System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user attributes; means for analyzing the received attributes and generating a user profile; means for creating a proper job list using a generation AI based on the generated user profile; means for collecting detailed information on a job selected by the user from the job list; means for constructing and transmitting a job experience game scenario; means for analyzing a result of the job experience game and providing feedback to the user; and means for making a new job suggestion based on the analyzed result.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] Today's children's main goal is to study at school, and they have few opportunities to think deeply about their future career choices. As a result, they often struggle to choose a career when they graduate from university, and it is often difficult for them to find a job that matches their aptitude and aspirations. To solve this problem, it is necessary to help children visualize their future careers in concrete terms and provide them with opportunities to understand the positive and negative aspects of various careers. However, traditional career education has focused on children as recipients of information, and there is an issue of few opportunities for them to gain actual work experience. [Means for solving the problem]

[0005] To address this issue, the present invention provides a system that utilizes a generation AI to suggest careers based on a child's attribute information. This system receives and analyzes the user's attribute information, such as age, gender, place of residence, and hobbies, to generate a user profile. Based on the generated profile, the generation AI creates a list of suitable careers, collects detailed information about the careers, and transmits it to the device. Furthermore, the system provides a job experience game for the career selected by the user, simulating actual work tasks to allow the user to concretely experience the rewards and challenges of the career. Finally, the system analyzes the results of the job experience game, provides feedback to the user, and makes further career suggestions, helping the user find the career that best suits them. In this way, children's anxiety about future career choices is reduced and they can find the career that best suits them at an early stage.

[0006] "User attribute information" refers to personal information and characteristic information about a user, such as age, gender, place of residence, and hobbies.

[0007] "Analysis" refers to the act of evaluating a user's personality traits and abilities based on the received data and generating a profile based on that.

[0008] A "user profile" is a collection of data that indicates a user's personality traits and abilities, created based on collected and analyzed user attribute information.

[0009] "Generative AI" refers to algorithms and systems that use artificial intelligence technology to analyze data and make predictions and suggestions.

[0010] The "occupation list" is a list of occupations suitable for the user, suggested by the generative AI based on the user profile.

[0011] "Detailed information" refers to specific information about a particular occupation, including the job content, required skills, rewards, difficulties, salary estimates, etc.

[0012] "Terminal" refers to an electronic device used by a user to input and confirm information and to play the job experience game.

[0013] A "job experience game" is a system that provides an interactive game-style experience that simulates real-life work tasks and situations based on a selected job.

[0014] "Scenario" refers to a virtual storyline that specifies the work tasks and situations that a user faces within a job experience game.

[0015] "Feedback" refers to the act of providing information based on a user's actions and choices, including evaluation results, areas for improvement, and next steps. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that uses a generative AI to suggest careers based on children's attribute information and provides a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[0038] Server Processing

[0039] Receiving user data

[0040] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[0041] Data analysis

[0042] The received attribute information is analyzed to generate a user profile, which includes information such as personality traits and abilities.

[0043] Career suggestions by generative AI

[0044] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[0045] Collecting and sending details

[0046] Detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimate, etc.) is collected and sent to the terminal.

[0047] Scenario construction for a job experience game

[0048] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[0049] Data storage and training

[0050] The results of the job experience game are analyzed and the user's actions and choices are stored in a database, which is used to continuously train the generative AI algorithm.

[0051] Feedback and next steps

[0052] Based on the results of the job experience game, the system provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[0053] Terminal handling

[0054] Providing a user interface

[0055] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0056] Sending data

[0057] The attribute information entered by the user is sent to the server.

[0058] Receiving and displaying job offers

[0059] The job list and its detailed information sent from the server are received and displayed to the user, who can then select the job that interests them.

[0060] Providing an interface for a job experience game

[0061] The interface displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0062] User operations

[0063] Entering attribute information

[0064] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0065] Review and select career suggestions

[0066] Check the list of occupations and detailed information displayed on your device and select the occupation that interests you.

[0067] Playing a job experience game

[0068] Participants can play a job experience game provided on their device and simulate specific work tasks. Through this interactive game, participants can experience the rewards and challenges of the job.

[0069] Specific examples

[0070] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[0071] In this way, the present invention helps children reduce anxiety about future career choices and helps them find the right career path.

[0072] The processing flow will be explained below.

[0073] Server Processing Steps

[0074] Step 1:

[0075] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[0076] Step 2:

[0077] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[0078] Step 3:

[0079] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[0080] Step 4:

[0081] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[0082] Step 5:

[0083] The server sends the collected detailed information to the terminal.

[0084] Step 6:

[0085] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[0086] Step 7:

[0087] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[0088] Step 8:

[0089] The server receives the results of the job experience game and analyzes the user's actions and choices.

[0090] Step 9:

[0091] The server provides feedback to the user based on the analysis results and suggests more suitable occupations.

[0092] Terminal processing steps

[0093] Step 1:

[0094] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0095] Step 2:

[0096] The terminal transmits the attribute information input by the user to the server.

[0097] Step 3:

[0098] The terminal displays the occupation list and detailed information received from the server, and provides the user with options.

[0099] Step 4:

[0100] The terminal again transmits information about the occupation selected by the user to the server.

[0101] Step 5:

[0102] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[0103] Step 6:

[0104] The terminal records the choices and actions that the user makes while playing the job experience game and transmits them to the server sequentially.

[0105] User operation steps

[0106] Step 1:

[0107] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0108] Step 2:

[0109] The user checks the occupation list and detailed information displayed on the terminal.

[0110] Step 3:

[0111] The user selects an occupation that interests them on the terminal.

[0112] Step 4:

[0113] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[0114] Specific examples

[0115] Example of User A

[0116] Step 1:

[0117] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[0118] Step 2:

[0119] The terminal transmits the input information to the server.

[0120] Step 3:

[0121] The server analyzes the attribute information of user A and generates a user profile.

[0122] Step 4:

[0123] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[0124] Step 5:

[0125] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[0126] Step 6:

[0127] User A selects "Game Developer."

[0128] Step 7:

[0129] The terminal transmits the selected occupation information to the server.

[0130] Step 8:

[0131] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[0132] Step 9:

[0133] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[0134] Step 10:

[0135] User A simulates character design and project management tasks.

[0136] Step 11:

[0137] The server receives the game results and analyzes User A's actions and choices.

[0138] Step 12:

[0139] The server provides feedback and next career suggestions to User A.

[0140] Example 1

[0141] 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."

[0142] Conventional career guidance systems do not adequately analyze users' attribute information or make career suggestions, making it difficult to suggest optimal careers based on individual characteristics and interests. Furthermore, they lack specific understanding through work experience, and there are insufficient means for users to fully experience the type of work they will actually be doing. Furthermore, there is also an insufficient mechanism for reflecting users' experience and behavioral data in the next career suggestion.

[0143] 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.

[0144] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the results of the job experience game and providing feedback to the user, means for making new job suggestions based on the analysis results, and means for storing the received user behavior data in a database and providing it for training by the generation AI. This enables optimal job suggestions based on the user's attribute information, making it possible to understand specific jobs through job experiences and continuously improve the accuracy of job suggestions.

[0145] "User attribute information" refers to personal information that a user inputs into a terminal, such as the user's age, gender, place of residence, and hobbies.

[0146] "Analysis" is the act of processing received information to extract and generate useful data.

[0147] A "user profile" is a summary of information including the user's personality traits and abilities, generated based on analyzed user attribute information.

[0148] "Generative AI" is an artificial intelligence technology that suggests suitable careers based on a user's characteristics and interests.

[0149] An "occupation list" is a list of multiple occupations suitable for a user, created by the generation AI based on the user profile.

[0150] "Detailed information" refers to information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[0151] A "job experience game" is an interactive game that simulates real-life work tasks based on a user's chosen job.

[0152] "Scenario construction" refers to the design and creation of content that recreates specific work tasks, project progress, and problem-solving scenes related to the user's chosen occupation.

[0153] "Feedback" refers to advice and information provided to users based on the results of the job experience game, such as strengths, areas for improvement, and suggestions for the next job to try.

[0154] A "database" is a system or mechanism for storing and managing collected data.

[0155] The present invention is a system that uses a generative AI model to suggest careers based on a child's attribute information and provides a career experience game. The embodiments of the present invention are described in detail below.

[0156] Server Processing

[0157] Receiving user data

[0158] The server receives the user's attribute information sent from the device. This attribute information includes age, gender, place of residence, hobbies, etc. Specifically, the server validates the received JSON format data.

[0159] Data analysis

[0160] The server uses the Python pandas library to analyze the received attribute information. Based on the analyzed data, it generates a user profile, which includes information such as personality traits and abilities.

[0161] Career suggestions by generative AI

[0162] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[0163] Collecting and sending details

[0164] The server extracts detailed information about the proposed job from a MySQL database and sends it to the device in JSON format, including details such as the job description, required skills, rewards, challenges, and salary estimates.

[0165] Scenario construction for a job experience game

[0166] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of daily work, project execution, and problem-solving.

[0167] Data storage and training

[0168] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used for continuous training of the generative AI model.

[0169] Feedback and next steps

[0170] The server provides feedback to the user based on the results of the job experience game, including suggestions for new jobs to try next, as well as the user's strengths and areas for improvement.

[0171] Terminal handling

[0172] Providing a user interface

[0173] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0174] Sending data

[0175] The terminal converts the attribute information entered by the user into JSON format and sends it to the server using the HTTPS protocol.

[0176] Receiving and displaying job offers

[0177] The terminal receives the occupation list and its detailed information sent from the server and displays it to the user, who can then select an occupation that interests them.

[0178] Providing an interface for a job experience game

[0179] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0180] User operations

[0181] Entering attribute information

[0182] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0183] Review and select career suggestions

[0184] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[0185] Playing a job experience game

[0186] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[0187] Specific examples

[0188] For example, let's say User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into his or her device, which then sends the information to the server. The server receives the information, analyzes it, and generates a user profile. The generation AI then suggests occupations such as "game developer" or "digital artist" based on the profile. The server collects detailed information about the suggested occupations and sends it to the device. User A selects "game developer" and receives a scenario for a job experience game built by the server using Unity. User A simulates character design and project management tasks on his or her device, experiencing the specific content of the work, as well as the rewards and difficulties involved. The server analyzes the game results, provides feedback to User A, and suggests more suitable occupations.

[0189] The system helps children reduce anxiety about future career choices and find the right career path.

[0190] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0191] Step 1:

[0192] Enter attribute information (user)

[0193] The user enters attribute information such as age, gender, place of residence, and hobbies into the device. For example, if the user is 14 years old and enters "programming" and "drawing" as his or her hobbies, the input data is acquired through a form. This form contains input fields, and data is collected as the user enters each item.

[0194] Input: User attribute information (age, gender, place of residence, hobbies, etc.)

[0195] Output: Input attribute information (JSON format)

[0196] Step 2:

[0197] Data transmission (terminal)

[0198] The device sends the attribute information entered by the user to the server as an HTTP POST request. At this time, the attribute information is converted to JSON format. The device uses the HTTPS protocol to send the data securely.

[0199] Input: User attribute information (JSON format)

[0200] Output: Attribute information sent to the server

[0201] Step 3:

[0202] Receiving attribute information (server)

[0203] The server receives the user's attribute information sent from the terminal, performs format validation on the received data, and identifies invalid data input.

[0204] Input: Attribute information sent from the device (JSON format)

[0205] Output: Attribute information that passes validation

[0206] Step 4:

[0207] Data analysis (server)

[0208] The server uses the Python pandas library to analyze the received attribute information and generates a user profile based on the analyzed data. This profile summarizes information such as the user's personality traits and abilities.

[0209] Input: Attribute information that has passed validation

[0210] Output: User profile

[0211] Step 5:

[0212] Occupation suggestions by generative AI (server)

[0213] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[0214] Input: User profile

[0215] Output: List of suitable occupations

[0216] Step 6:

[0217] Collecting and sending detailed information (server)

[0218] The server extracts detailed information about the proposed jobs from a MySQL database and sends it to the device in JSON format, including job description, required skills, rewards, challenges, salary estimates, etc.

[0219] Input: List of suitable occupations

[0220] Output: Detailed information in JSON format

[0221] Step 7:

[0222] Job experience game scenario construction (server)

[0223] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of the job's daily tasks, project progress, and problem-solving.

[0224] Input: Details of selected occupation

[0225] Output: The constructed work experience game scenario

[0226] Step 8:

[0227] Receive and display job suggestions and detailed information (terminal)

[0228] The terminal receives the list of occupations and detailed information sent from the server and displays them to the user. The user selects the occupations they are interested in from the suggested occupations. The interface includes a selection button.

[0229] Input: Occupation list and detailed information sent from the server

[0230] Output: The information displayed to the user and the selected occupation

[0231] Step 9:

[0232] Job experience game implementation (user)

[0233] The user launches an interactive job experience game provided on the device and simulates work tasks based on a scenario, such as character design or project management.

[0234] Input: A constructed work experience game scenario

[0235] Output: The results of the work task experienced by the user

[0236] Step 10:

[0237] Analysis and storage of game results (server)

[0238] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used to continuously train the generative AI model.

[0239] Input: The results of the work task experienced by the user

[0240] Output: Saved behavioral data

[0241] Step 11:

[0242] Feedback and New Suggestions (Server)

[0243] The server provides feedback to the user based on the game results, including suggestions for new professions, highlighting the user's strengths and areas for improvement, and suggesting the next profession to try.

[0244] Input: Stored behavioral data

[0245] Output: Feedback and new career suggestions provided to the user

[0246] (Application example 1)

[0247] 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."

[0248] Conventional work experience systems do not adequately help children find the most suitable occupation based on their own attribute information. Furthermore, because the work experience lacks realism and interactivity, it is difficult for children to grasp the appeal and challenges of actual work. Therefore, there is a need for a system that can reduce children's anxiety about choosing a future occupation and help them find the right career path.

[0249] 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.

[0250] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for allowing the user to interactively play the job experience game in a virtual environment, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the results of the analysis. This enables children to find the most suitable occupation based on their own attribute information and actually experience that occupation in a virtual environment.

[0251] "User attribute information" refers to personal characteristics such as the user's age, sex, place of residence, and hobbies.

[0252] "User profile" refers to data that indicates the characteristics and abilities of a user, generated based on the received attribute information.

[0253] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and suggestions.

[0254] "Suitable Occupation List" means a list of occupations suitable for the user suggested by the generative AI based on the user's profile.

[0255] "Detailed occupational information" refers to specific information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[0256] A "career experience game scenario" refers to a story that includes work tasks, project progress, problem-solving scenes, etc., constructed to simulate the career chosen by the user.

[0257] A "virtual environment" refers to a virtual space generated using computer technology in which users can have interactive experiences.

[0258] "Results of the job experience game" refers to data such as the results, selection history, and actions that the user has achieved through the job experience game.

[0259] "Feedback" refers to the evaluation and advice provided to the user after analyzing the results of the job experience game.

[0260] "New career suggestions" means that the generative AI will use feedback to suggest new careers for the user to try next.

[0261] This invention is a system that utilizes generative AI to suggest careers based on children's attribute information and provides a linked career experience game. The specific program processing flow and implementation method of the system are explained in detail below.

[0262] Server Processing

[0263] Receiving user data

[0264] The server receives attribute information entered by the user into the terminal, such as age, gender, place of residence, hobbies, etc. Any structured data format is used for this information.

[0265] Data analysis

[0266] The server analyzes the received attribute information and generates a user profile, which includes information such as personality traits and abilities, and the AI ​​operates based on this.

[0267] Career suggestions by generative AI

[0268] The server uses a generative AI based on the generated user profile to create a list of suitable occupations. The generative AI uses a large dataset and machine learning models to suggest occupations that match the user's characteristics and interests.

[0269] Collecting and sending details

[0270] The server collects detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimates, etc.) and sends it to the terminal.

[0271] Scenario construction for a job experience game

[0272] Based on the user's chosen occupation, the server creates a scenario for the job-experience game that simulates real-life work tasks, including scenes from daily work, project management, and problem-solving.

[0273] Providing a virtual environment

[0274] The server allows users to interactively experience the job experience game in a virtual environment, which is provided through devices such as smartphones and head-mounted displays.

[0275] Data storage and training

[0276] The server analyzes the results of the job experience game and stores the user's actions and choices in a database. The stored data is used to train the generative AI algorithm.

[0277] Feedback and next steps

[0278] Based on the results of the job experience game, the server provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[0279] Terminal handling

[0280] Providing a user interface

[0281] The terminal provides an interface for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0282] Sending data

[0283] The attribute information entered by the user is sent from the terminal to the server.

[0284] Receiving and displaying job offers

[0285] The terminal receives the list of occupations and their detailed information sent from the server and displays them to the user, who can then select the occupations that interest them.

[0286] Providing an interface for a job experience game

[0287] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0288] User operations

[0289] Entering attribute information

[0290] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0291] Review and select career suggestions

[0292] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[0293] Playing a job experience game

[0294] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[0295] Specific examples

[0296] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[0297] An example prompt might be something like, "The user is 14 years old and his hobbies are programming and drawing."

[0298] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0299] Step 1:

[0300] The server receives attribute information entered by the user into the terminal. Attribute information includes age, gender, place of residence, hobbies, etc. The server receives this information and temporarily stores it in a database (input: attribute information, output: stored attribute data).

[0301] Step 2:

[0302] The server analyzes the received attribute information and generates a user profile. The analysis process involves evaluating the user's personality and characteristics using a machine learning model (input: attribute information, output: user profile).

[0303] Step 3:

[0304] The server uses a generation AI to create a list of suitable occupations based on the generated user profile. The generation AI refers to a large dataset and the synthesized user profile to list suitable occupations (input: user profile, output: occupation list).

[0305] Step 4:

[0306] The server collects detailed information about the occupation selected by the user from the occupation list and sends it to the terminal. The detailed information includes the job description, required skills, rewards, difficulty, expected salary, etc. (Input: Occupation selected by the user, Output: Occupation details information).

[0307] Step 5:

[0308] The server constructs a scenario for the work experience game based on the user's selected occupation. This scenario includes scenes of daily work tasks, project progress, and problem solving (input: selected occupation, output: work experience game scenario).

[0309] Step 6:

[0310] The terminal displays the scenario of the job experience game received from the server, and the user plays the interactive job experience game through the terminal (input: job experience game scenario, output: interactive game interface).

[0311] Step 7:

[0312] The server analyzes the results of the job experience game and provides feedback to the user. The analysis is based on the user's behavioral history and selection results (input: job experience game results, output: feedback data).

[0313] Step 8:

[0314] The server uses a generative AI to suggest new occupations based on the feedback, which then suggests the next occupation for the user to try (input: feedback data, output: new occupation suggestion list).

[0315] 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.

[0316] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[0317] Server Processing

[0318] Receiving user data

[0319] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[0320] Data analysis

[0321] The received attribute information is analyzed to generate a user profile, which includes the user's personality traits and abilities.

[0322] Career suggestions by generative AI

[0323] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[0324] Collecting and sending details

[0325] Detailed information about the occupation selected from the list of occupations (job content, required skills, rewards, difficulty, salary estimates, etc.) is collected from the database and sent to the terminal.

[0326] Scenario construction for a job experience game

[0327] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[0328] Emotion recognition by emotion engine

[0329] The server uses an emotion engine to recognize the user's emotions, detecting them from data such as the user's facial expressions, voice, and text input, and using this information for analysis.

[0330] Data storage and training

[0331] The results of the job-experience game are analyzed, and the user's actions, choices, and emotional state are stored in a database. The stored data is used to continuously train the generative AI algorithm.

[0332] Feedback and next steps

[0333] Based on the results of the job experience game and the user's emotional state, the system provides feedback to the user and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[0334] Terminal handling

[0335] Providing a user interface

[0336] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0337] Sending data

[0338] The attribute information entered by the user is sent to the server.

[0339] Receiving and displaying job offers

[0340] The occupation list and detailed information sent from the server are displayed, and the user is given the option to select one.

[0341] Accepting user selections

[0342] The information about the occupation selected by the user is sent again to the server.

[0343] Providing an interface for a job experience game

[0344] The scenario of the job experience game received from the server is displayed, allowing the user to interactively experience it.

[0345] User operations

[0346] Entering attribute information

[0347] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0348] Review and select career suggestions

[0349] Check the list of occupations and detailed information displayed on the device, and select the occupation that interests you on the device.

[0350] Playing a job experience game

[0351] They play the job experience games provided on the device and perform work tasks and simulations.

[0352] Providing emotion data

[0353] While playing, the device collects emotional data such as the user's facial expressions, voice, and text input, and sends it to the server.

[0354] Specific examples

[0355] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[0356] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[0357] The processing flow will be explained below.

[0358] Server Processing Steps

[0359] Step 1:

[0360] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[0361] Step 2:

[0362] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[0363] Step 3:

[0364] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[0365] Step 4:

[0366] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[0367] Step 5:

[0368] The server sends the collected detailed information to the terminal.

[0369] Step 6:

[0370] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[0371] Step 7:

[0372] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[0373] Step 8:

[0374] The server receives the results of the job experience game as well as the user's emotional data (facial expressions, voice, text input, etc.) sent from the terminal.

[0375] Step 9:

[0376] The server analyzes the results of the job experience game and the emotional data, and evaluates the user's actions and choices.

[0377] Step 10:

[0378] The server provides feedback to the user based on the analysis results and suggests careers that are more suitable for them. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[0379] Terminal processing steps

[0380] Step 1:

[0381] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0382] Step 2:

[0383] The terminal transmits the attribute information input by the user to the server.

[0384] Step 3:

[0385] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[0386] Step 4:

[0387] The terminal again transmits information about the occupation selected by the user to the server.

[0388] Step 5:

[0389] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[0390] Step 6:

[0391] The device records the choices and actions the user makes while playing the job experience game, and simultaneously collects the user's emotional data (facial expressions, voice, text input, etc.) and transmits it to the server.

[0392] User operation steps

[0393] Step 1:

[0394] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0395] Step 2:

[0396] The user checks the occupation list and detailed information displayed on the terminal.

[0397] Step 3:

[0398] The user selects an occupation that interests them on the terminal.

[0399] Step 4:

[0400] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[0401] Step 5:

[0402] The user's emotion data is collected by the terminal and transmitted to the server.

[0403] Specific examples

[0404] Example of User A

[0405] Step 1:

[0406] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[0407] Step 2:

[0408] The terminal transmits the input information to the server.

[0409] Step 3:

[0410] The server analyzes the attribute information of user A and generates a user profile.

[0411] Step 4:

[0412] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[0413] Step 5:

[0414] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[0415] Step 6:

[0416] User A selects "Game Developer."

[0417] Step 7:

[0418] The terminal transmits the selected occupation information to the server.

[0419] Step 8:

[0420] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[0421] Step 9:

[0422] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[0423] Step 10:

[0424] While User A simulates character design and project management tasks, the device collects User A's facial expressions and voice data, analyzes them with an emotion engine, and sends the results to the server.

[0425] Step 11:

[0426] The server receives the game results and emotional data and analyzes User A's behavior and choices.

[0427] Step 12:

[0428] The server provides feedback to User A based on the analysis results and suggests the next occupation to try. For example, if User A is evaluated as having high concentration in addition to technical ability, the server will suggest a new occupation of "software engineer."

[0429] In this way, the invention uses a multifaceted approach that combines emotion recognition and generative AI to help children find the career that best suits them.

[0430] Example 2

[0431] 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."

[0432] Conventional career suggestion systems primarily suggest careers based on the user's attribute information, but do not take the user's emotional state into consideration. Furthermore, the scenarios for career experience games are monotonous and do not reflect the user's characteristics, interests, or emotions. This makes it difficult for users to effectively find a career that suits them. The objective of this invention is to provide a system that takes the user's emotional state into consideration, improves the accuracy of career suggestions, and provides an interactive career experience game.

[0433] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the emotion data, means for analyzing the results and emotion data of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This enables highly accurate occupation suggestions that take the user's emotional state into consideration, allowing the user to effectively find an occupation that suits them and providing a realistic work experience through an interactive job experience game.

[0434] "User attribute information" is data about an individual, including the user's age, gender, place of residence, hobbies, and the like.

[0435] A "user profile" is data that summarizes the user's personality traits and abilities by analyzing the received user attribute information.

[0436] "Generative AI" is an artificial intelligence algorithm that uses a user's profile and other data to generate a list of jobs.

[0437] An "occupation list" is a list of occupations suitable for a user, created by the generation AI based on the user's profile.

[0438] "Detailed information" refers to information including the job content, required skills, rewarding aspects, difficulty, salary estimates, etc. of the occupations included in the occupation list.

[0439] A "career experience game" is an interactive game that includes simulated work tasks and scenes based on a user-selected career.

[0440] "Emotion data" is data that indicates the emotional state of the user detected from facial expressions, voice, text input, and the like.

[0441] "Feedback" refers to information about improvements and advice provided to the user based on the results of the job experience game and emotional data.

[0442] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. The program processing of the system is explained in detail below.

[0443] Server Processing

[0444] Receiving user data

[0445] The server receives attribute information entered by the user on the device, such as age, gender, place of residence, hobbies, etc. This data is sent in JSON format using an HTTP POST request.

[0446] Data analysis

[0447] The server analyzes the received attribute information and generates a user profile using programming languages ​​such as Python and R. Based on the user's hobbies and characteristics, the server evaluates and records the user's personality traits and abilities.

[0448] Career suggestions by generative AI

[0449] The server uses a generative AI (e.g., GPT-4) to create a list of suitable occupations based on the generated user profile. By inputting a prompt sentence, the server suggests occupations suitable for the user.

[0450] Collecting and sending details

[0451] The server collects detailed information about the occupation selected from the occupation list from the database and sends it to the terminal, for example, using an SQL query to obtain the necessary occupational information (job description, required skills, rewards, difficulties, expected salary, etc.).

[0452] Scenario construction for a job experience game

[0453] The server creates and transmits a scenario for the job experience game based on the job selected by the user. This scenario includes scenes of daily work, project progress, and problem solving so that the user can actually experience the job.

[0454] Emotion recognition by emotion engine

[0455] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. It analyzes data such as the user's facial expressions, voice, and text input in real time and stores the user's emotional state in a database.

[0456] Data storage and training

[0457] The server analyzes the results of the job-experience game and stores the user's actions, choices, and emotional state in a database, which is used to continuously train the generative AI algorithm.

[0458] Feedback and next steps

[0459] The server provides feedback to the user based on the results of the job experience game and their emotional state, and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and changes in their emotions.

[0460] Terminal handling

[0461] Providing a user interface

[0462] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0463] Sending data

[0464] The terminal transmits the attribute information input by the user to the server.

[0465] Receiving and displaying job offers

[0466] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[0467] Accepting user selections

[0468] The terminal again transmits information about the occupation selected by the user to the server.

[0469] Providing an interface for a job experience game

[0470] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[0471] User operations

[0472] Entering attribute information

[0473] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0474] Review and select career suggestions

[0475] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them on the terminal.

[0476] Playing a job experience game

[0477] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[0478] Providing emotion data

[0479] While playing, users' emotional data, such as facial expressions, voice, and text input, is collected by the terminal and transmitted to the server.

[0480] Specific examples

[0481] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which is then received and analyzed by the server to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[0482] Prompt Sentence Examples

[0483] Below are some example prompts to input to a generative AI model (e.g., GPT-4):

[0484] User profile: Age 14, Gender male, Residence Tokyo, Hobbies programming, drawing

[0485] Please suggest three suitable jobs for this user. For each job, please also tell us the required skills, job content, rewards and difficulties, and salary estimates.

[0486] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[0487] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0488] Step 1: Receiving user data

[0489] Input: Attribute information entered by the user into the device (age, gender, place of residence, hobbies, etc.).

[0490] Processing: The device receives the attribute information entered by the user and sends it to the server. The server receives the data sent in JSON format using an HTTP POST request.

[0491] Output: JSON data of attribute information is stored on the server.

[0492] Step 2: Analyze the data

[0493] Input: User attribute information stored on the server (JSON format).

[0494] Processing: The server uses Python scripts to analyze the received data and generate a user profile (age, gender, location, hobbies, personality traits, abilities, etc.) using classification algorithms and clustering techniques.

[0495] Output: A user profile (in JSON format) is generated.

[0496] Step 3: Occupation suggestions by generative AI

[0497] Input: Server-generated user profile (JSON format).

[0498] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4), and the generative AI suggests occupations based on the user's characteristics and interests. For example, the server sends the following prompt to the generative AI: "User profile: Age 14, Gender: Male, Residence: Tokyo, Hobbies: Programming, Drawing. Please suggest three occupations suitable for this user."

[0499] Output: A list of occupations (text format) is generated.

[0500] Step 4: Collect and submit details

[0501] Input: A list of occupations (text format) generated by the generation AI.

[0502] Processing: The server uses an SQL query to retrieve detailed information about the selected occupation (job description, required skills, salary estimate, etc.) from the database. Example: SELECT FROM careers WHERE job_title='Game Developer';

[0503] Output: The list of occupations and their details (in JSON format) will be sent to the terminal.

[0504] Step 5: Building a scenario for the job experience game

[0505] Input: Details of the user's chosen occupation (JSON format).

[0506] Processing: The server generates a scenario for a job-experience game using a Python script based on the example of a "game developer" and sends it to the device in JSON format. The scenario includes tasks such as character design and project management.

[0507] Output: Job experience game scenario (JSON format).

[0508] Step 6: Emotion Recognition with the Emotion Engine

[0509] Input: User facial, voice, and text input data.

[0510] Processing: The device analyzes the user's emotional data in real time using an emotion engine (e.g., Affectiva API) and sends it to the server. The data includes emotional states such as "happy=70%, sad=10%, neutral=20%."

[0511] Output: Emotion data (JSON format).

[0512] Step 7: Data storage and training

[0513] Input: Job experience game results and sentiment data (JSON format).

[0514] Processing: The server stores the analysis results in a database (e.g., MongoDB or MySQL). The stored data is used to continuously train the generative AI algorithm.

[0515] Output: An updated database and a trained generative AI model.

[0516] Step 8: Feedback and next steps

[0517] Input: Job experience game results and sentiment data (JSON format).

[0518] Processing: The server uses the generative AI model to suggest the next suitable job for the user and generates feedback. For example, the generative AI might create a sentence such as, "Since you were successful in the game developer scenario, try project manager next."

[0519] Output: Feedback and next career suggestion information provided to the user.

[0520] (Application example 2)

[0521] 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."

[0522] Conventional work experience systems do not adequately suggest appropriate jobs based on the user's attribute information. Furthermore, it is difficult to interactively experience work experiences in real time and obtain feedback. Furthermore, there is a lack of systems that can recognize the user's emotional state in real time and provide appropriate feedback based on that information.

[0523] The identification processing by the identification 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 receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for providing the scenario for the job experience game to smart glasses or a tablet in a physical store, means for collecting and analyzing user emotion data using an emotion recognition engine, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This makes it possible to provide accurate job suggestions based on the user's attribute information, interactive job experiences that reflect the user's emotional state in real time, and appropriate feedback.

[0524] The "means for receiving user attribute information" is a function by which a server or terminal receives information such as age, sex, place of residence, and hobbies entered by a user.

[0525] The "means for generating a user profile" is a function that analyzes the received attribute information and creates a detailed profile including the user's personality traits and abilities.

[0526] The "means for creating a list of appropriate occupations using a generation AI" is a function in which the generation AI generates a list of occupations suitable for the user based on the generated user profile.

[0527] "Means for collecting detailed information and sending it to the terminal" refers to a function that collects detailed information about the selected occupation, such as the job content, required skills, rewards, difficulties, and salary estimates, and sends it to the user's terminal.

[0528] The "means for creating and transmitting a scenario for a job experience game" is a function for creating a scenario for a job experience game based on a job selected by the user and transmitting the scenario to the terminal.

[0529] "Means for providing to smart glasses or tablets in a physical store" refers to a function for providing users with a scenario of a job experience game through smart glasses or tablets installed in a physical store.

[0530] "Means for collecting and analyzing user emotional data using an emotion recognition engine" refers to a function that analyzes the user's facial expressions and voice using an emotion recognition engine to collect and analyze emotional data.

[0531] The "means for analyzing the results of the job experience game and providing feedback to the user" is a function for analyzing the results of playing the job experience game and providing feedback to the user such as strengths and areas for improvement.

[0532] The "means of making new career suggestions" is a function that allows the generation AI to make new career suggestions based on the results of the previous career experience game and emotional data.

[0533] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on a child's attribute information and provide an interactive career experience game in a physical store. This system receives and analyzes the user's attribute information and generates a user profile, then uses the generative AI to create a list of appropriate careers and sends the details and the scenario of the career experience game to the device. The system also provides the user with a career experience through smart glasses or a tablet, and uses the emotion engine to collect and analyze the user's emotional data and provide feedback.

[0534] Hardware and software used

[0535] The following hardware and software are used to realize this system.

[0536] Smart glasses (e.g. Google Glass)

[0537] Tablet (e.g. iPad)

[0538] Server (e.g. AWS EC2)

[0539] Database (e.g. MySQL)

[0540] Generative AI (e.g. OpenAI GPT-4)

[0541] Emotion recognition engine (e.g. Affectiva SDK)

[0542] Data processing and calculation process

[0543] The server receives the user's attribute information. The user uses a tablet or smart glasses to input information such as age, gender, place of residence, and hobbies. This data is sent to the server, where it is analyzed. A user profile is generated based on the analysis results. This profile includes the user's personality traits and abilities.

[0544] The generative AI (OpenAI GPT-4) creates a list of suitable occupations based on the user profile. For example, if the user is 14 years old and their hobbies are programming and drawing, the generative AI will suggest occupations such as "game developer" and "digital artist." Examples of prompts for this generative AI are as follows:

[0545] Example prompt sentence:

[0546] "I'm a 14-year-old kid whose hobbies are programming and drawing. What kind of job would be suitable for me?"

[0547] The server then collects detailed information about the selected job from a database and sends it to the tablet or smart glasses. Based on the user's selected job, the server creates a scenario for a job experience game and provides the scenario through the smart glasses or tablet in the physical store.

[0548] The emotion recognition engine (Affectiva SDK) analyzes the user's facial expressions and voice in real time to collect emotional data. This data is sent to the server and analyzed along with the results of the job-experience game. Based on the analysis results, feedback is provided to the user. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[0549] Finally, the generative AI will suggest new careers based on the analysis results. In this way, the system helps users find the career that best suits them and experience it interactively in real time.

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] Users use tablets or smart glasses to input attribute information such as age, gender, place of residence, and hobbies, and this input data is sent to the server by the device.

[0553] Input: User attribute information (age, gender, place of residence, hobbies)

[0554] Output: Sending attribute information to the server

[0555] Step 2:

[0556] The server analyzes the received attribute information and generates a user profile by comparing it with past data stored in a database.

[0557] Input: Received attribute information

[0558] Data processing: Comparison with past data stored in the database and analysis

[0559] Output: Generated user profile

[0560] Step 3:

[0561] The server uses a generation AI (OpenAI GPT-4) to create a list of suitable occupations based on the generated user profile. At this time, the server inputs a prompt sentence into the generation AI and receives occupation suggestions.

[0562] Input: User profile, prompt

[0563] Data Computation: Creating Occupation Lists with Generative AI

[0564] Output: List of suitable occupations

[0565] Example prompt: "I'm a 14-year-old kid whose hobbies are programming and drawing. What career would be suitable for me?"

[0566] Step 4:

[0567] The server collects detailed information (job content, required skills, rewarding work, difficulty, expected salary, etc.) about the occupation selected by the user from the generated occupation list from a database and sends it to the terminal.

[0568] Input: User-selected occupation, occupation list

[0569] Data processing: Collect detailed information from the database

[0570] Output: Send detailed job information

[0571] Step 5:

[0572] The server creates an interactive job experience game scenario based on the user's chosen occupation and sends it to smart glasses or tablets in the physical store.

[0573] Input: User-selected occupation, detailed information

[0574] Data calculation: Scenario construction for a job experience game

[0575] Output: Interactive job experience game scenario submission

[0576] Step 6:

[0577] Using the emotion recognition engine (Affectiva SDK), smart glasses or tablets analyze the user's facial expressions and voice in real time to collect emotional data, which is then sent from the device to a server.

[0578] Input: User's facial expression and voice data

[0579] Data processing: Analysis using emotion recognition engine

[0580] Output: Collect emotion data and send it to the server

[0581] Step 7:

[0582] The server analyzes the results of the job experience game and the collected emotional data, stores the results in a database, and provides feedback to the user based on this.

[0583] Input: Job experience game results, emotion data

[0584] Data Computing: Analyzing Outcome and Sentiment Data

[0585] Output: Generating and providing feedback

[0586] Step 8:

[0587] The server then uses the generative AI to suggest new occupations based on the analysis results. The user can then select an occupation from the list of suggested occupations and continue the interactive occupation experience.

[0588] Input: Analysis results, emotion data

[0589] Data Computation: New Career Proposals with Generative AI

[0590] Output: Generate a new list of occupations and repeat each step

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Second embodiment]

[0595] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0606] 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."

[0607] This invention is a system that uses a generative AI to suggest careers based on children's attribute information and provides a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[0608] Server Processing

[0609] Receiving user data

[0610] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[0611] Data analysis

[0612] The received attribute information is analyzed to generate a user profile, which includes information such as personality traits and abilities.

[0613] Career suggestions by generative AI

[0614] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[0615] Collecting and sending details

[0616] Detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimate, etc.) is collected and sent to the terminal.

[0617] Scenario construction for a job experience game

[0618] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[0619] Data storage and training

[0620] The results of the job experience game are analyzed and the user's actions and choices are stored in a database, which is used to continuously train the generative AI algorithm.

[0621] Feedback and next steps

[0622] Based on the results of the job experience game, the system provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[0623] Terminal handling

[0624] Providing a user interface

[0625] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0626] Sending data

[0627] The attribute information entered by the user is sent to the server.

[0628] Receiving and displaying job offers

[0629] The job list and its detailed information sent from the server are received and displayed to the user, who can then select the job that interests them.

[0630] Providing an interface for a job experience game

[0631] The interface displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0632] User operations

[0633] Entering attribute information

[0634] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0635] Review and select career suggestions

[0636] Check the list of occupations and detailed information displayed on your device and select the occupation that interests you.

[0637] Playing a job experience game

[0638] Participants can play a job experience game provided on their device and simulate specific work tasks. Through this interactive game, participants can experience the rewards and challenges of the job.

[0639] Specific examples

[0640] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[0641] In this way, the present invention helps children reduce anxiety about future career choices and helps them find the right career path.

[0642] The processing flow will be explained below.

[0643] Server Processing Steps

[0644] Step 1:

[0645] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[0646] Step 2:

[0647] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[0648] Step 3:

[0649] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[0650] Step 4:

[0651] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[0652] Step 5:

[0653] The server sends the collected detailed information to the terminal.

[0654] Step 6:

[0655] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[0656] Step 7:

[0657] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[0658] Step 8:

[0659] The server receives the results of the job experience game and analyzes the user's actions and choices.

[0660] Step 9:

[0661] The server provides feedback to the user based on the analysis results and suggests more suitable occupations.

[0662] Terminal processing steps

[0663] Step 1:

[0664] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0665] Step 2:

[0666] The terminal transmits the attribute information input by the user to the server.

[0667] Step 3:

[0668] The terminal displays the occupation list and detailed information received from the server, and provides the user with options.

[0669] Step 4:

[0670] The terminal again transmits information about the occupation selected by the user to the server.

[0671] Step 5:

[0672] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[0673] Step 6:

[0674] The terminal records the choices and actions that the user makes while playing the job experience game and transmits them to the server sequentially.

[0675] User operation steps

[0676] Step 1:

[0677] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0678] Step 2:

[0679] The user checks the occupation list and detailed information displayed on the terminal.

[0680] Step 3:

[0681] The user selects an occupation that interests them on the terminal.

[0682] Step 4:

[0683] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[0684] Specific examples

[0685] Example of User A

[0686] Step 1:

[0687] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[0688] Step 2:

[0689] The terminal transmits the input information to the server.

[0690] Step 3:

[0691] The server analyzes the attribute information of user A and generates a user profile.

[0692] Step 4:

[0693] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[0694] Step 5:

[0695] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[0696] Step 6:

[0697] User A selects "Game Developer."

[0698] Step 7:

[0699] The terminal transmits the selected occupation information to the server.

[0700] Step 8:

[0701] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[0702] Step 9:

[0703] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[0704] Step 10:

[0705] User A simulates character design and project management tasks.

[0706] Step 11:

[0707] The server receives the game results and analyzes User A's actions and choices.

[0708] Step 12:

[0709] The server provides feedback and next career suggestions to User A.

[0710] Example 1

[0711] 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."

[0712] Conventional career guidance systems do not adequately analyze users' attribute information or make career suggestions, making it difficult to suggest optimal careers based on individual characteristics and interests. Furthermore, they lack specific understanding through work experience, and there are insufficient means for users to fully experience the type of work they will actually be doing. Furthermore, there is also an insufficient mechanism for reflecting users' experience and behavioral data in the next career suggestion.

[0713] 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.

[0714] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the results of the job experience game and providing feedback to the user, means for making new job suggestions based on the analysis results, and means for storing the received user behavior data in a database and providing it for training by the generation AI. This enables optimal job suggestions based on the user's attribute information, making it possible to understand specific jobs through job experiences and continuously improve the accuracy of job suggestions.

[0715] "User attribute information" refers to personal information that a user inputs into a terminal, such as the user's age, gender, place of residence, and hobbies.

[0716] "Analysis" is the act of processing received information to extract and generate useful data.

[0717] A "user profile" is a summary of information including the user's personality traits and abilities, generated based on analyzed user attribute information.

[0718] "Generative AI" is an artificial intelligence technology that suggests suitable careers based on a user's characteristics and interests.

[0719] An "occupation list" is a list of multiple occupations suitable for a user, created by the generation AI based on the user profile.

[0720] "Detailed information" refers to information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[0721] A "job experience game" is an interactive game that simulates real-life work tasks based on a user's chosen job.

[0722] "Scenario construction" refers to the design and creation of content that recreates specific work tasks, project progress, and problem-solving scenes related to the user's chosen occupation.

[0723] "Feedback" refers to advice and information provided to users based on the results of the job experience game, such as strengths, areas for improvement, and suggestions for the next job to try.

[0724] A "database" is a system or mechanism for storing and managing collected data.

[0725] The present invention is a system that uses a generative AI model to suggest careers based on a child's attribute information and provides a career experience game. The embodiments of the present invention are described in detail below.

[0726] Server Processing

[0727] Receiving user data

[0728] The server receives the user's attribute information sent from the device. This attribute information includes age, gender, place of residence, hobbies, etc. Specifically, the server validates the received JSON format data.

[0729] Data analysis

[0730] The server uses the Python pandas library to analyze the received attribute information. Based on the analyzed data, it generates a user profile, which includes information such as personality traits and abilities.

[0731] Career suggestions by generative AI

[0732] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[0733] Collecting and sending details

[0734] The server extracts detailed information about the proposed job from a MySQL database and sends it to the device in JSON format, including details such as the job description, required skills, rewards, challenges, and salary estimates.

[0735] Scenario construction for a job experience game

[0736] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of daily work, project execution, and problem-solving.

[0737] Data storage and training

[0738] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used for continuous training of the generative AI model.

[0739] Feedback and next steps

[0740] The server provides feedback to the user based on the results of the job experience game, including suggestions for new jobs to try next, as well as the user's strengths and areas for improvement.

[0741] Terminal handling

[0742] Providing a user interface

[0743] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0744] Sending data

[0745] The terminal converts the attribute information entered by the user into JSON format and sends it to the server using the HTTPS protocol.

[0746] Receiving and displaying job offers

[0747] The terminal receives the occupation list and its detailed information sent from the server and displays it to the user, who can then select an occupation that interests them.

[0748] Providing an interface for a job experience game

[0749] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0750] User operations

[0751] Entering attribute information

[0752] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0753] Review and select career suggestions

[0754] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[0755] Playing a job experience game

[0756] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[0757] Specific examples

[0758] For example, let's say User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into his or her device, which then sends the information to the server. The server receives the information, analyzes it, and generates a user profile. The generation AI then suggests occupations such as "game developer" or "digital artist" based on the profile. The server collects detailed information about the suggested occupations and sends it to the device. User A selects "game developer" and receives a scenario for a job experience game built by the server using Unity. User A simulates character design and project management tasks on his or her device, experiencing the specific content of the work, as well as the rewards and difficulties involved. The server analyzes the game results, provides feedback to User A, and suggests more suitable occupations.

[0759] The system helps children reduce anxiety about future career choices and find the right career path.

[0760] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0761] Step 1:

[0762] Enter attribute information (user)

[0763] The user enters attribute information such as age, gender, place of residence, and hobbies into the device. For example, if the user is 14 years old and enters "programming" and "drawing" as his or her hobbies, the input data is acquired through a form. This form contains input fields, and data is collected as the user enters each item.

[0764] Input: User attribute information (age, gender, place of residence, hobbies, etc.)

[0765] Output: Input attribute information (JSON format)

[0766] Step 2:

[0767] Data transmission (terminal)

[0768] The device sends the attribute information entered by the user to the server as an HTTP POST request. At this time, the attribute information is converted to JSON format. The device uses the HTTPS protocol to send the data securely.

[0769] Input: User attribute information (JSON format)

[0770] Output: Attribute information sent to the server

[0771] Step 3:

[0772] Receiving attribute information (server)

[0773] The server receives the user's attribute information sent from the terminal, performs format validation on the received data, and identifies invalid data input.

[0774] Input: Attribute information sent from the device (JSON format)

[0775] Output: Attribute information that passes validation

[0776] Step 4:

[0777] Data analysis (server)

[0778] The server uses the Python pandas library to analyze the received attribute information and generates a user profile based on the analyzed data. This profile summarizes information such as the user's personality traits and abilities.

[0779] Input: Attribute information that has passed validation

[0780] Output: User profile

[0781] Step 5:

[0782] Occupation suggestions by generative AI (server)

[0783] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[0784] Input: User profile

[0785] Output: List of suitable occupations

[0786] Step 6:

[0787] Collecting and sending detailed information (server)

[0788] The server extracts detailed information about the proposed jobs from a MySQL database and sends it to the device in JSON format, including job description, required skills, rewards, challenges, salary estimates, etc.

[0789] Input: List of suitable occupations

[0790] Output: Detailed information in JSON format

[0791] Step 7:

[0792] Job experience game scenario construction (server)

[0793] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of the job's daily tasks, project progress, and problem-solving.

[0794] Input: Details of selected occupation

[0795] Output: The constructed work experience game scenario

[0796] Step 8:

[0797] Receive and display job suggestions and detailed information (terminal)

[0798] The terminal receives the list of occupations and detailed information sent from the server and displays them to the user. The user selects the occupations they are interested in from the suggested occupations. The interface includes a selection button.

[0799] Input: Occupation list and detailed information sent from the server

[0800] Output: The information displayed to the user and the selected occupation

[0801] Step 9:

[0802] Job experience game implementation (user)

[0803] The user launches an interactive job experience game provided on the device and simulates work tasks based on a scenario, such as character design or project management.

[0804] Input: A constructed work experience game scenario

[0805] Output: The results of the work task experienced by the user

[0806] Step 10:

[0807] Analysis and storage of game results (server)

[0808] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used to continuously train the generative AI model.

[0809] Input: The results of the work task experienced by the user

[0810] Output: Saved behavioral data

[0811] Step 11:

[0812] Feedback and New Suggestions (Server)

[0813] The server provides feedback to the user based on the game results, including suggestions for new professions, highlighting the user's strengths and areas for improvement, and suggesting the next profession to try.

[0814] Input: Stored behavioral data

[0815] Output: Feedback and new career suggestions provided to the user

[0816] (Application example 1)

[0817] 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."

[0818] Conventional work experience systems do not adequately help children find the most suitable occupation based on their own attribute information. Furthermore, because the work experience lacks realism and interactivity, it is difficult for children to grasp the appeal and challenges of actual work. Therefore, there is a need for a system that can reduce children's anxiety about choosing a future occupation and help them find the right career path.

[0819] 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.

[0820] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for allowing the user to interactively play the job experience game in a virtual environment, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the results of the analysis. This enables children to find the most suitable occupation based on their own attribute information and actually experience that occupation in a virtual environment.

[0821] "User attribute information" refers to personal characteristics such as the user's age, sex, place of residence, and hobbies.

[0822] "User profile" refers to data that indicates the characteristics and abilities of a user, generated based on the received attribute information.

[0823] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and suggestions.

[0824] "Suitable Occupation List" means a list of occupations suitable for the user suggested by the generative AI based on the user's profile.

[0825] "Detailed occupational information" refers to specific information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[0826] A "career experience game scenario" refers to a story that includes work tasks, project progress, problem-solving scenes, etc., constructed to simulate the career chosen by the user.

[0827] A "virtual environment" refers to a virtual space generated using computer technology in which users can have interactive experiences.

[0828] "Results of the job experience game" refers to data such as the results, selection history, and actions that the user has achieved through the job experience game.

[0829] "Feedback" refers to the evaluation and advice provided to the user after analyzing the results of the job experience game.

[0830] "New career suggestions" means that the generative AI will use feedback to suggest new careers for the user to try next.

[0831] This invention is a system that utilizes generative AI to suggest careers based on children's attribute information and provides a linked career experience game. The specific program processing flow and implementation method of the system are explained in detail below.

[0832] Server Processing

[0833] Receiving user data

[0834] The server receives attribute information entered by the user into the terminal, such as age, gender, place of residence, hobbies, etc. Any structured data format is used for this information.

[0835] Data analysis

[0836] The server analyzes the received attribute information and generates a user profile, which includes information such as personality traits and abilities, and the AI ​​operates based on this.

[0837] Career suggestions by generative AI

[0838] The server uses a generative AI based on the generated user profile to create a list of suitable occupations. The generative AI uses a large dataset and machine learning models to suggest occupations that match the user's characteristics and interests.

[0839] Collecting and sending details

[0840] The server collects detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimates, etc.) and sends it to the terminal.

[0841] Scenario construction for a job experience game

[0842] Based on the user's chosen occupation, the server creates a scenario for the job-experience game that simulates real-life work tasks, including scenes from daily work, project management, and problem-solving.

[0843] Providing a virtual environment

[0844] The server allows users to interactively experience the job experience game in a virtual environment, which is provided through devices such as smartphones and head-mounted displays.

[0845] Data storage and training

[0846] The server analyzes the results of the job experience game and stores the user's actions and choices in a database. The stored data is used to train the generative AI algorithm.

[0847] Feedback and next steps

[0848] Based on the results of the job experience game, the server provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[0849] Terminal handling

[0850] Providing a user interface

[0851] The terminal provides an interface for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0852] Sending data

[0853] The attribute information entered by the user is sent from the terminal to the server.

[0854] Receiving and displaying job offers

[0855] The terminal receives the list of occupations and their detailed information sent from the server and displays them to the user, who can then select the occupations that interest them.

[0856] Providing an interface for a job experience game

[0857] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[0858] User operations

[0859] Entering attribute information

[0860] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[0861] Review and select career suggestions

[0862] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[0863] Playing a job experience game

[0864] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[0865] Specific examples

[0866] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[0867] An example prompt might be something like, "The user is 14 years old and his hobbies are programming and drawing."

[0868] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0869] Step 1:

[0870] The server receives attribute information entered by the user into the terminal. Attribute information includes age, gender, place of residence, hobbies, etc. The server receives this information and temporarily stores it in a database (input: attribute information, output: stored attribute data).

[0871] Step 2:

[0872] The server analyzes the received attribute information and generates a user profile. The analysis process involves evaluating the user's personality and characteristics using a machine learning model (input: attribute information, output: user profile).

[0873] Step 3:

[0874] The server uses a generation AI to create a list of suitable occupations based on the generated user profile. The generation AI refers to a large dataset and the synthesized user profile to list suitable occupations (input: user profile, output: occupation list).

[0875] Step 4:

[0876] The server collects detailed information about the occupation selected by the user from the occupation list and sends it to the terminal. The detailed information includes the job description, required skills, rewards, difficulty, expected salary, etc. (Input: Occupation selected by the user, Output: Occupation details information).

[0877] Step 5:

[0878] The server constructs a scenario for the work experience game based on the user's selected occupation. This scenario includes scenes of daily work tasks, project progress, and problem solving (input: selected occupation, output: work experience game scenario).

[0879] Step 6:

[0880] The terminal displays the scenario of the job experience game received from the server, and the user plays the interactive job experience game through the terminal (input: job experience game scenario, output: interactive game interface).

[0881] Step 7:

[0882] The server analyzes the results of the job experience game and provides feedback to the user. The analysis is based on the user's behavioral history and selection results (input: job experience game results, output: feedback data).

[0883] Step 8:

[0884] The server uses a generative AI to suggest new occupations based on the feedback, which then suggests the next occupation for the user to try (input: feedback data, output: new occupation suggestion list).

[0885] 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.

[0886] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[0887] Server Processing

[0888] Receiving user data

[0889] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[0890] Data analysis

[0891] The received attribute information is analyzed to generate a user profile, which includes the user's personality traits and abilities.

[0892] Career suggestions by generative AI

[0893] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[0894] Collecting and sending details

[0895] Detailed information about the occupation selected from the list of occupations (job content, required skills, rewards, difficulty, salary estimates, etc.) is collected from the database and sent to the terminal.

[0896] Scenario construction for a job experience game

[0897] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[0898] Emotion recognition by emotion engine

[0899] The server uses an emotion engine to recognize the user's emotions, detecting them from data such as the user's facial expressions, voice, and text input, and using this information for analysis.

[0900] Data storage and training

[0901] The results of the job-experience game are analyzed, and the user's actions, choices, and emotional state are stored in a database. The stored data is used to continuously train the generative AI algorithm.

[0902] Feedback and next steps

[0903] Based on the results of the job experience game and the user's emotional state, the system provides feedback to the user and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[0904] Terminal handling

[0905] Providing a user interface

[0906] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0907] Sending data

[0908] The attribute information entered by the user is sent to the server.

[0909] Receiving and displaying job offers

[0910] The occupation list and detailed information sent from the server are displayed, and the user is given the option to select one.

[0911] Accepting user selections

[0912] The information about the occupation selected by the user is sent again to the server.

[0913] Providing an interface for a job experience game

[0914] The scenario of the job experience game received from the server is displayed, allowing the user to interactively experience it.

[0915] User operations

[0916] Entering attribute information

[0917] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0918] Review and select career suggestions

[0919] Check the list of occupations and detailed information displayed on the device, and select the occupation that interests you on the device.

[0920] Playing a job experience game

[0921] They play the job experience games provided on the device and perform work tasks and simulations.

[0922] Providing emotion data

[0923] While playing, the device collects emotional data such as the user's facial expressions, voice, and text input, and sends it to the server.

[0924] Specific examples

[0925] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[0926] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[0927] The processing flow will be explained below.

[0928] Server Processing Steps

[0929] Step 1:

[0930] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[0931] Step 2:

[0932] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[0933] Step 3:

[0934] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[0935] Step 4:

[0936] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[0937] Step 5:

[0938] The server sends the collected detailed information to the terminal.

[0939] Step 6:

[0940] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[0941] Step 7:

[0942] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[0943] Step 8:

[0944] The server receives the results of the job experience game as well as the user's emotional data (facial expressions, voice, text input, etc.) sent from the terminal.

[0945] Step 9:

[0946] The server analyzes the results of the job experience game and the emotional data, and evaluates the user's actions and choices.

[0947] Step 10:

[0948] The server provides feedback to the user based on the analysis results and suggests careers that are more suitable for them. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[0949] Terminal processing steps

[0950] Step 1:

[0951] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[0952] Step 2:

[0953] The terminal transmits the attribute information input by the user to the server.

[0954] Step 3:

[0955] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[0956] Step 4:

[0957] The terminal again transmits information about the occupation selected by the user to the server.

[0958] Step 5:

[0959] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[0960] Step 6:

[0961] The device records the choices and actions the user makes while playing the job experience game, and simultaneously collects the user's emotional data (facial expressions, voice, text input, etc.) and transmits it to the server.

[0962] User operation steps

[0963] Step 1:

[0964] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[0965] Step 2:

[0966] The user checks the occupation list and detailed information displayed on the terminal.

[0967] Step 3:

[0968] The user selects an occupation that interests them on the terminal.

[0969] Step 4:

[0970] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[0971] Step 5:

[0972] The user's emotion data is collected by the terminal and transmitted to the server.

[0973] Specific examples

[0974] Example of User A

[0975] Step 1:

[0976] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[0977] Step 2:

[0978] The terminal transmits the input information to the server.

[0979] Step 3:

[0980] The server analyzes the attribute information of user A and generates a user profile.

[0981] Step 4:

[0982] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[0983] Step 5:

[0984] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[0985] Step 6:

[0986] User A selects "Game Developer."

[0987] Step 7:

[0988] The terminal transmits the selected occupation information to the server.

[0989] Step 8:

[0990] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[0991] Step 9:

[0992] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[0993] Step 10:

[0994] While User A simulates character design and project management tasks, the device collects User A's facial expressions and voice data, analyzes them with an emotion engine, and sends the results to the server.

[0995] Step 11:

[0996] The server receives the game results and emotional data and analyzes User A's behavior and choices.

[0997] Step 12:

[0998] The server provides feedback to User A based on the analysis results and suggests the next occupation to try. For example, if User A is evaluated as having high concentration in addition to technical ability, the server will suggest a new occupation of "software engineer."

[0999] In this way, the invention uses a multifaceted approach that combines emotion recognition and generative AI to help children find the career that best suits them.

[1000] Example 2

[1001] 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."

[1002] Conventional career suggestion systems primarily suggest careers based on the user's attribute information, but do not take the user's emotional state into consideration. Furthermore, the scenarios for career experience games are monotonous and do not reflect the user's characteristics, interests, or emotions. This makes it difficult for users to effectively find a career that suits them. The objective of this invention is to provide a system that takes the user's emotional state into consideration, improves the accuracy of career suggestions, and provides an interactive career experience game.

[1003] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the emotion data, means for analyzing the results and emotion data of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This enables highly accurate occupation suggestions that take the user's emotional state into consideration, allowing the user to effectively find an occupation that suits them and providing a realistic work experience through an interactive job experience game.

[1004] "User attribute information" is data about an individual, including the user's age, gender, place of residence, hobbies, and the like.

[1005] A "user profile" is data that summarizes the user's personality traits and abilities by analyzing the received user attribute information.

[1006] "Generative AI" is an artificial intelligence algorithm that uses a user's profile and other data to generate a list of jobs.

[1007] An "occupation list" is a list of occupations suitable for a user, created by the generation AI based on the user's profile.

[1008] "Detailed information" refers to information including the job content, required skills, rewarding aspects, difficulty, salary estimates, etc. of the occupations included in the occupation list.

[1009] A "career experience game" is an interactive game that includes simulated work tasks and scenes based on a user-selected career.

[1010] "Emotion data" is data that indicates the emotional state of the user detected from facial expressions, voice, text input, and the like.

[1011] "Feedback" refers to information about improvements and advice provided to the user based on the results of the job experience game and emotional data.

[1012] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. The program processing of the system is explained in detail below.

[1013] Server Processing

[1014] Receiving user data

[1015] The server receives attribute information entered by the user on the device, such as age, gender, place of residence, hobbies, etc. This data is sent in JSON format using an HTTP POST request.

[1016] Data analysis

[1017] The server analyzes the received attribute information and generates a user profile using programming languages ​​such as Python and R. Based on the user's hobbies and characteristics, the server evaluates and records the user's personality traits and abilities.

[1018] Career suggestions by generative AI

[1019] The server uses a generative AI (e.g., GPT-4) to create a list of suitable occupations based on the generated user profile. By inputting a prompt sentence, the server suggests occupations suitable for the user.

[1020] Collecting and sending details

[1021] The server collects detailed information about the occupation selected from the occupation list from the database and sends it to the terminal, for example, using an SQL query to obtain the necessary occupational information (job description, required skills, rewards, difficulties, expected salary, etc.).

[1022] Scenario construction for a job experience game

[1023] The server creates and transmits a scenario for the job experience game based on the job selected by the user. This scenario includes scenes of daily work, project progress, and problem solving so that the user can actually experience the job.

[1024] Emotion recognition by emotion engine

[1025] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. It analyzes data such as the user's facial expressions, voice, and text input in real time and stores the user's emotional state in a database.

[1026] Data storage and training

[1027] The server analyzes the results of the job-experience game and stores the user's actions, choices, and emotional state in a database, which is used to continuously train the generative AI algorithm.

[1028] Feedback and next steps

[1029] The server provides feedback to the user based on the results of the job experience game and their emotional state, and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and changes in their emotions.

[1030] Terminal handling

[1031] Providing a user interface

[1032] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1033] Sending data

[1034] The terminal transmits the attribute information input by the user to the server.

[1035] Receiving and displaying job offers

[1036] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[1037] Accepting user selections

[1038] The terminal again transmits information about the occupation selected by the user to the server.

[1039] Providing an interface for a job experience game

[1040] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[1041] User operations

[1042] Entering attribute information

[1043] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1044] Review and select career suggestions

[1045] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them on the terminal.

[1046] Playing a job experience game

[1047] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[1048] Providing emotion data

[1049] While playing, users' emotional data, such as facial expressions, voice, and text input, is collected by the terminal and transmitted to the server.

[1050] Specific examples

[1051] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which is then received and analyzed by the server to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[1052] Prompt Sentence Examples

[1053] Below are some example prompts to input to a generative AI model (e.g., GPT-4):

[1054] User profile: Age 14, Gender male, Residence Tokyo, Hobbies programming, drawing

[1055] Please suggest three suitable jobs for this user. For each job, please also tell us the required skills, job content, rewards and difficulties, and salary estimates.

[1056] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[1057] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1058] Step 1: Receiving user data

[1059] Input: Attribute information entered by the user into the device (age, gender, place of residence, hobbies, etc.).

[1060] Processing: The device receives the attribute information entered by the user and sends it to the server. The server receives the data sent in JSON format using an HTTP POST request.

[1061] Output: JSON data of attribute information is stored on the server.

[1062] Step 2: Analyze the data

[1063] Input: User attribute information stored on the server (JSON format).

[1064] Processing: The server uses Python scripts to analyze the received data and generate a user profile (age, gender, location, hobbies, personality traits, abilities, etc.) using classification algorithms and clustering techniques.

[1065] Output: A user profile (in JSON format) is generated.

[1066] Step 3: Occupation suggestions by generative AI

[1067] Input: Server-generated user profile (JSON format).

[1068] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4), and the generative AI suggests occupations based on the user's characteristics and interests. For example, the server sends the following prompt to the generative AI: "User profile: Age 14, Gender: Male, Residence: Tokyo, Hobbies: Programming, Drawing. Please suggest three occupations suitable for this user."

[1069] Output: A list of occupations (text format) is generated.

[1070] Step 4: Collect and submit details

[1071] Input: A list of occupations (text format) generated by the generation AI.

[1072] Processing: The server uses an SQL query to retrieve detailed information about the selected occupation (job description, required skills, salary estimate, etc.) from the database. Example: SELECT FROM careers WHERE job_title='Game Developer';

[1073] Output: The list of occupations and their details (in JSON format) will be sent to the terminal.

[1074] Step 5: Building a scenario for the job experience game

[1075] Input: Details of the user's chosen occupation (JSON format).

[1076] Processing: The server generates a scenario for a job-experience game using a Python script based on the example of a "game developer" and sends it to the device in JSON format. The scenario includes tasks such as character design and project management.

[1077] Output: Job experience game scenario (JSON format).

[1078] Step 6: Emotion Recognition with the Emotion Engine

[1079] Input: User facial, voice, and text input data.

[1080] Processing: The device analyzes the user's emotional data in real time using an emotion engine (e.g., Affectiva API) and sends it to the server. The data includes emotional states such as "happy=70%, sad=10%, neutral=20%."

[1081] Output: Emotion data (JSON format).

[1082] Step 7: Data storage and training

[1083] Input: Job experience game results and sentiment data (JSON format).

[1084] Processing: The server stores the analysis results in a database (e.g., MongoDB or MySQL). The stored data is used to continuously train the generative AI algorithm.

[1085] Output: An updated database and a trained generative AI model.

[1086] Step 8: Feedback and next steps

[1087] Input: Job experience game results and sentiment data (JSON format).

[1088] Processing: The server uses the generative AI model to suggest the next suitable job for the user and generates feedback. For example, the generative AI might create a sentence such as, "Since you were successful in the game developer scenario, try project manager next."

[1089] Output: Feedback and next career suggestion information provided to the user.

[1090] (Application example 2)

[1091] 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."

[1092] Conventional work experience systems do not adequately suggest appropriate jobs based on the user's attribute information. Furthermore, it is difficult to interactively experience work experiences in real time and obtain feedback. Furthermore, there is a lack of systems that can recognize the user's emotional state in real time and provide appropriate feedback based on that information.

[1093] The identification processing by the identification 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 receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for providing the scenario for the job experience game to smart glasses or a tablet in a physical store, means for collecting and analyzing user emotion data using an emotion recognition engine, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This makes it possible to provide accurate job suggestions based on the user's attribute information, interactive job experiences that reflect the user's emotional state in real time, and appropriate feedback.

[1094] The "means for receiving user attribute information" is a function by which a server or terminal receives information such as age, sex, place of residence, and hobbies entered by a user.

[1095] The "means for generating a user profile" is a function that analyzes the received attribute information and creates a detailed profile including the user's personality traits and abilities.

[1096] The "means for creating a list of appropriate occupations using a generation AI" is a function in which the generation AI generates a list of occupations suitable for the user based on the generated user profile.

[1097] "Means for collecting detailed information and sending it to the terminal" refers to a function that collects detailed information about the selected occupation, such as the job content, required skills, rewards, difficulties, and salary estimates, and sends it to the user's terminal.

[1098] The "means for creating and transmitting a scenario for a job experience game" is a function for creating a scenario for a job experience game based on a job selected by the user and transmitting the scenario to the terminal.

[1099] "Means for providing to smart glasses or tablets in a physical store" refers to a function for providing users with a scenario of a job experience game through smart glasses or tablets installed in a physical store.

[1100] "Means for collecting and analyzing user emotional data using an emotion recognition engine" refers to a function that analyzes the user's facial expressions and voice using an emotion recognition engine to collect and analyze emotional data.

[1101] The "means for analyzing the results of the job experience game and providing feedback to the user" is a function for analyzing the results of playing the job experience game and providing feedback to the user such as strengths and areas for improvement.

[1102] The "means of making new career suggestions" is a function that allows the generation AI to make new career suggestions based on the results of the previous career experience game and emotional data.

[1103] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on a child's attribute information and provide an interactive career experience game in a physical store. This system receives and analyzes the user's attribute information and generates a user profile, then uses the generative AI to create a list of appropriate careers and sends the details and the scenario of the career experience game to the device. The system also provides the user with a career experience through smart glasses or a tablet, and uses the emotion engine to collect and analyze the user's emotional data and provide feedback.

[1104] Hardware and software used

[1105] The following hardware and software are used to realize this system.

[1106] Smart glasses (e.g. Google Glass)

[1107] Tablet (e.g. iPad)

[1108] Server (e.g. AWS EC2)

[1109] Database (e.g. MySQL)

[1110] Generative AI (e.g. OpenAI GPT-4)

[1111] Emotion recognition engine (e.g. Affectiva SDK)

[1112] Data processing and calculation process

[1113] The server receives the user's attribute information. The user uses a tablet or smart glasses to input information such as age, gender, place of residence, and hobbies. This data is sent to the server, where it is analyzed. A user profile is generated based on the analysis results. This profile includes the user's personality traits and abilities.

[1114] The generative AI (OpenAI GPT-4) creates a list of suitable occupations based on the user profile. For example, if the user is 14 years old and their hobbies are programming and drawing, the generative AI will suggest occupations such as "game developer" and "digital artist." Examples of prompts for this generative AI are as follows:

[1115] Example prompt sentence:

[1116] "I'm a 14-year-old kid whose hobbies are programming and drawing. What kind of job would be suitable for me?"

[1117] The server then collects detailed information about the selected job from a database and sends it to the tablet or smart glasses. Based on the user's selected job, the server creates a scenario for a job experience game and provides the scenario through the smart glasses or tablet in the physical store.

[1118] The emotion recognition engine (Affectiva SDK) analyzes the user's facial expressions and voice in real time to collect emotional data. This data is sent to the server and analyzed along with the results of the job-experience game. Based on the analysis results, feedback is provided to the user. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[1119] Finally, the generative AI will suggest new careers based on the analysis results. In this way, the system helps users find the career that best suits them and experience it interactively in real time.

[1120] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1121] Step 1:

[1122] Users use tablets or smart glasses to input attribute information such as age, gender, place of residence, and hobbies, and this input data is sent to the server by the device.

[1123] Input: User attribute information (age, gender, place of residence, hobbies)

[1124] Output: Sending attribute information to the server

[1125] Step 2:

[1126] The server analyzes the received attribute information and generates a user profile by comparing it with past data stored in a database.

[1127] Input: Received attribute information

[1128] Data processing: Comparison with past data stored in the database and analysis

[1129] Output: Generated user profile

[1130] Step 3:

[1131] The server uses a generation AI (OpenAI GPT-4) to create a list of suitable occupations based on the generated user profile. At this time, the server inputs a prompt sentence into the generation AI and receives occupation suggestions.

[1132] Input: User profile, prompt

[1133] Data Computation: Creating Occupation Lists with Generative AI

[1134] Output: List of suitable occupations

[1135] Example prompt: "I'm a 14-year-old kid whose hobbies are programming and drawing. What career would be suitable for me?"

[1136] Step 4:

[1137] The server collects detailed information (job content, required skills, rewarding work, difficulty, expected salary, etc.) about the occupation selected by the user from the generated occupation list from a database and sends it to the terminal.

[1138] Input: User-selected occupation, occupation list

[1139] Data processing: Collect detailed information from the database

[1140] Output: Send detailed job information

[1141] Step 5:

[1142] The server creates an interactive job experience game scenario based on the user's chosen occupation and sends it to smart glasses or tablets in the physical store.

[1143] Input: User-selected occupation, detailed information

[1144] Data calculation: Scenario construction for a job experience game

[1145] Output: Interactive job experience game scenario submission

[1146] Step 6:

[1147] Using the emotion recognition engine (Affectiva SDK), smart glasses or tablets analyze the user's facial expressions and voice in real time to collect emotional data, which is then sent from the device to a server.

[1148] Input: User's facial expression and voice data

[1149] Data processing: Analysis using emotion recognition engine

[1150] Output: Collect emotion data and send it to the server

[1151] Step 7:

[1152] The server analyzes the results of the job experience game and the collected emotional data, stores the results in a database, and provides feedback to the user based on this.

[1153] Input: Job experience game results, emotion data

[1154] Data Computing: Analyzing Outcome and Sentiment Data

[1155] Output: Generating and providing feedback

[1156] Step 8:

[1157] The server then uses the generative AI to suggest new occupations based on the analysis results. The user can then select an occupation from the list of suggested occupations and continue the interactive occupation experience.

[1158] Input: Analysis results, emotion data

[1159] Data Computation: New Career Proposals with Generative AI

[1160] Output: Generate a new list of occupations and repeat each step

[1161] 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.

[1162] 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.

[1163] 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.

[1164] [Third embodiment]

[1165] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1166] 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.

[1167] 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).

[1168] 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.

[1169] 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.

[1170] 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).

[1171] 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.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] 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."

[1177] This invention is a system that uses a generative AI to suggest careers based on children's attribute information and provides a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[1178] Server Processing

[1179] Receiving user data

[1180] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[1181] Data analysis

[1182] The received attribute information is analyzed to generate a user profile, which includes information such as personality traits and abilities.

[1183] Career suggestions by generative AI

[1184] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[1185] Collecting and sending details

[1186] Detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimate, etc.) is collected and sent to the terminal.

[1187] Scenario construction for a job experience game

[1188] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[1189] Data storage and training

[1190] The results of the job experience game are analyzed and the user's actions and choices are stored in a database, which is used to continuously train the generative AI algorithm.

[1191] Feedback and next steps

[1192] Based on the results of the job experience game, the system provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[1193] Terminal handling

[1194] Providing a user interface

[1195] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1196] Sending data

[1197] The attribute information entered by the user is sent to the server.

[1198] Receiving and displaying job offers

[1199] The job list and its detailed information sent from the server are received and displayed to the user, who can then select the job that interests them.

[1200] Providing an interface for a job experience game

[1201] The interface displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1202] User operations

[1203] Entering attribute information

[1204] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[1205] Review and select career suggestions

[1206] Check the list of occupations and detailed information displayed on your device and select the occupation that interests you.

[1207] Playing a job experience game

[1208] Participants can play a job experience game provided on their device and simulate specific work tasks. Through this interactive game, participants can experience the rewards and challenges of the job.

[1209] Specific examples

[1210] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[1211] In this way, the present invention helps children reduce anxiety about future career choices and helps them find the right career path.

[1212] The processing flow will be explained below.

[1213] Server Processing Steps

[1214] Step 1:

[1215] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[1216] Step 2:

[1217] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[1218] Step 3:

[1219] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[1220] Step 4:

[1221] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[1222] Step 5:

[1223] The server sends the collected detailed information to the terminal.

[1224] Step 6:

[1225] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[1226] Step 7:

[1227] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[1228] Step 8:

[1229] The server receives the results of the job experience game and analyzes the user's actions and choices.

[1230] Step 9:

[1231] The server provides feedback to the user based on the analysis results and suggests more suitable occupations.

[1232] Terminal processing steps

[1233] Step 1:

[1234] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1235] Step 2:

[1236] The terminal transmits the attribute information input by the user to the server.

[1237] Step 3:

[1238] The terminal displays the occupation list and detailed information received from the server, and provides the user with options.

[1239] Step 4:

[1240] The terminal again transmits information about the occupation selected by the user to the server.

[1241] Step 5:

[1242] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[1243] Step 6:

[1244] The terminal records the choices and actions that the user makes while playing the job experience game and transmits them to the server sequentially.

[1245] User operation steps

[1246] Step 1:

[1247] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1248] Step 2:

[1249] The user checks the occupation list and detailed information displayed on the terminal.

[1250] Step 3:

[1251] The user selects an occupation that interests them on the terminal.

[1252] Step 4:

[1253] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[1254] Specific examples

[1255] Example of User A

[1256] Step 1:

[1257] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[1258] Step 2:

[1259] The terminal transmits the input information to the server.

[1260] Step 3:

[1261] The server analyzes the attribute information of user A and generates a user profile.

[1262] Step 4:

[1263] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[1264] Step 5:

[1265] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[1266] Step 6:

[1267] User A selects "Game Developer."

[1268] Step 7:

[1269] The terminal transmits the selected occupation information to the server.

[1270] Step 8:

[1271] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[1272] Step 9:

[1273] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[1274] Step 10:

[1275] User A simulates character design and project management tasks.

[1276] Step 11:

[1277] The server receives the game results and analyzes User A's actions and choices.

[1278] Step 12:

[1279] The server provides feedback and next career suggestions to User A.

[1280] Example 1

[1281] 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."

[1282] Conventional career guidance systems do not adequately analyze users' attribute information or make career suggestions, making it difficult to suggest optimal careers based on individual characteristics and interests. Furthermore, they lack specific understanding through work experience, and there are insufficient means for users to fully experience the type of work they will actually be doing. Furthermore, there is also an insufficient mechanism for reflecting users' experience and behavioral data in the next career suggestion.

[1283] 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.

[1284] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the results of the job experience game and providing feedback to the user, means for making new job suggestions based on the analysis results, and means for storing the received user behavior data in a database and providing it for training by the generation AI. This enables optimal job suggestions based on the user's attribute information, making it possible to understand specific jobs through job experiences and continuously improve the accuracy of job suggestions.

[1285] "User attribute information" refers to personal information that a user inputs into a terminal, such as the user's age, gender, place of residence, and hobbies.

[1286] "Analysis" is the act of processing received information to extract and generate useful data.

[1287] A "user profile" is a summary of information including the user's personality traits and abilities, generated based on analyzed user attribute information.

[1288] "Generative AI" is an artificial intelligence technology that suggests suitable careers based on a user's characteristics and interests.

[1289] An "occupation list" is a list of multiple occupations suitable for a user, created by the generation AI based on the user profile.

[1290] "Detailed information" refers to information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[1291] A "job experience game" is an interactive game that simulates real-life work tasks based on a user's chosen job.

[1292] "Scenario construction" refers to the design and creation of content that recreates specific work tasks, project progress, and problem-solving scenes related to the user's chosen occupation.

[1293] "Feedback" refers to advice and information provided to users based on the results of the job experience game, such as strengths, areas for improvement, and suggestions for the next job to try.

[1294] A "database" is a system or mechanism for storing and managing collected data.

[1295] The present invention is a system that uses a generative AI model to suggest careers based on a child's attribute information and provides a career experience game. The embodiments of the present invention are described in detail below.

[1296] Server Processing

[1297] Receiving user data

[1298] The server receives the user's attribute information sent from the device. This attribute information includes age, gender, place of residence, hobbies, etc. Specifically, the server validates the received JSON format data.

[1299] Data analysis

[1300] The server uses the Python pandas library to analyze the received attribute information. Based on the analyzed data, it generates a user profile, which includes information such as personality traits and abilities.

[1301] Career suggestions by generative AI

[1302] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[1303] Collecting and sending details

[1304] The server extracts detailed information about the proposed job from a MySQL database and sends it to the device in JSON format, including details such as the job description, required skills, rewards, challenges, and salary estimates.

[1305] Scenario construction for a job experience game

[1306] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of daily work, project execution, and problem-solving.

[1307] Data storage and training

[1308] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used for continuous training of the generative AI model.

[1309] Feedback and next steps

[1310] The server provides feedback to the user based on the results of the job experience game, including suggestions for new jobs to try next, as well as the user's strengths and areas for improvement.

[1311] Terminal handling

[1312] Providing a user interface

[1313] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1314] Sending data

[1315] The terminal converts the attribute information entered by the user into JSON format and sends it to the server using the HTTPS protocol.

[1316] Receiving and displaying job offers

[1317] The terminal receives the occupation list and its detailed information sent from the server and displays it to the user, who can then select an occupation that interests them.

[1318] Providing an interface for a job experience game

[1319] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1320] User operations

[1321] Entering attribute information

[1322] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[1323] Review and select career suggestions

[1324] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[1325] Playing a job experience game

[1326] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[1327] Specific examples

[1328] For example, let's say User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into his or her device, which then sends the information to the server. The server receives the information, analyzes it, and generates a user profile. The generation AI then suggests occupations such as "game developer" or "digital artist" based on the profile. The server collects detailed information about the suggested occupations and sends it to the device. User A selects "game developer" and receives a scenario for a job experience game built by the server using Unity. User A simulates character design and project management tasks on his or her device, experiencing the specific content of the work, as well as the rewards and difficulties involved. The server analyzes the game results, provides feedback to User A, and suggests more suitable occupations.

[1329] The system helps children reduce anxiety about future career choices and find the right career path.

[1330] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1331] Step 1:

[1332] Enter attribute information (user)

[1333] The user enters attribute information such as age, gender, place of residence, and hobbies into the device. For example, if the user is 14 years old and enters "programming" and "drawing" as his or her hobbies, the input data is acquired through a form. This form contains input fields, and data is collected as the user enters each item.

[1334] Input: User attribute information (age, gender, place of residence, hobbies, etc.)

[1335] Output: Input attribute information (JSON format)

[1336] Step 2:

[1337] Data transmission (terminal)

[1338] The device sends the attribute information entered by the user to the server as an HTTP POST request. At this time, the attribute information is converted to JSON format. The device uses the HTTPS protocol to send the data securely.

[1339] Input: User attribute information (JSON format)

[1340] Output: Attribute information sent to the server

[1341] Step 3:

[1342] Receiving attribute information (server)

[1343] The server receives the user's attribute information sent from the terminal, performs format validation on the received data, and identifies invalid data input.

[1344] Input: Attribute information sent from the device (JSON format)

[1345] Output: Attribute information that passes validation

[1346] Step 4:

[1347] Data analysis (server)

[1348] The server uses the Python pandas library to analyze the received attribute information and generates a user profile based on the analyzed data. This profile summarizes information such as the user's personality traits and abilities.

[1349] Input: Attribute information that has passed validation

[1350] Output: User profile

[1351] Step 5:

[1352] Occupation suggestions by generative AI (server)

[1353] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[1354] Input: User profile

[1355] Output: List of suitable occupations

[1356] Step 6:

[1357] Collecting and sending detailed information (server)

[1358] The server extracts detailed information about the proposed jobs from a MySQL database and sends it to the device in JSON format, including job description, required skills, rewards, challenges, salary estimates, etc.

[1359] Input: List of suitable occupations

[1360] Output: Detailed information in JSON format

[1361] Step 7:

[1362] Job experience game scenario construction (server)

[1363] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of the job's daily tasks, project progress, and problem-solving.

[1364] Input: Details of selected occupation

[1365] Output: The constructed work experience game scenario

[1366] Step 8:

[1367] Receive and display job suggestions and detailed information (terminal)

[1368] The terminal receives the list of occupations and detailed information sent from the server and displays them to the user. The user selects the occupations they are interested in from the suggested occupations. The interface includes a selection button.

[1369] Input: Occupation list and detailed information sent from the server

[1370] Output: The information displayed to the user and the selected occupation

[1371] Step 9:

[1372] Job experience game implementation (user)

[1373] The user launches an interactive job experience game provided on the device and simulates work tasks based on a scenario, such as character design or project management.

[1374] Input: A constructed work experience game scenario

[1375] Output: The results of the work task experienced by the user

[1376] Step 10:

[1377] Analysis and storage of game results (server)

[1378] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used to continuously train the generative AI model.

[1379] Input: The results of the work task experienced by the user

[1380] Output: Saved behavioral data

[1381] Step 11:

[1382] Feedback and New Suggestions (Server)

[1383] The server provides feedback to the user based on the game results, including suggestions for new professions, highlighting the user's strengths and areas for improvement, and suggesting the next profession to try.

[1384] Input: Stored behavioral data

[1385] Output: Feedback and new career suggestions provided to the user

[1386] (Application example 1)

[1387] 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."

[1388] Conventional work experience systems do not adequately help children find the most suitable occupation based on their own attribute information. Furthermore, because the work experience lacks realism and interactivity, it is difficult for children to grasp the appeal and challenges of actual work. Therefore, there is a need for a system that can reduce children's anxiety about choosing a future occupation and help them find the right career path.

[1389] 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.

[1390] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for allowing the user to interactively play the job experience game in a virtual environment, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the results of the analysis. This enables children to find the most suitable occupation based on their own attribute information and actually experience that occupation in a virtual environment.

[1391] "User attribute information" refers to personal characteristics such as the user's age, sex, place of residence, and hobbies.

[1392] "User profile" refers to data that indicates the characteristics and abilities of a user, generated based on the received attribute information.

[1393] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and suggestions.

[1394] "Suitable Occupation List" means a list of occupations suitable for the user suggested by the generative AI based on the user's profile.

[1395] "Detailed occupational information" refers to specific information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[1396] A "career experience game scenario" refers to a story that includes work tasks, project progress, problem-solving scenes, etc., constructed to simulate the career chosen by the user.

[1397] A "virtual environment" refers to a virtual space generated using computer technology in which users can have interactive experiences.

[1398] "Results of the job experience game" refers to data such as the results, selection history, and actions that the user has achieved through the job experience game.

[1399] "Feedback" refers to the evaluation and advice provided to the user after analyzing the results of the job experience game.

[1400] "New career suggestions" means that the generative AI will use feedback to suggest new careers for the user to try next.

[1401] This invention is a system that utilizes generative AI to suggest careers based on children's attribute information and provides a linked career experience game. The specific program processing flow and implementation method of the system are explained in detail below.

[1402] Server Processing

[1403] Receiving user data

[1404] The server receives attribute information entered by the user into the terminal, such as age, gender, place of residence, hobbies, etc. Any structured data format is used for this information.

[1405] Data analysis

[1406] The server analyzes the received attribute information and generates a user profile, which includes information such as personality traits and abilities, and the AI ​​operates based on this.

[1407] Career suggestions by generative AI

[1408] The server uses a generative AI based on the generated user profile to create a list of suitable occupations. The generative AI uses a large dataset and machine learning models to suggest occupations that match the user's characteristics and interests.

[1409] Collecting and sending details

[1410] The server collects detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimates, etc.) and sends it to the terminal.

[1411] Scenario construction for a job experience game

[1412] Based on the user's chosen occupation, the server creates a scenario for the job-experience game that simulates real-life work tasks, including scenes from daily work, project management, and problem-solving.

[1413] Providing a virtual environment

[1414] The server allows users to interactively experience the job experience game in a virtual environment, which is provided through devices such as smartphones and head-mounted displays.

[1415] Data storage and training

[1416] The server analyzes the results of the job experience game and stores the user's actions and choices in a database. The stored data is used to train the generative AI algorithm.

[1417] Feedback and next steps

[1418] Based on the results of the job experience game, the server provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[1419] Terminal handling

[1420] Providing a user interface

[1421] The terminal provides an interface for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1422] Sending data

[1423] The attribute information entered by the user is sent from the terminal to the server.

[1424] Receiving and displaying job offers

[1425] The terminal receives the list of occupations and their detailed information sent from the server and displays them to the user, who can then select the occupations that interest them.

[1426] Providing an interface for a job experience game

[1427] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1428] User operations

[1429] Entering attribute information

[1430] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[1431] Review and select career suggestions

[1432] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[1433] Playing a job experience game

[1434] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[1435] Specific examples

[1436] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[1437] An example prompt might be something like, "The user is 14 years old and his hobbies are programming and drawing."

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Step 1:

[1440] The server receives attribute information entered by the user into the terminal. Attribute information includes age, gender, place of residence, hobbies, etc. The server receives this information and temporarily stores it in a database (input: attribute information, output: stored attribute data).

[1441] Step 2:

[1442] The server analyzes the received attribute information and generates a user profile. The analysis process involves evaluating the user's personality and characteristics using a machine learning model (input: attribute information, output: user profile).

[1443] Step 3:

[1444] The server uses a generation AI to create a list of suitable occupations based on the generated user profile. The generation AI refers to a large dataset and the synthesized user profile to list suitable occupations (input: user profile, output: occupation list).

[1445] Step 4:

[1446] The server collects detailed information about the occupation selected by the user from the occupation list and sends it to the terminal. The detailed information includes the job description, required skills, rewards, difficulty, expected salary, etc. (Input: Occupation selected by the user, Output: Occupation details information).

[1447] Step 5:

[1448] The server constructs a scenario for the work experience game based on the user's selected occupation. This scenario includes scenes of daily work tasks, project progress, and problem solving (input: selected occupation, output: work experience game scenario).

[1449] Step 6:

[1450] The terminal displays the scenario of the job experience game received from the server, and the user plays the interactive job experience game through the terminal (input: job experience game scenario, output: interactive game interface).

[1451] Step 7:

[1452] The server analyzes the results of the job experience game and provides feedback to the user. The analysis is based on the user's behavioral history and selection results (input: job experience game results, output: feedback data).

[1453] Step 8:

[1454] The server uses a generative AI to suggest new occupations based on the feedback, which then suggests the next occupation for the user to try (input: feedback data, output: new occupation suggestion list).

[1455] 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.

[1456] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[1457] Server Processing

[1458] Receiving user data

[1459] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[1460] Data analysis

[1461] The received attribute information is analyzed to generate a user profile, which includes the user's personality traits and abilities.

[1462] Career suggestions by generative AI

[1463] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[1464] Collecting and sending details

[1465] Detailed information about the occupation selected from the list of occupations (job content, required skills, rewards, difficulty, salary estimates, etc.) is collected from the database and sent to the terminal.

[1466] Scenario construction for a job experience game

[1467] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[1468] Emotion recognition by emotion engine

[1469] The server uses an emotion engine to recognize the user's emotions, detecting them from data such as the user's facial expressions, voice, and text input, and using this information for analysis.

[1470] Data storage and training

[1471] The results of the job-experience game are analyzed, and the user's actions, choices, and emotional state are stored in a database. The stored data is used to continuously train the generative AI algorithm.

[1472] Feedback and next steps

[1473] Based on the results of the job experience game and the user's emotional state, the system provides feedback to the user and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[1474] Terminal handling

[1475] Providing a user interface

[1476] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1477] Sending data

[1478] The attribute information entered by the user is sent to the server.

[1479] Receiving and displaying job offers

[1480] The occupation list and detailed information sent from the server are displayed, and the user is given the option to select one.

[1481] Accepting user selections

[1482] The information about the occupation selected by the user is sent again to the server.

[1483] Providing an interface for a job experience game

[1484] The scenario of the job experience game received from the server is displayed, allowing the user to interactively experience it.

[1485] User operations

[1486] Entering attribute information

[1487] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1488] Review and select career suggestions

[1489] Check the list of occupations and detailed information displayed on the device, and select the occupation that interests you on the device.

[1490] Playing a job experience game

[1491] They play the job experience games provided on the device and perform work tasks and simulations.

[1492] Providing emotion data

[1493] While playing, the device collects emotional data such as the user's facial expressions, voice, and text input, and sends it to the server.

[1494] Specific examples

[1495] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[1496] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[1497] The processing flow will be explained below.

[1498] Server Processing Steps

[1499] Step 1:

[1500] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[1501] Step 2:

[1502] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[1503] Step 3:

[1504] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[1505] Step 4:

[1506] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[1507] Step 5:

[1508] The server sends the collected detailed information to the terminal.

[1509] Step 6:

[1510] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[1511] Step 7:

[1512] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[1513] Step 8:

[1514] The server receives the results of the job experience game as well as the user's emotional data (facial expressions, voice, text input, etc.) sent from the terminal.

[1515] Step 9:

[1516] The server analyzes the results of the job experience game and the emotional data, and evaluates the user's actions and choices.

[1517] Step 10:

[1518] The server provides feedback to the user based on the analysis results and suggests careers that are more suitable for them. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[1519] Terminal processing steps

[1520] Step 1:

[1521] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1522] Step 2:

[1523] The terminal transmits the attribute information input by the user to the server.

[1524] Step 3:

[1525] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[1526] Step 4:

[1527] The terminal again transmits information about the occupation selected by the user to the server.

[1528] Step 5:

[1529] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[1530] Step 6:

[1531] The device records the choices and actions the user makes while playing the job experience game, and simultaneously collects the user's emotional data (facial expressions, voice, text input, etc.) and transmits it to the server.

[1532] User operation steps

[1533] Step 1:

[1534] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1535] Step 2:

[1536] The user checks the occupation list and detailed information displayed on the terminal.

[1537] Step 3:

[1538] The user selects an occupation that interests them on the terminal.

[1539] Step 4:

[1540] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[1541] Step 5:

[1542] The user's emotion data is collected by the terminal and transmitted to the server.

[1543] Specific examples

[1544] Example of User A

[1545] Step 1:

[1546] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[1547] Step 2:

[1548] The terminal transmits the input information to the server.

[1549] Step 3:

[1550] The server analyzes the attribute information of user A and generates a user profile.

[1551] Step 4:

[1552] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[1553] Step 5:

[1554] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[1555] Step 6:

[1556] User A selects "Game Developer."

[1557] Step 7:

[1558] The terminal transmits the selected occupation information to the server.

[1559] Step 8:

[1560] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[1561] Step 9:

[1562] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[1563] Step 10:

[1564] While User A simulates character design and project management tasks, the device collects User A's facial expressions and voice data, analyzes them with an emotion engine, and sends the results to the server.

[1565] Step 11:

[1566] The server receives the game results and emotional data and analyzes User A's behavior and choices.

[1567] Step 12:

[1568] The server provides feedback to User A based on the analysis results and suggests the next occupation to try. For example, if User A is evaluated as having high concentration in addition to technical ability, the server will suggest a new occupation of "software engineer."

[1569] In this way, the invention uses a multifaceted approach that combines emotion recognition and generative AI to help children find the career that best suits them.

[1570] Example 2

[1571] 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."

[1572] Conventional career suggestion systems primarily suggest careers based on the user's attribute information, but do not take the user's emotional state into consideration. Furthermore, the scenarios for career experience games are monotonous and do not reflect the user's characteristics, interests, or emotions. This makes it difficult for users to effectively find a career that suits them. The objective of this invention is to provide a system that takes the user's emotional state into consideration, improves the accuracy of career suggestions, and provides an interactive career experience game.

[1573] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the emotion data, means for analyzing the results and emotion data of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This enables highly accurate occupation suggestions that take the user's emotional state into consideration, allowing the user to effectively find an occupation that suits them and providing a realistic work experience through an interactive job experience game.

[1574] "User attribute information" is data about an individual, including the user's age, gender, place of residence, hobbies, and the like.

[1575] A "user profile" is data that summarizes the user's personality traits and abilities by analyzing the received user attribute information.

[1576] "Generative AI" is an artificial intelligence algorithm that uses a user's profile and other data to generate a list of jobs.

[1577] An "occupation list" is a list of occupations suitable for a user, created by the generation AI based on the user's profile.

[1578] "Detailed information" refers to information including the job content, required skills, rewarding aspects, difficulty, salary estimates, etc. of the occupations included in the occupation list.

[1579] A "career experience game" is an interactive game that includes simulated work tasks and scenes based on a user-selected career.

[1580] "Emotion data" is data that indicates the emotional state of the user detected from facial expressions, voice, text input, and the like.

[1581] "Feedback" refers to information about improvements and advice provided to the user based on the results of the job experience game and emotional data.

[1582] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. The program processing of the system is explained in detail below.

[1583] Server Processing

[1584] Receiving user data

[1585] The server receives attribute information entered by the user on the device, such as age, gender, place of residence, hobbies, etc. This data is sent in JSON format using an HTTP POST request.

[1586] Data analysis

[1587] The server analyzes the received attribute information and generates a user profile using programming languages ​​such as Python and R. Based on the user's hobbies and characteristics, the server evaluates and records the user's personality traits and abilities.

[1588] Career suggestions by generative AI

[1589] The server uses a generative AI (e.g., GPT-4) to create a list of suitable occupations based on the generated user profile. By inputting a prompt sentence, the server suggests occupations suitable for the user.

[1590] Collecting and sending details

[1591] The server collects detailed information about the occupation selected from the occupation list from the database and sends it to the terminal, for example, using an SQL query to obtain the necessary occupational information (job description, required skills, rewards, difficulties, expected salary, etc.).

[1592] Scenario construction for a job experience game

[1593] The server creates and transmits a scenario for the job experience game based on the job selected by the user. This scenario includes scenes of daily work, project progress, and problem solving so that the user can actually experience the job.

[1594] Emotion recognition by emotion engine

[1595] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. It analyzes data such as the user's facial expressions, voice, and text input in real time and stores the user's emotional state in a database.

[1596] Data storage and training

[1597] The server analyzes the results of the job-experience game and stores the user's actions, choices, and emotional state in a database, which is used to continuously train the generative AI algorithm.

[1598] Feedback and next steps

[1599] The server provides feedback to the user based on the results of the job experience game and their emotional state, and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and changes in their emotions.

[1600] Terminal handling

[1601] Providing a user interface

[1602] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1603] Sending data

[1604] The terminal transmits the attribute information input by the user to the server.

[1605] Receiving and displaying job offers

[1606] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[1607] Accepting user selections

[1608] The terminal again transmits information about the occupation selected by the user to the server.

[1609] Providing an interface for a job experience game

[1610] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[1611] User operations

[1612] Entering attribute information

[1613] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1614] Review and select career suggestions

[1615] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them on the terminal.

[1616] Playing a job experience game

[1617] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[1618] Providing emotion data

[1619] While playing, users' emotional data, such as facial expressions, voice, and text input, is collected by the terminal and transmitted to the server.

[1620] Specific examples

[1621] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which is then received and analyzed by the server to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[1622] Prompt Sentence Examples

[1623] Below are some example prompts to input to a generative AI model (e.g., GPT-4):

[1624] User profile: Age 14, Gender male, Residence Tokyo, Hobbies programming, drawing

[1625] Please suggest three suitable jobs for this user. For each job, please also tell us the required skills, job content, rewards and difficulties, and salary estimates.

[1626] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[1627] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1628] Step 1: Receiving user data

[1629] Input: Attribute information entered by the user into the device (age, gender, place of residence, hobbies, etc.).

[1630] Processing: The device receives the attribute information entered by the user and sends it to the server. The server receives the data sent in JSON format using an HTTP POST request.

[1631] Output: JSON data of attribute information is stored on the server.

[1632] Step 2: Analyze the data

[1633] Input: User attribute information stored on the server (JSON format).

[1634] Processing: The server uses Python scripts to analyze the received data and generate a user profile (age, gender, location, hobbies, personality traits, abilities, etc.) using classification algorithms and clustering techniques.

[1635] Output: A user profile (in JSON format) is generated.

[1636] Step 3: Occupation suggestions by generative AI

[1637] Input: Server-generated user profile (JSON format).

[1638] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4), and the generative AI suggests occupations based on the user's characteristics and interests. For example, the server sends the following prompt to the generative AI: "User profile: Age 14, Gender: Male, Residence: Tokyo, Hobbies: Programming, Drawing. Please suggest three occupations suitable for this user."

[1639] Output: A list of occupations (text format) is generated.

[1640] Step 4: Collect and submit details

[1641] Input: A list of occupations (text format) generated by the generation AI.

[1642] Processing: The server uses an SQL query to retrieve detailed information about the selected occupation (job description, required skills, salary estimate, etc.) from the database. Example: SELECT FROM careers WHERE job_title='Game Developer';

[1643] Output: The list of occupations and their details (in JSON format) will be sent to the terminal.

[1644] Step 5: Building a scenario for the job experience game

[1645] Input: Details of the user's chosen occupation (JSON format).

[1646] Processing: The server generates a scenario for a job-experience game using a Python script based on the example of a "game developer" and sends it to the device in JSON format. The scenario includes tasks such as character design and project management.

[1647] Output: Job experience game scenario (JSON format).

[1648] Step 6: Emotion Recognition with the Emotion Engine

[1649] Input: User facial, voice, and text input data.

[1650] Processing: The device analyzes the user's emotional data in real time using an emotion engine (e.g., Affectiva API) and sends it to the server. The data includes emotional states such as "happy=70%, sad=10%, neutral=20%."

[1651] Output: Emotion data (JSON format).

[1652] Step 7: Data storage and training

[1653] Input: Job experience game results and sentiment data (JSON format).

[1654] Processing: The server stores the analysis results in a database (e.g., MongoDB or MySQL). The stored data is used to continuously train the generative AI algorithm.

[1655] Output: An updated database and a trained generative AI model.

[1656] Step 8: Feedback and next steps

[1657] Input: Job experience game results and sentiment data (JSON format).

[1658] Processing: The server uses the generative AI model to suggest the next suitable job for the user and generates feedback. For example, the generative AI might create a sentence such as, "Since you were successful in the game developer scenario, try project manager next."

[1659] Output: Feedback and next career suggestion information provided to the user.

[1660] (Application example 2)

[1661] 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."

[1662] Conventional work experience systems do not adequately suggest appropriate jobs based on the user's attribute information. Furthermore, it is difficult to interactively experience work experiences in real time and obtain feedback. Furthermore, there is a lack of systems that can recognize the user's emotional state in real time and provide appropriate feedback based on that information.

[1663] The identification processing by the identification 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 receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for providing the scenario for the job experience game to smart glasses or a tablet in a physical store, means for collecting and analyzing user emotion data using an emotion recognition engine, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This makes it possible to provide accurate job suggestions based on the user's attribute information, interactive job experiences that reflect the user's emotional state in real time, and appropriate feedback.

[1664] The "means for receiving user attribute information" is a function by which a server or terminal receives information such as age, sex, place of residence, and hobbies entered by a user.

[1665] The "means for generating a user profile" is a function that analyzes the received attribute information and creates a detailed profile including the user's personality traits and abilities.

[1666] The "means for creating a list of appropriate occupations using a generation AI" is a function in which the generation AI generates a list of occupations suitable for the user based on the generated user profile.

[1667] "Means for collecting detailed information and sending it to the terminal" refers to a function that collects detailed information about the selected occupation, such as the job content, required skills, rewards, difficulties, and salary estimates, and sends it to the user's terminal.

[1668] The "means for creating and transmitting a scenario for a job experience game" is a function for creating a scenario for a job experience game based on a job selected by the user and transmitting the scenario to the terminal.

[1669] "Means for providing to smart glasses or tablets in a physical store" refers to a function for providing users with a scenario of a job experience game through smart glasses or tablets installed in a physical store.

[1670] "Means for collecting and analyzing user emotional data using an emotion recognition engine" refers to a function that analyzes the user's facial expressions and voice using an emotion recognition engine to collect and analyze emotional data.

[1671] The "means for analyzing the results of the job experience game and providing feedback to the user" is a function for analyzing the results of playing the job experience game and providing feedback to the user such as strengths and areas for improvement.

[1672] The "means of making new career suggestions" is a function that allows the generation AI to make new career suggestions based on the results of the previous career experience game and emotional data.

[1673] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on a child's attribute information and provide an interactive career experience game in a physical store. This system receives and analyzes the user's attribute information and generates a user profile, then uses the generative AI to create a list of appropriate careers and sends the details and the scenario of the career experience game to the device. The system also provides the user with a career experience through smart glasses or a tablet, and uses the emotion engine to collect and analyze the user's emotional data and provide feedback.

[1674] Hardware and software used

[1675] The following hardware and software are used to realize this system.

[1676] Smart glasses (e.g. Google Glass)

[1677] Tablet (e.g. iPad)

[1678] Server (e.g. AWS EC2)

[1679] Database (e.g. MySQL)

[1680] Generative AI (e.g. OpenAI GPT-4)

[1681] Emotion recognition engine (e.g. Affectiva SDK)

[1682] Data processing and calculation process

[1683] The server receives the user's attribute information. The user uses a tablet or smart glasses to input information such as age, gender, place of residence, and hobbies. This data is sent to the server, where it is analyzed. A user profile is generated based on the analysis results. This profile includes the user's personality traits and abilities.

[1684] The generative AI (OpenAI GPT-4) creates a list of suitable occupations based on the user profile. For example, if the user is 14 years old and their hobbies are programming and drawing, the generative AI will suggest occupations such as "game developer" and "digital artist." Examples of prompts for this generative AI are as follows:

[1685] Example prompt sentence:

[1686] "I'm a 14-year-old kid whose hobbies are programming and drawing. What kind of job would be suitable for me?"

[1687] The server then collects detailed information about the selected job from a database and sends it to the tablet or smart glasses. Based on the user's selected job, the server creates a scenario for a job experience game and provides the scenario through the smart glasses or tablet in the physical store.

[1688] The emotion recognition engine (Affectiva SDK) analyzes the user's facial expressions and voice in real time to collect emotional data. This data is sent to the server and analyzed along with the results of the job-experience game. Based on the analysis results, feedback is provided to the user. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[1689] Finally, the generative AI will suggest new careers based on the analysis results. In this way, the system helps users find the career that best suits them and experience it interactively in real time.

[1690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1691] Step 1:

[1692] Users use tablets or smart glasses to input attribute information such as age, gender, place of residence, and hobbies, and this input data is sent to the server by the device.

[1693] Input: User attribute information (age, gender, place of residence, hobbies)

[1694] Output: Sending attribute information to the server

[1695] Step 2:

[1696] The server analyzes the received attribute information and generates a user profile by comparing it with past data stored in a database.

[1697] Input: Received attribute information

[1698] Data processing: Comparison with past data stored in the database and analysis

[1699] Output: Generated user profile

[1700] Step 3:

[1701] The server uses a generation AI (OpenAI GPT-4) to create a list of suitable occupations based on the generated user profile. At this time, the server inputs a prompt sentence into the generation AI and receives occupation suggestions.

[1702] Input: User profile, prompt

[1703] Data Computation: Creating Occupation Lists with Generative AI

[1704] Output: List of suitable occupations

[1705] Example prompt: "I'm a 14-year-old kid whose hobbies are programming and drawing. What career would be suitable for me?"

[1706] Step 4:

[1707] The server collects detailed information (job content, required skills, rewarding work, difficulty, expected salary, etc.) about the occupation selected by the user from the generated occupation list from a database and sends it to the terminal.

[1708] Input: User-selected occupation, occupation list

[1709] Data processing: Collect detailed information from the database

[1710] Output: Send detailed job information

[1711] Step 5:

[1712] The server creates an interactive job experience game scenario based on the user's chosen occupation and sends it to smart glasses or tablets in the physical store.

[1713] Input: User-selected occupation, detailed information

[1714] Data calculation: Scenario construction for a job experience game

[1715] Output: Interactive job experience game scenario submission

[1716] Step 6:

[1717] Using the emotion recognition engine (Affectiva SDK), smart glasses or tablets analyze the user's facial expressions and voice in real time to collect emotional data, which is then sent from the device to a server.

[1718] Input: User's facial expression and voice data

[1719] Data processing: Analysis using emotion recognition engine

[1720] Output: Collect emotion data and send it to the server

[1721] Step 7:

[1722] The server analyzes the results of the job experience game and the collected emotional data, stores the results in a database, and provides feedback to the user based on this.

[1723] Input: Job experience game results, emotion data

[1724] Data Computing: Analyzing Outcome and Sentiment Data

[1725] Output: Generating and providing feedback

[1726] Step 8:

[1727] The server then uses the generative AI to suggest new occupations based on the analysis results. The user can then select an occupation from the list of suggested occupations and continue the interactive occupation experience.

[1728] Input: Analysis results, emotion data

[1729] Data Computation: New Career Proposals with Generative AI

[1730] Output: Generate a new list of occupations and repeat each step

[1731] 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.

[1732] 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.

[1733] 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.

[1734] [Fourth embodiment]

[1735] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1736] 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.

[1737] 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).

[1738] 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.

[1739] 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.

[1740] 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).

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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."

[1748] This invention is a system that uses a generative AI to suggest careers based on children's attribute information and provides a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[1749] Server Processing

[1750] Receiving user data

[1751] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[1752] Data analysis

[1753] The received attribute information is analyzed to generate a user profile, which includes information such as personality traits and abilities.

[1754] Career suggestions by generative AI

[1755] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[1756] Collecting and sending details

[1757] Detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimate, etc.) is collected and sent to the terminal.

[1758] Scenario construction for a job experience game

[1759] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[1760] Data storage and training

[1761] The results of the job experience game are analyzed and the user's actions and choices are stored in a database, which is used to continuously train the generative AI algorithm.

[1762] Feedback and next steps

[1763] Based on the results of the job experience game, the system provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[1764] Terminal handling

[1765] Providing a user interface

[1766] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1767] Sending data

[1768] The attribute information entered by the user is sent to the server.

[1769] Receiving and displaying job offers

[1770] The job list and its detailed information sent from the server are received and displayed to the user, who can then select the job that interests them.

[1771] Providing an interface for a job experience game

[1772] The interface displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1773] User operations

[1774] Entering attribute information

[1775] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[1776] Review and select career suggestions

[1777] Check the list of occupations and detailed information displayed on your device and select the occupation that interests you.

[1778] Playing a job experience game

[1779] Participants can play a job experience game provided on their device and simulate specific work tasks. Through this interactive game, participants can experience the rewards and challenges of the job.

[1780] Specific examples

[1781] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[1782] In this way, the present invention helps children reduce anxiety about future career choices and helps them find the right career path.

[1783] The processing flow will be explained below.

[1784] Server Processing Steps

[1785] Step 1:

[1786] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[1787] Step 2:

[1788] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[1789] Step 3:

[1790] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[1791] Step 4:

[1792] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[1793] Step 5:

[1794] The server sends the collected detailed information to the terminal.

[1795] Step 6:

[1796] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[1797] Step 7:

[1798] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[1799] Step 8:

[1800] The server receives the results of the job experience game and analyzes the user's actions and choices.

[1801] Step 9:

[1802] The server provides feedback to the user based on the analysis results and suggests more suitable occupations.

[1803] Terminal processing steps

[1804] Step 1:

[1805] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1806] Step 2:

[1807] The terminal transmits the attribute information input by the user to the server.

[1808] Step 3:

[1809] The terminal displays the occupation list and detailed information received from the server, and provides the user with options.

[1810] Step 4:

[1811] The terminal again transmits information about the occupation selected by the user to the server.

[1812] Step 5:

[1813] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[1814] Step 6:

[1815] The terminal records the choices and actions that the user makes while playing the job experience game and transmits them to the server sequentially.

[1816] User operation steps

[1817] Step 1:

[1818] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[1819] Step 2:

[1820] The user checks the occupation list and detailed information displayed on the terminal.

[1821] Step 3:

[1822] The user selects an occupation that interests them on the terminal.

[1823] Step 4:

[1824] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[1825] Specific examples

[1826] Example of User A

[1827] Step 1:

[1828] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[1829] Step 2:

[1830] The terminal transmits the input information to the server.

[1831] Step 3:

[1832] The server analyzes the attribute information of user A and generates a user profile.

[1833] Step 4:

[1834] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[1835] Step 5:

[1836] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[1837] Step 6:

[1838] User A selects "Game Developer."

[1839] Step 7:

[1840] The terminal transmits the selected occupation information to the server.

[1841] Step 8:

[1842] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[1843] Step 9:

[1844] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[1845] Step 10:

[1846] User A simulates character design and project management tasks.

[1847] Step 11:

[1848] The server receives the game results and analyzes User A's actions and choices.

[1849] Step 12:

[1850] The server provides feedback and next career suggestions to User A.

[1851] Example 1

[1852] 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."

[1853] Conventional career guidance systems do not adequately analyze users' attribute information or make career suggestions, making it difficult to suggest optimal careers based on individual characteristics and interests. Furthermore, they lack specific understanding through work experience, and there are insufficient means for users to fully experience the type of work they will actually be doing. Furthermore, there is also an insufficient mechanism for reflecting users' experience and behavioral data in the next career suggestion.

[1854] 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.

[1855] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the results of the job experience game and providing feedback to the user, means for making new job suggestions based on the analysis results, and means for storing the received user behavior data in a database and providing it for training by the generation AI. This enables optimal job suggestions based on the user's attribute information, making it possible to understand specific jobs through job experiences and continuously improve the accuracy of job suggestions.

[1856] "User attribute information" refers to personal information that a user inputs into a terminal, such as the user's age, gender, place of residence, and hobbies.

[1857] "Analysis" is the act of processing received information to extract and generate useful data.

[1858] A "user profile" is a summary of information including the user's personality traits and abilities, generated based on analyzed user attribute information.

[1859] "Generative AI" is an artificial intelligence technology that suggests suitable careers based on a user's characteristics and interests.

[1860] An "occupation list" is a list of multiple occupations suitable for a user, created by the generation AI based on the user profile.

[1861] "Detailed information" refers to information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[1862] A "job experience game" is an interactive game that simulates real-life work tasks based on a user's chosen job.

[1863] "Scenario construction" refers to the design and creation of content that recreates specific work tasks, project progress, and problem-solving scenes related to the user's chosen occupation.

[1864] "Feedback" refers to advice and information provided to users based on the results of the job experience game, such as strengths, areas for improvement, and suggestions for the next job to try.

[1865] A "database" is a system or mechanism for storing and managing collected data.

[1866] The present invention is a system that uses a generative AI model to suggest careers based on a child's attribute information and provides a career experience game. The embodiments of the present invention are described in detail below.

[1867] Server Processing

[1868] Receiving user data

[1869] The server receives the user's attribute information sent from the device. This attribute information includes age, gender, place of residence, hobbies, etc. Specifically, the server validates the received JSON format data.

[1870] Data analysis

[1871] The server uses the Python pandas library to analyze the received attribute information. Based on the analyzed data, it generates a user profile, which includes information such as personality traits and abilities.

[1872] Career suggestions by generative AI

[1873] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[1874] Collecting and sending details

[1875] The server extracts detailed information about the proposed job from a MySQL database and sends it to the device in JSON format, including details such as the job description, required skills, rewards, challenges, and salary estimates.

[1876] Scenario construction for a job experience game

[1877] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of daily work, project execution, and problem-solving.

[1878] Data storage and training

[1879] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used for continuous training of the generative AI model.

[1880] Feedback and next steps

[1881] The server provides feedback to the user based on the results of the job experience game, including suggestions for new jobs to try next, as well as the user's strengths and areas for improvement.

[1882] Terminal handling

[1883] Providing a user interface

[1884] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1885] Sending data

[1886] The terminal converts the attribute information entered by the user into JSON format and sends it to the server using the HTTPS protocol.

[1887] Receiving and displaying job offers

[1888] The terminal receives the occupation list and its detailed information sent from the server and displays it to the user, who can then select an occupation that interests them.

[1889] Providing an interface for a job experience game

[1890] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1891] User operations

[1892] Entering attribute information

[1893] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[1894] Review and select career suggestions

[1895] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[1896] Playing a job experience game

[1897] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[1898] Specific examples

[1899] For example, let's say User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into his or her device, which then sends the information to the server. The server receives the information, analyzes it, and generates a user profile. The generation AI then suggests occupations such as "game developer" or "digital artist" based on the profile. The server collects detailed information about the suggested occupations and sends it to the device. User A selects "game developer" and receives a scenario for a job experience game built by the server using Unity. User A simulates character design and project management tasks on his or her device, experiencing the specific content of the work, as well as the rewards and difficulties involved. The server analyzes the game results, provides feedback to User A, and suggests more suitable occupations.

[1900] The system helps children reduce anxiety about future career choices and find the right career path.

[1901] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1902] Step 1:

[1903] Enter attribute information (user)

[1904] The user enters attribute information such as age, gender, place of residence, and hobbies into the device. For example, if the user is 14 years old and enters "programming" and "drawing" as his or her hobbies, the input data is acquired through a form. This form contains input fields, and data is collected as the user enters each item.

[1905] Input: User attribute information (age, gender, place of residence, hobbies, etc.)

[1906] Output: Input attribute information (JSON format)

[1907] Step 2:

[1908] Data transmission (terminal)

[1909] The device sends the attribute information entered by the user to the server as an HTTP POST request. At this time, the attribute information is converted to JSON format. The device uses the HTTPS protocol to send the data securely.

[1910] Input: User attribute information (JSON format)

[1911] Output: Attribute information sent to the server

[1912] Step 3:

[1913] Receiving attribute information (server)

[1914] The server receives the user's attribute information sent from the terminal, performs format validation on the received data, and identifies invalid data input.

[1915] Input: Attribute information sent from the device (JSON format)

[1916] Output: Attribute information that passes validation

[1917] Step 4:

[1918] Data analysis (server)

[1919] The server uses the Python pandas library to analyze the received attribute information and generates a user profile based on the analyzed data. This profile summarizes information such as the user's personality traits and abilities.

[1920] Input: Attribute information that has passed validation

[1921] Output: User profile

[1922] Step 5:

[1923] Occupation suggestions by generative AI (server)

[1924] Based on the generated user profile, the server inputs a prompt into a generative AI model (e.g., OpenAI's GPT-3) to generate a list of suitable occupations. The prompt is in the form of "What occupation would be suitable for a 14-year-old user whose hobbies are programming and drawing?"

[1925] Input: User profile

[1926] Output: List of suitable occupations

[1927] Step 6:

[1928] Collecting and sending detailed information (server)

[1929] The server extracts detailed information about the proposed jobs from a MySQL database and sends it to the device in JSON format, including job description, required skills, rewards, challenges, salary estimates, etc.

[1930] Input: List of suitable occupations

[1931] Output: Detailed information in JSON format

[1932] Step 7:

[1933] Job experience game scenario construction (server)

[1934] The server uses Unity or Unreal Engine to build experiential scenarios for the proposed jobs, including scenes of the job's daily tasks, project progress, and problem-solving.

[1935] Input: Details of selected occupation

[1936] Output: The constructed work experience game scenario

[1937] Step 8:

[1938] Receive and display job suggestions and detailed information (terminal)

[1939] The terminal receives the list of occupations and detailed information sent from the server and displays them to the user. The user selects the occupations they are interested in from the suggested occupations. The interface includes a selection button.

[1940] Input: Occupation list and detailed information sent from the server

[1941] Output: The information displayed to the user and the selected occupation

[1942] Step 9:

[1943] Job experience game implementation (user)

[1944] The user launches an interactive job experience game provided on the device and simulates work tasks based on a scenario, such as character design or project management.

[1945] Input: A constructed work experience game scenario

[1946] Output: The results of the work task experienced by the user

[1947] Step 10:

[1948] Analysis and storage of game results (server)

[1949] The results of the job experience game are analyzed and user behavior data is stored in a database, which is used to continuously train the generative AI model.

[1950] Input: The results of the work task experienced by the user

[1951] Output: Saved behavioral data

[1952] Step 11:

[1953] Feedback and New Suggestions (Server)

[1954] The server provides feedback to the user based on the game results, including suggestions for new professions, highlighting the user's strengths and areas for improvement, and suggesting the next profession to try.

[1955] Input: Stored behavioral data

[1956] Output: Feedback and new career suggestions provided to the user

[1957] (Application example 1)

[1958] 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."

[1959] Conventional work experience systems do not adequately help children find the most suitable occupation based on their own attribute information. Furthermore, because the work experience lacks realism and interactivity, it is difficult for children to grasp the appeal and challenges of actual work. Therefore, there is a need for a system that can reduce children's anxiety about choosing a future occupation and help them find the right career path.

[1960] 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.

[1961] In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for allowing the user to interactively play the job experience game in a virtual environment, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the results of the analysis. This enables children to find the most suitable occupation based on their own attribute information and actually experience that occupation in a virtual environment.

[1962] "User attribute information" refers to personal characteristics such as the user's age, sex, place of residence, and hobbies.

[1963] "User profile" refers to data that indicates the characteristics and abilities of a user, generated based on the received attribute information.

[1964] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and suggestions.

[1965] "Suitable Occupation List" means a list of occupations suitable for the user suggested by the generative AI based on the user's profile.

[1966] "Detailed occupational information" refers to specific information about a particular occupation, such as the job content, required skills, rewards, difficulties, and salary estimates.

[1967] A "career experience game scenario" refers to a story that includes work tasks, project progress, problem-solving scenes, etc., constructed to simulate the career chosen by the user.

[1968] A "virtual environment" refers to a virtual space generated using computer technology in which users can have interactive experiences.

[1969] "Results of the job experience game" refers to data such as the results, selection history, and actions that the user has achieved through the job experience game.

[1970] "Feedback" refers to the evaluation and advice provided to the user after analyzing the results of the job experience game.

[1971] "New career suggestions" means that the generative AI will use feedback to suggest new careers for the user to try next.

[1972] This invention is a system that utilizes generative AI to suggest careers based on children's attribute information and provides a linked career experience game. The specific program processing flow and implementation method of the system are explained in detail below.

[1973] Server Processing

[1974] Receiving user data

[1975] The server receives attribute information entered by the user into the terminal, such as age, gender, place of residence, hobbies, etc. Any structured data format is used for this information.

[1976] Data analysis

[1977] The server analyzes the received attribute information and generates a user profile, which includes information such as personality traits and abilities, and the AI ​​operates based on this.

[1978] Career suggestions by generative AI

[1979] The server uses a generative AI based on the generated user profile to create a list of suitable occupations. The generative AI uses a large dataset and machine learning models to suggest occupations that match the user's characteristics and interests.

[1980] Collecting and sending details

[1981] The server collects detailed information about the occupation selected from the occupation list (job content, required skills, rewards, difficulty, salary estimates, etc.) and sends it to the terminal.

[1982] Scenario construction for a job experience game

[1983] Based on the user's chosen occupation, the server creates a scenario for the job-experience game that simulates real-life work tasks, including scenes from daily work, project management, and problem-solving.

[1984] Providing a virtual environment

[1985] The server allows users to interactively experience the job experience game in a virtual environment, which is provided through devices such as smartphones and head-mounted displays.

[1986] Data storage and training

[1987] The server analyzes the results of the job experience game and stores the user's actions and choices in a database. The stored data is used to train the generative AI algorithm.

[1988] Feedback and next steps

[1989] Based on the results of the job experience game, the server provides feedback to the user and suggests new jobs. The feedback includes the user's strengths and areas for improvement, as well as suggestions for the next job to try.

[1990] Terminal handling

[1991] Providing a user interface

[1992] The terminal provides an interface for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[1993] Sending data

[1994] The attribute information entered by the user is sent from the terminal to the server.

[1995] Receiving and displaying job offers

[1996] The terminal receives the list of occupations and their detailed information sent from the server and displays them to the user, who can then select the occupations that interest them.

[1997] Providing an interface for a job experience game

[1998] The terminal displays the scenario of the job experience game received from the server, allowing the user to interactively experience the game. The interface includes work tasks and simulations.

[1999] User operations

[2000] Entering attribute information

[2001] The user inputs attribute information such as age, sex, place of residence, and hobbies into the terminal.

[2002] Review and select career suggestions

[2003] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them.

[2004] Playing a job experience game

[2005] Users can play the job experience game provided on the device and simulate specific work tasks. Through the interactive game, they can experience the rewards and challenges of the job.

[2006] Specific examples

[2007] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his or her attribute information into a device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job experience game to the device. User A simulates character design and project management tasks on the device, experiencing the specifics of the work, its rewards, and its challenges. The server then analyzes the results of the experience, provides feedback to User A, and suggests more suitable occupations.

[2008] An example prompt might be something like, "The user is 14 years old and his hobbies are programming and drawing."

[2009] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2010] Step 1:

[2011] The server receives attribute information entered by the user into the terminal. Attribute information includes age, gender, place of residence, hobbies, etc. The server receives this information and temporarily stores it in a database (input: attribute information, output: stored attribute data).

[2012] Step 2:

[2013] The server analyzes the received attribute information and generates a user profile. The analysis process involves evaluating the user's personality and characteristics using a machine learning model (input: attribute information, output: user profile).

[2014] Step 3:

[2015] The server uses a generation AI to create a list of suitable occupations based on the generated user profile. The generation AI refers to a large dataset and the synthesized user profile to list suitable occupations (input: user profile, output: occupation list).

[2016] Step 4:

[2017] The server collects detailed information about the occupation selected by the user from the occupation list and sends it to the terminal. The detailed information includes the job description, required skills, rewards, difficulty, expected salary, etc. (Input: Occupation selected by the user, Output: Occupation details information).

[2018] Step 5:

[2019] The server constructs a scenario for the work experience game based on the user's selected occupation. This scenario includes scenes of daily work tasks, project progress, and problem solving (input: selected occupation, output: work experience game scenario).

[2020] Step 6:

[2021] The terminal displays the scenario of the job experience game received from the server, and the user plays the interactive job experience game through the terminal (input: job experience game scenario, output: interactive game interface).

[2022] Step 7:

[2023] The server analyzes the results of the job experience game and provides feedback to the user. The analysis is based on the user's behavioral history and selection results (input: job experience game results, output: feedback data).

[2024] Step 8:

[2025] The server uses a generative AI to suggest new occupations based on the feedback, which then suggests the next occupation for the user to try (input: feedback data, output: new occupation suggestion list).

[2026] 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.

[2027] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. Below, the flow of the system's program processing is explained in detail in natural language.

[2028] Server Processing

[2029] Receiving user data

[2030] The server receives attribute information entered by the user into the terminal, including age, gender, place of residence, and hobbies.

[2031] Data analysis

[2032] The received attribute information is analyzed to generate a user profile, which includes the user's personality traits and abilities.

[2033] Career suggestions by generative AI

[2034] Based on the generated user profile, the AI ​​creates a list of suitable occupations, suggesting multiple occupations based on the user's characteristics and interests.

[2035] Collecting and sending details

[2036] Detailed information about the occupation selected from the list of occupations (job content, required skills, rewards, difficulty, salary estimates, etc.) is collected from the database and sent to the terminal.

[2037] Scenario construction for a job experience game

[2038] Based on the user's chosen occupation, we create scenarios for a work experience game that simulates real-life work tasks, including scenes from daily work, project progress, and problem-solving.

[2039] Emotion recognition by emotion engine

[2040] The server uses an emotion engine to recognize the user's emotions, detecting them from data such as the user's facial expressions, voice, and text input, and using this information for analysis.

[2041] Data storage and training

[2042] The results of the job-experience game are analyzed, and the user's actions, choices, and emotional state are stored in a database. The stored data is used to continuously train the generative AI algorithm.

[2043] Feedback and next steps

[2044] Based on the results of the job experience game and the user's emotional state, the system provides feedback to the user and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[2045] Terminal handling

[2046] Providing a user interface

[2047] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[2048] Sending data

[2049] The attribute information entered by the user is sent to the server.

[2050] Receiving and displaying job offers

[2051] The occupation list and detailed information sent from the server are displayed, and the user is given the option to select one.

[2052] Accepting user selections

[2053] The information about the occupation selected by the user is sent again to the server.

[2054] Providing an interface for a job experience game

[2055] The scenario of the job experience game received from the server is displayed, allowing the user to interactively experience it.

[2056] User operations

[2057] Entering attribute information

[2058] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[2059] Review and select career suggestions

[2060] Check the list of occupations and detailed information displayed on the device, and select the occupation that interests you on the device.

[2061] Playing a job experience game

[2062] They play the job experience games provided on the device and perform work tasks and simulations.

[2063] Providing emotion data

[2064] While playing, the device collects emotional data such as the user's facial expressions, voice, and text input, and sends it to the server.

[2065] Specific examples

[2066] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which the server receives and analyzes to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[2067] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[2068] The processing flow will be explained below.

[2069] Server Processing Steps

[2070] Step 1:

[2071] The server receives the user's attribute information sent from the device, including age, gender, place of residence, hobbies, etc.

[2072] Step 2:

[2073] The server analyzes the received attribute information and generates a user profile, which includes the user's personality traits and abilities.

[2074] Step 3:

[2075] The server uses a generative AI to create a list of suitable occupations based on the user profile, which suggests multiple occupations based on the user's characteristics and interests.

[2076] Step 4:

[2077] The server collects detailed information from a database about the job the user selects from the list of suggested jobs, including the job description, required skills, rewards, challenges, and expected salary.

[2078] Step 5:

[2079] The server sends the collected detailed information to the terminal.

[2080] Step 6:

[2081] The server creates a scenario for the job experience game based on the user's chosen job, including real-life work tasks and situations.

[2082] Step 7:

[2083] The server transmits the constructed scenario to the terminal, allowing the user to play the job experience game.

[2084] Step 8:

[2085] The server receives the results of the job experience game as well as the user's emotional data (facial expressions, voice, text input, etc.) sent from the terminal.

[2086] Step 9:

[2087] The server analyzes the results of the job experience game and the emotional data, and evaluates the user's actions and choices.

[2088] Step 10:

[2089] The server provides feedback to the user based on the analysis results and suggests careers that are more suitable for them. The feedback includes advice based on the user's strengths, areas for improvement, and emotional changes.

[2090] Terminal processing steps

[2091] Step 1:

[2092] The terminal displays a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[2093] Step 2:

[2094] The terminal transmits the attribute information input by the user to the server.

[2095] Step 3:

[2096] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[2097] Step 4:

[2098] The terminal again transmits information about the occupation selected by the user to the server.

[2099] Step 5:

[2100] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[2101] Step 6:

[2102] The device records the choices and actions the user makes while playing the job experience game, and simultaneously collects the user's emotional data (facial expressions, voice, text input, etc.) and transmits it to the server.

[2103] User operation steps

[2104] Step 1:

[2105] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[2106] Step 2:

[2107] The user checks the occupation list and detailed information displayed on the terminal.

[2108] Step 3:

[2109] The user selects an occupation that interests them on the terminal.

[2110] Step 4:

[2111] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[2112] Step 5:

[2113] The user's emotion data is collected by the terminal and transmitted to the server.

[2114] Specific examples

[2115] Example of User A

[2116] Step 1:

[2117] User A enters into the terminal that he is 14 years old and his hobbies are programming and drawing.

[2118] Step 2:

[2119] The terminal transmits the input information to the server.

[2120] Step 3:

[2121] The server analyzes the attribute information of user A and generates a user profile.

[2122] Step 4:

[2123] Using generative AI, create a list of occupations suitable for user A (e.g., "game developer" or "digital artist").

[2124] Step 5:

[2125] The server sends the occupation list and detailed information to the terminal and displays it to User A.

[2126] Step 6:

[2127] User A selects "Game Developer."

[2128] Step 7:

[2129] The terminal transmits the selected occupation information to the server.

[2130] Step 8:

[2131] The server creates scenarios for game developers (such as character design and project management tasks) and sends them to the terminal.

[2132] Step 9:

[2133] The terminal displays the scenario of the job experience game to User A and allows him to play the game.

[2134] Step 10:

[2135] While User A simulates character design and project management tasks, the device collects User A's facial expressions and voice data, analyzes them with an emotion engine, and sends the results to the server.

[2136] Step 11:

[2137] The server receives the game results and emotional data and analyzes User A's behavior and choices.

[2138] Step 12:

[2139] The server provides feedback to User A based on the analysis results and suggests the next occupation to try. For example, if User A is evaluated as having high concentration in addition to technical ability, the server will suggest a new occupation of "software engineer."

[2140] In this way, the invention uses a multifaceted approach that combines emotion recognition and generative AI to help children find the career that best suits them.

[2141] Example 2

[2142] 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."

[2143] Conventional career suggestion systems primarily suggest careers based on the user's attribute information, but do not take the user's emotional state into consideration. Furthermore, the scenarios for career experience games are monotonous and do not reflect the user's characteristics, interests, or emotions. This makes it difficult for users to effectively find a career that suits them. The objective of this invention is to provide a system that takes the user's emotional state into consideration, improves the accuracy of career suggestions, and provides an interactive career experience game.

[2144] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for analyzing the emotion data, means for analyzing the results and emotion data of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This enables highly accurate occupation suggestions that take the user's emotional state into consideration, allowing the user to effectively find an occupation that suits them and providing a realistic work experience through an interactive job experience game.

[2145] "User attribute information" is data about an individual, including the user's age, gender, place of residence, hobbies, and the like.

[2146] A "user profile" is data that summarizes the user's personality traits and abilities by analyzing the received user attribute information.

[2147] "Generative AI" is an artificial intelligence algorithm that uses a user's profile and other data to generate a list of jobs.

[2148] An "occupation list" is a list of occupations suitable for a user, created by the generation AI based on the user's profile.

[2149] "Detailed information" refers to information including the job content, required skills, rewarding aspects, difficulty, salary estimates, etc. of the occupations included in the occupation list.

[2150] A "career experience game" is an interactive game that includes simulated work tasks and scenes based on a user-selected career.

[2151] "Emotion data" is data that indicates the emotional state of the user detected from facial expressions, voice, text input, and the like.

[2152] "Feedback" refers to information about improvements and advice provided to the user based on the results of the job experience game and emotional data.

[2153] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on children's attribute information and provide a career experience game. The program processing of the system is explained in detail below.

[2154] Server Processing

[2155] Receiving user data

[2156] The server receives attribute information entered by the user on the device, such as age, gender, place of residence, hobbies, etc. This data is sent in JSON format using an HTTP POST request.

[2157] Data analysis

[2158] The server analyzes the received attribute information and generates a user profile using programming languages ​​such as Python and R. Based on the user's hobbies and characteristics, the server evaluates and records the user's personality traits and abilities.

[2159] Career suggestions by generative AI

[2160] The server uses a generative AI (e.g., GPT-4) to create a list of suitable occupations based on the generated user profile. By inputting a prompt sentence, the server suggests occupations suitable for the user.

[2161] Collecting and sending details

[2162] The server collects detailed information about the occupation selected from the occupation list from the database and sends it to the terminal, for example, using an SQL query to obtain the necessary occupational information (job description, required skills, rewards, difficulties, expected salary, etc.).

[2163] Scenario construction for a job experience game

[2164] The server creates and transmits a scenario for the job experience game based on the job selected by the user. This scenario includes scenes of daily work, project progress, and problem solving so that the user can actually experience the job.

[2165] Emotion recognition by emotion engine

[2166] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. It analyzes data such as the user's facial expressions, voice, and text input in real time and stores the user's emotional state in a database.

[2167] Data storage and training

[2168] The server analyzes the results of the job-experience game and stores the user's actions, choices, and emotional state in a database, which is used to continuously train the generative AI algorithm.

[2169] Feedback and next steps

[2170] The server provides feedback to the user based on the results of the job experience game and their emotional state, and suggests new jobs. The feedback includes advice based on the user's strengths, areas for improvement, and changes in their emotions.

[2171] Terminal handling

[2172] Providing a user interface

[2173] The terminal provides a form for the user to input attribute information, including input fields for age, gender, place of residence, hobbies, etc.

[2174] Sending data

[2175] The terminal transmits the attribute information input by the user to the server.

[2176] Receiving and displaying job offers

[2177] The terminal displays the occupation list and detailed information sent from the server, and offers the user a choice.

[2178] Accepting user selections

[2179] The terminal again transmits information about the occupation selected by the user to the server.

[2180] Providing an interface for a job experience game

[2181] The terminal displays the scenario of the job experience game received from the server, allowing the user to experience it interactively.

[2182] User operations

[2183] Entering attribute information

[2184] The user inputs his / her own attribute information (age, gender, place of residence, hobbies, etc.) into the terminal.

[2185] Review and select career suggestions

[2186] The user checks the list of occupations and detailed information displayed on the terminal and selects an occupation that interests them on the terminal.

[2187] Playing a job experience game

[2188] The user plays the job experience game provided on the terminal and performs work tasks and simulations.

[2189] Providing emotion data

[2190] While playing, users' emotional data, such as facial expressions, voice, and text input, is collected by the terminal and transmitted to the server.

[2191] Specific examples

[2192] For example, suppose User A is 14 years old and his hobbies are programming and drawing. User A enters his attribute information into his device, which is then received and analyzed by the server to generate a user profile. The generation AI suggests occupations such as "game developer" and "digital artist" to User A. If User A selects "game developer," the server sends the details and a scenario for a job-experience game to his device. User A simulates character design and project management tasks on his device, and while playing, the device collects User A's facial expressions and voice data. Based on this, the emotion engine recognizes his emotions and sends them to the server. The server analyzes the game results and emotion data, provides feedback to User A, and suggests more suitable occupations.

[2193] Prompt Sentence Examples

[2194] Below are some example prompts to input to a generative AI model (e.g., GPT-4):

[2195] User profile: Age 14, Gender male, Residence Tokyo, Hobbies programming, drawing

[2196] Please suggest three suitable jobs for this user. For each job, please also tell us the required skills, job content, rewards and difficulties, and salary estimates.

[2197] In this way, the present invention uses a multifaceted approach, including emotion recognition, to help children find the career that best suits them.

[2198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2199] Step 1: Receiving user data

[2200] Input: Attribute information entered by the user into the device (age, gender, place of residence, hobbies, etc.).

[2201] Processing: The device receives the attribute information entered by the user and sends it to the server. The server receives the data sent in JSON format using an HTTP POST request.

[2202] Output: JSON data of attribute information is stored on the server.

[2203] Step 2: Analyze the data

[2204] Input: User attribute information stored on the server (JSON format).

[2205] Processing: The server uses Python scripts to analyze the received data and generate a user profile (age, gender, location, hobbies, personality traits, abilities, etc.) using classification algorithms and clustering techniques.

[2206] Output: A user profile (in JSON format) is generated.

[2207] Step 3: Occupation suggestions by generative AI

[2208] Input: Server-generated user profile (JSON format).

[2209] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4), and the generative AI suggests occupations based on the user's characteristics and interests. For example, the server sends the following prompt to the generative AI: "User profile: Age 14, Gender: Male, Residence: Tokyo, Hobbies: Programming, Drawing. Please suggest three occupations suitable for this user."

[2210] Output: A list of occupations (text format) is generated.

[2211] Step 4: Collect and submit details

[2212] Input: A list of occupations (text format) generated by the generation AI.

[2213] Processing: The server uses an SQL query to retrieve detailed information about the selected occupation (job description, required skills, salary estimate, etc.) from the database. Example: SELECT FROM careers WHERE job_title='Game Developer';

[2214] Output: The list of occupations and their details (in JSON format) will be sent to the terminal.

[2215] Step 5: Building a scenario for the job experience game

[2216] Input: Details of the user's chosen occupation (JSON format).

[2217] Processing: The server generates a scenario for a job-experience game using a Python script based on the example of a "game developer" and sends it to the device in JSON format. The scenario includes tasks such as character design and project management.

[2218] Output: Job experience game scenario (JSON format).

[2219] Step 6: Emotion Recognition with the Emotion Engine

[2220] Input: User facial, voice, and text input data.

[2221] Processing: The device analyzes the user's emotional data in real time using an emotion engine (e.g., Affectiva API) and sends it to the server. The data includes emotional states such as "happy=70%, sad=10%, neutral=20%."

[2222] Output: Emotion data (JSON format).

[2223] Step 7: Data storage and training

[2224] Input: Job experience game results and sentiment data (JSON format).

[2225] Processing: The server stores the analysis results in a database (e.g., MongoDB or MySQL). The stored data is used to continuously train the generative AI algorithm.

[2226] Output: An updated database and a trained generative AI model.

[2227] Step 8: Feedback and next steps

[2228] Input: Job experience game results and sentiment data (JSON format).

[2229] Processing: The server uses the generative AI model to suggest the next suitable job for the user and generates feedback. For example, the generative AI might create a sentence such as, "Since you were successful in the game developer scenario, try project manager next."

[2230] Output: Feedback and next career suggestion information provided to the user.

[2231] (Application example 2)

[2232] 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."

[2233] Conventional work experience systems do not adequately suggest appropriate jobs based on the user's attribute information. Furthermore, it is difficult to interactively experience work experiences in real time and obtain feedback. Furthermore, there is a lack of systems that can recognize the user's emotional state in real time and provide appropriate feedback based on that information.

[2234] The identification processing by the identification 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 receiving user attribute information, means for analyzing the received attribute information and generating a user profile, means for creating a list of appropriate occupations using a generation AI based on the generated user profile, means for collecting detailed information about occupations selected by the user from the occupation list and transmitting it to the terminal, means for constructing and transmitting a scenario for a job experience game based on the occupation selected by the user, means for providing the scenario for the job experience game to smart glasses or a tablet in a physical store, means for collecting and analyzing user emotion data using an emotion recognition engine, means for analyzing the results of the job experience game and providing feedback to the user, and means for suggesting new occupations based on the analysis results. This makes it possible to provide accurate job suggestions based on the user's attribute information, interactive job experiences that reflect the user's emotional state in real time, and appropriate feedback.

[2235] The "means for receiving user attribute information" is a function by which a server or terminal receives information such as age, sex, place of residence, and hobbies entered by a user.

[2236] The "means for generating a user profile" is a function that analyzes the received attribute information and creates a detailed profile including the user's personality traits and abilities.

[2237] The "means for creating a list of appropriate occupations using a generation AI" is a function in which the generation AI generates a list of occupations suitable for the user based on the generated user profile.

[2238] "Means for collecting detailed information and sending it to the terminal" refers to a function that collects detailed information about the selected occupation, such as the job content, required skills, rewards, difficulties, and salary estimates, and sends it to the user's terminal.

[2239] The "means for creating and transmitting a scenario for a job experience game" is a function for creating a scenario for a job experience game based on a job selected by the user and transmitting the scenario to the terminal.

[2240] "Means for providing to smart glasses or tablets in a physical store" refers to a function for providing users with a scenario of a job experience game through smart glasses or tablets installed in a physical store.

[2241] "Means for collecting and analyzing user emotional data using an emotion recognition engine" refers to a function that analyzes the user's facial expressions and voice using an emotion recognition engine to collect and analyze emotional data.

[2242] The "means for analyzing the results of the job experience game and providing feedback to the user" is a function for analyzing the results of playing the job experience game and providing feedback to the user such as strengths and areas for improvement.

[2243] The "means of making new career suggestions" is a function that allows the generation AI to make new career suggestions based on the results of the previous career experience game and emotional data.

[2244] This invention is a system that combines a generative AI and an emotion engine to suggest careers based on a child's attribute information and provide an interactive career experience game in a physical store. This system receives and analyzes the user's attribute information and generates a user profile, then uses the generative AI to create a list of appropriate careers and sends the details and the scenario of the career experience game to the device. The system also provides the user with a career experience through smart glasses or a tablet, and uses the emotion engine to collect and analyze the user's emotional data and provide feedback.

[2245] Hardware and software used

[2246] The following hardware and software are used to realize this system.

[2247] Smart glasses (e.g. Google Glass)

[2248] Tablet (e.g. iPad)

[2249] Server (e.g. AWS EC2)

[2250] Database (e.g. MySQL)

[2251] Generative AI (e.g. OpenAI GPT-4)

[2252] Emotion recognition engine (e.g. Affectiva SDK)

[2253] Data processing and calculation process

[2254] The server receives the user's attribute information. The user uses a tablet or smart glasses to input information such as age, gender, place of residence, and hobbies. This data is sent to the server, where it is analyzed. A user profile is generated based on the analysis results. This profile includes the user's personality traits and abilities.

[2255] The generative AI (OpenAI GPT-4) creates a list of suitable occupations based on the user profile. For example, if the user is 14 years old and their hobbies are programming and drawing, the generative AI will suggest occupations such as "game developer" and "digital artist." Examples of prompts for this generative AI are as follows:

[2256] Example prompt sentence:

[2257] "I'm a 14-year-old kid whose hobbies are programming and drawing. What kind of job would be suitable for me?"

[2258] The server then collects detailed information about the selected job from a database and sends it to the tablet or smart glasses. Based on the user's selected job, the server creates a scenario for a job experience game and provides the scenario through the smart glasses or tablet in the physical store.

[2259] The emotion recognition engine (Affectiva SDK) analyzes the user's facial expressions and voice in real time to collect emotional data. This data is sent to the server and analyzed along with the results of the job-experience game. Based on the analysis results, feedback is provided to the user. The feedback includes advice based on the user's strengths, areas for im...

Claims

1. means for receiving user attribute information; means for analyzing the received attribute information and generating a user profile; A means for generating an appropriate occupation list using a generation AI based on the generated user profile; means for collecting detailed information about an occupation selected by the user from the occupation list and transmitting the collected information to a terminal; A means for constructing and transmitting a scenario for a job experience game based on a job selected by a user; means for analyzing the results of the job experience game and providing feedback to the user; means for making new career suggestions based on the analysis results; A system including:

2. 2. The system of claim 1, wherein the generating AI includes means for continuously learning the analyzed user profile to improve the accuracy of the career suggestions.

3. 2. The system according to claim 1, wherein the terminal includes means for providing an interface for inputting user attribute information, displaying received career suggestions and detailed information, and providing an interface for allowing the user to select a career.

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