System

Abstract

To provide an interview practice support system using a generated AI for examinees, job seekers, or job changers.SOLUTION: Acquiring basic information based on an input from a user, acquiring a question for self-analysis such as a suitability test or a personality diagnosis from a server, presenting the acquired question to the user and acquiring an answer of the user, transmitting the answer data of the user to the server and storing the answer data, acquiring a candidate list of a desired destination based on the self-analysis data of the user, transmitting desired destination information to the server and storing the desired destination information, and acquiring desired destination information based on the self-analysis data and the desired destination information of the user. A system, comprising: means for generating interview questions by a generating AI; means for providing the generated interview questions to users to obtain user answers; means for generating feedback based on the user answers and providing the feedback to the users; and means for storing the user interview results and the feedback in a server to improve a AI model.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] For students taking entrance exams, job seekers, and those seeking a new job, interviews are a crucial moment. However, due to a lack of adequate practice environments, many people approach the actual interview with anxiety. Traditional interview practice methods have limitations on the number of practice sessions and the quality of feedback, often leaving people unable to approach the interview with confidence. It is also difficult to determine the degree to which the results of one's self-analysis match the profile of the person desired by the school or company one is applying to. This can easily lead to mismatches with desired employers, resulting in potential failures and hiring mistakes. [Means for solving the problem]

[0005] This invention provides an interview practice support system using generative AI for applicants, job seekers, and career changers. Based on basic information entered by the user, this system retrieves aptitude tests and personality assessment questions for self-analysis from a server, presents them to the user, and obtains answers. The obtained answer data is stored on the server, and the user's self-analysis results are generated. Next, a list of potential employers is retrieved from the server, and the employer information selected by the user is stored on the server. Based on this information, the generative AI generates interview questions and provides them to the user. Feedback is generated based on the user's answers and provided to the user, allowing the user to improve the quality of their interview. This system also has a function to automatically determine the degree of match based on the self-analysis results and the desired personality profile of the employer, thereby reducing mismatches with the employer. Furthermore, the AI ​​model continuously learns based on the feedback, improving the accuracy of interview practice, allowing users to practice more effectively. This provides an environment in which users can approach actual interviews with confidence.

[0006] "Exam candidates, job seekers, or job-changers" refers to individuals taking entrance exams, seeking employment, or seeking a new job.

[0007] "Interview practice" is an activity in which participants practice answering questions in preparation for an actual interview.

[0008] "Generative AI" refers to a system that uses artificial intelligence technology to dynamically generate questions and feedback.

[0009] "Basic information" refers to personal information such as the user's name, email address, and date of birth.

[0010] "Aptitude tests" and "personality tests" are tests used to evaluate a user's abilities and personality.

[0011] "Self-analysis" is a process by which users themselves understand their own personality and abilities.

[0012] A "question" is a question that requests the user to answer.

[0013] An "answer" is a response that a user provides to a question.

[0014] A "server" is a computer system for processing, managing, and storing data.

[0015] A "database" is a collection of data, a collection of information organized for easy management and retrieval.

[0016] The "list of potential schools" is a list of schools or companies that the user wishes to attend or work for.

[0017] "Feedback" is information that includes evaluations of the user's answers and suggestions for improvement.

[0018] The "matching degree" is an index showing the degree of agreement between the results of the user's self-analysis and the type of person the desired employer is looking for.

[0019] An "AI model" is a computational model that has been trained to perform a specific task using machine learning techniques. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is an interview practice support system designed for test takers, job seekers, or people looking to change jobs, and it uses a generative AI to provide users with appropriate questions and feedback. This system consists of a server, a terminal used by the user, and the user.

[0042] Handling user registration and profile entries

[0043] Terminal

[0044] When the app is launched for the first time, it displays a user registration screen, providing input fields for the user's name, email address, date of birth, etc.

[0045] server

[0046] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[0047] User

[0048] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[0049] Conducting self-analysis

[0050] Terminal

[0051] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[0052] server

[0053] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[0054] User

[0055] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[0056] Choosing your preferred destination

[0057] Terminal

[0058] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[0059] server

[0060] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[0061] User

[0062] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[0063] Conducting interview simulations

[0064] Terminal

[0065] It displays questions generated by the AI ​​one by one and provides an interface where users can enter answers. After entering the answers, they are sent to the server.

[0066] server

[0067] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database.

[0068] User

[0069] The user inputs an answer to the displayed question and transmits the answer data to the server.

[0070] Providing feedback

[0071] Terminal

[0072] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[0073] server

[0074] Based on the user's response data, the generation AI generates feedback, which is then delivered to the device.

[0075] User

[0076] Review the feedback provided and understand areas for improvement.

[0077] Saving results and improving AI models

[0078] Terminal

[0079] The results of the interview simulation are sent to the server, which stores the user's practice history.

[0080] server

[0081] The stored interview result data is used to train the generative AI model to improve it. As the AI ​​model continues to learn, it will be able to provide users with more accurate questions and feedback in the future.

[0082] User

[0083] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[0084] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[0085] The processing flow will be explained below.

[0086] Handling user registration and profile entries

[0087] Step 1:

[0088] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[0089] Step 2:

[0090] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[0091] Step 3:

[0092] Terminal: Checks the entered information and sends it to the server.

[0093] Step 4:

[0094] Server: Stores the received user information in a database.

[0095] Conducting self-analysis

[0096] Step 1:

[0097] User: Presses a button to start self-analysis.

[0098] Step 2:

[0099] Device: Retrieves aptitude and personality test questions from the server.

[0100] Step 3:

[0101] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[0102] Step 4:

[0103] User: Enter answers to the questions displayed.

[0104] Step 5:

[0105] Terminal: Sends the entered answer data to the server.

[0106] Step 6:

[0107] Server: Stores the received response data in a database.

[0108] Choosing your preferred destination

[0109] Step 1:

[0110] User: Select the "Select School of Interest" option from the menu.

[0111] Step 2:

[0112] Terminal: Obtain a list of universities and companies of interest from the server.

[0113] Step 3:

[0114] Terminal: Display the obtained list and let the user select.

[0115] Step 4:

[0116] User: Select the school or company of your choice from the list of preferred schools.

[0117] Step 5:

[0118] Terminal: Sends the selected desired school information to the server.

[0119] Step 6:

[0120] Server: Save the received information about the desired school in a database.

[0121] Conducting interview simulations

[0122] Step 1:

[0123] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[0124] Step 2:

[0125] Terminal: Displays the generated questions one by one.

[0126] Step 3:

[0127] User: Enter answers to the questions displayed.

[0128] Step 4:

[0129] Terminal: Temporarily holds the entered answers.

[0130] Step 5:

[0131] Terminal: Sends the response data to the server.

[0132] Step 6:

[0133] Server: Stores the received response data in a database.

[0134] Providing feedback

[0135] Step 1:

[0136] Server: The generative AI generates feedback based on the user's answers.

[0137] Step 2:

[0138] Server: Delivers the generated feedback to the device.

[0139] Step 3:

[0140] Terminal: Display feedback to the user.

[0141] Step 4:

[0142] Users: Review and understand the feedback.

[0143] Saving results and improving AI models

[0144] Step 1:

[0145] Terminal: Sends the results of the interview simulation to the server.

[0146] Step 2:

[0147] Server: Stores the received interview result data in a database.

[0148] Step 3:

[0149] Server: Uses the saved result data to learn and improve the AI ​​model.

[0150] Step 4:

[0151] Server: The AI ​​model continuously learns and generates more accurate questions and feedback.

[0152] The above is a specific processing flow for carrying out the present invention. The user, terminal, and server each play their respective roles and cooperate to form the entire system.

[0153] Example 1

[0154] 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."

[0155] Conventional interview practice support systems have difficulty automatically generating appropriate interview questions tailored to the user's individual characteristics and desired employer, and are unable to provide specific and accurate feedback. Furthermore, they lack a continuous learning function to improve the accuracy of interview practice, leaving a need for a system that can adequately meet user needs. Furthermore, they lack a support function for users to formulate specific action plans based on feedback, limiting the effectiveness of users' self-improvement.

[0156] 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.

[0157] In this invention, the server includes: means for acquiring basic information based on user input; means for acquiring self-analysis questions such as aptitude tests and personality tests from the server; means for presenting the acquired questions to the user and acquiring the user's answers; means for transmitting and storing the user's answer data to the server; means for acquiring a list of candidate employers based on the user's self-analysis data; means for transmitting and storing the user's selected employer information to the server; means for a generation AI to generate interview questions based on the user's self-analysis data and employer information; means for providing the generated interview questions to the user and acquiring the user's answers; means for generating and providing feedback based on the user's answers; means for saving the user's interview result data and feedback to the server and improving the AI ​​model; means for generating appropriate feedback using the generation AI model; means for providing a timer function during the interview simulation to automatically prompt the user to answer; and means for providing a memo function when displaying the feedback so that the user can create a specific action plan. This allows the user to receive interview questions tailored to their characteristics and employers of choice, receive specific and accurate feedback, and improve the accuracy of their interview practice through continuous learning. In addition, the effectiveness of self-improvement can be enhanced by utilizing the support functions for formulating specific action plans.

[0158] "User" refers to a candidate, job seeker, or job seeker who uses this system.

[0159] "Basic information" refers to information such as name, email address, and date of birth that a user enters when registering with the system.

[0160] A "server" is a device that processes and stores various data, and generates and distributes aptitude tests, self-analysis questions, interview questions, etc.

[0161] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet, that provides an input interface and display functions.

[0162] An "aptitude test" is a question or test that assesses a user's characteristics or abilities.

[0163] A "personality test" is a question or test that analyzes a user's personality and behavioral patterns.

[0164] "Self-analysis data" refers to the answers and results of the user's responses to aptitude tests and personality tests.

[0165] "Desired destination" refers to the university, company, or other destination the user desires to attend.

[0166] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate interview questions and feedback based on user information.

[0167] "Interview questions" are questions presented to the user in the interview simulation.

[0168] "Feedback" refers to evaluations and improvements on the user's answers.

[0169] "Interview result data" is data that includes the answers given by the user in the interview simulation and the feedback given to those answers.

[0170] The "timer function" is a function that prompts the user to enter an answer within a certain time period.

[0171] The "memo function" is a function that allows users to create specific action plans based on feedback.

[0172] An "AI model" is an algorithm that learns from user data and improves the accuracy of interview practice.

[0173] This invention is a support system that allows test takers, job seekers, and people looking to change jobs to effectively practice for interviews. The system consists of a terminal used by the user and a server that processes data and generates questions. Users can perform the entire process from registration to interview practice and receiving feedback.

[0174] User registration and profile entry

[0175] Terminal

[0176] When a user launches the app for the first time, a user registration screen is displayed. The user enters information such as their name, email address, and date of birth. For example, the user enters "Yamada Hanako" in the name field, "hanako@example.com" in the email address field, and "1995-06-20" in the date of birth field.

[0177] server

[0178] It receives user information sent from the device and stores it in a MySQL (registered trademark) database, thereby accumulating basic user information.

[0179] User

[0180] By inputting information and pressing the send button, the terminal transmits the information to the server.

[0181] Conducting self-analysis

[0182] Terminal

[0183] The app displays aptitude or personality questions, such as "What are your strengths?" The user answers the questions and the device sends the answers to a server.

[0184] server

[0185] The server receives the data from the receiving side and stores it in an SQLite database. The question and answer data is later used as self-analysis results.

[0186] User

[0187] The user enters an answer to the displayed question and presses the send button to send the answer data to the server.

[0188] Choosing your preferred destination

[0189] Terminal

[0190] The app displays a list of universities and companies you are interested in. You select an option from the list and send the selected information to the server. For example, let's say you select "XYZ Company."

[0191] server

[0192] The server receives the selected information about the desired school and stores it in a database.

[0193] User

[0194] The user selects the desired destination and presses the OK button to send the selection to the server.

[0195] Conducting interview simulations

[0196] server

[0197] Based on the user's self-analysis and desired company information, Generating AI generates appropriate interview questions. For example, it might ask, "What are your reasons for applying?"

[0198] Terminal

[0199] The generated question is displayed to the user, who answers the question and the terminal transmits the answer data to the server.

[0200] User

[0201] In response to the question that appears, enter "I sympathize with your philosophy of contributing to society and would like to work for your company," and press the send button.

[0202] Providing feedback

[0203] server

[0204] The generative AI generates feedback based on the user's answers. For example, it might say, "It would be better if you included specific examples in your motivation for applying."

[0205] Terminal

[0206] Feedback is displayed to the user, who can review it and understand where improvements can be made.

[0207] User

[0208] The feedback provided can be used to improve your next practice.

[0209] Saving results and improving AI models

[0210] Terminal

[0211] The results of the interview simulation are sent to a server, and the user's practice history is saved.

[0212] server

[0213] The server stores the resulting data and uses it to train the Generating AI model, which will improve the accuracy of interview questions and feedback for future interviews.

[0214] User

[0215] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[0216] concrete action

[0217] Timer function: During the interview simulation, the user is prompted to enter answers within a certain time.

[0218] Notes function: While viewing feedback, users can enter notes to help them create specific action plans.

[0219] Prompt Sentence Examples

[0220] "What are your hobbies?"

[0221] "What has been the most challenging experience you have had so far?"

[0222] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[0223] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0224] Step 1: User registration and profile entry

[0225] User: Launch the app and the user registration screen will appear. Enter information such as "Yamada Hanako," "hanako@example.com," and "1995-06-20," and press the registration button.

[0226] Terminal: Receives information entered by the user, converts it into JSON format, and sends it to the server.

[0227] Server: Receives JSON data and saves it to a database. Specifically, it saves the information "Hanako Yamada", "hanako@example.com", and "1995-06-20" in a MySQL database. The input is user information, and the output is saved to the database.

[0228] Step 2: Conduct a self-analysis

[0229] Terminal: Displays aptitude test and personality assessment questions received from the server. The question "What are your strengths?" is displayed.

[0230] User: Answers the question by typing "Analytical ability" and presses the send button.

[0231] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[0232] Server: Receives the answer data and saves it in a database. Specifically, it saves the answer "analytical ability" in an SQLite database. The input is the question and answer, and the output is saved in the database.

[0233] Step 3: Choose your preferred school

[0234] Terminal: Displays the list of companies of interest received from the server. Lists such as "ABC Company" and "XYZ Company" are displayed.

[0235] User: Selects "XYZ Company" as the company of choice and presses the OK button.

[0236] Terminal: Converts the information selected by the user into JSON format and sends it to the server.

[0237] Server: Receives the selected company information and saves it in the database. Specifically, it saves the selected company "XYZ Company" in the database. The input is the company information, and the output is saving it in the database.

[0238] Step 4: Conduct a simulated interview

[0239] Server: Based on the user's self-analysis results and desired employer information, the generative AI model generates appropriate interview questions. For example, it generates a question such as, "What is your motivation for applying?"

[0240] Terminal: The generated interview questions are displayed to the user.

[0241] User: Answers the question by saying, "I sympathize with your philosophy of contributing to society and would like to work for your company," and presses the send button.

[0242] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[0243] Server: Receives the response data and stores it in a database. Specifically, the response "I sympathize with your philosophy of social contribution and would like to work for your company" is stored in the database. The input is the question and the response, and the output is stored in the database.

[0244] Step 5: Provide feedback

[0245] Server: The generative AI model generates feedback based on the user's response data. For example, it might generate feedback such as, "It would be better if you included specific examples in your motivation for applying."

[0246] Terminal: The generated feedback is displayed to the user.

[0247] User: Check the feedback and use it for the next practice. As a concrete example, the user will try to provide more specific answers in the next simulation based on the feedback that "please include specific examples in your motivation for applying." The input is the answer data, and the output is the feedback.

[0248] Step 6: Saving the results and improving the AI ​​model

[0249] Terminal: Converts the interview simulation results into JSON format and sends them to the server.

[0250] Server: Receives the result data and stores it in a database. In addition, the generative AI model learns based on the result data, improving the accuracy of interview questions and feedback. The input is the simulation result data, and the output is the learning and accuracy improvement of the AI ​​model.

[0251] Users: The next time they take a simulated interview, they will receive improved questions and feedback, which will lead to more effective practice and improved performance in the actual interview.

[0252] This allows users to consistently practice interviews effectively and gain confidence in real interviews. Furthermore, the Generating AI model continuously learns based on feedback and simulated interview results, enabling it to provide high-quality support.

[0253] (Application example 1)

[0254] 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."

[0255] Conventional interview practice support systems have the problem of only being able to provide limited support to test takers, job seekers, or people looking to change jobs. Similarly, there is a lack of effective support for training new store clerks to improve their customer service skills. This presents a problem in that customer service practice cannot be effectively conducted in physical stores, making it difficult to improve service. To solve these problems, the present invention aims to expand the interview practice support system and provide a comprehensive practice support system that can also accommodate customer service practice for store clerks.

[0256] 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.

[0257] In this invention, the server comprises: means for acquiring basic information based on an input from a user;

[0258] A means for acquiring questions for self-analysis such as aptitude tests and personality tests from a server;

[0259] means for presenting the acquired question to a user and acquiring an answer from the user;

[0260] means for transmitting and storing user response data on a server;

[0261] A means for acquiring a list of candidates for desired employment based on the user's self-analysis data;

[0262] A means for transmitting the desired school information selected by the user to a server and storing it;

[0263] A means for the AI ​​to generate interview questions based on the user's self-analysis data and desired employer information;

[0264] means for providing the generated interview questions to a user and obtaining answers from the user;

[0265] means for generating and providing feedback to the user based on the user's responses;

[0266] A means for storing the user's interview result data and feedback on a server and improving the AI ​​model;

[0267] To support sales staff in their customer service practice, a means is provided for acquiring profile information from users and for a generation AI to generate questions necessary for customer service based on the type of store.

[0268] A means for providing the generated customer service question to a store clerk and obtaining an answer from the store clerk;

[0269] a means for generating and providing feedback to the sales associate based on the sales associate's responses;

[0270] A means to suggest areas for improvement to improve customer service skills based on feedback

[0271] This will not only help prepare students for interviews, job seekers, and people looking to change jobs for interview practice, but will also improve the accuracy of customer service practice for new store employees.

[0272] "Exam takers" are students who intend to take an exam.

[0273] A "job seeker" is an individual who is actively seeking new employment with a company or organization.

[0274] A "job seeker" is an individual who is seeking a job change from their current workplace to another workplace.

[0275] An "interview practice support system" is an information processing system designed to help users practice for interviews effectively.

[0276] "Basic information" refers to basic information about the user, such as the user's name, email address, years of experience, etc.

[0277] An "aptitude test" is a test used to evaluate an individual's abilities and personality, and is used to understand the user's aptitude.

[0278] A "personality test" is a test for assessing an individual's personality traits and is used to understand a user's personality.

[0279] "Self-analysis data" is a collection of information obtained from the results of a user's responses to aptitude tests and personality assessments.

[0280] "Preferences" refers to universities, companies, etc. that the user wishes to attend or work for.

[0281] "Generative AI" is a system that uses artificial intelligence technology to generate interview questions and feedback based on user data.

[0282] "Customer service practice" refers to new store employees practicing the skills necessary to effectively serve customers in a physical store.

[0283] "Profile information" refers to information registered by a user, such as name, email address, years of experience, and type of store.

[0284] "Store type" refers to store categories such as fashion, restaurants, supermarkets, etc.

[0285] "Feedback" is information about evaluations and areas for improvement that are generated based on the user's responses.

[0286] "Areas for improvement" refers to points or suggestions that users or store clerks need to improve through practice.

[0287] This invention provides a practice support system for test takers, job seekers, people looking to change jobs, and new store clerks. To implement this invention, a server, a user terminal, and a generative AI model are used.

[0288] Handling user registration and profile entries

[0289] Terminal

[0290] When a user launches the app for the first time, they are presented with a user registration screen, which provides fields for entering profile information such as name, email address, date of birth, years of experience, etc. Once the user enters this information and presses the submit button, the information is sent to the server.

[0291] server

[0292] The server receives the user information sent from the terminal and stores it in a database, thereby accumulating basic user information for use in subsequent processes.

[0293] Conducting self-analysis

[0294] Terminal

[0295] Aptitude tests and personality tests are displayed to the user, who then enters answers to the questions and presses the send button to send the answer data to the server.

[0296] server

[0297] The server distributes self-analysis questions to the user's terminal and stores the answer data received from the terminal in a database, which is later used as the user's self-analysis results.

[0298] Choosing your preferred destination

[0299] Terminal

[0300] It displays a list of universities and companies that the user is interested in and provides an interface for them to select from. The user selects the university or company they want, presses the OK button, and sends the selection to the server.

[0301] server

[0302] The server distributes the list of desired companies and stores the information on the desired companies selected by the user in a database.

[0303] Interview simulation and customer service practice

[0304] Terminal

[0305] It displays questions generated by the AI ​​and provides an interface for users to input their answers. Users input answers to the displayed questions and press the send button to send the answer data to the server.

[0306] server

[0307] The server uses a generation AI to generate appropriate interview questions based on the user's self-analysis results and desired employer information. Similarly, it generates customer service practice questions based on the employee's profile information and the type of store. The generated questions are sent to the device, which receives the user's response data and stores it in a database.

[0308] Providing feedback

[0309] Terminal

[0310] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[0311] server

[0312] The server uses AI to generate feedback based on the user's response data and delivers it to the device, allowing the user to practice based on the feedback.

[0313] Saving results and improving AI models

[0314] Terminal

[0315] The results of the user's interview simulation and customer service practice are sent to the server, which stores the user's practice history.

[0316] server

[0317] The server uses the stored interview result data and feedback data to improve the AI ​​model. This continuous learning allows the generative AI to provide more accurate questions and feedback in the future.

[0318] For example, in the case of a customer service simulation for a fashion store, the following prompts could be used:

[0319] "Customer service simulation for new sales associates: Fashion\nPlease generate questions."

[0320] By using such prompt sentences, the generative AI can generate questions that correspond to specific store situations.

[0321] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0322] Step 1:

[0323] User registration and profile entry

[0324] When a user launches the app on their device for the first time, a user registration screen appears. Here, the user enters profile information such as name, email address, date of birth, and years of experience. Once the information is entered, this data is sent from the device to the server. The server saves the received data in a database and accumulates the user's basic information.

[0325] Input: Name, email address, date of birth, years of experience

[0326] Output: User information stored in the database

[0327] Step 2:

[0328] Conducting self-analysis

[0329] Aptitude tests and personality assessment questions for self-analysis are sent from the server to the user's device. The device presents these questions to the user, who then enters their answers. Once the answers are entered, the device sends them to the server. The server receives the answer data and stores it in a database.

[0330] Input: Self-assessment questions, user answers

[0331] Output: Answer data stored in a database

[0332] Step 3:

[0333] Choosing your preferred destination

[0334] A list of universities and companies of choice is sent from the server to the user's device. The device displays the list to the user, who then selects the desired university. Once the selection is complete, the user's device sends the information to the server. The server stores the received information in a database.

[0335] Input: Preferred list, user selection

[0336] Output: Information about the desired school saved in the database

[0337] Step 4:

[0338] Interview simulation and customer service practice

[0339] The generation AI generates interview questions based on the user's self-analysis results and information about the company they wish to work for. In the case of customer service practice for a store clerk, customer service questions are generated based on the user's profile information and the type of store. The generated questions are sent from the server to the user's device, and the user answers them. The device sends the user's answers to the server, which stores them in a database.

[0340] Input: Self-analysis results, desired employer information, AI prompt

[0341] Output: Generated interview questions, answers stored in a database

[0342] Step 5:

[0343] Providing feedback

[0344] The server uses the AI ​​to generate feedback based on the user's response data. The generated feedback is then sent from the server to the user's device. The device then displays the feedback to the user and suggests areas for improvement.

[0345] Input: User response data, prompts for the AI ​​generator

[0346] Output: generated feedback, presented to the user

[0347] Step 6:

[0348] Saving results and improving AI models

[0349] The results and feedback data of the user's interview simulation and customer service practice are sent to a server and stored in a database. The server continuously trains the generative AI model based on this stored data, enabling it to provide more accurate questions and feedback in the future.

[0350] Input: Interview result data, feedback data

[0351] Output: Result data stored in a database, improved AI model

[0352] 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.

[0353] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a server, a terminal, and a user.

[0354] Handling user registration and profile entries

[0355] Terminal

[0356] When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[0357] server

[0358] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[0359] User

[0360] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[0361] Conducting self-analysis

[0362] Terminal

[0363] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[0364] server

[0365] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[0366] User

[0367] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[0368] Choosing your preferred destination

[0369] Terminal

[0370] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[0371] server

[0372] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[0373] User

[0374] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[0375] Conducting interview simulations

[0376] Terminal

[0377] It displays questions generated by the generative AI one by one, provides an interface where the user can enter answers, and operates an emotion engine that recognizes the user's emotions.

[0378] server

[0379] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database. In addition, emotional data generated by the emotion engine is also stored.

[0380] User

[0381] Answers are entered to the questions displayed, and sentiment analysis is performed using an emotion engine.

[0382] Providing feedback

[0383] server

[0384] The AI ​​generates feedback based on the user's response data and emotional data, and delivers the generated feedback to the device.

[0385] Terminal

[0386] Feedback is displayed to the user, including an evaluation of the answer, suggestions for improvement, and advice on how to respond based on emotional data.

[0387] User

[0388] Review and understand the feedback provided, and use it to understand how to manage your emotions and improve.

[0389] Saving results and improving AI models

[0390] Terminal

[0391] The results of the interview simulation and emotional data are sent to the server, which stores the user's practice history.

[0392] server

[0393] The AI ​​model uses the saved interview result data and emotion data to learn and improve itself. By continuously learning, the AI ​​model will be able to provide users with more accurate questions and feedback in the future.

[0394] User

[0395] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[0396] In this way, the present invention provides an environment that allows users to approach real interviews with confidence. Furthermore, the generative AI and emotion engine create a practice environment that takes the user's feelings into consideration by generating appropriate feedback based on the user's emotional data. This allows users to practice interviews in a more relaxed state and effectively identify areas for improvement.

[0397] The processing flow will be explained below.

[0398] Handling user registration and profile entries

[0399] Step 1:

[0400] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[0401] Step 2:

[0402] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[0403] Step 3:

[0404] Terminal: Checks the entered information and sends it to the server.

[0405] Step 4:

[0406] Server: Stores the received user information in a database.

[0407] Conducting self-analysis

[0408] Step 1:

[0409] User: Presses a button to start self-analysis.

[0410] Step 2:

[0411] Device: Retrieves aptitude and personality test questions from the server.

[0412] Step 3:

[0413] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[0414] Step 4:

[0415] User: Enter answers to the questions displayed.

[0416] Step 5:

[0417] Terminal: Sends the entered answer data to the server.

[0418] Step 6:

[0419] Server: Stores the received response data in a database.

[0420] Choosing your preferred destination

[0421] Step 1:

[0422] User: Select the "Select School of Interest" option from the menu.

[0423] Step 2:

[0424] Terminal: Obtain a list of universities and companies of interest from the server.

[0425] Step 3:

[0426] Terminal: Display the obtained list and let the user select.

[0427] Step 4:

[0428] User: Select the school or company of your choice from the list of preferred schools.

[0429] Step 5:

[0430] Terminal: Sends the selected desired school information to the server.

[0431] Step 6:

[0432] Server: Save the received information about the desired school in a database.

[0433] Conducting interview simulations

[0434] Step 1:

[0435] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[0436] Step 2:

[0437] Terminal: Displays the generated questions one by one and also runs an emotion engine to recognize the user's emotions during the interview.

[0438] Step 3:

[0439] User: Enter answers to the questions displayed.

[0440] Step 4:

[0441] Emotion engine: Obtains emotional data from the user's facial expressions and tone of voice during the interview.

[0442] Step 5:

[0443] Terminal: Temporarily stores the user's answers and emotion data.

[0444] Step 6:

[0445] Terminal: Sends response data and emotion data to the server.

[0446] Step 7:

[0447] Server: Stores the received response data and emotion data in a database.

[0448] Providing feedback

[0449] Step 1:

[0450] Server: The AI ​​generates feedback based on the user's response data and emotional data.

[0451] Step 2:

[0452] Server: Delivers the generated feedback to the device.

[0453] Step 3:

[0454] On the device, feedback is displayed to the user, including a rating of the answer, suggestions for improvement, and emotional advice based on the emotional data.

[0455] Step 4:

[0456] Users: Review the feedback provided and understand areas for improvement.

[0457] Saving results and improving AI models

[0458] Step 1:

[0459] Terminal: Sends the results of the interview simulation and emotional data to the server.

[0460] Step 2:

[0461] Server: Stores the received interview result data and emotion data in a database.

[0462] Step 3:

[0463] Server: Uses the stored result data and emotion data to learn and improve the AI ​​model.

[0464] Step 4:

[0465] Server: The AI ​​model continuously learns, enabling it to generate more accurate questions and feedback in the future.

[0466] The above is a specific processing flow for implementing the present invention. The user, terminal, server, and emotion engine each play their own roles while working together to form the entire system.

[0467] Example 2

[0468] 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."

[0469] Conventional interview practice systems have not been able to generate appropriate interview questions based on the user's self-analysis results and information about their desired employer, nor have they provided sufficient feedback that reflects real-time emotional data. Furthermore, there have been no systems that improve the accuracy of interview practice by continuously training a generative AI model using the user's practice results and emotional data. This has made it difficult for users to effectively improve their practical interview skills.

[0470] 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.

[0471] In this invention, the server includes a means for the generation AI to generate interview questions based on the user's self-analysis data and desired employer information, a means for the terminal to acquire emotional data and recognize the user's emotional state, and a means for continuously training the generation AI model using the saved interview result data and emotional data. This allows the user to receive appropriate questions based on the self-analysis results and desired employer information, and furthermore, by receiving feedback based on the user's emotional state, enables practical and effective interview practice.

[0472] "User" refers to an individual who uses the information processing system, such as a student taking an exam, a job seeker, or a person looking to change jobs.

[0473] "Basic information" refers to information that identifies and locates an individual, such as a user's name, email address, and date of birth.

[0474] An "aptitude test" is a question or test designed to assess a user's aptitudes, interests, abilities, etc.

[0475] A "personality assessment" is a set of questions or tests designed to assess a user's personality and behavioral traits.

[0476] "Self-Analysis Questions" refers to a list of questions used to deepen a user's self-understanding.

[0477] "Preferred school" refers to the school or company for which the user wishes to take an entrance exam, get a job, or change jobs.

[0478] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate interview questions and feedback.

[0479] "Interview Questions" are questions asked to the user and provided by the generative AI as part of the interview simulation.

[0480] "Feedback" refers to the evaluation and improvement suggestions provided to the user regarding their interview practice.

[0481] "Emotion data" refers to information about the user's emotional state that is analyzed based on facial expressions, tone of voice, etc.

[0482] "AI Model" refers to a computational model that learns and improves using artificial intelligence techniques.

[0483] "Continuous learning" refers to the means by which AI models are regularly updated and improved based on collected data.

[0484] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a user, a terminal, and a server.

[0485] First, when a user launches the application, a user registration screen appears on the device. After the user enters basic information such as name, email address, and date of birth, the device sends this information to the server. The server stores the received basic information in a database. Through this process, the system accumulates user profile information.

[0486] When the user moves to the next screen for self-analysis, the server sends a list of questions for aptitude tests and personality assessments to the terminal. The terminal displays these questions to the user, and the user enters answers to each question. Once the answers are completed, the terminal sends the answer data to the server, which stores it in a database.

[0487] The user then moves to a screen for selecting their preferred universities and companies, and the server sends a list of their preferred universities and companies to the terminal. Once the user selects their preferred universities and companies, the terminal sends the selected university information to the server, which then stores it in a database.

[0488] When the interview simulation stage begins, the server sends prompts to the generation AI based on the user's self-analysis results and information about the company they wish to work for. The generation AI generates appropriate interview questions, which the server then sends to the device. The device displays the questions, and the user enters their answers. While the answers are being entered, the device activates an emotion engine to analyze the user's emotional state and obtain emotional data. This data is then sent from the device to the server, which stores it in a database.

[0489] The server then generates feedback by sending prompts to the AI ​​based on the user's response data and emotion data. The generated feedback is sent to the device and displayed to the user. The feedback includes an evaluation of the response, suggestions for improvement, and advice based on the emotion data.

[0490] Finally, the results of the interview simulation and emotional data are sent to and stored on a server, which uses this data to continuously train the generative AI model and provide more accurate questions and feedback to users in the future.

[0491] For example, the generative AI will ask the question, "Please introduce yourself in one minute." It will also provide advice based on emotion recognition, such as, "Take a deep breath and relax." This system allows users to practice effectively so they can approach the actual interview with confidence.

[0492] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0493] Step 1:

[0494] The user launches the app.

[0495] Input: A user launches an application on their smartphone or PC.

[0496] Output: The user is taken to the login or sign up screen.

[0497] What happens: The user taps or clicks the icon and the application displays its welcome screen.

[0498] Step 2:

[0499] The user enters basic information.

[0500] Input: The user enters basic information such as name, email address, and date of birth.

[0501] Output: The user's basic information is temporarily stored on the device.

[0502] Specific actions: The user fills in the required information in the form and clicks the "Register" button.

[0503] Step 3:

[0504] The device sends basic information to the server.

[0505] Input: Basic information entered by the user.

[0506] Output: The server receives the basic information and stores it in a database.

[0507] Specific behavior: The device sends basic information to the server using an HTTP POST request.

[0508] Step 4:

[0509] The server stores user information.

[0510] Input: Basic information sent from the device.

[0511] Output: Basic information is saved in the database.

[0512] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[0513] Step 5:

[0514] The user moves to the self-analysis screen.

[0515] Input: User selects "Self-Analysis" from the navigation menu.

[0516] Output: A self-analysis question is displayed.

[0517] What happens: The user clicks on a menu item and the screen switches to a self-analysis interface.

[0518] Step 6:

[0519] The server sends a self-analysis question.

[0520] Input: User ID and request information.

[0521] Output: A list of self-analysis questions will be displayed on the terminal.

[0522] Specific operation: The server sends a predefined list of questions in JSON format to the terminal.

[0523] Step 7:

[0524] The user answers self-assessment questions.

[0525] Input: Self-analysis questions.

[0526] Output: User's answer data is temporarily saved on the device.

[0527] Specific action: The user enters an answer to a question using a text box or options.

[0528] Step 8:

[0529] The terminal transmits the response data to the server.

[0530] Input: User response data.

[0531] Output: The server receives the response data and stores it in a database.

[0532] Specific operation: The device sends the response data to the server using an HTTP POST request.

[0533] Step 9:

[0534] The server stores the response data.

[0535] Input: Response data sent from the device.

[0536] Output: The response data is saved in a database.

[0537] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[0538] Step 10:

[0539] The user moves to the desired school selection screen.

[0540] Input: User instructions.

[0541] Output: A list of preferred destinations is displayed.

[0542] Specific behavior: The user selects "Select preferred school" from the navigation menu and the screen changes.

[0543] Step 11:

[0544] The server sends the list of preferred destinations.

[0545] Input: User ID and request information.

[0546] Output: The list of preferred schools is displayed on the terminal.

[0547] Specific operation: The server sends a predefined list of preferred schools in JSON format to the terminal.

[0548] Step 12:

[0549] The user selects the desired destination.

[0550] Input: Preferred list.

[0551] Output: The selected school information is temporarily saved on the device.

[0552] Specific behavior: The user selects the university or company they want to apply to from a drop-down menu.

[0553] Step 13:

[0554] The terminal transmits the desired school information to the server.

[0555] Input: User selected school information.

[0556] Output: The server receives the desired school information and stores it in a database.

[0557] Specific operation: The device sends the desired school information to the server using an HTTP POST request.

[0558] Step 14:

[0559] The server stores the desired school information.

[0560] Input: Application information sent from the device.

[0561] Output: The desired school information is saved in the database.

[0562] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[0563] Step 15:

[0564] The user navigates to the interview simulation screen.

[0565] Input: User instructions.

[0566] Output: The interface for the interview simulation is displayed.

[0567] Specific behavior: The user selects "Interview Simulation" from the navigation menu and the screen changes.

[0568] Step 16:

[0569] The server sends a prompt to the spawned AI.

[0570] Input: User's self-analysis results and desired company information.

[0571] Output: Generative AI generates interview questions.

[0572] Specific operation: The server sends an API request to the generation AI based on the self-analysis results and desired employer information.

[0573] Step 17:

[0574] The server generates a question and sends it to the terminal.

[0575] Input: Interview questions generated by generative AI.

[0576] Output: The question is displayed on the terminal.

[0577] Specific operation: The server sends the generated question in JSON format to the device.

[0578] Step 18:

[0579] The user answers the interview questions.

[0580] Input: Interview questions generated by the generative AI.

[0581] Output: User's answer data is temporarily saved on the device.

[0582] Specific operation: The user enters an answer to a question using a text box or voice input.

[0583] Step 19:

[0584] The device operates an emotion engine and acquires emotion data.

[0585] Input: The facial expression and tone of voice when the user answers.

[0586] Output: The emotional state is recognized and the emotional data is temporarily stored on the device.

[0587] Specific operation: The device uses the built-in camera and microphone to analyze facial expressions and tone of voice to determine emotions.

[0588] Step 20:

[0589] The terminal transmits the response data and emotion data to the server.

[0590] Input: User response data and sentiment data.

[0591] Output: The server receives these data and stores them in a database.

[0592] Specific operation: The device sends answer data and emotion data to the server using an HTTP POST request.

[0593] Step 21:

[0594] The server stores the response data and emotion data.

[0595] Input: Answer data and emotion data sent from the device.

[0596] Output: Response data and emotion data are stored in a database.

[0597] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[0598] Step 22:

[0599] The server sends prompts to the generation AI to generate feedback.

[0600] Input: Response data and sentiment data.

[0601] Output: Feedback is generated by the generative AI.

[0602] Specific operation: The server sends an API request to the generation AI based on the response data and emotion data.

[0603] Step 23:

[0604] The server generates feedback and sends it to the device.

[0605] Input: Feedback generated by the generative AI.

[0606] Output: Feedback is displayed on the terminal.

[0607] Specific behavior: The server sends the generated feedback in JSON format to the device.

[0608] Step 24:

[0609] The user checks the feedback.

[0610] Input: Feedback content.

[0611] Output: User reads and understands the feedback.

[0612] Specific actions: The device displays the feedback content and the user confirms it.

[0613] (Application example 2)

[0614] 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."

[0615] There are currently no interview practice support systems for autonomous vehicles, and in particular, no methods have been developed that use generative AI or emotion engines to provide real-time interview questions and feedback while driving.There is a need for a system that allows users to efficiently practice interviews and make appropriate preparations while driving.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating interview questions using a generation AI based on the user's self-analysis data and desired employer information, means for generating feedback based on the user's emotions with an emotion engine that acquires and analyzes the user's emotion data, and means for saving the user's interview result data and feedback on the server and improving the AI ​​model. This enables the user to effectively practice for interviews and receive real-time feedback while driving an autonomous vehicle.

[0617] "User Basic Information" refers to the user's name, email address, date of birth, and other personal information.

[0618] "Self-analysis data" is data obtained as a result of an aptitude test or personality diagnosis, and indicates the user's personality and aptitude.

[0619] "Preference information" is information about the user's desired university, company, or other destination.

[0620] "Generative AI" is a type of artificial intelligence, an algorithm or system that generates appropriate interview questions based on the user's self-analysis data and information about the company they are applying to.

[0621] "Emotion engine" is a general term for software or hardware used to acquire and analyze user emotion data.

[0622] "Interview questions" are questions for question and answer session that are presented to the user in the interview simulation.

[0623] "Feedback" is information that includes an evaluation of the user's interview practice and suggestions for improvement.

[0624] "Interview result data" refers to data that includes answers given by the user in the interview simulation, as well as evaluations and emotional data regarding those answers.

[0625] "Improving an AI model" is the process of continuously improving the performance and accuracy of an AI using collected data.

[0626] This invention relates to an interview practice support system for an autonomous vehicle. This system combines a generative AI model and an emotion engine to provide effective interview practice and feedback to users. The following describes in detail the embodiments of this invention.

[0627] Hardware and Software

[0628] 1. Hardware:

[0629] In-vehicle infotainment system: ANDROID® Auto or Apple CarPlay® compatible device.

[0630] Camera system: A camera to capture the driver's facial expressions.

[0631] Microphone: A microphone for collecting the driver's voice.

[0632] 2. Software:

[0633] Cloud server: Data storage and AI model hosting are performed by AWS (registered trademark) or Google (registered trademark) Cloud.

[0634] Emotion Analysis SDK: Software that performs facial expression recognition and voice analysis, such as the Affectiva SDK.

[0635] Generative AI model: Generates interview questions using GPT-4 (registered trademark). Powered by Azure (registered trademark) OpenAI (registered trademark) services.

[0636] Program processing flow

[0637] Register and fill out your profile:

[0638] When the device starts up, the user enters basic information, which is then sent to a cloud server and stored in a database.

[0639] Conduct a self-analysis:

[0640] The device displays aptitude tests and personality assessment questions, and the user answers them. The answers are sent to the server and saved. At this point, the user's self-analysis data is accumulated.

[0641] Select your preferred school:

[0642] The terminal displays a list of candidate schools of choice, and sends the information on the schools selected by the user to the server for storage, which accumulates the information in a database.

[0643] Conducting a simulated interview:

[0644] The generative AI model generates interview questions based on the user's self-analysis data and information about the company they are applying to, and presents them to the user via their device. The user's answers are collected in real time and sent to a cloud server. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[0645] Providing feedback:

[0646] The server generates feedback based on the response data and emotion data and delivers it to the device. The feedback includes an evaluation of the response content, suggestions for improvement, and advice on feelings based on the emotion data.

[0647] Storing interview result data and improving AI models:

[0648] The results and feedback data from the interview simulation are stored on a server and used to train the AI ​​model, which allows the AI ​​model to continually improve and provide more accurate questions and feedback in the future.

[0649] Examples of specific examples and prompts

[0650] Examples:

[0651] When the driver gets into the self-driving car and says, "Please start the interview practice," the in-car infotainment system starts up and the interview practice assistant begins asking interview questions. In response to the questions, the driver is asked, "Please introduce yourself." After the driver responds, the generative AI suggests the next question, and the emotion engine analyzes the tone of voice and facial expressions to provide feedback in real time.

[0652] Example prompt sentence:

[0653] "Generate five software engineer interview questions based on user background information."

[0654] "Use the sentiment data to provide feedback on interview responses and explain why."

[0655] In this way, the present invention provides a highly supportive environment for conducting effective interview practice even in an autonomous vehicle.

[0656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0657] Step 1:

[0658] User registration and profile entry

[0659] When the device is first started up, a user registration screen is displayed, and the user enters basic information such as name, email address, and date of birth.

[0660] Input: Name, Email Address, Date of Birth.

[0661] Data processing: Convert basic information into JSON format and send it to the server using the HTTPS protocol.

[0662] The server stores the received data in a database.

[0663] Output: Basic information stored in a database.

[0664] Step 2:

[0665] Conducting self-analysis

[0666] The terminal displays aptitude test and personality assessment questions and provides an interface for the user to answer the questions.

[0667] Input: The user's answer.

[0668] Data processing: Convert the response data into JSON format and send it to the server.

[0669] The server stores the received response data in a database.

[0670] Output: Self-analysis data is saved in a database.

[0671] Step 3:

[0672] Choosing your preferred destination

[0673] The terminal displays a list of candidate companies of interest, and transmits the information on the company selected by the user to the server and stores it.

[0674] Input: Select your preferred destination.

[0675] Data processing: Convert the selected desired school information into JSON format and send it to the server.

[0676] The server stores the received desired school information in a database.

[0677] Output: The desired school information is saved in the database.

[0678] Step 4:

[0679] Conducting interview simulations

[0680] The server uses a generation AI to generate interview questions based on self-analysis data and information about the company of choice, and delivers them to the device.

[0681] Input: Self-analysis data, desired employer information.

[0682] Data calculation: Create prompts that the generative AI model uses to generate questions, and then call the model to generate questions.

[0683] Output: Generated interview questions.

[0684] The device displays the generated questions to the user and obtains answers. The emotion engine also analyzes the user's facial expressions and voice to obtain emotion data.

[0685] Input: User answers and sentiment data.

[0686] Data processing: Response data and emotion data are converted into JSON format and sent to the server.

[0687] Output: Answer data and sentiment data stored on the server.

[0688] Step 5:

[0689] Providing feedback

[0690] The server uses a generation AI to generate feedback based on the response data and emotion data, and delivers it to the device.

[0691] Input: Response data, emotion data.

[0692] Data calculation: Analyzes response data and sentiment data to generate feedback, and invokes a generative AI model to generate feedback.

[0693] Output: The generated feedback.

[0694] The terminal displays the feedback to the user.

[0695] Input: Feedback data.

[0696] Output: User feedback confirmation.

[0697] Step 6:

[0698] Storing interview result data and improving AI models

[0699] The server stores the results data and feedback data of the interview simulation in a database.

[0700] Input: Interview result data, feedback data.

[0701] Data processing: Converting data into an analyzable format and storing it in a database.

[0702] Output: Result data stored in a database.

[0703] This data is used to continuously train generative AI models to improve the accuracy of interview questions and feedback.

[0704] Data computation: Combining historical and new data to train models.

[0705] Output: An improved AI model.

[0706] 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.

[0707] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0708] 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.

[0709] [Second embodiment]

[0710] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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).

[0716] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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."

[0722] This invention is an interview practice support system designed for test takers, job seekers, or people looking to change jobs, and it uses a generative AI to provide users with appropriate questions and feedback. This system consists of a server, a terminal used by the user, and the user.

[0723] Handling user registration and profile entries

[0724] Terminal

[0725] When the app is launched for the first time, it displays a user registration screen, providing input fields for the user's name, email address, date of birth, etc.

[0726] server

[0727] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[0728] User

[0729] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[0730] Conducting self-analysis

[0731] Terminal

[0732] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[0733] server

[0734] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[0735] User

[0736] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[0737] Choosing your preferred destination

[0738] Terminal

[0739] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[0740] server

[0741] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[0742] User

[0743] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[0744] Conducting interview simulations

[0745] Terminal

[0746] It displays questions generated by the AI ​​one by one and provides an interface where users can enter answers. After entering the answers, they are sent to the server.

[0747] server

[0748] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database.

[0749] User

[0750] The user inputs an answer to the displayed question and transmits the answer data to the server.

[0751] Providing feedback

[0752] Terminal

[0753] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[0754] server

[0755] Based on the user's response data, the generation AI generates feedback, which is then delivered to the device.

[0756] User

[0757] Review the feedback provided and understand areas for improvement.

[0758] Saving results and improving AI models

[0759] Terminal

[0760] The results of the interview simulation are sent to the server, which stores the user's practice history.

[0761] server

[0762] The stored interview result data is used to train the generative AI model to improve it. As the AI ​​model continues to learn, it will be able to provide users with more accurate questions and feedback in the future.

[0763] User

[0764] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[0765] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[0766] The processing flow will be explained below.

[0767] Handling user registration and profile entries

[0768] Step 1:

[0769] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[0770] Step 2:

[0771] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[0772] Step 3:

[0773] Terminal: Checks the entered information and sends it to the server.

[0774] Step 4:

[0775] Server: Stores the received user information in a database.

[0776] Conducting self-analysis

[0777] Step 1:

[0778] User: Presses a button to start self-analysis.

[0779] Step 2:

[0780] Device: Retrieves aptitude and personality test questions from the server.

[0781] Step 3:

[0782] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[0783] Step 4:

[0784] User: Enter answers to the questions displayed.

[0785] Step 5:

[0786] Terminal: Sends the entered answer data to the server.

[0787] Step 6:

[0788] Server: Stores the received response data in a database.

[0789] Choosing your preferred destination

[0790] Step 1:

[0791] User: Select the "Select School of Interest" option from the menu.

[0792] Step 2:

[0793] Terminal: Obtain a list of universities and companies of interest from the server.

[0794] Step 3:

[0795] Terminal: Display the obtained list and let the user select.

[0796] Step 4:

[0797] User: Select the school or company of your choice from the list of preferred schools.

[0798] Step 5:

[0799] Terminal: Sends the selected desired school information to the server.

[0800] Step 6:

[0801] Server: Save the received information about the desired school in a database.

[0802] Conducting interview simulations

[0803] Step 1:

[0804] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[0805] Step 2:

[0806] Terminal: Displays the generated questions one by one.

[0807] Step 3:

[0808] User: Enter answers to the questions displayed.

[0809] Step 4:

[0810] Terminal: Temporarily holds the entered answers.

[0811] Step 5:

[0812] Terminal: Sends the response data to the server.

[0813] Step 6:

[0814] Server: Stores the received response data in a database.

[0815] Providing feedback

[0816] Step 1:

[0817] Server: The generative AI generates feedback based on the user's answers.

[0818] Step 2:

[0819] Server: Delivers the generated feedback to the device.

[0820] Step 3:

[0821] Terminal: Display feedback to the user.

[0822] Step 4:

[0823] Users: Review and understand the feedback.

[0824] Saving results and improving AI models

[0825] Step 1:

[0826] Terminal: Sends the results of the interview simulation to the server.

[0827] Step 2:

[0828] Server: Stores the received interview result data in a database.

[0829] Step 3:

[0830] Server: Uses the saved result data to learn and improve the AI ​​model.

[0831] Step 4:

[0832] Server: The AI ​​model continuously learns and generates more accurate questions and feedback.

[0833] The above is a specific processing flow for carrying out the present invention. The user, terminal, and server each play their respective roles and cooperate to form the entire system.

[0834] Example 1

[0835] 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."

[0836] Conventional interview practice support systems have difficulty automatically generating appropriate interview questions tailored to the user's individual characteristics and desired employer, and are unable to provide specific and accurate feedback. Furthermore, they lack a continuous learning function to improve the accuracy of interview practice, leaving a need for a system that can adequately meet user needs. Furthermore, they lack a support function for users to formulate specific action plans based on feedback, limiting the effectiveness of users' self-improvement.

[0837] 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.

[0838] In this invention, the server includes: means for acquiring basic information based on user input; means for acquiring self-analysis questions such as aptitude tests and personality tests from the server; means for presenting the acquired questions to the user and acquiring the user's answers; means for transmitting and storing the user's answer data to the server; means for acquiring a list of candidate employers based on the user's self-analysis data; means for transmitting and storing the user's selected employer information to the server; means for a generation AI to generate interview questions based on the user's self-analysis data and employer information; means for providing the generated interview questions to the user and acquiring the user's answers; means for generating and providing feedback based on the user's answers; means for saving the user's interview result data and feedback to the server and improving the AI ​​model; means for generating appropriate feedback using the generation AI model; means for providing a timer function during the interview simulation to automatically prompt the user to answer; and means for providing a memo function when displaying the feedback so that the user can create a specific action plan. This allows the user to receive interview questions tailored to their characteristics and employers of choice, receive specific and accurate feedback, and improve the accuracy of their interview practice through continuous learning. In addition, the effectiveness of self-improvement can be enhanced by utilizing the support functions for formulating specific action plans.

[0839] "User" refers to a candidate, job seeker, or job seeker who uses this system.

[0840] "Basic information" refers to information such as name, email address, and date of birth that a user enters when registering with the system.

[0841] A "server" is a device that processes and stores various data, and generates and distributes aptitude tests, self-analysis questions, interview questions, etc.

[0842] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet, that provides an input interface and display functions.

[0843] An "aptitude test" is a question or test that assesses a user's characteristics or abilities.

[0844] A "personality test" is a question or test that analyzes a user's personality and behavioral patterns.

[0845] "Self-analysis data" refers to the answers and results of the user's responses to aptitude tests and personality tests.

[0846] "Desired destination" refers to the university, company, or other destination the user desires to attend.

[0847] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate interview questions and feedback based on user information.

[0848] "Interview questions" are questions presented to the user in the interview simulation.

[0849] "Feedback" refers to evaluations and improvements on the user's answers.

[0850] "Interview result data" is data that includes the answers given by the user in the interview simulation and the feedback given to those answers.

[0851] The "timer function" is a function that prompts the user to enter an answer within a certain time period.

[0852] The "memo function" is a function that allows users to create specific action plans based on feedback.

[0853] An "AI model" is an algorithm that learns from user data and improves the accuracy of interview practice.

[0854] This invention is a support system that allows test takers, job seekers, and people looking to change jobs to effectively practice for interviews. The system consists of a terminal used by the user and a server that processes data and generates questions. Users can perform the entire process from registration to interview practice and receiving feedback.

[0855] User registration and profile entry

[0856] Terminal

[0857] When a user launches the app for the first time, a user registration screen is displayed. The user enters information such as their name, email address, and date of birth. For example, the user enters "Yamada Hanako" in the name field, "hanako@example.com" in the email address field, and "1995-06-20" in the date of birth field.

[0858] server

[0859] It receives user information sent from the device and stores it in a MySQL database, thereby accumulating basic user information.

[0860] User

[0861] By inputting information and pressing the send button, the terminal transmits the information to the server.

[0862] Conducting self-analysis

[0863] Terminal

[0864] The app displays aptitude or personality questions, such as "What are your strengths?" The user answers the questions and the device sends the answers to a server.

[0865] server

[0866] The server receives the data from the receiving side and stores it in an SQLite database. The question and answer data is later used as self-analysis results.

[0867] User

[0868] The user enters an answer to the displayed question and presses the send button to send the answer data to the server.

[0869] Choosing your preferred destination

[0870] Terminal

[0871] The app displays a list of universities and companies you are interested in. You select an option from the list and send the selected information to the server. For example, let's say you select "XYZ Company."

[0872] server

[0873] The server receives the selected information about the desired school and stores it in a database.

[0874] User

[0875] The user selects the desired destination and presses the OK button to send the selection to the server.

[0876] Conducting interview simulations

[0877] server

[0878] Based on the user's self-analysis and desired company information, Generating AI generates appropriate interview questions. For example, it might ask, "What are your reasons for applying?"

[0879] Terminal

[0880] The generated question is displayed to the user, who answers the question and the terminal transmits the answer data to the server.

[0881] User

[0882] In response to the question that appears, enter "I sympathize with your philosophy of contributing to society and would like to work for your company," and press the send button.

[0883] Providing feedback

[0884] server

[0885] The generative AI generates feedback based on the user's answers. For example, it might say, "It would be better if you included specific examples in your motivation for applying."

[0886] Terminal

[0887] Feedback is displayed to the user, who can review it and understand where improvements can be made.

[0888] User

[0889] The feedback provided can be used to improve your next practice.

[0890] Saving results and improving AI models

[0891] Terminal

[0892] The results of the interview simulation are sent to a server, and the user's practice history is saved.

[0893] server

[0894] The server stores the resulting data and uses it to train the Generating AI model, which will improve the accuracy of interview questions and feedback for future interviews.

[0895] User

[0896] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[0897] concrete action

[0898] Timer function: During the interview simulation, the user is prompted to enter answers within a certain time.

[0899] Notes function: While viewing feedback, users can enter notes to help them create specific action plans.

[0900] Prompt Sentence Examples

[0901] "What are your hobbies?"

[0902] "What has been the most challenging experience you have had so far?"

[0903] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[0904] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0905] Step 1: User registration and profile entry

[0906] User: Launch the app and the user registration screen will appear. Enter information such as "Yamada Hanako," "hanako@example.com," and "1995-06-20," and press the registration button.

[0907] Terminal: Receives information entered by the user, converts it into JSON format, and sends it to the server.

[0908] Server: Receives JSON data and saves it to a database. Specifically, it saves the information "Hanako Yamada", "hanako@example.com", and "1995-06-20" in a MySQL database. The input is user information, and the output is saved to the database.

[0909] Step 2: Conduct a self-analysis

[0910] Terminal: Displays aptitude test and personality assessment questions received from the server. The question "What are your strengths?" is displayed.

[0911] User: Answers the question by typing "Analytical ability" and presses the send button.

[0912] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[0913] Server: Receives the answer data and saves it in a database. Specifically, it saves the answer "analytical ability" in an SQLite database. The input is the question and answer, and the output is saved in the database.

[0914] Step 3: Choose your preferred school

[0915] Terminal: Displays the list of companies of interest received from the server. Lists such as "ABC Company" and "XYZ Company" are displayed.

[0916] User: Selects "XYZ Company" as the company of choice and presses the OK button.

[0917] Terminal: Converts the information selected by the user into JSON format and sends it to the server.

[0918] Server: Receives the selected company information and saves it in the database. Specifically, it saves the selected company "XYZ Company" in the database. The input is the company information, and the output is saving it in the database.

[0919] Step 4: Conduct a simulated interview

[0920] Server: Based on the user's self-analysis results and desired employer information, the generative AI model generates appropriate interview questions. For example, it generates a question such as, "What is your motivation for applying?"

[0921] Terminal: The generated interview questions are displayed to the user.

[0922] User: Answers the question by saying, "I sympathize with your philosophy of contributing to society and would like to work for your company," and presses the send button.

[0923] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[0924] Server: Receives the response data and stores it in a database. Specifically, the response "I sympathize with your philosophy of social contribution and would like to work for your company" is stored in the database. The input is the question and the response, and the output is stored in the database.

[0925] Step 5: Provide feedback

[0926] Server: The generative AI model generates feedback based on the user's response data. For example, it might generate feedback such as, "It would be better if you included specific examples in your motivation for applying."

[0927] Terminal: The generated feedback is displayed to the user.

[0928] User: Check the feedback and use it for the next practice. As a concrete example, the user will try to provide more specific answers in the next simulation based on the feedback that "please include specific examples in your motivation for applying." The input is the answer data, and the output is the feedback.

[0929] Step 6: Saving the results and improving the AI ​​model

[0930] Terminal: Converts the interview simulation results into JSON format and sends them to the server.

[0931] Server: Receives the result data and stores it in a database. In addition, the generative AI model learns based on the result data, improving the accuracy of interview questions and feedback. The input is the simulation result data, and the output is the learning and accuracy improvement of the AI ​​model.

[0932] Users: The next time they take a simulated interview, they will receive improved questions and feedback, which will lead to more effective practice and improved performance in the actual interview.

[0933] This allows users to consistently practice interviews effectively and gain confidence in real interviews. Furthermore, the Generating AI model continuously learns based on feedback and simulated interview results, enabling it to provide high-quality support.

[0934] (Application example 1)

[0935] 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."

[0936] Conventional interview practice support systems have the problem of only being able to provide limited support to test takers, job seekers, or people looking to change jobs. Similarly, there is a lack of effective support for training new store clerks to improve their customer service skills. This presents a problem in that customer service practice cannot be effectively conducted in physical stores, making it difficult to improve service. To solve these problems, the present invention aims to expand the interview practice support system and provide a comprehensive practice support system that can also accommodate customer service practice for store clerks.

[0937] 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.

[0938] In this invention, the server comprises: means for acquiring basic information based on an input from a user;

[0939] A means for acquiring questions for self-analysis such as aptitude tests and personality tests from a server;

[0940] means for presenting the acquired question to a user and acquiring an answer from the user;

[0941] means for transmitting and storing user response data on a server;

[0942] A means for acquiring a list of candidates for desired employment based on the user's self-analysis data;

[0943] A means for transmitting the desired school information selected by the user to a server and storing it;

[0944] A means for the AI ​​to generate interview questions based on the user's self-analysis data and desired employer information;

[0945] means for providing the generated interview questions to a user and obtaining answers from the user;

[0946] means for generating and providing feedback to the user based on the user's responses;

[0947] A means for storing the user's interview result data and feedback on a server and improving the AI ​​model;

[0948] To support sales staff in their customer service practice, a means is provided for acquiring profile information from users and for a generation AI to generate questions necessary for customer service based on the type of store.

[0949] A means for providing the generated customer service question to a store clerk and obtaining an answer from the store clerk;

[0950] a means for generating and providing feedback to the sales associate based on the sales associate's responses;

[0951] A means to suggest areas for improvement to improve customer service skills based on feedback

[0952] This will not only help prepare students for interviews, job seekers, and people looking to change jobs for interview practice, but will also improve the accuracy of customer service practice for new store employees.

[0953] "Exam takers" are students who intend to take an exam.

[0954] A "job seeker" is an individual who is actively seeking new employment with a company or organization.

[0955] A "job seeker" is an individual who is seeking a job change from their current workplace to another workplace.

[0956] An "interview practice support system" is an information processing system designed to help users practice for interviews effectively.

[0957] "Basic information" refers to basic information about the user, such as the user's name, email address, years of experience, etc.

[0958] An "aptitude test" is a test used to evaluate an individual's abilities and personality, and is used to understand the user's aptitude.

[0959] A "personality test" is a test for assessing an individual's personality traits and is used to understand a user's personality.

[0960] "Self-analysis data" is a collection of information obtained from the results of a user's responses to aptitude tests and personality assessments.

[0961] "Preferences" refers to universities, companies, etc. that the user wishes to attend or work for.

[0962] "Generative AI" is a system that uses artificial intelligence technology to generate interview questions and feedback based on user data.

[0963] "Customer service practice" refers to new store employees practicing the skills necessary to effectively serve customers in a physical store.

[0964] "Profile information" refers to information registered by a user, such as name, email address, years of experience, and type of store.

[0965] "Store type" refers to store categories such as fashion, restaurants, supermarkets, etc.

[0966] "Feedback" is information about evaluations and areas for improvement that are generated based on the user's responses.

[0967] "Areas for improvement" refers to points or suggestions that users or store clerks need to improve through practice.

[0968] This invention provides a practice support system for test takers, job seekers, people looking to change jobs, and new store clerks. To implement this invention, a server, a user terminal, and a generative AI model are used.

[0969] Handling user registration and profile entries

[0970] Terminal

[0971] When a user launches the app for the first time, they are presented with a user registration screen, which provides fields for entering profile information such as name, email address, date of birth, years of experience, etc. Once the user enters this information and presses the submit button, the information is sent to the server.

[0972] server

[0973] The server receives the user information sent from the terminal and stores it in a database, thereby accumulating basic user information for use in subsequent processes.

[0974] Conducting self-analysis

[0975] Terminal

[0976] Aptitude tests and personality tests are displayed to the user, who then enters answers to the questions and presses the send button to send the answer data to the server.

[0977] server

[0978] The server distributes self-analysis questions to the user's terminal and stores the answer data received from the terminal in a database, which is later used as the user's self-analysis results.

[0979] Choosing your preferred destination

[0980] Terminal

[0981] It displays a list of universities and companies that the user is interested in and provides an interface for them to select from. The user selects the university or company they want, presses the OK button, and sends the selection to the server.

[0982] server

[0983] The server distributes the list of desired companies and stores the information on the desired companies selected by the user in a database.

[0984] Interview simulation and customer service practice

[0985] Terminal

[0986] It displays questions generated by the AI ​​and provides an interface for users to input their answers. Users input answers to the displayed questions and press the send button to send the answer data to the server.

[0987] server

[0988] The server uses a generation AI to generate appropriate interview questions based on the user's self-analysis results and desired employer information. Similarly, it generates customer service practice questions based on the employee's profile information and the type of store. The generated questions are sent to the device, which receives the user's response data and stores it in a database.

[0989] Providing feedback

[0990] Terminal

[0991] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[0992] server

[0993] The server uses AI to generate feedback based on the user's response data and delivers it to the device, allowing the user to practice based on the feedback.

[0994] Saving results and improving AI models

[0995] Terminal

[0996] The results of the user's interview simulation and customer service practice are sent to the server, which stores the user's practice history.

[0997] server

[0998] The server uses the stored interview result data and feedback data to improve the AI ​​model. This continuous learning allows the generative AI to provide more accurate questions and feedback in the future.

[0999] For example, in the case of a customer service simulation for a fashion store, the following prompts could be used:

[1000] "Customer service simulation for new sales associates: Fashion\nPlease generate questions."

[1001] By using such prompt sentences, the generative AI can generate questions that correspond to specific store situations.

[1002] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1003] Step 1:

[1004] User registration and profile entry

[1005] When a user launches the app on their device for the first time, a user registration screen appears. Here, the user enters profile information such as name, email address, date of birth, and years of experience. Once the information is entered, this data is sent from the device to the server. The server saves the received data in a database and accumulates the user's basic information.

[1006] Input: Name, email address, date of birth, years of experience

[1007] Output: User information stored in the database

[1008] Step 2:

[1009] Conducting self-analysis

[1010] Aptitude tests and personality assessment questions for self-analysis are sent from the server to the user's device. The device presents these questions to the user, who then enters their answers. Once the answers are entered, the device sends them to the server. The server receives the answer data and stores it in a database.

[1011] Input: Self-assessment questions, user answers

[1012] Output: Answer data stored in a database

[1013] Step 3:

[1014] Choosing your preferred destination

[1015] A list of universities and companies of choice is sent from the server to the user's device. The device displays the list to the user, who then selects the desired university. Once the selection is complete, the user's device sends the information to the server. The server stores the received information in a database.

[1016] Input: Preferred list, user selection

[1017] Output: Information about the desired school saved in the database

[1018] Step 4:

[1019] Interview simulation and customer service practice

[1020] The generation AI generates interview questions based on the user's self-analysis results and information about the company they wish to work for. In the case of customer service practice for a store clerk, customer service questions are generated based on the user's profile information and the type of store. The generated questions are sent from the server to the user's device, and the user answers them. The device sends the user's answers to the server, which stores them in a database.

[1021] Input: Self-analysis results, desired employer information, AI prompt

[1022] Output: Generated interview questions, answers stored in a database

[1023] Step 5:

[1024] Providing feedback

[1025] The server uses the AI ​​to generate feedback based on the user's response data. The generated feedback is then sent from the server to the user's device. The device then displays the feedback to the user and suggests areas for improvement.

[1026] Input: User response data, prompts for the AI ​​generator

[1027] Output: generated feedback, presented to the user

[1028] Step 6:

[1029] Saving results and improving AI models

[1030] The results and feedback data of the user's interview simulation and customer service practice are sent to a server and stored in a database. The server continuously trains the generative AI model based on this stored data, enabling it to provide more accurate questions and feedback in the future.

[1031] Input: Interview result data, feedback data

[1032] Output: Result data stored in a database, improved AI model

[1033] 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.

[1034] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a server, a terminal, and a user.

[1035] Handling user registration and profile entries

[1036] Terminal

[1037] When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[1038] server

[1039] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[1040] User

[1041] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[1042] Conducting self-analysis

[1043] Terminal

[1044] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[1045] server

[1046] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[1047] User

[1048] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[1049] Choosing your preferred destination

[1050] Terminal

[1051] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[1052] server

[1053] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[1054] User

[1055] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[1056] Conducting interview simulations

[1057] Terminal

[1058] It displays questions generated by the generative AI one by one, provides an interface where the user can enter answers, and operates an emotion engine that recognizes the user's emotions.

[1059] server

[1060] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database. In addition, emotional data generated by the emotion engine is also stored.

[1061] User

[1062] Answers are entered to the questions displayed, and sentiment analysis is performed using an emotion engine.

[1063] Providing feedback

[1064] server

[1065] The AI ​​generates feedback based on the user's response data and emotional data, and delivers the generated feedback to the device.

[1066] Terminal

[1067] Feedback is displayed to the user, including an evaluation of the answer, suggestions for improvement, and advice on how to respond based on emotional data.

[1068] User

[1069] Review and understand the feedback provided, and use it to understand how to manage your emotions and improve.

[1070] Saving results and improving AI models

[1071] Terminal

[1072] The results of the interview simulation and emotional data are sent to the server, which stores the user's practice history.

[1073] server

[1074] The AI ​​model uses the saved interview result data and emotion data to learn and improve itself. By continuously learning, the AI ​​model will be able to provide users with more accurate questions and feedback in the future.

[1075] User

[1076] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[1077] In this way, the present invention provides an environment that allows users to approach real interviews with confidence. Furthermore, the generative AI and emotion engine create a practice environment that takes the user's feelings into consideration by generating appropriate feedback based on the user's emotional data. This allows users to practice interviews in a more relaxed state and effectively identify areas for improvement.

[1078] The processing flow will be explained below.

[1079] Handling user registration and profile entries

[1080] Step 1:

[1081] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[1082] Step 2:

[1083] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[1084] Step 3:

[1085] Terminal: Checks the entered information and sends it to the server.

[1086] Step 4:

[1087] Server: Stores the received user information in a database.

[1088] Conducting self-analysis

[1089] Step 1:

[1090] User: Presses a button to start self-analysis.

[1091] Step 2:

[1092] Device: Retrieves aptitude and personality test questions from the server.

[1093] Step 3:

[1094] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[1095] Step 4:

[1096] User: Enter answers to the questions displayed.

[1097] Step 5:

[1098] Terminal: Sends the entered answer data to the server.

[1099] Step 6:

[1100] Server: Stores the received response data in a database.

[1101] Choosing your preferred destination

[1102] Step 1:

[1103] User: Select the "Select School of Interest" option from the menu.

[1104] Step 2:

[1105] Terminal: Obtain a list of universities and companies of interest from the server.

[1106] Step 3:

[1107] Terminal: Display the obtained list and let the user select.

[1108] Step 4:

[1109] User: Select the school or company of your choice from the list of preferred schools.

[1110] Step 5:

[1111] Terminal: Sends the selected desired school information to the server.

[1112] Step 6:

[1113] Server: Save the received information about the desired school in a database.

[1114] Conducting interview simulations

[1115] Step 1:

[1116] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[1117] Step 2:

[1118] Terminal: Displays the generated questions one by one and also runs an emotion engine to recognize the user's emotions during the interview.

[1119] Step 3:

[1120] User: Enter answers to the questions displayed.

[1121] Step 4:

[1122] Emotion engine: Obtains emotional data from the user's facial expressions and tone of voice during the interview.

[1123] Step 5:

[1124] Terminal: Temporarily stores the user's answers and emotion data.

[1125] Step 6:

[1126] Terminal: Sends response data and emotion data to the server.

[1127] Step 7:

[1128] Server: Stores the received response data and emotion data in a database.

[1129] Providing feedback

[1130] Step 1:

[1131] Server: The AI ​​generates feedback based on the user's response data and emotional data.

[1132] Step 2:

[1133] Server: Delivers the generated feedback to the device.

[1134] Step 3:

[1135] On the device, feedback is displayed to the user, including a rating of the answer, suggestions for improvement, and emotional advice based on the emotional data.

[1136] Step 4:

[1137] Users: Review the feedback provided and understand areas for improvement.

[1138] Saving results and improving AI models

[1139] Step 1:

[1140] Terminal: Sends the results of the interview simulation and emotional data to the server.

[1141] Step 2:

[1142] Server: Stores the received interview result data and emotion data in a database.

[1143] Step 3:

[1144] Server: Uses the stored result data and emotion data to learn and improve the AI ​​model.

[1145] Step 4:

[1146] Server: The AI ​​model continuously learns, enabling it to generate more accurate questions and feedback in the future.

[1147] The above is a specific processing flow for implementing the present invention. The user, terminal, server, and emotion engine each play their own roles while working together to form the entire system.

[1148] Example 2

[1149] 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."

[1150] Conventional interview practice systems have not been able to generate appropriate interview questions based on the user's self-analysis results and information about their desired employer, nor have they provided sufficient feedback that reflects real-time emotional data. Furthermore, there have been no systems that improve the accuracy of interview practice by continuously training a generative AI model using the user's practice results and emotional data. This has made it difficult for users to effectively improve their practical interview skills.

[1151] 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.

[1152] In this invention, the server includes a means for the generation AI to generate interview questions based on the user's self-analysis data and desired employer information, a means for the terminal to acquire emotional data and recognize the user's emotional state, and a means for continuously training the generation AI model using the saved interview result data and emotional data. This allows the user to receive appropriate questions based on the self-analysis results and desired employer information, and furthermore, by receiving feedback based on the user's emotional state, enables practical and effective interview practice.

[1153] "User" refers to an individual who uses the information processing system, such as a student taking an exam, a job seeker, or a person looking to change jobs.

[1154] "Basic information" refers to information that identifies and locates an individual, such as a user's name, email address, and date of birth.

[1155] An "aptitude test" is a question or test designed to assess a user's aptitudes, interests, abilities, etc.

[1156] A "personality assessment" is a set of questions or tests designed to assess a user's personality and behavioral traits.

[1157] "Self-Analysis Questions" refers to a list of questions used to deepen a user's self-understanding.

[1158] "Preferred school" refers to the school or company for which the user wishes to take an entrance exam, get a job, or change jobs.

[1159] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate interview questions and feedback.

[1160] "Interview Questions" are questions asked to the user and provided by the generative AI as part of the interview simulation.

[1161] "Feedback" refers to the evaluation and improvement suggestions provided to the user regarding their interview practice.

[1162] "Emotion data" refers to information about the user's emotional state that is analyzed based on facial expressions, tone of voice, etc.

[1163] "AI Model" refers to a computational model that learns and improves using artificial intelligence techniques.

[1164] "Continuous learning" refers to the means by which AI models are regularly updated and improved based on collected data.

[1165] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a user, a terminal, and a server.

[1166] First, when a user launches the application, a user registration screen appears on the device. After the user enters basic information such as name, email address, and date of birth, the device sends this information to the server. The server stores the received basic information in a database. Through this process, the system accumulates user profile information.

[1167] When the user moves to the next screen for self-analysis, the server sends a list of questions for aptitude tests and personality assessments to the terminal. The terminal displays these questions to the user, and the user enters answers to each question. Once the answers are completed, the terminal sends the answer data to the server, which stores it in a database.

[1168] The user then moves to a screen for selecting their preferred universities and companies, and the server sends a list of their preferred universities and companies to the terminal. Once the user selects their preferred universities and companies, the terminal sends the selected university information to the server, which then stores it in a database.

[1169] When the interview simulation stage begins, the server sends prompts to the generation AI based on the user's self-analysis results and information about the company they wish to work for. The generation AI generates appropriate interview questions, which the server then sends to the device. The device displays the questions, and the user enters their answers. While the answers are being entered, the device activates an emotion engine to analyze the user's emotional state and obtain emotional data. This data is then sent from the device to the server, which stores it in a database.

[1170] The server then generates feedback by sending prompts to the AI ​​based on the user's response data and emotion data. The generated feedback is sent to the device and displayed to the user. The feedback includes an evaluation of the response, suggestions for improvement, and advice based on the emotion data.

[1171] Finally, the results of the interview simulation and emotional data are sent to and stored on a server, which uses this data to continuously train the generative AI model and provide more accurate questions and feedback to users in the future.

[1172] For example, the generative AI will ask the question, "Please introduce yourself in one minute." It will also provide advice based on emotion recognition, such as, "Take a deep breath and relax." This system allows users to practice effectively so they can approach the actual interview with confidence.

[1173] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] The user launches the app.

[1176] Input: A user launches an application on their smartphone or PC.

[1177] Output: The user is taken to the login or sign up screen.

[1178] What happens: The user taps or clicks the icon and the application displays its welcome screen.

[1179] Step 2:

[1180] The user enters basic information.

[1181] Input: The user enters basic information such as name, email address, and date of birth.

[1182] Output: The user's basic information is temporarily stored on the device.

[1183] Specific actions: The user fills in the required information in the form and clicks the "Register" button.

[1184] Step 3:

[1185] The device sends basic information to the server.

[1186] Input: Basic information entered by the user.

[1187] Output: The server receives the basic information and stores it in a database.

[1188] Specific behavior: The device sends basic information to the server using an HTTP POST request.

[1189] Step 4:

[1190] The server stores user information.

[1191] Input: Basic information sent from the device.

[1192] Output: Basic information is saved in the database.

[1193] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1194] Step 5:

[1195] The user moves to the self-analysis screen.

[1196] Input: User selects "Self-Analysis" from the navigation menu.

[1197] Output: A self-analysis question is displayed.

[1198] What happens: The user clicks on a menu item and the screen switches to a self-analysis interface.

[1199] Step 6:

[1200] The server sends a self-analysis question.

[1201] Input: User ID and request information.

[1202] Output: A list of self-analysis questions will be displayed on the terminal.

[1203] Specific operation: The server sends a predefined list of questions in JSON format to the terminal.

[1204] Step 7:

[1205] The user answers self-assessment questions.

[1206] Input: Self-analysis questions.

[1207] Output: User's answer data is temporarily saved on the device.

[1208] Specific action: The user enters an answer to a question using a text box or options.

[1209] Step 8:

[1210] The terminal transmits the response data to the server.

[1211] Input: User response data.

[1212] Output: The server receives the response data and stores it in a database.

[1213] Specific operation: The device sends the response data to the server using an HTTP POST request.

[1214] Step 9:

[1215] The server stores the response data.

[1216] Input: Response data sent from the device.

[1217] Output: The response data is saved in a database.

[1218] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1219] Step 10:

[1220] The user moves to the desired school selection screen.

[1221] Input: User instructions.

[1222] Output: A list of preferred destinations is displayed.

[1223] Specific behavior: The user selects "Select preferred school" from the navigation menu and the screen changes.

[1224] Step 11:

[1225] The server sends the list of preferred destinations.

[1226] Input: User ID and request information.

[1227] Output: The list of preferred schools is displayed on the terminal.

[1228] Specific operation: The server sends a predefined list of preferred schools in JSON format to the terminal.

[1229] Step 12:

[1230] The user selects the desired destination.

[1231] Input: Preferred list.

[1232] Output: The selected school information is temporarily saved on the device.

[1233] Specific behavior: The user selects the university or company they want to apply to from a drop-down menu.

[1234] Step 13:

[1235] The terminal transmits the desired school information to the server.

[1236] Input: User selected school information.

[1237] Output: The server receives the desired school information and stores it in a database.

[1238] Specific operation: The device sends the desired school information to the server using an HTTP POST request.

[1239] Step 14:

[1240] The server stores the desired school information.

[1241] Input: Application information sent from the device.

[1242] Output: The desired school information is saved in the database.

[1243] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1244] Step 15:

[1245] The user navigates to the interview simulation screen.

[1246] Input: User instructions.

[1247] Output: The interface for the interview simulation is displayed.

[1248] Specific behavior: The user selects "Interview Simulation" from the navigation menu and the screen changes.

[1249] Step 16:

[1250] The server sends a prompt to the spawned AI.

[1251] Input: User's self-analysis results and desired company information.

[1252] Output: Generative AI generates interview questions.

[1253] Specific operation: The server sends an API request to the generation AI based on the self-analysis results and desired employer information.

[1254] Step 17:

[1255] The server generates a question and sends it to the terminal.

[1256] Input: Interview questions generated by generative AI.

[1257] Output: The question is displayed on the terminal.

[1258] Specific operation: The server sends the generated question in JSON format to the device.

[1259] Step 18:

[1260] The user answers the interview questions.

[1261] Input: Interview questions generated by the generative AI.

[1262] Output: User's answer data is temporarily saved on the device.

[1263] Specific operation: The user enters an answer to a question using a text box or voice input.

[1264] Step 19:

[1265] The device operates an emotion engine and acquires emotion data.

[1266] Input: The facial expression and tone of voice when the user answers.

[1267] Output: The emotional state is recognized and the emotional data is temporarily stored on the device.

[1268] Specific operation: The device uses the built-in camera and microphone to analyze facial expressions and tone of voice to determine emotions.

[1269] Step 20:

[1270] The terminal transmits the response data and emotion data to the server.

[1271] Input: User response data and sentiment data.

[1272] Output: The server receives these data and stores them in a database.

[1273] Specific operation: The device sends answer data and emotion data to the server using an HTTP POST request.

[1274] Step 21:

[1275] The server stores the response data and emotion data.

[1276] Input: Answer data and emotion data sent from the device.

[1277] Output: Response data and emotion data are stored in a database.

[1278] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1279] Step 22:

[1280] The server sends prompts to the generation AI to generate feedback.

[1281] Input: Response data and sentiment data.

[1282] Output: Feedback is generated by the generative AI.

[1283] Specific operation: The server sends an API request to the generation AI based on the response data and emotion data.

[1284] Step 23:

[1285] The server generates feedback and sends it to the device.

[1286] Input: Feedback generated by the generative AI.

[1287] Output: Feedback is displayed on the terminal.

[1288] Specific behavior: The server sends the generated feedback in JSON format to the device.

[1289] Step 24:

[1290] The user checks the feedback.

[1291] Input: Feedback content.

[1292] Output: User reads and understands the feedback.

[1293] Specific actions: The device displays the feedback content and the user confirms it.

[1294] (Application example 2)

[1295] 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."

[1296] There are currently no interview practice support systems for autonomous vehicles, and in particular, no methods have been developed that use generative AI or emotion engines to provide real-time interview questions and feedback while driving.There is a need for a system that allows users to efficiently practice interviews and make appropriate preparations while driving.

[1297] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating interview questions using a generation AI based on the user's self-analysis data and desired employer information, means for generating feedback based on the user's emotions with an emotion engine that acquires and analyzes the user's emotion data, and means for saving the user's interview result data and feedback on the server and improving the AI ​​model. This enables the user to effectively practice for interviews and receive real-time feedback while driving an autonomous vehicle.

[1298] "User Basic Information" refers to the user's name, email address, date of birth, and other personal information.

[1299] "Self-analysis data" is data obtained as a result of an aptitude test or personality diagnosis, and indicates the user's personality and aptitude.

[1300] "Preference information" is information about the user's desired university, company, or other destination.

[1301] "Generative AI" is a type of artificial intelligence, an algorithm or system that generates appropriate interview questions based on the user's self-analysis data and information about the company they are applying to.

[1302] "Emotion engine" is a general term for software or hardware used to acquire and analyze user emotion data.

[1303] "Interview questions" are questions for question and answer session that are presented to the user in the interview simulation.

[1304] "Feedback" is information that includes an evaluation of the user's interview practice and suggestions for improvement.

[1305] "Interview result data" refers to data that includes answers given by the user in the interview simulation, as well as evaluations and emotional data regarding those answers.

[1306] "Improving an AI model" is the process of continuously improving the performance and accuracy of an AI using collected data.

[1307] This invention relates to an interview practice support system for an autonomous vehicle. This system combines a generative AI model and an emotion engine to provide effective interview practice and feedback to users. The following describes in detail the embodiments of this invention.

[1308] Hardware and Software

[1309] 1. Hardware:

[1310] In-car infotainment system: Android Auto or Apple CarPlay compatible device.

[1311] Camera system: A camera to capture the driver's facial expressions.

[1312] Microphone: A microphone for collecting the driver's voice.

[1313] 2. Software:

[1314] Cloud server: Data storage and AI model hosting are performed by AWS or Google Cloud.

[1315] Emotion Analysis SDK: Software that performs facial expression recognition and voice analysis, such as the Affectiva SDK.

[1316] Generative AI model: Generates interview questions using GPT-4. Powered by Azure OpenAI services.

[1317] Program processing flow

[1318] Register and fill out your profile:

[1319] When the device starts up, the user enters basic information, which is then sent to a cloud server and stored in a database.

[1320] Conduct a self-analysis:

[1321] The device displays aptitude tests and personality assessment questions, and the user answers them. The answers are sent to the server and saved. At this point, the user's self-analysis data is accumulated.

[1322] Select your preferred school:

[1323] The terminal displays a list of candidate schools of choice, and sends the information on the schools selected by the user to the server for storage, which accumulates the information in a database.

[1324] Conducting a simulated interview:

[1325] The generative AI model generates interview questions based on the user's self-analysis data and information about the company they are applying to, and presents them to the user via their device. The user's answers are collected in real time and sent to a cloud server. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[1326] Providing feedback:

[1327] The server generates feedback based on the response data and emotion data and delivers it to the device. The feedback includes an evaluation of the response content, suggestions for improvement, and advice on feelings based on the emotion data.

[1328] Storing interview result data and improving AI models:

[1329] The results and feedback data from the interview simulation are stored on a server and used to train the AI ​​model, which allows the AI ​​model to continually improve and provide more accurate questions and feedback in the future.

[1330] Examples of specific examples and prompts

[1331] Examples:

[1332] When the driver gets into the self-driving car and says, "Please start the interview practice," the in-car infotainment system starts up and the interview practice assistant begins asking interview questions. In response to the questions, the driver is asked, "Please introduce yourself." After the driver responds, the generative AI suggests the next question, and the emotion engine analyzes the tone of voice and facial expressions to provide feedback in real time.

[1333] Example prompt sentence:

[1334] "Generate five software engineer interview questions based on user background information."

[1335] "Use the sentiment data to provide feedback on interview responses and explain why."

[1336] In this way, the present invention provides a highly supportive environment for conducting effective interview practice even in an autonomous vehicle.

[1337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1338] Step 1:

[1339] User registration and profile entry

[1340] When the device is first started up, a user registration screen is displayed, and the user enters basic information such as name, email address, and date of birth.

[1341] Input: Name, Email Address, Date of Birth.

[1342] Data processing: Convert basic information into JSON format and send it to the server using the HTTPS protocol.

[1343] The server stores the received data in a database.

[1344] Output: Basic information stored in a database.

[1345] Step 2:

[1346] Conducting self-analysis

[1347] The terminal displays aptitude test and personality assessment questions and provides an interface for the user to answer the questions.

[1348] Input: The user's answer.

[1349] Data processing: Convert the response data into JSON format and send it to the server.

[1350] The server stores the received response data in a database.

[1351] Output: Self-analysis data is saved in a database.

[1352] Step 3:

[1353] Choosing your preferred destination

[1354] The terminal displays a list of candidate companies of interest, and transmits the information on the company selected by the user to the server and stores it.

[1355] Input: Select your preferred destination.

[1356] Data processing: Convert the selected desired school information into JSON format and send it to the server.

[1357] The server stores the received desired school information in a database.

[1358] Output: The desired school information is saved in the database.

[1359] Step 4:

[1360] Conducting interview simulations

[1361] The server uses a generation AI to generate interview questions based on self-analysis data and information about the company of choice, and delivers them to the device.

[1362] Input: Self-analysis data, desired employer information.

[1363] Data calculation: Create prompts that the generative AI model uses to generate questions, and then call the model to generate questions.

[1364] Output: Generated interview questions.

[1365] The device displays the generated questions to the user and obtains answers. The emotion engine also analyzes the user's facial expressions and voice to obtain emotion data.

[1366] Input: User answers and sentiment data.

[1367] Data processing: Response data and emotion data are converted into JSON format and sent to the server.

[1368] Output: Answer data and sentiment data stored on the server.

[1369] Step 5:

[1370] Providing feedback

[1371] The server uses a generation AI to generate feedback based on the response data and emotion data, and delivers it to the device.

[1372] Input: Response data, emotion data.

[1373] Data calculation: Analyzes response data and sentiment data to generate feedback, and invokes a generative AI model to generate feedback.

[1374] Output: The generated feedback.

[1375] The terminal displays the feedback to the user.

[1376] Input: Feedback data.

[1377] Output: User feedback confirmation.

[1378] Step 6:

[1379] Storing interview result data and improving AI models

[1380] The server stores the results data and feedback data of the interview simulation in a database.

[1381] Input: Interview result data, feedback data.

[1382] Data processing: Converting data into an analyzable format and storing it in a database.

[1383] Output: Result data stored in a database.

[1384] This data is used to continuously train generative AI models to improve the accuracy of interview questions and feedback.

[1385] Data computation: Combining historical and new data to train models.

[1386] Output: An improved AI model.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] [Third embodiment]

[1391] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1392] 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.

[1393] 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).

[1394] 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.

[1395] 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.

[1396] 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).

[1397] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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."

[1403] This invention is an interview practice support system designed for test takers, job seekers, or people looking to change jobs, and it uses a generative AI to provide users with appropriate questions and feedback. This system consists of a server, a terminal used by the user, and the user.

[1404] Handling user registration and profile entries

[1405] Terminal

[1406] When the app is launched for the first time, it displays a user registration screen, providing input fields for the user's name, email address, date of birth, etc.

[1407] server

[1408] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[1409] User

[1410] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[1411] Conducting self-analysis

[1412] Terminal

[1413] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[1414] server

[1415] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[1416] User

[1417] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[1418] Choosing your preferred destination

[1419] Terminal

[1420] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[1421] server

[1422] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[1423] User

[1424] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[1425] Conducting interview simulations

[1426] Terminal

[1427] It displays questions generated by the AI ​​one by one and provides an interface where users can enter answers. After entering the answers, they are sent to the server.

[1428] server

[1429] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database.

[1430] User

[1431] The user inputs an answer to the displayed question and transmits the answer data to the server.

[1432] Providing feedback

[1433] Terminal

[1434] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[1435] server

[1436] Based on the user's response data, the generation AI generates feedback, which is then delivered to the device.

[1437] User

[1438] Review the feedback provided and understand areas for improvement.

[1439] Saving results and improving AI models

[1440] Terminal

[1441] The results of the interview simulation are sent to the server, which stores the user's practice history.

[1442] server

[1443] The stored interview result data is used to train the generative AI model to improve it. As the AI ​​model continues to learn, it will be able to provide users with more accurate questions and feedback in the future.

[1444] User

[1445] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[1446] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[1447] The processing flow will be explained below.

[1448] Handling user registration and profile entries

[1449] Step 1:

[1450] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[1451] Step 2:

[1452] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[1453] Step 3:

[1454] Terminal: Checks the entered information and sends it to the server.

[1455] Step 4:

[1456] Server: Stores the received user information in a database.

[1457] Conducting self-analysis

[1458] Step 1:

[1459] User: Presses a button to start self-analysis.

[1460] Step 2:

[1461] Device: Retrieves aptitude and personality test questions from the server.

[1462] Step 3:

[1463] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[1464] Step 4:

[1465] User: Enter answers to the questions displayed.

[1466] Step 5:

[1467] Terminal: Sends the entered answer data to the server.

[1468] Step 6:

[1469] Server: Stores the received response data in a database.

[1470] Choosing your preferred destination

[1471] Step 1:

[1472] User: Select the "Select School of Interest" option from the menu.

[1473] Step 2:

[1474] Terminal: Obtain a list of universities and companies of interest from the server.

[1475] Step 3:

[1476] Terminal: Display the obtained list and let the user select.

[1477] Step 4:

[1478] User: Select the school or company of your choice from the list of preferred schools.

[1479] Step 5:

[1480] Terminal: Sends the selected desired school information to the server.

[1481] Step 6:

[1482] Server: Save the received information about the desired school in a database.

[1483] Conducting interview simulations

[1484] Step 1:

[1485] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[1486] Step 2:

[1487] Terminal: Displays the generated questions one by one.

[1488] Step 3:

[1489] User: Enter answers to the questions displayed.

[1490] Step 4:

[1491] Terminal: Temporarily holds the entered answers.

[1492] Step 5:

[1493] Terminal: Sends the response data to the server.

[1494] Step 6:

[1495] Server: Stores the received response data in a database.

[1496] Providing feedback

[1497] Step 1:

[1498] Server: The generative AI generates feedback based on the user's answers.

[1499] Step 2:

[1500] Server: Delivers the generated feedback to the device.

[1501] Step 3:

[1502] Terminal: Display feedback to the user.

[1503] Step 4:

[1504] Users: Review and understand the feedback.

[1505] Saving results and improving AI models

[1506] Step 1:

[1507] Terminal: Sends the results of the interview simulation to the server.

[1508] Step 2:

[1509] Server: Stores the received interview result data in a database.

[1510] Step 3:

[1511] Server: Uses the saved result data to learn and improve the AI ​​model.

[1512] Step 4:

[1513] Server: The AI ​​model continuously learns and generates more accurate questions and feedback.

[1514] The above is a specific processing flow for carrying out the present invention. The user, terminal, and server each play their respective roles and cooperate to form the entire system.

[1515] Example 1

[1516] 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."

[1517] Conventional interview practice support systems have difficulty automatically generating appropriate interview questions tailored to the user's individual characteristics and desired employer, and are unable to provide specific and accurate feedback. Furthermore, they lack a continuous learning function to improve the accuracy of interview practice, leaving a need for a system that can adequately meet user needs. Furthermore, they lack a support function for users to formulate specific action plans based on feedback, limiting the effectiveness of users' self-improvement.

[1518] 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.

[1519] In this invention, the server includes: means for acquiring basic information based on user input; means for acquiring self-analysis questions such as aptitude tests and personality tests from the server; means for presenting the acquired questions to the user and acquiring the user's answers; means for transmitting and storing the user's answer data to the server; means for acquiring a list of candidate employers based on the user's self-analysis data; means for transmitting and storing the user's selected employer information to the server; means for a generation AI to generate interview questions based on the user's self-analysis data and employer information; means for providing the generated interview questions to the user and acquiring the user's answers; means for generating and providing feedback based on the user's answers; means for saving the user's interview result data and feedback to the server and improving the AI ​​model; means for generating appropriate feedback using the generation AI model; means for providing a timer function during the interview simulation to automatically prompt the user to answer; and means for providing a memo function when displaying the feedback so that the user can create a specific action plan. This allows the user to receive interview questions tailored to their characteristics and employers of choice, receive specific and accurate feedback, and improve the accuracy of their interview practice through continuous learning. In addition, the effectiveness of self-improvement can be enhanced by utilizing the support functions for formulating specific action plans.

[1520] "User" refers to a candidate, job seeker, or job seeker who uses this system.

[1521] "Basic information" refers to information such as name, email address, and date of birth that a user enters when registering with the system.

[1522] A "server" is a device that processes and stores various data, and generates and distributes aptitude tests, self-analysis questions, interview questions, etc.

[1523] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet, that provides an input interface and display functions.

[1524] An "aptitude test" is a question or test that assesses a user's characteristics or abilities.

[1525] A "personality test" is a question or test that analyzes a user's personality and behavioral patterns.

[1526] "Self-analysis data" refers to the answers and results of the user's responses to aptitude tests and personality tests.

[1527] "Desired destination" refers to the university, company, or other destination the user desires to attend.

[1528] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate interview questions and feedback based on user information.

[1529] "Interview questions" are questions presented to the user in the interview simulation.

[1530] "Feedback" refers to evaluations and improvements on the user's answers.

[1531] "Interview result data" is data that includes the answers given by the user in the interview simulation and the feedback given to those answers.

[1532] The "timer function" is a function that prompts the user to enter an answer within a certain time period.

[1533] The "memo function" is a function that allows users to create specific action plans based on feedback.

[1534] An "AI model" is an algorithm that learns from user data and improves the accuracy of interview practice.

[1535] This invention is a support system that allows test takers, job seekers, and people looking to change jobs to effectively practice for interviews. The system consists of a terminal used by the user and a server that processes data and generates questions. Users can perform the entire process from registration to interview practice and receiving feedback.

[1536] User registration and profile entry

[1537] Terminal

[1538] When a user launches the app for the first time, a user registration screen is displayed. The user enters information such as their name, email address, and date of birth. For example, the user enters "Yamada Hanako" in the name field, "hanako@example.com" in the email address field, and "1995-06-20" in the date of birth field.

[1539] server

[1540] It receives user information sent from the device and stores it in a MySQL database, thereby accumulating basic user information.

[1541] User

[1542] By inputting information and pressing the send button, the terminal transmits the information to the server.

[1543] Conducting self-analysis

[1544] Terminal

[1545] The app displays aptitude or personality questions, such as "What are your strengths?" The user answers the questions and the device sends the answers to a server.

[1546] server

[1547] The server receives the data from the receiving side and stores it in an SQLite database. The question and answer data is later used as self-analysis results.

[1548] User

[1549] The user enters an answer to the displayed question and presses the send button to send the answer data to the server.

[1550] Choosing your preferred destination

[1551] Terminal

[1552] The app displays a list of universities and companies you are interested in. You select an option from the list and send the selected information to the server. For example, let's say you select "XYZ Company."

[1553] server

[1554] The server receives the selected information about the desired school and stores it in a database.

[1555] User

[1556] The user selects the desired destination and presses the OK button to send the selection to the server.

[1557] Conducting interview simulations

[1558] server

[1559] Based on the user's self-analysis and desired company information, Generating AI generates appropriate interview questions. For example, it might ask, "What are your reasons for applying?"

[1560] Terminal

[1561] The generated question is displayed to the user, who answers the question and the terminal transmits the answer data to the server.

[1562] User

[1563] In response to the question that appears, enter "I sympathize with your philosophy of contributing to society and would like to work for your company," and press the send button.

[1564] Providing feedback

[1565] server

[1566] The generative AI generates feedback based on the user's answers. For example, it might say, "It would be better if you included specific examples in your motivation for applying."

[1567] Terminal

[1568] Feedback is displayed to the user, who can review it and understand where improvements can be made.

[1569] User

[1570] The feedback provided can be used to improve your next practice.

[1571] Saving results and improving AI models

[1572] Terminal

[1573] The results of the interview simulation are sent to a server, and the user's practice history is saved.

[1574] server

[1575] The server stores the resulting data and uses it to train the Generating AI model, which will improve the accuracy of interview questions and feedback for future interviews.

[1576] User

[1577] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[1578] concrete action

[1579] Timer function: During the interview simulation, the user is prompted to enter answers within a certain time.

[1580] Notes function: While viewing feedback, users can enter notes to help them create specific action plans.

[1581] Prompt Sentence Examples

[1582] "What are your hobbies?"

[1583] "What has been the most challenging experience you have had so far?"

[1584] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[1585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1586] Step 1: User registration and profile entry

[1587] User: Launch the app and the user registration screen will appear. Enter information such as "Yamada Hanako," "hanako@example.com," and "1995-06-20," and press the registration button.

[1588] Terminal: Receives information entered by the user, converts it into JSON format, and sends it to the server.

[1589] Server: Receives JSON data and saves it to a database. Specifically, it saves the information "Hanako Yamada", "hanako@example.com", and "1995-06-20" in a MySQL database. The input is user information, and the output is saved to the database.

[1590] Step 2: Conduct a self-analysis

[1591] Terminal: Displays aptitude test and personality assessment questions received from the server. The question "What are your strengths?" is displayed.

[1592] User: Answers the question by typing "Analytical ability" and presses the send button.

[1593] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[1594] Server: Receives the answer data and saves it in a database. Specifically, it saves the answer "analytical ability" in an SQLite database. The input is the question and answer, and the output is saved in the database.

[1595] Step 3: Choose your preferred school

[1596] Terminal: Displays the list of companies of interest received from the server. Lists such as "ABC Company" and "XYZ Company" are displayed.

[1597] User: Selects "XYZ Company" as the company of choice and presses the OK button.

[1598] Terminal: Converts the information selected by the user into JSON format and sends it to the server.

[1599] Server: Receives the selected company information and saves it in the database. Specifically, it saves the selected company "XYZ Company" in the database. The input is the company information, and the output is saving it in the database.

[1600] Step 4: Conduct a simulated interview

[1601] Server: Based on the user's self-analysis results and desired employer information, the generative AI model generates appropriate interview questions. For example, it generates a question such as, "What is your motivation for applying?"

[1602] Terminal: The generated interview questions are displayed to the user.

[1603] User: Answers the question by saying, "I sympathize with your philosophy of contributing to society and would like to work for your company," and presses the send button.

[1604] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[1605] Server: Receives the response data and stores it in a database. Specifically, the response "I sympathize with your philosophy of social contribution and would like to work for your company" is stored in the database. The input is the question and the response, and the output is stored in the database.

[1606] Step 5: Provide feedback

[1607] Server: The generative AI model generates feedback based on the user's response data. For example, it might generate feedback such as, "It would be better if you included specific examples in your motivation for applying."

[1608] Terminal: The generated feedback is displayed to the user.

[1609] User: Check the feedback and use it for the next practice. As a concrete example, the user will try to provide more specific answers in the next simulation based on the feedback that "please include specific examples in your motivation for applying." The input is the answer data, and the output is the feedback.

[1610] Step 6: Saving the results and improving the AI ​​model

[1611] Terminal: Converts the interview simulation results into JSON format and sends them to the server.

[1612] Server: Receives the result data and stores it in a database. In addition, the generative AI model learns based on the result data, improving the accuracy of interview questions and feedback. The input is the simulation result data, and the output is the learning and accuracy improvement of the AI ​​model.

[1613] Users: The next time they take a simulated interview, they will receive improved questions and feedback, which will lead to more effective practice and improved performance in the actual interview.

[1614] This allows users to consistently practice interviews effectively and gain confidence in real interviews. Furthermore, the Generating AI model continuously learns based on feedback and simulated interview results, enabling it to provide high-quality support.

[1615] (Application example 1)

[1616] 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."

[1617] Conventional interview practice support systems have the problem of only being able to provide limited support to test takers, job seekers, or people looking to change jobs. Similarly, there is a lack of effective support for training new store clerks to improve their customer service skills. This presents a problem in that customer service practice cannot be effectively conducted in physical stores, making it difficult to improve service. To solve these problems, the present invention aims to expand the interview practice support system and provide a comprehensive practice support system that can also accommodate customer service practice for store clerks.

[1618] 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.

[1619] In this invention, the server comprises: means for acquiring basic information based on an input from a user;

[1620] A means for acquiring questions for self-analysis such as aptitude tests and personality tests from a server;

[1621] means for presenting the acquired question to a user and acquiring an answer from the user;

[1622] means for transmitting and storing user response data on a server;

[1623] A means for acquiring a list of candidates for desired employment based on the user's self-analysis data;

[1624] A means for transmitting the desired school information selected by the user to a server and storing it;

[1625] A means for the AI ​​to generate interview questions based on the user's self-analysis data and desired employer information;

[1626] means for providing the generated interview questions to a user and obtaining answers from the user;

[1627] means for generating and providing feedback to the user based on the user's responses;

[1628] A means for storing the user's interview result data and feedback on a server and improving the AI ​​model;

[1629] To support sales staff in their customer service practice, a means is provided for acquiring profile information from users and for a generation AI to generate questions necessary for customer service based on the type of store.

[1630] A means for providing the generated customer service question to a store clerk and obtaining an answer from the store clerk;

[1631] a means for generating and providing feedback to the sales associate based on the sales associate's responses;

[1632] A means to suggest areas for improvement to improve customer service skills based on feedback

[1633] This will not only help prepare students for interviews, job seekers, and people looking to change jobs for interview practice, but will also improve the accuracy of customer service practice for new store employees.

[1634] "Exam takers" are students who intend to take an exam.

[1635] A "job seeker" is an individual who is actively seeking new employment with a company or organization.

[1636] A "job seeker" is an individual who is seeking a job change from their current workplace to another workplace.

[1637] An "interview practice support system" is an information processing system designed to help users practice for interviews effectively.

[1638] "Basic information" refers to basic information about the user, such as the user's name, email address, years of experience, etc.

[1639] An "aptitude test" is a test used to evaluate an individual's abilities and personality, and is used to understand the user's aptitude.

[1640] A "personality test" is a test for assessing an individual's personality traits and is used to understand a user's personality.

[1641] "Self-analysis data" is a collection of information obtained from the results of a user's responses to aptitude tests and personality assessments.

[1642] "Preferences" refers to universities, companies, etc. that the user wishes to attend or work for.

[1643] "Generative AI" is a system that uses artificial intelligence technology to generate interview questions and feedback based on user data.

[1644] "Customer service practice" refers to new store employees practicing the skills necessary to effectively serve customers in a physical store.

[1645] "Profile information" refers to information registered by a user, such as name, email address, years of experience, and type of store.

[1646] "Store type" refers to store categories such as fashion, restaurants, supermarkets, etc.

[1647] "Feedback" is information about evaluations and areas for improvement that are generated based on the user's responses.

[1648] "Areas for improvement" refers to points or suggestions that users or store clerks need to improve through practice.

[1649] This invention provides a practice support system for test takers, job seekers, people looking to change jobs, and new store clerks. To implement this invention, a server, a user terminal, and a generative AI model are used.

[1650] Handling user registration and profile entries

[1651] Terminal

[1652] When a user launches the app for the first time, they are presented with a user registration screen, which provides fields for entering profile information such as name, email address, date of birth, years of experience, etc. Once the user enters this information and presses the submit button, the information is sent to the server.

[1653] server

[1654] The server receives the user information sent from the terminal and stores it in a database, thereby accumulating basic user information for use in subsequent processes.

[1655] Conducting self-analysis

[1656] Terminal

[1657] Aptitude tests and personality tests are displayed to the user, who then enters answers to the questions and presses the send button to send the answer data to the server.

[1658] server

[1659] The server distributes self-analysis questions to the user's terminal and stores the answer data received from the terminal in a database, which is later used as the user's self-analysis results.

[1660] Choosing your preferred destination

[1661] Terminal

[1662] It displays a list of universities and companies that the user is interested in and provides an interface for them to select from. The user selects the university or company they want, presses the OK button, and sends the selection to the server.

[1663] server

[1664] The server distributes the list of desired companies and stores the information on the desired companies selected by the user in a database.

[1665] Interview simulation and customer service practice

[1666] Terminal

[1667] It displays questions generated by the AI ​​and provides an interface for users to input their answers. Users input answers to the displayed questions and press the send button to send the answer data to the server.

[1668] server

[1669] The server uses a generation AI to generate appropriate interview questions based on the user's self-analysis results and desired employer information. Similarly, it generates customer service practice questions based on the employee's profile information and the type of store. The generated questions are sent to the device, which receives the user's response data and stores it in a database.

[1670] Providing feedback

[1671] Terminal

[1672] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[1673] server

[1674] The server uses AI to generate feedback based on the user's response data and delivers it to the device, allowing the user to practice based on the feedback.

[1675] Saving results and improving AI models

[1676] Terminal

[1677] The results of the user's interview simulation and customer service practice are sent to the server, which stores the user's practice history.

[1678] server

[1679] The server uses the stored interview result data and feedback data to improve the AI ​​model. This continuous learning allows the generative AI to provide more accurate questions and feedback in the future.

[1680] For example, in the case of a customer service simulation for a fashion store, the following prompts could be used:

[1681] "Customer service simulation for new sales associates: Fashion\nPlease generate questions."

[1682] By using such prompt sentences, the generative AI can generate questions that correspond to specific store situations.

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] User registration and profile entry

[1686] When a user launches the app on their device for the first time, a user registration screen appears. Here, the user enters profile information such as name, email address, date of birth, and years of experience. Once the information is entered, this data is sent from the device to the server. The server saves the received data in a database and accumulates the user's basic information.

[1687] Input: Name, email address, date of birth, years of experience

[1688] Output: User information stored in the database

[1689] Step 2:

[1690] Conducting self-analysis

[1691] Aptitude tests and personality assessment questions for self-analysis are sent from the server to the user's device. The device presents these questions to the user, who then enters their answers. Once the answers are entered, the device sends them to the server. The server receives the answer data and stores it in a database.

[1692] Input: Self-assessment questions, user answers

[1693] Output: Answer data stored in a database

[1694] Step 3:

[1695] Choosing your preferred destination

[1696] A list of universities and companies of choice is sent from the server to the user's device. The device displays the list to the user, who then selects the desired university. Once the selection is complete, the user's device sends the information to the server. The server stores the received information in a database.

[1697] Input: Preferred list, user selection

[1698] Output: Information about the desired school saved in the database

[1699] Step 4:

[1700] Interview simulation and customer service practice

[1701] The generation AI generates interview questions based on the user's self-analysis results and information about the company they wish to work for. In the case of customer service practice for a store clerk, customer service questions are generated based on the user's profile information and the type of store. The generated questions are sent from the server to the user's device, and the user answers them. The device sends the user's answers to the server, which stores them in a database.

[1702] Input: Self-analysis results, desired employer information, AI prompt

[1703] Output: Generated interview questions, answers stored in a database

[1704] Step 5:

[1705] Providing feedback

[1706] The server uses the AI ​​to generate feedback based on the user's response data. The generated feedback is then sent from the server to the user's device. The device then displays the feedback to the user and suggests areas for improvement.

[1707] Input: User response data, prompts for the AI ​​generator

[1708] Output: generated feedback, presented to the user

[1709] Step 6:

[1710] Saving results and improving AI models

[1711] The results and feedback data of the user's interview simulation and customer service practice are sent to a server and stored in a database. The server continuously trains the generative AI model based on this stored data, enabling it to provide more accurate questions and feedback in the future.

[1712] Input: Interview result data, feedback data

[1713] Output: Result data stored in a database, improved AI model

[1714] 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.

[1715] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a server, a terminal, and a user.

[1716] Handling user registration and profile entries

[1717] Terminal

[1718] When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[1719] server

[1720] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[1721] User

[1722] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[1723] Conducting self-analysis

[1724] Terminal

[1725] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[1726] server

[1727] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[1728] User

[1729] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[1730] Choosing your preferred destination

[1731] Terminal

[1732] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[1733] server

[1734] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[1735] User

[1736] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[1737] Conducting interview simulations

[1738] Terminal

[1739] It displays questions generated by the generative AI one by one, provides an interface where the user can enter answers, and operates an emotion engine that recognizes the user's emotions.

[1740] server

[1741] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database. In addition, emotional data generated by the emotion engine is also stored.

[1742] User

[1743] Answers are entered to the questions displayed, and sentiment analysis is performed using an emotion engine.

[1744] Providing feedback

[1745] server

[1746] The AI ​​generates feedback based on the user's response data and emotional data, and delivers the generated feedback to the device.

[1747] Terminal

[1748] Feedback is displayed to the user, including an evaluation of the answer, suggestions for improvement, and advice on how to respond based on emotional data.

[1749] User

[1750] Review and understand the feedback provided, and use it to understand how to manage your emotions and improve.

[1751] Saving results and improving AI models

[1752] Terminal

[1753] The results of the interview simulation and emotional data are sent to the server, which stores the user's practice history.

[1754] server

[1755] The AI ​​model uses the saved interview result data and emotion data to learn and improve itself. By continuously learning, the AI ​​model will be able to provide users with more accurate questions and feedback in the future.

[1756] User

[1757] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[1758] In this way, the present invention provides an environment that allows users to approach real interviews with confidence. Furthermore, the generative AI and emotion engine create a practice environment that takes the user's feelings into consideration by generating appropriate feedback based on the user's emotional data. This allows users to practice interviews in a more relaxed state and effectively identify areas for improvement.

[1759] The processing flow will be explained below.

[1760] Handling user registration and profile entries

[1761] Step 1:

[1762] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[1763] Step 2:

[1764] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[1765] Step 3:

[1766] Terminal: Checks the entered information and sends it to the server.

[1767] Step 4:

[1768] Server: Stores the received user information in a database.

[1769] Conducting self-analysis

[1770] Step 1:

[1771] User: Presses a button to start self-analysis.

[1772] Step 2:

[1773] Device: Retrieves aptitude and personality test questions from the server.

[1774] Step 3:

[1775] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[1776] Step 4:

[1777] User: Enter answers to the questions displayed.

[1778] Step 5:

[1779] Terminal: Sends the entered answer data to the server.

[1780] Step 6:

[1781] Server: Stores the received response data in a database.

[1782] Choosing your preferred destination

[1783] Step 1:

[1784] User: Select the "Select School of Interest" option from the menu.

[1785] Step 2:

[1786] Terminal: Obtain a list of universities and companies of interest from the server.

[1787] Step 3:

[1788] Terminal: Display the obtained list and let the user select.

[1789] Step 4:

[1790] User: Select the school or company of your choice from the list of preferred schools.

[1791] Step 5:

[1792] Terminal: Sends the selected desired school information to the server.

[1793] Step 6:

[1794] Server: Save the received information about the desired school in a database.

[1795] Conducting interview simulations

[1796] Step 1:

[1797] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[1798] Step 2:

[1799] Terminal: Displays the generated questions one by one and also runs an emotion engine to recognize the user's emotions during the interview.

[1800] Step 3:

[1801] User: Enter answers to the questions displayed.

[1802] Step 4:

[1803] Emotion engine: Obtains emotional data from the user's facial expressions and tone of voice during the interview.

[1804] Step 5:

[1805] Terminal: Temporarily stores the user's answers and emotion data.

[1806] Step 6:

[1807] Terminal: Sends response data and emotion data to the server.

[1808] Step 7:

[1809] Server: Stores the received response data and emotion data in a database.

[1810] Providing feedback

[1811] Step 1:

[1812] Server: The AI ​​generates feedback based on the user's response data and emotional data.

[1813] Step 2:

[1814] Server: Delivers the generated feedback to the device.

[1815] Step 3:

[1816] On the device, feedback is displayed to the user, including a rating of the answer, suggestions for improvement, and emotional advice based on the emotional data.

[1817] Step 4:

[1818] Users: Review the feedback provided and understand areas for improvement.

[1819] Saving results and improving AI models

[1820] Step 1:

[1821] Terminal: Sends the results of the interview simulation and emotional data to the server.

[1822] Step 2:

[1823] Server: Stores the received interview result data and emotion data in a database.

[1824] Step 3:

[1825] Server: Uses the stored result data and emotion data to learn and improve the AI ​​model.

[1826] Step 4:

[1827] Server: The AI ​​model continuously learns, enabling it to generate more accurate questions and feedback in the future.

[1828] The above is a specific processing flow for implementing the present invention. The user, terminal, server, and emotion engine each play their own roles while working together to form the entire system.

[1829] Example 2

[1830] 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."

[1831] Conventional interview practice systems have not been able to generate appropriate interview questions based on the user's self-analysis results and information about their desired employer, nor have they provided sufficient feedback that reflects real-time emotional data. Furthermore, there have been no systems that improve the accuracy of interview practice by continuously training a generative AI model using the user's practice results and emotional data. This has made it difficult for users to effectively improve their practical interview skills.

[1832] 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.

[1833] In this invention, the server includes a means for the generation AI to generate interview questions based on the user's self-analysis data and desired employer information, a means for the terminal to acquire emotional data and recognize the user's emotional state, and a means for continuously training the generation AI model using the saved interview result data and emotional data. This allows the user to receive appropriate questions based on the self-analysis results and desired employer information, and furthermore, by receiving feedback based on the user's emotional state, enables practical and effective interview practice.

[1834] "User" refers to an individual who uses the information processing system, such as a student taking an exam, a job seeker, or a person looking to change jobs.

[1835] "Basic information" refers to information that identifies and locates an individual, such as a user's name, email address, and date of birth.

[1836] An "aptitude test" is a question or test designed to assess a user's aptitudes, interests, abilities, etc.

[1837] A "personality assessment" is a set of questions or tests designed to assess a user's personality and behavioral traits.

[1838] "Self-Analysis Questions" refers to a list of questions used to deepen a user's self-understanding.

[1839] "Preferred school" refers to the school or company for which the user wishes to take an entrance exam, get a job, or change jobs.

[1840] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate interview questions and feedback.

[1841] "Interview Questions" are questions asked to the user and provided by the generative AI as part of the interview simulation.

[1842] "Feedback" refers to the evaluation and improvement suggestions provided to the user regarding their interview practice.

[1843] "Emotion data" refers to information about the user's emotional state that is analyzed based on facial expressions, tone of voice, etc.

[1844] "AI Model" refers to a computational model that learns and improves using artificial intelligence techniques.

[1845] "Continuous learning" refers to the means by which AI models are regularly updated and improved based on collected data.

[1846] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a user, a terminal, and a server.

[1847] First, when a user launches the application, a user registration screen appears on the device. After the user enters basic information such as name, email address, and date of birth, the device sends this information to the server. The server stores the received basic information in a database. Through this process, the system accumulates user profile information.

[1848] When the user moves to the next screen for self-analysis, the server sends a list of questions for aptitude tests and personality assessments to the terminal. The terminal displays these questions to the user, and the user enters answers to each question. Once the answers are completed, the terminal sends the answer data to the server, which stores it in a database.

[1849] The user then moves to a screen for selecting their preferred universities and companies, and the server sends a list of their preferred universities and companies to the terminal. Once the user selects their preferred universities and companies, the terminal sends the selected university information to the server, which then stores it in a database.

[1850] When the interview simulation stage begins, the server sends prompts to the generation AI based on the user's self-analysis results and information about the company they wish to work for. The generation AI generates appropriate interview questions, which the server then sends to the device. The device displays the questions, and the user enters their answers. While the answers are being entered, the device activates an emotion engine to analyze the user's emotional state and obtain emotional data. This data is then sent from the device to the server, which stores it in a database.

[1851] The server then generates feedback by sending prompts to the AI ​​based on the user's response data and emotion data. The generated feedback is sent to the device and displayed to the user. The feedback includes an evaluation of the response, suggestions for improvement, and advice based on the emotion data.

[1852] Finally, the results of the interview simulation and emotional data are sent to and stored on a server, which uses this data to continuously train the generative AI model and provide more accurate questions and feedback to users in the future.

[1853] For example, the generative AI will ask the question, "Please introduce yourself in one minute." It will also provide advice based on emotion recognition, such as, "Take a deep breath and relax." This system allows users to practice effectively so they can approach the actual interview with confidence.

[1854] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1855] Step 1:

[1856] The user launches the app.

[1857] Input: A user launches an application on their smartphone or PC.

[1858] Output: The user is taken to the login or sign up screen.

[1859] What happens: The user taps or clicks the icon and the application displays its welcome screen.

[1860] Step 2:

[1861] The user enters basic information.

[1862] Input: The user enters basic information such as name, email address, and date of birth.

[1863] Output: The user's basic information is temporarily stored on the device.

[1864] Specific actions: The user fills in the required information in the form and clicks the "Register" button.

[1865] Step 3:

[1866] The device sends basic information to the server.

[1867] Input: Basic information entered by the user.

[1868] Output: The server receives the basic information and stores it in a database.

[1869] Specific behavior: The device sends basic information to the server using an HTTP POST request.

[1870] Step 4:

[1871] The server stores user information.

[1872] Input: Basic information sent from the device.

[1873] Output: Basic information is saved in the database.

[1874] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1875] Step 5:

[1876] The user moves to the self-analysis screen.

[1877] Input: User selects "Self-Analysis" from the navigation menu.

[1878] Output: A self-analysis question is displayed.

[1879] What happens: The user clicks on a menu item and the screen switches to a self-analysis interface.

[1880] Step 6:

[1881] The server sends a self-analysis question.

[1882] Input: User ID and request information.

[1883] Output: A list of self-analysis questions will be displayed on the terminal.

[1884] Specific operation: The server sends a predefined list of questions in JSON format to the terminal.

[1885] Step 7:

[1886] The user answers self-assessment questions.

[1887] Input: Self-analysis questions.

[1888] Output: User's answer data is temporarily saved on the device.

[1889] Specific action: The user enters an answer to a question using a text box or options.

[1890] Step 8:

[1891] The terminal transmits the response data to the server.

[1892] Input: User response data.

[1893] Output: The server receives the response data and stores it in a database.

[1894] Specific operation: The device sends the response data to the server using an HTTP POST request.

[1895] Step 9:

[1896] The server stores the response data.

[1897] Input: Response data sent from the device.

[1898] Output: The response data is saved in a database.

[1899] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1900] Step 10:

[1901] The user moves to the desired school selection screen.

[1902] Input: User instructions.

[1903] Output: A list of preferred destinations is displayed.

[1904] Specific behavior: The user selects "Select preferred school" from the navigation menu and the screen changes.

[1905] Step 11:

[1906] The server sends the list of preferred destinations.

[1907] Input: User ID and request information.

[1908] Output: The list of preferred schools is displayed on the terminal.

[1909] Specific operation: The server sends a predefined list of preferred schools in JSON format to the terminal.

[1910] Step 12:

[1911] The user selects the desired destination.

[1912] Input: Preferred list.

[1913] Output: The selected school information is temporarily saved on the device.

[1914] Specific behavior: The user selects the university or company they want to apply to from a drop-down menu.

[1915] Step 13:

[1916] The terminal transmits the desired school information to the server.

[1917] Input: User selected school information.

[1918] Output: The server receives the desired school information and stores it in a database.

[1919] Specific operation: The device sends the desired school information to the server using an HTTP POST request.

[1920] Step 14:

[1921] The server stores the desired school information.

[1922] Input: Application information sent from the device.

[1923] Output: The desired school information is saved in the database.

[1924] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1925] Step 15:

[1926] The user navigates to the interview simulation screen.

[1927] Input: User instructions.

[1928] Output: The interface for the interview simulation is displayed.

[1929] Specific behavior: The user selects "Interview Simulation" from the navigation menu and the screen changes.

[1930] Step 16:

[1931] The server sends a prompt to the spawned AI.

[1932] Input: User's self-analysis results and desired company information.

[1933] Output: Generative AI generates interview questions.

[1934] Specific operation: The server sends an API request to the generation AI based on the self-analysis results and desired employer information.

[1935] Step 17:

[1936] The server generates a question and sends it to the terminal.

[1937] Input: Interview questions generated by generative AI.

[1938] Output: The question is displayed on the terminal.

[1939] Specific operation: The server sends the generated question in JSON format to the device.

[1940] Step 18:

[1941] The user answers the interview questions.

[1942] Input: Interview questions generated by the generative AI.

[1943] Output: User's answer data is temporarily saved on the device.

[1944] Specific operation: The user enters an answer to a question using a text box or voice input.

[1945] Step 19:

[1946] The device operates an emotion engine and acquires emotion data.

[1947] Input: The facial expression and tone of voice when the user answers.

[1948] Output: The emotional state is recognized and the emotional data is temporarily stored on the device.

[1949] Specific operation: The device uses the built-in camera and microphone to analyze facial expressions and tone of voice to determine emotions.

[1950] Step 20:

[1951] The terminal transmits the response data and emotion data to the server.

[1952] Input: User response data and sentiment data.

[1953] Output: The server receives these data and stores them in a database.

[1954] Specific operation: The device sends answer data and emotion data to the server using an HTTP POST request.

[1955] Step 21:

[1956] The server stores the response data and emotion data.

[1957] Input: Answer data and emotion data sent from the device.

[1958] Output: Response data and emotion data are stored in a database.

[1959] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[1960] Step 22:

[1961] The server sends prompts to the generation AI to generate feedback.

[1962] Input: Response data and sentiment data.

[1963] Output: Feedback is generated by the generative AI.

[1964] Specific operation: The server sends an API request to the generation AI based on the response data and emotion data.

[1965] Step 23:

[1966] The server generates feedback and sends it to the device.

[1967] Input: Feedback generated by the generative AI.

[1968] Output: Feedback is displayed on the terminal.

[1969] Specific behavior: The server sends the generated feedback in JSON format to the device.

[1970] Step 24:

[1971] The user checks the feedback.

[1972] Input: Feedback content.

[1973] Output: User reads and understands the feedback.

[1974] Specific actions: The device displays the feedback content and the user confirms it.

[1975] (Application example 2)

[1976] 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."

[1977] There are currently no interview practice support systems for autonomous vehicles, and in particular, no methods have been developed that use generative AI or emotion engines to provide real-time interview questions and feedback while driving.There is a need for a system that allows users to efficiently practice interviews and make appropriate preparations while driving.

[1978] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating interview questions using a generation AI based on the user's self-analysis data and desired employer information, means for generating feedback based on the user's emotions with an emotion engine that acquires and analyzes the user's emotion data, and means for saving the user's interview result data and feedback on the server and improving the AI ​​model. This enables the user to effectively practice for interviews and receive real-time feedback while driving an autonomous vehicle.

[1979] "User Basic Information" refers to the user's name, email address, date of birth, and other personal information.

[1980] "Self-analysis data" is data obtained as a result of an aptitude test or personality diagnosis, and indicates the user's personality and aptitude.

[1981] "Preference information" is information about the user's desired university, company, or other destination.

[1982] "Generative AI" is a type of artificial intelligence, an algorithm or system that generates appropriate interview questions based on the user's self-analysis data and information about the company they are applying to.

[1983] "Emotion engine" is a general term for software or hardware used to acquire and analyze user emotion data.

[1984] "Interview questions" are questions for question and answer session that are presented to the user in the interview simulation.

[1985] "Feedback" is information that includes an evaluation of the user's interview practice and suggestions for improvement.

[1986] "Interview result data" refers to data that includes answers given by the user in the interview simulation, as well as evaluations and emotional data regarding those answers.

[1987] "Improving an AI model" is the process of continuously improving the performance and accuracy of an AI using collected data.

[1988] This invention relates to an interview practice support system for an autonomous vehicle. This system combines a generative AI model and an emotion engine to provide effective interview practice and feedback to users. The following describes in detail the embodiments of this invention.

[1989] Hardware and Software

[1990] 1. Hardware:

[1991] In-car infotainment system: Android Auto or Apple CarPlay compatible device.

[1992] Camera system: A camera to capture the driver's facial expressions.

[1993] Microphone: A microphone for collecting the driver's voice.

[1994] 2. Software:

[1995] Cloud server: Data storage and AI model hosting are performed by AWS or Google Cloud.

[1996] Emotion Analysis SDK: Software that performs facial expression recognition and voice analysis, such as the Affectiva SDK.

[1997] Generative AI model: Generates interview questions using GPT-4. Powered by Azure OpenAI services.

[1998] Program processing flow

[1999] Register and fill out your profile:

[2000] When the device starts up, the user enters basic information, which is then sent to a cloud server and stored in a database.

[2001] Conduct a self-analysis:

[2002] The device displays aptitude tests and personality assessment questions, and the user answers them. The answers are sent to the server and saved. At this point, the user's self-analysis data is accumulated.

[2003] Select your preferred school:

[2004] The terminal displays a list of candidate schools of choice, and sends the information on the schools selected by the user to the server for storage, which accumulates the information in a database.

[2005] Conducting a simulated interview:

[2006] The generative AI model generates interview questions based on the user's self-analysis data and information about the company they are applying to, and presents them to the user via their device. The user's answers are collected in real time and sent to a cloud server. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[2007] Providing feedback:

[2008] The server generates feedback based on the response data and emotion data and delivers it to the device. The feedback includes an evaluation of the response content, suggestions for improvement, and advice on feelings based on the emotion data.

[2009] Storing interview result data and improving AI models:

[2010] The results and feedback data from the interview simulation are stored on a server and used to train the AI ​​model, which allows the AI ​​model to continually improve and provide more accurate questions and feedback in the future.

[2011] Examples of specific examples and prompts

[2012] Examples:

[2013] When the driver gets into the self-driving car and says, "Please start the interview practice," the in-car infotainment system starts up and the interview practice assistant begins asking interview questions. In response to the questions, the driver is asked, "Please introduce yourself." After the driver responds, the generative AI suggests the next question, and the emotion engine analyzes the tone of voice and facial expressions to provide feedback in real time.

[2014] Example prompt sentence:

[2015] "Generate five software engineer interview questions based on user background information."

[2016] "Use the sentiment data to provide feedback on interview responses and explain why."

[2017] In this way, the present invention provides a highly supportive environment for conducting effective interview practice even in an autonomous vehicle.

[2018] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2019] Step 1:

[2020] User registration and profile entry

[2021] When the device is first started up, a user registration screen is displayed, and the user enters basic information such as name, email address, and date of birth.

[2022] Input: Name, Email Address, Date of Birth.

[2023] Data processing: Convert basic information into JSON format and send it to the server using the HTTPS protocol.

[2024] The server stores the received data in a database.

[2025] Output: Basic information stored in a database.

[2026] Step 2:

[2027] Conducting self-analysis

[2028] The terminal displays aptitude test and personality assessment questions and provides an interface for the user to answer the questions.

[2029] Input: The user's answer.

[2030] Data processing: Convert the response data into JSON format and send it to the server.

[2031] The server stores the received response data in a database.

[2032] Output: Self-analysis data is saved in a database.

[2033] Step 3:

[2034] Choosing your preferred destination

[2035] The terminal displays a list of candidate companies of interest, and transmits the information on the company selected by the user to the server and stores it.

[2036] Input: Select your preferred destination.

[2037] Data processing: Convert the selected desired school information into JSON format and send it to the server.

[2038] The server stores the received desired school information in a database.

[2039] Output: The desired school information is saved in the database.

[2040] Step 4:

[2041] Conducting interview simulations

[2042] The server uses a generation AI to generate interview questions based on self-analysis data and information about the company of choice, and delivers them to the device.

[2043] Input: Self-analysis data, desired employer information.

[2044] Data calculation: Create prompts that the generative AI model uses to generate questions, and then call the model to generate questions.

[2045] Output: Generated interview questions.

[2046] The device displays the generated questions to the user and obtains answers. The emotion engine also analyzes the user's facial expressions and voice to obtain emotion data.

[2047] Input: User answers and sentiment data.

[2048] Data processing: Response data and emotion data are converted into JSON format and sent to the server.

[2049] Output: Answer data and sentiment data stored on the server.

[2050] Step 5:

[2051] Providing feedback

[2052] The server uses a generation AI to generate feedback based on the response data and emotion data, and delivers it to the device.

[2053] Input: Response data, emotion data.

[2054] Data calculation: Analyzes response data and sentiment data to generate feedback, and invokes a generative AI model to generate feedback.

[2055] Output: The generated feedback.

[2056] The terminal displays the feedback to the user.

[2057] Input: Feedback data.

[2058] Output: User feedback confirmation.

[2059] Step 6:

[2060] Storing interview result data and improving AI models

[2061] The server stores the results data and feedback data of the interview simulation in a database.

[2062] Input: Interview result data, feedback data.

[2063] Data processing: Converting data into an analyzable format and storing it in a database.

[2064] Output: Result data stored in a database.

[2065] This data is used to continuously train generative AI models to improve the accuracy of interview questions and feedback.

[2066] Data computation: Combining historical and new data to train models.

[2067] Output: An improved AI model.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] [Fourth embodiment]

[2072] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2073] 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.

[2074] 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).

[2075] 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.

[2076] 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.

[2077] 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).

[2078] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2079] 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.

[2080] 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.

[2081] 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.

[2082] 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.

[2083] 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.

[2084] 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."

[2085] This invention is an interview practice support system designed for test takers, job seekers, or people looking to change jobs, and it uses a generative AI to provide users with appropriate questions and feedback. This system consists of a server, a terminal used by the user, and the user.

[2086] Handling user registration and profile entries

[2087] Terminal

[2088] When the app is launched for the first time, it displays a user registration screen, providing input fields for the user's name, email address, date of birth, etc.

[2089] server

[2090] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[2091] User

[2092] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[2093] Conducting self-analysis

[2094] Terminal

[2095] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[2096] server

[2097] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[2098] User

[2099] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[2100] Choosing your preferred destination

[2101] Terminal

[2102] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[2103] server

[2104] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[2105] User

[2106] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[2107] Conducting interview simulations

[2108] Terminal

[2109] It displays questions generated by the AI ​​one by one and provides an interface where users can enter answers. After entering the answers, they are sent to the server.

[2110] server

[2111] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database.

[2112] User

[2113] The user inputs an answer to the displayed question and transmits the answer data to the server.

[2114] Providing feedback

[2115] Terminal

[2116] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[2117] server

[2118] Based on the user's response data, the generation AI generates feedback, which is then delivered to the device.

[2119] User

[2120] Review the feedback provided and understand areas for improvement.

[2121] Saving results and improving AI models

[2122] Terminal

[2123] The results of the interview simulation are sent to the server, which stores the user's practice history.

[2124] server

[2125] The stored interview result data is used to train the generative AI model to improve it. As the AI ​​model continues to learn, it will be able to provide users with more accurate questions and feedback in the future.

[2126] User

[2127] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[2128] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[2129] The processing flow will be explained below.

[2130] Handling user registration and profile entries

[2131] Step 1:

[2132] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[2133] Step 2:

[2134] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[2135] Step 3:

[2136] Terminal: Checks the entered information and sends it to the server.

[2137] Step 4:

[2138] Server: Stores the received user information in a database.

[2139] Conducting self-analysis

[2140] Step 1:

[2141] User: Presses a button to start self-analysis.

[2142] Step 2:

[2143] Device: Retrieves aptitude and personality test questions from the server.

[2144] Step 3:

[2145] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[2146] Step 4:

[2147] User: Enter answers to the questions displayed.

[2148] Step 5:

[2149] Terminal: Sends the entered answer data to the server.

[2150] Step 6:

[2151] Server: Stores the received response data in a database.

[2152] Choosing your preferred destination

[2153] Step 1:

[2154] User: Select the "Select School of Interest" option from the menu.

[2155] Step 2:

[2156] Terminal: Obtain a list of universities and companies of interest from the server.

[2157] Step 3:

[2158] Terminal: Display the obtained list and let the user select.

[2159] Step 4:

[2160] User: Select the school or company of your choice from the list of preferred schools.

[2161] Step 5:

[2162] Terminal: Sends the selected desired school information to the server.

[2163] Step 6:

[2164] Server: Save the received information about the desired school in a database.

[2165] Conducting interview simulations

[2166] Step 1:

[2167] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[2168] Step 2:

[2169] Terminal: Displays the generated questions one by one.

[2170] Step 3:

[2171] User: Enter answers to the questions displayed.

[2172] Step 4:

[2173] Terminal: Temporarily holds the entered answers.

[2174] Step 5:

[2175] Terminal: Sends the response data to the server.

[2176] Step 6:

[2177] Server: Stores the received response data in a database.

[2178] Providing feedback

[2179] Step 1:

[2180] Server: The generative AI generates feedback based on the user's answers.

[2181] Step 2:

[2182] Server: Delivers the generated feedback to the device.

[2183] Step 3:

[2184] Terminal: Display feedback to the user.

[2185] Step 4:

[2186] Users: Review and understand the feedback.

[2187] Saving results and improving AI models

[2188] Step 1:

[2189] Terminal: Sends the results of the interview simulation to the server.

[2190] Step 2:

[2191] Server: Stores the received interview result data in a database.

[2192] Step 3:

[2193] Server: Uses the saved result data to learn and improve the AI ​​model.

[2194] Step 4:

[2195] Server: The AI ​​model continuously learns and generates more accurate questions and feedback.

[2196] The above is a specific processing flow for carrying out the present invention. The user, terminal, and server each play their respective roles and cooperate to form the entire system.

[2197] Example 1

[2198] 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."

[2199] Conventional interview practice support systems have difficulty automatically generating appropriate interview questions tailored to the user's individual characteristics and desired employer, and are unable to provide specific and accurate feedback. Furthermore, they lack a continuous learning function to improve the accuracy of interview practice, leaving a need for a system that can adequately meet user needs. Furthermore, they lack a support function for users to formulate specific action plans based on feedback, limiting the effectiveness of users' self-improvement.

[2200] 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.

[2201] In this invention, the server includes: means for acquiring basic information based on user input; means for acquiring self-analysis questions such as aptitude tests and personality tests from the server; means for presenting the acquired questions to the user and acquiring the user's answers; means for transmitting and storing the user's answer data to the server; means for acquiring a list of candidate employers based on the user's self-analysis data; means for transmitting and storing the user's selected employer information to the server; means for a generation AI to generate interview questions based on the user's self-analysis data and employer information; means for providing the generated interview questions to the user and acquiring the user's answers; means for generating and providing feedback based on the user's answers; means for saving the user's interview result data and feedback to the server and improving the AI ​​model; means for generating appropriate feedback using the generation AI model; means for providing a timer function during the interview simulation to automatically prompt the user to answer; and means for providing a memo function when displaying the feedback so that the user can create a specific action plan. This allows the user to receive interview questions tailored to their characteristics and employers of choice, receive specific and accurate feedback, and improve the accuracy of their interview practice through continuous learning. In addition, the effectiveness of self-improvement can be enhanced by utilizing the support functions for formulating specific action plans.

[2202] "User" refers to a candidate, job seeker, or job seeker who uses this system.

[2203] "Basic information" refers to information such as name, email address, and date of birth that a user enters when registering with the system.

[2204] A "server" is a device that processes and stores various data, and generates and distributes aptitude tests, self-analysis questions, interview questions, etc.

[2205] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet, that provides an input interface and display functions.

[2206] An "aptitude test" is a question or test that assesses a user's characteristics or abilities.

[2207] A "personality test" is a question or test that analyzes a user's personality and behavioral patterns.

[2208] "Self-analysis data" refers to the answers and results of the user's responses to aptitude tests and personality tests.

[2209] "Desired destination" refers to the university, company, or other destination the user desires to attend.

[2210] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate interview questions and feedback based on user information.

[2211] "Interview questions" are questions presented to the user in the interview simulation.

[2212] "Feedback" refers to evaluations and improvements on the user's answers.

[2213] "Interview result data" is data that includes the answers given by the user in the interview simulation and the feedback given to those answers.

[2214] The "timer function" is a function that prompts the user to enter an answer within a certain time period.

[2215] The "memo function" is a function that allows users to create specific action plans based on feedback.

[2216] An "AI model" is an algorithm that learns from user data and improves the accuracy of interview practice.

[2217] This invention is a support system that allows test takers, job seekers, and people looking to change jobs to effectively practice for interviews. The system consists of a terminal used by the user and a server that processes data and generates questions. Users can perform the entire process from registration to interview practice and receiving feedback.

[2218] User registration and profile entry

[2219] Terminal

[2220] When a user launches the app for the first time, a user registration screen is displayed. The user enters information such as their name, email address, and date of birth. For example, the user enters "Yamada Hanako" in the name field, "hanako@example.com" in the email address field, and "1995-06-20" in the date of birth field.

[2221] server

[2222] It receives user information sent from the device and stores it in a MySQL database, thereby accumulating basic user information.

[2223] User

[2224] By inputting information and pressing the send button, the terminal transmits the information to the server.

[2225] Conducting self-analysis

[2226] Terminal

[2227] The app displays aptitude or personality questions, such as "What are your strengths?" The user answers the questions and the device sends the answers to a server.

[2228] server

[2229] The server receives the data from the receiving side and stores it in an SQLite database. The question and answer data is later used as self-analysis results.

[2230] User

[2231] The user enters an answer to the displayed question and presses the send button to send the answer data to the server.

[2232] Choosing your preferred destination

[2233] Terminal

[2234] The app displays a list of universities and companies you are interested in. You select an option from the list and send the selected information to the server. For example, let's say you select "XYZ Company."

[2235] server

[2236] The server receives the selected information about the desired school and stores it in a database.

[2237] User

[2238] The user selects the desired destination and presses the OK button to send the selection to the server.

[2239] Conducting interview simulations

[2240] server

[2241] Based on the user's self-analysis and desired company information, Generating AI generates appropriate interview questions. For example, it might ask, "What are your reasons for applying?"

[2242] Terminal

[2243] The generated question is displayed to the user, who answers the question and the terminal transmits the answer data to the server.

[2244] User

[2245] In response to the question that appears, enter "I sympathize with your philosophy of contributing to society and would like to work for your company," and press the send button.

[2246] Providing feedback

[2247] server

[2248] The generative AI generates feedback based on the user's answers. For example, it might say, "It would be better if you included specific examples in your motivation for applying."

[2249] Terminal

[2250] Feedback is displayed to the user, who can review it and understand where improvements can be made.

[2251] User

[2252] The feedback provided can be used to improve your next practice.

[2253] Saving results and improving AI models

[2254] Terminal

[2255] The results of the interview simulation are sent to a server, and the user's practice history is saved.

[2256] server

[2257] The server stores the resulting data and uses it to train the Generating AI model, which will improve the accuracy of interview questions and feedback for future interviews.

[2258] User

[2259] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[2260] concrete action

[2261] Timer function: During the interview simulation, the user is prompted to enter answers within a certain time.

[2262] Notes function: While viewing feedback, users can enter notes to help them create specific action plans.

[2263] Prompt Sentence Examples

[2264] "What are your hobbies?"

[2265] "What has been the most challenging experience you have had so far?"

[2266] In this way, the present invention provides an environment in which users can approach real interviews with confidence. Furthermore, the server and generating AI continuously learn and improve, providing optimal support to users.

[2267] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2268] Step 1: User registration and profile entry

[2269] User: Launch the app and the user registration screen will appear. Enter information such as "Yamada Hanako," "hanako@example.com," and "1995-06-20," and press the registration button.

[2270] Terminal: Receives information entered by the user, converts it into JSON format, and sends it to the server.

[2271] Server: Receives JSON data and saves it to a database. Specifically, it saves the information "Hanako Yamada", "hanako@example.com", and "1995-06-20" in a MySQL database. The input is user information, and the output is saved to the database.

[2272] Step 2: Conduct a self-analysis

[2273] Terminal: Displays aptitude test and personality assessment questions received from the server. The question "What are your strengths?" is displayed.

[2274] User: Answers the question by typing "Analytical ability" and presses the send button.

[2275] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[2276] Server: Receives the answer data and saves it in a database. Specifically, it saves the answer "analytical ability" in an SQLite database. The input is the question and answer, and the output is saved in the database.

[2277] Step 3: Choose your preferred school

[2278] Terminal: Displays the list of companies of interest received from the server. Lists such as "ABC Company" and "XYZ Company" are displayed.

[2279] User: Selects "XYZ Company" as the company of choice and presses the OK button.

[2280] Terminal: Converts the information selected by the user into JSON format and sends it to the server.

[2281] Server: Receives the selected company information and saves it in the database. Specifically, it saves the selected company "XYZ Company" in the database. The input is the company information, and the output is saving it in the database.

[2282] Step 4: Conduct a simulated interview

[2283] Server: Based on the user's self-analysis results and desired employer information, the generative AI model generates appropriate interview questions. For example, it generates a question such as, "What is your motivation for applying?"

[2284] Terminal: The generated interview questions are displayed to the user.

[2285] User: Answers the question by saying, "I sympathize with your philosophy of contributing to society and would like to work for your company," and presses the send button.

[2286] Terminal: Receives the user's response, converts it into JSON format, and sends it to the server.

[2287] Server: Receives the response data and stores it in a database. Specifically, the response "I sympathize with your philosophy of social contribution and would like to work for your company" is stored in the database. The input is the question and the response, and the output is stored in the database.

[2288] Step 5: Provide feedback

[2289] Server: The generative AI model generates feedback based on the user's response data. For example, it might generate feedback such as, "It would be better if you included specific examples in your motivation for applying."

[2290] Terminal: The generated feedback is displayed to the user.

[2291] User: Check the feedback and use it for the next practice. As a concrete example, the user will try to provide more specific answers in the next simulation based on the feedback that "please include specific examples in your motivation for applying." The input is the answer data, and the output is the feedback.

[2292] Step 6: Saving the results and improving the AI ​​model

[2293] Terminal: Converts the interview simulation results into JSON format and sends them to the server.

[2294] Server: Receives the result data and stores it in a database. In addition, the generative AI model learns based on the result data, improving the accuracy of interview questions and feedback. The input is the simulation result data, and the output is the learning and accuracy improvement of the AI ​​model.

[2295] Users: The next time they take a simulated interview, they will receive improved questions and feedback, which will lead to more effective practice and improved performance in the actual interview.

[2296] This allows users to consistently practice interviews effectively and gain confidence in real interviews. Furthermore, the Generating AI model continuously learns based on feedback and simulated interview results, enabling it to provide high-quality support.

[2297] (Application example 1)

[2298] 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."

[2299] Conventional interview practice support systems have the problem of only being able to provide limited support to test takers, job seekers, or people looking to change jobs. Similarly, there is a lack of effective support for training new store clerks to improve their customer service skills. This presents a problem in that customer service practice cannot be effectively conducted in physical stores, making it difficult to improve service. To solve these problems, the present invention aims to expand the interview practice support system and provide a comprehensive practice support system that can also accommodate customer service practice for store clerks.

[2300] 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.

[2301] In this invention, the server comprises: means for acquiring basic information based on an input from a user;

[2302] A means for acquiring questions for self-analysis such as aptitude tests and personality tests from a server;

[2303] means for presenting the acquired question to a user and acquiring an answer from the user;

[2304] means for transmitting and storing user response data on a server;

[2305] A means for acquiring a list of candidates for desired employment based on the user's self-analysis data;

[2306] A means for transmitting the desired school information selected by the user to a server and storing it;

[2307] A means for the AI ​​to generate interview questions based on the user's self-analysis data and desired employer information;

[2308] means for providing the generated interview questions to a user and obtaining answers from the user;

[2309] means for generating and providing feedback to the user based on the user's responses;

[2310] A means for storing the user's interview result data and feedback on a server and improving the AI ​​model;

[2311] To support sales staff in their customer service practice, a means is provided for acquiring profile information from users and for a generation AI to generate questions necessary for customer service based on the type of store.

[2312] A means for providing the generated customer service question to a store clerk and obtaining an answer from the store clerk;

[2313] a means for generating and providing feedback to the sales associate based on the sales associate's responses;

[2314] A means to suggest areas for improvement to improve customer service skills based on feedback

[2315] This will not only help prepare students for interviews, job seekers, and people looking to change jobs for interview practice, but will also improve the accuracy of customer service practice for new store employees.

[2316] "Exam takers" are students who intend to take an exam.

[2317] A "job seeker" is an individual who is actively seeking new employment with a company or organization.

[2318] A "job seeker" is an individual who is seeking a job change from their current workplace to another workplace.

[2319] An "interview practice support system" is an information processing system designed to help users practice for interviews effectively.

[2320] "Basic information" refers to basic information about the user, such as the user's name, email address, years of experience, etc.

[2321] An "aptitude test" is a test used to evaluate an individual's abilities and personality, and is used to understand the user's aptitude.

[2322] A "personality test" is a test for assessing an individual's personality traits and is used to understand a user's personality.

[2323] "Self-analysis data" is a collection of information obtained from the results of a user's responses to aptitude tests and personality assessments.

[2324] "Preferences" refers to universities, companies, etc. that the user wishes to attend or work for.

[2325] "Generative AI" is a system that uses artificial intelligence technology to generate interview questions and feedback based on user data.

[2326] "Customer service practice" refers to new store employees practicing the skills necessary to effectively serve customers in a physical store.

[2327] "Profile information" refers to information registered by a user, such as name, email address, years of experience, and type of store.

[2328] "Store type" refers to store categories such as fashion, restaurants, supermarkets, etc.

[2329] "Feedback" is information about evaluations and areas for improvement that are generated based on the user's responses.

[2330] "Areas for improvement" refers to points or suggestions that users or store clerks need to improve through practice.

[2331] This invention provides a practice support system for test takers, job seekers, people looking to change jobs, and new store clerks. To implement this invention, a server, a user terminal, and a generative AI model are used.

[2332] Handling user registration and profile entries

[2333] Terminal

[2334] When a user launches the app for the first time, they are presented with a user registration screen, which provides fields for entering profile information such as name, email address, date of birth, years of experience, etc. Once the user enters this information and presses the submit button, the information is sent to the server.

[2335] server

[2336] The server receives the user information sent from the terminal and stores it in a database, thereby accumulating basic user information for use in subsequent processes.

[2337] Conducting self-analysis

[2338] Terminal

[2339] Aptitude tests and personality tests are displayed to the user, who then enters answers to the questions and presses the send button to send the answer data to the server.

[2340] server

[2341] The server distributes self-analysis questions to the user's terminal and stores the answer data received from the terminal in a database, which is later used as the user's self-analysis results.

[2342] Choosing your preferred destination

[2343] Terminal

[2344] It displays a list of universities and companies that the user is interested in and provides an interface for them to select from. The user selects the university or company they want, presses the OK button, and sends the selection to the server.

[2345] server

[2346] The server distributes the list of desired companies and stores the information on the desired companies selected by the user in a database.

[2347] Interview simulation and customer service practice

[2348] Terminal

[2349] It displays questions generated by the AI ​​and provides an interface for users to input their answers. Users input answers to the displayed questions and press the send button to send the answer data to the server.

[2350] server

[2351] The server uses a generation AI to generate appropriate interview questions based on the user's self-analysis results and desired employer information. Similarly, it generates customer service practice questions based on the employee's profile information and the type of store. The generated questions are sent to the device, which receives the user's response data and stores it in a database.

[2352] Providing feedback

[2353] Terminal

[2354] Displays feedback received from the server to the user, including a rating for the answer and suggestions for improvement.

[2355] server

[2356] The server uses AI to generate feedback based on the user's response data and delivers it to the device, allowing the user to practice based on the feedback.

[2357] Saving results and improving AI models

[2358] Terminal

[2359] The results of the user's interview simulation and customer service practice are sent to the server, which stores the user's practice history.

[2360] server

[2361] The server uses the stored interview result data and feedback data to improve the AI ​​model. This continuous learning allows the generative AI to provide more accurate questions and feedback in the future.

[2362] For example, in the case of a customer service simulation for a fashion store, the following prompts could be used:

[2363] "Customer service simulation for new sales associates: Fashion\nPlease generate questions."

[2364] By using such prompt sentences, the generative AI can generate questions that correspond to specific store situations.

[2365] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2366] Step 1:

[2367] User registration and profile entry

[2368] When a user launches the app on their device for the first time, a user registration screen appears. Here, the user enters profile information such as name, email address, date of birth, and years of experience. Once the information is entered, this data is sent from the device to the server. The server saves the received data in a database and accumulates the user's basic information.

[2369] Input: Name, email address, date of birth, years of experience

[2370] Output: User information stored in the database

[2371] Step 2:

[2372] Conducting self-analysis

[2373] Aptitude tests and personality assessment questions for self-analysis are sent from the server to the user's device. The device presents these questions to the user, who then enters their answers. Once the answers are entered, the device sends them to the server. The server receives the answer data and stores it in a database.

[2374] Input: Self-assessment questions, user answers

[2375] Output: Answer data stored in a database

[2376] Step 3:

[2377] Choosing your preferred destination

[2378] A list of universities and companies of choice is sent from the server to the user's device. The device displays the list to the user, who then selects the desired university. Once the selection is complete, the user's device sends the information to the server. The server stores the received information in a database.

[2379] Input: Preferred list, user selection

[2380] Output: Information about the desired school saved in the database

[2381] Step 4:

[2382] Interview simulation and customer service practice

[2383] The generation AI generates interview questions based on the user's self-analysis results and information about the company they wish to work for. In the case of customer service practice for a store clerk, customer service questions are generated based on the user's profile information and the type of store. The generated questions are sent from the server to the user's device, and the user answers them. The device sends the user's answers to the server, which stores them in a database.

[2384] Input: Self-analysis results, desired employer information, AI prompt

[2385] Output: Generated interview questions, answers stored in a database

[2386] Step 5:

[2387] Providing feedback

[2388] The server uses the AI ​​to generate feedback based on the user's response data. The generated feedback is then sent from the server to the user's device. The device then displays the feedback to the user and suggests areas for improvement.

[2389] Input: User response data, prompts for the AI ​​generator

[2390] Output: generated feedback, presented to the user

[2391] Step 6:

[2392] Saving results and improving AI models

[2393] The results and feedback data of the user's interview simulation and customer service practice are sent to a server and stored in a database. The server continuously trains the generative AI model based on this stored data, enabling it to provide more accurate questions and feedback in the future.

[2394] Input: Interview result data, feedback data

[2395] Output: Result data stored in a database, improved AI model

[2396] 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.

[2397] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a server, a terminal, and a user.

[2398] Handling user registration and profile entries

[2399] Terminal

[2400] When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[2401] server

[2402] It receives user information sent from the terminal and stores it in a database, thereby accumulating basic user information.

[2403] User

[2404] On the profile entry screen, enter basic information such as your name, email address, date of birth, etc. After completing the entry, the information is sent from your device to the server.

[2405] Conducting self-analysis

[2406] Terminal

[2407] It displays aptitude tests and personality tests and provides an interface for users to answer them. After the user enters their answers, the answer data is sent to the server.

[2408] server

[2409] Self-analysis questions are sent to the device, and the response data is received and stored in a database. The stored data is later used as the user's self-analysis results.

[2410] User

[2411] Enter your answer to the displayed question. After entering your answer, press the send button to send the answer data to the server.

[2412] Choosing your preferred destination

[2413] Terminal

[2414] A list of universities and companies of the user's choice is displayed, allowing the user to select. The selected information is then sent to the server.

[2415] server

[2416] The list of desired companies is delivered to the terminal, and the selected desired company information is stored in a database.

[2417] User

[2418] Select the desired university or company from the list of preferred schools provided, press the decision button and send it to the server.

[2419] Conducting interview simulations

[2420] Terminal

[2421] It displays questions generated by the generative AI one by one, provides an interface where the user can enter answers, and operates an emotion engine that recognizes the user's emotions.

[2422] server

[2423] Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions. The generated questions are sent to the device, and the answer data is received and stored in a database. In addition, emotional data generated by the emotion engine is also stored.

[2424] User

[2425] Answers are entered to the questions displayed, and sentiment analysis is performed using an emotion engine.

[2426] Providing feedback

[2427] server

[2428] The AI ​​generates feedback based on the user's response data and emotional data, and delivers the generated feedback to the device.

[2429] Terminal

[2430] Feedback is displayed to the user, including an evaluation of the answer, suggestions for improvement, and advice on how to respond based on emotional data.

[2431] User

[2432] Review and understand the feedback provided, and use it to understand how to manage your emotions and improve.

[2433] Saving results and improving AI models

[2434] Terminal

[2435] The results of the interview simulation and emotional data are sent to the server, which stores the user's practice history.

[2436] server

[2437] The AI ​​model uses the saved interview result data and emotion data to learn and improve itself. By continuously learning, the AI ​​model will be able to provide users with more accurate questions and feedback in the future.

[2438] User

[2439] When conducting an interview simulation again, more effective practice can be carried out based on the accumulated data.

[2440] In this way, the present invention provides an environment that allows users to approach real interviews with confidence. Furthermore, the generative AI and emotion engine create a practice environment that takes the user's feelings into consideration by generating appropriate feedback based on the user's emotional data. This allows users to practice interviews in a more relaxed state and effectively identify areas for improvement.

[2441] The processing flow will be explained below.

[2442] Handling user registration and profile entries

[2443] Step 1:

[2444] On the device: When the app is launched for the first time, it displays a user registration screen, providing fields where the user can enter basic information such as name, email address, and date of birth.

[2445] Step 2:

[2446] User: Enter basic information such as name, email address, and date of birth on the profile entry screen.

[2447] Step 3:

[2448] Terminal: Checks the entered information and sends it to the server.

[2449] Step 4:

[2450] Server: Stores the received user information in a database.

[2451] Conducting self-analysis

[2452] Step 1:

[2453] User: Presses a button to start self-analysis.

[2454] Step 2:

[2455] Device: Retrieves aptitude and personality test questions from the server.

[2456] Step 3:

[2457] Terminal: Presents an interface that displays questions one at a time and allows the user to enter answers.

[2458] Step 4:

[2459] User: Enter answers to the questions displayed.

[2460] Step 5:

[2461] Terminal: Sends the entered answer data to the server.

[2462] Step 6:

[2463] Server: Stores the received response data in a database.

[2464] Choosing your preferred destination

[2465] Step 1:

[2466] User: Select the "Select School of Interest" option from the menu.

[2467] Step 2:

[2468] Terminal: Obtain a list of universities and companies of interest from the server.

[2469] Step 3:

[2470] Terminal: Display the obtained list and let the user select.

[2471] Step 4:

[2472] User: Select the school or company of your choice from the list of preferred schools.

[2473] Step 5:

[2474] Terminal: Sends the selected desired school information to the server.

[2475] Step 6:

[2476] Server: Save the received information about the desired school in a database.

[2477] Conducting interview simulations

[2478] Step 1:

[2479] Server: Based on the user's self-analysis results and desired employer information, the AI ​​generates appropriate interview questions.

[2480] Step 2:

[2481] Terminal: Displays the generated questions one by one and also runs an emotion engine to recognize the user's emotions during the interview.

[2482] Step 3:

[2483] User: Enter answers to the questions displayed.

[2484] Step 4:

[2485] Emotion engine: Obtains emotional data from the user's facial expressions and tone of voice during the interview.

[2486] Step 5:

[2487] Terminal: Temporarily stores the user's answers and emotion data.

[2488] Step 6:

[2489] Terminal: Sends response data and emotion data to the server.

[2490] Step 7:

[2491] Server: Stores the received response data and emotion data in a database.

[2492] Providing feedback

[2493] Step 1:

[2494] Server: The AI ​​generates feedback based on the user's response data and emotional data.

[2495] Step 2:

[2496] Server: Delivers the generated feedback to the device.

[2497] Step 3:

[2498] On the device, feedback is displayed to the user, including a rating of the answer, suggestions for improvement, and emotional advice based on the emotional data.

[2499] Step 4:

[2500] Users: Review the feedback provided and understand areas for improvement.

[2501] Saving results and improving AI models

[2502] Step 1:

[2503] Terminal: Sends the results of the interview simulation and emotional data to the server.

[2504] Step 2:

[2505] Server: Stores the received interview result data and emotion data in a database.

[2506] Step 3:

[2507] Server: Uses the stored result data and emotion data to learn and improve the AI ​​model.

[2508] Step 4:

[2509] Server: The AI ​​model continuously learns, enabling it to generate more accurate questions and feedback in the future.

[2510] The above is a specific processing flow for implementing the present invention. The user, terminal, server, and emotion engine each play their own roles while working together to form the entire system.

[2511] Example 2

[2512] 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."

[2513] Conventional interview practice systems have not been able to generate appropriate interview questions based on the user's self-analysis results and information about their desired employer, nor have they provided sufficient feedback that reflects real-time emotional data. Furthermore, there have been no systems that improve the accuracy of interview practice by continuously training a generative AI model using the user's practice results and emotional data. This has made it difficult for users to effectively improve their practical interview skills.

[2514] 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.

[2515] In this invention, the server includes a means for the generation AI to generate interview questions based on the user's self-analysis data and desired employer information, a means for the terminal to acquire emotional data and recognize the user's emotional state, and a means for continuously training the generation AI model using the saved interview result data and emotional data. This allows the user to receive appropriate questions based on the self-analysis results and desired employer information, and furthermore, by receiving feedback based on the user's emotional state, enables practical and effective interview practice.

[2516] "User" refers to an individual who uses the information processing system, such as a student taking an exam, a job seeker, or a person looking to change jobs.

[2517] "Basic information" refers to information that identifies and locates an individual, such as a user's name, email address, and date of birth.

[2518] An "aptitude test" is a question or test designed to assess a user's aptitudes, interests, abilities, etc.

[2519] A "personality assessment" is a set of questions or tests designed to assess a user's personality and behavioral traits.

[2520] "Self-Analysis Questions" refers to a list of questions used to deepen a user's self-understanding.

[2521] "Preferred school" refers to the school or company for which the user wishes to take an entrance exam, get a job, or change jobs.

[2522] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate interview questions and feedback.

[2523] "Interview Questions" are questions asked to the user and provided by the generative AI as part of the interview simulation.

[2524] "Feedback" refers to the evaluation and improvement suggestions provided to the user regarding their interview practice.

[2525] "Emotion data" refers to information about the user's emotional state that is analyzed based on facial expressions, tone of voice, etc.

[2526] "AI Model" refers to a computational model that learns and improves using artificial intelligence techniques.

[2527] "Continuous learning" refers to the means by which AI models are regularly updated and improved based on collected data.

[2528] This invention is an interview practice support system for examinees, job seekers, or people looking to change jobs, which combines a generative AI and an emotion engine to provide users with more effective interview practice and feedback. This system is composed of a user, a terminal, and a server.

[2529] First, when a user launches the application, a user registration screen appears on the device. After the user enters basic information such as name, email address, and date of birth, the device sends this information to the server. The server stores the received basic information in a database. Through this process, the system accumulates user profile information.

[2530] When the user moves to the next screen for self-analysis, the server sends a list of questions for aptitude tests and personality assessments to the terminal. The terminal displays these questions to the user, and the user enters answers to each question. Once the answers are completed, the terminal sends the answer data to the server, which stores it in a database.

[2531] The user then moves to a screen for selecting their preferred universities and companies, and the server sends a list of their preferred universities and companies to the terminal. Once the user selects their preferred universities and companies, the terminal sends the selected university information to the server, which then stores it in a database.

[2532] When the interview simulation stage begins, the server sends prompts to the generation AI based on the user's self-analysis results and information about the company they wish to work for. The generation AI generates appropriate interview questions, which the server then sends to the device. The device displays the questions, and the user enters their answers. While the answers are being entered, the device activates an emotion engine to analyze the user's emotional state and obtain emotional data. This data is then sent from the device to the server, which stores it in a database.

[2533] The server then generates feedback by sending prompts to the AI ​​based on the user's response data and emotion data. The generated feedback is sent to the device and displayed to the user. The feedback includes an evaluation of the response, suggestions for improvement, and advice based on the emotion data.

[2534] Finally, the results of the interview simulation and emotional data are sent to and stored on a server, which uses this data to continuously train the generative AI model and provide more accurate questions and feedback to users in the future.

[2535] For example, the generative AI will ask the question, "Please introduce yourself in one minute." It will also provide advice based on emotion recognition, such as, "Take a deep breath and relax." This system allows users to practice effectively so they can approach the actual interview with confidence.

[2536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2537] Step 1:

[2538] The user launches the app.

[2539] Input: A user launches an application on their smartphone or PC.

[2540] Output: The user is taken to the login or sign up screen.

[2541] What happens: The user taps or clicks the icon and the application displays its welcome screen.

[2542] Step 2:

[2543] The user enters basic information.

[2544] Input: The user enters basic information such as name, email address, and date of birth.

[2545] Output: The user's basic information is temporarily stored on the device.

[2546] Specific actions: The user fills in the required information in the form and clicks the "Register" button.

[2547] Step 3:

[2548] The device sends basic information to the server.

[2549] Input: Basic information entered by the user.

[2550] Output: The server receives the basic information and stores it in a database.

[2551] Specific behavior: The device sends basic information to the server using an HTTP POST request.

[2552] Step 4:

[2553] The server stores user information.

[2554] Input: Basic information sent from the device.

[2555] Output: Basic information is saved in the database.

[2556] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[2557] Step 5:

[2558] The user moves to the self-analysis screen.

[2559] Input: User selects "Self-Analysis" from the navigation menu.

[2560] Output: A self-analysis question is displayed.

[2561] What happens: The user clicks on a menu item and the screen switches to a self-analysis interface.

[2562] Step 6:

[2563] The server sends a self-analysis question.

[2564] Input: User ID and request information.

[2565] Output: A list of self-analysis questions will be displayed on the terminal.

[2566] Specific operation: The server sends a predefined list of questions in JSON format to the terminal.

[2567] Step 7:

[2568] The user answers self-assessment questions.

[2569] Input: Self-analysis questions.

[2570] Output: User's answer data is temporarily saved on the device.

[2571] Specific action: The user enters an answer to a question using a text box or options.

[2572] Step 8:

[2573] The terminal transmits the response data to the server.

[2574] Input: User response data.

[2575] Output: The server receives the response data and stores it in a database.

[2576] Specific operation: The device sends the response data to the server using an HTTP POST request.

[2577] Step 9:

[2578] The server stores the response data.

[2579] Input: Response data sent from the device.

[2580] Output: The response data is saved in a database.

[2581] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[2582] Step 10:

[2583] The user moves to the desired school selection screen.

[2584] Input: User instructions.

[2585] Output: A list of preferred destinations is displayed.

[2586] Specific behavior: The user selects "Select preferred school" from the navigation menu and the screen changes.

[2587] Step 11:

[2588] The server sends the list of preferred destinations.

[2589] Input: User ID and request information.

[2590] Output: The list of preferred schools is displayed on the terminal.

[2591] Specific operation: The server sends a predefined list of preferred schools in JSON format to the terminal.

[2592] Step 12:

[2593] The user selects the desired destination.

[2594] Input: Preferred list.

[2595] Output: The selected school information is temporarily saved on the device.

[2596] Specific behavior: The user selects the university or company they want to apply to from a drop-down menu.

[2597] Step 13:

[2598] The terminal transmits the desired school information to the server.

[2599] Input: User selected school information.

[2600] Output: The server receives the desired school information and stores it in a database.

[2601] Specific operation: The device sends the desired school information to the server using an HTTP POST request.

[2602] Step 14:

[2603] The server stores the desired school information.

[2604] Input: Application information sent from the device.

[2605] Output: The desired school information is saved in the database.

[2606] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[2607] Step 15:

[2608] The user navigates to the interview simulation screen.

[2609] Input: User instructions.

[2610] Output: The interface for the interview simulation is displayed.

[2611] Specific behavior: The user selects "Interview Simulation" from the navigation menu and the screen changes.

[2612] Step 16:

[2613] The server sends a prompt to the spawned AI.

[2614] Input: User's self-analysis results and desired company information.

[2615] Output: Generative AI generates interview questions.

[2616] Specific operation: The server sends an API request to the generation AI based on the self-analysis results and desired employer information.

[2617] Step 17:

[2618] The server generates a question and sends it to the terminal.

[2619] Input: Interview questions generated by generative AI.

[2620] Output: The question is displayed on the terminal.

[2621] Specific operation: The server sends the generated question in JSON format to the device.

[2622] Step 18:

[2623] The user answers the interview questions.

[2624] Input: Interview questions generated by the generative AI.

[2625] Output: User's answer data is temporarily saved on the device.

[2626] Specific operation: The user enters an answer to a question using a text box or voice input.

[2627] Step 19:

[2628] The device operates an emotion engine and acquires emotion data.

[2629] Input: The facial expression and tone of voice when the user answers.

[2630] Output: The emotional state is recognized and the emotional data is temporarily stored on the device.

[2631] Specific operation: The device uses the built-in camera and microphone to analyze facial expressions and tone of voice to determine emotions.

[2632] Step 20:

[2633] The terminal transmits the response data and emotion data to the server.

[2634] Input: User response data and sentiment data.

[2635] Output: The server receives these data and stores them in a database.

[2636] Specific operation: The device sends answer data and emotion data to the server using an HTTP POST request.

[2637] Step 21:

[2638] The server stores the response data and emotion data.

[2639] Input: Answer data and emotion data sent from the device.

[2640] Output: Response data and emotion data are stored in a database.

[2641] What happens: The server parses the incoming data and inserts it into the database using an SQL query.

[2642] Step 22:

[2643] The server sends prompts to the generation AI to generate feedback.

[2644] Input: Response data and sentiment data.

[2645] Output: Feedback is generated by the generative AI.

[2646] Specific operation: The server sends an API request to the generation AI based on the response data and emotion data.

[2647] Step 23:

[2648] The server generates feedback and sends it to the device.

[2649] Input: Feedback generated by the generative AI.

[2650] Output: Feedback is displayed on the terminal.

[2651] Specific behavior: The server sends the generated feedback in JSON format to the device.

[2652] Step 24:

[2653] The user checks the feedback.

[2654] Input: Feedback content.

[2655] Output: User reads and understands the feedback.

[2656] Specific actions: The device displays the feedback content and the user confirms it.

[2657] (Application example 2)

[2658] 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."

[2659] There are currently no interview practice support systems for autonomous vehicles, and in particular, no methods have been developed that use generative AI or emotion engines to provide real-time interview questions and feedback while driving.There is a need for a system that allows users to efficiently practice interviews and make appropriate preparations while driving.

[2660] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating interview questions using a generation AI based on the user's self-analysis data and desired employer information, means for generating feedback based on the user's emotions with an emotion engine that acquires and analyzes the user's emotion data, and means for saving the user's interview result data and feedback on the server and improving the AI ​​model. This enables the user to effectively practice for interviews and receive real-time feedback while driving an autonomous vehicle.

[2661] "User Basic Information" refers to the user's name, email address, date of birth, and other personal information.

[2662] "Self-analysis data" is data obtained as a result of an aptitude test or personality diagnosis, and indicates the user's personality and aptitude.

[2663] "Preference information" is information about the user's desired university, company, or other destination.

[2664] "Generative AI" is a type of artificial intelligence, an algorithm or system that generates appropriate interview questions based on the user's self-analysis data and information about the company they are applying to.

[2665] "Emotion engine" is a general term for software or hardware used to acquire and analyze user emotion data.

[2666] "Interview questions" are questions for question and answer session that are presented to the user in the interview simulation.

[2667] "Feedback" is information that includes an evaluation of the user's interview practice and suggestions for improvement.

[2668] "Interview result data" refers to data that includes answers given by the user in the interview simulation, as well as evaluations and emotional data regarding those answers.

[2669] "Improving an AI model" is the process of continuously improving the performance and accuracy of an AI using collected data.

[2670] This invention relates to an interview practice support system for an autonomous vehicle. This system combines a generative AI model and an emotion engine to provide effective interview practice and feedback to users. The following describes in detail the embodiments of this invention.

[2671] Hardware and Software

[2672] 1. Hardware:

[2673] In-car infotainment system: Android Auto or Apple CarPlay compatible device.

[2674] Camera system: A cam...

Claims

1. An information processing system that supports interview practice for examinees, job seekers, or job-changers, means for obtaining basic information based on input from a user; A means for acquiring questions for self-analysis such as aptitude tests and personality tests from a server; means for presenting the acquired question to a user and acquiring an answer from the user; means for transmitting and storing user response data on a server; A means for acquiring a list of candidates for desired employment based on the user's self-analysis data; A means for transmitting the desired school information selected by the user to a server and storing it; A means for the AI ​​to generate interview questions based on the user's self-analysis data and desired employer information; means for providing the generated interview questions to a user and obtaining answers from the user; means for generating and providing feedback to the user based on the user's responses; The system includes a means for storing user interview result data and feedback on a server and improving the AI ​​model.

2. 2. The system according to claim 1, further comprising means for automatically determining the degree of matching based on the results of the user's self-analysis and information on the type of person the desired employer is looking for.

3. 10. The system of claim 1, further comprising means for the AI ​​model to continuously learn based on user feedback and improve the accuracy of the interview practice.

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