System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting information, work experience, skills, interests, and career goals of a job seeker; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and the collected market data and suggesting an optimal job type and career path using a machine learning algorithm; and means for updating and evolving the suggested career path according to the progress of the job seeker and visually displaying the career path.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's labor market, it is difficult for job seekers to find a job that matches their skills, interests, and career goals, requiring a lot of time and effort. Companies also face difficulties in placing the right people in the right positions, resulting in a high incidence of mismatches. Furthermore, there is a lack of objective advice and support available to job seekers to efficiently progress in their careers and build up their skills. This project aims to solve these problems. [Means for solving the problem]

[0005] It provides a means for entering job seeker information, work history, skills, interests, and career goals, as well as a means for collecting information on market trends, required skills, and career paths. It also provides a means for analyzing the entered information and collected market data and using machine learning algorithms to suggest optimal job types and career paths. Furthermore, it incorporates a means for updating and evolving the suggested career paths according to the job seeker's progress and visually displaying them, thereby continuously providing the optimal career plan for the job seeker. It also incorporates a means for managing subscription status, processing billing for usage fees, managing collaboration information with companies, and providing job seekers with job information and direct requests from companies, thereby creating a valuable service for both companies and job seekers.

[0006] "Job applicant information" refers to personal information such as name, address, contact details, and all career-related information that job applicants enter into the system.

[0007] "Work history" refers to information such as details of the jobs a job seeker has previously held, the duration of the job, the job content, and the job title.

[0008] "Skills" are specific abilities, knowledge, and techniques possessed by job seekers. Examples include programming skills, language proficiency, and professional qualifications.

[0009] "Interests" are information related to fields, topics, or industries that a job seeker is particularly interested in.

[0010] "Career goals" are goals such as the job or position a job seeker wants to achieve in the future, or the skills or qualifications they want to acquire.

[0011] "Market trends" refers to information about the latest developments, changes in demand, technological innovations, etc. in a particular industry or occupation.

[0012] "Demand skills" are the abilities, knowledge, and techniques currently required in a particular occupation or industry.

[0013] A "career path" is a series of steps or positions that a job seeker must take to reach their desired career goal.

[0014] A "machine learning algorithm" is a computational method for recognizing patterns based on large amounts of data and automatically learning and making predictions.

[0015] "Analysis" is the technique of analyzing collected data in detail, extracting necessary information, and drawing appropriate conclusions.

[0016] "Suggestions" are specific guidelines the system provides job seekers for the best career path, next steps, and skill improvement.

[0017] "Subscription status" refers to information such as the user's subscription service usage status, plan type, and payment status.

[0018] "Progress" is information that indicates the stage at which a job seeker is in their individual career plan and the extent of their achievements.

[0019] "Visual display" refers to displaying analysis results and proposals in an easy-to-read format such as graphs, charts, or lists.

[0020] "Job information" refers to information provided by companies, such as details of the job position, conditions, and required skills.

[0021] A "direct request from a company" is any direct communication from a company to a specific job seeker, such as an employment offer or interview request. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] ---

[0044] This invention is a job matching system that utilizes a generative AI model to identify optimal jobs based on information obtained from users and support the development and progression of career paths. The system uses job seeker input, market trends, and machine learning algorithms to provide users with personalized career plans.

[0045] overview

[0046] The system has three main roles:

[0047] 1. Enter user information

[0048] 2. Data analysis and career path generation by the server

[0049] 3. Displaying and interacting with results on the device

[0050] Enter user information

[0051] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[0052] Server-based data analysis and career path generation

[0053] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[0054] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[0055] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0056] Displaying and interacting with results on the device

[0057] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[0058] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[0059] Specific examples

[0060] Case 1: Career planning for new graduates

[0061] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0062] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0063] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0064] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0065] 5. The user reviews the suggested career path and enrolls in the recommended online course as their next step.

[0066] Case 2: Mid-career professionals

[0067] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0068] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0069] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0070] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0071] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[0072] Business model implementation

[0073] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[0074] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development.

[0075] The processing flow will be explained below.

[0076] ---

[0077] Step 1:

[0078] A user logs in to the system.

[0079] The user accesses the login screen and enters their user ID and password.

[0080] Click the Login button to submit your credentials.

[0081] Step 2:

[0082] The server authenticates the user.

[0083] The server checks the received authentication information against its database to see if a matching user exists.

[0084] If the authentication is successful, the session is started and the home screen is returned to the user.

[0085] If authentication fails, an error message is returned.

[0086] Step 3:

[0087] Users enter data such as work history, skills, interests, and career goals.

[0088] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[0089] After completing the input, click the send button to save the data.

[0090] Step 4:

[0091] The terminal transmits the user input data to the server.

[0092] The device converts the input data into the appropriate format and sends it to the server as an API request.

[0093] Step 5:

[0094] The server collects information on market trends, required skills, and career paths for each industry and job position.

[0095] The server uses external APIs and web scraping technology to collect the latest industry data and store it in a database.

[0096] Step 6:

[0097] The server analyzes user data and market data.

[0098] The server uses machine learning algorithms to analyze user information and market data.

[0099] The analysis uses input data to identify optimal job types, recommended skill sets, career paths, and more.

[0100] Step 7:

[0101] A server generates career path suggestions.

[0102] The server generates suggestions for the user based on the analysis results.

[0103] Suggestions include next career steps, how to acquire recommended skills, and available resources.

[0104] Step 8:

[0105] The device displays the career path to the user.

[0106] The terminal visualizes the proposals received from the server and displays them in an easy-to-understand manner for the user.

[0107] Step 9:

[0108] The user reviews the proposal and selects the next step.

[0109] Users review suggested career paths and choose next actions, such as using recommended resources to learn new skills.

[0110] Step 10:

[0111] The server periodically updates the user's progress data.

[0112] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[0113] Update and evolve your career plan based on the data received.

[0114] Step 11:

[0115] The server manages the subscription model.

[0116] The server manages the user's subscription status and handles billing for usage fees.

[0117] Send reminders to users when their subscriptions are about to expire.

[0118] Step 12:

[0119] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[0120] The server stores job information provided by companies in a database and notifies users as appropriate.

[0121] Forwarding direct requests from companies (e.g., interview offers) to users.

[0122] ---

[0123] Example 1

[0124] 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."

[0125] In today's job search and career development process, job seekers spend a great deal of time and effort organizing their information and finding the right job or career path based on that information. Furthermore, a lack of awareness of market trends and required skill sets makes it difficult to plan their careers. To solve these challenges, a system is needed that provides individually customized career advice and dynamically updates plans based on progress.

[0126] 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.

[0127] In this invention, the server includes a means for inputting job seeker information, work history, skills, interests, and career goals, a means for collecting information on market trends, required abilities, and career paths, and a means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal occupations and career paths. This makes it possible to provide individually customized career advice and dynamically update career plans according to progress.

[0128] A "job seeker" is an individual seeking employment, someone who is looking for a new position that utilizes their skills and experience.

[0129] "Information" refers to data about job seekers, including detailed data entered about their work history, skills, interests, career goals, etc.

[0130] "Work history" refers to the history of jobs and positions held by a job seeker in the past, including details such as the duration and nature of the work.

[0131] "Skills" refers to the specialized knowledge and skills that a job seeker possesses, such as programming languages ​​or project management skills.

[0132] "Interests" refers to areas or topics that a job seeker is particularly interested in for their future career.

[0133] "Career goals" refer to the professional goals or positions that job seekers hope to achieve in the future.

[0134] "Market trends" refers to current labor market conditions and trends, including the demand for specific skills and occupations, and employer needs.

[0135] "Demanding competencies" refer to the skills and expertise required in the labor market or in specific occupations.

[0136] "Career path" refers to the specific steps and direction a job seeker takes to advance their career.

[0137] "Analysis" refers to the activity of analyzing input job seeker information and market data to identify the most suitable jobs and career paths.

[0138] A "machine learning algorithm" is a type of program that automatically finds patterns based on large amounts of data and makes predictions and judgments.

[0139] "Suggestion" refers to the act of specifically indicating suitable occupations and career paths for job seekers based on the results of the analysis.

[0140] "Progress" refers to the degree of progress achieved as a result of a job seeker taking action based on a suggestion.

[0141] "Update and evolution" refers to the process of dynamically revising the suggested career path based on the job seeker's latest situation and progress, and making more appropriate suggestions.

[0142] "Visual display" refers to displaying analysis results and proposals using visual elements such as graphs and charts.

[0143] "Subscription Status" refers to the status of the recurring fee paid by a Job Seeker for use of the System.

[0144] "Usage Fee Billing Process" refers to the process for billing Job Seekers for the fees required to use the Subscription Service.

[0145] "Information on collaboration with companies" refers to job information and requests collected by the system in collaboration with companies.

[0146] "Job information" refers to information about employment opportunities provided by businesses to job seekers.

[0147] A "direct request" is a specific request or proposal sent directly by an employer to a job seeker.

[0148] This invention is a career advisory system that uses a generative AI model to support career development by proposing optimal occupations and career paths based on information entered by job seekers. This system has multiple functions, including job seeker information entry, market trend collection, data analysis, career path generation, progress tracking and updates, and visual information display.

[0149] overview

[0150] This system has three roles:

[0151] 1. Enter user information

[0152] 2. Data analysis and career path generation by the server

[0153] 3. Displaying and interacting with results on the device

[0154] Enter user information

[0155] Users log in to the system and enter information such as their work history, skills, interests, and career goals on the profile setting screen. For example, a recent graduate might enter the major they studied at university and the industry they are interested in (e.g., information engineering, AI technology). This input data is stored in a database and used for later analysis.

[0156] Server-based data analysis and career path generation

[0157] The server performs the following functions:

[0158] 1. Data collection: The server periodically collects information on market trends, required skills, and career paths using external APIs and web scraping technology. For example, it uses the LinkedIn API to collect market trend data.

[0159] 2. Data analysis: The server uses machine learning algorithms such as "scikit-learn" to analyze job seeker information and collected market data, comparing the user's skill set with the skills in demand in the market and identifying the most suitable job and recommended skill set.

[0160] 3. Career path generation: Based on the analysis results, the server generates personalized career path suggestions for job seekers, such as recommending advanced Python skills to become a "data scientist" and offering specific Udemy courses.

[0161] Displaying and interacting with results on the device

[0162] The terminal is responsible for:

[0163] 1. Displaying the results: Receive the career path suggestions sent from the server and display them visually to the user. For example, use "D3.js" or "Chart.js" to display the career paths and recommended skill sets in graphs and charts.

[0164] 2. Interaction: The user reviews the proposed career path and selects their next step. If the user enrolls in a recommended online course, the server records that information and tracks their progress. The system dynamically updates the career plan and makes new suggestions based on their progress.

[0165] Specific examples

[0166] Case 1: Career planning for new graduates

[0167] 1. A user (a recent graduate student) logs in to the system and enters in their profile that they are majoring in information engineering and interested in AI technology.

[0168] 2. The server receives this information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0169] 3. The server uses "scikit-learn" to analyze user data and market data and identify "Python" and "basic machine learning" as important skills.

[0170] 4. The server suggests career paths to becoming a "data scientist," recommending, for example, "take a Udemy course to acquire advanced Python skills."

[0171] 5. The device displays suggested career paths and skill sets to the user, and suggests specific next steps to take.

[0172] 6. The user follows the suggestion and enrolls in a Python course on Udemy.

[0173] 7. The server tracks the user's progress through the online course and suggests next steps when a new skill is mastered.

[0174] Case 2: Mid-career professionals

[0175] 1. A user (mid-career professional) logs into the system, currently works as a lead engineer, and enters recent projects and new skills (e.g., project management) into their profile.

[0176] 2. The server takes the user's input data and compares it with the latest industry trends. For example, it determines that there is a growing demand for project management.

[0177] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0178] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0179] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[0180] Prompt Sentence Examples

[0181] "Create a career path suitable for new graduates majoring in information engineering and interested in AI technology."

[0182] "Please suggest the next career step for a user currently working as a lead engineer."

[0183] In this way, the system of the present invention uses a generative AI model to provide job seekers with customized career advice and dynamically update their career plans according to their progress, thereby supporting efficient and effective career development.

[0184] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0185] Step 1:

[0186] A user accesses the system and logs in by entering their login information, which is an email address and a password, which authenticates the user and allows them to access their user profile.

[0187] Step 2:

[0188] Users enter information such as work history, skills, interests, and career goals on the profile setting screen. For example, they might enter "3 years as a software engineer" as their work history and "Python, data analysis" as their skills. This information is sent to the server and stored in a database.

[0189] Step 3:

[0190] The server periodically uses external APIs and web scraping technologies (e.g., "LinkedIn API") to collect data on market trends and desired skills. The collected data is stored in a database for later analysis. The input is the external data source, and the output is updated market data.

[0191] Step 4:

[0192] The server combines the received user data and market data and performs data analysis using machine learning algorithms (e.g., "scikit-learn"). The analysis identifies the best jobs and recommended skill sets. The inputs for this step are user data and market data, and the output is the analysis results.

[0193] Step 5:

[0194] Based on the analysis results, the server proposes a personalized career path to the user. This proposal includes the next skills to acquire and online courses to study. For example, a specific proposal may be made such as, "Your next step is to acquire advanced skills in Python. We recommend Udemy course X." The input is the analysis results, and the output is a career path proposal.

[0195] Step 6:

[0196] The terminal receives the career path suggestions sent from the server and visually displays them to the user using graphs and charts (e.g., "D3.js"). The displayed information includes details of the career path and recommended skill sets. The input is the career path suggestions, and the output is the visual display.

[0197] Step 7:

[0198] The user reviews the proposed career path and takes the next step by enrolling in a recommended online course, such as enrolling in a Python course on Udemy. This information is sent back to the server, allowing the system to track their progress.

[0199] Step 8:

[0200] The server periodically checks the user's progress (e.g., progress in online courses) and dynamically updates the career plan based on new skills and advancements acquired. The input to this step is the user's progress, and the output is the updated career plan.

[0201] Step 9:

[0202] The server sends the updated carrier plan back to the terminal, which then visually displays it to the user. This allows the user to always check the latest carrier plan and know the specific action to take next. The input is the updated carrier plan, and the output is another visual display.

[0203] In this way, the system allows users, servers, and terminals to play their respective roles and work together to support job seekers' career development.

[0204] (Application example 1)

[0205] 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."

[0206] Currently, store staff and managers lack the means to plan their career paths and receive support to acquire the most suitable jobs and skills. As a result, they face the challenge of spending a great deal of time and effort gathering appropriate information and self-studying to achieve their career goals. Furthermore, with industry trends changing rapidly, career plans need to be updated based on the latest information, and there is a need for automation of this process.

[0207] 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.

[0208] In this invention, the server includes means for inputting a user's work history, skills, interests, and career goals, means for collecting information on market trends, required skills, and career paths, means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal jobs and career paths, means for updating and evolving the suggested career paths according to the job seeker's progress and visually displaying them, and means for suggesting optimal career paths based on the work history, skills, and interests of store users and showing learning resources and skill improvement methods related to work at the physical store. This makes it easier for staff and managers working at physical stores to efficiently obtain and update career plans based on the latest market trends and acquire appropriate skill sets.

[0209] "Job Seeker" means an individual who is searching for employment.

[0210] "Work history" refers to work experience, which is the history of the jobs that an individual has held up to now.

[0211] "Skills" refers to the skills and abilities a job seeker possesses to perform a specific task or job.

[0212] "Interests" refers to areas that interest the job seeker or are relevant to the job.

[0213] "Career goals" are goals related to the job or position that a job seeker wants to achieve in the future.

[0214] "Market trends" refers to the general movements and demands in the current industry or job market.

[0215] "Demand skills" refers to the specialized techniques and knowledge required for a particular job or industry.

[0216] A "career path" refers to the route or path of professional growth that a job seeker aims to achieve.

[0217] A "machine learning algorithm" is a statistical model or method used by a computer to predict future outcomes based on past data.

[0218] "Recommendation" is the process of showing the user how to acquire the job or skills that are deemed most suitable for them.

[0219] "Progress" is a measure of how far a job seeker is moving toward their career goals.

[0220] "Visually displaying" refers to displaying information or data to a user in a visual format such as a graph or chart.

[0221] "Subscription status" refers to the status of a contract that allows a user to regularly use a particular service.

[0222] "Billing" is the process of collecting fees from users for subscription services.

[0223] "Linked information" refers to information and data related to companies and external organizations.

[0224] "Job information" is information published by a company about the positions it plans to hire.

[0225] "Request" refers to a specific request or request made by a company to a job seeker.

[0226] "Users working in stores" refers to staff and managers who actually work in physical stores.

[0227] "Learning resources" are materials and content used to improve skills and knowledge.

[0228] This invention is a job matching system that utilizes generative AI models to support the career development of staff and managers working in brick-and-mortar stores. This system identifies the most suitable job based on information obtained from the user and supports the development and progression of career paths. The system consists of three main components: user information input, data analysis and career path generation by the server, and result display and interaction.

[0229] Entering user information

[0230] Users log in to the system and enter personal information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields. Based on this information, a detailed profile of the user is created.

[0231] Server-based data analysis and career path generation

[0232] The server includes the following means:

[0233] 1. Collect user information: Collect information entered by the user, such as work history, skills, interests, and career goals.

[0234] 2. Market data collection: Use external APIs and web scraping techniques to gather information on market trends, required skills, and career paths.

[0235] 3. Data Analysis: Collected user information and market data are analyzed using machine learning algorithms (e.g., Random Forest Classifier). This analysis identifies the best job type and career path for the user, recommended skill sets, and next actions to take.

[0236] 4. Career Path Generation: Based on the analysis results, we generate personalized career path suggestions for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0237] Viewing and interacting with results

[0238] The server generates recommendations that are visually displayed to the user on their device. The user can review the recommendations and choose a next step, such as enrolling in a recommended online course or acquiring a skill. The system tracks the user's progress and dynamically updates the career plan.

[0239] Application examples for brick-and-mortar store staff

[0240] One application of this system is to support career planning for store staff and managers. For example, if a staff member aims to become a store manager in the future, the system will suggest the optimal career path based on their current work history, skills, and interests. It will also provide learning resources and ways to improve skills related to work in the store.

[0241] Specific examples

[0242] Let's say a staff member at a brick-and-mortar store aspires to become a store manager in the future. He / she is currently working as a stock manager and has the following skill set:

[0243] Work experience: "Cashier", "Stock manager"

[0244] Skills: "Customer Service," "Inventory Management," "Report Writing"

[0245] Interests: Leadership, Merchandising

[0246] Career goal: "Store manager"

[0247] Prompt Sentence Examples

[0248] Area of ​​expertise: Store management

[0249] Work experience: Cashier, Stock Manager

[0250] Skills: Customer service, inventory management, report writing

[0251] Interests: Leadership, Merchandising

[0252] Career goal: Store manager

[0253] Market Trends: Providing the latest store operation trend data

[0254] The generative AI model will suggest career paths and recommended skill sets.

[0255] In this way, the job matching system of the present invention provides individualized career plans and effectively supports the career development of store staff and managers.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] A user logs into the system and enters information such as work history, skills, interests, and career goals.

[0259] Input: User's work history, skills, interests, and career goals

[0260] Output: A dataset of user information

[0261] Specific operation: The user enters information on a dedicated profile setting screen, and the information is saved in a database.

[0262] Step 2:

[0263] The server uses external APIs and web scraping technology to collect information on market trends, required skills, and career paths.

[0264] Input: Market information obtained from external APIs and websites

[0265] Output: Market data

[0266] What it does: The server periodically makes API requests and web scraping to store market data in a database.

[0267] Step 3:

[0268] The server collects user information and market data and analyzes the data using machine learning algorithms.

[0269] Input: User information dataset, market data

[0270] Output: Analysis results (optimal job type, career path, recommended skill set)

[0271] Specific operation: The server uses the collected data to train a machine learning model (e.g., RandomForestClassifier), and then uses that model to predict the optimal job type and career path.

[0272] Step 4:

[0273] The server generates personalized career path suggestions for the user based on the analysis results.

[0274] Input: Analysis results

[0275] Output: Career path suggestions

[0276] Specific operation: Based on the analysis results, the server lists recommended career paths, skill acquisition methods, and related resources for the user.

[0277] Step 5:

[0278] The terminal receives the proposal sent from the server and visually displays it to the user.

[0279] Input: Career path proposal

[0280] Output: A visual representation that the user can see

[0281] Specific behavior: The device displays the suggestions to the user in the form of graphs and charts, and the user confirms the next step.

[0282] Step 6:

[0283] The user reviews the suggested career path and selects their next action.

[0284] Input: The user's selected action

[0285] Output: New progress information

[0286] Specific Action: The user enrolls in a suggested online course or begins learning a new skill and enters their progress into the system.

[0287] Step 7:

[0288] The server dynamically updates the career plan based on the progress information and presents the latest proposal to the user again.

[0289] Input: New progress information

[0290] Output: Updated career path suggestions

[0291] Specific operation: The server collects and analyzes the user's progress information and presents new career paths and skill sets to the user.

[0292] 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.

[0293] ---

[0294] This invention is a job matching system that utilizes a generative AI model to identify the most suitable occupation based on information obtained from the user and support the development and progression of a career path. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized career advice.

[0295] overview

[0296] The system has four main roles:

[0297] 1. Enter user information

[0298] 2. Data analysis and career path generation by the server

[0299] 3. Displaying and interacting with results on the device

[0300] 4. Emotion engine recognizes user emotions and adjusts career paths

[0301] Enter user information

[0302] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[0303] Server-based data analysis and career path generation

[0304] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[0305] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[0306] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0307] Displaying and interacting with results on the device

[0308] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[0309] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[0310] Emotion recognition and regulation with emotion engine

[0311] 7. The emotion engine analyzes the user's emotions from their facial expressions, tone of voice, text content, etc. For example, it uses a webcam or microphone to collect facial expressions and tone of voice, and then reads emotions from the text through text analysis.

[0312] 8. The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results, making it possible to suggest career paths that match the user's current psychological state.

[0313] 9. The server then uses the results of the emotion analysis to tailor career advice to the user's motivation and stress level. For example, if the user is feeling stressed, it will suggest resources and activities that will help them relax.

[0314] Specific examples

[0315] Case 1: Career planning for new graduates

[0316] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0317] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0318] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0319] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0320] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[0321] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[0322] Case 2: Mid-career professionals

[0323] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0324] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0325] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0326] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0327] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest a challenging project.

[0328] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[0329] Business model implementation

[0330] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[0331] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development. By introducing an emotion engine, the system can provide optimal advice according to the user's psychological state, thereby achieving more accurate career planning.

[0332] The processing flow will be explained below.

[0333] ---

[0334] Step 1:

[0335] A user logs in to the system.

[0336] The user accesses the login screen and enters their user ID and password.

[0337] Click the Login button and submit your authentication information.

[0338] Step 2:

[0339] The server authenticates the user.

[0340] The server checks the received authentication information against its database to see if a matching user exists.

[0341] If authentication is successful, the session begins and the home screen is returned to the user.

[0342] If authentication fails, an error message is returned.

[0343] Step 3:

[0344] Users enter data such as work history, skills, interests, and career goals.

[0345] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[0346] Once you have completed the entry, click the submit button to save the data.

[0347] Step 4:

[0348] The terminal transmits the user input data to the server.

[0349] The device structures the input data and sends it to the server as an API request.

[0350] Step 5:

[0351] The server collects information on market trends, required skills, and career paths for each industry and job position.

[0352] The server periodically collects the latest market data using external APIs and web scraping technology and stores it in a database.

[0353] Step 6:

[0354] The server analyzes user data and market data.

[0355] The server uses machine learning algorithms to analyze the data and identify the jobs and career paths that are best suited for the user.

[0356] The analysis involves matching user input data with market trend data.

[0357] Step 7:

[0358] The server generates career path suggestions based on the analysis results.

[0359] Suggestions include next career steps, recommended ways to acquire skills, and related resources (e.g., online courses).

[0360] The suggestions are customized to each user's individual situation.

[0361] Step 8:

[0362] The device displays the career path to the user.

[0363] The terminal visually displays the proposal received from the server and presents it in a format that is easy for the user to understand.

[0364] Step 9:

[0365] The user reviews the suggested career path and selects their next steps.

[0366] Based on the suggestions, the user decides on the next action (e.g., taking a recommended course or acquiring a skill).

[0367] Step 10:

[0368] The emotion engine recognizes the user's emotions.

[0369] The emotion engine uses a webcam and microphone to collect the user's facial expressions and voice.

[0370] Text analysis is used to read emotions from user written text.

[0371] Step 11:

[0372] The server collects emotional data and reflects it in career path suggestions.

[0373] The server analyzes the data received from the emotion engine and adjusts the suggestions according to the user's emotional state.

[0374] For example, if a user is feeling stressed, additional resources to help them relax may be suggested.

[0375] Step 12:

[0376] The server periodically updates the user's progress data.

[0377] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[0378] Based on this data, career plans are updated and evolved.

[0379] Step 13:

[0380] The server manages the subscription model.

[0381] The server manages the user's subscription status and handles billing for usage fees.

[0382] Send reminders to users when their subscriptions are about to expire.

[0383] Step 14:

[0384] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[0385] The server stores job information provided by companies in a database and notifies job seekers as appropriate.

[0386] Forwarding direct requests from companies (e.g., interview offers) to users.

[0387] ---

[0388] Example 2

[0389] 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."

[0390] Conventional job matching systems have limitations in suggesting career paths based on a job seeker's specific skills and work history. They also lack the ability to provide advice that takes into account the job seeker's emotions and current psychological state, making it difficult to provide personalized support. As a result, job seekers often fail to find a career path or job type that truly satisfies them.

[0391] The specification process by the specification 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 inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and proposing optimal occupations and career paths using a machine learning algorithm; means for updating and evolving the proposed career path according to the job seeker's progress and visually displaying it; emotion recognition means for recognizing the job seeker's emotions and reflecting them in the analysis results; and means for adjusting the proposal content based on the emotion analysis results. This enables personalized career advice that takes into account not only the job seeker's skills and work history, but also their emotions and psychological state.

[0392] A "job seeker" is an individual who is seeking employment in a particular industry or occupation.

[0393] "Information" refers to data about a job seeker's profile, including, for example, work history, skills, interests, and career goals.

[0394] "Work history" refers to the jobs a job seeker has held in the past and for how long.

[0395] "Skills" refer to the specific skills, abilities, and knowledge that a job seeker possesses.

[0396] "Interests" refers to the industries or fields that a job seeker is interested in.

[0397] "Career goals" refer to the professional goals and aspirations that job seekers hope to achieve in the future.

[0398] "Market trends" refer to the current trends and demands in a particular industry or occupation.

[0399] "Collection methods" refer to the means used to gather information on market trends, required skills, and career paths from external data sources.

[0400] A "machine learning algorithm" is a system or method for analyzing large amounts of data to make predictions and classifications.

[0401] A "career path" refers to the specific path or steps that a job seeker takes to reach the job or position they aim for in the future.

[0402] "Progress" refers to the current stage of a job seeker's progress toward achieving their set career path.

[0403] "Emotion recognition means" refers to a means for analyzing the emotions and psychological state of job seekers and reflecting them in the system.

[0404] "Suggestions" refer to the career paths and recommended actions that the system presents to job seekers based on the analysis results.

[0405] "Visual display means" refers to means for displaying career paths and progress to users using visual designs such as graphs and charts.

[0406] MODE FOR CARRYING OUT THE INVENTION

[0407] This invention is a job matching system that optimizes a user's career path by utilizing a generative AI model. The system mainly consists of user information input, data analysis and career path generation by a server, display and interaction of the results on a terminal, and emotion recognition and career path adjustment by an emotion engine. It also includes an emotion engine for providing personalized advice based on the user's emotions.

[0408] Enter user information

[0409] Users log in to the system and enter information such as their work history, skills, interests, and career goals by filling in the required fields on a profile setting screen. This information is then sent to the server and used for analysis.

[0410] Server-based data analysis and career path generation

[0411] The server stores the received user information in a database. It periodically collects information on market trends, required skills, and career paths using external APIs and web scraping techniques. For example, it retrieves job listings using the Indeed API. The server uses machine learning algorithms (e.g., sklearn's RandomForestClassifier) ​​to analyze user data and market data and identify optimal jobs, career paths, recommended skill sets, and next actions to take.

[0412] Displaying and interacting with results on the device

[0413] The device receives the suggestions sent from the server and displays them visually to the user. Here, visualization libraries such as D3.js are used to display career paths and recommended skill sets in graphs and charts. The user can review the displayed career path and select their next step. As a specific action, a link to a recommended online course is displayed. When the user selects the next action, their progress is sent to the server, and the career plan is dynamically updated.

[0414] Emotion recognition and regulation with emotion engine

[0415] The emotion engine collects emotional data such as the user's facial expressions, tone of voice, and text content, using a webcam and microphone to capture facial expressions and tone of voice. It then uses NLP technology to analyze text and recognize the user's emotions. The server collects the emotional data obtained from the emotion engine and incorporates it into the analysis results, enabling it to suggest career paths tailored to the user's current psychological state. For example, if it determines that the user is feeling stressed, it provides advice based on the emotion analysis results, such as suggesting online resources for relaxation.

[0416] Specific examples

[0417] Case 1: Career planning for new graduates

[0418] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0419] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0420] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0421] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0422] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[0423] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[0424] Case 2: Mid-career professionals

[0425] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0426] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0427] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0428] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0429] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest challenging projects.

[0430] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[0431] Prompt Sentence Examples

[0432] Examples of prompts for users to enter information into the system:

[0433] "Tell me more about your work history and skills. What kind of work have you done so far?"

[0434] "What are your future career goals? What type of job or industry would you like to work in?"

[0435] "Let me know if you plan on gaining any new skills or qualifications."

[0436] "Please tell me more about your recent projects and accomplishments."

[0437] "Tell me your current emotional state. Are you stressed or confident?"

[0438] As described above, this invention provides personalized career advice by taking into account not only the user's skills and work history, but also their emotions and psychological state. Based on a generative AI model, and in conjunction with an emotion engine, it enables more accurate job matching and career path suggestions.

[0439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] A user logs in to the system by entering their username and password on the login screen and clicking the "Login" button. The input data (username and password) is sent to the server, and user authentication is performed. If authentication is successful, the server sends the user information to the client terminal, and the user can proceed to the next screen.

[0442] Step 2:

[0443] The user enters information such as work history, skills, interests, and career goals in the profile setting screen. For example, the user enters past jobs in the "Work History" input field and technical skills in the "Skills" input field. After the data is entered and the "Submit" button is pressed, the input data is sent to the server. The server receives it and stores it in the database. A new record is created in the user profile table based on the input data.

[0444] Step 3:

[0445] The server collects information on market trends, required skills, and career paths from external data sources based on user information. For example, it calls an external API to obtain job information and stores it in an internal database. The collected data is stored in a market data table.

[0446] Step 4:

[0447] The server combines user data with collected market data and inputs it into a machine learning model. Here, data preprocessing is performed, for example, using Python's pandas library. The formatted data is then fed into a machine learning algorithm (for example, sklearn's RandomForestClassifier) ​​for analysis. The analysis results identify the most suitable job type or career path, recommended skill sets, and next actions to take. The analysis results are output in JSON format and stored in the server's temporary data storage.

[0448] Step 5:

[0449] The server sends the analysis results to the client device. The sent data is in JSON format, and the device receives it. After receiving the data, the device uses D3.js or other visualization libraries to visually display career paths and recommended skill sets. The user checks the displayed career path and clicks the "Select next step" button. This action sends the selected data to the server.

[0450] Step 6:

[0451] The server updates the user's progress based on the selected next step. The progress data is stored in a database, dynamically updating the user's career plan. The updated data is then sent back to the device, which visually displays the updates.

[0452] Step 7:

[0453] The emotion engine uses a webcam and microphone to collect the user's facial expressions, tone of voice, and text content. The collected data is analyzed in real time. For example, OpenCV is used for facial analysis, and NLP techniques are used to recognize emotions in voice data.

[0454] Step 8:

[0455] The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results. Based on the emotional data, career path suggestions are made according to the user's current psychological state. For example, if the user is feeling high stress, online resources to help them relax are suggested, and specific advice based on their progress is also included. The suggestions are again output in JSON format and sent to the device.

[0456] (Application example 2)

[0457] 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."

[0458] Current job matching systems typically make suggestions based on a job seeker's work history and skills, but the accuracy of these suggestions is limited because they cannot adjust the system to take into account the job seeker's emotions and stress level. Furthermore, there is no system that can analyze passenger emotions in real time in autonomous vehicles and provide optimal routes and entertainment options. Therefore, there is a need for a system that can suggest career paths that take into account the job seeker's emotional state and provide optimal suggestions based on passenger emotions.

[0459] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and using a machine learning algorithm to propose optimal jobs and career paths; means for updating and evolving the proposed career paths in accordance with the job seeker's progress and visually displaying them; means for analyzing the job seeker's emotions using an emotion recognition engine and adjusting the career path based on the analysis results; and means for linking with the vehicle's infotainment system to propose optimal routes and entertainment options based on the passenger's emotional state. This makes it possible to provide career advice tailored to the job seeker's emotional state, and to make optimal suggestions in accordance with the passenger's emotions in self-driving vehicles.

[0460] definition statement

[0461] "Job Seeker Information" refers to personal information entered by a job seeker, such as work history, skills, interests, and career goals.

[0462] "Market trends" refer to the trends in demand for skills and occupations in the current employment market.

[0463] "Skills" refers to the abilities, expertise, and techniques that a job seeker possesses.

[0464] "Career path" refers to the future occupational path or course in a certain occupation.

[0465] A "machine learning algorithm" is an automated learning process for data analysis and prediction.

[0466] An "emotion recognition engine" is a technology for analyzing a user's emotions and identifying their state.

[0467] An "infotainment system" is a system that provides integrated information and entertainment within a vehicle.

[0468] "Route" refers to a path to a particular destination.

[0469] "Entertainment options" refers to entertainment options such as music, movies, and games offered to passengers.

[0470] "Real-time" means that the system responds immediately, processes data, and provides results quickly.

[0471] patent specification

[0472] The present invention relates to a job matching system that combines a generative AI model and an emotion engine. An embodiment of the system will be described in detail below.

[0473] Overall system overview

[0474] The system features the ability to input job seeker information, analyze data, generate and display career paths, and connect with the car's infotainment system.

[0475] Enter user information

[0476] 1. A user logs in to the system using a smartphone or other device. After logging in, the user enters personal information such as work history, skills, interests, and career goals.

[0477] 2. The emotion engine analyzes the user's emotional state at the time of input, for example, by analyzing the user's facial expressions and tone of voice using a camera or microphone.

[0478] 3. The terminal sends the input information to the server.

[0479] Server-based data analysis and career path generation

[0480] 1. The server compares the received job seeker information with market trends and required skill information collected using external APIs and web scraping technology.

[0481] 2. The server uses machine learning algorithms to analyze user data and market data to identify the best jobs and career paths.

[0482] 3. Taking into account the analysis results from the emotion engine, we suggest a career path based on the user's emotional state.

[0483] Viewing and interacting with results

[0484] 1. The terminal visually displays the career path suggestions and recommended skill sets sent from the server, using graphs and charts.

[0485] 2. The user reviews the proposed career path and selects their next steps. After selection, the system tracks their progress and updates their career plan.

[0486] Integration with car infotainment systems

[0487] 1. The emotion engine recognizes passengers' emotions and collects their facial expressions and tone of voice through cameras and microphones, allowing it to analyze their emotional state in real time.

[0488] 2. The server then uses the analysis results to generate optimal routes and entertainment options for passengers, for example, suggesting relaxing music and quiet routes for stressed passengers.

[0489] 3. The infotainment system displays the suggestions sent from the server and allows the passenger to interactively make a selection.

[0490] Specific examples

[0491] Case 1: Career planning for new graduates

[0492] 1. Students log in to the system and enter their major and industry of interest.

[0493] 2. The server collects market trends and required skills and suggests the most suitable job, for example, a career path as a data scientist.

[0494] 3. The emotion engine analyzes the user's emotional state, and if it determines that the user is feeling stressed, it suggests relaxing learning resources.

[0495] Example prompt sentence:

[0496] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[0497] Hardware and software used

[0498] 1. Hardware

[0499] Smartphones, PCs, infotainment systems (e.g., Android Auto, Apple CarPlay)

[0500] Camera, microphone

[0501] 2. Software

[0502] Server-side data analysis systems (e.g., Python, Django, Flask)

[0503] Emotion recognition software (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text)

[0504] External APIs and web scraping technologies for market data collection

[0505] This will enable career advice tailored to the job seeker's emotional state and optimal suggestions based on the emotions of passengers in autonomous vehicles.

[0506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0507] Program processing flow

[0508] Step 1:

[0509] Users log in to the system using a smartphone or PC. After logging in, they enter personal information such as their work history, skills, interests, and career goals. The entered information is stored in a database by the device and sent to the server for analysis. The input data is the user's work history, skills, interests, and career goals, and the output is organized job seeker information sent to the server.

[0510] Step 2:

[0511] The emotion engine performs emotion analysis using facial expressions and tone of voice when the user inputs. The hardware used is the device's camera and microphone, and the software is an emotion recognition algorithm (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text). The analysis results are sent to the server as emotion data. The input is raw data obtained from the camera and microphone, and the output is an analysis result indicating the user's emotional state.

[0512] Step 3:

[0513] The server uses the received job seeker information and sentiment data to collect market trends and required skills information through external APIs and web scraping technology. The collected data is stored in a database. The input is job seeker information and market data from external data sources, and the output is the collected market data.

[0514] Step 4:

[0515] The server uses machine learning algorithms to analyze job seeker data and collected market data to generate optimal job types and career paths. It also takes into account emotional data obtained from the emotion engine to propose personalized career paths. The input is job seeker information, market data, and emotional data, and the output is optimal job type and career path proposals.

[0516] Step 5:

[0517] The terminal visually displays the career path proposals sent from the server. The presentation is done using graphs and charts in a format that is easy for the user to understand. The input is the career path proposal sent from the server, and the output is the visual presentation for the user.

[0518] Step 6:

[0519] Users review the proposed career path and select their next steps. After making a selection, the system tracks their progress and updates their career plan. The input is the user's selection, and the output is an updated career plan.

[0520] Step 7:

[0521] The emotion recognition engine is integrated into the car's infotainment system and analyzes passengers' facial expressions and tone of voice in real time. The hardware used is a camera and microphone, and the software is an emotion analysis algorithm. The input is facial expression and voice data collected in real time, and the output is analyzed emotion data.

[0522] Step 8:

[0523] Based on the analysis results, the server suggests optimal routes and entertainment options according to the passenger's emotional state. For example, it suggests relaxing music and quiet routes for stressed passengers. The input is emotional data sent from the emotion recognition engine, and the output is recommendations for optimal routes and entertainment options for passengers.

[0524] Step 9:

[0525] The infotainment system displays suggestions sent from the server and allows passengers to interactively select from them. The input is the suggestions sent from the server, and the output is the information displayed to the passenger.

[0526] Example prompt sentence:

[0527] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] [Second embodiment]

[0532] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0533] 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.

[0534] 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).

[0535] 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.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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."

[0544] ---

[0545] This invention is a job matching system that utilizes a generative AI model to identify optimal jobs based on information obtained from users and support the development and progression of career paths. The system uses job seeker input, market trends, and machine learning algorithms to provide users with personalized career plans.

[0546] overview

[0547] The system has three main roles:

[0548] 1. Enter user information

[0549] 2. Data analysis and career path generation by the server

[0550] 3. Displaying and interacting with results on the device

[0551] Enter user information

[0552] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[0553] Server-based data analysis and career path generation

[0554] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[0555] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[0556] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0557] Displaying and interacting with results on the device

[0558] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[0559] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[0560] Specific examples

[0561] Case 1: Career planning for new graduates

[0562] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0563] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0564] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0565] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0566] 5. The user reviews the suggested career path and enrolls in the recommended online course as their next step.

[0567] Case 2: Mid-career professionals

[0568] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0569] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0570] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0571] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0572] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[0573] Business model implementation

[0574] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[0575] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development.

[0576] The processing flow will be explained below.

[0577] ---

[0578] Step 1:

[0579] A user logs in to the system.

[0580] The user accesses the login screen and enters their user ID and password.

[0581] Click the Login button to submit your credentials.

[0582] Step 2:

[0583] The server authenticates the user.

[0584] The server checks the received authentication information against its database to see if a matching user exists.

[0585] If the authentication is successful, the session is started and the home screen is returned to the user.

[0586] If authentication fails, an error message is returned.

[0587] Step 3:

[0588] Users enter data such as work history, skills, interests, and career goals.

[0589] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[0590] After completing the input, click the send button to save the data.

[0591] Step 4:

[0592] The terminal transmits the user input data to the server.

[0593] The device converts the input data into the appropriate format and sends it to the server as an API request.

[0594] Step 5:

[0595] The server collects information on market trends, required skills, and career paths for each industry and job position.

[0596] The server uses external APIs and web scraping technology to collect the latest industry data and store it in a database.

[0597] Step 6:

[0598] The server analyzes user data and market data.

[0599] The server uses machine learning algorithms to analyze user information and market data.

[0600] The analysis uses input data to identify optimal job types, recommended skill sets, career paths, and more.

[0601] Step 7:

[0602] A server generates career path suggestions.

[0603] The server generates suggestions for the user based on the analysis results.

[0604] Suggestions include next career steps, how to acquire recommended skills, and available resources.

[0605] Step 8:

[0606] The device displays the career path to the user.

[0607] The terminal visualizes the proposals received from the server and displays them in an easy-to-understand manner for the user.

[0608] Step 9:

[0609] The user reviews the proposal and selects the next step.

[0610] Users review suggested career paths and choose next actions, such as using recommended resources to learn new skills.

[0611] Step 10:

[0612] The server periodically updates the user's progress data.

[0613] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[0614] Update and evolve your career plan based on the data received.

[0615] Step 11:

[0616] The server manages the subscription model.

[0617] The server manages the user's subscription status and handles billing for usage fees.

[0618] Send reminders to users when their subscriptions are about to expire.

[0619] Step 12:

[0620] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[0621] The server stores job information provided by companies in a database and notifies users as appropriate.

[0622] Forwarding direct requests from companies (e.g., interview offers) to users.

[0623] ---

[0624] Example 1

[0625] 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."

[0626] In today's job search and career development process, job seekers spend a great deal of time and effort organizing their information and finding the right job or career path based on that information. Furthermore, a lack of awareness of market trends and required skill sets makes it difficult to plan their careers. To solve these challenges, a system is needed that provides individually customized career advice and dynamically updates plans based on progress.

[0627] 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.

[0628] In this invention, the server includes a means for inputting job seeker information, work history, skills, interests, and career goals, a means for collecting information on market trends, required abilities, and career paths, and a means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal occupations and career paths. This makes it possible to provide individually customized career advice and dynamically update career plans according to progress.

[0629] A "job seeker" is an individual seeking employment, someone who is looking for a new position that utilizes their skills and experience.

[0630] "Information" refers to data about job seekers, including detailed data entered about their work history, skills, interests, career goals, etc.

[0631] "Work history" refers to the history of jobs and positions held by a job seeker in the past, including details such as the duration and nature of the work.

[0632] "Skills" refers to the specialized knowledge and skills that a job seeker possesses, such as programming languages ​​or project management skills.

[0633] "Interests" refers to areas or topics that a job seeker is particularly interested in for their future career.

[0634] "Career goals" refer to the professional goals or positions that job seekers hope to achieve in the future.

[0635] "Market trends" refers to current labor market conditions and trends, including the demand for specific skills and occupations, and employer needs.

[0636] "Demanding competencies" refer to the skills and expertise required in the labor market or in specific occupations.

[0637] "Career path" refers to the specific steps and direction a job seeker takes to advance their career.

[0638] "Analysis" refers to the activity of analyzing input job seeker information and market data to identify the most suitable jobs and career paths.

[0639] A "machine learning algorithm" is a type of program that automatically finds patterns based on large amounts of data and makes predictions and judgments.

[0640] "Suggestion" refers to the act of specifically indicating suitable occupations and career paths for job seekers based on the results of the analysis.

[0641] "Progress" refers to the degree of progress achieved as a result of a job seeker taking action based on a suggestion.

[0642] "Update and evolution" refers to the process of dynamically revising the suggested career path based on the job seeker's latest situation and progress, and making more appropriate suggestions.

[0643] "Visual display" refers to displaying analysis results and proposals using visual elements such as graphs and charts.

[0644] "Subscription Status" refers to the status of the recurring fee paid by a Job Seeker for use of the System.

[0645] "Usage Fee Billing Process" refers to the process for billing Job Seekers for the fees required to use the Subscription Service.

[0646] "Information on collaboration with companies" refers to job information and requests collected by the system in collaboration with companies.

[0647] "Job information" refers to information about employment opportunities provided by businesses to job seekers.

[0648] A "direct request" is a specific request or proposal sent directly by an employer to a job seeker.

[0649] This invention is a career advisory system that uses a generative AI model to support career development by proposing optimal occupations and career paths based on information entered by job seekers. This system has multiple functions, including job seeker information entry, market trend collection, data analysis, career path generation, progress tracking and updates, and visual information display.

[0650] overview

[0651] This system has three roles:

[0652] 1. Enter user information

[0653] 2. Data analysis and career path generation by the server

[0654] 3. Displaying and interacting with results on the device

[0655] Enter user information

[0656] Users log in to the system and enter information such as their work history, skills, interests, and career goals on the profile setting screen. For example, a recent graduate might enter the major they studied at university and the industry they are interested in (e.g., information engineering, AI technology). This input data is stored in a database and used for later analysis.

[0657] Server-based data analysis and career path generation

[0658] The server performs the following functions:

[0659] 1. Data collection: The server periodically collects information on market trends, required skills, and career paths using external APIs and web scraping technology. For example, it uses the LinkedIn API to collect market trend data.

[0660] 2. Data analysis: The server uses machine learning algorithms such as "scikit-learn" to analyze job seeker information and collected market data, comparing the user's skill set with the skills in demand in the market and identifying the most suitable job and recommended skill set.

[0661] 3. Career path generation: Based on the analysis results, the server generates personalized career path suggestions for job seekers, such as recommending advanced Python skills to become a "data scientist" and offering specific Udemy courses.

[0662] Displaying and interacting with results on the device

[0663] The terminal is responsible for:

[0664] 1. Displaying the results: Receive the career path suggestions sent from the server and display them visually to the user. For example, use "D3.js" or "Chart.js" to display the career paths and recommended skill sets in graphs and charts.

[0665] 2. Interaction: The user reviews the proposed career path and selects their next step. If the user enrolls in a recommended online course, the server records that information and tracks their progress. The system dynamically updates the career plan and makes new suggestions based on their progress.

[0666] Specific examples

[0667] Case 1: Career planning for new graduates

[0668] 1. A user (a recent graduate student) logs in to the system and enters in their profile that they are majoring in information engineering and interested in AI technology.

[0669] 2. The server receives this information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0670] 3. The server uses "scikit-learn" to analyze user data and market data and identify "Python" and "basic machine learning" as important skills.

[0671] 4. The server suggests career paths to becoming a "data scientist," recommending, for example, "take a Udemy course to acquire advanced Python skills."

[0672] 5. The device displays suggested career paths and skill sets to the user, and suggests specific next steps to take.

[0673] 6. The user follows the suggestion and enrolls in a Python course on Udemy.

[0674] 7. The server tracks the user's progress through the online course and suggests next steps when a new skill is mastered.

[0675] Case 2: Mid-career professionals

[0676] 1. A user (mid-career professional) logs into the system, currently works as a lead engineer, and enters recent projects and new skills (e.g., project management) into their profile.

[0677] 2. The server takes the user's input data and compares it with the latest industry trends. For example, it determines that there is a growing demand for project management.

[0678] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0679] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0680] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[0681] Prompt Sentence Examples

[0682] "Create a career path suitable for new graduates majoring in information engineering and interested in AI technology."

[0683] "Please suggest the next career step for a user currently working as a lead engineer."

[0684] In this way, the system of the present invention uses a generative AI model to provide job seekers with customized career advice and dynamically update their career plans according to their progress, thereby supporting efficient and effective career development.

[0685] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0686] Step 1:

[0687] A user accesses the system and logs in by entering their login information, which is an email address and a password, which authenticates the user and allows them to access their user profile.

[0688] Step 2:

[0689] Users enter information such as work history, skills, interests, and career goals on the profile setting screen. For example, they might enter "3 years as a software engineer" as their work history and "Python, data analysis" as their skills. This information is sent to the server and stored in a database.

[0690] Step 3:

[0691] The server periodically uses external APIs and web scraping technologies (e.g., "LinkedIn API") to collect data on market trends and desired skills. The collected data is stored in a database for later analysis. The input is the external data source, and the output is updated market data.

[0692] Step 4:

[0693] The server combines the received user data and market data and performs data analysis using machine learning algorithms (e.g., "scikit-learn"). The analysis identifies the best jobs and recommended skill sets. The inputs for this step are user data and market data, and the output is the analysis results.

[0694] Step 5:

[0695] Based on the analysis results, the server proposes a personalized career path to the user. This proposal includes the next skills to acquire and online courses to study. For example, a specific proposal may be made such as, "Your next step is to acquire advanced skills in Python. We recommend Udemy course X." The input is the analysis results, and the output is a career path proposal.

[0696] Step 6:

[0697] The terminal receives the career path suggestions sent from the server and visually displays them to the user using graphs and charts (e.g., "D3.js"). The displayed information includes details of the career path and recommended skill sets. The input is the career path suggestions, and the output is the visual display.

[0698] Step 7:

[0699] The user reviews the proposed career path and takes the next step by enrolling in a recommended online course, such as enrolling in a Python course on Udemy. This information is sent back to the server, allowing the system to track their progress.

[0700] Step 8:

[0701] The server periodically checks the user's progress (e.g., progress in online courses) and dynamically updates the career plan based on new skills and advancements acquired. The input to this step is the user's progress, and the output is the updated career plan.

[0702] Step 9:

[0703] The server sends the updated carrier plan back to the terminal, which then visually displays it to the user. This allows the user to always check the latest carrier plan and know the specific action to take next. The input is the updated carrier plan, and the output is another visual display.

[0704] In this way, the system allows users, servers, and terminals to play their respective roles and work together to support job seekers' career development.

[0705] (Application example 1)

[0706] 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."

[0707] Currently, store staff and managers lack the means to plan their career paths and receive support to acquire the most suitable jobs and skills. As a result, they face the challenge of spending a great deal of time and effort gathering appropriate information and self-studying to achieve their career goals. Furthermore, with industry trends changing rapidly, career plans need to be updated based on the latest information, and there is a need for automation of this process.

[0708] 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.

[0709] In this invention, the server includes means for inputting a user's work history, skills, interests, and career goals, means for collecting information on market trends, required skills, and career paths, means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal jobs and career paths, means for updating and evolving the suggested career paths according to the job seeker's progress and visually displaying them, and means for suggesting optimal career paths based on the work history, skills, and interests of store users and showing learning resources and skill improvement methods related to work at the physical store. This makes it easier for staff and managers working at physical stores to efficiently obtain and update career plans based on the latest market trends and acquire appropriate skill sets.

[0710] "Job Seeker" means an individual who is searching for employment.

[0711] "Work history" refers to work experience, which is the history of the jobs that an individual has held up to now.

[0712] "Skills" refers to the skills and abilities a job seeker possesses to perform a specific task or job.

[0713] "Interests" refers to areas that interest the job seeker or are relevant to the job.

[0714] "Career goals" are goals related to the job or position that a job seeker wants to achieve in the future.

[0715] "Market trends" refers to the general movements and demands in the current industry or job market.

[0716] "Demand skills" refers to the specialized techniques and knowledge required for a particular job or industry.

[0717] A "career path" refers to the route or path of professional growth that a job seeker aims to achieve.

[0718] A "machine learning algorithm" is a statistical model or method used by a computer to predict future outcomes based on past data.

[0719] "Recommendation" is the process of showing the user how to acquire the job or skills that are deemed most suitable for them.

[0720] "Progress" is a measure of how far a job seeker is moving toward their career goals.

[0721] "Visually displaying" refers to displaying information or data to a user in a visual format such as a graph or chart.

[0722] "Subscription status" refers to the status of a contract that allows a user to regularly use a particular service.

[0723] "Billing" is the process of collecting fees from users for subscription services.

[0724] "Linked information" refers to information and data related to companies and external organizations.

[0725] "Job information" is information published by a company about the positions it plans to hire.

[0726] "Request" refers to a specific request or request made by a company to a job seeker.

[0727] "Users working in stores" refers to staff and managers who actually work in physical stores.

[0728] "Learning resources" are materials and content used to improve skills and knowledge.

[0729] This invention is a job matching system that utilizes generative AI models to support the career development of staff and managers working in brick-and-mortar stores. This system identifies the most suitable job based on information obtained from the user and supports the development and progression of career paths. The system consists of three main components: user information input, data analysis and career path generation by the server, and result display and interaction.

[0730] Entering user information

[0731] Users log in to the system and enter personal information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields. Based on this information, a detailed profile of the user is created.

[0732] Server-based data analysis and career path generation

[0733] The server includes the following means:

[0734] 1. Collect user information: Collect information entered by the user, such as work history, skills, interests, and career goals.

[0735] 2. Market data collection: Use external APIs and web scraping techniques to gather information on market trends, required skills, and career paths.

[0736] 3. Data Analysis: Collected user information and market data are analyzed using machine learning algorithms (e.g., Random Forest Classifier). This analysis identifies the best job type and career path for the user, recommended skill sets, and next actions to take.

[0737] 4. Career Path Generation: Based on the analysis results, we generate personalized career path suggestions for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0738] Viewing and interacting with results

[0739] The server generates recommendations that are visually displayed to the user on their device. The user can review the recommendations and choose a next step, such as enrolling in a recommended online course or acquiring a skill. The system tracks the user's progress and dynamically updates the career plan.

[0740] Application examples for brick-and-mortar store staff

[0741] One application of this system is to support career planning for store staff and managers. For example, if a staff member aims to become a store manager in the future, the system will suggest the optimal career path based on their current work history, skills, and interests. It will also provide learning resources and ways to improve skills related to work in the store.

[0742] Specific examples

[0743] Let's say a staff member at a brick-and-mortar store aspires to become a store manager in the future. He / she is currently working as a stock manager and has the following skill set:

[0744] Work experience: "Cashier", "Stock manager"

[0745] Skills: "Customer Service," "Inventory Management," "Report Writing"

[0746] Interests: Leadership, Merchandising

[0747] Career goal: "Store manager"

[0748] Prompt Sentence Examples

[0749] Area of ​​expertise: Store management

[0750] Work experience: Cashier, Stock Manager

[0751] Skills: Customer service, inventory management, report writing

[0752] Interests: Leadership, Merchandising

[0753] Career goal: Store manager

[0754] Market Trends: Providing the latest store operation trend data

[0755] The generative AI model will suggest career paths and recommended skill sets.

[0756] In this way, the job matching system of the present invention provides individualized career plans and effectively supports the career development of store staff and managers.

[0757] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0758] Step 1:

[0759] A user logs into the system and enters information such as work history, skills, interests, and career goals.

[0760] Input: User's work history, skills, interests, and career goals

[0761] Output: A dataset of user information

[0762] Specific operation: The user enters information on a dedicated profile setting screen, and the information is saved in a database.

[0763] Step 2:

[0764] The server uses external APIs and web scraping technology to collect information on market trends, required skills, and career paths.

[0765] Input: Market information obtained from external APIs and websites

[0766] Output: Market data

[0767] What it does: The server periodically makes API requests and web scraping to store market data in a database.

[0768] Step 3:

[0769] The server collects user information and market data and analyzes the data using machine learning algorithms.

[0770] Input: User information dataset, market data

[0771] Output: Analysis results (optimal job type, career path, recommended skill set)

[0772] Specific operation: The server uses the collected data to train a machine learning model (e.g., RandomForestClassifier), and then uses that model to predict the optimal job type and career path.

[0773] Step 4:

[0774] The server generates personalized career path suggestions for the user based on the analysis results.

[0775] Input: Analysis results

[0776] Output: Career path suggestions

[0777] Specific operation: Based on the analysis results, the server lists recommended career paths, skill acquisition methods, and related resources for the user.

[0778] Step 5:

[0779] The terminal receives the proposal sent from the server and visually displays it to the user.

[0780] Input: Career path proposal

[0781] Output: A visual representation that the user can see

[0782] Specific behavior: The device displays the suggestions to the user in the form of graphs and charts, and the user confirms the next step.

[0783] Step 6:

[0784] The user reviews the suggested career path and selects their next action.

[0785] Input: The user's selected action

[0786] Output: New progress information

[0787] Specific Action: The user enrolls in a suggested online course or begins learning a new skill and enters their progress into the system.

[0788] Step 7:

[0789] The server dynamically updates the career plan based on the progress information and presents the latest proposal to the user again.

[0790] Input: New progress information

[0791] Output: Updated career path suggestions

[0792] Specific operation: The server collects and analyzes the user's progress information and presents new career paths and skill sets to the user.

[0793] 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.

[0794] ---

[0795] This invention is a job matching system that utilizes a generative AI model to identify the most suitable occupation based on information obtained from the user and support the development and progression of a career path. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized career advice.

[0796] overview

[0797] The system has four main roles:

[0798] 1. Enter user information

[0799] 2. Data analysis and career path generation by the server

[0800] 3. Displaying and interacting with results on the device

[0801] 4. Emotion engine recognizes user emotions and adjusts career paths

[0802] Enter user information

[0803] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[0804] Server-based data analysis and career path generation

[0805] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[0806] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[0807] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[0808] Displaying and interacting with results on the device

[0809] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[0810] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[0811] Emotion recognition and regulation with emotion engine

[0812] 7. The emotion engine analyzes the user's emotions from their facial expressions, tone of voice, text content, etc. For example, it uses a webcam or microphone to collect facial expressions and tone of voice, and then reads emotions from the text through text analysis.

[0813] 8. The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results, making it possible to suggest career paths that match the user's current psychological state.

[0814] 9. The server then uses the results of the emotion analysis to tailor career advice to the user's motivation and stress level. For example, if the user is feeling stressed, it will suggest resources and activities that will help them relax.

[0815] Specific examples

[0816] Case 1: Career planning for new graduates

[0817] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0818] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0819] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0820] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0821] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[0822] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[0823] Case 2: Mid-career professionals

[0824] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0825] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0826] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0827] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0828] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest a challenging project.

[0829] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[0830] Business model implementation

[0831] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[0832] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development. By introducing an emotion engine, the system can provide optimal advice according to the user's psychological state, thereby achieving more accurate career planning.

[0833] The processing flow will be explained below.

[0834] ---

[0835] Step 1:

[0836] A user logs in to the system.

[0837] The user accesses the login screen and enters their user ID and password.

[0838] Click the Login button and submit your authentication information.

[0839] Step 2:

[0840] The server authenticates the user.

[0841] The server checks the received authentication information against its database to see if a matching user exists.

[0842] If authentication is successful, the session begins and the home screen is returned to the user.

[0843] If authentication fails, an error message is returned.

[0844] Step 3:

[0845] Users enter data such as work history, skills, interests, and career goals.

[0846] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[0847] Once you have completed the entry, click the submit button to save the data.

[0848] Step 4:

[0849] The terminal transmits the user input data to the server.

[0850] The device structures the input data and sends it to the server as an API request.

[0851] Step 5:

[0852] The server collects information on market trends, required skills, and career paths for each industry and job position.

[0853] The server periodically collects the latest market data using external APIs and web scraping technology and stores it in a database.

[0854] Step 6:

[0855] The server analyzes user data and market data.

[0856] The server uses machine learning algorithms to analyze the data and identify the jobs and career paths that are best suited for the user.

[0857] The analysis involves matching user input data with market trend data.

[0858] Step 7:

[0859] The server generates career path suggestions based on the analysis results.

[0860] Suggestions include next career steps, recommended ways to acquire skills, and related resources (e.g., online courses).

[0861] The suggestions are customized to each user's individual situation.

[0862] Step 8:

[0863] The device displays the career path to the user.

[0864] The terminal visually displays the proposal received from the server and presents it in a format that is easy for the user to understand.

[0865] Step 9:

[0866] The user reviews the suggested career path and selects their next steps.

[0867] Based on the suggestions, the user decides on the next action (e.g., taking a recommended course or acquiring a skill).

[0868] Step 10:

[0869] The emotion engine recognizes the user's emotions.

[0870] The emotion engine uses a webcam and microphone to collect the user's facial expressions and voice.

[0871] Text analysis is used to read emotions from user written text.

[0872] Step 11:

[0873] The server collects emotional data and reflects it in career path suggestions.

[0874] The server analyzes the data received from the emotion engine and adjusts the suggestions according to the user's emotional state.

[0875] For example, if a user is feeling stressed, additional resources to help them relax may be suggested.

[0876] Step 12:

[0877] The server periodically updates the user's progress data.

[0878] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[0879] Based on this data, career plans are updated and evolved.

[0880] Step 13:

[0881] The server manages the subscription model.

[0882] The server manages the user's subscription status and handles billing for usage fees.

[0883] Send reminders to users when their subscriptions are about to expire.

[0884] Step 14:

[0885] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[0886] The server stores job information provided by companies in a database and notifies job seekers as appropriate.

[0887] Forwarding direct requests from companies (e.g., interview offers) to users.

[0888] ---

[0889] Example 2

[0890] 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."

[0891] Conventional job matching systems have limitations in suggesting career paths based on a job seeker's specific skills and work history. They also lack the ability to provide advice that takes into account the job seeker's emotions and current psychological state, making it difficult to provide personalized support. As a result, job seekers often fail to find a career path or job type that truly satisfies them.

[0892] The specification process by the specification 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 inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and proposing optimal occupations and career paths using a machine learning algorithm; means for updating and evolving the proposed career path according to the job seeker's progress and visually displaying it; emotion recognition means for recognizing the job seeker's emotions and reflecting them in the analysis results; and means for adjusting the proposal content based on the emotion analysis results. This enables personalized career advice that takes into account not only the job seeker's skills and work history, but also their emotions and psychological state.

[0893] A "job seeker" is an individual who is seeking employment in a particular industry or occupation.

[0894] "Information" refers to data about a job seeker's profile, including, for example, work history, skills, interests, and career goals.

[0895] "Work history" refers to the jobs a job seeker has held in the past and for how long.

[0896] "Skills" refer to the specific skills, abilities, and knowledge that a job seeker possesses.

[0897] "Interests" refers to the industries or fields that a job seeker is interested in.

[0898] "Career goals" refer to the professional goals and aspirations that job seekers hope to achieve in the future.

[0899] "Market trends" refer to the current trends and demands in a particular industry or occupation.

[0900] "Collection methods" refer to the means used to gather information on market trends, required skills, and career paths from external data sources.

[0901] A "machine learning algorithm" is a system or method for analyzing large amounts of data to make predictions and classifications.

[0902] A "career path" refers to the specific path or steps that a job seeker takes to reach the job or position they aim for in the future.

[0903] "Progress" refers to the current stage of a job seeker's progress toward achieving their set career path.

[0904] "Emotion recognition means" refers to a means for analyzing the emotions and psychological state of job seekers and reflecting them in the system.

[0905] "Suggestions" refer to the career paths and recommended actions that the system presents to job seekers based on the analysis results.

[0906] "Visual display means" refers to means for displaying career paths and progress to users using visual designs such as graphs and charts.

[0907] MODE FOR CARRYING OUT THE INVENTION

[0908] This invention is a job matching system that optimizes a user's career path by utilizing a generative AI model. The system mainly consists of user information input, data analysis and career path generation by a server, display and interaction of the results on a terminal, and emotion recognition and career path adjustment by an emotion engine. It also includes an emotion engine for providing personalized advice based on the user's emotions.

[0909] Enter user information

[0910] Users log in to the system and enter information such as their work history, skills, interests, and career goals by filling in the required fields on a profile setting screen. This information is then sent to the server and used for analysis.

[0911] Server-based data analysis and career path generation

[0912] The server stores the received user information in a database. It periodically collects information on market trends, required skills, and career paths using external APIs and web scraping techniques. For example, it retrieves job listings using the Indeed API. The server uses machine learning algorithms (e.g., sklearn's RandomForestClassifier) ​​to analyze user data and market data and identify optimal jobs, career paths, recommended skill sets, and next actions to take.

[0913] Displaying and interacting with results on the device

[0914] The device receives the suggestions sent from the server and displays them visually to the user. Here, visualization libraries such as D3.js are used to display career paths and recommended skill sets in graphs and charts. The user can review the displayed career path and select their next step. As a specific action, a link to a recommended online course is displayed. When the user selects the next action, their progress is sent to the server, and the career plan is dynamically updated.

[0915] Emotion recognition and regulation with emotion engine

[0916] The emotion engine collects emotional data such as the user's facial expressions, tone of voice, and text content, using a webcam and microphone to capture facial expressions and tone of voice. It then uses NLP technology to analyze text and recognize the user's emotions. The server collects the emotional data obtained from the emotion engine and incorporates it into the analysis results, enabling it to suggest career paths tailored to the user's current psychological state. For example, if it determines that the user is feeling stressed, it provides advice based on the emotion analysis results, such as suggesting online resources for relaxation.

[0917] Specific examples

[0918] Case 1: Career planning for new graduates

[0919] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[0920] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[0921] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[0922] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[0923] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[0924] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[0925] Case 2: Mid-career professionals

[0926] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[0927] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[0928] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[0929] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[0930] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest challenging projects.

[0931] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[0932] Prompt Sentence Examples

[0933] Examples of prompts for users to enter information into the system:

[0934] "Tell me more about your work history and skills. What kind of work have you done so far?"

[0935] "What are your future career goals? What type of job or industry would you like to work in?"

[0936] "Let me know if you plan on gaining any new skills or qualifications."

[0937] "Please tell me more about your recent projects and accomplishments."

[0938] "Tell me your current emotional state. Are you stressed or confident?"

[0939] As described above, this invention provides personalized career advice by taking into account not only the user's skills and work history, but also their emotions and psychological state. Based on a generative AI model, and in conjunction with an emotion engine, it enables more accurate job matching and career path suggestions.

[0940] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0941] Step 1:

[0942] A user logs in to the system by entering their username and password on the login screen and clicking the "Login" button. The input data (username and password) is sent to the server, and user authentication is performed. If authentication is successful, the server sends the user information to the client terminal, and the user can proceed to the next screen.

[0943] Step 2:

[0944] The user enters information such as work history, skills, interests, and career goals in the profile setting screen. For example, the user enters past jobs in the "Work History" input field and technical skills in the "Skills" input field. After the data is entered and the "Submit" button is pressed, the input data is sent to the server. The server receives it and stores it in the database. A new record is created in the user profile table based on the input data.

[0945] Step 3:

[0946] The server collects information on market trends, required skills, and career paths from external data sources based on user information. For example, it calls an external API to obtain job information and stores it in an internal database. The collected data is stored in a market data table.

[0947] Step 4:

[0948] The server combines user data with collected market data and inputs it into a machine learning model. Here, data preprocessing is performed, for example, using Python's pandas library. The formatted data is then fed into a machine learning algorithm (for example, sklearn's RandomForestClassifier) ​​for analysis. The analysis results identify the most suitable job type or career path, recommended skill sets, and next actions to take. The analysis results are output in JSON format and stored in the server's temporary data storage.

[0949] Step 5:

[0950] The server sends the analysis results to the client device. The sent data is in JSON format, and the device receives it. After receiving the data, the device uses D3.js or other visualization libraries to visually display career paths and recommended skill sets. The user checks the displayed career path and clicks the "Select next step" button. This action sends the selected data to the server.

[0951] Step 6:

[0952] The server updates the user's progress based on the selected next step. The progress data is stored in a database, dynamically updating the user's career plan. The updated data is then sent back to the device, which visually displays the updates.

[0953] Step 7:

[0954] The emotion engine uses a webcam and microphone to collect the user's facial expressions, tone of voice, and text content. The collected data is analyzed in real time. For example, OpenCV is used for facial analysis, and NLP techniques are used to recognize emotions in voice data.

[0955] Step 8:

[0956] The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results. Based on the emotional data, career path suggestions are made according to the user's current psychological state. For example, if the user is feeling high stress, online resources to help them relax are suggested, and specific advice based on their progress is also included. The suggestions are again output in JSON format and sent to the device.

[0957] (Application example 2)

[0958] 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."

[0959] Current job matching systems typically make suggestions based on a job seeker's work history and skills, but the accuracy of these suggestions is limited because they cannot adjust the system to take into account the job seeker's emotions and stress level. Furthermore, there is no system that can analyze passenger emotions in real time in autonomous vehicles and provide optimal routes and entertainment options. Therefore, there is a need for a system that can suggest career paths that take into account the job seeker's emotional state and provide optimal suggestions based on passenger emotions.

[0960] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and using a machine learning algorithm to propose optimal jobs and career paths; means for updating and evolving the proposed career paths in accordance with the job seeker's progress and visually displaying them; means for analyzing the job seeker's emotions using an emotion recognition engine and adjusting the career path based on the analysis results; and means for linking with the vehicle's infotainment system to propose optimal routes and entertainment options based on the passenger's emotional state. This makes it possible to provide career advice tailored to the job seeker's emotional state, and to make optimal suggestions in accordance with the passenger's emotions in self-driving vehicles.

[0961] definition statement

[0962] "Job Seeker Information" refers to personal information entered by a job seeker, such as work history, skills, interests, and career goals.

[0963] "Market trends" refer to the trends in demand for skills and occupations in the current employment market.

[0964] "Skills" refers to the abilities, expertise, and techniques that a job seeker possesses.

[0965] "Career path" refers to the future occupational path or course in a certain occupation.

[0966] A "machine learning algorithm" is an automated learning process for data analysis and prediction.

[0967] An "emotion recognition engine" is a technology for analyzing a user's emotions and identifying their state.

[0968] An "infotainment system" is a system that provides integrated information and entertainment within a vehicle.

[0969] "Route" refers to a path to a particular destination.

[0970] "Entertainment options" refers to entertainment options such as music, movies, and games offered to passengers.

[0971] "Real-time" means that the system responds immediately, processes data, and provides results quickly.

[0972] patent specification

[0973] The present invention relates to a job matching system that combines a generative AI model and an emotion engine. An embodiment of the system will be described in detail below.

[0974] Overall system overview

[0975] The system features the ability to input job seeker information, analyze data, generate and display career paths, and connect with the car's infotainment system.

[0976] Enter user information

[0977] 1. A user logs in to the system using a smartphone or other device. After logging in, the user enters personal information such as work history, skills, interests, and career goals.

[0978] 2. The emotion engine analyzes the user's emotional state at the time of input, for example, by analyzing the user's facial expressions and tone of voice using a camera or microphone.

[0979] 3. The terminal sends the input information to the server.

[0980] Server-based data analysis and career path generation

[0981] 1. The server compares the received job seeker information with market trends and required skill information collected using external APIs and web scraping technology.

[0982] 2. The server uses machine learning algorithms to analyze user data and market data to identify the best jobs and career paths.

[0983] 3. Taking into account the analysis results from the emotion engine, we suggest a career path based on the user's emotional state.

[0984] Viewing and interacting with results

[0985] 1. The terminal visually displays the career path suggestions and recommended skill sets sent from the server, using graphs and charts.

[0986] 2. The user reviews the proposed career path and selects their next steps. After selection, the system tracks their progress and updates their career plan.

[0987] Integration with car infotainment systems

[0988] 1. The emotion engine recognizes passengers' emotions and collects their facial expressions and tone of voice through cameras and microphones, allowing it to analyze their emotional state in real time.

[0989] 2. The server then uses the analysis results to generate optimal routes and entertainment options for passengers, for example, suggesting relaxing music and quiet routes for stressed passengers.

[0990] 3. The infotainment system displays the suggestions sent from the server and allows the passenger to interactively make a selection.

[0991] Specific examples

[0992] Case 1: Career planning for new graduates

[0993] 1. Students log in to the system and enter their major and industry of interest.

[0994] 2. The server collects market trends and required skills and suggests the most suitable job, for example, a career path as a data scientist.

[0995] 3. The emotion engine analyzes the user's emotional state, and if it determines that the user is feeling stressed, it suggests relaxing learning resources.

[0996] Example prompt sentence:

[0997] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[0998] Hardware and software used

[0999] 1. Hardware

[1000] Smartphones, PCs, infotainment systems (e.g., Android Auto, Apple CarPlay)

[1001] Camera, microphone

[1002] 2. Software

[1003] Server-side data analysis systems (e.g., Python, Django, Flask)

[1004] Emotion recognition software (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text)

[1005] External APIs and web scraping technologies for market data collection

[1006] This will enable career advice tailored to the job seeker's emotional state and optimal suggestions based on the emotions of passengers in autonomous vehicles.

[1007] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1008] Program processing flow

[1009] Step 1:

[1010] Users log in to the system using a smartphone or PC. After logging in, they enter personal information such as their work history, skills, interests, and career goals. The entered information is stored in a database by the device and sent to the server for analysis. The input data is the user's work history, skills, interests, and career goals, and the output is organized job seeker information sent to the server.

[1011] Step 2:

[1012] The emotion engine performs emotion analysis using facial expressions and tone of voice when the user inputs. The hardware used is the device's camera and microphone, and the software is an emotion recognition algorithm (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text). The analysis results are sent to the server as emotion data. The input is raw data obtained from the camera and microphone, and the output is an analysis result indicating the user's emotional state.

[1013] Step 3:

[1014] The server uses the received job seeker information and sentiment data to collect market trends and required skills information through external APIs and web scraping technology. The collected data is stored in a database. The input is job seeker information and market data from external data sources, and the output is the collected market data.

[1015] Step 4:

[1016] The server uses machine learning algorithms to analyze job seeker data and collected market data to generate optimal job types and career paths. It also takes into account emotional data obtained from the emotion engine to propose personalized career paths. The input is job seeker information, market data, and emotional data, and the output is optimal job type and career path proposals.

[1017] Step 5:

[1018] The terminal visually displays the career path proposals sent from the server. The presentation is done using graphs and charts in a format that is easy for the user to understand. The input is the career path proposal sent from the server, and the output is the visual presentation for the user.

[1019] Step 6:

[1020] Users review the proposed career path and select their next steps. After making a selection, the system tracks their progress and updates their career plan. The input is the user's selection, and the output is an updated career plan.

[1021] Step 7:

[1022] The emotion recognition engine is integrated into the car's infotainment system and analyzes passengers' facial expressions and tone of voice in real time. The hardware used is a camera and microphone, and the software is an emotion analysis algorithm. The input is facial expression and voice data collected in real time, and the output is analyzed emotion data.

[1023] Step 8:

[1024] Based on the analysis results, the server suggests optimal routes and entertainment options according to the passenger's emotional state. For example, it suggests relaxing music and quiet routes for stressed passengers. The input is emotional data sent from the emotion recognition engine, and the output is recommendations for optimal routes and entertainment options for passengers.

[1025] Step 9:

[1026] The infotainment system displays suggestions sent from the server and allows passengers to interactively select from them. The input is the suggestions sent from the server, and the output is the information displayed to the passenger.

[1027] Example prompt sentence:

[1028] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] [Third embodiment]

[1033] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1034] 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.

[1035] 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).

[1036] 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.

[1037] 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.

[1038] 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).

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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."

[1045] ---

[1046] This invention is a job matching system that utilizes a generative AI model to identify optimal jobs based on information obtained from users and support the development and progression of career paths. The system uses job seeker input, market trends, and machine learning algorithms to provide users with personalized career plans.

[1047] overview

[1048] The system has three main roles:

[1049] 1. Enter user information

[1050] 2. Data analysis and career path generation by the server

[1051] 3. Displaying and interacting with results on the device

[1052] Enter user information

[1053] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[1054] Server-based data analysis and career path generation

[1055] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[1056] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[1057] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1058] Displaying and interacting with results on the device

[1059] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[1060] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[1061] Specific examples

[1062] Case 1: Career planning for new graduates

[1063] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1064] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1065] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1066] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1067] 5. The user reviews the suggested career path and enrolls in the recommended online course as their next step.

[1068] Case 2: Mid-career professionals

[1069] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1070] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1071] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1072] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1073] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[1074] Business model implementation

[1075] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[1076] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development.

[1077] The processing flow will be explained below.

[1078] ---

[1079] Step 1:

[1080] A user logs in to the system.

[1081] The user accesses the login screen and enters their user ID and password.

[1082] Click the Login button to submit your credentials.

[1083] Step 2:

[1084] The server authenticates the user.

[1085] The server checks the received authentication information against its database to see if a matching user exists.

[1086] If the authentication is successful, the session is started and the home screen is returned to the user.

[1087] If authentication fails, an error message is returned.

[1088] Step 3:

[1089] Users enter data such as work history, skills, interests, and career goals.

[1090] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[1091] After completing the input, click the send button to save the data.

[1092] Step 4:

[1093] The terminal transmits the user input data to the server.

[1094] The device converts the input data into the appropriate format and sends it to the server as an API request.

[1095] Step 5:

[1096] The server collects information on market trends, required skills, and career paths for each industry and job position.

[1097] The server uses external APIs and web scraping technology to collect the latest industry data and store it in a database.

[1098] Step 6:

[1099] The server analyzes user data and market data.

[1100] The server uses machine learning algorithms to analyze user information and market data.

[1101] The analysis uses input data to identify optimal job types, recommended skill sets, career paths, and more.

[1102] Step 7:

[1103] A server generates career path suggestions.

[1104] The server generates suggestions for the user based on the analysis results.

[1105] Suggestions include next career steps, how to acquire recommended skills, and available resources.

[1106] Step 8:

[1107] The device displays the career path to the user.

[1108] The terminal visualizes the proposals received from the server and displays them in an easy-to-understand manner for the user.

[1109] Step 9:

[1110] The user reviews the proposal and selects the next step.

[1111] Users review suggested career paths and choose next actions, such as using recommended resources to learn new skills.

[1112] Step 10:

[1113] The server periodically updates the user's progress data.

[1114] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[1115] Update and evolve your career plan based on the data received.

[1116] Step 11:

[1117] The server manages the subscription model.

[1118] The server manages the user's subscription status and handles billing for usage fees.

[1119] Send reminders to users when their subscriptions are about to expire.

[1120] Step 12:

[1121] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[1122] The server stores job information provided by companies in a database and notifies users as appropriate.

[1123] Forwarding direct requests from companies (e.g., interview offers) to users.

[1124] ---

[1125] Example 1

[1126] 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."

[1127] In today's job search and career development process, job seekers spend a great deal of time and effort organizing their information and finding the right job or career path based on that information. Furthermore, a lack of awareness of market trends and required skill sets makes it difficult to plan their careers. To solve these challenges, a system is needed that provides individually customized career advice and dynamically updates plans based on progress.

[1128] 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.

[1129] In this invention, the server includes a means for inputting job seeker information, work history, skills, interests, and career goals, a means for collecting information on market trends, required abilities, and career paths, and a means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal occupations and career paths. This makes it possible to provide individually customized career advice and dynamically update career plans according to progress.

[1130] A "job seeker" is an individual seeking employment, someone who is looking for a new position that utilizes their skills and experience.

[1131] "Information" refers to data about job seekers, including detailed data entered about their work history, skills, interests, career goals, etc.

[1132] "Work history" refers to the history of jobs and positions held by a job seeker in the past, including details such as the duration and nature of the work.

[1133] "Skills" refers to the specialized knowledge and skills that a job seeker possesses, such as programming languages ​​or project management skills.

[1134] "Interests" refers to areas or topics that a job seeker is particularly interested in for their future career.

[1135] "Career goals" refer to the professional goals or positions that job seekers hope to achieve in the future.

[1136] "Market trends" refers to current labor market conditions and trends, including the demand for specific skills and occupations, and employer needs.

[1137] "Demanding competencies" refer to the skills and expertise required in the labor market or in specific occupations.

[1138] "Career path" refers to the specific steps and direction a job seeker takes to advance their career.

[1139] "Analysis" refers to the activity of analyzing input job seeker information and market data to identify the most suitable jobs and career paths.

[1140] A "machine learning algorithm" is a type of program that automatically finds patterns based on large amounts of data and makes predictions and judgments.

[1141] "Suggestion" refers to the act of specifically indicating suitable occupations and career paths for job seekers based on the results of the analysis.

[1142] "Progress" refers to the degree of progress achieved as a result of a job seeker taking action based on a suggestion.

[1143] "Update and evolution" refers to the process of dynamically revising the suggested career path based on the job seeker's latest situation and progress, and making more appropriate suggestions.

[1144] "Visual display" refers to displaying analysis results and proposals using visual elements such as graphs and charts.

[1145] "Subscription Status" refers to the status of the recurring fee paid by a Job Seeker for use of the System.

[1146] "Usage Fee Billing Process" refers to the process for billing Job Seekers for the fees required to use the Subscription Service.

[1147] "Information on collaboration with companies" refers to job information and requests collected by the system in collaboration with companies.

[1148] "Job information" refers to information about employment opportunities provided by businesses to job seekers.

[1149] A "direct request" is a specific request or proposal sent directly by an employer to a job seeker.

[1150] This invention is a career advisory system that uses a generative AI model to support career development by proposing optimal occupations and career paths based on information entered by job seekers. This system has multiple functions, including job seeker information entry, market trend collection, data analysis, career path generation, progress tracking and updates, and visual information display.

[1151] overview

[1152] This system has three roles:

[1153] 1. Enter user information

[1154] 2. Data analysis and career path generation by the server

[1155] 3. Displaying and interacting with results on the device

[1156] Enter user information

[1157] Users log in to the system and enter information such as their work history, skills, interests, and career goals on the profile setting screen. For example, a recent graduate might enter the major they studied at university and the industry they are interested in (e.g., information engineering, AI technology). This input data is stored in a database and used for later analysis.

[1158] Server-based data analysis and career path generation

[1159] The server performs the following functions:

[1160] 1. Data collection: The server periodically collects information on market trends, required skills, and career paths using external APIs and web scraping technology. For example, it uses the LinkedIn API to collect market trend data.

[1161] 2. Data analysis: The server uses machine learning algorithms such as "scikit-learn" to analyze job seeker information and collected market data, comparing the user's skill set with the skills in demand in the market and identifying the most suitable job and recommended skill set.

[1162] 3. Career path generation: Based on the analysis results, the server generates personalized career path suggestions for job seekers, such as recommending advanced Python skills to become a "data scientist" and offering specific Udemy courses.

[1163] Displaying and interacting with results on the device

[1164] The terminal is responsible for:

[1165] 1. Displaying the results: Receive the career path suggestions sent from the server and display them visually to the user. For example, use "D3.js" or "Chart.js" to display the career paths and recommended skill sets in graphs and charts.

[1166] 2. Interaction: The user reviews the proposed career path and selects their next step. If the user enrolls in a recommended online course, the server records that information and tracks their progress. The system dynamically updates the career plan and makes new suggestions based on their progress.

[1167] Specific examples

[1168] Case 1: Career planning for new graduates

[1169] 1. A user (a recent graduate student) logs in to the system and enters in their profile that they are majoring in information engineering and interested in AI technology.

[1170] 2. The server receives this information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1171] 3. The server uses "scikit-learn" to analyze user data and market data and identify "Python" and "basic machine learning" as important skills.

[1172] 4. The server suggests career paths to becoming a "data scientist," recommending, for example, "take a Udemy course to acquire advanced Python skills."

[1173] 5. The device displays suggested career paths and skill sets to the user, and suggests specific next steps to take.

[1174] 6. The user follows the suggestion and enrolls in a Python course on Udemy.

[1175] 7. The server tracks the user's progress through the online course and suggests next steps when a new skill is mastered.

[1176] Case 2: Mid-career professionals

[1177] 1. A user (mid-career professional) logs into the system, currently works as a lead engineer, and enters recent projects and new skills (e.g., project management) into their profile.

[1178] 2. The server takes the user's input data and compares it with the latest industry trends. For example, it determines that there is a growing demand for project management.

[1179] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1180] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1181] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[1182] Prompt Sentence Examples

[1183] "Create a career path suitable for new graduates majoring in information engineering and interested in AI technology."

[1184] "Please suggest the next career step for a user currently working as a lead engineer."

[1185] In this way, the system of the present invention uses a generative AI model to provide job seekers with customized career advice and dynamically update their career plans according to their progress, thereby supporting efficient and effective career development.

[1186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1187] Step 1:

[1188] A user accesses the system and logs in by entering their login information, which is an email address and a password, which authenticates the user and allows them to access their user profile.

[1189] Step 2:

[1190] Users enter information such as work history, skills, interests, and career goals on the profile setting screen. For example, they might enter "3 years as a software engineer" as their work history and "Python, data analysis" as their skills. This information is sent to the server and stored in a database.

[1191] Step 3:

[1192] The server periodically uses external APIs and web scraping technologies (e.g., "LinkedIn API") to collect data on market trends and desired skills. The collected data is stored in a database for later analysis. The input is the external data source, and the output is updated market data.

[1193] Step 4:

[1194] The server combines the received user data and market data and performs data analysis using machine learning algorithms (e.g., "scikit-learn"). The analysis identifies the best jobs and recommended skill sets. The inputs for this step are user data and market data, and the output is the analysis results.

[1195] Step 5:

[1196] Based on the analysis results, the server proposes a personalized career path to the user. This proposal includes the next skills to acquire and online courses to study. For example, a specific proposal may be made such as, "Your next step is to acquire advanced skills in Python. We recommend Udemy course X." The input is the analysis results, and the output is a career path proposal.

[1197] Step 6:

[1198] The terminal receives the career path suggestions sent from the server and visually displays them to the user using graphs and charts (e.g., "D3.js"). The displayed information includes details of the career path and recommended skill sets. The input is the career path suggestions, and the output is the visual display.

[1199] Step 7:

[1200] The user reviews the proposed career path and takes the next step by enrolling in a recommended online course, such as enrolling in a Python course on Udemy. This information is sent back to the server, allowing the system to track their progress.

[1201] Step 8:

[1202] The server periodically checks the user's progress (e.g., progress in online courses) and dynamically updates the career plan based on new skills and advancements acquired. The input to this step is the user's progress, and the output is the updated career plan.

[1203] Step 9:

[1204] The server sends the updated carrier plan back to the terminal, which then visually displays it to the user. This allows the user to always check the latest carrier plan and know the specific action to take next. The input is the updated carrier plan, and the output is another visual display.

[1205] In this way, the system allows users, servers, and terminals to play their respective roles and work together to support job seekers' career development.

[1206] (Application example 1)

[1207] 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."

[1208] Currently, store staff and managers lack the means to plan their career paths and receive support to acquire the most suitable jobs and skills. As a result, they face the challenge of spending a great deal of time and effort gathering appropriate information and self-studying to achieve their career goals. Furthermore, with industry trends changing rapidly, career plans need to be updated based on the latest information, and there is a need for automation of this process.

[1209] 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.

[1210] In this invention, the server includes means for inputting a user's work history, skills, interests, and career goals, means for collecting information on market trends, required skills, and career paths, means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal jobs and career paths, means for updating and evolving the suggested career paths according to the job seeker's progress and visually displaying them, and means for suggesting optimal career paths based on the work history, skills, and interests of store users and showing learning resources and skill improvement methods related to work at the physical store. This makes it easier for staff and managers working at physical stores to efficiently obtain and update career plans based on the latest market trends and acquire appropriate skill sets.

[1211] "Job Seeker" means an individual who is searching for employment.

[1212] "Work history" refers to work experience, which is the history of the jobs that an individual has held up to now.

[1213] "Skills" refers to the skills and abilities a job seeker possesses to perform a specific task or job.

[1214] "Interests" refers to areas that interest the job seeker or are relevant to the job.

[1215] "Career goals" are goals related to the job or position that a job seeker wants to achieve in the future.

[1216] "Market trends" refers to the general movements and demands in the current industry or job market.

[1217] "Demand skills" refers to the specialized techniques and knowledge required for a particular job or industry.

[1218] A "career path" refers to the route or path of professional growth that a job seeker aims to achieve.

[1219] A "machine learning algorithm" is a statistical model or method used by a computer to predict future outcomes based on past data.

[1220] "Recommendation" is the process of showing the user how to acquire the job or skills that are deemed most suitable for them.

[1221] "Progress" is a measure of how far a job seeker is moving toward their career goals.

[1222] "Visually displaying" refers to displaying information or data to a user in a visual format such as a graph or chart.

[1223] "Subscription status" refers to the status of a contract that allows a user to regularly use a particular service.

[1224] "Billing" is the process of collecting fees from users for subscription services.

[1225] "Linked information" refers to information and data related to companies and external organizations.

[1226] "Job information" is information published by a company about the positions it plans to hire.

[1227] "Request" refers to a specific request or request made by a company to a job seeker.

[1228] "Users working in stores" refers to staff and managers who actually work in physical stores.

[1229] "Learning resources" are materials and content used to improve skills and knowledge.

[1230] This invention is a job matching system that utilizes generative AI models to support the career development of staff and managers working in brick-and-mortar stores. This system identifies the most suitable job based on information obtained from the user and supports the development and progression of career paths. The system consists of three main components: user information input, data analysis and career path generation by the server, and result display and interaction.

[1231] Entering user information

[1232] Users log in to the system and enter personal information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields. Based on this information, a detailed profile of the user is created.

[1233] Server-based data analysis and career path generation

[1234] The server includes the following means:

[1235] 1. Collect user information: Collect information entered by the user, such as work history, skills, interests, and career goals.

[1236] 2. Market data collection: Use external APIs and web scraping techniques to gather information on market trends, required skills, and career paths.

[1237] 3. Data Analysis: Collected user information and market data are analyzed using machine learning algorithms (e.g., Random Forest Classifier). This analysis identifies the best job type and career path for the user, recommended skill sets, and next actions to take.

[1238] 4. Career Path Generation: Based on the analysis results, we generate personalized career path suggestions for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1239] Viewing and interacting with results

[1240] The server generates recommendations that are visually displayed to the user on their device. The user can review the recommendations and choose a next step, such as enrolling in a recommended online course or acquiring a skill. The system tracks the user's progress and dynamically updates the career plan.

[1241] Application examples for brick-and-mortar store staff

[1242] One application of this system is to support career planning for store staff and managers. For example, if a staff member aims to become a store manager in the future, the system will suggest the optimal career path based on their current work history, skills, and interests. It will also provide learning resources and ways to improve skills related to work in the store.

[1243] Specific examples

[1244] Let's say a staff member at a brick-and-mortar store aspires to become a store manager in the future. He / she is currently working as a stock manager and has the following skill set:

[1245] Work experience: "Cashier", "Stock manager"

[1246] Skills: "Customer Service," "Inventory Management," "Report Writing"

[1247] Interests: Leadership, Merchandising

[1248] Career goal: "Store manager"

[1249] Prompt Sentence Examples

[1250] Area of ​​expertise: Store management

[1251] Work experience: Cashier, Stock Manager

[1252] Skills: Customer service, inventory management, report writing

[1253] Interests: Leadership, Merchandising

[1254] Career goal: Store manager

[1255] Market Trends: Providing the latest store operation trend data

[1256] The generative AI model will suggest career paths and recommended skill sets.

[1257] In this way, the job matching system of the present invention provides individualized career plans and effectively supports the career development of store staff and managers.

[1258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1259] Step 1:

[1260] A user logs into the system and enters information such as work history, skills, interests, and career goals.

[1261] Input: User's work history, skills, interests, and career goals

[1262] Output: A dataset of user information

[1263] Specific operation: The user enters information on a dedicated profile setting screen, and the information is saved in a database.

[1264] Step 2:

[1265] The server uses external APIs and web scraping technology to collect information on market trends, required skills, and career paths.

[1266] Input: Market information obtained from external APIs and websites

[1267] Output: Market data

[1268] What it does: The server periodically makes API requests and web scraping to store market data in a database.

[1269] Step 3:

[1270] The server collects user information and market data and analyzes the data using machine learning algorithms.

[1271] Input: User information dataset, market data

[1272] Output: Analysis results (optimal job type, career path, recommended skill set)

[1273] Specific operation: The server uses the collected data to train a machine learning model (e.g., RandomForestClassifier), and then uses that model to predict the optimal job type and career path.

[1274] Step 4:

[1275] The server generates personalized career path suggestions for the user based on the analysis results.

[1276] Input: Analysis results

[1277] Output: Career path suggestions

[1278] Specific operation: Based on the analysis results, the server lists recommended career paths, skill acquisition methods, and related resources for the user.

[1279] Step 5:

[1280] The terminal receives the proposal sent from the server and visually displays it to the user.

[1281] Input: Career path proposal

[1282] Output: A visual representation that the user can see

[1283] Specific behavior: The device displays the suggestions to the user in the form of graphs and charts, and the user confirms the next step.

[1284] Step 6:

[1285] The user reviews the suggested career path and selects their next action.

[1286] Input: The user's selected action

[1287] Output: New progress information

[1288] Specific Action: The user enrolls in a suggested online course or begins learning a new skill and enters their progress into the system.

[1289] Step 7:

[1290] The server dynamically updates the career plan based on the progress information and presents the latest proposal to the user again.

[1291] Input: New progress information

[1292] Output: Updated career path suggestions

[1293] Specific operation: The server collects and analyzes the user's progress information and presents new career paths and skill sets to the user.

[1294] 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.

[1295] ---

[1296] This invention is a job matching system that utilizes a generative AI model to identify the most suitable occupation based on information obtained from the user and support the development and progression of a career path. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized career advice.

[1297] overview

[1298] The system has four main roles:

[1299] 1. Enter user information

[1300] 2. Data analysis and career path generation by the server

[1301] 3. Displaying and interacting with results on the device

[1302] 4. Emotion engine recognizes user emotions and adjusts career paths

[1303] Enter user information

[1304] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[1305] Server-based data analysis and career path generation

[1306] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[1307] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[1308] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1309] Displaying and interacting with results on the device

[1310] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[1311] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[1312] Emotion recognition and regulation with emotion engine

[1313] 7. The emotion engine analyzes the user's emotions from their facial expressions, tone of voice, text content, etc. For example, it uses a webcam or microphone to collect facial expressions and tone of voice, and then reads emotions from the text through text analysis.

[1314] 8. The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results, making it possible to suggest career paths that match the user's current psychological state.

[1315] 9. The server then uses the results of the emotion analysis to tailor career advice to the user's motivation and stress level. For example, if the user is feeling stressed, it will suggest resources and activities that will help them relax.

[1316] Specific examples

[1317] Case 1: Career planning for new graduates

[1318] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1319] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1320] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1321] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1322] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[1323] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[1324] Case 2: Mid-career professionals

[1325] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1326] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1327] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1328] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1329] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest a challenging project.

[1330] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[1331] Business model implementation

[1332] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[1333] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development. By introducing an emotion engine, the system can provide optimal advice according to the user's psychological state, thereby achieving more accurate career planning.

[1334] The processing flow will be explained below.

[1335] ---

[1336] Step 1:

[1337] A user logs in to the system.

[1338] The user accesses the login screen and enters their user ID and password.

[1339] Click the Login button and submit your authentication information.

[1340] Step 2:

[1341] The server authenticates the user.

[1342] The server checks the received authentication information against its database to see if a matching user exists.

[1343] If authentication is successful, the session begins and the home screen is returned to the user.

[1344] If authentication fails, an error message is returned.

[1345] Step 3:

[1346] Users enter data such as work history, skills, interests, and career goals.

[1347] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[1348] Once you have completed the entry, click the submit button to save the data.

[1349] Step 4:

[1350] The terminal transmits the user input data to the server.

[1351] The device structures the input data and sends it to the server as an API request.

[1352] Step 5:

[1353] The server collects information on market trends, required skills, and career paths for each industry and job position.

[1354] The server periodically collects the latest market data using external APIs and web scraping technology and stores it in a database.

[1355] Step 6:

[1356] The server analyzes user data and market data.

[1357] The server uses machine learning algorithms to analyze the data and identify the jobs and career paths that are best suited for the user.

[1358] The analysis involves matching user input data with market trend data.

[1359] Step 7:

[1360] The server generates career path suggestions based on the analysis results.

[1361] Suggestions include next career steps, recommended ways to acquire skills, and related resources (e.g., online courses).

[1362] The suggestions are customized to each user's individual situation.

[1363] Step 8:

[1364] The device displays the career path to the user.

[1365] The terminal visually displays the proposal received from the server and presents it in a format that is easy for the user to understand.

[1366] Step 9:

[1367] The user reviews the suggested career path and selects their next steps.

[1368] Based on the suggestions, the user decides on the next action (e.g., taking a recommended course or acquiring a skill).

[1369] Step 10:

[1370] The emotion engine recognizes the user's emotions.

[1371] The emotion engine uses a webcam and microphone to collect the user's facial expressions and voice.

[1372] Text analysis is used to read emotions from user written text.

[1373] Step 11:

[1374] The server collects emotional data and reflects it in career path suggestions.

[1375] The server analyzes the data received from the emotion engine and adjusts the suggestions according to the user's emotional state.

[1376] For example, if a user is feeling stressed, additional resources to help them relax may be suggested.

[1377] Step 12:

[1378] The server periodically updates the user's progress data.

[1379] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[1380] Based on this data, career plans are updated and evolved.

[1381] Step 13:

[1382] The server manages the subscription model.

[1383] The server manages the user's subscription status and handles billing for usage fees.

[1384] Send reminders to users when their subscriptions are about to expire.

[1385] Step 14:

[1386] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[1387] The server stores job information provided by companies in a database and notifies job seekers as appropriate.

[1388] Forwarding direct requests from companies (e.g., interview offers) to users.

[1389] ---

[1390] Example 2

[1391] 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."

[1392] Conventional job matching systems have limitations in suggesting career paths based on a job seeker's specific skills and work history. They also lack the ability to provide advice that takes into account the job seeker's emotions and current psychological state, making it difficult to provide personalized support. As a result, job seekers often fail to find a career path or job type that truly satisfies them.

[1393] The specification process by the specification 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 inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and proposing optimal occupations and career paths using a machine learning algorithm; means for updating and evolving the proposed career path according to the job seeker's progress and visually displaying it; emotion recognition means for recognizing the job seeker's emotions and reflecting them in the analysis results; and means for adjusting the proposal content based on the emotion analysis results. This enables personalized career advice that takes into account not only the job seeker's skills and work history, but also their emotions and psychological state.

[1394] A "job seeker" is an individual who is seeking employment in a particular industry or occupation.

[1395] "Information" refers to data about a job seeker's profile, including, for example, work history, skills, interests, and career goals.

[1396] "Work history" refers to the jobs a job seeker has held in the past and for how long.

[1397] "Skills" refer to the specific skills, abilities, and knowledge that a job seeker possesses.

[1398] "Interests" refers to the industries or fields that a job seeker is interested in.

[1399] "Career goals" refer to the professional goals and aspirations that job seekers hope to achieve in the future.

[1400] "Market trends" refer to the current trends and demands in a particular industry or occupation.

[1401] "Collection methods" refer to the means used to gather information on market trends, required skills, and career paths from external data sources.

[1402] A "machine learning algorithm" is a system or method for analyzing large amounts of data to make predictions and classifications.

[1403] A "career path" refers to the specific path or steps that a job seeker takes to reach the job or position they aim for in the future.

[1404] "Progress" refers to the current stage of a job seeker's progress toward achieving their set career path.

[1405] "Emotion recognition means" refers to a means for analyzing the emotions and psychological state of job seekers and reflecting them in the system.

[1406] "Suggestions" refer to the career paths and recommended actions that the system presents to job seekers based on the analysis results.

[1407] "Visual display means" refers to means for displaying career paths and progress to users using visual designs such as graphs and charts.

[1408] MODE FOR CARRYING OUT THE INVENTION

[1409] This invention is a job matching system that optimizes a user's career path by utilizing a generative AI model. The system mainly consists of user information input, data analysis and career path generation by a server, display and interaction of the results on a terminal, and emotion recognition and career path adjustment by an emotion engine. It also includes an emotion engine for providing personalized advice based on the user's emotions.

[1410] Enter user information

[1411] Users log in to the system and enter information such as their work history, skills, interests, and career goals by filling in the required fields on a profile setting screen. This information is then sent to the server and used for analysis.

[1412] Server-based data analysis and career path generation

[1413] The server stores the received user information in a database. It periodically collects information on market trends, required skills, and career paths using external APIs and web scraping techniques. For example, it retrieves job listings using the Indeed API. The server uses machine learning algorithms (e.g., sklearn's RandomForestClassifier) ​​to analyze user data and market data and identify optimal jobs, career paths, recommended skill sets, and next actions to take.

[1414] Displaying and interacting with results on the device

[1415] The device receives the suggestions sent from the server and displays them visually to the user. Here, visualization libraries such as D3.js are used to display career paths and recommended skill sets in graphs and charts. The user can review the displayed career path and select their next step. As a specific action, a link to a recommended online course is displayed. When the user selects the next action, their progress is sent to the server, and the career plan is dynamically updated.

[1416] Emotion recognition and regulation with emotion engine

[1417] The emotion engine collects emotional data such as the user's facial expressions, tone of voice, and text content, using a webcam and microphone to capture facial expressions and tone of voice. It then uses NLP technology to analyze text and recognize the user's emotions. The server collects the emotional data obtained from the emotion engine and incorporates it into the analysis results, enabling it to suggest career paths tailored to the user's current psychological state. For example, if it determines that the user is feeling stressed, it provides advice based on the emotion analysis results, such as suggesting online resources for relaxation.

[1418] Specific examples

[1419] Case 1: Career planning for new graduates

[1420] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1421] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1422] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1423] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1424] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[1425] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[1426] Case 2: Mid-career professionals

[1427] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1428] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1429] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1430] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1431] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest challenging projects.

[1432] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[1433] Prompt Sentence Examples

[1434] Examples of prompts for users to enter information into the system:

[1435] "Tell me more about your work history and skills. What kind of work have you done so far?"

[1436] "What are your future career goals? What type of job or industry would you like to work in?"

[1437] "Let me know if you plan on gaining any new skills or qualifications."

[1438] "Please tell me more about your recent projects and accomplishments."

[1439] "Tell me your current emotional state. Are you stressed or confident?"

[1440] As described above, this invention provides personalized career advice by taking into account not only the user's skills and work history, but also their emotions and psychological state. Based on a generative AI model, and in conjunction with an emotion engine, it enables more accurate job matching and career path suggestions.

[1441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1442] Step 1:

[1443] A user logs in to the system by entering their username and password on the login screen and clicking the "Login" button. The input data (username and password) is sent to the server, and user authentication is performed. If authentication is successful, the server sends the user information to the client terminal, and the user can proceed to the next screen.

[1444] Step 2:

[1445] The user enters information such as work history, skills, interests, and career goals in the profile setting screen. For example, the user enters past jobs in the "Work History" input field and technical skills in the "Skills" input field. After the data is entered and the "Submit" button is pressed, the input data is sent to the server. The server receives it and stores it in the database. A new record is created in the user profile table based on the input data.

[1446] Step 3:

[1447] The server collects information on market trends, required skills, and career paths from external data sources based on user information. For example, it calls an external API to obtain job information and stores it in an internal database. The collected data is stored in a market data table.

[1448] Step 4:

[1449] The server combines user data with collected market data and inputs it into a machine learning model. Here, data preprocessing is performed, for example, using Python's pandas library. The formatted data is then fed into a machine learning algorithm (for example, sklearn's RandomForestClassifier) ​​for analysis. The analysis results identify the most suitable job type or career path, recommended skill sets, and next actions to take. The analysis results are output in JSON format and stored in the server's temporary data storage.

[1450] Step 5:

[1451] The server sends the analysis results to the client device. The sent data is in JSON format, and the device receives it. After receiving the data, the device uses D3.js or other visualization libraries to visually display career paths and recommended skill sets. The user checks the displayed career path and clicks the "Select next step" button. This action sends the selected data to the server.

[1452] Step 6:

[1453] The server updates the user's progress based on the selected next step. The progress data is stored in a database, dynamically updating the user's career plan. The updated data is then sent back to the device, which visually displays the updates.

[1454] Step 7:

[1455] The emotion engine uses a webcam and microphone to collect the user's facial expressions, tone of voice, and text content. The collected data is analyzed in real time. For example, OpenCV is used for facial analysis, and NLP techniques are used to recognize emotions in voice data.

[1456] Step 8:

[1457] The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results. Based on the emotional data, career path suggestions are made according to the user's current psychological state. For example, if the user is feeling high stress, online resources to help them relax are suggested, and specific advice based on their progress is also included. The suggestions are again output in JSON format and sent to the device.

[1458] (Application example 2)

[1459] 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."

[1460] Current job matching systems typically make suggestions based on a job seeker's work history and skills, but the accuracy of these suggestions is limited because they cannot adjust the system to take into account the job seeker's emotions and stress level. Furthermore, there is no system that can analyze passenger emotions in real time in autonomous vehicles and provide optimal routes and entertainment options. Therefore, there is a need for a system that can suggest career paths that take into account the job seeker's emotional state and provide optimal suggestions based on passenger emotions.

[1461] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and using a machine learning algorithm to propose optimal jobs and career paths; means for updating and evolving the proposed career paths in accordance with the job seeker's progress and visually displaying them; means for analyzing the job seeker's emotions using an emotion recognition engine and adjusting the career path based on the analysis results; and means for linking with the vehicle's infotainment system to propose optimal routes and entertainment options based on the passenger's emotional state. This makes it possible to provide career advice tailored to the job seeker's emotional state, and to make optimal suggestions in accordance with the passenger's emotions in self-driving vehicles.

[1462] definition statement

[1463] "Job Seeker Information" refers to personal information entered by a job seeker, such as work history, skills, interests, and career goals.

[1464] "Market trends" refer to the trends in demand for skills and occupations in the current employment market.

[1465] "Skills" refers to the abilities, expertise, and techniques that a job seeker possesses.

[1466] "Career path" refers to the future occupational path or course in a certain occupation.

[1467] A "machine learning algorithm" is an automated learning process for data analysis and prediction.

[1468] An "emotion recognition engine" is a technology for analyzing a user's emotions and identifying their state.

[1469] An "infotainment system" is a system that provides integrated information and entertainment within a vehicle.

[1470] "Route" refers to a path to a particular destination.

[1471] "Entertainment options" refers to entertainment options such as music, movies, and games offered to passengers.

[1472] "Real-time" means that the system responds immediately, processes data, and provides results quickly.

[1473] patent specification

[1474] The present invention relates to a job matching system that combines a generative AI model and an emotion engine. An embodiment of the system will be described in detail below.

[1475] Overall system overview

[1476] The system features the ability to input job seeker information, analyze data, generate and display career paths, and connect with the car's infotainment system.

[1477] Enter user information

[1478] 1. A user logs in to the system using a smartphone or other device. After logging in, the user enters personal information such as work history, skills, interests, and career goals.

[1479] 2. The emotion engine analyzes the user's emotional state at the time of input, for example, by analyzing the user's facial expressions and tone of voice using a camera or microphone.

[1480] 3. The terminal sends the input information to the server.

[1481] Server-based data analysis and career path generation

[1482] 1. The server compares the received job seeker information with market trends and required skill information collected using external APIs and web scraping technology.

[1483] 2. The server uses machine learning algorithms to analyze user data and market data to identify the best jobs and career paths.

[1484] 3. Taking into account the analysis results from the emotion engine, we suggest a career path based on the user's emotional state.

[1485] Viewing and interacting with results

[1486] 1. The terminal visually displays the career path suggestions and recommended skill sets sent from the server, using graphs and charts.

[1487] 2. The user reviews the proposed career path and selects their next steps. After selection, the system tracks their progress and updates their career plan.

[1488] Integration with car infotainment systems

[1489] 1. The emotion engine recognizes passengers' emotions and collects their facial expressions and tone of voice through cameras and microphones, allowing it to analyze their emotional state in real time.

[1490] 2. The server then uses the analysis results to generate optimal routes and entertainment options for passengers, for example, suggesting relaxing music and quiet routes for stressed passengers.

[1491] 3. The infotainment system displays the suggestions sent from the server and allows the passenger to interactively make a selection.

[1492] Specific examples

[1493] Case 1: Career planning for new graduates

[1494] 1. Students log in to the system and enter their major and industry of interest.

[1495] 2. The server collects market trends and required skills and suggests the most suitable job, for example, a career path as a data scientist.

[1496] 3. The emotion engine analyzes the user's emotional state, and if it determines that the user is feeling stressed, it suggests relaxing learning resources.

[1497] Example prompt sentence:

[1498] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[1499] Hardware and software used

[1500] 1. Hardware

[1501] Smartphones, PCs, infotainment systems (e.g., Android Auto, Apple CarPlay)

[1502] Camera, microphone

[1503] 2. Software

[1504] Server-side data analysis systems (e.g., Python, Django, Flask)

[1505] Emotion recognition software (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text)

[1506] External APIs and web scraping technologies for market data collection

[1507] This will enable career advice tailored to the job seeker's emotional state and optimal suggestions based on the emotions of passengers in autonomous vehicles.

[1508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1509] Program processing flow

[1510] Step 1:

[1511] Users log in to the system using a smartphone or PC. After logging in, they enter personal information such as their work history, skills, interests, and career goals. The entered information is stored in a database by the device and sent to the server for analysis. The input data is the user's work history, skills, interests, and career goals, and the output is organized job seeker information sent to the server.

[1512] Step 2:

[1513] The emotion engine performs emotion analysis using facial expressions and tone of voice when the user inputs. The hardware used is the device's camera and microphone, and the software is an emotion recognition algorithm (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text). The analysis results are sent to the server as emotion data. The input is raw data obtained from the camera and microphone, and the output is an analysis result indicating the user's emotional state.

[1514] Step 3:

[1515] The server uses the received job seeker information and sentiment data to collect market trends and required skills information through external APIs and web scraping technology. The collected data is stored in a database. The input is job seeker information and market data from external data sources, and the output is the collected market data.

[1516] Step 4:

[1517] The server uses machine learning algorithms to analyze job seeker data and collected market data to generate optimal job types and career paths. It also takes into account emotional data obtained from the emotion engine to propose personalized career paths. The input is job seeker information, market data, and emotional data, and the output is optimal job type and career path proposals.

[1518] Step 5:

[1519] The terminal visually displays the career path proposals sent from the server. The presentation is done using graphs and charts in a format that is easy for the user to understand. The input is the career path proposal sent from the server, and the output is the visual presentation for the user.

[1520] Step 6:

[1521] Users review the proposed career path and select their next steps. After making a selection, the system tracks their progress and updates their career plan. The input is the user's selection, and the output is an updated career plan.

[1522] Step 7:

[1523] The emotion recognition engine is integrated into the car's infotainment system and analyzes passengers' facial expressions and tone of voice in real time. The hardware used is a camera and microphone, and the software is an emotion analysis algorithm. The input is facial expression and voice data collected in real time, and the output is analyzed emotion data.

[1524] Step 8:

[1525] Based on the analysis results, the server suggests optimal routes and entertainment options according to the passenger's emotional state. For example, it suggests relaxing music and quiet routes for stressed passengers. The input is emotional data sent from the emotion recognition engine, and the output is recommendations for optimal routes and entertainment options for passengers.

[1526] Step 9:

[1527] The infotainment system displays suggestions sent from the server and allows passengers to interactively select from them. The input is the suggestions sent from the server, and the output is the information displayed to the passenger.

[1528] Example prompt sentence:

[1529] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] [Fourth embodiment]

[1534] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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).

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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."

[1547] ---

[1548] This invention is a job matching system that utilizes a generative AI model to identify optimal jobs based on information obtained from users and support the development and progression of career paths. The system uses job seeker input, market trends, and machine learning algorithms to provide users with personalized career plans.

[1549] overview

[1550] The system has three main roles:

[1551] 1. Enter user information

[1552] 2. Data analysis and career path generation by the server

[1553] 3. Displaying and interacting with results on the device

[1554] Enter user information

[1555] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[1556] Server-based data analysis and career path generation

[1557] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[1558] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[1559] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1560] Displaying and interacting with results on the device

[1561] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[1562] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[1563] Specific examples

[1564] Case 1: Career planning for new graduates

[1565] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1566] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1567] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1568] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1569] 5. The user reviews the suggested career path and enrolls in the recommended online course as their next step.

[1570] Case 2: Mid-career professionals

[1571] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1572] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1573] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1574] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1575] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[1576] Business model implementation

[1577] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[1578] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development.

[1579] The processing flow will be explained below.

[1580] ---

[1581] Step 1:

[1582] A user logs in to the system.

[1583] The user accesses the login screen and enters their user ID and password.

[1584] Click the Login button to submit your credentials.

[1585] Step 2:

[1586] The server authenticates the user.

[1587] The server checks the received authentication information against its database to see if a matching user exists.

[1588] If the authentication is successful, the session is started and the home screen is returned to the user.

[1589] If authentication fails, an error message is returned.

[1590] Step 3:

[1591] Users enter data such as work history, skills, interests, and career goals.

[1592] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[1593] After completing the input, click the send button to save the data.

[1594] Step 4:

[1595] The terminal transmits the user input data to the server.

[1596] The device converts the input data into the appropriate format and sends it to the server as an API request.

[1597] Step 5:

[1598] The server collects information on market trends, required skills, and career paths for each industry and job position.

[1599] The server uses external APIs and web scraping technology to collect the latest industry data and store it in a database.

[1600] Step 6:

[1601] The server analyzes user data and market data.

[1602] The server uses machine learning algorithms to analyze user information and market data.

[1603] The analysis uses input data to identify optimal job types, recommended skill sets, career paths, and more.

[1604] Step 7:

[1605] A server generates career path suggestions.

[1606] The server generates suggestions for the user based on the analysis results.

[1607] Suggestions include next career steps, how to acquire recommended skills, and available resources.

[1608] Step 8:

[1609] The device displays the career path to the user.

[1610] The terminal visualizes the proposals received from the server and displays them in an easy-to-understand manner for the user.

[1611] Step 9:

[1612] The user reviews the proposal and selects the next step.

[1613] Users review suggested career paths and choose next actions, such as using recommended resources to learn new skills.

[1614] Step 10:

[1615] The server periodically updates the user's progress data.

[1616] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[1617] Update and evolve your career plan based on the data received.

[1618] Step 11:

[1619] The server manages the subscription model.

[1620] The server manages the user's subscription status and handles billing for usage fees.

[1621] Send reminders to users when their subscriptions are about to expire.

[1622] Step 12:

[1623] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[1624] The server stores job information provided by companies in a database and notifies users as appropriate.

[1625] Forwarding direct requests from companies (e.g., interview offers) to users.

[1626] ---

[1627] Example 1

[1628] 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."

[1629] In today's job search and career development process, job seekers spend a great deal of time and effort organizing their information and finding the right job or career path based on that information. Furthermore, a lack of awareness of market trends and required skill sets makes it difficult to plan their careers. To solve these challenges, a system is needed that provides individually customized career advice and dynamically updates plans based on progress.

[1630] 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.

[1631] In this invention, the server includes a means for inputting job seeker information, work history, skills, interests, and career goals, a means for collecting information on market trends, required abilities, and career paths, and a means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal occupations and career paths. This makes it possible to provide individually customized career advice and dynamically update career plans according to progress.

[1632] A "job seeker" is an individual seeking employment, someone who is looking for a new position that utilizes their skills and experience.

[1633] "Information" refers to data about job seekers, including detailed data entered about their work history, skills, interests, career goals, etc.

[1634] "Work history" refers to the history of jobs and positions held by a job seeker in the past, including details such as the duration and nature of the work.

[1635] "Skills" refers to the specialized knowledge and skills that a job seeker possesses, such as programming languages ​​or project management skills.

[1636] "Interests" refers to areas or topics that a job seeker is particularly interested in for their future career.

[1637] "Career goals" refer to the professional goals or positions that job seekers hope to achieve in the future.

[1638] "Market trends" refers to current labor market conditions and trends, including the demand for specific skills and occupations, and employer needs.

[1639] "Demanding competencies" refer to the skills and expertise required in the labor market or in specific occupations.

[1640] "Career path" refers to the specific steps and direction a job seeker takes to advance their career.

[1641] "Analysis" refers to the activity of analyzing input job seeker information and market data to identify the most suitable jobs and career paths.

[1642] A "machine learning algorithm" is a type of program that automatically finds patterns based on large amounts of data and makes predictions and judgments.

[1643] "Suggestion" refers to the act of specifically indicating suitable occupations and career paths for job seekers based on the results of the analysis.

[1644] "Progress" refers to the degree of progress achieved as a result of a job seeker taking action based on a suggestion.

[1645] "Update and evolution" refers to the process of dynamically revising the suggested career path based on the job seeker's latest situation and progress, and making more appropriate suggestions.

[1646] "Visual display" refers to displaying analysis results and proposals using visual elements such as graphs and charts.

[1647] "Subscription Status" refers to the status of the recurring fee paid by a Job Seeker for use of the System.

[1648] "Usage Fee Billing Process" refers to the process for billing Job Seekers for the fees required to use the Subscription Service.

[1649] "Information on collaboration with companies" refers to job information and requests collected by the system in collaboration with companies.

[1650] "Job information" refers to information about employment opportunities provided by businesses to job seekers.

[1651] A "direct request" is a specific request or proposal sent directly by an employer to a job seeker.

[1652] This invention is a career advisory system that uses a generative AI model to support career development by proposing optimal occupations and career paths based on information entered by job seekers. This system has multiple functions, including job seeker information entry, market trend collection, data analysis, career path generation, progress tracking and updates, and visual information display.

[1653] overview

[1654] This system has three roles:

[1655] 1. Enter user information

[1656] 2. Data analysis and career path generation by the server

[1657] 3. Displaying and interacting with results on the device

[1658] Enter user information

[1659] Users log in to the system and enter information such as their work history, skills, interests, and career goals on the profile setting screen. For example, a recent graduate might enter the major they studied at university and the industry they are interested in (e.g., information engineering, AI technology). This input data is stored in a database and used for later analysis.

[1660] Server-based data analysis and career path generation

[1661] The server performs the following functions:

[1662] 1. Data collection: The server periodically collects information on market trends, required skills, and career paths using external APIs and web scraping technology. For example, it uses the LinkedIn API to collect market trend data.

[1663] 2. Data analysis: The server uses machine learning algorithms such as "scikit-learn" to analyze job seeker information and collected market data, comparing the user's skill set with the skills in demand in the market and identifying the most suitable job and recommended skill set.

[1664] 3. Career path generation: Based on the analysis results, the server generates personalized career path suggestions for job seekers, such as recommending advanced Python skills to become a "data scientist" and offering specific Udemy courses.

[1665] Displaying and interacting with results on the device

[1666] The terminal is responsible for:

[1667] 1. Displaying the results: Receive the career path suggestions sent from the server and display them visually to the user. For example, use "D3.js" or "Chart.js" to display the career paths and recommended skill sets in graphs and charts.

[1668] 2. Interaction: The user reviews the proposed career path and selects their next step. If the user enrolls in a recommended online course, the server records that information and tracks their progress. The system dynamically updates the career plan and makes new suggestions based on their progress.

[1669] Specific examples

[1670] Case 1: Career planning for new graduates

[1671] 1. A user (a recent graduate student) logs in to the system and enters in their profile that they are majoring in information engineering and interested in AI technology.

[1672] 2. The server receives this information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1673] 3. The server uses "scikit-learn" to analyze user data and market data and identify "Python" and "basic machine learning" as important skills.

[1674] 4. The server suggests career paths to becoming a "data scientist," recommending, for example, "take a Udemy course to acquire advanced Python skills."

[1675] 5. The device displays suggested career paths and skill sets to the user, and suggests specific next steps to take.

[1676] 6. The user follows the suggestion and enrolls in a Python course on Udemy.

[1677] 7. The server tracks the user's progress through the online course and suggests next steps when a new skill is mastered.

[1678] Case 2: Mid-career professionals

[1679] 1. A user (mid-career professional) logs into the system, currently works as a lead engineer, and enters recent projects and new skills (e.g., project management) into their profile.

[1680] 2. The server takes the user's input data and compares it with the latest industry trends. For example, it determines that there is a growing demand for project management.

[1681] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1682] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1683] 5. The user reviews the suggestions and chooses to take the recommended online leadership course as their next step.

[1684] Prompt Sentence Examples

[1685] "Create a career path suitable for new graduates majoring in information engineering and interested in AI technology."

[1686] "Please suggest the next career step for a user currently working as a lead engineer."

[1687] In this way, the system of the present invention uses a generative AI model to provide job seekers with customized career advice and dynamically update their career plans according to their progress, thereby supporting efficient and effective career development.

[1688] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1689] Step 1:

[1690] A user accesses the system and logs in by entering their login information, which is an email address and a password, which authenticates the user and allows them to access their user profile.

[1691] Step 2:

[1692] Users enter information such as work history, skills, interests, and career goals on the profile setting screen. For example, they might enter "3 years as a software engineer" as their work history and "Python, data analysis" as their skills. This information is sent to the server and stored in a database.

[1693] Step 3:

[1694] The server periodically uses external APIs and web scraping technologies (e.g., "LinkedIn API") to collect data on market trends and desired skills. The collected data is stored in a database for later analysis. The input is the external data source, and the output is updated market data.

[1695] Step 4:

[1696] The server combines the received user data and market data and performs data analysis using machine learning algorithms (e.g., "scikit-learn"). The analysis identifies the best jobs and recommended skill sets. The inputs for this step are user data and market data, and the output is the analysis results.

[1697] Step 5:

[1698] Based on the analysis results, the server proposes a personalized career path to the user. This proposal includes the next skills to acquire and online courses to study. For example, a specific proposal may be made such as, "Your next step is to acquire advanced skills in Python. We recommend Udemy course X." The input is the analysis results, and the output is a career path proposal.

[1699] Step 6:

[1700] The terminal receives the career path suggestions sent from the server and visually displays them to the user using graphs and charts (e.g., "D3.js"). The displayed information includes details of the career path and recommended skill sets. The input is the career path suggestions, and the output is the visual display.

[1701] Step 7:

[1702] The user reviews the proposed career path and takes the next step by enrolling in a recommended online course, such as enrolling in a Python course on Udemy. This information is sent back to the server, allowing the system to track their progress.

[1703] Step 8:

[1704] The server periodically checks the user's progress (e.g., progress in online courses) and dynamically updates the career plan based on new skills and advancements acquired. The input to this step is the user's progress, and the output is the updated career plan.

[1705] Step 9:

[1706] The server sends the updated carrier plan back to the terminal, which then visually displays it to the user. This allows the user to always check the latest carrier plan and know the specific action to take next. The input is the updated carrier plan, and the output is another visual display.

[1707] In this way, the system allows users, servers, and terminals to play their respective roles and work together to support job seekers' career development.

[1708] (Application example 1)

[1709] 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."

[1710] Currently, store staff and managers lack the means to plan their career paths and receive support to acquire the most suitable jobs and skills. As a result, they face the challenge of spending a great deal of time and effort gathering appropriate information and self-studying to achieve their career goals. Furthermore, with industry trends changing rapidly, career plans need to be updated based on the latest information, and there is a need for automation of this process.

[1711] 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.

[1712] In this invention, the server includes means for inputting a user's work history, skills, interests, and career goals, means for collecting information on market trends, required skills, and career paths, means for analyzing the input information and collected market data and using a machine learning algorithm to suggest optimal jobs and career paths, means for updating and evolving the suggested career paths according to the job seeker's progress and visually displaying them, and means for suggesting optimal career paths based on the work history, skills, and interests of store users and showing learning resources and skill improvement methods related to work at the physical store. This makes it easier for staff and managers working at physical stores to efficiently obtain and update career plans based on the latest market trends and acquire appropriate skill sets.

[1713] "Job Seeker" means an individual who is searching for employment.

[1714] "Work history" refers to work experience, which is the history of the jobs that an individual has held up to now.

[1715] "Skills" refers to the skills and abilities a job seeker possesses to perform a specific task or job.

[1716] "Interests" refers to areas that interest the job seeker or are relevant to the job.

[1717] "Career goals" are goals related to the job or position that a job seeker wants to achieve in the future.

[1718] "Market trends" refers to the general movements and demands in the current industry or job market.

[1719] "Demand skills" refers to the specialized techniques and knowledge required for a particular job or industry.

[1720] A "career path" refers to the route or path of professional growth that a job seeker aims to achieve.

[1721] A "machine learning algorithm" is a statistical model or method used by a computer to predict future outcomes based on past data.

[1722] "Recommendation" is the process of showing the user how to acquire the job or skills that are deemed most suitable for them.

[1723] "Progress" is a measure of how far a job seeker is moving toward their career goals.

[1724] "Visually displaying" refers to displaying information or data to a user in a visual format such as a graph or chart.

[1725] "Subscription status" refers to the status of a contract that allows a user to regularly use a particular service.

[1726] "Billing" is the process of collecting fees from users for subscription services.

[1727] "Linked information" refers to information and data related to companies and external organizations.

[1728] "Job information" is information published by a company about the positions it plans to hire.

[1729] "Request" refers to a specific request or request made by a company to a job seeker.

[1730] "Users working in stores" refers to staff and managers who actually work in physical stores.

[1731] "Learning resources" are materials and content used to improve skills and knowledge.

[1732] This invention is a job matching system that utilizes generative AI models to support the career development of staff and managers working in brick-and-mortar stores. This system identifies the most suitable job based on information obtained from the user and supports the development and progression of career paths. The system consists of three main components: user information input, data analysis and career path generation by the server, and result display and interaction.

[1733] Entering user information

[1734] Users log in to the system and enter personal information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields. Based on this information, a detailed profile of the user is created.

[1735] Server-based data analysis and career path generation

[1736] The server includes the following means:

[1737] 1. Collect user information: Collect information entered by the user, such as work history, skills, interests, and career goals.

[1738] 2. Market data collection: Use external APIs and web scraping techniques to gather information on market trends, required skills, and career paths.

[1739] 3. Data Analysis: Collected user information and market data are analyzed using machine learning algorithms (e.g., Random Forest Classifier). This analysis identifies the best job type and career path for the user, recommended skill sets, and next actions to take.

[1740] 4. Career Path Generation: Based on the analysis results, we generate personalized career path suggestions for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1741] Viewing and interacting with results

[1742] The server generates recommendations that are visually displayed to the user on their device. The user can review the recommendations and choose a next step, such as enrolling in a recommended online course or acquiring a skill. The system tracks the user's progress and dynamically updates the career plan.

[1743] Application examples for brick-and-mortar store staff

[1744] One application of this system is to support career planning for store staff and managers. For example, if a staff member aims to become a store manager in the future, the system will suggest the optimal career path based on their current work history, skills, and interests. It will also provide learning resources and ways to improve skills related to work in the store.

[1745] Specific examples

[1746] Let's say a staff member at a brick-and-mortar store aspires to become a store manager in the future. He / she is currently working as a stock manager and has the following skill set:

[1747] Work experience: "Cashier", "Stock manager"

[1748] Skills: "Customer Service," "Inventory Management," "Report Writing"

[1749] Interests: Leadership, Merchandising

[1750] Career goal: "Store manager"

[1751] Prompt Sentence Examples

[1752] Area of ​​expertise: Store management

[1753] Work experience: Cashier, Stock Manager

[1754] Skills: Customer service, inventory management, report writing

[1755] Interests: Leadership, Merchandising

[1756] Career goal: Store manager

[1757] Market Trends: Providing the latest store operation trend data

[1758] The generative AI model will suggest career paths and recommended skill sets.

[1759] In this way, the job matching system of the present invention provides individualized career plans and effectively supports the career development of store staff and managers.

[1760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1761] Step 1:

[1762] A user logs into the system and enters information such as work history, skills, interests, and career goals.

[1763] Input: User's work history, skills, interests, and career goals

[1764] Output: A dataset of user information

[1765] Specific operation: The user enters information on a dedicated profile setting screen, and the information is saved in a database.

[1766] Step 2:

[1767] The server uses external APIs and web scraping technology to collect information on market trends, required skills, and career paths.

[1768] Input: Market information obtained from external APIs and websites

[1769] Output: Market data

[1770] What it does: The server periodically makes API requests and web scraping to store market data in a database.

[1771] Step 3:

[1772] The server collects user information and market data and analyzes the data using machine learning algorithms.

[1773] Input: User information dataset, market data

[1774] Output: Analysis results (optimal job type, career path, recommended skill set)

[1775] Specific operation: The server uses the collected data to train a machine learning model (e.g., RandomForestClassifier), and then uses that model to predict the optimal job type and career path.

[1776] Step 4:

[1777] The server generates personalized career path suggestions for the user based on the analysis results.

[1778] Input: Analysis results

[1779] Output: Career path suggestions

[1780] Specific operation: Based on the analysis results, the server lists recommended career paths, skill acquisition methods, and related resources for the user.

[1781] Step 5:

[1782] The terminal receives the proposal sent from the server and visually displays it to the user.

[1783] Input: Career path proposal

[1784] Output: A visual representation that the user can see

[1785] Specific behavior: The device displays the suggestions to the user in the form of graphs and charts, and the user confirms the next step.

[1786] Step 6:

[1787] The user reviews the suggested career path and selects their next action.

[1788] Input: The user's selected action

[1789] Output: New progress information

[1790] Specific Action: The user enrolls in a suggested online course or begins learning a new skill and enters their progress into the system.

[1791] Step 7:

[1792] The server dynamically updates the career plan based on the progress information and presents the latest proposal to the user again.

[1793] Input: New progress information

[1794] Output: Updated career path suggestions

[1795] Specific operation: The server collects and analyzes the user's progress information and presents new career paths and skill sets to the user.

[1796] 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.

[1797] ---

[1798] This invention is a job matching system that utilizes a generative AI model to identify the most suitable occupation based on information obtained from the user and support the development and progression of a career path. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized career advice.

[1799] overview

[1800] The system has four main roles:

[1801] 1. Enter user information

[1802] 2. Data analysis and career path generation by the server

[1803] 3. Displaying and interacting with results on the device

[1804] 4. Emotion engine recognizes user emotions and adjusts career paths

[1805] Enter user information

[1806] 1. A user logs into the system and enters information such as work history, skills, interests, and career goals. This information is entered in the user's profile settings screen, filling in the required fields.

[1807] Server-based data analysis and career path generation

[1808] 2. The server collects the received user information and matches it with market trends, required skills, and career paths by industry and job title, including by periodically gathering market data using external APIs and web scraping technology.

[1809] 3. The server uses machine learning algorithms to analyze user and market data, identifying optimal jobs and career paths, recommended skill sets, and next steps.

[1810] 4. Based on the analysis, the server generates personalized career path recommendations for the user, including next steps, recommended skill acquisition methods, and related resources (e.g., online courses).

[1811] Displaying and interacting with results on the device

[1812] 5. The device receives the suggestions sent from the server and displays them visually to the user. Career paths and recommended skill sets are displayed in graphs and charts that are easy for the user to understand.

[1813] 6. The user reviews the proposed career path and selects their next steps. As the user performs their next actions (e.g., taking recommended courses, acquiring skills), the system tracks their progress and dynamically updates their career plan.

[1814] Emotion recognition and regulation with emotion engine

[1815] 7. The emotion engine analyzes the user's emotions from their facial expressions, tone of voice, text content, etc. For example, it uses a webcam or microphone to collect facial expressions and tone of voice, and then reads emotions from the text through text analysis.

[1816] 8. The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results, making it possible to suggest career paths that match the user's current psychological state.

[1817] 9. The server then uses the results of the emotion analysis to tailor career advice to the user's motivation and stress level. For example, if the user is feeling stressed, it will suggest resources and activities that will help them relax.

[1818] Specific examples

[1819] Case 1: Career planning for new graduates

[1820] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1821] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1822] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1823] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1824] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[1825] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[1826] Case 2: Mid-career professionals

[1827] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1828] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1829] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1830] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1831] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest a challenging project.

[1832] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[1833] Business model implementation

[1834] The company will introduce a subscription model to enable users to continue using the service, and will also provide job information through partnerships with companies, helping them place the right people in the right positions, thereby providing value to both users and companies.

[1835] In this way, the job matching system of the present invention provides individualized career plans and effectively supports job seekers in their career development. By introducing an emotion engine, the system can provide optimal advice according to the user's psychological state, thereby achieving more accurate career planning.

[1836] The processing flow will be explained below.

[1837] ---

[1838] Step 1:

[1839] A user logs in to the system.

[1840] The user accesses the login screen and enters their user ID and password.

[1841] Click the Login button and submit your authentication information.

[1842] Step 2:

[1843] The server authenticates the user.

[1844] The server checks the received authentication information against its database to see if a matching user exists.

[1845] If authentication is successful, the session begins and the home screen is returned to the user.

[1846] If authentication fails, an error message is returned.

[1847] Step 3:

[1848] Users enter data such as work history, skills, interests, and career goals.

[1849] The user opens the profile setting screen and enters the required information in each input field (work history, skills, interests, career goals, etc.).

[1850] Once you have completed the entry, click the submit button to save the data.

[1851] Step 4:

[1852] The terminal transmits the user input data to the server.

[1853] The device structures the input data and sends it to the server as an API request.

[1854] Step 5:

[1855] The server collects information on market trends, required skills, and career paths for each industry and job position.

[1856] The server periodically collects the latest market data using external APIs and web scraping technology and stores it in a database.

[1857] Step 6:

[1858] The server analyzes user data and market data.

[1859] The server uses machine learning algorithms to analyze the data and identify the jobs and career paths that are best suited for the user.

[1860] The analysis involves matching user input data with market trend data.

[1861] Step 7:

[1862] The server generates career path suggestions based on the analysis results.

[1863] Suggestions include next career steps, recommended ways to acquire skills, and related resources (e.g., online courses).

[1864] The suggestions are customized to each user's individual situation.

[1865] Step 8:

[1866] The device displays the career path to the user.

[1867] The terminal visually displays the proposal received from the server and presents it in a format that is easy for the user to understand.

[1868] Step 9:

[1869] The user reviews the suggested career path and selects their next steps.

[1870] Based on the suggestions, the user decides on the next action (e.g., taking a recommended course or acquiring a skill).

[1871] Step 10:

[1872] The emotion engine recognizes the user's emotions.

[1873] The emotion engine uses a webcam and microphone to collect the user's facial expressions and voice.

[1874] Text analysis is used to read emotions from user written text.

[1875] Step 11:

[1876] The server collects emotional data and reflects it in career path suggestions.

[1877] The server analyzes the data received from the emotion engine and adjusts the suggestions according to the user's emotional state.

[1878] For example, if a user is feeling stressed, additional resources to help them relax may be suggested.

[1879] Step 12:

[1880] The server periodically updates the user's progress data.

[1881] The server receives new input data and activities from users (e.g., course completion, certification acquisition).

[1882] Based on this data, career plans are updated and evolved.

[1883] Step 13:

[1884] The server manages the subscription model.

[1885] The server manages the user's subscription status and handles billing for usage fees.

[1886] Send reminders to users when their subscriptions are about to expire.

[1887] Step 14:

[1888] The server manages information on collaboration with companies and provides job information and direct requests from companies to job seekers.

[1889] The server stores job information provided by companies in a database and notifies job seekers as appropriate.

[1890] Forwarding direct requests from companies (e.g., interview offers) to users.

[1891] ---

[1892] Example 2

[1893] 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."

[1894] Conventional job matching systems have limitations in suggesting career paths based on a job seeker's specific skills and work history. They also lack the ability to provide advice that takes into account the job seeker's emotions and current psychological state, making it difficult to provide personalized support. As a result, job seekers often fail to find a career path or job type that truly satisfies them.

[1895] The specification process by the specification 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 inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and proposing optimal occupations and career paths using a machine learning algorithm; means for updating and evolving the proposed career path according to the job seeker's progress and visually displaying it; emotion recognition means for recognizing the job seeker's emotions and reflecting them in the analysis results; and means for adjusting the proposal content based on the emotion analysis results. This enables personalized career advice that takes into account not only the job seeker's skills and work history, but also their emotions and psychological state.

[1896] A "job seeker" is an individual who is seeking employment in a particular industry or occupation.

[1897] "Information" refers to data about a job seeker's profile, including, for example, work history, skills, interests, and career goals.

[1898] "Work history" refers to the jobs a job seeker has held in the past and for how long.

[1899] "Skills" refer to the specific skills, abilities, and knowledge that a job seeker possesses.

[1900] "Interests" refers to the industries or fields that a job seeker is interested in.

[1901] "Career goals" refer to the professional goals and aspirations that job seekers hope to achieve in the future.

[1902] "Market trends" refer to the current trends and demands in a particular industry or occupation.

[1903] "Collection methods" refer to the means used to gather information on market trends, required skills, and career paths from external data sources.

[1904] A "machine learning algorithm" is a system or method for analyzing large amounts of data to make predictions and classifications.

[1905] A "career path" refers to the specific path or steps that a job seeker takes to reach the job or position they aim for in the future.

[1906] "Progress" refers to the current stage of a job seeker's progress toward achieving their set career path.

[1907] "Emotion recognition means" refers to a means for analyzing the emotions and psychological state of job seekers and reflecting them in the system.

[1908] "Suggestions" refer to the career paths and recommended actions that the system presents to job seekers based on the analysis results.

[1909] "Visual display means" refers to means for displaying career paths and progress to users using visual designs such as graphs and charts.

[1910] MODE FOR CARRYING OUT THE INVENTION

[1911] This invention is a job matching system that optimizes a user's career path by utilizing a generative AI model. The system mainly consists of user information input, data analysis and career path generation by a server, display and interaction of the results on a terminal, and emotion recognition and career path adjustment by an emotion engine. It also includes an emotion engine for providing personalized advice based on the user's emotions.

[1912] Enter user information

[1913] Users log in to the system and enter information such as their work history, skills, interests, and career goals by filling in the required fields on a profile setting screen. This information is then sent to the server and used for analysis.

[1914] Server-based data analysis and career path generation

[1915] The server stores the received user information in a database. It periodically collects information on market trends, required skills, and career paths using external APIs and web scraping techniques. For example, it retrieves job listings using the Indeed API. The server uses machine learning algorithms (e.g., sklearn's RandomForestClassifier) ​​to analyze user data and market data and identify optimal jobs, career paths, recommended skill sets, and next actions to take.

[1916] Displaying and interacting with results on the device

[1917] The device receives the suggestions sent from the server and displays them visually to the user. Here, visualization libraries such as D3.js are used to display career paths and recommended skill sets in graphs and charts. The user can review the displayed career path and select their next step. As a specific action, a link to a recommended online course is displayed. When the user selects the next action, their progress is sent to the server, and the career plan is dynamically updated.

[1918] Emotion recognition and regulation with emotion engine

[1919] The emotion engine collects emotional data such as the user's facial expressions, tone of voice, and text content, using a webcam and microphone to capture facial expressions and tone of voice. It then uses NLP technology to analyze text and recognize the user's emotions. The server collects the emotional data obtained from the emotion engine and incorporates it into the analysis results, enabling it to suggest career paths tailored to the user's current psychological state. For example, if it determines that the user is feeling stressed, it provides advice based on the emotion analysis results, such as suggesting online resources for relaxation.

[1920] Specific examples

[1921] Case 1: Career planning for new graduates

[1922] 1. A user (a recent graduate) logs in to the system and enters the major they studied at university and the industry they are interested in. For example, if they majored in information engineering and are interested in AI technology, they enter that information in their profile.

[1923] 2. The server receives the input information and collects market trends and required skills related to AI technology (e.g., Python, basic machine learning).

[1924] 3. Based on the collected data, the server suggests "data scientist" as the most suitable job and provides recommended skill sets and learning resources.

[1925] 4. The device displays the user's career path and recommended skill sets, and suggests specific next steps to take.

[1926] 5. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if it determines that the user is feeling stressed, it will suggest online resources to help them relax.

[1927] 6. The server adjusts its suggestions based on the emotional data and includes advice to reduce stress.

[1928] Case 2: Mid-career professionals

[1929] 1. A user (mid-career professional) logs into the system and enters their latest projects and acquired qualifications. For example, if they are currently working as a lead engineer, they enter their recent projects and new skills (e.g., project management).

[1930] 2. The server takes the user's input data and compares it with current industry trends. For example, it determines that there is a growing demand for project management.

[1931] 3. Based on the analysis results, the server suggests "Project Manager" as the next career step and provides recommended skills (e.g., leadership, project management) and related resources.

[1932] 4. The device visualizes the proposal and displays it to the user, showing a detailed career plan.

[1933] 5. The emotion engine analyzes the user's text and tone of voice to understand their current emotional state. For example, if it determines that the user is confident, it will suggest challenging projects.

[1934] 6. The server adjusts the suggestions based on the emotional data and includes advice to further develop the user's confidence.

[1935] Prompt Sentence Examples

[1936] Examples of prompts for users to enter information into the system:

[1937] "Tell me more about your work history and skills. What kind of work have you done so far?"

[1938] "What are your future career goals? What type of job or industry would you like to work in?"

[1939] "Let me know if you plan on gaining any new skills or qualifications."

[1940] "Please tell me more about your recent projects and accomplishments."

[1941] "Tell me your current emotional state. Are you stressed or confident?"

[1942] As described above, this invention provides personalized career advice by taking into account not only the user's skills and work history, but also their emotions and psychological state. Based on a generative AI model, and in conjunction with an emotion engine, it enables more accurate job matching and career path suggestions.

[1943] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1944] Step 1:

[1945] A user logs in to the system by entering their username and password on the login screen and clicking the "Login" button. The input data (username and password) is sent to the server, and user authentication is performed. If authentication is successful, the server sends the user information to the client terminal, and the user can proceed to the next screen.

[1946] Step 2:

[1947] The user enters information such as work history, skills, interests, and career goals in the profile setting screen. For example, the user enters past jobs in the "Work History" input field and technical skills in the "Skills" input field. After the data is entered and the "Submit" button is pressed, the input data is sent to the server. The server receives it and stores it in the database. A new record is created in the user profile table based on the input data.

[1948] Step 3:

[1949] The server collects information on market trends, required skills, and career paths from external data sources based on user information. For example, it calls an external API to obtain job information and stores it in an internal database. The collected data is stored in a market data table.

[1950] Step 4:

[1951] The server combines user data with collected market data and inputs it into a machine learning model. Here, data preprocessing is performed, for example, using Python's pandas library. The formatted data is then fed into a machine learning algorithm (for example, sklearn's RandomForestClassifier) ​​for analysis. The analysis results identify the most suitable job type or career path, recommended skill sets, and next actions to take. The analysis results are output in JSON format and stored in the server's temporary data storage.

[1952] Step 5:

[1953] The server sends the analysis results to the client device. The sent data is in JSON format, and the device receives it. After receiving the data, the device uses D3.js or other visualization libraries to visually display career paths and recommended skill sets. The user checks the displayed career path and clicks the "Select next step" button. This action sends the selected data to the server.

[1954] Step 6:

[1955] The server updates the user's progress based on the selected next step. The progress data is stored in a database, dynamically updating the user's career plan. The updated data is then sent back to the device, which visually displays the updates.

[1956] Step 7:

[1957] The emotion engine uses a webcam and microphone to collect the user's facial expressions, tone of voice, and text content. The collected data is analyzed in real time. For example, OpenCV is used for facial analysis, and NLP techniques are used to recognize emotions in voice data.

[1958] Step 8:

[1959] The server collects the emotional data obtained from the emotion engine and reflects it in the analysis results. Based on the emotional data, career path suggestions are made according to the user's current psychological state. For example, if the user is feeling high stress, online resources to help them relax are suggested, and specific advice based on their progress is also included. The suggestions are again output in JSON format and sent to the device.

[1960] (Application example 2)

[1961] 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."

[1962] Current job matching systems typically make suggestions based on a job seeker's work history and skills, but the accuracy of these suggestions is limited because they cannot adjust the system to take into account the job seeker's emotions and stress level. Furthermore, there is no system that can analyze passenger emotions in real time in autonomous vehicles and provide optimal routes and entertainment options. Therefore, there is a need for a system that can suggest career paths that take into account the job seeker's emotional state and provide optimal suggestions based on passenger emotions.

[1963] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a job seeker's information, work history, skills, interests, and career goals; means for collecting information on market trends, required skills, and career paths; means for analyzing the input information and collected market data and using a machine learning algorithm to propose optimal jobs and career paths; means for updating and evolving the proposed career paths in accordance with the job seeker's progress and visually displaying them; means for analyzing the job seeker's emotions using an emotion recognition engine and adjusting the career path based on the analysis results; and means for linking with the vehicle's infotainment system to propose optimal routes and entertainment options based on the passenger's emotional state. This makes it possible to provide career advice tailored to the job seeker's emotional state, and to make optimal suggestions in accordance with the passenger's emotions in self-driving vehicles.

[1964] definition statement

[1965] "Job Seeker Information" refers to personal information entered by a job seeker, such as work history, skills, interests, and career goals.

[1966] "Market trends" refer to the trends in demand for skills and occupations in the current employment market.

[1967] "Skills" refers to the abilities, expertise, and techniques that a job seeker possesses.

[1968] "Career path" refers to the future occupational path or course in a certain occupation.

[1969] A "machine learning algorithm" is an automated learning process for data analysis and prediction.

[1970] An "emotion recognition engine" is a technology for analyzing a user's emotions and identifying their state.

[1971] An "infotainment system" is a system that provides integrated information and entertainment within a vehicle.

[1972] "Route" refers to a path to a particular destination.

[1973] "Entertainment options" refers to entertainment options such as music, movies, and games offered to passengers.

[1974] "Real-time" means that the system responds immediately, processes data, and provides results quickly.

[1975] patent specification

[1976] The present invention relates to a job matching system that combines a generative AI model and an emotion engine. An embodiment of the system will be described in detail below.

[1977] Overall system overview

[1978] The system features the ability to input job seeker information, analyze data, generate and display career paths, and connect with the car's infotainment system.

[1979] Enter user information

[1980] 1. A user logs in to the system using a smartphone or other device. After logging in, the user enters personal information such as work history, skills, interests, and career goals.

[1981] 2. The emotion engine analyzes the user's emotional state at the time of input, for example, by analyzing the user's facial expressions and tone of voice using a camera or microphone.

[1982] 3. The terminal sends the input information to the server.

[1983] Server-based data analysis and career path generation

[1984] 1. The server compares the received job seeker information with market trends and required skill information collected using external APIs and web scraping technology.

[1985] 2. The server uses machine learning algorithms to analyze user data and market data to identify the best jobs and career paths.

[1986] 3. Taking into account the analysis results from the emotion engine, we suggest a career path based on the user's emotional state.

[1987] Viewing and interacting with results

[1988] 1. The terminal visually displays the career path suggestions and recommended skill sets sent from the server, using graphs and charts.

[1989] 2. The user reviews the proposed career path and selects their next steps. After selection, the system tracks their progress and updates their career plan.

[1990] Integration with car infotainment systems

[1991] 1. The emotion engine recognizes passengers' emotions and collects their facial expressions and tone of voice through cameras and microphones, allowing it to analyze their emotional state in real time.

[1992] 2. The server then uses the analysis results to generate optimal routes and entertainment options for passengers, for example, suggesting relaxing music and quiet routes for stressed passengers.

[1993] 3. The infotainment system displays the suggestions sent from the server and allows the passenger to interactively make a selection.

[1994] Specific examples

[1995] Case 1: Career planning for new graduates

[1996] 1. Students log in to the system and enter their major and industry of interest.

[1997] 2. The server collects market trends and required skills and suggests the most suitable job, for example, a career path as a data scientist.

[1998] 3. The emotion engine analyzes the user's emotional state, and if it determines that the user is feeling stressed, it suggests relaxing learning resources.

[1999] Example prompt sentence:

[2000] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[2001] Hardware and software used

[2002] 1. Hardware

[2003] Smartphones, PCs, infotainment systems (e.g., Android Auto, Apple CarPlay)

[2004] Camera, microphone

[2005] 2. Software

[2006] Server-side data analysis systems (e.g., Python, Django, Flask)

[2007] Emotion recognition software (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text)

[2008] External APIs and web scraping technologies for market data collection

[2009] This will enable career advice tailored to the job seeker's emotional state and optimal suggestions based on the emotions of passengers in autonomous vehicles.

[2010] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2011] Program processing flow

[2012] Step 1:

[2013] Users log in to the system using a smartphone or PC. After logging in, they enter personal information such as their work history, skills, interests, and career goals. The entered information is stored in a database by the device and sent to the server for analysis. The input data is the user's work history, skills, interests, and career goals, and the output is organized job seeker information sent to the server.

[2014] Step 2:

[2015] The emotion engine performs emotion analysis using facial expressions and tone of voice when the user inputs. The hardware used is the device's camera and microphone, and the software is an emotion recognition algorithm (e.g., Google Cloud Vision API, Google Cloud Speech-to-Text). The analysis results are sent to the server as emotion data. The input is raw data obtained from the camera and microphone, and the output is an analysis result indicating the user's emotional state.

[2016] Step 3:

[2017] The server uses the received job seeker information and sentiment data to collect market trends and required skills information through external APIs and web scraping technology. The collected data is stored in a database. The input is job seeker information and market data from external data sources, and the output is the collected market data.

[2018] Step 4:

[2019] The server uses machine learning algorithms to analyze job seeker data and collected market data to generate optimal job types and career paths. It also takes into account emotional data obtained from the emotion engine to propose personalized career paths. The input is job seeker information, market data, and emotional data, and the output is optimal job type and career path proposals.

[2020] Step 5:

[2021] The terminal visually displays the career path proposals sent from the server. The presentation is done using graphs and charts in a format that is easy for the user to understand. The input is the career path proposal sent from the server, and the output is the visual presentation for the user.

[2022] Step 6:

[2023] Users review the proposed career path and select their next steps. After making a selection, the system tracks their progress and updates their career plan. The input is the user's selection, and the output is an updated career plan.

[2024] Step 7:

[2025] The emotion recognition engine is integrated into the car's infotainment system and analyzes passengers' facial expressions and tone of voice in real time. The hardware used is a camera and microphone, and the software is an emotion analysis algorithm. The input is facial expression and voice data collected in real time, and the output is analyzed emotion data.

[2026] Step 8:

[2027] Based on the analysis results, the server suggests optimal routes and entertainment options according to the passenger's emotional state. For example, it suggests relaxing music and quiet routes for stressed passengers. The input is emotional data sent from the emotion recognition engine, and the output is recommendations for optimal routes and entertainment options for passengers.

[2028] Step 9:

[2029] The infotainment system displays suggestions sent from the server and allows passengers to interactively select from them. The input is the suggestions sent from the server, and the output is the information displayed to the passenger.

[2030] Example prompt sentence:

[2031] The user may be tired after a long train ride, so suggest relaxing music and scenic routes.

[2032] 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.

[2033] 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.

[2034] 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.

[2035] 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 identific...

Claims

1. a means of inputting job seeker information, work history, skills, interests, and career goals; A means of gathering information on market trends, required skills, and career paths, A means of analyzing input information and collected market data and using machine learning algorithms to suggest optimal job types and career paths; A system that includes a means to update and evolve suggested career paths based on the job seeker's progress and visually display them.

2. 2. The system according to claim 1, further comprising means for managing the subscription status of job seekers and for processing billing for usage fees.

3. 2. The system according to claim 1, further comprising means for managing information on collaboration with companies and providing job seekers with recruitment information and direct requests from companies.

Images