to review the provided solution and take actual action according to the guidelines.

[1284] After implementing a solution, users can input the results and feedback back into the system through the collection means. The collection means stores the user feedback in a database and uses it to improve the accuracy of the generation AI means. This enables the generation AI to provide action guidelines with even higher accuracy in future solution generation.

[1285] The following cases are specific examples:

[1286] Example 1:

[1287] If an employee is experiencing power harassment at work, they open the application and enter the details of their complaint. This information is sent to the server, and the analysis means classifies it as a case of power harassment. The generation AI means provides "ways to deal with power harassment," such as "ways to avoid talking to your boss," "ways to collect evidence," and "ways to officially report to the human resources department." The employee checks the proposed solutions and puts them into action.

[1288] Specific prompt examples:

[1289] How to deal with power harassment from your boss

[1290] "I want to know how to solve bullying in the workplace."

[1291] The system allows for the anonymous and effective resolution of ethical issues in the workplace, and is an advanced tool to ensure employee safety and security.

[1292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1293] Step 1:

[1294] Users download the smartphone application and go through a simple registration process when they use it for the first time.

[1295] Input: Username, Email Address, Password

[1296] Output: User ID (unique identifier)

[1297] What happens: A user opens the application and enters their username, email address, and password, which are then sent to the server, which generates a unique user ID for the user and completes the registration.

[1298] Step 2:

[1299] A user logs into the system using authentication methods.

[1300] Input: Email address, password

[1301] Output: Login session (authentication token)

[1302] How it works: The user enters their email address and password into the application and the authentication process begins. The server verifies this information and, if authentication is successful, creates a login session and returns an authentication token.

[1303] Step 3:

[1304] The user inputs the consultation content using the input means.

[1305] Input: Consultation content (text format)

[1306] Output: Consultation data object

[1307] Specific operation: The user enters the consultation details into the form in the application and presses the send button. This information is sent from the terminal to the server as a consultation data object.

[1308] Step 4:

[1309] The server analyzes the consultation contents received by the server using an analysis means and classifies them into categories.

[1310] Input: Consultation data object

[1311] Output: Category information (bullying, harassment, whistleblowing, etc.)

[1312] Specific operation: The server analyzes the received consultation data using natural language processing technology and tokenizes the consultation content. The analysis means classifies the consultation content into appropriate categories based on this data.

[1313] Step 5:

[1314] Based on the consultation content classified by the analysis means, the generation AI means generates a solution.

[1315] Input: Category information, consultation data object

[1316] Output: Solution data object

[1317] Specific operation: Based on the categorical information generated by the analytical means, the generative AI model generates an optimal solution, which is formatted as a solution data object.

[1318] Step 6:

[1319] The solution generated by the generation AI means is presented to the user through the presentation means.

[1320] Input: Solution data object

[1321] Output: The solution displayed in the user interface

[1322] Specific Actions: Solution data objects are transformed into a user-friendly format and displayed to the user through presentation means within the application, allowing the user to view and understand them.

[1323] Step 7:

[1324] The user implements the provided solution and inputs the results and feedback into the system through a collection means.

[1325] Input: Feedback data (text format, feedback content)

[1326] Output: Feedback data object

[1327] Specific operation: The user implements the provided solution and inputs the results and impressions into the system through the collection means. The feedback data is sent to the server as a feedback data object.

[1328] Step 8:

[1329] The server stores the feedback collected by the collection means in a database to improve the accuracy of the generation AI means.

[1330] Input: Feedback data object

[1331] Output: Updated generative AI model

[1332] How it works: The server stores the received feedback in a database and uses it as training data to retrain the generative AI model, enabling it to provide more accurate solutions in the future.

[1333] 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.

[1334] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. The system incorporates an emotion engine that recognizes and analyzes users' emotions, thereby improving the accuracy and applicability of solutions.

[1335] First, the user downloads the application and goes through a simple registration process the first time they use it. They then use an input method to anonymously enter their concerns in free-form. For example, they could write something like, "There's bullying in my class and I don't know what to do about it."

[1336] Next, the input consultation content is sent from the terminal to the server. The server passes the received consultation content to an analysis means, which uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. At this point, the emotion engine incorporated in the present invention is activated and recognizes the user's emotions from the consultation content. For example, the emotion engine may detect from the input content that the user is feeling emotions such as "anxiety" or "fear."

[1337] The AI ​​generator then generates an optimal solution based on the consultation content classified by the analysis unit and the recognized emotional data. The generated solution is tailored to the user's emotional state and provides specific and appropriate advice. For example, if the user is feeling "fear" about a bullying issue, the generated solution would include how to seek psychological help.

[1338] The generated solutions are formatted on the server and sent to the device, which then displays them on the screen in a user-friendly format. The user can then review the solutions and implement them. For example, in the case of a bullying problem, the solution suggests "talking to a trusted teacher" and "taking a break or finding ways to relax."

[1339] After implementing a solution, the user enters the results and experience into the terminal through a feedback input form. The terminal then sends the entered feedback to the server and stores it in a database via the collection means. In particular, since the feedback includes emotion data collected by the emotion engine of the present invention, it contributes to improving the accuracy of the generation AI means in subsequent solution generation.

[1340] The following cases are specific examples:

[1341] Example 1:

[1342] User: A, a second-year junior high school student

[1343] Consultation content: "I saw my friend being bullied in class, but I don't know what to do."

[1344] Person A opens the application and enters the details of his or her problem. This information is sent to the server, where the analysis means classifies it as a case of bullying. At the same time, the emotion engine detects that Person A is feeling "helpless." The generation AI means analyzes "how to respond when witnessing bullying" and generates "how to report it to a trusted teacher or counselor" and "actions to increase self-esteem." Person A checks the proposed solutions and implements them. Later, feedback is provided, contributing to improving the accuracy of the system.

[1345] Example 2:

[1346] User: Mother of B, a fourth-grader

[1347] Consultation: "My son is getting angry at a strict teacher at school and comes home crying every day. What should I do?"

[1348] B's mother opens the application, inputs the details of her consultation, and sends them to the server. The analysis means classifies it as a case of moral harassment, and the emotion engine detects emotions such as "concern." The generative AI means generates "methods of communication between teachers and parents" and "methods of formal feedback to the school." B's mother checks the solutions and implements them. Feedback after implementation is provided through the application, which will help further improve the system.

[1349] As described above, the present invention provides a system that supports problem solving in educational settings and reduces the mental burden on those involved. The incorporation of an emotion engine improves the applicability and accuracy of solutions, enabling support that is more suited to the user's needs.

[1350] The processing flow will be explained below.

[1351] Step 1:

[1352] The user downloads and opens the application. When the application launches, first-time users are presented with a simple registration form. They enter the minimum information, such as a username, email address, and password, and then tap the "Register" button.

[1353] Step 2:

[1354] The terminal sends the entered information to the server, which stores the received information in a database, generates a confirmation code along with a registration completion message, and sends them to the user's email address.

[1355] Step 3:

[1356] The user receives a verification code via email and enters it into the application. The device sends the verification code to the server and performs authentication. The server verifies the verification code and allows the user to log in if authentication is successful.

[1357] Step 4:

[1358] After logging in, the user selects the "New Consultation" button from the main menu within the application. The device displays a consultation content input form. The user enters the consultation content in free text format and taps the "Send" button.

[1359] Step 5:

[1360] The device sends the input consultation content to the server. The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories (bullying, moral harassment, power harassment, sexual harassment, etc.). During this classification process, an emotion engine is activated and recognizes the user's emotions from the consultation content.

[1361] Step 6:

[1362] The server operates the generative AI means based on the consultation content and emotion data analyzed by the emotion engine. The generative AI means identifies the root cause of the problem and the extent of its impact, and generates an optimal solution that takes the user's emotions into consideration.

[1363] Step 7:

[1364] The server formats the generated solution and converts it into a user-friendly format, then transmits the formatted solution to the terminal.

[1365] Step 8:

[1366] The device displays the solutions received from the server on the screen. The user can then review the solutions and take the necessary action. For example, in the case of a bullying issue, the device displays "ways to calm down" along with the action to "consult a trusted teacher or counselor."

[1367] Step 9:

[1368] After implementing the solution, the user reopens the application and enters the results and impressions in the feedback input form. The device then sends the entered feedback to the server.

[1369] Step 10:

[1370] The server collects the received feedback and stores it in a database. The collected feedback is used to improve the accuracy of the generative AI method. In particular, the feedback includes emotional data collected by the emotion engine, which further improves the accuracy of solution generation in future.

[1371] Example 2

[1372] 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."

[1373] Ethical issues such as bullying, moral harassment, power harassment, and sexual harassment, which are difficult to resolve in traditional educational environments, can have serious psychological and health effects if left unaddressed. Users are also required to provide their concerns anonymously, which protects their privacy. However, traditional systems often lack the means to resolve these issues efficiently and accurately. In particular, there has been no system that can properly recognize the emotions in the content of a consultation and provide appropriate solutions based on this.

[1374] 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.

[1375] In this invention, the server includes an input / output device for users to anonymously input consultation details, an analysis device for analyzing and categorizing the consultation details received from the input / output device, a device using natural language processing technology for tokenizing the consultation details classified by the analysis device and classifying them into appropriate categories, an emotion recognition device for recognizing users' emotions based on the natural language processing technology, a generation algorithm device for generating solutions based on the user's emotion data recognized by the emotion recognition device, a presentation device for presenting the solutions generated by the generation algorithm device to users, and a collection device for collecting user feedback received via the presentation device and improving the accuracy of the generation algorithm device. This enables the generation and provision of appropriate solutions that take user emotions into consideration. Furthermore, the collected feedback data can be used to improve the accuracy of the system, contributing to improved accuracy in future solution generation.

[1376] A "user" is an individual who accesses the system, anonymously enters a request, and reviews and implements the solutions provided.

[1377] The "input / output device" is a device that allows users to anonymously input the details of their inquiries and displays solutions from the server.

[1378] The "analysis device" is a device that analyzes the consultation content received from the input / output device and performs tokenization and category classification using natural language processing technology.

[1379] "Natural language processing technology" is a technology for understanding and analyzing human language, tokenizing the content of consultations, and classifying them into appropriate categories.

[1380] An "emotion recognition device" is a device that uses natural language processing technology to recognize emotions from the content of a user's consultation and generates emotion data.

[1381] The "generative algorithm device" is a device that includes an artificial intelligence model for generating optimal solutions based on the emotion data recognized by the emotion recognition device.

[1382] A "presentation device" is a device for presenting to a user a solution generated by a generation algorithm device.

[1383] A "collection device" is a device that collects user feedback received via a presentation device and uses it to improve the accuracy of the generation algorithm device.

[1384] "Feedback" is information that a user records and sends to the system the results and experiences of implementing a provided solution.

[1385] MODE FOR CARRYING OUT THE INVENTION

[1386] This invention is an anonymous consultation system for preventing and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. This system allows users to anonymously input their consultation details and provides optimal solutions based on the details of the consultation.

[1387] User usage of the application

[1388] Users first download the system's application from an application store. When using the system for the first time, they register an account by entering basic information, which ensures the user's anonymity.

[1389] Enter the consultation details

[1390] Users tap "New Consultation" on the main screen of the application and enter the content of their consultation in free-form. For example, they could write specific details such as "I'm having trouble with bullying in my class and I don't know how to deal with it." Once they've finished entering their information, they tap the "Send" button.

[1391] Data transmission and reception

[1392] The device encrypts the consultation content entered by the user and sends it to the server using the HTTPS protocol. The server then passes the received consultation content to an analysis method (for example, using natural language processing libraries such as SpaCy or NLTK).

[1393] Data analysis and emotion recognition

[1394] The server uses natural language processing technology to tokenize the consultation content and classify it into appropriate categories. Based on this, an emotion engine (e.g., emotion recognition models BERT or GPT-3) is activated to recognize the user's emotions. Specifically, it generates emotion tags such as "anxiety" or "fear" from the text.

[1395] Solution Generation

[1396] The server inputs the analyzed consultation content and detected emotion tags as prompts into a generative AI model (e.g., OpenAI's GPT-3), which then generates an optimal solution.

[1397] Examples of prompts include:

[1398] Consultation content: Bullying is an issue in class and I don't know how to deal with it.

[1399] Emotion: Fear

[1400] The generated solutions are formatted at the server and sent to the terminal.

[1401] View and implement solutions

[1402] The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, specific actions such as "Consult a trusted teacher" are displayed on the screen.

[1403] Collecting feedback

[1404] After implementing the solution, the user enters the results and experience as feedback into the application, which is then sent by tapping the "Feedback" button.

[1405] The device sends the feedback information to the server. This communication is also encrypted using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation. Emotional data in particular is useful for model training, contributing to improving the accuracy of future solutions generated by the generative AI model.

[1406] The above is a specific embodiment of this system. The present invention aims to provide a more appropriate and practical solution by taking into account the user's emotions.

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1: User downloads and registers the application

[1409] How it works: A user downloads the system's application from an application store. When using it for the first time, they register an account by entering basic information such as their name, age, and school name. The information they enter is hashed to ensure anonymity.

[1410] Input: User's basic information (name, age, school name, etc.).

[1411] Output: The registered user account.

[1412] Step 2: User inputs the content of the consultation

[1413] Operation: The user selects "New Consultation" from the main screen of the application, enters the consultation details in free text format, and presses the "Send" button to send the data.

[1414] Input: The user's problem (e.g., "I'm having trouble with bullying in my class and I don't know what to do about it.").

[1415] Output: Consultation details saved on the device.

[1416] Step 3: Send data from the device to the server

[1417] How it works: The device encrypts the consultation information entered by the user and sends it to the server using the HTTPS protocol. The data is encrypted to ensure the security of the communication.

[1418] Input: User's consultation (encrypted).

[1419] Output: The consultation details sent to the server.

[1420] Step 4: Data analysis and emotion recognition by the server

[1421] How it works: The server passes the received consultation content to an analysis tool (e.g., a natural language processing library like SpaCy or NLTK), tokenizes the content, and classifies it into appropriate categories. It then uses an emotion recognition model (e.g., BERT or GPT-3) to recognize the user's emotions.

[1422] Input: Received consultation content.

[1423] Output: Tokenized data, emotion tags (e.g., "anxiety", "fear").

[1424] Step 5: Generative AI model generates solutions

[1425] How it works: The server inputs the analyzed consultation content and the recognized emotion tag as a prompt sentence into a generative AI model (e.g., OpenAI's GPT-3). Based on this, the generative AI model generates an optimal solution.

[1426] Input: Prompt statement (e.g., "Contact: Bullying in class is an issue and I don't know what to do. Emotion: Fear").

[1427] Output: Optimal solution (in text format).

[1428] Step 6: Formatting and sending the solution on the server

[1429] How it works: The solution generated by the generative AI model is formatted in an appropriate format (e.g., JSON or XML) and sent to the device.

[1430] Input: The generated solution.

[1431] Output: A formatted solution.

[1432] Step 7: User confirms and implements the solution

[1433] Operation: The device decodes the solution received from the server and displays it on the screen in a user-friendly format. The user can then confirm the solution and take action. For example, a specific action such as "Consult a trusted teacher" is displayed on the screen.

[1434] Input: A formatted solution.

[1435] Output: A specific action to be taken.

[1436] Step 8: Collecting user feedback and sending it to the server

[1437] How it works: After the user implements a solution, they input their results and experience as feedback into the application. The input feedback is sent by tapping the "Feedback" button. The device encrypts the feedback information and sends it to the server using the HTTPS protocol. The server stores the received feedback in a database and uses it to improve the accuracy of future solution generation.

[1438] Input: User feedback.

[1439] Output: Feedback data stored in a database.

[1440] The above is a description of each processing step and its specific operation of this system. By taking into consideration the user's feelings and providing appropriate solutions quickly, it helps prevent and resolve ethical issues.

[1441] (Application example 2)

[1442] 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."

[1443] In today's educational environment, ethical issues such as bullying, moral harassment, power harassment, and sexual harassment frequently occur, often causing mental stress. Conventional problem-solving methods have not provided effective support because they are unable to properly understand the client's emotions and provide applicable solutions. For this reason, there is a need to provide an environment where users can seek advice anonymously, as well as incorporate functions to recognize and analyze emotions in order to provide more specific and appropriate solutions.

[1444] 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.

[1445] In this invention, the server includes input means for users to anonymously input consultation details, analysis means for analyzing and categorizing the consultation details received from the input means, generation AI means for generating solutions based on the consultation details categorized by the analysis means, emotion analysis means for recognizing and analyzing the user's emotions when generating solutions by the emotion analysis means, and means for incorporating emotion data recognized by the emotion analysis means into the solution generation process, thereby making it possible to provide highly applicable solutions that take the user's emotions into consideration.

[1446] "User" refers to an individual who uses the system to input their inquiry and receive a solution.

[1447] "Anonymity" refers to a state in which a user's personal information or name is not revealed, with the aim of protecting the user's privacy.

[1448] "Input means" refers to the method or device by which a user enters the details of their consultation. For example, this includes a smartphone or computer input form.

[1449] "Analysis means" refers to the technology or algorithm used to analyze the received consultation content and classify it into an appropriate category.

[1450] "Generative AI means" refers to artificial intelligence technologies and algorithms that generate optimal solutions based on the consultation content classified by analytical means.

[1451] "Emotion analysis means" refers to the technology and algorithms used to recognize and analyze the user's emotions from the consultation content entered.

[1452] "Presentation Means" refers to a method or device for visually or audibly presenting the solution generated by the Generative AI Means to the user.

[1453] "Collection Method" refers to the technology or algorithm used to collect feedback provided by users on proposed solutions and store it in the system.

[1454] "Authentication methods" refers to techniques and methods for ensuring and guaranteeing the anonymity of users.

[1455] "Database" refers to an electronic information storage system for storing collected feedback and sentiment data and utilizing it for subsequent solution generation.

[1456] A "prompt" refers to the input format that a generative AI model uses to generate a solution and get the optimal output.

[1457] The embodiment of this invention provides a system for anonymously consulting and resolving ethical issues such as bullying, moral harassment, power harassment, and sexual harassment in educational environments. In particular, by incorporating a function to analyze the user's emotions, the system provides more appropriate solutions.

[1458] Hardware and software used

[1459] Hardware: Smartphone (iOS and Android compatible)

[1460] Software: Python, Hugging Face Transformers library, requests library

[1461] System configuration

[1462] User device:

[1463] Users access the system using a smartphone, enter their concerns anonymously through the application, and receive solutions.

[1464] server

[1465] The server includes the following components:

[1466] 1. Input means: A means for receiving the consultation content input by the user.

[1467] 2. Analysis method: The received consultation content is analyzed using natural language processing technology and classified into appropriate categories.

[1468] 3. Emotion analysis method: A method for recognizing and analyzing the user's emotions from the analyzed consultation content. It uses the Hugging Face Transformers library.

[1469] 4. Generative AI method: Based on the user's consultation content and recognized emotion data, the optimal solution is generated using a generative AI model.

[1470] 5. Presentation means: A means for presenting the generated solution to the user.

[1471] 6. Collection methods: User-provided feedback is collected and used to improve the accuracy of the generative AI methods.

[1472] Data Flow

[1473] 1. Input of consultation content: The user inputs the consultation content into the application and sends it to the server.

[1474] 2. Analysis of consultation content: The server passes the received consultation content to an analysis means, tokenizes it using natural language processing technology, and classifies it into appropriate categories.

[1475] 3. Emotion analysis: The analyzed data is input into the emotion analysis means to recognize and analyze the user's emotions.

[1476] 4. Solution generation: The generative AI means generates a solution based on the data obtained by the analysis means and the sentiment analysis means.

[1477] 5. Presentation of the solution: The generated solution is formatted and sent to the user's terminal through a presentation means.

[1478] 6. Feedback collection: The user implements the proposed solution and provides feedback through the application. The collection mechanism sends this feedback to the server and stores it in the database.

[1479] Specific examples

[1480] Example 1:

[1481] If a second-year junior high school student, A, sees a friend being bullied in class but doesn't know what to do, the emotion analysis means will detect A's "helplessness." The generative AI means will suggest ways to deal with bullying, such as consulting a trusted teacher or taking actions to improve self-esteem.

[1482] Example prompt sentence:

[1483] Input: My classmate is being bullied and I don't know what to do about it.

[1484] Emotion: helplessness

[1485] Output formats:

[1486] Suggestion 1: Talk to a trusted teacher or counselor

[1487] Suggestion 2: Actions to improve self-esteem

[1488] Example 2:

[1489] If the mother of a fourth-grader named B says that her son comes home crying every day because he is being strict with a teacher at school, the emotion analysis means will detect the concern. The AI ​​generation means will generate and suggest ways to communicate with the teacher and provide formal feedback to the school.

[1490] Example prompt sentence:

[1491] My son comes home crying every day because he has a strict teacher at school. What should I do?

[1492] Emotion: Concern

[1493] Output formats:

[1494] Suggestion 1: How to communicate with teachers

[1495] Suggestion 2: Formal feedback methods for schools

[1496] In this way, the present invention realizes a system that supports problem solving in educational settings and provides specific and appropriate advice according to the user's emotions.

[1497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1498] Step 1:

[1499] The user anonymously inputs the details of the consultation using a device (smartphone), and the input details are sent to the server.

[1500] Input: User's problem (e.g., "My friend is being bullied in class")

[1501] Output: The consultation content is sent to the server

[1502] Step 2:

[1503] The server passes the received consultation content to an analysis means, where it tokenizes it using natural language processing technology and classifies it into appropriate categories.

[1504] Input: Consultation content received by the server

[1505] Data processing: tokenization and categorization using natural language processing

[1506] Output: Consultation contents categorized

[1507] Step 3:

[1508] The analyzed consultation content is input into the emotion analysis means, and the user's emotions are recognized and analyzed.

[1509] Input: Consultation content categorized

[1510] Data Computation: Sentiment Analysis Using Hugging Face's Transformers Library

[1511] Output: Recognized emotion data (e.g., "helplessness")

[1512] Step 4:

[1513] Based on the data obtained by the analysis means and the sentiment analysis means, the generation AI means generates the optimal solution.

[1514] Input: Categorized consultation content and recognized emotion data

[1515] Data calculation: Enter a prompt into the generative AI model to generate the optimal solution

[1516] Output: Generated solutions (e.g., "How to talk to a trusted teacher" or "What to do to improve self-esteem")

[1517] Step 5:

[1518] The generated solution is formatted and sent to the user's terminal via a presentation means.

[1519] Input: Generated solution

[1520] Data processing: formatting the solution

[1521] Output: The solution sent to the user's device

[1522] Step 6:

[1523] The user reviews and implements the proposed solution, then provides feedback through the application.

[1524] Input: User feedback (e.g., "I felt relieved after implementing the suggested solution")

[1525] Data Processing: Feedback Data Collection and Storage

[1526] Output: The collected feedback data is stored on the server.

[1527] Step 7:

[1528] The collection method uses the provided feedback to improve the accuracy of the generative AI method, and the feedback and emotion data are stored in a database and used for subsequent solution generation.

[1529] Input: Collected feedback and sentiment data

[1530] Data processing: Used as training data for generative AI models

[1531] Output: Improved accuracy in solution generation from next time onwards

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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).

[1539] 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.

[1540] 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."

[1541] 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.

[1542] 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).

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] The following is further disclosed regarding the above embodiment.

[1554] (Claim 1)

[1555] an input means for a user to input the content of a consultation anonymously;

[1556] analysis means for analyzing the consultation contents received from the input means and categorizing them;

[1557] a generation AI means for generating a solution based on the consultation content classified by the analysis means;

[1558] a presentation means for presenting the solution generated by the generation AI means to a user;

[1559] a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means;

[1560] A system including:

[1561] (Claim 2)

[1562] 2. The system according to claim 1, further comprising an authentication means for ensuring the anonymity of a user, and the authentication means guarantees the anonymity of the user.

[1563] (Claim 3)

[1564] 10. The system of claim 1, further comprising means for storing the feedback collected by said collecting means in a database and utilizing the feedback for subsequent solution generation.

[1565] "Example 1"

[1566] (Claim 1)

[1567] an input means for a user to input the content of a consultation anonymously;

[1568] analysis means for analyzing the consultation contents received from the input means and categorizing them;

[1569] a generation AI means for generating a solution based on the consultation content classified by the analysis means;

[1570] a presentation means for presenting the solution generated by the generation AI means to a user;

[1571] a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means;

[1572] an authentication means for authenticating users and controlling access to the system;

[1573] A communication method for encrypting the transmission and reception of consultation contents;

[1574] A system including:

[1575] (Claim 2)

[1576] 10. The system of claim 1, including authentication means for ensuring user anonymity.

[1577] (Claim 3)

[1578] 10. The system of claim 1, further comprising means for storing the feedback collected by said collecting means in a database and utilizing the feedback for subsequent solution generation.

[1579] "Application Example 1"

[1580] (Claim 1)

[1581] an input means for a user to input the content of a consultation anonymously;

[1582] analysis means for analyzing the consultation contents received from the input means and categorizing them;

[1583] a generation AI means for generating a solution based on the consultation content classified by the analysis means;

[1584] a presentation means for presenting the solution generated by the generation AI means to a user;

[1585] A system that includes a means for company employees to anonymously consult and report issues such as harassment, bullying, and whistle-blowing that they face in the workplace, and

[1586] a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means;

[1587] A system including:

[1588] (Claim 2)

[1589] 2. The system according to claim 1, wherein an authentication means is provided to ensure the anonymity of users, and the authentication means is used to guarantee the anonymity of users, allowing company employees to anonymously seek advice on problems.

[1590] (Claim 3)

[1591] 10. The system of claim 1, further comprising means for storing the feedback collected by said collecting means in a database and utilizing the feedback for subsequent solution generation.

[1592] "Example 2: Combining Emotion Engines"

[1593] (Claim 1)

[1594] an input / output device for users to anonymously input their consultation details;

[1595] an analysis device for analyzing and categorizing the consultation content received from the input / output device;

[1596] a device including a natural language processing technology that tokenizes the consultation content classified by the analysis device and classifies it into appropriate categories;

[1597] an emotion recognition device that recognizes a user's emotion based on the natural language processing technology;

[1598] a generation algorithm device that generates a solution based on the user's emotion data recognized by the emotion recognition device;

[1599] a presentation device for presenting the solution generated by the generation algorithm device to a user;

[1600] a collection device for collecting user feedback received via the presentation device to improve the accuracy of the generation algorithm device;

[1601] A system including:

[1602] (Claim 2)

[1603] 2. The system according to claim 1, further comprising an authentication device for ensuring the anonymity of a user, wherein the authentication device guarantees the anonymity of the user.

[1604] (Claim 3)

[1605] 10. The system of claim 1, further comprising means for storing the feedback collected by the collection device in a database and utilizing the feedback for subsequent solution generation.

[1606] "Application example 2 when combining emotion engines"

[1607] Rewritten claims

[1608] (Claim 1)

[1609] an input means for a user to input the content of a consultation anonymously;

[1610] analysis means for analyzing the consultation contents received from the input means and categorizing them;

[1611] a generation AI means for generating a solution based on the consultation content classified by the analysis means;

[1612] emotion analysis means for recognizing and analyzing a user's emotion when generating a solution by the generation AI means;

[1613] means for incorporating emotion data recognized by said emotion analysis means into a solution generation process;

[1614] a presentation means for presenting the solution generated by the generation AI means to a user;

[1615] a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means;

[1616] A system including:

[1617] (Claim 2)

[1618] 2. The system according to claim 1, further comprising an authentication means for ensuring the anonymity of a user, and the authentication means guarantees the anonymity of the user.

[1619] (Claim 3)

[1620] The system of claim 1 further comprising: means for storing the feedback collected by the collection means in a database and using the feedback for subsequent solution generation; and means for adjusting the generation prompts of the generation AI means based on the emotion data collected by the emotion analysis means. [Explanation of symbols]

[1621] 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. an input means for a user to input the content of a consultation anonymously; analysis means for analyzing the consultation contents received from the input means and categorizing them; a generation AI means for generating a solution based on the consultation content classified by the analysis means; a presentation means for presenting the solution generated by the generation AI means to a user; a collection means for collecting user feedback received via the presentation means and improving the accuracy of the generation AI means; A system including:

2. 2. The system according to claim 1, further comprising an authentication means for ensuring the anonymity of a user, wherein the anonymity of the user is guaranteed by said authentication means.

3. 2. The system of claim 1, further comprising means for storing the feedback collected by said collecting means in a database and utilizing the feedback for subsequent solution generation.

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