System

Abstract

A system is provided.SOLUTION: A system comprising: means for displaying a screen for a user to answer a question; means for collecting answers from the user and transmitting the answers to a server; means for the server receiving the answers from the user and analyzing an optimal occupation by comparing the answers with a database of past successful persons using a generative AI model; means for the server storing an analysis result in a user profile and transmitting the analysis result to a client; and means for the client displaying the transmitted analysis 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] The problem this invention aims to solve is the difficulty individuals have in finding the occupation that best suits their abilities and aptitudes. In particular, there is a lack of systems that help young men and women aged 15 to 30 find the occupation that best suits them. Conventional career aptitude tests do not fully utilize data from past successful people and lack a method for statistically determining an individual's aptitude. As a result, people often miss opportunities to find the right occupation. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes: a means for displaying a screen for users to answer questions; a means for collecting user response data and transmitting the data to a server; a means for the server to receive the user response data and compare it with a database of past successful people using a generative AI model to analyze the most suitable occupation; a means for the server to save the analysis results in the user's profile and transmit them to a terminal; and a means for the terminal to display the transmitted analysis results. The system also includes a means for the server to format the user's response data and convert it into a format suitable for input into the generative AI model, and a means for displaying options for users to obtain more information about occupations in which they are interested. This system allows users to statistically identify the occupation that best suits them, improving the reliability of their occupational choices.

[0006] "User" refers to an individual who uses the system, and in particular, a person who is attempting to diagnose career aptitude.

[0007] "Question" refers to a series of questions designed to identify a user's characteristics and preferences.

[0008] "Answer data" refers to information selected or entered by a user in response to a question.

[0009] "Server" refers to a computer system that receives user response data, analyzes it using a generative AI model, and sends the results to the user.

[0010] A "generative AI model" refers to an artificial intelligence algorithm that uses a database of past successful people to compare the user's response data and analyze the most suitable occupation.

[0011] The "database of past successes" refers to a collection of data that stores data on people who have been successful in various professions and that the generative AI model can use for analysis.

[0012] "Analysis results" refers to suitable occupational candidates that the generative AI model statistically derives based on the user's response data.

[0013] A "profile" is a digital folder that aggregates and stores information about a user.

[0014] "Terminal" refers to the device (e.g., smartphone, tablet, PC) used by a User to access the System.

[0015] "Appropriate occupation" refers to the occupation that the generative AI model determines to be optimal based on the user's characteristics and preferences.

[0016] "Options" refers to additional choices or features that allow a user to obtain more information about a career that interests them. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system for analyzing an individual's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, and a means for the server to receive and analyze the user's answer data and present the most suitable occupation. The system also includes a means for the server to send the analysis results to a terminal, which then displays them.

[0039] The system works as follows:

[0040] 1. User Initiation

[0041] The user launches the application and is presented with a login screen.

[0042] The user logs in with an existing account or creates a new account.

[0043] 2. Start of vocational aptitude test

[0044] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device.

[0045] 3. Answering questions

[0046] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0047] 4. Processing the invisible parts

[0048] The terminal temporarily stores the user's answers and transmits the answer data to the server when all questions have been answered.

[0049] 5. Server-side analysis

[0050] The server receives the user's response data and formats the data.

[0051] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable occupation.

[0052] For example, if answer 1 is 'yes' and answer 2 is 'no', then the appropriate occupation for this person is 'project manager'.

[0053] 6. Submitting the results

[0054] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0055] 7. Displaying the results

[0056] The device displays the analysis results to the user, presenting a list of occupations that are suitable for the user.

[0057] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[0058] Specific examples

[0059] For example, a user might use an app to take a career aptitude test. The user first presses the "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0060] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0061] Finally, the server saves the analysis results in the user's profile and sends them to the device. The device displays the results, allowing the user to review them. Furthermore, if the user is interested in "project manager," an option to obtain more information about that occupation is displayed. In this way, the present invention helps users find the occupation that best suits them.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user launches the application and is presented with a login screen. The user logs in with an existing account or creates a new account. The server checks the authentication information and displays the dashboard screen on the device.

[0065] Step 2:

[0066] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0067] Step 3:

[0068] The user answers a question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0069] Step 4:

[0070] The device temporarily stores the answer and displays the next question, and this process is repeated for all questions.

[0071] Step 5:

[0072] After the device has answered all the questions, it sends the answer data to the server in JSON format, for example as follows:

[0073] json

[0074] {

[0075] "user_id": "12345",

[0076] "responses": [

[0077] {"question_id": "1", "answer": "yes"},

[0078] {"question_id": "2", "answer": "no"},

[0079] ...

[0080] ]

[0081] }

[0082] Step 6:

[0083] The server receives the user's response data and performs a data processing process, converting the processed data into a format that can be input into the generative AI model.

[0084] Step 7:

[0085] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data with a database of past successful users.

[0086] Step 8:

[0087] The generative AI model uses statistical algorithms to analyze the best career aptitudes and generate a list of the resulting jobs. For example, it might generate the following list:

[0088] json

[0089] {

[0090] "recommended_jobs": [

[0091] "Project Manager",

[0092] "consultant",

[0093] "Marketing Manager"

[0094] ]

[0095] }

[0096] Step 9:

[0097] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[0098] Step 10:

[0099] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[0100] Step 11:

[0101] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[0102] The above are the specific processing steps of this system, and the overall flow for users to find the job that best suits them.

[0103] Example 1

[0104] 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."

[0105] There is a need for a system that can quickly and accurately determine an individual's career aptitude. Existing methods require manual analysis of a large number of user responses, which takes a long time to produce results and is inaccurate in recommending suitable careers. Furthermore, they lack a means to obtain additional details about the careers that interest the user.

[0106] 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.

[0107] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and analyze the most suitable occupation by comparing it with a database of past successful people using a generative AI model, means for the server to save the analysis results in the user's profile and transmit them to the terminal, and means for the terminal to display the transmitted analysis results. This makes it possible to quickly and accurately determine an individual's occupational aptitude and further obtain detailed information about occupations in which the user is interested.

[0108] A "user" is an individual who uses the system to take a career aptitude test.

[0109] A "server" is a computer system that receives user response data, analyzes it using a generative AI model, and provides the results to the user.

[0110] A "terminal" is a device that a user uses to take a career aptitude test and that provides an interface for displaying the analysis results.

[0111] "Means for displaying a screen" refers to a function for displaying an interface on a terminal for the user to answer questions.

[0112] "Means for collecting data" refers to the function of recording and temporarily saving answers entered by users to questions.

[0113] "Means for transmitting data" refers to a communication function for transmitting the user's response data to the server.

[0114] A "generative AI model" is a type of artificial intelligence technology used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[0115] A "database" is a collection of information that stores information about successful people of the past.

[0116] "Means for analysis" refers to the function of using a generative AI model to analyze user response data and identify the most suitable occupation.

[0117] A "profile" is a collection of information where data related to a user is stored and where analysis results are stored.

[0118] "Display means" refers to a function that displays the analysis results sent from the server on the user's terminal.

[0119] "Means for obtaining detailed information" refers to an interface that provides further information about the occupation in which the user is interested.

[0120] This invention relates to a system for analyzing an individual's career aptitude. The system operates through a series of processes that display a screen for the user to answer questions, collect the user's answer data, and send it to a server. The following hardware and software are used to implement this system.

[0121] Hardware and software used

[0122] Hardware

[0123] Devices: smartphones, tablets, computers

[0124] Server: Cloud server (e.g. AWS, Google Cloud)

[0125] software

[0126] Application: iOS app, Android app or web application

[0127] Generative AI model: GPT (e.g., OpenAI's GPT-4)

[0128] Natural language processing explanation

[0129] 1. User Initiation

[0130] A user launches an application and is presented with a login screen, where they can log in with an existing account or create a new account.

[0131] 2. Start of vocational aptitude test

[0132] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device. Each question is typically answered with a "yes" or "no."

[0133] 3. Answering questions

[0134] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no." The device temporarily stores the answers.

[0135] 4. Data transmission

[0136] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[0137] 5. Data Analysis

[0138] The server formats the received data and converts it into a format that can be input into the generative AI model. The generative AI model then compares it with a database of past successful people and analyzes the most suitable occupation for that person. For example, if answer 1 is 'yes' and answer 2 is 'no', it determines that the appropriate occupation for this person is 'project manager'.

[0139] 6. Submitting the results

[0140] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0141] 7. Displaying the results

[0142] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[0143] Specific examples

[0144] For example, a user might use an app to take a career aptitude test. The user presses a "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0145] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0146] Finally, the server saves the analysis results in the user's profile and sends them to the device, where they are displayed and the user can review the results. Furthermore, if the user is interested in "project manager," they are given the option to get more information about that occupation.

[0147] Example prompts for generative AI models

[0148] The user clicked the "Start Career Aptitude Test" button and answered the questions. The user answered as follows: "Question 1: Yes, Question 2: No, Question 3: Yes." Please recommend the best career for them based on this.

[0149] This invention allows users to quickly and accurately find the occupation that best suits them, and also allows users to easily obtain detailed information about the occupation.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Step 1:

[0152] The user launches the application. The device displays a login screen, and the user can log in with an existing account or create a new account.

[0153] input:

[0154] User account information (email address, password).

[0155] output:

[0156] Authentication success or failure message for the user.

[0157] Specific behavior:

[0158] The user taps the application to launch it, enters their email address and password, and presses the "Login" button. If creating a new account, they enter the required information and press the "Register" button.

[0159] Step 2:

[0160] The user presses the "Start Career Aptitude Test" button. The device displays the career aptitude test question screen.

[0161] input:

[0162] User action (clicking the "Start Career Aptitude Test" button).

[0163] output:

[0164] Occupational aptitude test question screen.

[0165] Specific behavior:

[0166] Click the "Start Career Aptitude Test" button on the home screen, and the screen will display buttons to move to the next question (such as "Next" or "Answer").

[0167] Step 3:

[0168] The user answers each question, and the device temporarily stores the answers.

[0169] input:

[0170] The user's answer (for example, "yes" or "no").

[0171] output:

[0172] Temporarily stored response data.

[0173] Specific behavior:

[0174] For example, to the question "Do you like working in a team?", select "Yes" or "No" and click the "Next" button. The next question will be displayed.

[0175] Step 4:

[0176] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[0177] input:

[0178] Temporarily stored response data.

[0179] output:

[0180] The response data sent to the server.

[0181] Specific behavior:

[0182] After answering the last question, click the "Done" button. The device formats and sends the answer data. The server receives the data and returns a confirmation message to the device.

[0183] Step 5:

[0184] The server formats the received data and converts it into a format that can be input into a generative AI model, which then compares it with a database of past successful people to analyze the most suitable occupation.

[0185] input:

[0186] The response data sent to the server.

[0187] output:

[0188] Analyzed vocational aptitude results.

[0189] Specific behavior:

[0190] The generative AI model is fed the collected user response data along with the prompt text. The AI ​​model analyzes the data and recommends roles such as "project manager," "consultant," and "marketing manager."

[0191] Step 6:

[0192] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0193] input:

[0194] Analyzed vocational aptitude results.

[0195] output:

[0196] Analysis results stored in the user's profile and data sent to the device.

[0197] Specific behavior:

[0198] The server saves the analysis results in a database, and sends an API call to the device to receive the results once the analysis is complete.

[0199] Step 7:

[0200] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[0201] input:

[0202] Analysis results sent to the device.

[0203] output:

[0204] Career aptitude results and detailed information displayed to the user.

[0205] Specific behavior:

[0206] On the results screen, you will see a list of recommended jobs. Click on each job's icon or description to view more information.

[0207] (Application example 1)

[0208] 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."

[0209] Conventional career aptitude analysis systems lack interactive counseling for users and are unable to provide sufficient information for career selection. They also lack a means to effectively receive career aptitude analysis from remote locations. Furthermore, it is difficult to obtain detailed information about careers that users are interested in in real time.

[0210] 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.

[0211] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for interactive career aptitude counseling by a counselor in the virtual realm, and means for displaying options for the user to obtain detailed information about occupations they are interested in. This allows the user to receive an interactive career aptitude analysis even from home and obtain detailed information in real time.

[0212] "User" refers to an individual who takes a career aptitude test and uses the results to find a suitable career.

[0213] The term "means for displaying a screen" refers to a device or program that visually presents the interface and questions necessary for the user to answer the questions.

[0214] "Means for collecting response data" refers to a device or program that compiles the responses provided by users as data and stores them in a form that can be used later.

[0215] "Means for transmitting to a server" refers to a device or program for transmitting collected response data to a remote server via a communication network such as the Internet.

[0216] "Generative AI model" refers to an artificial intelligence model, a type of machine learning, that is used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[0217] The "database of past successes" is a database that collects data on individuals who have passed career aptitude tests in the past, and is used as a basis for determining the career aptitude of new users.

[0218] A "profile" is a database that compiles information about a user, and stores analysis results, the user's response history, and other information.

[0219] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet, which displays the analysis results sent from the server.

[0220] "Virtual realm" refers to a virtual reality space that a user accesses through a device such as a head-mounted display, in which interactive career aptitude counseling takes place.

[0221] "Counselor" refers to an avatar or actual person whose role is to provide career aptitude counseling to users within the virtual reality space.

[0222] "Interactive career aptitude counseling" refers to an interactive dialogue in which users answer questions and interact with counselors in real time to analyze career aptitude and find the most suitable career.

[0223] "Options for obtaining more information" refers to settings and functions that allow users to check more detailed information about occupations that interest them, allowing users to learn more about the details of the occupation and the skills required.

[0224] This invention is a system used by users to analyze their career aptitude, and its specific configuration and operation are described below. This system includes a terminal for users to take a career aptitude test, a server that analyzes the user's response data, and a head-mounted display and counselor for providing interactive career counseling within a virtual realm.

[0225] First, a user answers a career aptitude test using a device equipped with a screen display. The answers to the questions are collected on the device, and the collected data is sent to a server. The server formats the answers and converts them into a format that can be input into a generative AI model. This generative AI model compares the answers with a database of past successful candidates and analyzes the most suitable career.

[0226] The analysis results are stored in the user's profile and sent from the server to the device, where they are displayed. The user can then wear a head-mounted display and enter the virtual space, where they can interact with a virtual counselor. The counseling session also provides the user with the option to obtain detailed information about careers they are interested in in real time.

[0227] Hardware and software used

[0228] Hardware:

[0229] 1. Head-mounted displays (e.g., Oculus Rift, HTC Vive)

[0230] 2. Server (e.g. AWS EC2)

[0231] 3. Computers, smartphones, tablets, and other devices

[0232] software:

[0233] 1. Flask: A Python-based web server framework

[0234] 2. Gensim: A framework for Word2Vec generative AI models

[0235] 3. JSON: Data exchange format between client and server

[0236] Details of the process

[0237] 1. User startup:

[0238] The user uses the device to access the career aptitude test screen and answer questions such as "Are you good at planning?" and "Do you enjoy creative work?"

[0239] 2. Sending and analyzing response data:

[0240] The device sends the collected response data to a server, which formats the received data and inputs it into a generative AI model. The generative AI model compares the data with a database of past successful people and analyzes suitable occupations.

[0241] 3. Save and display results:

[0242] The server saves the analysis results in the user's profile and sends them to the user's device, which displays them to the user.

[0243] 4. Virtual Counseling:

[0244] The user puts on a head-mounted display and enters a virtual realm, where a virtual counselor appears and interacts with the user, asking follow-up questions and providing more detailed career information.

[0245] Specific examples

[0246] For example, if a user answers "yes" or "no" to a question, the generative AI model will use these answers to analyze whether occupations such as "marketing manager" or "project manager" are suitable. If the user enters the virtual space and interacts with a counselor, expressing interest in "project manager," they can obtain new detailed information about that occupation.

[0247] Prompt Sentence Examples

[0248] Identify the best occupation for you based on this user's response data. The user's responses are "I'm good at planning" and "I like creative work."

[0249] The system allows users to receive an interactive career aptitude analysis from the comfort of their own home, and enjoy effective counseling to obtain detailed information in real time.

[0250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0251] Step 1:

[0252] The user uses a terminal to display the screen of a career aptitude test. The user answers the questions. The input is the user's answer, and the output is the answer data temporarily saved on the terminal. Specifically, the user answers "Yes" or "No" to the question "Are you good at making plans?"

[0253] Step 2:

[0254] The device collects the user's response data and sends it to the server. The input is the temporarily saved user response data, and the output is the data sent to the server. A specific example of this behavior is when the device sends the response data to the server in JSON format.

[0255] Step 3:

[0256] The server formats the received user response data and converts it into a format that can be input to the generative AI model. The input is the received user response data, and the output is the formatted data. Specifically, the server converts the response data into a vector and passes it to the generative AI model.

[0257] Step 4:

[0258] The server uses a generative AI model to compare the response data with a database of past successful people and analyze the most suitable occupation. The input is the formatted data and a database of past successful people, and the output is the analysis results. A specific operation is the process in which the generative AI model determines that "the occupation suitable for this user is 'project manager'."

[0259] Step 5:

[0260] The server saves the analysis results in the user's profile and sends them to the device. The input is the analysis results and the user's profile information, and the output is the analysis results saved in the user's profile and the results sent to the device. Specifically, the server saves the analysis results in a database and sends them to the device in JSON format.

[0261] Step 6:

[0262] The terminal displays the analysis results sent to the user. The input is the analysis results sent from the server, and the output is the results displayed on the terminal. Specifically, the message "The job suitable for you is 'Project Manager'" is displayed on the user's screen.

[0263] Step 7:

[0264] The user wears a head-mounted display, enters the virtual space, and interacts with the counselor. The input is the analysis results and the user's selection, and the output is the start of virtual counseling. Specifically, the user becomes interested in the "project manager" and requests more information in the virtual space.

[0265] Step 8:

[0266] In the virtual realm, a counselor provides detailed career information to the user. The input is the user's selection and the counselor's questions, and the output is the detailed career information provided. Specifically, the counselor explains the role of a "project manager" and the skills required.

[0267] Through the above steps, users can receive an interactive career aptitude analysis from home and enjoy effective counseling to obtain detailed information in real time.

[0268] 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.

[0269] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting and sending the user's answer data to a server, a means for the server to receive the user's answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results.

[0270] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[0271] Specific operation of the system

[0272] The operation of the system is as follows.

[0273] 1. User Initiation

[0274] The user launches the application and is presented with a login screen. They can log in with an existing account or create a new one. The server checks the authentication information and displays the dashboard screen on the device.

[0275] 2. Start of vocational aptitude test

[0276] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0277] 3. Emotion Recognition by Emotion Engine

[0278] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions a user makes when answering questions can be classified as "happiness," "anger," "sadness," etc.

[0279] 4. Answering questions and collecting sentiment data

[0280] The user answers a question, for example, "Do you like working in a team?", and selects "yes" or "no." At the same time, the emotion engine collects the user's emotional data at that time.

[0281] 5. Sending data to the server

[0282] The device temporarily stores the answer data and emotion data, and once all questions have been answered, it sends this data to the server. The data is structured as follows:

[0283] json

[0284] {

[0285] "user_id": "12345",

[0286] "responses": [

[0287] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[0288] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[0289] ...

[0290] ]

[0291] }

[0292] 6. Server-side analysis

[0293] The server receives the user's response data and emotion data and formats the data.

[0294] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable career.

[0295] For example, if answer 1 is 'yes' and emotion is 'happy', then the suitable occupation for this person is 'project manager'.

[0296] 7. Sending and displaying results

[0297] The server stores the generated analysis results in the user's profile and sends them to the device.

[0298] The device displays the analysis results to the user, who can then review the results and select the occupations that interest them.

[0299] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[0300] The above is a specific embodiment for carrying out the present invention. This system allows users to find the most suitable occupation for themselves with high accuracy.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] When a user launches the application, the login screen is displayed. The user logs in with an existing account or creates a new account. The server checks the user's authentication information, and if authentication is successful, the dashboard screen is displayed on the device.

[0304] Step 2:

[0305] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0306] Step 3:

[0307] The device will activate the emotion engine and begin collecting and analyzing the user's facial expressions and voice in real time. Specifically, the user will be required to enable the camera and microphone.

[0308] Step 4:

[0309] The user answers a question. For example, "Do you like working in a team?" and selects "yes" or "no." During this time, the emotion engine recognizes emotional data (such as "happiness," "sadness," or "surprise") from the user's facial expressions and voice.

[0310] Step 5:

[0311] The device temporarily stores the user's response and emotional data. For example, the following data is stored:

[0312] json

[0313] {

[0314] "question_id": "1",

[0315] "answer": "yes",

[0316] "emotion": "happy"

[0317] }

[0318] Step 6:

[0319] After the device has completed collecting all the answers to the questions and emotional data, it sends this data in JSON format to the server.

[0320] Step 7:

[0321] The server receives the user's response data and emotion data, formats the data, and converts it into a format that can be input into the generative AI model.

[0322] Step 8:

[0323] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data and emotional data with a database of past successful users.

[0324] Step 9:

[0325] The generative AI model uses statistical algorithms to analyze the most suitable occupations and generate a list of the results. For example, the AI ​​model might determine that "if answer 1 is 'yes' and emotion is 'happy', then the appropriate occupation for this person is 'project manager'."

[0326] Step 10:

[0327] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[0328] Step 11:

[0329] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[0330] Step 12:

[0331] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[0332] The above are the specific processing steps of this system and the overall flow for users to find the occupation that best suits them. By adding an emotion engine, it is possible to reflect the user's emotional state and make more accurate occupational aptitude diagnosis.

[0333] Example 2

[0334] 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."

[0335] Conventional occupational aptitude diagnosis systems analyzed occupations based solely on user response data, which meant that they were unable to reflect the user's emotions and psychological state, resulting in a decrease in the accuracy of the aptitude diagnosis.In addition, the fixed order and content of questions sometimes made it difficult for users to operate the system.

[0336] 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 displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and compare it with a database of past successful people using a generative AI model to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for the terminal to analyze the user's facial expressions and voice in real time and recognize emotions, means for adding emotion data to the question answer data, and means for dynamically changing the content and order of questions based on the emotion recognition results. This enables more accurate occupational aptitude diagnosis that reflects the user's emotions.

[0337] The "screen for answering questions" refers to an interface that allows the user to input answers to questions that are displayed.

[0338] "Answer data" refers to data describing the answer entered by the user to the question.

[0339] A "generative AI model" refers to a mathematical model that uses artificial intelligence to perform analysis and prediction.

[0340] A "database of past successful people" refers to a database that accumulates data on successful people from the past.

[0341] "Analysis results" refers to the results of the server analyzing the user's response data using the generated AI model.

[0342] A "profile" refers to data that compiles information about a user.

[0343] "Terminal" refers to an electronic device that is used by a user for operation.

[0344] "Real-time analysis of facial expressions and voice" refers to the device instantly analyzing the user's facial expressions and voice.

[0345] "Emotion recognition" refers to the process of identifying emotions from a user's facial expressions and voice.

[0346] "Emotion data" refers to data related to the user's emotions obtained through emotion recognition.

[0347] "Dynamic change of the content and order of questions" refers to changing the order and content of questions in real time based on the user's emotions.

[0348] "Means for displaying options" refers to an interface that displays options for a user to obtain more information about a job that interests them.

[0349] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's career aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, a means for the server to receive the user's answer data and compare it with a database of past successful candidates using a generative AI model to analyze the most suitable career, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results. Furthermore, the emotion engine allows the system to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[0350] The following hardware and software are used to implement this system.

[0351] First, when a user launches an application, a device such as a smartphone or tablet is used. When the user logs in, the server verifies the authentication information and displays the dashboard screen on the device. The main software used at this stage includes a web server and a database management system.

[0352] Next, when the user starts the career aptitude test, the device displays the first question screen. The question includes specific content such as "Do you like working in a team?" As the user enters their answer, the device analyzes their facial expressions and voice in real time, and an emotion engine recognizes their emotions. The emotion analysis software used here may be technology such as OpenFace or IBM Watson.

[0353] Once all questions have been answered, the device sends the collected answer data and emotion data to the server. The data is structured as follows, for example:

[0354] Response data:

[0355] Question 1: "Yes", Emotion: "Joy"

[0356] Question 2: "No", Sentiment: "Neutral"

[0357] When the server receives this data, it formats it and converts it into a format that can be input to a generative AI model, such as GPT-3 or BERT. Analysis can be performed using prompts like the following:

[0358] Example prompt:

[0359] Receive the user's answer data in the following format and compare it with a database of past successes to determine the most suitable occupation. Answer data: Question 1: "Yes", Emotion: "Joy" Question 2: "No", Emotion: "Neutral"

[0360] The generative AI model performs the analysis and generates results identifying the most suitable occupation, such as a suggested occupation title like "Project Manager." The server stores these results in the user's profile and sends them to the device, where the user can review the results and receive options for further information.

[0361] This system allows users to find the occupation that best suits them with greater accuracy. The introduction of an emotion engine enables aptitude assessment that reflects the user's psychological state more than conventional systems, providing more reliable diagnostic results.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] When a user starts the application, a login screen is displayed. The user can log in with an existing account or create a new account. The input is the user's ID and password, and the output is the authentication result. The server checks the authentication information, and if authentication is successful, it sends dashboard data to the terminal. Specifically, the terminal displays the login screen, accepts the user's input, and sends it to the server. The server then compares it with a database to perform authentication.

[0365] Step 2:

[0366] When the user presses the "Start Occupational Aptitude Test" button, the terminal displays the first question screen. The input is the user's button operation, and the output is the first question screen. Specifically, the terminal detects the click event of the test start button, obtains the first question data of the aptitude test, and displays it.

[0367] Step 3:

[0368] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. The input is the user's facial expression and voice data, and the output is the recognized emotion data. Specifically, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed in real time by the emotion engine.

[0369] Step 4:

[0370] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." The input is the user's answer and real-time recognized emotion data, and the output is a set of answer data and emotion data. Specifically, the device records the answer and saves it together with the emotion data obtained from the emotion engine.

[0371] Step 5:

[0372] The device temporarily stores answer data and emotion data, and once all questions have been answered, it sends this data to the server. The input is the stored answer data and emotion data, and the output is a data package sent to the server. Specifically, the answer and emotion data are converted into JSON format and sent to the server.

[0373] Step 6:

[0374] The server receives the user's response data and emotion data and formats the data. The input is the submitted response data and emotion data, and the output is the formatted data. The server then inputs the formatted data into a generative AI model for analysis. The input is the formatted data, and the output is the analysis result (the best occupation). The server uses the generative AI model to compare the user's data with a database of past successful people and determine the best occupation.

[0375] Step 7:

[0376] The server saves the generated analysis results in the user's profile and sends them to the device. The input is the analysis results, and the output is the data sent to the device. Specifically, the device displays the results and, if the user selects an occupation they are interested in, displays options to provide more detailed information and related information.

[0377] The above are the specific processing steps of the program for this system. This flow allows users to receive a highly accurate career aptitude diagnosis that takes their own emotions into consideration.

[0378] (Application example 2)

[0379] 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."

[0380] Conventional occupational aptitude analysis systems analyze optimal occupations based on user response data, but they have the problem of low aptitude accuracy because they cannot view the user's emotions or work aptitude. Another issue is that it is difficult to dynamically assign tasks that reflect the emotions and aptitudes of workers at work sites such as factories. This invention incorporates user emotion data into occupational aptitude analysis to improve the accuracy of aptitude diagnosis and to optimize the work content and assignments of factory workers based on their emotions and aptitudes.

[0381] The identification process 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 shaping user response data and converting it into a format for input to the generative AI model, means for displaying a screen on which the user can answer questions, and means for collecting user response data and transmitting the data to the server. This allows user emotional data to be incorporated into occupational aptitude diagnosis, making it possible to determine optimal work content and placement based on the emotions and aptitudes of factory workers.

[0382] "User" refers to an individual or group that uses the System.

[0383] A "question answer screen" refers to an interface that allows a user to enter an answer to a particular question.

[0384] "Answer data" refers to information entered by a user in response to a question.

[0385] "Server" refers to a computing device that analyzes collected data and uses generative AI models to determine optimal occupations and tasks.

[0386] A "generative AI model" refers to an artificial intelligence algorithm that predicts and suggests optimal occupations and work content based on user response data and emotional data.

[0387] A "database of past successes" refers to a database based on individuals and cases of past success.

[0388] "Analysis" refers to the process of evaluating suitability based on data collected by the server and calculating the most suitable occupation and work content based on that.

[0389] "Profile" refers to a collection of personal data that stores information about a user.

[0390] "Terminal" refers to a device that receives and displays the analysis results sent from the server, including smartphones, computers, tablets, etc.

[0391] "Factory workers" refers to individual workers who perform work in a factory.

[0392] "Emotion" refers to the mental state a user expresses in response to a particular situation or question, such as "happiness," "sadness," or "stress."

[0393] "Aptitude" refers to the ability or aptitude a user has for a particular occupation or task.

[0394] "Optimization" refers to determining the most efficient and appropriate work content and placement based on collected data.

[0395] The present invention is a system that analyzes users' occupational aptitude with higher accuracy and realizes optimal work content and placement based on the emotions and aptitudes of workers in a factory. The system involves a process in which users answer questions, collect and analyze data based on the answers, and display the final results.

[0396] The system includes the following hardware and software:

[0397] A device that displays a screen for answering the user's questions: a smartphone, tablet, computer, etc.

[0398] Means for collecting and sending data to a server: a communication module with an internet connection.

[0399] Server that analyzes data using generative AI models: High-performance computing environment (e.g., cloud server).

[0400] External libraries for emotion recognition: Libraries for facial recognition and speech analysis (e.g. emotion_recognition, ai_model).

[0401] The operation of the system is as follows.

[0402] When a user starts the career aptitude test, questions are displayed on the device. As the user answers the questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time and adds the results to the answer data. The collected data, including the user's emotional data, is sent to the server. When the server receives the data, it formats it and inputs it into a generative AI model, which then compares it with a database of past successful candidates to analyze the most suitable career.

[0403] The system also collects data on the emotions and aptitudes of factory workers regarding specific tasks. Questions about the work content and aptitude are displayed to the workers, and their answers and emotional data are sent to the server. The server then analyzes the workers' response data and emotional data to determine the optimal work content and placement.

[0404] For example, if a worker feels "joy" in "assembly work" and is judged to be "good at" it, the frequency with which he or she is in charge of that work will be increased. On the other hand, if another worker feels "stress" in "inspection work" and is judged to be "unskilled" at it, the worker's work will be changed.

[0405] Here are some examples of prompts to input to a generative AI model:

[0406] Analyze the worker data below and suggest the best tasks for them.

[0407] Worker ID: worker_001

[0408] Response data:

[0409] 1. Task: assembly, Answer: yes, Emotion: happy

[0410] 2. Task: Inspection, Answer: no, Emotion: stress

[0411] 3. Task: Packing, Answer: yes, Emotion: neutral

[0412] The system allows users to more accurately find the jobs that best suit them, and enables dynamic work placement within factories based on workers' emotions and aptitudes.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The user starts a career aptitude test. The user launches the application and logs in with an existing account or creates a new account on the login screen. The device checks the authentication information and displays the dashboard screen. The input is the user's authentication information, and the output is the dashboard screen.

[0416] Step 2:

[0417] The user presses the "Start Career Aptitude Test" button. The terminal displays the first question screen and provides an interface for the user to answer the questions. The input is pressing the button, and the output is the first question screen.

[0418] Step 3:

[0419] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions of the user when answering questions can be classified as "happiness" or "sadness." The input is the user's video and audio data, and the output is emotion data.

[0420] Step 4:

[0421] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." At the same time, the emotion engine collects the user's emotion data at that time. The input is the user's answer and emotion data, and the output is a pair of answer data and emotion data.

[0422] Step 5:

[0423] The device temporarily stores the answer data and emotion data, and sends them to the server once all questions have been answered. The data consists of the following:

[0424] {

[0425] "user_id": "12345",

[0426] "responses": [

[0427] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[0428] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[0429] ...

[0430] ]

[0431] }

[0432] The input is response data and emotion data, and the output is data to be sent to the server.

[0433] Step 6:

[0434] The server receives the user's response data and emotion data and formats the data. The input is the transmitted data, and the output is the formatted data.

[0435] Step 7:

[0436] The server uses a generative AI model to compare the user's data with a database of past successful people to determine the most suitable occupation. For example, if answer 1 is 'yes' and emotion is 'happy', it determines that the appropriate occupation for this person is 'project manager'. The input is the formatted data, and the output is the analysis result.

[0437] Step 8:

[0438] The server saves the generated analysis results in the user's profile and sends them to the terminal. The input is the analysis results, and the output is the data saved in the profile.

[0439] Step 9:

[0440] The terminal displays the analysis results to the user. The user can check the results and select the occupation they are interested in. The input is profile data, and the output is display data.

[0441] Step 10:

[0442] A similar process is performed for factory workers: they are asked questions about their work and aptitude, and their responses and emotional data are collected. The input is the worker's responses and emotional data, and the output is worker data.

[0443] Step 11:

[0444] The server analyzes the workers' response data and emotional data to determine the optimal work content and placement. The input is the workers' response data and emotional data, and the output is the optimal work content and placement data.

[0445] Step 12:

[0446] The server notifies the factory system of the analysis results and instructs workers on the appropriate work content and placement. The input is the analysis results, and the output is instruction data for the factory system.

[0447] Through this series of steps, users can find the occupation that best suits them, and factory workers are assigned optimal work content and placement based on their emotions and aptitudes.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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."

[0464] The present invention is a system for analyzing an individual's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, and a means for the server to receive and analyze the user's answer data and present the most suitable occupation. The system also includes a means for the server to send the analysis results to a terminal, which then displays them.

[0465] The system works as follows:

[0466] 1. User Initiation

[0467] The user launches the application and is presented with a login screen.

[0468] The user logs in with an existing account or creates a new account.

[0469] 2. Start of vocational aptitude test

[0470] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device.

[0471] 3. Answering questions

[0472] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0473] 4. Processing the invisible parts

[0474] The terminal temporarily stores the user's answers and transmits the answer data to the server when all questions have been answered.

[0475] 5. Server-side analysis

[0476] The server receives the user's response data and formats the data.

[0477] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable occupation.

[0478] For example, if answer 1 is 'yes' and answer 2 is 'no', then the appropriate occupation for this person is 'project manager'.

[0479] 6. Submitting the results

[0480] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0481] 7. Displaying the results

[0482] The device displays the analysis results to the user, presenting a list of occupations that are suitable for the user.

[0483] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[0484] Specific examples

[0485] For example, a user might use an app to take a career aptitude test. The user first presses the "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0486] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0487] Finally, the server saves the analysis results in the user's profile and sends them to the device. The device displays the results, allowing the user to review them. Furthermore, if the user is interested in "project manager," an option to obtain more information about that occupation is displayed. In this way, the present invention helps users find the occupation that best suits them.

[0488] The processing flow will be explained below.

[0489] Step 1:

[0490] The user launches the application and is presented with a login screen. The user logs in with an existing account or creates a new account. The server checks the authentication information and displays the dashboard screen on the device.

[0491] Step 2:

[0492] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0493] Step 3:

[0494] The user answers a question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0495] Step 4:

[0496] The device temporarily stores the answer and displays the next question, and this process is repeated for all questions.

[0497] Step 5:

[0498] After the device has answered all the questions, it sends the answer data to the server in JSON format, for example as follows:

[0499] json

[0500] {

[0501] "user_id": "12345",

[0502] "responses": [

[0503] {"question_id": "1", "answer": "yes"},

[0504] {"question_id": "2", "answer": "no"},

[0505] ...

[0506] ]

[0507] }

[0508] Step 6:

[0509] The server receives the user's response data and performs a data processing process, converting the processed data into a format that can be input into the generative AI model.

[0510] Step 7:

[0511] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data with a database of past successful users.

[0512] Step 8:

[0513] The generative AI model uses statistical algorithms to analyze the best career aptitudes and generate a list of the resulting jobs. For example, it might generate the following list:

[0514] json

[0515] {

[0516] "recommended_jobs": [

[0517] "Project Manager",

[0518] "consultant",

[0519] "Marketing Manager"

[0520] ]

[0521] }

[0522] Step 9:

[0523] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[0524] Step 10:

[0525] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[0526] Step 11:

[0527] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[0528] The above are the specific processing steps of this system, and the overall flow for users to find the job that best suits them.

[0529] Example 1

[0530] 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."

[0531] There is a need for a system that can quickly and accurately determine an individual's career aptitude. Existing methods require manual analysis of a large number of user responses, which takes a long time to produce results and is inaccurate in recommending suitable careers. Furthermore, they lack a means to obtain additional details about the careers that interest the user.

[0532] 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.

[0533] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and analyze the most suitable occupation by comparing it with a database of past successful people using a generative AI model, means for the server to save the analysis results in the user's profile and transmit them to the terminal, and means for the terminal to display the transmitted analysis results. This makes it possible to quickly and accurately determine an individual's occupational aptitude and further obtain detailed information about occupations in which the user is interested.

[0534] A "user" is an individual who uses the system to take a career aptitude test.

[0535] A "server" is a computer system that receives user response data, analyzes it using a generative AI model, and provides the results to the user.

[0536] A "terminal" is a device that a user uses to take a career aptitude test and that provides an interface for displaying the analysis results.

[0537] "Means for displaying a screen" refers to a function for displaying an interface on a terminal for the user to answer questions.

[0538] "Means for collecting data" refers to the function of recording and temporarily saving answers entered by users to questions.

[0539] "Means for transmitting data" refers to a communication function for transmitting the user's response data to the server.

[0540] A "generative AI model" is a type of artificial intelligence technology used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[0541] A "database" is a collection of information that stores information about successful people of the past.

[0542] "Means for analysis" refers to the function of using a generative AI model to analyze user response data and identify the most suitable occupation.

[0543] A "profile" is a collection of information where data related to a user is stored and where analysis results are stored.

[0544] "Display means" refers to a function that displays the analysis results sent from the server on the user's terminal.

[0545] "Means for obtaining detailed information" refers to an interface that provides further information about the occupation in which the user is interested.

[0546] This invention relates to a system for analyzing an individual's career aptitude. The system operates through a series of processes that display a screen for the user to answer questions, collect the user's answer data, and send it to a server. The following hardware and software are used to implement this system.

[0547] Hardware and software used

[0548] Hardware

[0549] Devices: smartphones, tablets, computers

[0550] Server: Cloud server (e.g. AWS, Google Cloud)

[0551] software

[0552] Application: iOS app, Android app or web application

[0553] Generative AI model: GPT (e.g., OpenAI's GPT-4)

[0554] Natural language processing explanation

[0555] 1. User Initiation

[0556] A user launches an application and is presented with a login screen, where they can log in with an existing account or create a new account.

[0557] 2. Start of vocational aptitude test

[0558] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device. Each question is typically answered with a "yes" or "no."

[0559] 3. Answering questions

[0560] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no." The device temporarily stores the answers.

[0561] 4. Data transmission

[0562] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[0563] 5. Data Analysis

[0564] The server formats the received data and converts it into a format that can be input into the generative AI model. The generative AI model then compares it with a database of past successful people and analyzes the most suitable occupation for that person. For example, if answer 1 is 'yes' and answer 2 is 'no', it determines that the appropriate occupation for this person is 'project manager'.

[0565] 6. Submitting the results

[0566] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0567] 7. Displaying the results

[0568] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[0569] Specific examples

[0570] For example, a user might use an app to take a career aptitude test. The user presses a "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0571] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0572] Finally, the server saves the analysis results in the user's profile and sends them to the device, where they are displayed and the user can review the results. Furthermore, if the user is interested in "project manager," they are given the option to get more information about that occupation.

[0573] Example prompts for generative AI models

[0574] The user clicked the "Start Career Aptitude Test" button and answered the questions. The user answered as follows: "Question 1: Yes, Question 2: No, Question 3: Yes." Please recommend the best career for them based on this.

[0575] This invention allows users to quickly and accurately find the occupation that best suits them, and also allows users to easily obtain detailed information about the occupation.

[0576] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0577] Step 1:

[0578] The user launches the application. The device displays a login screen, and the user can log in with an existing account or create a new account.

[0579] input:

[0580] User account information (email address, password).

[0581] output:

[0582] Authentication success or failure message for the user.

[0583] Specific behavior:

[0584] The user taps the application to launch it, enters their email address and password, and presses the "Login" button. If creating a new account, they enter the required information and press the "Register" button.

[0585] Step 2:

[0586] The user presses the "Start Career Aptitude Test" button. The device displays the career aptitude test question screen.

[0587] input:

[0588] User action (clicking the "Start Career Aptitude Test" button).

[0589] output:

[0590] Occupational aptitude test question screen.

[0591] Specific behavior:

[0592] Click the "Start Career Aptitude Test" button on the home screen, and the screen will display buttons to move to the next question (such as "Next" or "Answer").

[0593] Step 3:

[0594] The user answers each question, and the device temporarily stores the answers.

[0595] input:

[0596] The user's answer (for example, "yes" or "no").

[0597] output:

[0598] Temporarily stored response data.

[0599] Specific behavior:

[0600] For example, to the question "Do you like working in a team?", select "Yes" or "No" and click the "Next" button. The next question will be displayed.

[0601] Step 4:

[0602] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[0603] input:

[0604] Temporarily stored response data.

[0605] output:

[0606] The response data sent to the server.

[0607] Specific behavior:

[0608] After answering the last question, click the "Done" button. The device formats and sends the answer data. The server receives the data and returns a confirmation message to the device.

[0609] Step 5:

[0610] The server formats the received data and converts it into a format that can be input into a generative AI model, which then compares it with a database of past successful people to analyze the most suitable occupation.

[0611] input:

[0612] The response data sent to the server.

[0613] output:

[0614] Analyzed vocational aptitude results.

[0615] Specific behavior:

[0616] The generative AI model is fed the collected user response data along with the prompt text. The AI ​​model analyzes the data and recommends roles such as "project manager," "consultant," and "marketing manager."

[0617] Step 6:

[0618] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0619] input:

[0620] Analyzed vocational aptitude results.

[0621] output:

[0622] Analysis results stored in the user's profile and data sent to the device.

[0623] Specific behavior:

[0624] The server saves the analysis results in a database, and sends an API call to the device to receive the results once the analysis is complete.

[0625] Step 7:

[0626] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[0627] input:

[0628] Analysis results sent to the device.

[0629] output:

[0630] Career aptitude results and detailed information displayed to the user.

[0631] Specific behavior:

[0632] On the results screen, you will see a list of recommended jobs. Click on each job's icon or description to view more information.

[0633] (Application example 1)

[0634] 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."

[0635] Conventional career aptitude analysis systems lack interactive counseling for users and are unable to provide sufficient information for career selection. They also lack a means to effectively receive career aptitude analysis from remote locations. Furthermore, it is difficult to obtain detailed information about careers that users are interested in in real time.

[0636] 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.

[0637] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for interactive career aptitude counseling by a counselor in the virtual realm, and means for displaying options for the user to obtain detailed information about occupations they are interested in. This allows the user to receive an interactive career aptitude analysis even from home and obtain detailed information in real time.

[0638] "User" refers to an individual who takes a career aptitude test and uses the results to find a suitable career.

[0639] The term "means for displaying a screen" refers to a device or program that visually presents the interface and questions necessary for the user to answer the questions.

[0640] "Means for collecting response data" refers to a device or program that compiles the responses provided by users as data and stores them in a form that can be used later.

[0641] "Means for transmitting to a server" refers to a device or program for transmitting collected response data to a remote server via a communication network such as the Internet.

[0642] "Generative AI model" refers to an artificial intelligence model, a type of machine learning, that is used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[0643] The "database of past successes" is a database that collects data on individuals who have passed career aptitude tests in the past, and is used as a basis for determining the career aptitude of new users.

[0644] A "profile" is a database that compiles information about a user, and stores analysis results, the user's response history, and other information.

[0645] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet, which displays the analysis results sent from the server.

[0646] "Virtual realm" refers to a virtual reality space that a user accesses through a device such as a head-mounted display, in which interactive career aptitude counseling takes place.

[0647] "Counselor" refers to an avatar or actual person whose role is to provide career aptitude counseling to users within the virtual reality space.

[0648] "Interactive career aptitude counseling" refers to an interactive dialogue in which users answer questions and interact with counselors in real time to analyze career aptitude and find the most suitable career.

[0649] "Options for obtaining more information" refers to settings and functions that allow users to check more detailed information about occupations that interest them, allowing users to learn more about the details of the occupation and the skills required.

[0650] This invention is a system used by users to analyze their career aptitude, and its specific configuration and operation are described below. This system includes a terminal for users to take a career aptitude test, a server that analyzes the user's response data, and a head-mounted display and counselor for providing interactive career counseling within a virtual realm.

[0651] First, a user answers a career aptitude test using a device equipped with a screen display. The answers to the questions are collected on the device, and the collected data is sent to a server. The server formats the answers and converts them into a format that can be input into a generative AI model. This generative AI model compares the answers with a database of past successful candidates and analyzes the most suitable career.

[0652] The analysis results are stored in the user's profile and sent from the server to the device, where they are displayed. The user can then wear a head-mounted display and enter the virtual space, where they can interact with a virtual counselor. The counseling session also provides the user with the option to obtain detailed information about careers they are interested in in real time.

[0653] Hardware and software used

[0654] Hardware:

[0655] 1. Head-mounted displays (e.g., Oculus Rift, HTC Vive)

[0656] 2. Server (e.g. AWS EC2)

[0657] 3. Computers, smartphones, tablets, and other devices

[0658] software:

[0659] 1. Flask: A Python-based web server framework

[0660] 2. Gensim: A framework for Word2Vec generative AI models

[0661] 3. JSON: Data exchange format between client and server

[0662] Details of the process

[0663] 1. User startup:

[0664] The user uses the device to access the career aptitude test screen and answer questions such as "Are you good at planning?" and "Do you enjoy creative work?"

[0665] 2. Sending and analyzing response data:

[0666] The device sends the collected response data to a server, which formats the received data and inputs it into a generative AI model. The generative AI model compares the data with a database of past successful people and analyzes suitable occupations.

[0667] 3. Save and display results:

[0668] The server saves the analysis results in the user's profile and sends them to the user's device, which displays them to the user.

[0669] 4. Virtual Counseling:

[0670] The user puts on a head-mounted display and enters a virtual realm, where a virtual counselor appears and interacts with the user, asking follow-up questions and providing more detailed career information.

[0671] Specific examples

[0672] For example, if a user answers "yes" or "no" to a question, the generative AI model will use these answers to analyze whether occupations such as "marketing manager" or "project manager" are suitable. If the user enters the virtual space and interacts with a counselor, expressing interest in "project manager," they can obtain new detailed information about that occupation.

[0673] Prompt Sentence Examples

[0674] Identify the best occupation for you based on this user's response data. The user's responses are "I'm good at planning" and "I like creative work."

[0675] The system allows users to receive an interactive career aptitude analysis from the comfort of their own home, and enjoy effective counseling to obtain detailed information in real time.

[0676] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0677] Step 1:

[0678] The user uses a terminal to display the screen of a career aptitude test. The user answers the questions. The input is the user's answer, and the output is the answer data temporarily saved on the terminal. Specifically, the user answers "Yes" or "No" to the question "Are you good at making plans?"

[0679] Step 2:

[0680] The device collects the user's response data and sends it to the server. The input is the temporarily saved user response data, and the output is the data sent to the server. A specific example of this behavior is when the device sends the response data to the server in JSON format.

[0681] Step 3:

[0682] The server formats the received user response data and converts it into a format that can be input to the generative AI model. The input is the received user response data, and the output is the formatted data. Specifically, the server converts the response data into a vector and passes it to the generative AI model.

[0683] Step 4:

[0684] The server uses a generative AI model to compare the response data with a database of past successful people and analyze the most suitable occupation. The input is the formatted data and a database of past successful people, and the output is the analysis results. A specific operation is the process in which the generative AI model determines that "the occupation suitable for this user is 'project manager'."

[0685] Step 5:

[0686] The server saves the analysis results in the user's profile and sends them to the device. The input is the analysis results and the user's profile information, and the output is the analysis results saved in the user's profile and the results sent to the device. Specifically, the server saves the analysis results in a database and sends them to the device in JSON format.

[0687] Step 6:

[0688] The terminal displays the analysis results sent to the user. The input is the analysis results sent from the server, and the output is the results displayed on the terminal. Specifically, the message "The job suitable for you is 'Project Manager'" is displayed on the user's screen.

[0689] Step 7:

[0690] The user wears a head-mounted display, enters the virtual space, and interacts with the counselor. The input is the analysis results and the user's selection, and the output is the start of virtual counseling. Specifically, the user becomes interested in the "project manager" and requests more information in the virtual space.

[0691] Step 8:

[0692] In the virtual realm, a counselor provides detailed career information to the user. The input is the user's selection and the counselor's questions, and the output is the detailed career information provided. Specifically, the counselor explains the role of a "project manager" and the skills required.

[0693] Through the above steps, users can receive an interactive career aptitude analysis from home and enjoy effective counseling to obtain detailed information in real time.

[0694] 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.

[0695] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting and sending the user's answer data to a server, a means for the server to receive the user's answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results.

[0696] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[0697] Specific operation of the system

[0698] The operation of the system is as follows.

[0699] 1. User Initiation

[0700] The user launches the application and is presented with a login screen. They can log in with an existing account or create a new one. The server checks the authentication information and displays the dashboard screen on the device.

[0701] 2. Start of vocational aptitude test

[0702] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0703] 3. Emotion Recognition by Emotion Engine

[0704] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions a user makes when answering questions can be classified as "happiness," "anger," "sadness," etc.

[0705] 4. Answering questions and collecting sentiment data

[0706] The user answers a question, for example, "Do you like working in a team?", and selects "yes" or "no." At the same time, the emotion engine collects the user's emotional data at that time.

[0707] 5. Sending data to the server

[0708] The device temporarily stores the answer data and emotion data, and once all questions have been answered, it sends this data to the server. The data is structured as follows:

[0709] json

[0710] {

[0711] "user_id": "12345",

[0712] "responses": [

[0713] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[0714] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[0715] ...

[0716] ]

[0717] }

[0718] 6. Server-side analysis

[0719] The server receives the user's response data and emotion data and formats the data.

[0720] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable career.

[0721] For example, if answer 1 is 'yes' and emotion is 'happy', then the suitable occupation for this person is 'project manager'.

[0722] 7. Sending and displaying results

[0723] The server stores the generated analysis results in the user's profile and sends them to the device.

[0724] The device displays the analysis results to the user, who can then review the results and select the occupations that interest them.

[0725] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[0726] The above is a specific embodiment for carrying out the present invention. This system allows users to find the most suitable occupation for themselves with high accuracy.

[0727] The processing flow will be explained below.

[0728] Step 1:

[0729] When a user launches the application, the login screen is displayed. The user logs in with an existing account or creates a new account. The server checks the user's authentication information, and if authentication is successful, the dashboard screen is displayed on the device.

[0730] Step 2:

[0731] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0732] Step 3:

[0733] The device will activate the emotion engine and begin collecting and analyzing the user's facial expressions and voice in real time. Specifically, the user will be required to enable the camera and microphone.

[0734] Step 4:

[0735] The user answers a question. For example, "Do you like working in a team?" and selects "yes" or "no." During this time, the emotion engine recognizes emotional data (such as "happiness," "sadness," or "surprise") from the user's facial expressions and voice.

[0736] Step 5:

[0737] The device temporarily stores the user's response and emotional data. For example, the following data is stored:

[0738] json

[0739] {

[0740] "question_id": "1",

[0741] "answer": "yes",

[0742] "emotion": "happy"

[0743] }

[0744] Step 6:

[0745] After the device has completed collecting all the answers to the questions and emotional data, it sends this data in JSON format to the server.

[0746] Step 7:

[0747] The server receives the user's response data and emotion data, formats the data, and converts it into a format that can be input into the generative AI model.

[0748] Step 8:

[0749] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data and emotional data with a database of past successful users.

[0750] Step 9:

[0751] The generative AI model uses statistical algorithms to analyze the most suitable occupations and generate a list of the results. For example, the AI ​​model might determine that "if answer 1 is 'yes' and emotion is 'happy', then the appropriate occupation for this person is 'project manager'."

[0752] Step 10:

[0753] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[0754] Step 11:

[0755] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[0756] Step 12:

[0757] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[0758] The above are the specific processing steps of this system and the overall flow for users to find the occupation that best suits them. By adding an emotion engine, it is possible to reflect the user's emotional state and make more accurate occupational aptitude diagnosis.

[0759] Example 2

[0760] 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."

[0761] Conventional occupational aptitude diagnosis systems analyzed occupations based solely on user response data, which meant that they were unable to reflect the user's emotions and psychological state, resulting in a decrease in the accuracy of the aptitude diagnosis.In addition, the fixed order and content of questions sometimes made it difficult for users to operate the system.

[0762] 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 displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and compare it with a database of past successful people using a generative AI model to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for the terminal to analyze the user's facial expressions and voice in real time and recognize emotions, means for adding emotion data to the question answer data, and means for dynamically changing the content and order of questions based on the emotion recognition results. This enables more accurate occupational aptitude diagnosis that reflects the user's emotions.

[0763] The "screen for answering questions" refers to an interface that allows the user to input answers to questions that are displayed.

[0764] "Answer data" refers to data describing the answer entered by the user to the question.

[0765] A "generative AI model" refers to a mathematical model that uses artificial intelligence to perform analysis and prediction.

[0766] A "database of past successful people" refers to a database that accumulates data on successful people from the past.

[0767] "Analysis results" refers to the results of the server analyzing the user's response data using the generated AI model.

[0768] A "profile" refers to data that compiles information about a user.

[0769] "Terminal" refers to an electronic device that is used by a user for operation.

[0770] "Real-time analysis of facial expressions and voice" refers to the device instantly analyzing the user's facial expressions and voice.

[0771] "Emotion recognition" refers to the process of identifying emotions from a user's facial expressions and voice.

[0772] "Emotion data" refers to data related to the user's emotions obtained through emotion recognition.

[0773] "Dynamic change of the content and order of questions" refers to changing the order and content of questions in real time based on the user's emotions.

[0774] "Means for displaying options" refers to an interface that displays options for a user to obtain more information about a job that interests them.

[0775] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's career aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, a means for the server to receive the user's answer data and compare it with a database of past successful candidates using a generative AI model to analyze the most suitable career, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results. Furthermore, the emotion engine allows the system to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[0776] The following hardware and software are used to implement this system.

[0777] First, when a user launches an application, a device such as a smartphone or tablet is used. When the user logs in, the server verifies the authentication information and displays the dashboard screen on the device. The main software used at this stage includes a web server and a database management system.

[0778] Next, when the user starts the career aptitude test, the device displays the first question screen. The question includes specific content such as "Do you like working in a team?" As the user enters their answer, the device analyzes their facial expressions and voice in real time, and an emotion engine recognizes their emotions. The emotion analysis software used here may be technology such as OpenFace or IBM Watson.

[0779] Once all questions have been answered, the device sends the collected answer data and emotion data to the server. The data is structured as follows, for example:

[0780] Response data:

[0781] Question 1: "Yes", Emotion: "Joy"

[0782] Question 2: "No", Sentiment: "Neutral"

[0783] When the server receives this data, it formats it and converts it into a format that can be input to a generative AI model, such as GPT-3 or BERT. Analysis can be performed using prompts like the following:

[0784] Example prompt:

[0785] Receive the user's answer data in the following format and compare it with a database of past successes to determine the most suitable occupation. Answer data: Question 1: "Yes", Emotion: "Joy" Question 2: "No", Emotion: "Neutral"

[0786] The generative AI model performs the analysis and generates results identifying the most suitable occupation, such as a suggested occupation title like "Project Manager." The server stores these results in the user's profile and sends them to the device, where the user can review the results and receive options for further information.

[0787] This system allows users to find the occupation that best suits them with greater accuracy. The introduction of an emotion engine enables aptitude assessment that reflects the user's psychological state more than conventional systems, providing more reliable diagnostic results.

[0788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0789] Step 1:

[0790] When a user starts the application, a login screen is displayed. The user can log in with an existing account or create a new account. The input is the user's ID and password, and the output is the authentication result. The server checks the authentication information, and if authentication is successful, it sends dashboard data to the terminal. Specifically, the terminal displays the login screen, accepts the user's input, and sends it to the server. The server then compares it with a database to perform authentication.

[0791] Step 2:

[0792] When the user presses the "Start Occupational Aptitude Test" button, the terminal displays the first question screen. The input is the user's button operation, and the output is the first question screen. Specifically, the terminal detects the click event of the test start button, obtains the first question data of the aptitude test, and displays it.

[0793] Step 3:

[0794] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. The input is the user's facial expression and voice data, and the output is the recognized emotion data. Specifically, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed in real time by the emotion engine.

[0795] Step 4:

[0796] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." The input is the user's answer and real-time recognized emotion data, and the output is a set of answer data and emotion data. Specifically, the device records the answer and saves it together with the emotion data obtained from the emotion engine.

[0797] Step 5:

[0798] The device temporarily stores answer data and emotion data, and once all questions have been answered, it sends this data to the server. The input is the stored answer data and emotion data, and the output is a data package sent to the server. Specifically, the answer and emotion data are converted into JSON format and sent to the server.

[0799] Step 6:

[0800] The server receives the user's response data and emotion data and formats the data. The input is the submitted response data and emotion data, and the output is the formatted data. The server then inputs the formatted data into a generative AI model for analysis. The input is the formatted data, and the output is the analysis result (the best occupation). The server uses the generative AI model to compare the user's data with a database of past successful people and determine the best occupation.

[0801] Step 7:

[0802] The server saves the generated analysis results in the user's profile and sends them to the device. The input is the analysis results, and the output is the data sent to the device. Specifically, the device displays the results and, if the user selects an occupation they are interested in, displays options to provide more detailed information and related information.

[0803] The above are the specific processing steps of the program for this system. This flow allows users to receive a highly accurate career aptitude diagnosis that takes their own emotions into consideration.

[0804] (Application example 2)

[0805] 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."

[0806] Conventional occupational aptitude analysis systems analyze optimal occupations based on user response data, but they have the problem of low aptitude accuracy because they cannot view the user's emotions or work aptitude. Another issue is that it is difficult to dynamically assign tasks that reflect the emotions and aptitudes of workers at work sites such as factories. This invention incorporates user emotion data into occupational aptitude analysis to improve the accuracy of aptitude diagnosis and to optimize the work content and assignments of factory workers based on their emotions and aptitudes.

[0807] The identification process 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 shaping user response data and converting it into a format for input to the generative AI model, means for displaying a screen on which the user can answer questions, and means for collecting user response data and transmitting the data to the server. This allows user emotional data to be incorporated into occupational aptitude diagnosis, making it possible to determine optimal work content and placement based on the emotions and aptitudes of factory workers.

[0808] "User" refers to an individual or group that uses the System.

[0809] A "question answer screen" refers to an interface that allows a user to enter an answer to a particular question.

[0810] "Answer data" refers to information entered by a user in response to a question.

[0811] "Server" refers to a computing device that analyzes collected data and uses generative AI models to determine optimal occupations and tasks.

[0812] A "generative AI model" refers to an artificial intelligence algorithm that predicts and suggests optimal occupations and work content based on user response data and emotional data.

[0813] A "database of past successes" refers to a database based on individuals and cases of past success.

[0814] "Analysis" refers to the process of evaluating suitability based on data collected by the server and calculating the most suitable occupation and work content based on that.

[0815] "Profile" refers to a collection of personal data that stores information about a user.

[0816] "Terminal" refers to a device that receives and displays the analysis results sent from the server, including smartphones, computers, tablets, etc.

[0817] "Factory workers" refers to individual workers who perform work in a factory.

[0818] "Emotion" refers to the mental state a user expresses in response to a particular situation or question, such as "happiness," "sadness," or "stress."

[0819] "Aptitude" refers to the ability or aptitude a user has for a particular occupation or task.

[0820] "Optimization" refers to determining the most efficient and appropriate work content and placement based on collected data.

[0821] The present invention is a system that analyzes users' occupational aptitude with higher accuracy and realizes optimal work content and placement based on the emotions and aptitudes of workers in a factory. The system involves a process in which users answer questions, collect and analyze data based on the answers, and display the final results.

[0822] The system includes the following hardware and software:

[0823] A device that displays a screen for answering the user's questions: a smartphone, tablet, computer, etc.

[0824] Means for collecting and sending data to a server: a communication module with an internet connection.

[0825] Server that analyzes data using generative AI models: High-performance computing environment (e.g., cloud server).

[0826] External libraries for emotion recognition: Libraries for facial recognition and speech analysis (e.g. emotion_recognition, ai_model).

[0827] The operation of the system is as follows.

[0828] When a user starts the career aptitude test, questions are displayed on the device. As the user answers the questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time and adds the results to the answer data. The collected data, including the user's emotional data, is sent to the server. When the server receives the data, it formats it and inputs it into a generative AI model, which then compares it with a database of past successful candidates to analyze the most suitable career.

[0829] The system also collects data on the emotions and aptitudes of factory workers regarding specific tasks. Questions about the work content and aptitude are displayed to the workers, and their answers and emotional data are sent to the server. The server then analyzes the workers' response data and emotional data to determine the optimal work content and placement.

[0830] For example, if a worker feels "joy" in "assembly work" and is judged to be "good at" it, the frequency with which he or she is in charge of that work will be increased. On the other hand, if another worker feels "stress" in "inspection work" and is judged to be "unskilled" at it, the worker's work will be changed.

[0831] Here are some examples of prompts to input to a generative AI model:

[0832] Analyze the worker data below and suggest the best tasks for them.

[0833] Worker ID: worker_001

[0834] Response data:

[0835] 1. Task: assembly, Answer: yes, Emotion: happy

[0836] 2. Task: Inspection, Answer: no, Emotion: stress

[0837] 3. Task: Packing, Answer: yes, Emotion: neutral

[0838] The system allows users to more accurately find the jobs that best suit them, and enables dynamic work placement within factories based on workers' emotions and aptitudes.

[0839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0840] Step 1:

[0841] The user starts a career aptitude test. The user launches the application and logs in with an existing account or creates a new account on the login screen. The device checks the authentication information and displays the dashboard screen. The input is the user's authentication information, and the output is the dashboard screen.

[0842] Step 2:

[0843] The user presses the "Start Career Aptitude Test" button. The terminal displays the first question screen and provides an interface for the user to answer the questions. The input is pressing the button, and the output is the first question screen.

[0844] Step 3:

[0845] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions of the user when answering questions can be classified as "happiness" or "sadness." The input is the user's video and audio data, and the output is emotion data.

[0846] Step 4:

[0847] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." At the same time, the emotion engine collects the user's emotion data at that time. The input is the user's answer and emotion data, and the output is a pair of answer data and emotion data.

[0848] Step 5:

[0849] The device temporarily stores the answer data and emotion data, and sends them to the server once all questions have been answered. The data consists of the following:

[0850] {

[0851] "user_id": "12345",

[0852] "responses": [

[0853] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[0854] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[0855] ...

[0856] ]

[0857] }

[0858] The input is response data and emotion data, and the output is data to be sent to the server.

[0859] Step 6:

[0860] The server receives the user's response data and emotion data and formats the data. The input is the transmitted data, and the output is the formatted data.

[0861] Step 7:

[0862] The server uses a generative AI model to compare the user's data with a database of past successful people to determine the most suitable occupation. For example, if answer 1 is 'yes' and emotion is 'happy', it determines that the appropriate occupation for this person is 'project manager'. The input is the formatted data, and the output is the analysis result.

[0863] Step 8:

[0864] The server saves the generated analysis results in the user's profile and sends them to the terminal. The input is the analysis results, and the output is the data saved in the profile.

[0865] Step 9:

[0866] The terminal displays the analysis results to the user. The user can check the results and select the occupation they are interested in. The input is profile data, and the output is display data.

[0867] Step 10:

[0868] A similar process is performed for factory workers: they are asked questions about their work and aptitude, and their responses and emotional data are collected. The input is the worker's responses and emotional data, and the output is worker data.

[0869] Step 11:

[0870] The server analyzes the workers' response data and emotional data to determine the optimal work content and placement. The input is the workers' response data and emotional data, and the output is the optimal work content and placement data.

[0871] Step 12:

[0872] The server notifies the factory system of the analysis results and instructs workers on the appropriate work content and placement. The input is the analysis results, and the output is instruction data for the factory system.

[0873] Through this series of steps, users can find the occupation that best suits them, and factory workers are assigned optimal work content and placement based on their emotions and aptitudes.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] [Third embodiment]

[0878] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0879] 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.

[0880] 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).

[0881] 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.

[0882] 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.

[0883] 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).

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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."

[0890] The present invention is a system for analyzing an individual's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, and a means for the server to receive and analyze the user's answer data and present the most suitable occupation. The system also includes a means for the server to send the analysis results to a terminal, which then displays them.

[0891] The system works as follows:

[0892] 1. User Initiation

[0893] The user launches the application and is presented with a login screen.

[0894] The user logs in with an existing account or creates a new account.

[0895] 2. Start of vocational aptitude test

[0896] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device.

[0897] 3. Answering questions

[0898] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0899] 4. Processing the invisible parts

[0900] The terminal temporarily stores the user's answers and transmits the answer data to the server when all questions have been answered.

[0901] 5. Server-side analysis

[0902] The server receives the user's response data and formats the data.

[0903] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable occupation.

[0904] For example, if answer 1 is 'yes' and answer 2 is 'no', then the appropriate occupation for this person is 'project manager'.

[0905] 6. Submitting the results

[0906] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0907] 7. Displaying the results

[0908] The device displays the analysis results to the user, presenting a list of occupations that are suitable for the user.

[0909] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[0910] Specific examples

[0911] For example, a user might use an app to take a career aptitude test. The user first presses the "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0912] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0913] Finally, the server saves the analysis results in the user's profile and sends them to the device. The device displays the results, allowing the user to review them. Furthermore, if the user is interested in "project manager," an option to obtain more information about that occupation is displayed. In this way, the present invention helps users find the occupation that best suits them.

[0914] The processing flow will be explained below.

[0915] Step 1:

[0916] The user launches the application and is presented with a login screen. The user logs in with an existing account or creates a new account. The server checks the authentication information and displays the dashboard screen on the device.

[0917] Step 2:

[0918] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[0919] Step 3:

[0920] The user answers a question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[0921] Step 4:

[0922] The device temporarily stores the answer and displays the next question, and this process is repeated for all questions.

[0923] Step 5:

[0924] After the device has answered all the questions, it sends the answer data to the server in JSON format, for example as follows:

[0925] json

[0926] {

[0927] "user_id": "12345",

[0928] "responses": [

[0929] {"question_id": "1", "answer": "yes"},

[0930] {"question_id": "2", "answer": "no"},

[0931] ...

[0932] ]

[0933] }

[0934] Step 6:

[0935] The server receives the user's response data and performs a data processing process, converting the processed data into a format that can be input into the generative AI model.

[0936] Step 7:

[0937] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data with a database of past successful users.

[0938] Step 8:

[0939] The generative AI model uses statistical algorithms to analyze the best career aptitudes and generate a list of the resulting jobs. For example, it might generate the following list:

[0940] json

[0941] {

[0942] "recommended_jobs": [

[0943] "Project Manager",

[0944] "consultant",

[0945] "Marketing Manager"

[0946] ]

[0947] }

[0948] Step 9:

[0949] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[0950] Step 10:

[0951] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[0952] Step 11:

[0953] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[0954] The above are the specific processing steps of this system, and the overall flow for users to find the job that best suits them.

[0955] Example 1

[0956] 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."

[0957] There is a need for a system that can quickly and accurately determine an individual's career aptitude. Existing methods require manual analysis of a large number of user responses, which takes a long time to produce results and is inaccurate in recommending suitable careers. Furthermore, they lack a means to obtain additional details about the careers that interest the user.

[0958] 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.

[0959] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and analyze the most suitable occupation by comparing it with a database of past successful people using a generative AI model, means for the server to save the analysis results in the user's profile and transmit them to the terminal, and means for the terminal to display the transmitted analysis results. This makes it possible to quickly and accurately determine an individual's occupational aptitude and further obtain detailed information about occupations in which the user is interested.

[0960] A "user" is an individual who uses the system to take a career aptitude test.

[0961] A "server" is a computer system that receives user response data, analyzes it using a generative AI model, and provides the results to the user.

[0962] A "terminal" is a device that a user uses to take a career aptitude test and that provides an interface for displaying the analysis results.

[0963] "Means for displaying a screen" refers to a function for displaying an interface on a terminal for the user to answer questions.

[0964] "Means for collecting data" refers to the function of recording and temporarily saving answers entered by users to questions.

[0965] "Means for transmitting data" refers to a communication function for transmitting the user's response data to the server.

[0966] A "generative AI model" is a type of artificial intelligence technology used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[0967] A "database" is a collection of information that stores information about successful people of the past.

[0968] "Means for analysis" refers to the function of using a generative AI model to analyze user response data and identify the most suitable occupation.

[0969] A "profile" is a collection of information where data related to a user is stored and where analysis results are stored.

[0970] "Display means" refers to a function that displays the analysis results sent from the server on the user's terminal.

[0971] "Means for obtaining detailed information" refers to an interface that provides further information about the occupation in which the user is interested.

[0972] This invention relates to a system for analyzing an individual's career aptitude. The system operates through a series of processes that display a screen for the user to answer questions, collect the user's answer data, and send it to a server. The following hardware and software are used to implement this system.

[0973] Hardware and software used

[0974] Hardware

[0975] Devices: smartphones, tablets, computers

[0976] Server: Cloud server (e.g. AWS, Google Cloud)

[0977] software

[0978] Application: iOS app, Android app or web application

[0979] Generative AI model: GPT (e.g., OpenAI's GPT-4)

[0980] Natural language processing explanation

[0981] 1. User Initiation

[0982] A user launches an application and is presented with a login screen, where they can log in with an existing account or create a new account.

[0983] 2. Start of vocational aptitude test

[0984] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device. Each question is typically answered with a "yes" or "no."

[0985] 3. Answering questions

[0986] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no." The device temporarily stores the answers.

[0987] 4. Data transmission

[0988] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[0989] 5. Data Analysis

[0990] The server formats the received data and converts it into a format that can be input into the generative AI model. The generative AI model then compares it with a database of past successful people and analyzes the most suitable occupation for that person. For example, if answer 1 is 'yes' and answer 2 is 'no', it determines that the appropriate occupation for this person is 'project manager'.

[0991] 6. Submitting the results

[0992] The server saves the analysis results in the user's profile and sends the results to the user's device.

[0993] 7. Displaying the results

[0994] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[0995] Specific examples

[0996] For example, a user might use an app to take a career aptitude test. The user presses a "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[0997] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[0998] Finally, the server saves the analysis results in the user's profile and sends them to the device, where they are displayed and the user can review the results. Furthermore, if the user is interested in "project manager," they are given the option to get more information about that occupation.

[0999] Example prompts for generative AI models

[1000] The user clicked the "Start Career Aptitude Test" button and answered the questions. The user answered as follows: "Question 1: Yes, Question 2: No, Question 3: Yes." Please recommend the best career for them based on this.

[1001] This invention allows users to quickly and accurately find the occupation that best suits them, and also allows users to easily obtain detailed information about the occupation.

[1002] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] The user launches the application. The device displays a login screen, and the user can log in with an existing account or create a new account.

[1005] input:

[1006] User account information (email address, password).

[1007] output:

[1008] Authentication success or failure message for the user.

[1009] Specific behavior:

[1010] The user taps the application to launch it, enters their email address and password, and presses the "Login" button. If creating a new account, they enter the required information and press the "Register" button.

[1011] Step 2:

[1012] The user presses the "Start Career Aptitude Test" button. The device displays the career aptitude test question screen.

[1013] input:

[1014] User action (clicking the "Start Career Aptitude Test" button).

[1015] output:

[1016] Occupational aptitude test question screen.

[1017] Specific behavior:

[1018] Click the "Start Career Aptitude Test" button on the home screen, and the screen will display buttons to move to the next question (such as "Next" or "Answer").

[1019] Step 3:

[1020] The user answers each question, and the device temporarily stores the answers.

[1021] input:

[1022] The user's answer (for example, "yes" or "no").

[1023] output:

[1024] Temporarily stored response data.

[1025] Specific behavior:

[1026] For example, to the question "Do you like working in a team?", select "Yes" or "No" and click the "Next" button. The next question will be displayed.

[1027] Step 4:

[1028] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[1029] input:

[1030] Temporarily stored response data.

[1031] output:

[1032] The response data sent to the server.

[1033] Specific behavior:

[1034] After answering the last question, click the "Done" button. The device formats and sends the answer data. The server receives the data and returns a confirmation message to the device.

[1035] Step 5:

[1036] The server formats the received data and converts it into a format that can be input into a generative AI model, which then compares it with a database of past successful people to analyze the most suitable occupation.

[1037] input:

[1038] The response data sent to the server.

[1039] output:

[1040] Analyzed vocational aptitude results.

[1041] Specific behavior:

[1042] The generative AI model is fed the collected user response data along with the prompt text. The AI ​​model analyzes the data and recommends roles such as "project manager," "consultant," and "marketing manager."

[1043] Step 6:

[1044] The server saves the analysis results in the user's profile and sends the results to the user's device.

[1045] input:

[1046] Analyzed vocational aptitude results.

[1047] output:

[1048] Analysis results stored in the user's profile and data sent to the device.

[1049] Specific behavior:

[1050] The server saves the analysis results in a database, and sends an API call to the device to receive the results once the analysis is complete.

[1051] Step 7:

[1052] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[1053] input:

[1054] Analysis results sent to the device.

[1055] output:

[1056] Career aptitude results and detailed information displayed to the user.

[1057] Specific behavior:

[1058] On the results screen, you will see a list of recommended jobs. Click on each job's icon or description to view more information.

[1059] (Application example 1)

[1060] 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."

[1061] Conventional career aptitude analysis systems lack interactive counseling for users and are unable to provide sufficient information for career selection. They also lack a means to effectively receive career aptitude analysis from remote locations. Furthermore, it is difficult to obtain detailed information about careers that users are interested in in real time.

[1062] 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.

[1063] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for interactive career aptitude counseling by a counselor in the virtual realm, and means for displaying options for the user to obtain detailed information about occupations they are interested in. This allows the user to receive an interactive career aptitude analysis even from home and obtain detailed information in real time.

[1064] "User" refers to an individual who takes a career aptitude test and uses the results to find a suitable career.

[1065] The term "means for displaying a screen" refers to a device or program that visually presents the interface and questions necessary for the user to answer the questions.

[1066] "Means for collecting response data" refers to a device or program that compiles the responses provided by users as data and stores them in a form that can be used later.

[1067] "Means for transmitting to a server" refers to a device or program for transmitting collected response data to a remote server via a communication network such as the Internet.

[1068] "Generative AI model" refers to an artificial intelligence model, a type of machine learning, that is used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[1069] The "database of past successes" is a database that collects data on individuals who have passed career aptitude tests in the past, and is used as a basis for determining the career aptitude of new users.

[1070] A "profile" is a database that compiles information about a user, and stores analysis results, the user's response history, and other information.

[1071] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet, which displays the analysis results sent from the server.

[1072] "Virtual realm" refers to a virtual reality space that a user accesses through a device such as a head-mounted display, in which interactive career aptitude counseling takes place.

[1073] "Counselor" refers to an avatar or actual person whose role is to provide career aptitude counseling to users within the virtual reality space.

[1074] "Interactive career aptitude counseling" refers to an interactive dialogue in which users answer questions and interact with counselors in real time to analyze career aptitude and find the most suitable career.

[1075] "Options for obtaining more information" refers to settings and functions that allow users to check more detailed information about occupations that interest them, allowing users to learn more about the details of the occupation and the skills required.

[1076] This invention is a system used by users to analyze their career aptitude, and its specific configuration and operation are described below. This system includes a terminal for users to take a career aptitude test, a server that analyzes the user's response data, and a head-mounted display and counselor for providing interactive career counseling within a virtual realm.

[1077] First, a user answers a career aptitude test using a device equipped with a screen display. The answers to the questions are collected on the device, and the collected data is sent to a server. The server formats the answers and converts them into a format that can be input into a generative AI model. This generative AI model compares the answers with a database of past successful candidates and analyzes the most suitable career.

[1078] The analysis results are stored in the user's profile and sent from the server to the device, where they are displayed. The user can then wear a head-mounted display and enter the virtual space, where they can interact with a virtual counselor. The counseling session also provides the user with the option to obtain detailed information about careers they are interested in in real time.

[1079] Hardware and software used

[1080] Hardware:

[1081] 1. Head-mounted displays (e.g., Oculus Rift, HTC Vive)

[1082] 2. Server (e.g. AWS EC2)

[1083] 3. Computers, smartphones, tablets, and other devices

[1084] software:

[1085] 1. Flask: A Python-based web server framework

[1086] 2. Gensim: A framework for Word2Vec generative AI models

[1087] 3. JSON: Data exchange format between client and server

[1088] Details of the process

[1089] 1. User startup:

[1090] The user uses the device to access the career aptitude test screen and answer questions such as "Are you good at planning?" and "Do you enjoy creative work?"

[1091] 2. Sending and analyzing response data:

[1092] The device sends the collected response data to a server, which formats the received data and inputs it into a generative AI model. The generative AI model compares the data with a database of past successful people and analyzes suitable occupations.

[1093] 3. Save and display results:

[1094] The server saves the analysis results in the user's profile and sends them to the user's device, which displays them to the user.

[1095] 4. Virtual Counseling:

[1096] The user puts on a head-mounted display and enters a virtual realm, where a virtual counselor appears and interacts with the user, asking follow-up questions and providing more detailed career information.

[1097] Specific examples

[1098] For example, if a user answers "yes" or "no" to a question, the generative AI model will use these answers to analyze whether occupations such as "marketing manager" or "project manager" are suitable. If the user enters the virtual space and interacts with a counselor, expressing interest in "project manager," they can obtain new detailed information about that occupation.

[1099] Prompt Sentence Examples

[1100] Identify the best occupation for you based on this user's response data. The user's responses are "I'm good at planning" and "I like creative work."

[1101] The system allows users to receive an interactive career aptitude analysis from the comfort of their own home, and enjoy effective counseling to obtain detailed information in real time.

[1102] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1103] Step 1:

[1104] The user uses a terminal to display the screen of a career aptitude test. The user answers the questions. The input is the user's answer, and the output is the answer data temporarily saved on the terminal. Specifically, the user answers "Yes" or "No" to the question "Are you good at making plans?"

[1105] Step 2:

[1106] The device collects the user's response data and sends it to the server. The input is the temporarily saved user response data, and the output is the data sent to the server. A specific example of this behavior is when the device sends the response data to the server in JSON format.

[1107] Step 3:

[1108] The server formats the received user response data and converts it into a format that can be input to the generative AI model. The input is the received user response data, and the output is the formatted data. Specifically, the server converts the response data into a vector and passes it to the generative AI model.

[1109] Step 4:

[1110] The server uses a generative AI model to compare the response data with a database of past successful people and analyze the most suitable occupation. The input is the formatted data and a database of past successful people, and the output is the analysis results. A specific operation is the process in which the generative AI model determines that "the occupation suitable for this user is 'project manager'."

[1111] Step 5:

[1112] The server saves the analysis results in the user's profile and sends them to the device. The input is the analysis results and the user's profile information, and the output is the analysis results saved in the user's profile and the results sent to the device. Specifically, the server saves the analysis results in a database and sends them to the device in JSON format.

[1113] Step 6:

[1114] The terminal displays the analysis results sent to the user. The input is the analysis results sent from the server, and the output is the results displayed on the terminal. Specifically, the message "The job suitable for you is 'Project Manager'" is displayed on the user's screen.

[1115] Step 7:

[1116] The user wears a head-mounted display, enters the virtual space, and interacts with the counselor. The input is the analysis results and the user's selection, and the output is the start of virtual counseling. Specifically, the user becomes interested in the "project manager" and requests more information in the virtual space.

[1117] Step 8:

[1118] In the virtual realm, a counselor provides detailed career information to the user. The input is the user's selection and the counselor's questions, and the output is the detailed career information provided. Specifically, the counselor explains the role of a "project manager" and the skills required.

[1119] Through the above steps, users can receive an interactive career aptitude analysis from home and enjoy effective counseling to obtain detailed information in real time.

[1120] 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.

[1121] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting and sending the user's answer data to a server, a means for the server to receive the user's answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results.

[1122] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[1123] Specific operation of the system

[1124] The operation of the system is as follows.

[1125] 1. User Initiation

[1126] The user launches the application and is presented with a login screen. They can log in with an existing account or create a new one. The server checks the authentication information and displays the dashboard screen on the device.

[1127] 2. Start of vocational aptitude test

[1128] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[1129] 3. Emotion Recognition by Emotion Engine

[1130] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions a user makes when answering questions can be classified as "happiness," "anger," "sadness," etc.

[1131] 4. Answering questions and collecting sentiment data

[1132] The user answers a question, for example, "Do you like working in a team?", and selects "yes" or "no." At the same time, the emotion engine collects the user's emotional data at that time.

[1133] 5. Sending data to the server

[1134] The device temporarily stores the answer data and emotion data, and once all questions have been answered, it sends this data to the server. The data is structured as follows:

[1135] json

[1136] {

[1137] "user_id": "12345",

[1138] "responses": [

[1139] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[1140] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[1141] ...

[1142] ]

[1143] }

[1144] 6. Server-side analysis

[1145] The server receives the user's response data and emotion data and formats the data.

[1146] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable career.

[1147] For example, if answer 1 is 'yes' and emotion is 'happy', then the suitable occupation for this person is 'project manager'.

[1148] 7. Sending and displaying results

[1149] The server stores the generated analysis results in the user's profile and sends them to the device.

[1150] The device displays the analysis results to the user, who can then review the results and select the occupations that interest them.

[1151] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[1152] The above is a specific embodiment for carrying out the present invention. This system allows users to find the most suitable occupation for themselves with high accuracy.

[1153] The processing flow will be explained below.

[1154] Step 1:

[1155] When a user launches the application, the login screen is displayed. The user logs in with an existing account or creates a new account. The server checks the user's authentication information, and if authentication is successful, the dashboard screen is displayed on the device.

[1156] Step 2:

[1157] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[1158] Step 3:

[1159] The device will activate the emotion engine and begin collecting and analyzing the user's facial expressions and voice in real time. Specifically, the user will be required to enable the camera and microphone.

[1160] Step 4:

[1161] The user answers a question. For example, "Do you like working in a team?" and selects "yes" or "no." During this time, the emotion engine recognizes emotional data (such as "happiness," "sadness," or "surprise") from the user's facial expressions and voice.

[1162] Step 5:

[1163] The device temporarily stores the user's response and emotional data. For example, the following data is stored:

[1164] json

[1165] {

[1166] "question_id": "1",

[1167] "answer": "yes",

[1168] "emotion": "happy"

[1169] }

[1170] Step 6:

[1171] After the device has completed collecting all the answers to the questions and emotional data, it sends this data in JSON format to the server.

[1172] Step 7:

[1173] The server receives the user's response data and emotion data, formats the data, and converts it into a format that can be input into the generative AI model.

[1174] Step 8:

[1175] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data and emotional data with a database of past successful users.

[1176] Step 9:

[1177] The generative AI model uses statistical algorithms to analyze the most suitable occupations and generate a list of the results. For example, the AI ​​model might determine that "if answer 1 is 'yes' and emotion is 'happy', then the appropriate occupation for this person is 'project manager'."

[1178] Step 10:

[1179] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[1180] Step 11:

[1181] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[1182] Step 12:

[1183] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[1184] The above are the specific processing steps of this system and the overall flow for users to find the occupation that best suits them. By adding an emotion engine, it is possible to reflect the user's emotional state and make more accurate occupational aptitude diagnosis.

[1185] Example 2

[1186] 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."

[1187] Conventional occupational aptitude diagnosis systems analyzed occupations based solely on user response data, which meant that they were unable to reflect the user's emotions and psychological state, resulting in a decrease in the accuracy of the aptitude diagnosis.In addition, the fixed order and content of questions sometimes made it difficult for users to operate the system.

[1188] 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 displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and compare it with a database of past successful people using a generative AI model to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for the terminal to analyze the user's facial expressions and voice in real time and recognize emotions, means for adding emotion data to the question answer data, and means for dynamically changing the content and order of questions based on the emotion recognition results. This enables more accurate occupational aptitude diagnosis that reflects the user's emotions.

[1189] The "screen for answering questions" refers to an interface that allows the user to input answers to questions that are displayed.

[1190] "Answer data" refers to data describing the answer entered by the user to the question.

[1191] A "generative AI model" refers to a mathematical model that uses artificial intelligence to perform analysis and prediction.

[1192] A "database of past successful people" refers to a database that accumulates data on successful people from the past.

[1193] "Analysis results" refers to the results of the server analyzing the user's response data using the generated AI model.

[1194] A "profile" refers to data that compiles information about a user.

[1195] "Terminal" refers to an electronic device that is used by a user for operation.

[1196] "Real-time analysis of facial expressions and voice" refers to the device instantly analyzing the user's facial expressions and voice.

[1197] "Emotion recognition" refers to the process of identifying emotions from a user's facial expressions and voice.

[1198] "Emotion data" refers to data related to the user's emotions obtained through emotion recognition.

[1199] "Dynamic change of the content and order of questions" refers to changing the order and content of questions in real time based on the user's emotions.

[1200] "Means for displaying options" refers to an interface that displays options for a user to obtain more information about a job that interests them.

[1201] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's career aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, a means for the server to receive the user's answer data and compare it with a database of past successful candidates using a generative AI model to analyze the most suitable career, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results. Furthermore, the emotion engine allows the system to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[1202] The following hardware and software are used to implement this system.

[1203] First, when a user launches an application, a device such as a smartphone or tablet is used. When the user logs in, the server verifies the authentication information and displays the dashboard screen on the device. The main software used at this stage includes a web server and a database management system.

[1204] Next, when the user starts the career aptitude test, the device displays the first question screen. The question includes specific content such as "Do you like working in a team?" As the user enters their answer, the device analyzes their facial expressions and voice in real time, and an emotion engine recognizes their emotions. The emotion analysis software used here may be technology such as OpenFace or IBM Watson.

[1205] Once all questions have been answered, the device sends the collected answer data and emotion data to the server. The data is structured as follows, for example:

[1206] Response data:

[1207] Question 1: "Yes", Emotion: "Joy"

[1208] Question 2: "No", Sentiment: "Neutral"

[1209] When the server receives this data, it formats it and converts it into a format that can be input to a generative AI model, such as GPT-3 or BERT. Analysis can be performed using prompts like the following:

[1210] Example prompt:

[1211] Receive the user's answer data in the following format and compare it with a database of past successes to determine the most suitable occupation. Answer data: Question 1: "Yes", Emotion: "Joy" Question 2: "No", Emotion: "Neutral"

[1212] The generative AI model performs the analysis and generates results identifying the most suitable occupation, such as a suggested occupation title like "Project Manager." The server stores these results in the user's profile and sends them to the device, where the user can review the results and receive options for further information.

[1213] This system allows users to find the occupation that best suits them with greater accuracy. The introduction of an emotion engine enables aptitude assessment that reflects the user's psychological state more than conventional systems, providing more reliable diagnostic results.

[1214] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1215] Step 1:

[1216] When a user starts the application, a login screen is displayed. The user can log in with an existing account or create a new account. The input is the user's ID and password, and the output is the authentication result. The server checks the authentication information, and if authentication is successful, it sends dashboard data to the terminal. Specifically, the terminal displays the login screen, accepts the user's input, and sends it to the server. The server then compares it with a database to perform authentication.

[1217] Step 2:

[1218] When the user presses the "Start Occupational Aptitude Test" button, the terminal displays the first question screen. The input is the user's button operation, and the output is the first question screen. Specifically, the terminal detects the click event of the test start button, obtains the first question data of the aptitude test, and displays it.

[1219] Step 3:

[1220] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. The input is the user's facial expression and voice data, and the output is the recognized emotion data. Specifically, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed in real time by the emotion engine.

[1221] Step 4:

[1222] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." The input is the user's answer and real-time recognized emotion data, and the output is a set of answer data and emotion data. Specifically, the device records the answer and saves it together with the emotion data obtained from the emotion engine.

[1223] Step 5:

[1224] The device temporarily stores answer data and emotion data, and once all questions have been answered, it sends this data to the server. The input is the stored answer data and emotion data, and the output is a data package sent to the server. Specifically, the answer and emotion data are converted into JSON format and sent to the server.

[1225] Step 6:

[1226] The server receives the user's response data and emotion data and formats the data. The input is the submitted response data and emotion data, and the output is the formatted data. The server then inputs the formatted data into a generative AI model for analysis. The input is the formatted data, and the output is the analysis result (the best occupation). The server uses the generative AI model to compare the user's data with a database of past successful people and determine the best occupation.

[1227] Step 7:

[1228] The server saves the generated analysis results in the user's profile and sends them to the device. The input is the analysis results, and the output is the data sent to the device. Specifically, the device displays the results and, if the user selects an occupation they are interested in, displays options to provide more detailed information and related information.

[1229] The above are the specific processing steps of the program for this system. This flow allows users to receive a highly accurate career aptitude diagnosis that takes their own emotions into consideration.

[1230] (Application example 2)

[1231] 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."

[1232] Conventional occupational aptitude analysis systems analyze optimal occupations based on user response data, but they have the problem of low aptitude accuracy because they cannot view the user's emotions or work aptitude. Another issue is that it is difficult to dynamically assign tasks that reflect the emotions and aptitudes of workers at work sites such as factories. This invention incorporates user emotion data into occupational aptitude analysis to improve the accuracy of aptitude diagnosis and to optimize the work content and assignments of factory workers based on their emotions and aptitudes.

[1233] The identification process 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 shaping user response data and converting it into a format for input to the generative AI model, means for displaying a screen on which the user can answer questions, and means for collecting user response data and transmitting the data to the server. This allows user emotional data to be incorporated into occupational aptitude diagnosis, making it possible to determine optimal work content and placement based on the emotions and aptitudes of factory workers.

[1234] "User" refers to an individual or group that uses the System.

[1235] A "question answer screen" refers to an interface that allows a user to enter an answer to a particular question.

[1236] "Answer data" refers to information entered by a user in response to a question.

[1237] "Server" refers to a computing device that analyzes collected data and uses generative AI models to determine optimal occupations and tasks.

[1238] A "generative AI model" refers to an artificial intelligence algorithm that predicts and suggests optimal occupations and work content based on user response data and emotional data.

[1239] A "database of past successes" refers to a database based on individuals and cases of past success.

[1240] "Analysis" refers to the process of evaluating suitability based on data collected by the server and calculating the most suitable occupation and work content based on that.

[1241] "Profile" refers to a collection of personal data that stores information about a user.

[1242] "Terminal" refers to a device that receives and displays the analysis results sent from the server, including smartphones, computers, tablets, etc.

[1243] "Factory workers" refers to individual workers who perform work in a factory.

[1244] "Emotion" refers to the mental state a user expresses in response to a particular situation or question, such as "happiness," "sadness," or "stress."

[1245] "Aptitude" refers to the ability or aptitude a user has for a particular occupation or task.

[1246] "Optimization" refers to determining the most efficient and appropriate work content and placement based on collected data.

[1247] The present invention is a system that analyzes users' occupational aptitude with higher accuracy and realizes optimal work content and placement based on the emotions and aptitudes of workers in a factory. The system involves a process in which users answer questions, collect and analyze data based on the answers, and display the final results.

[1248] The system includes the following hardware and software:

[1249] A device that displays a screen for answering the user's questions: a smartphone, tablet, computer, etc.

[1250] Means for collecting and sending data to a server: a communication module with an internet connection.

[1251] Server that analyzes data using generative AI models: High-performance computing environment (e.g., cloud server).

[1252] External libraries for emotion recognition: Libraries for facial recognition and speech analysis (e.g. emotion_recognition, ai_model).

[1253] The operation of the system is as follows.

[1254] When a user starts the career aptitude test, questions are displayed on the device. As the user answers the questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time and adds the results to the answer data. The collected data, including the user's emotional data, is sent to the server. When the server receives the data, it formats it and inputs it into a generative AI model, which then compares it with a database of past successful candidates to analyze the most suitable career.

[1255] The system also collects data on the emotions and aptitudes of factory workers regarding specific tasks. Questions about the work content and aptitude are displayed to the workers, and their answers and emotional data are sent to the server. The server then analyzes the workers' response data and emotional data to determine the optimal work content and placement.

[1256] For example, if a worker feels "joy" in "assembly work" and is judged to be "good at" it, the frequency with which he or she is in charge of that work will be increased. On the other hand, if another worker feels "stress" in "inspection work" and is judged to be "unskilled" at it, the worker's work will be changed.

[1257] Here are some examples of prompts to input to a generative AI model:

[1258] Analyze the worker data below and suggest the best tasks for them.

[1259] Worker ID: worker_001

[1260] Response data:

[1261] 1. Task: assembly, Answer: yes, Emotion: happy

[1262] 2. Task: Inspection, Answer: no, Emotion: stress

[1263] 3. Task: Packing, Answer: yes, Emotion: neutral

[1264] The system allows users to more accurately find the jobs that best suit them, and enables dynamic work placement within factories based on workers' emotions and aptitudes.

[1265] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1266] Step 1:

[1267] The user starts a career aptitude test. The user launches the application and logs in with an existing account or creates a new account on the login screen. The device checks the authentication information and displays the dashboard screen. The input is the user's authentication information, and the output is the dashboard screen.

[1268] Step 2:

[1269] The user presses the "Start Career Aptitude Test" button. The terminal displays the first question screen and provides an interface for the user to answer the questions. The input is pressing the button, and the output is the first question screen.

[1270] Step 3:

[1271] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions of the user when answering questions can be classified as "happiness" or "sadness." The input is the user's video and audio data, and the output is emotion data.

[1272] Step 4:

[1273] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." At the same time, the emotion engine collects the user's emotion data at that time. The input is the user's answer and emotion data, and the output is a pair of answer data and emotion data.

[1274] Step 5:

[1275] The device temporarily stores the answer data and emotion data, and sends them to the server once all questions have been answered. The data consists of the following:

[1276] {

[1277] "user_id": "12345",

[1278] "responses": [

[1279] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[1280] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[1281] ...

[1282] ]

[1283] }

[1284] The input is response data and emotion data, and the output is data to be sent to the server.

[1285] Step 6:

[1286] The server receives the user's response data and emotion data and formats the data. The input is the transmitted data, and the output is the formatted data.

[1287] Step 7:

[1288] The server uses a generative AI model to compare the user's data with a database of past successful people to determine the most suitable occupation. For example, if answer 1 is 'yes' and emotion is 'happy', it determines that the appropriate occupation for this person is 'project manager'. The input is the formatted data, and the output is the analysis result.

[1289] Step 8:

[1290] The server saves the generated analysis results in the user's profile and sends them to the terminal. The input is the analysis results, and the output is the data saved in the profile.

[1291] Step 9:

[1292] The terminal displays the analysis results to the user. The user can check the results and select the occupation they are interested in. The input is profile data, and the output is display data.

[1293] Step 10:

[1294] A similar process is performed for factory workers: they are asked questions about their work and aptitude, and their responses and emotional data are collected. The input is the worker's responses and emotional data, and the output is worker data.

[1295] Step 11:

[1296] The server analyzes the workers' response data and emotional data to determine the optimal work content and placement. The input is the workers' response data and emotional data, and the output is the optimal work content and placement data.

[1297] Step 12:

[1298] The server notifies the factory system of the analysis results and instructs workers on the appropriate work content and placement. The input is the analysis results, and the output is instruction data for the factory system.

[1299] Through this series of steps, users can find the occupation that best suits them, and factory workers are assigned optimal work content and placement based on their emotions and aptitudes.

[1300] 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.

[1301] 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.

[1302] 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.

[1303] [Fourth embodiment]

[1304] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1305] 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.

[1306] 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).

[1307] 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.

[1308] 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.

[1309] 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).

[1310] 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.

[1311] 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.

[1312] 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.

[1313] 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.

[1314] 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.

[1315] 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.

[1316] 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."

[1317] The present invention is a system for analyzing an individual's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, and a means for the server to receive and analyze the user's answer data and present the most suitable occupation. The system also includes a means for the server to send the analysis results to a terminal, which then displays them.

[1318] The system works as follows:

[1319] 1. User Initiation

[1320] The user launches the application and is presented with a login screen.

[1321] The user logs in with an existing account or creates a new account.

[1322] 2. Start of vocational aptitude test

[1323] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device.

[1324] 3. Answering questions

[1325] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[1326] 4. Processing the invisible parts

[1327] The terminal temporarily stores the user's answers and transmits the answer data to the server when all questions have been answered.

[1328] 5. Server-side analysis

[1329] The server receives the user's response data and formats the data.

[1330] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable occupation.

[1331] For example, if answer 1 is 'yes' and answer 2 is 'no', then the appropriate occupation for this person is 'project manager'.

[1332] 6. Submitting the results

[1333] The server saves the analysis results in the user's profile and sends the results to the user's device.

[1334] 7. Displaying the results

[1335] The device displays the analysis results to the user, presenting a list of occupations that are suitable for the user.

[1336] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[1337] Specific examples

[1338] For example, a user might use an app to take a career aptitude test. The user first presses the "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[1339] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[1340] Finally, the server saves the analysis results in the user's profile and sends them to the device. The device displays the results, allowing the user to review them. Furthermore, if the user is interested in "project manager," an option to obtain more information about that occupation is displayed. In this way, the present invention helps users find the occupation that best suits them.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The user launches the application and is presented with a login screen. The user logs in with an existing account or creates a new account. The server checks the authentication information and displays the dashboard screen on the device.

[1344] Step 2:

[1345] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[1346] Step 3:

[1347] The user answers a question, for example, "Do you like working in a team?" by selecting "yes" or "no."

[1348] Step 4:

[1349] The device temporarily stores the answer and displays the next question, and this process is repeated for all questions.

[1350] Step 5:

[1351] After the device has answered all the questions, it sends the answer data to the server in JSON format, for example as follows:

[1352] json

[1353] {

[1354] "user_id": "12345",

[1355] "responses": [

[1356] {"question_id": "1", "answer": "yes"},

[1357] {"question_id": "2", "answer": "no"},

[1358] ...

[1359] ]

[1360] }

[1361] Step 6:

[1362] The server receives the user's response data and performs a data processing process, converting the processed data into a format that can be input into the generative AI model.

[1363] Step 7:

[1364] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data with a database of past successful users.

[1365] Step 8:

[1366] The generative AI model uses statistical algorithms to analyze the best career aptitudes and generate a list of the resulting jobs. For example, it might generate the following list:

[1367] json

[1368] {

[1369] "recommended_jobs": [

[1370] "Project Manager",

[1371] "consultant",

[1372] "Marketing Manager"

[1373] ]

[1374] }

[1375] Step 9:

[1376] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[1377] Step 10:

[1378] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[1379] Step 11:

[1380] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[1381] The above are the specific processing steps of this system, and the overall flow for users to find the job that best suits them.

[1382] Example 1

[1383] 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."

[1384] There is a need for a system that can quickly and accurately determine an individual's career aptitude. Existing methods require manual analysis of a large number of user responses, which takes a long time to produce results and is inaccurate in recommending suitable careers. Furthermore, they lack a means to obtain additional details about the careers that interest the user.

[1385] 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.

[1386] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and analyze the most suitable occupation by comparing it with a database of past successful people using a generative AI model, means for the server to save the analysis results in the user's profile and transmit them to the terminal, and means for the terminal to display the transmitted analysis results. This makes it possible to quickly and accurately determine an individual's occupational aptitude and further obtain detailed information about occupations in which the user is interested.

[1387] A "user" is an individual who uses the system to take a career aptitude test.

[1388] A "server" is a computer system that receives user response data, analyzes it using a generative AI model, and provides the results to the user.

[1389] A "terminal" is a device that a user uses to take a career aptitude test and that provides an interface for displaying the analysis results.

[1390] "Means for displaying a screen" refers to a function for displaying an interface on a terminal for the user to answer questions.

[1391] "Means for collecting data" refers to the function of recording and temporarily saving answers entered by users to questions.

[1392] "Means for transmitting data" refers to a communication function for transmitting the user's response data to the server.

[1393] A "generative AI model" is a type of artificial intelligence technology used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[1394] A "database" is a collection of information that stores information about successful people of the past.

[1395] "Means for analysis" refers to the function of using a generative AI model to analyze user response data and identify the most suitable occupation.

[1396] A "profile" is a collection of information where data related to a user is stored and where analysis results are stored.

[1397] "Display means" refers to a function that displays the analysis results sent from the server on the user's terminal.

[1398] "Means for obtaining detailed information" refers to an interface that provides further information about the occupation in which the user is interested.

[1399] This invention relates to a system for analyzing an individual's career aptitude. The system operates through a series of processes that display a screen for the user to answer questions, collect the user's answer data, and send it to a server. The following hardware and software are used to implement this system.

[1400] Hardware and software used

[1401] Hardware

[1402] Devices: smartphones, tablets, computers

[1403] Server: Cloud server (e.g. AWS, Google Cloud)

[1404] software

[1405] Application: iOS app, Android app or web application

[1406] Generative AI model: GPT (e.g., OpenAI's GPT-4)

[1407] Natural language processing explanation

[1408] 1. User Initiation

[1409] A user launches an application and is presented with a login screen, where they can log in with an existing account or create a new account.

[1410] 2. Start of vocational aptitude test

[1411] When the user presses the "Start Career Aptitude Test" button, a question screen will appear on the device. Each question is typically answered with a "yes" or "no."

[1412] 3. Answering questions

[1413] The user answers each question, for example, "Do you like working in a team?" by selecting "yes" or "no." The device temporarily stores the answers.

[1414] 4. Data transmission

[1415] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[1416] 5. Data Analysis

[1417] The server formats the received data and converts it into a format that can be input into the generative AI model. The generative AI model then compares it with a database of past successful people and analyzes the most suitable occupation for that person. For example, if answer 1 is 'yes' and answer 2 is 'no', it determines that the appropriate occupation for this person is 'project manager'.

[1418] 6. Submitting the results

[1419] The server saves the analysis results in the user's profile and sends the results to the user's device.

[1420] 7. Displaying the results

[1421] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[1422] Specific examples

[1423] For example, a user might use an app to take a career aptitude test. The user presses a "Start Career Aptitude Test" button and answers a series of questions. For example, the user might answer "Yes" to the question "Are you good at planning?" and then "Do you enjoy creative work?"

[1424] Once the user has completed the survey, the device sends the data to a server. The server receives the data and inputs it into a generative AI model. The model compares the data with a database of successful people in the past and determines whether the user is suitable for a job such as a "project manager," "consultant," or "marketing manager."

[1425] Finally, the server saves the analysis results in the user's profile and sends them to the device, where they are displayed and the user can review the results. Furthermore, if the user is interested in "project manager," they are given the option to get more information about that occupation.

[1426] Example prompts for generative AI models

[1427] The user clicked the "Start Career Aptitude Test" button and answered the questions. The user answered as follows: "Question 1: Yes, Question 2: No, Question 3: Yes." Please recommend the best career for them based on this.

[1428] This invention allows users to quickly and accurately find the occupation that best suits them, and also allows users to easily obtain detailed information about the occupation.

[1429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1430] Step 1:

[1431] The user launches the application. The device displays a login screen, and the user can log in with an existing account or create a new account.

[1432] input:

[1433] User account information (email address, password).

[1434] output:

[1435] Authentication success or failure message for the user.

[1436] Specific behavior:

[1437] The user taps the application to launch it, enters their email address and password, and presses the "Login" button. If creating a new account, they enter the required information and press the "Register" button.

[1438] Step 2:

[1439] The user presses the "Start Career Aptitude Test" button. The device displays the career aptitude test question screen.

[1440] input:

[1441] User action (clicking the "Start Career Aptitude Test" button).

[1442] output:

[1443] Occupational aptitude test question screen.

[1444] Specific behavior:

[1445] Click the "Start Career Aptitude Test" button on the home screen, and the screen will display buttons to move to the next question (such as "Next" or "Answer").

[1446] Step 3:

[1447] The user answers each question, and the device temporarily stores the answers.

[1448] input:

[1449] The user's answer (for example, "yes" or "no").

[1450] output:

[1451] Temporarily stored response data.

[1452] Specific behavior:

[1453] For example, to the question "Do you like working in a team?", select "Yes" or "No" and click the "Next" button. The next question will be displayed.

[1454] Step 4:

[1455] When all questions have been answered, the terminal sends the user's answer data to the server, which receives the answer data.

[1456] input:

[1457] Temporarily stored response data.

[1458] output:

[1459] The response data sent to the server.

[1460] Specific behavior:

[1461] After answering the last question, click the "Done" button. The device formats and sends the answer data. The server receives the data and returns a confirmation message to the device.

[1462] Step 5:

[1463] The server formats the received data and converts it into a format that can be input into a generative AI model, which then compares it with a database of past successful people to analyze the most suitable occupation.

[1464] input:

[1465] The response data sent to the server.

[1466] output:

[1467] Analyzed vocational aptitude results.

[1468] Specific behavior:

[1469] The generative AI model is fed the collected user response data along with the prompt text. The AI ​​model analyzes the data and recommends roles such as "project manager," "consultant," and "marketing manager."

[1470] Step 6:

[1471] The server saves the analysis results in the user's profile and sends the results to the user's device.

[1472] input:

[1473] Analyzed vocational aptitude results.

[1474] output:

[1475] Analysis results stored in the user's profile and data sent to the device.

[1476] Specific behavior:

[1477] The server saves the analysis results in a database, and sends an API call to the device to receive the results once the analysis is complete.

[1478] Step 7:

[1479] The device displays the analysis results to the user, presenting a list of suitable occupations, and when the user selects an occupation that interests them, they are given the option to view more detailed and related information about that occupation.

[1480] input:

[1481] Analysis results sent to the device.

[1482] output:

[1483] Career aptitude results and detailed information displayed to the user.

[1484] Specific behavior:

[1485] On the results screen, you will see a list of recommended jobs. Click on each job's icon or description to view more information.

[1486] (Application example 1)

[1487] 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."

[1488] Conventional career aptitude analysis systems lack interactive counseling for users and are unable to provide sufficient information for career selection. They also lack a means to effectively receive career aptitude analysis from remote locations. Furthermore, it is difficult to obtain detailed information about careers that users are interested in in real time.

[1489] 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.

[1490] In this invention, the server includes means for displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for interactive career aptitude counseling by a counselor in the virtual realm, and means for displaying options for the user to obtain detailed information about occupations they are interested in. This allows the user to receive an interactive career aptitude analysis even from home and obtain detailed information in real time.

[1491] "User" refers to an individual who takes a career aptitude test and uses the results to find a suitable career.

[1492] The term "means for displaying a screen" refers to a device or program that visually presents the interface and questions necessary for the user to answer the questions.

[1493] "Means for collecting response data" refers to a device or program that compiles the responses provided by users as data and stores them in a form that can be used later.

[1494] "Means for transmitting to a server" refers to a device or program for transmitting collected response data to a remote server via a communication network such as the Internet.

[1495] "Generative AI model" refers to an artificial intelligence model, a type of machine learning, that is used to analyze the most suitable occupation by comparing a user's response data with a database of past successful people.

[1496] The "database of past successes" is a database that collects data on individuals who have passed career aptitude tests in the past, and is used as a basis for determining the career aptitude of new users.

[1497] A "profile" is a database that compiles information about a user, and stores analysis results, the user's response history, and other information.

[1498] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet, which displays the analysis results sent from the server.

[1499] "Virtual realm" refers to a virtual reality space that a user accesses through a device such as a head-mounted display, in which interactive career aptitude counseling takes place.

[1500] "Counselor" refers to an avatar or actual person whose role is to provide career aptitude counseling to users within the virtual reality space.

[1501] "Interactive career aptitude counseling" refers to an interactive dialogue in which users answer questions and interact with counselors in real time to analyze career aptitude and find the most suitable career.

[1502] "Options for obtaining more information" refers to settings and functions that allow users to check more detailed information about occupations that interest them, allowing users to learn more about the details of the occupation and the skills required.

[1503] This invention is a system used by users to analyze their career aptitude, and its specific configuration and operation are described below. This system includes a terminal for users to take a career aptitude test, a server that analyzes the user's response data, and a head-mounted display and counselor for providing interactive career counseling within a virtual realm.

[1504] First, a user answers a career aptitude test using a device equipped with a screen display. The answers to the questions are collected on the device, and the collected data is sent to a server. The server formats the answers and converts them into a format that can be input into a generative AI model. This generative AI model compares the answers with a database of past successful candidates and analyzes the most suitable career.

[1505] The analysis results are stored in the user's profile and sent from the server to the device, where they are displayed. The user can then wear a head-mounted display and enter the virtual space, where they can interact with a virtual counselor. The counseling session also provides the user with the option to obtain detailed information about careers they are interested in in real time.

[1506] Hardware and software used

[1507] Hardware:

[1508] 1. Head-mounted displays (e.g., Oculus Rift, HTC Vive)

[1509] 2. Server (e.g. AWS EC2)

[1510] 3. Computers, smartphones, tablets, and other devices

[1511] software:

[1512] 1. Flask: A Python-based web server framework

[1513] 2. Gensim: A framework for Word2Vec generative AI models

[1514] 3. JSON: Data exchange format between client and server

[1515] Details of the process

[1516] 1. User startup:

[1517] The user uses the device to access the career aptitude test screen and answer questions such as "Are you good at planning?" and "Do you enjoy creative work?"

[1518] 2. Sending and analyzing response data:

[1519] The device sends the collected response data to a server, which formats the received data and inputs it into a generative AI model. The generative AI model compares the data with a database of past successful people and analyzes suitable occupations.

[1520] 3. Save and display results:

[1521] The server saves the analysis results in the user's profile and sends them to the user's device, which displays them to the user.

[1522] 4. Virtual Counseling:

[1523] The user puts on a head-mounted display and enters a virtual realm, where a virtual counselor appears and interacts with the user, asking follow-up questions and providing more detailed career information.

[1524] Specific examples

[1525] For example, if a user answers "yes" or "no" to a question, the generative AI model will use these answers to analyze whether occupations such as "marketing manager" or "project manager" are suitable. If the user enters the virtual space and interacts with a counselor, expressing interest in "project manager," they can obtain new detailed information about that occupation.

[1526] Prompt Sentence Examples

[1527] Identify the best occupation for you based on this user's response data. The user's responses are "I'm good at planning" and "I like creative work."

[1528] The system allows users to receive an interactive career aptitude analysis from the comfort of their own home, and enjoy effective counseling to obtain detailed information in real time.

[1529] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1530] Step 1:

[1531] The user uses a terminal to display the screen of a career aptitude test. The user answers the questions. The input is the user's answer, and the output is the answer data temporarily saved on the terminal. Specifically, the user answers "Yes" or "No" to the question "Are you good at making plans?"

[1532] Step 2:

[1533] The device collects the user's response data and sends it to the server. The input is the temporarily saved user response data, and the output is the data sent to the server. A specific example of this behavior is when the device sends the response data to the server in JSON format.

[1534] Step 3:

[1535] The server formats the received user response data and converts it into a format that can be input to the generative AI model. The input is the received user response data, and the output is the formatted data. Specifically, the server converts the response data into a vector and passes it to the generative AI model.

[1536] Step 4:

[1537] The server uses a generative AI model to compare the response data with a database of past successful people and analyze the most suitable occupation. The input is the formatted data and a database of past successful people, and the output is the analysis results. A specific operation is the process in which the generative AI model determines that "the occupation suitable for this user is 'project manager'."

[1538] Step 5:

[1539] The server saves the analysis results in the user's profile and sends them to the device. The input is the analysis results and the user's profile information, and the output is the analysis results saved in the user's profile and the results sent to the device. Specifically, the server saves the analysis results in a database and sends them to the device in JSON format.

[1540] Step 6:

[1541] The terminal displays the analysis results sent to the user. The input is the analysis results sent from the server, and the output is the results displayed on the terminal. Specifically, the message "The job suitable for you is 'Project Manager'" is displayed on the user's screen.

[1542] Step 7:

[1543] The user wears a head-mounted display, enters the virtual space, and interacts with the counselor. The input is the analysis results and the user's selection, and the output is the start of virtual counseling. Specifically, the user becomes interested in the "project manager" and requests more information in the virtual space.

[1544] Step 8:

[1545] In the virtual realm, a counselor provides detailed career information to the user. The input is the user's selection and the counselor's questions, and the output is the detailed career information provided. Specifically, the counselor explains the role of a "project manager" and the skills required.

[1546] Through the above steps, users can receive an interactive career aptitude analysis from home and enjoy effective counseling to obtain detailed information in real time.

[1547] 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.

[1548] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's occupational aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting and sending the user's answer data to a server, a means for the server to receive the user's answer data and use a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results.

[1549] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[1550] Specific operation of the system

[1551] The operation of the system is as follows.

[1552] 1. User Initiation

[1553] The user launches the application and is presented with a login screen. They can log in with an existing account or create a new one. The server checks the authentication information and displays the dashboard screen on the device.

[1554] 2. Start of vocational aptitude test

[1555] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[1556] 3. Emotion Recognition by Emotion Engine

[1557] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions a user makes when answering questions can be classified as "happiness," "anger," "sadness," etc.

[1558] 4. Answering questions and collecting sentiment data

[1559] The user answers a question, for example, "Do you like working in a team?", and selects "yes" or "no." At the same time, the emotion engine collects the user's emotional data at that time.

[1560] 5. Sending data to the server

[1561] The device temporarily stores the answer data and emotion data, and once all questions have been answered, it sends this data to the server. The data is structured as follows:

[1562] json

[1563] {

[1564] "user_id": "12345",

[1565] "responses": [

[1566] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[1567] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[1568] ...

[1569] ]

[1570] }

[1571] 6. Server-side analysis

[1572] The server receives the user's response data and emotion data and formats the data.

[1573] The server uses a generative AI model to match the user's data with a database of past successes to determine the most suitable career.

[1574] For example, if answer 1 is 'yes' and emotion is 'happy', then the suitable occupation for this person is 'project manager'.

[1575] 7. Sending and displaying results

[1576] The server stores the generated analysis results in the user's profile and sends them to the device.

[1577] The device displays the analysis results to the user, who can then review the results and select the occupations that interest them.

[1578] Once users select a career they are interested in, they are given the option to view more detailed and related information.

[1579] The above is a specific embodiment for carrying out the present invention. This system allows users to find the most suitable occupation for themselves with high accuracy.

[1580] The processing flow will be explained below.

[1581] Step 1:

[1582] When a user launches the application, the login screen is displayed. The user logs in with an existing account or creates a new account. The server checks the user's authentication information, and if authentication is successful, the dashboard screen is displayed on the device.

[1583] Step 2:

[1584] When the user presses the "Start Career Aptitude Test" button, the device displays the first question screen.

[1585] Step 3:

[1586] The device will activate the emotion engine and begin collecting and analyzing the user's facial expressions and voice in real time. Specifically, the user will be required to enable the camera and microphone.

[1587] Step 4:

[1588] The user answers a question. For example, "Do you like working in a team?" and selects "yes" or "no." During this time, the emotion engine recognizes emotional data (such as "happiness," "sadness," or "surprise") from the user's facial expressions and voice.

[1589] Step 5:

[1590] The device temporarily stores the user's response and emotional data. For example, the following data is stored:

[1591] json

[1592] {

[1593] "question_id": "1",

[1594] "answer": "yes",

[1595] "emotion": "happy"

[1596] }

[1597] Step 6:

[1598] After the device has completed collecting all the answers to the questions and emotional data, it sends this data in JSON format to the server.

[1599] Step 7:

[1600] The server receives the user's response data and emotion data, formats the data, and converts it into a format that can be input into the generative AI model.

[1601] Step 8:

[1602] The server calls the generative AI model and begins analysis based on the received data. The generative AI model compares the user's response data and emotional data with a database of past successful users.

[1603] Step 9:

[1604] The generative AI model uses statistical algorithms to analyze the most suitable occupations and generate a list of the results. For example, the AI ​​model might determine that "if answer 1 is 'yes' and emotion is 'happy', then the appropriate occupation for this person is 'project manager'."

[1605] Step 10:

[1606] The server saves the generated suitable occupation list in the user's profile and transmits it to the terminal.

[1607] Step 11:

[1608] The terminal displays the list of suitable occupations sent to the user, who can then review the results and select the occupations that interest them.

[1609] Step 12:

[1610] Once the user selects a job they are interested in, the device will provide more information related to that job, as well as the option to view further job listing pages.

[1611] The above are the specific processing steps of this system and the overall flow for users to find the occupation that best suits them. By adding an emotion engine, it is possible to reflect the user's emotional state and make more accurate occupational aptitude diagnosis.

[1612] Example 2

[1613] 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."

[1614] Conventional occupational aptitude diagnosis systems analyzed occupations based solely on user response data, which meant that they were unable to reflect the user's emotions and psychological state, resulting in a decrease in the accuracy of the aptitude diagnosis.In addition, the fixed order and content of questions sometimes made it difficult for users to operate the system.

[1615] 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 displaying a screen for the user to answer questions, means for collecting user answer data and transmitting the data to the server, means for the server to receive the user answer data and compare it with a database of past successful people using a generative AI model to analyze the most suitable occupation, means for the server to save the analysis results in the user's profile and transmit them to the terminal, means for the terminal to display the transmitted analysis results, means for the terminal to analyze the user's facial expressions and voice in real time and recognize emotions, means for adding emotion data to the question answer data, and means for dynamically changing the content and order of questions based on the emotion recognition results. This enables more accurate occupational aptitude diagnosis that reflects the user's emotions.

[1616] The "screen for answering questions" refers to an interface that allows the user to input answers to questions that are displayed.

[1617] "Answer data" refers to data describing the answer entered by the user to the question.

[1618] A "generative AI model" refers to a mathematical model that uses artificial intelligence to perform analysis and prediction.

[1619] A "database of past successful people" refers to a database that accumulates data on successful people from the past.

[1620] "Analysis results" refers to the results of the server analyzing the user's response data using the generated AI model.

[1621] A "profile" refers to data that compiles information about a user.

[1622] "Terminal" refers to an electronic device that is used by a user for operation.

[1623] "Real-time analysis of facial expressions and voice" refers to the device instantly analyzing the user's facial expressions and voice.

[1624] "Emotion recognition" refers to the process of identifying emotions from a user's facial expressions and voice.

[1625] "Emotion data" refers to data related to the user's emotions obtained through emotion recognition.

[1626] "Dynamic change of the content and order of questions" refers to changing the order and content of questions in real time based on the user's emotions.

[1627] "Means for displaying options" refers to an interface that displays options for a user to obtain more information about a job that interests them.

[1628] The present invention improves the accuracy of aptitude tests by combining an emotion engine with a system for analyzing a user's career aptitude. This system includes a means for displaying a screen on which the user can answer questions, a means for collecting the user's answer data and sending it to a server, a means for the server to receive the user's answer data and compare it with a database of past successful candidates using a generative AI model to analyze the most suitable career, a means for the server to save the analysis results in the user's profile and send them to the device, and a means for the device to display the analysis results. Furthermore, the emotion engine allows the system to recognize emotions from the user's facial expressions and voice in real time and add the results to the question answer data, as well as dynamically change the content and order of questions based on the emotion recognition results.

[1629] The following hardware and software are used to implement this system.

[1630] First, when a user launches an application, a device such as a smartphone or tablet is used. When the user logs in, the server verifies the authentication information and displays the dashboard screen on the device. The main software used at this stage includes a web server and a database management system.

[1631] Next, when the user starts the career aptitude test, the device displays the first question screen. The question includes specific content such as "Do you like working in a team?" As the user enters their answer, the device analyzes their facial expressions and voice in real time, and an emotion engine recognizes their emotions. The emotion analysis software used here may be technology such as OpenFace or IBM Watson.

[1632] Once all questions have been answered, the device sends the collected answer data and emotion data to the server. The data is structured as follows, for example:

[1633] Response data:

[1634] Question 1: "Yes", Emotion: "Joy"

[1635] Question 2: "No", Sentiment: "Neutral"

[1636] When the server receives this data, it formats it and converts it into a format that can be input to a generative AI model, such as GPT-3 or BERT. Analysis can be performed using prompts like the following:

[1637] Example prompt:

[1638] Receive the user's answer data in the following format and compare it with a database of past successes to determine the most suitable occupation. Answer data: Question 1: "Yes", Emotion: "Joy" Question 2: "No", Emotion: "Neutral"

[1639] The generative AI model performs the analysis and generates results identifying the most suitable occupation, such as a suggested occupation title like "Project Manager." The server stores these results in the user's profile and sends them to the device, where the user can review the results and receive options for further information.

[1640] This system allows users to find the occupation that best suits them with greater accuracy. The introduction of an emotion engine enables aptitude assessment that reflects the user's psychological state more than conventional systems, providing more reliable diagnostic results.

[1641] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1642] Step 1:

[1643] When a user starts the application, a login screen is displayed. The user can log in with an existing account or create a new account. The input is the user's ID and password, and the output is the authentication result. The server checks the authentication information, and if authentication is successful, it sends dashboard data to the terminal. Specifically, the terminal displays the login screen, accepts the user's input, and sends it to the server. The server then compares it with a database to perform authentication.

[1644] Step 2:

[1645] When the user presses the "Start Occupational Aptitude Test" button, the terminal displays the first question screen. The input is the user's button operation, and the output is the first question screen. Specifically, the terminal detects the click event of the test start button, obtains the first question data of the aptitude test, and displays it.

[1646] Step 3:

[1647] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. The input is the user's facial expression and voice data, and the output is the recognized emotion data. Specifically, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed in real time by the emotion engine.

[1648] Step 4:

[1649] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." The input is the user's answer and real-time recognized emotion data, and the output is a set of answer data and emotion data. Specifically, the device records the answer and saves it together with the emotion data obtained from the emotion engine.

[1650] Step 5:

[1651] The device temporarily stores answer data and emotion data, and once all questions have been answered, it sends this data to the server. The input is the stored answer data and emotion data, and the output is a data package sent to the server. Specifically, the answer and emotion data are converted into JSON format and sent to the server.

[1652] Step 6:

[1653] The server receives the user's response data and emotion data and formats the data. The input is the submitted response data and emotion data, and the output is the formatted data. The server then inputs the formatted data into a generative AI model for analysis. The input is the formatted data, and the output is the analysis result (the best occupation). The server uses the generative AI model to compare the user's data with a database of past successful people and determine the best occupation.

[1654] Step 7:

[1655] The server saves the generated analysis results in the user's profile and sends them to the device. The input is the analysis results, and the output is the data sent to the device. Specifically, the device displays the results and, if the user selects an occupation they are interested in, displays options to provide more detailed information and related information.

[1656] The above are the specific processing steps of the program for this system. This flow allows users to receive a highly accurate career aptitude diagnosis that takes their own emotions into consideration.

[1657] (Application example 2)

[1658] 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."

[1659] Conventional occupational aptitude analysis systems analyze optimal occupations based on user response data, but they have the problem of low aptitude accuracy because they cannot view the user's emotions or work aptitude. Another issue is that it is difficult to dynamically assign tasks that reflect the emotions and aptitudes of workers at work sites such as factories. This invention incorporates user emotion data into occupational aptitude analysis to improve the accuracy of aptitude diagnosis and to optimize the work content and assignments of factory workers based on their emotions and aptitudes.

[1660] The identification process 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 shaping user response data and converting it into a format for input to the generative AI model, means for displaying a screen on which the user can answer questions, and means for collecting user response data and transmitting the data to the server. This allows user emotional data to be incorporated into occupational aptitude diagnosis, making it possible to determine optimal work content and placement based on the emotions and aptitudes of factory workers.

[1661] "User" refers to an individual or group that uses the System.

[1662] A "question answer screen" refers to an interface that allows a user to enter an answer to a particular question.

[1663] "Answer data" refers to information entered by a user in response to a question.

[1664] "Server" refers to a computing device that analyzes collected data and uses generative AI models to determine optimal occupations and tasks.

[1665] A "generative AI model" refers to an artificial intelligence algorithm that predicts and suggests optimal occupations and work content based on user response data and emotional data.

[1666] A "database of past successes" refers to a database based on individuals and cases of past success.

[1667] "Analysis" refers to the process of evaluating suitability based on data collected by the server and calculating the most suitable occupation and work content based on that.

[1668] "Profile" refers to a collection of personal data that stores information about a user.

[1669] "Terminal" refers to a device that receives and displays the analysis results sent from the server, including smartphones, computers, tablets, etc.

[1670] "Factory workers" refers to individual workers who perform work in a factory.

[1671] "Emotion" refers to the mental state a user expresses in response to a particular situation or question, such as "happiness," "sadness," or "stress."

[1672] "Aptitude" refers to the ability or aptitude a user has for a particular occupation or task.

[1673] "Optimization" refers to determining the most efficient and appropriate work content and placement based on collected data.

[1674] The present invention is a system that analyzes users' occupational aptitude with higher accuracy and realizes optimal work content and placement based on the emotions and aptitudes of workers in a factory. The system involves a process in which users answer questions, collect and analyze data based on the answers, and display the final results.

[1675] The system includes the following hardware and software:

[1676] A device that displays a screen for answering the user's questions: a smartphone, tablet, computer, etc.

[1677] Means for collecting and sending data to a server: a communication module with an internet connection.

[1678] Server that analyzes data using generative AI models: High-performance computing environment (e.g., cloud server).

[1679] External libraries for emotion recognition: Libraries for facial recognition and speech analysis (e.g. emotion_recognition, ai_model).

[1680] The operation of the system is as follows.

[1681] When a user starts the career aptitude test, questions are displayed on the device. As the user answers the questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time and adds the results to the answer data. The collected data, including the user's emotional data, is sent to the server. When the server receives the data, it formats it and inputs it into a generative AI model, which then compares it with a database of past successful candidates to analyze the most suitable career.

[1682] The system also collects data on the emotions and aptitudes of factory workers regarding specific tasks. Questions about the work content and aptitude are displayed to the workers, and their answers and emotional data are sent to the server. The server then analyzes the workers' response data and emotional data to determine the optimal work content and placement.

[1683] For example, if a worker feels "joy" in "assembly work" and is judged to be "good at" it, the frequency with which he or she is in charge of that work will be increased. On the other hand, if another worker feels "stress" in "inspection work" and is judged to be "unskilled" at it, the worker's work will be changed.

[1684] Here are some examples of prompts to input to a generative AI model:

[1685] Analyze the worker data below and suggest the best tasks for them.

[1686] Worker ID: worker_001

[1687] Response data:

[1688] 1. Task: assembly, Answer: yes, Emotion: happy

[1689] 2. Task: Inspection, Answer: no, Emotion: stress

[1690] 3. Task: Packing, Answer: yes, Emotion: neutral

[1691] The system allows users to more accurately find the jobs that best suit them, and enables dynamic work placement within factories based on workers' emotions and aptitudes.

[1692] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1693] Step 1:

[1694] The user starts a career aptitude test. The user launches the application and logs in with an existing account or creates a new account on the login screen. The device checks the authentication information and displays the dashboard screen. The input is the user's authentication information, and the output is the dashboard screen.

[1695] Step 2:

[1696] The user presses the "Start Career Aptitude Test" button. The terminal displays the first question screen and provides an interface for the user to answer the questions. The input is pressing the button, and the output is the first question screen.

[1697] Step 3:

[1698] The device analyzes the user's facial expressions and voice in real time, and the emotion engine recognizes the user's emotions. For example, the facial expressions of the user when answering questions can be classified as "happiness" or "sadness." The input is the user's video and audio data, and the output is emotion data.

[1699] Step 4:

[1700] The user answers a question. For example, "Do you like working in a team?" and selects "Yes" or "No." At the same time, the emotion engine collects the user's emotion data at that time. The input is the user's answer and emotion data, and the output is a pair of answer data and emotion data.

[1701] Step 5:

[1702] The device temporarily stores the answer data and emotion data, and sends them to the server once all questions have been answered. The data consists of the following:

[1703] {

[1704] "user_id": "12345",

[1705] "responses": [

[1706] {"question_id": "1", "answer": "yes", "emotion": "happy"},

[1707] {"question_id": "2", "answer": "no", "emotion": "neutral"},

[1708] ...

[1709] ]

[1710] }

[1711] The input is response data and emotion data, and the output is data to be sent to the server.

[1712] Step 6:

[1713] The server receives the user's response data and emotion data and formats the data. The input is the transmitted data, and the output is the formatted data.

[1714] Step 7:

[1715] The server uses a generative AI model to compare the user's data with a database of past successful people to determine the most suitable occupation. For example, if answer 1 is 'yes' and emotion is 'happy', it determines that the appropriate occupation for this person is 'project manager'. The input is the formatted data, and the output is the analysis result.

[1716] Step 8:

[1717] The server saves the generated analysis results in the user's profile and sends them to the terminal. The input is the analysis results, and the output is the data saved in the profile.

[1718] Step 9:

[1719] The terminal displays the analysis results to the user. The user can check the results and select the occupation they are interested in. The input is profile data, and the output is display data.

[1720] Step 10:

[1721] A similar process is performed for factory workers: they are asked questions about their work and aptitude, and their responses and emotional data are collected. The input is the worker's responses and emotional data, and the output is worker data.

[1722] Step 11:

[1723] The server analyzes the workers' response data and emotional data to determine the optimal work content and placement. The input is the workers' response data and emotional data, and the output is the optimal work content and placement data.

[1724] Step 12:

[1725] The server notifies the factory system of the analysis results and instructs workers on the appropriate work content and placement. The input is the analysis results, and the output is instruction data for the factory system.

[1726] Through this series of steps, users can find the occupation that best suits them, and factory workers are assigned optimal work content and placement based on their emotions and aptitudes.

[1727] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1728] 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.

[1729] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1730] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1731] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1732] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1733] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1734] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1735] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1736] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1737] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1738] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1739] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1740] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1741] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1742] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1743] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1744] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1745] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1746] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1747] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1748] The following is further disclosed regarding the above embodiment.

[1749] (Claim 1)

[1750] means for displaying a screen for the user to answer questions;

[1751] means for collecting user response data and transmitting the data to a server;

[1752] The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation;

[1753] The server stores the analysis results in the user's profile and transmits them to the device;

[1754] A means for the device to display the analysis results sent

[1755] A system including:

[1756] (Claim 2)

[1757] 2. The system of claim 1, further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

[1758] (Claim 3)

[1759] 10. The system of claim 1, further comprising means for displaying an option for the user to obtain more information about a career of interest.

[1760] "Example 1"

[1761] (Claim 1)

[1762] means for displaying a screen for the user to answer questions;

[1763] means for collecting user response data and transmitting the data to a server;

[1764] The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation;

[1765] The server stores the analysis results in the user's profile and transmits them to the device;

[1766] The system includes a means for displaying the transmitted analysis results at the terminal.

[1767] (Claim 2)

[1768] 2. The system of claim 1, further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

[1769] (Claim 3)

[1770] 10. The system of claim 1, further comprising means for displaying an option for the user to obtain more information about a career of interest.

[1771] "Application Example 1"

[1772] (Claim 1)

[1773] means for displaying a screen for the user to answer questions;

[1774] means for collecting user response data and transmitting the data to a server;

[1775] The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation;

[1776] The server stores the analysis results in the user's profile and transmits them to the device;

[1777] a means for displaying the transmitted analysis results by the terminal;

[1778] A means for providing interactive career aptitude counseling with a counselor in the virtual realm

[1779] A system including:

[1780] (Claim 2)

[1781] 2. The system of claim 1, further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

[1782] (Claim 3)

[1783] 10. The system of claim 1, further comprising means for displaying an option for the user to obtain more information about a career of interest.

[1784] "Example 2: Combining Emotion Engines"

[1785] (Claim 1)

[1786] means for displaying a screen for the user to answer questions;

[1787] means for collecting user response data and transmitting the data to a server;

[1788] The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation;

[1789] The server stores the analysis results in the user's profile and transmits them to the device;

[1790] a means for displaying the transmitted analysis results by the terminal;

[1791] A means for the device to analyze the user's facial expressions and voice in real time and perform emotion recognition;

[1792] means for adding emotion data to question response data;

[1793] A method for dynamically changing the content and order of questions based on emotion recognition results

[1794] A system including:

[1795] (Claim 2)

[1796] 2. The system of claim 1, further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

[1797] (Claim 3)

[1798] 10. The system of claim 1, further comprising means for displaying an option for the user to obtain more information about a career of interest.

[1799] "Application example 2 when combining emotion engines"

[1800] (Claim 1)

[1801] means for displaying a screen for the user to answer questions;

[1802] means for collecting user response data and transmitting the data to a server;

[1803] The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation;

[1804] The server stores the analysis results in the user's profile and transmits them to the device;

[1805] a means for displaying the transmitted analysis results by the terminal;

[1806] A means of analyzing the emotions and aptitudes of workers in a factory in real time and optimizing their work content and placement based on that information

[1807] A system including:

[1808] (Claim 2)

[1809] 2. The system of claim 1, further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

[1810] (Claim 3)

[1811] 10. The system of claim 1, further comprising means for displaying an option for the user to obtain more information about a career of interest. [Explanation of symbols]

[1812] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for displaying a screen for the user to answer questions; means for collecting user response data and transmitting the data to a server; The server receives the user's response data and uses a generative AI model to compare it with a database of past successful people to analyze the most suitable occupation; The server stores the analysis results in the user's profile and transmits them to the device; A means for the device to display the analysis results sent A system including:

2. The system of claim 1 further comprising means for the server to format the user's response data and convert it into a format for input to the generative AI model.

3. 10. The system of claim 1, further comprising means for displaying options for a user to obtain more information about a career of interest.

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