System

Abstract

A system is provided.SOLUTION: A system comprising: means for using a generative model to suggest appropriate jobs based on a user's interests, abilities, and values; means for providing job opportunities for the suggested jobs; means for providing support for specific path planning and skill improvement to reach the user's selected job; and means for analyzing industry and market trends and providing the results of the analysis to the user.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 modern society, occupational diversification and rapid industrial change are making it difficult for individual users to find the occupation that best suits them. As a result, opportunities to find a job that matches one's interests, abilities, and values ​​are decreasing, resulting in problems such as increased employee turnover and declining labor productivity. The purpose of this invention is to provide users with the information and support they need to select the optimal occupation and build their career based on their individual characteristics. Furthermore, it aims to improve future employment stability and job satisfaction by analyzing industry trends and market demand and supporting career choices based on these. [Means for solving the problem]

[0005] The present invention provides a means for using generative models to suggest careers based on a user's interests, abilities, and values. Specifically, the system suggests suitable careers by interacting with the user through a chatbot interface and analyzing the information obtained. It also provides job opportunities related to the suggested careers and supports the user in making specific career plans and improving their skills to achieve those careers. Furthermore, the system analyzes industry trends and market demand and provides the results to the user, enabling the user to make career choices that take future market demands into account. In this way, the present invention provides comprehensive support to users, helping them make appropriate career choices, stabilizing the labor market and improving their job satisfaction.

[0006] A "generative model" is an artificial intelligence or machine learning algorithm that suggests optimal careers based on data provided by users.

[0007] "User" refers to a person who uses this system and is trying to make a career choice based on their interests, abilities, and values.

[0008] "Career suggestion" is the act of recommending the most suitable occupation to a user, taking into account their characteristics.

[0009] "Job Opportunity" refers to specific job recruitment information provided to users.

[0010] A "career plan" details the learning path and specific course of action a user needs to take to reach a specific career.

[0011] "Support for improving competence" refers to the provision of various educational resources and training to help users acquire the necessary skills and knowledge.

[0012] "Industry trends" refers to data and information that indicates the current state and future development trends of a particular industry.

[0013] "Market demand" is an indicator that shows the extent to which the market has a need for a particular occupation or skill.

[0014] "Analysis results" means the views and predictions derived by the generative model based on the collected data. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[0037] System Overview

[0038] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[0039] Program processing

[0040] Career diagnosis chatbot

[0041] User access and interaction initiation:

[0042] The user accesses the employment and career change support service from their device, logs in, and selects the career diagnosis function. The server connects to the generative AI model, generates a chatbot interface, and presents it to the user. The user inputs information about their interests, abilities, and values ​​in response to the chatbot's questions.

[0043] Analysis and career suggestions using generative models:

[0044] The server analyzes the user's answers using a generative AI model to suggest the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[0045] Career Advice and Planning

[0046] Providing career advice:

[0047] If the user is interested in the suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. The learning plan includes information about online courses and training programs.

[0048] Implementing your career plan:

[0049] The server compiles the user's learning plan into a detailed course plan and presents it in the form of a timeline, which specifies the goals and necessary resources for each step.

[0050] Industry trends and market demand analysis

[0051] Data collection and analysis:

[0052] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[0053] Providing analysis results:

[0054] If a user wants to find trend information for a particular industry or occupation, the server generates and provides the analysis results to the user, allowing the user to consider careers based on market demand.

[0055] Specific examples

[0056] Career Assessment Chatbot Use Cases:

[0057] When a user types "I want to find a new career" into the chatbot, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes this using a generative AI model and suggests "software developer." It also retrieves related job information from a database and displays it to the user.

[0058] Examples of career advice provided:

[0059] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are a programming language (Python, Java®) and an understanding of algorithms." It also provides information about online courses and training programs.

[0060] Industry trends and market demand analysis examples:

[0061] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[0062] In this way, the system provides users with comprehensive career choice support and provides specific means to support career success.

[0063] The processing flow will be explained below.

[0064] Specific processing flow of the career diagnosis chatbot program

[0065] Processing flow

[0066] Step 1:

[0067] The user accesses the employment and career change support service from their device and enters their login information.

[0068] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[0069] Step 2:

[0070] The server authenticates the user and displays the home screen.

[0071] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[0072] Step 3:

[0073] The user selects the "Career Diagnosis" function on the home screen.

[0074] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[0075] Step 4:

[0076] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[0077] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[0078] Step 5:

[0079] The user answers the chatbot's question (e.g., "I like reading and programming").

[0080] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[0081] Step 6:

[0082] The server analyzes the user's answers using a generative AI model.

[0083] How it works: The server inputs the user's answers into a natural language processing engine, and passes the analysis results to a generative model, which then calculates the best possible job candidates.

[0084] Step 7:

[0085] The server will suggest the best career aptitudes.

[0086] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[0087] Step 8:

[0088] The server displays job listings related to the proposed occupation.

[0089] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[0090] Career Advice and Planning Program Process

[0091] Processing flow

[0092] Step 1:

[0093] If the user is interested in the careers presented, they will ask for detailed career advice.

[0094] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[0095] Step 2:

[0096] The server retrieves the skills and qualifications required for the user's career choice from a database.

[0097] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[0098] Step 3:

[0099] The server generates the lesson plan.

[0100] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[0101] Step 4:

[0102] The server will then concretely present the course plan.

[0103] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[0104] Specific processing flow of the industry trend and market demand analysis program

[0105] Processing flow

[0106] Step 1:

[0107] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[0108] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[0109] Step 2:

[0110] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[0111] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[0112] Step 3:

[0113] The server generates the analysis results.

[0114] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result.

[0115] Step 4:

[0116] The server presents the analysis results to the user.

[0117] Operation: The generated analysis results are compiled into a report, HTML code is generated to display in the user interface, and the report is sent to the terminal.

[0118] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[0119] Example 1

[0120] 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."

[0121] Conventional career selection support systems have had difficulty suggesting appropriate careers based on the user's interests, abilities, and values. They also have had issues with providing effective career advice, specific career plans, and demand forecasts that reflect industry and market trends. The purpose of this invention is to solve these issues and provide optimal career selection support for users.

[0122] 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.

[0123] In this invention, the server includes means for using a generative model to suggest careers based on the user's interests, abilities, and values, means for analyzing the user's responses using a generative AI model and suggesting appropriate careers, means for generating a study plan related to the skills and qualifications required when the user requests career advice, and means for analyzing collected data on industry and market trends and generating trend information. This allows the user to receive career suggestions based on their individual abilities and interests, receive specific career advice and career plans, and obtain information that reflects the latest industry and market trends.

[0124] A "generative model" is a type of artificial intelligence that makes predictions and suggestions based on user input data, and has functions such as document generation, data analysis, and job recommendations.

[0125] "Interest" refers to a user's interest or curiosity in a particular thing or activity.

[0126] "Ability" refers to the skills, knowledge, and abilities that a user possesses to perform a specific task or work.

[0127] "Values" refers to the basic ideas and standards that serve as the basis for users' beliefs and choices of behavior.

[0128] "Means" refer to the methods or processes used to achieve a particular goal.

[0129] "Job Opportunities" refers to job postings and employment opportunities related to occupations that interest users.

[0130] A "career plan" is a plan that specifies the steps and actions a user needs to take to reach a specific career.

[0131] "Competence development" refers to the process by which clients improve the skills and knowledge required for a particular occupation.

[0132] "Support" refers to the support and assistance provided to users to achieve their goals.

[0133] An "industry" is a part of economic activity that produces or provides a particular product or service.

[0134] A "market" refers to an economic venue or area where goods and services are bought and sold.

[0135] "Trends" refers to the current situation and future direction in a particular field or area.

[0136] "Data" refers to information that is collected, stored and processed for a specific purpose.

[0137] "Analysis" refers to the investigation or processing of data for the purpose of detailed examination or evaluation.

[0138] "User interface" refers to the means or method by which a user and a system interact with each other to exchange information.

[0139] A "learning plan" refers to a set of learning activities and schedules designed to acquire specific skills or knowledge.

[0140] "Trend information" refers to information about the latest developments and trends in a particular industry or market.

[0141] "Answer" refers to the information a user enters in response to a question or prompt from the system.

[0142] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[0143] System Overview

[0144] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[0145] Career diagnosis chatbot

[0146] When a user logs in from their device and selects the career diagnosis function, the server connects to a generative AI model (e.g., OpenAI (registered trademark) GPT-4 (registered trademark)) and generates a chatbot interface. The user inputs information about their interests, abilities, and values ​​in response to questions from the chatbot. At that time, the server proceeds with a dialogue with the user as follows:

[0147] Example prompt sentence:

[0148] User: "I want to find a new career."

[0149] Server: "What are your hobbies?"

[0150] User: "I like reading and programming."

[0151] The server then uses a generative AI model to analyze the user's answers and suggest "software developer" as the most suitable occupation. It also retrieves related job information from a database and displays it to the user.

[0152] Career Advice and Planning

[0153] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a study plan based on that information. For example, if the user asks, "What should I do to become a software developer?" the server will respond, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information on online courses and training programs. The server then compiles the generated study plan into a specific career plan and presents it in the form of a timeline. The career plan will specify the goals and necessary resources for each step.

[0154] Industry trends and market demand analysis

[0155] The server regularly collects data on industry and market trends and analyzes them using a proprietary analytical algorithm. The data is extracted from publicly available market reports and statistical information. When a user wants to research trend information for a specific industry or occupation, the server generates analytical results and provides them to the user. For example, if a user requests, "I want to know the future outlook for the IT industry," the server will provide the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path toward becoming an AI engineer.

[0156] Hardware or software used

[0157] The server uses high-performance hardware such as NVIDIA GPUs and generative AI models. The terminal provides a user interface through a browser or dedicated application. It also uses MySQL (registered trademark), PostgreSQL, Coursera, Udemy, etc. to obtain information through the APIs of database management systems and online education platforms. Furthermore, scraping tools and big data processing platforms (e.g., Beautiful Soup, Scrapy, Apache (registered trademark), Hadoop, Spark) are used for data collection and analysis.

[0158] In this way, the server, terminal, and user each play their own role, and the entire system works together to provide comprehensive career selection support.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1:

[0161] A user accesses the system from a terminal and logs in.

[0162] Input: User login information (username, password).

[0163] Data processing and calculation: The terminal sends the login information to the server, which then checks it against a database for authentication.

[0164] Output: Login success message, access to occupational diagnostic function.

[0165] Specific operation: The device launches a website or app, enters a username and password on the login screen, and after successful authentication, the menu screen is displayed.

[0166] Step 2:

[0167] The user selects the career assessment function.

[0168] Input: User's choice of occupational diagnostic function.

[0169] Data processing and calculation: The device sends the selected information to the server, which connects it to the generated AI model and generates the chatbot interface.

[0170] Output: Initial message from the career assessment chatbot.

[0171] What happens: The user clicks the "Start Career Assessment" button and the chatbot asks, "What are your hobbies?"

[0172] Step 3:

[0173] The user answers the chatbot's questions.

[0174] Input: Information about the user's interests, abilities, and values.

[0175] Data processing and calculation: The server collects user input and inputs it as a prompt to the generative AI model, which then performs analysis.

[0176] Output: Career suggestions and related job listings.

[0177] Specific behavior: The user responds, "I like reading and programming," and the server suggests "Software Developer" and also displays related job listings.

[0178] Step 4:

[0179] A user seeks career advice regarding a suggested occupation.

[0180] Input: User's career advice question.

[0181] Data processing and calculation: The server retrieves the necessary skills and qualifications from the database, analyzes them using a generative AI model, and generates a learning plan.

[0182] Output: Learning plan and training program information.

[0183] What it does: A user types, "What do I need to do to become a software developer?" The server responds, "You need Python or Java skills," and provides a link to an online course.

[0184] Step 5:

[0185] The server compiles the user's study plan into a career plan.

[0186] Enter: Study Plan.

[0187] Data processing and calculation: The server organizes the learning plan in a timeline format, specifying the goals and required resources for each step.

[0188] Output: A timeline of your career plan.

[0189] What it does: The server displays a timeline like "Learn Python within 3 months, then learn Java."

[0190] Step 6:

[0191] The server periodically collects and analyzes data on industry and market trends.

[0192] Input: Public market reports and statistics.

[0193] Data processing and calculation: The server collects data using a scraping tool and analyzes it on the big data processing platform.

[0194] Output: Industry and market trend information.

[0195] What it does: The server collects public market reports every month, analyzes them using Apache Spark, and stores the trend information in a database.

[0196] Step 7:

[0197] A user requests trending information for a particular industry or occupation.

[0198] Input: User's request for trend information.

[0199] Data processing and calculation: The server retrieves the analysis results from the database and generates the necessary information.

[0200] Output: Analysis results and trend information.

[0201] Specific operation: The user requests, "I want to know the future outlook for the IT industry," and the server presents the analysis result, such as, "The fields of AI and machine learning will grow."

[0202] In this way, the entire system works together according to the user's needs, providing comprehensive support for career choices, career advice, career planning, and industry trend analysis.

[0203] (Application example 1)

[0204] 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."

[0205] In modern society, many people face the challenge of finding the most suitable occupation based on their aptitudes and interests. Furthermore, it is difficult to obtain information on appropriate career plans and market demand, creating many barriers to career development. Furthermore, existing career diagnosis systems have limited user interfaces and lack a way to visually display career diagnosis information and career plans received in real time.

[0206] 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.

[0207] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing industry and market trends and providing the analysis results to the user, and means for displaying the career diagnosis results and career plans in an interactive format using smart glasses, thereby enabling the user to select an occupation according to their aptitude and obtain specific career plans and market trend information in real time.

[0208] A "generative model" is an artificial intelligence algorithm that generates new data and predictions based on user-provided information.

[0209] "Career suggestions" are the act of determining the most suitable occupation based on the user's interests, abilities, and values, and presenting it to the user.

[0210] "Job opportunities" refers to job information provided to users for employment or career changes.

[0211] A "career plan" is a plan that includes specific steps and a timeline for how a client will reach their chosen career.

[0212] "Support for capacity building" refers to the provision of learning paths and training programs that enable users to acquire the skills and knowledge required for a specific occupation.

[0213] "Industry and Market Trends" is information about current and future market trends and demands in various industries.

[0214] "Analysis results" refer to the specific conclusions and findings of the analysis conducted based on the collected data.

[0215] "Smart glasses" are wearable devices used to display information, and are eyeglass-type devices that have the ability to display visual data in an interactive format.

[0216] "Dialogue" refers to a method of communication in which the user and the system exchange information in a two-way manner.

[0217] "Career diagnosis results" refer to career suggestions provided based on the user's characteristics through a generative model.

[0218] In order to implement the present invention, it is necessary to construct a system including the following steps.

[0219] System Overview

[0220] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, career advice and path planning functions, and analysis of industry trends and market demands. These functions are realized through server-side processing and a user interface using smart glasses.

[0221] Program processing flow and hardware used

[0222] Career diagnosis chatbot

[0223] User access and interaction initiation:

[0224] The user uses smart glasses to access the employment and career change support service and selects the career diagnosis function. Using the smart glasses' HUD (head-up display), the server displays a chatbot interface based on the generative AI model and begins a dialogue with the user. The user inputs information about their interests, abilities, and values ​​by answering the chatbot's questions.

[0225] Analysis and career suggestions using generative models:

[0226] The server analyzes the user's answers using a generative model and suggests the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[0227] Career Advice and Planning

[0228] Providing career advice:

[0229] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. This learning plan includes information about online courses and training programs.

[0230] Implementing your career plan:

[0231] The server compiles the user's learning plan into a detailed course plan, which is displayed in a timeline format on the smart glasses' HUD. The course plan specifies the goals and necessary resources for each step.

[0232] Industry trends and market demand analysis

[0233] Data collection and analysis:

[0234] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[0235] Providing analysis results:

[0236] When a user wants to find out trend information for a particular industry or occupation, the server will generate analysis results and provide them to the user through the smart glasses, allowing the user to consider careers based on market demand.

[0237] Specific examples of functions

[0238] Career Assessment Chatbot Use Cases:

[0239] When a user types "I want to find a new career" into the smart glasses, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes the answer with a generative model and suggests "software developer." It also retrieves related job listings from a database and displays them to the user.

[0240] Examples of career advice provided:

[0241] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[0242] Industry trends and market demand analysis examples:

[0243] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[0244] Examples of prompt statements

[0245] Suggest the best career for you based on your interests, abilities, and values:

[0246] Interests: I like reading and programming

[0247] Ability to use Python and Java

[0248] Values: I want to do creative work

[0249] In this way, the system provides users with comprehensive career selection assistance and allows real-time feedback through the smart glasses.

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

[0251] Step 1:

[0252] To begin using the service, users access the employment and career change support service using smart glasses and select the career diagnosis function. The career diagnosis chatbot interface will then be displayed on the smart glasses' HUD.

[0253] Step 2:

[0254] By answering questions from the chatbot, users input information about their interests, abilities, and values, which is then sent to a server and used to analyze the generative AI model.

[0255] Step 3:

[0256] The server uses a generative AI model to analyze data entered by the user. Specifically, it analyzes information on interests, abilities, and values, and inputs prompts to the generative AI model to suggest the most suitable occupation. For example, a prompt such as "Interests: I like reading and programming. Abilities: I can use Python and Java. Values: I want to work in a creative field" is passed to the generative AI model. Based on this, the model generates data suggesting appropriate occupations. The output is the suggested occupations and related job information.

[0257] Step 4:

[0258] The server presents the user with suggested occupations derived from the generative AI model. The suggested occupation, for example, "Software Developer," is displayed on the smart glasses' HUD. Related job listings are also displayed simultaneously.

[0259] Step 5:

[0260] If the user is interested in the suggested careers, they can ask for career advice through the smart glasses. The user can ask, "What should I do to become a software developer?" and the question is sent to the server.

[0261] Step 6:

[0262] The server retrieves the required skills and qualifications from a database based on the user's questions, and generates a learning plan based on that information. For example, it prepares an answer such as "The required skills are programming languages ​​(Python, Java) and understanding of algorithms," and displays it on the smart glasses' HUD. It also provides information on related online courses and training programs.

[0263] Step 7:

[0264] The server then compiles the generated learning plan into a specific course plan, which is displayed in timeline format on the smart glasses' HUD. The plan specifies the goals and necessary resources for each step, allowing the user to learn and gain experience accordingly.

[0265] Step 8:

[0266] When a user wants to find out trend information about a particular industry or occupation, they send a request to the server through the smart glasses. For example, they might request, "I want to know the future outlook for the IT industry."

[0267] Step 9:

[0268] The server analyzes industry and market trend data collected periodically and generates analysis results based on the request, such as "The fields of AI and machine learning are expected to continue to grow," which is displayed on the smart glasses' HUD.

[0269] This will realize a system that allows users to select a career that suits their aptitude and obtain specific career plans and market trend information in real time.

[0270] 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.

[0271] The present invention uses a system that combines a generative model and an emotion engine to provide a unified service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. Specific embodiments for implementing the present invention are described below.

[0272] System Overview

[0273] This system is composed of a generative model, an emotion engine, a user interface, and a database. The generative model is responsible for suggesting the most suitable occupation based on the user's interests, abilities, and values, while the emotion engine analyzes the user's emotional state and reflects it in each function of the system.

[0274] Program processing

[0275] Career diagnosis chatbot

[0276] User access and interaction initiation:

[0277] The user accesses the employment and career change support service from their device and logs in. The server authenticates the user and displays the home screen. The user selects the "career diagnosis" function and begins a dialogue through the chatbot interface. The generative model collects information about the user's interests, abilities, and values, and gathers data to determine suggested careers.

[0278] Analysis and career suggestions using generative models

[0279] Analysis by generative model:

[0280] The server passes the user's input data to a generative model that then analyzes the user's responses using natural language processing techniques to generate a list of the most suitable occupations based on the user's interests, abilities, and values.

[0281] The role of the Emotion Engine:

[0282] The emotion engine analyzes the user's dialogue in real time to determine their emotional state. The results of the emotion engine's analysis are reflected in the generative model's analysis results, resulting in more precise career suggestions.

[0283] Job information provided by:

[0284] The server retrieves job opportunities (job information) related to the proposed occupation from the database and presents them to the user, who can use them to make further career choices.

[0285] Career Advice and Planning

[0286] Providing career advice:

[0287] If the user is interested in the suggested career, they can request detailed career advice. The server retrieves the skills and qualifications required for the user's career choice from a database. The emotion engine adjusts the advice content according to the user's emotional state.

[0288] Implementing your career plan:

[0289] Based on the acquired information, the server generates a specific career plan (study plan, training program list, etc.) and presents it to the user in a timeline format. The information provided by the emotion engine allows the server to provide the optimal plan according to the user's emotional state.

[0290] Industry trends and market demand analysis

[0291] Data collection and analysis:

[0292] The server periodically collects industry and market trend data, including publicly available market reports and statistics, and analyzes the trends using proprietary analytical algorithms.

[0293] Providing analysis results:

[0294] When a user wants to find trend information for a particular industry or profession, the server generates and provides the latest analysis results to the user, and the emotion engine adjusts the presentation of the analysis results based on the user's emotional state.

[0295] Specific examples

[0296] Career Assessment Chatbot Use Cases:

[0297] When a user types "I want to find a new career" into the chatbot, the server asks, "Tell me about your interests and skills." If the user responds, "I like reading and programming," the server analyzes the information using a generative model and suggests "software developer." At this point, an emotion engine distinguishes between the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly. Related job information is also retrieved from the database and displayed.

[0298] Examples of career advice provided:

[0299] When a user asks, "What should I do to become a software developer?", the server provides the necessary skills (e.g., programming languages, understanding algorithms, etc.). The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy or anxiety. For example, if the user is feeling anxious, it presents a more detailed plan.

[0300] Industry trends and market demand analysis examples:

[0301] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." The emotion engine analyzes the user's emotions at that time and adjusts the presentation method, such as emphasizing positive information.

[0302] In this way, the system provides users with comprehensive career selection support and concrete means to support their career success. Furthermore, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[0303] The processing flow will be explained below.

[0304] Specific processing flow of the career diagnosis chatbot program

[0305] Processing flow

[0306] Step 1:

[0307] The user accesses the employment and career change support service from their device and enters their login information.

[0308] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[0309] Step 2:

[0310] The server authenticates the user and displays the home screen.

[0311] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[0312] Step 3:

[0313] The user selects the "Career Diagnosis" function on the home screen.

[0314] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[0315] Step 4:

[0316] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[0317] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[0318] Step 5:

[0319] The user answers the chatbot's question (e.g., "I like reading and programming").

[0320] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[0321] Step 6:

[0322] The server analyzes the user's responses and emotional data using a generative AI model and emotion engine.

[0323] How it works: The server inputs the user's response into the natural language processing engine, passes the analysis results to the generative model, and then uses the emotion engine to analyze the user's emotional state and reflects it in the analysis results of the generative model.

[0324] Step 7:

[0325] The server will suggest the best career aptitudes.

[0326] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[0327] Step 8:

[0328] The server displays job listings related to the proposed occupation.

[0329] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[0330] Career Advice and Planning Program Process

[0331] Processing flow

[0332] Step 1:

[0333] If the user is interested in the careers presented, they will ask for detailed career advice.

[0334] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[0335] Step 2:

[0336] The server retrieves the skills and qualifications required for the user's career choice from a database.

[0337] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[0338] Step 3:

[0339] The server generates the lesson plan.

[0340] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[0341] Step 4:

[0342] The server will then concretely present the course plan.

[0343] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[0344] Specific processing flow of the industry trend and market demand analysis program

[0345] Processing flow

[0346] Step 1:

[0347] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[0348] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[0349] Step 2:

[0350] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[0351] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[0352] Step 3:

[0353] The server generates the analysis results.

[0354] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result. The emotion engine takes the user's emotional state into account to generate a customized analysis result.

[0355] Step 4:

[0356] The server presents the analysis results to the user.

[0357] Operation: The generated analysis results are compiled into a report, and HTML code for displaying them in a user interface is generated and sent to the device. The content and method of presentation are adjusted depending on the user's emotional state.

[0358] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[0359] Example 2

[0360] 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."

[0361] While conventional career suggestion systems suggest careers based on users' interests and abilities, they are inadequate in taking into account their emotional state and adjusting their career plans. Furthermore, they struggle to analyze and provide market and industry trends, making it difficult for users to obtain specific future predictions. This makes it difficult for users to make appropriate career choices and develop career plans.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0363] In this invention, the server includes means for using a generative model to suggest appropriate occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and ability improvement support to reach the user's selected occupation, means for analyzing industry and market trends and providing the user with the analysis results, and means for analyzing the user's emotional state using an emotion engine and adjusting the content of the career suggestions and career plans made by the generative model. This allows users to not only receive career suggestions based on their own interests, abilities, and values, but also receive optimal advice and career plans according to their emotional state at the time, and further enables them to obtain future predictions based on an understanding of market and industry trends.

[0364] A "generative model" is the part of the system that uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[0365] The "emotion engine" is a technology that analyzes the user's emotional state in real time and reflects the results of that analysis in career suggestions and career planning.

[0366] A "career plan" is a specific action plan that includes a list of study plans and training programs to help the user reach their chosen career.

[0367] "Job Opportunities" means job postings or employment opportunities related to the proposed occupation.

[0368] "Industry and Market Trends" is information that indicates current and future changes and trends in a particular industry or market.

[0369] "Analysis Results" refers to the data and conclusions derived by generative models, emotion engines, and other analytical algorithms.

[0370] "User" refers to an individual who uses the System to receive career suggestions and career plans.

[0371] The present invention is a system that combines a generative model and an emotion engine to provide career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state in an integrated manner.

[0372] System Configuration

[0373] The system includes the following components:

[0374] 1. Generative Model:

[0375] It uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[0376] 2. Emotion Engine:

[0377] The user's emotional state is analyzed in real time, and the results of that analysis are reflected in career suggestions and career planning.

[0378] 3. User Interface:

[0379] The interface is designed to make it easy for users to interact with the system, allowing them to input and retrieve information in chatbot format.

[0380] 4. Database:

[0381] Store data on occupational information, job openings, skill acquisition information, and industry and market trends, and provide this data as needed.

[0382] Hardware and software used

[0383] Hardware: Servers, terminals

[0384] The servers use a cloud-based infrastructure.

[0385] Terminals include PCs and mobile devices.

[0386] Software: Generative AI models, natural language processing technology, sentiment analysis technology, database management systems

[0387] Use TENSORFLOW (registered trademark) or PyTorch for generative models.

[0388] Use a natural language processing library such as the Natural Language Toolkit (NLTK) for your sentiment engine.

[0389] Use MySQL or PostgreSQL as your database management system.

[0390] Program processing

[0391] Specific examples

[0392] 1. Use cases for the career assessment chatbot:

[0393] A user types into a chatbot, "I want to find a new career."

[0394] The server asks, "Tell me about your interests and skills."

[0395] The user responds, "I like reading and programming."

[0396] The server performs analysis using a generative model and suggests a "software developer."

[0397] The emotion engine determines the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly.

[0398] Related job information is also retrieved from the database and displayed.

[0399] 2. Examples of career advice provided:

[0400] A user asks, "How do I become a software developer?"

[0401] The server provides the necessary skills (e.g., programming languages, understanding of algorithms, etc.).

[0402] The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy and anxiety.

[0403] For example, if the user is feeling anxious, the plan will be presented in more detail.

[0404] 3. Industry Trends and Market Demand Analysis Examples:

[0405] A user requests, "I want to know about the future outlook for the IT industry."

[0406] The server provides the analysis results, stating, "The fields of AI and machine learning are expected to continue to grow."

[0407] The emotion engine analyzes the user's emotions at that time and adjusts the presentation method by, for example, emphasizing positive information.

[0408] Prompt Sentence Examples

[0409] "I want to find a new career"

[0410] "Tell me about your interests and skills"

[0411] I like reading and programming.

[0412] "How do I become a software developer?"

[0413] "I want to know the future outlook for the IT industry."

[0414] As described above, this system provides comprehensive career selection support to users and offers concrete means to support their career success. In addition, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[0415] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0416] Step 1:

[0417] The user accesses the employment and career change support service from the terminal and logs in.

[0418] Input: User ID and password.

[0419] Data processing / calculation: The server passes the received user ID and password to the authentication system and performs hash matching.

[0420] Output: Authentication result (success / failure).

[0421] Specific operation: If authentication is successful, the server generates a home screen and sends it to the device. If authentication fails, an error message is displayed.

[0422] Step 2:

[0423] The user selects the "Career Diagnosis" feature on the home screen and begins a dialogue through the chatbot interface.

[0424] Input: User's selection of the "Career Assessment" feature.

[0425] Data processing / computation: The server receives the user's selection and initializes the chatbot interface.

[0426] Output: Display of the chatbot interface.

[0427] Specific operation: The server sends the UI component to the device, and the chatbot displays a message prompting the user to "Tell us about your interests and skills."

[0428] Step 3:

[0429] Users input their interests, abilities, and values ​​into the chatbot.

[0430] Input: User's answer (e.g., "I like reading" or "I have programming experience").

[0431] Data processing / calculation: The server passes the response data to the generative model and performs natural language processing.

[0432] Output: Analysis results (profile of user interests, abilities, and values).

[0433] Specific operation: The server temporarily stores the analysis results obtained from the generative model, and the chatbot displays the next question.

[0434] Step 4:

[0435] A generative model analyzes the user's answers and makes career suggestions.

[0436] Input: User response data passed to the generative model.

[0437] Data processing / computation: The generative model applies natural language processing techniques and analyzes the answers based on the career suggestion algorithm.

[0438] Output: A list of occupations based on the analysis results.

[0439] Specific operation: The server receives the analysis results of the generative model and presents a list of occupations to the user through the chatbot.

[0440] Step 5:

[0441] The emotion engine analyzes the user's emotional state and feeds the analysis results back to the generative model.

[0442] Input: User interaction.

[0443] Data processing / calculation: The emotion engine analyzes the dialogue content and determines the emotional state (e.g., joy, anxiety).

[0444] Output: Emotion analysis results.

[0445] Specific operation: The server feeds back the emotion analysis results to the generative model and adjusts it to optimal career suggestions.

[0446] Step 6:

[0447] Providing relevant job information based on career suggestions.

[0448] Input: Best Occupation List.

[0449] Data processing / calculation: The server extracts relevant job information from the database.

[0450] Output:Related job listings.

[0451] Specific operation: The server sends the job information to the terminal and displays it to the user.

[0452] Step 7:

[0453] When users seek career advice, they provide detailed skills and qualifications.

[0454] Input: User's career advice request.

[0455] Data processing / calculation: The server retrieves the required skills and qualifications from the database.

[0456] Output: Skills and qualifications.

[0457] Specific operation: The server adjusts the acquired information based on the emotion engine and displays appropriate advice to the user.

[0458] Step 8:

[0459] The career plan is concretized and presented to the user.

[0460] Input: Information needed to choose a career.

[0461] Data processing / calculation: The server generates a list of learning plans and training programs and organizes them into a timeline format.

[0462] Output: A concrete career plan.

[0463] Specific operation: The server sends the course plan to the terminal and displays it to the user. The emotion engine also adjusts it.

[0464] Step 9:

[0465] Provides analysis of industry trends and market demand.

[0466] Input: User request for trend information.

[0467] Data processing / calculation: The server collects and analyzes market reports and statistical information to predict future supply and demand.

[0468] Output: Market trend analysis results.

[0469] Specific operation: The server generates the latest analysis results, adjusts them using the emotion engine, sends them to the device, and displays them to the user.

[0470] (Application example 2)

[0471] 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."

[0472] Modern society requires diverse career choices and career support, but there is a lack of systems that make appropriate career suggestions that are tailored to each user's interests, abilities, and values. There is also a need for services that take into account the user's emotional state, but current systems do not adequately achieve this. Furthermore, there are few systems that provide comprehensive, specific career plans and market trend analysis linked to career suggestions. Therefore, there is a need for a system that analyzes users' daily behavioral data, such as their purchasing history and product browsing history, to provide more personalized, emotionally sensitive career support.

[0473] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0474] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job-seeking opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing the user's interests, abilities, and values ​​based on purchase history and product browsing history, and means for analyzing the user's emotional state and adjusting the content of suggestions and screen display according to the emotional state. This makes it possible to make occupation suggestions and career support based on the user's personal data, and to provide detailed consulting that takes the user's emotional state into consideration.

[0475] A "generative model" refers to an algorithm that suggests suitable occupations based on information such as a user's interests, abilities, and values.

[0476] "Career suggestions" refers to presenting appropriate occupations and career paths to users based on the results of analysis by the generative model.

[0477] "Job Opportunities" refers to the provision of job postings or employment opportunities related to the proposed occupation.

[0478] "Career planning" refers to the specific learning plan or training program that will lead a user to their chosen career.

[0479] "Support for capacity building" refers to providing assistance to users to acquire the skills and knowledge necessary for their chosen occupation.

[0480] "Industry and Market Trends" refers to trends and changes in specific industry sectors and markets.

[0481] "Purchase history" refers to a record of products and services purchased by a user in the past.

[0482] "Product browsing history" refers to the record of products a user has viewed on an online shopping site or app.

[0483] "Emotional state" refers to the psychological state, such as joy, anxiety, or sadness, that a user feels at a given moment.

[0484] "Analysis" refers to the process of analyzing collected data using generative models and sentiment analysis engines to derive meaning from it.

[0485] "Screen display" refers to visually presenting the analyzed results to the user through a user interface.

[0486] "System" refers to the computer-based infrastructure that integrates and operates the various means mentioned above.

[0487] This invention is a system that provides an integrated service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. This system is realized by combining a generative model, an emotion engine, a user interface, and a database.

[0488] System Configuration

[0489] The system consists of the following elements:

[0490] Generative Model

[0491] Emotion Engine

[0492] User Interface

[0493] Database

[0494] Program processing

[0495] User Access and Data Collection

[0496] Users access the system using a smartphone. When the user logs in, the application on the device collects their purchase history and product browsing history and sends it to the server. This data collection is done using a REST API.

[0497] Analysis using generative models

[0498] The server inputs the collected data into a generative model to analyze the user's interests, abilities, and values. This analysis is performed using natural language processing technology, specifically using Hugging Face's Transformers. The generative model then uses the analysis results to suggest the most suitable occupation.

[0499] Analysis by emotion engine

[0500] Furthermore, the emotion engine analyzes the user's emotional state in real time, using tools such as Luxand's Face SDK. Based on the user's emotional state, the suggestions and screen display are adjusted. For example, if the user is feeling anxious, more detailed and thoughtful explanations are displayed.

[0501] Providing job opportunities and career planning

[0502] Job information related to the occupations suggested by the generative model is retrieved from a database and displayed to the user. The learning paths and training programs required for the occupation selected by the user are also presented, along with specific career planning and skill development support.

[0503] Industry and market trend analysis

[0504] The server periodically collects market data and analyzes industry and market trends. The analysis results are provided to users to help them make career choices. This analysis utilizes statistical information and publicly available market reports.

[0505] Specific examples

[0506] For example, if a user "frequently purchases tech-related products," the generative model analyzes that data and suggests the occupation "software developer." The emotion engine analyzes the user's emotions at the time and adjusts the suggestions based on their level of joy or anxiety.

[0507] Example prompt sentence:

[0508] Purchase history: I frequently purchase tech-related products, programming books, gadgets, etc. What is your suitable occupation?

[0509] This system allows users to receive career suggestions based on their personal data, and also provides detailed consulting that takes into account their emotional state.

[0510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0511] Step 1:

[0512] The user accesses the system from a smartphone.

[0513] Input: User login information (ID, password)

[0514] Output: User authentication result

[0515] The server receives the user's login information and performs authentication. If authentication is successful, the home screen is displayed.

[0516] Step 2:

[0517] The user's purchase history and product browsing history are collected and sent to the server.

[0518] Input: Purchase history and product browsing history data

[0519] Output: Send data to the server

[0520] The device collects data on products the user has previously purchased or viewed and sends it to the server. Specifically, it creates a data set containing information such as product category, purchase date, and viewing time, and sends it using a REST API.

[0521] Step 3:

[0522] The server inputs the submitted data into a generative model to analyze the user's interests, abilities, and values.

[0523] Input: Purchase history and product browsing history data

[0524] Output: Analysis results based on the user's interests, abilities, and values

[0525] The server inputs the received data into a generative model (such as Hugging Face's Transformers) and processes the data using natural language processing techniques. As a result of the analysis, it generates a list of suitable occupations.

[0526] Step 4:

[0527] An emotion engine is used to analyze the user's emotional state.

[0528] Input: User's facial image or voice data

[0529] Output: Emotional state analysis results

[0530] To analyze the user's emotional state in real time, the device uses a camera and microphone to capture facial images or voice data and sends them to a server. The server then analyzes the emotional state using software such as Luxand Face SDK and outputs the degree of emotion as a numerical value.

[0531] Step 5:

[0532] The server integrates the analysis results from the generative model and the results from the emotion engine to make appropriate career suggestions.

[0533] Input: Analysis results of the generative model, analysis results of the emotion engine

[0534] Output: Adjusted list of career suggestions

[0535] The server integrates data from the generative model and the emotion engine to generate a list of career suggestions based on the user's emotional state. For example, if the user feels anxious, the server adjusts the list by adding more specific and detailed descriptions.

[0536] Step 6:

[0537] Job listings related to the suggested occupation are retrieved from a database and displayed to the user.

[0538] Input: Career suggestion list

[0539] Output: List of job postings

[0540] The server searches the database for job listings related to the proposed occupation and transmits the acquired job listings to the terminal, which visually displays them to the user.

[0541] Step 7:

[0542] Provide users with the learning paths and specific career plans required for their chosen career.

[0543] Input: User's occupation choice

[0544] Output: Learning path, career plan

[0545] The server retrieves the skills and qualifications required for the job selected by the user from a database and generates a learning path or training program, which is displayed in a timeline format.

[0546] Step 8:

[0547] Analyze industry and market trends and provide the results to users.

[0548] Input: Market Data

[0549] Output: Market trend analysis results

[0550] The server analyzes industry and market trends based on regularly collected market data, using statistical information and publicly available market reports, and presents the analysis results to users to help them make career choices.

[0551] 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.

[0552] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0553] 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.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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."

[0567] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[0568] System Overview

[0569] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[0570] Program processing

[0571] Career diagnosis chatbot

[0572] User access and interaction initiation:

[0573] The user accesses the employment and career change support service from their device, logs in, and selects the career diagnosis function. The server connects to the generative AI model, generates a chatbot interface, and presents it to the user. The user inputs information about their interests, abilities, and values ​​in response to the chatbot's questions.

[0574] Analysis and career suggestions using generative models:

[0575] The server analyzes the user's answers using a generative AI model to suggest the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[0576] Career Advice and Planning

[0577] Providing career advice:

[0578] If the user is interested in the suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. The learning plan includes information about online courses and training programs.

[0579] Implementing your career plan:

[0580] The server compiles the user's learning plan into a detailed course plan and presents it in the form of a timeline, which specifies the goals and necessary resources for each step.

[0581] Industry trends and market demand analysis

[0582] Data collection and analysis:

[0583] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[0584] Providing analysis results:

[0585] If a user wants to find trend information for a particular industry or occupation, the server generates and provides the analysis results to the user, allowing the user to consider careers based on market demand.

[0586] Specific examples

[0587] Career Assessment Chatbot Use Cases:

[0588] When a user types "I want to find a new career" into the chatbot, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes this using a generative AI model and suggests "software developer." It also retrieves related job information from a database and displays it to the user.

[0589] Examples of career advice provided:

[0590] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[0591] Industry trends and market demand analysis examples:

[0592] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[0593] In this way, the system provides users with comprehensive career choice support and provides specific means to support career success.

[0594] The processing flow will be explained below.

[0595] Specific processing flow of the career diagnosis chatbot program

[0596] Processing flow

[0597] Step 1:

[0598] The user accesses the employment and career change support service from their device and enters their login information.

[0599] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[0600] Step 2:

[0601] The server authenticates the user and displays the home screen.

[0602] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[0603] Step 3:

[0604] The user selects the "Career Diagnosis" function on the home screen.

[0605] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[0606] Step 4:

[0607] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[0608] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[0609] Step 5:

[0610] The user answers the chatbot's question (e.g., "I like reading and programming").

[0611] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[0612] Step 6:

[0613] The server analyzes the user's answers using a generative AI model.

[0614] How it works: The server inputs the user's answers into a natural language processing engine, and passes the analysis results to a generative model, which then calculates the best possible job candidates.

[0615] Step 7:

[0616] The server will suggest the best career aptitudes.

[0617] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[0618] Step 8:

[0619] The server displays job listings related to the proposed occupation.

[0620] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[0621] Career Advice and Planning Program Process

[0622] Processing flow

[0623] Step 1:

[0624] If the user is interested in the careers presented, they will ask for detailed career advice.

[0625] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[0626] Step 2:

[0627] The server retrieves the skills and qualifications required for the user's career choice from a database.

[0628] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[0629] Step 3:

[0630] The server generates the lesson plan.

[0631] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[0632] Step 4:

[0633] The server will then concretely present the course plan.

[0634] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[0635] Specific processing flow of the industry trend and market demand analysis program

[0636] Processing flow

[0637] Step 1:

[0638] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[0639] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[0640] Step 2:

[0641] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[0642] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[0643] Step 3:

[0644] The server generates the analysis results.

[0645] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result.

[0646] Step 4:

[0647] The server presents the analysis results to the user.

[0648] Operation: The generated analysis results are compiled into a report, HTML code is generated to display in the user interface, and the report is sent to the terminal.

[0649] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[0650] Example 1

[0651] 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."

[0652] Conventional career selection support systems have had difficulty suggesting appropriate careers based on the user's interests, abilities, and values. They also have had issues with providing effective career advice, specific career plans, and demand forecasts that reflect industry and market trends. The purpose of this invention is to solve these issues and provide optimal career selection support for users.

[0653] 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.

[0654] In this invention, the server includes means for using a generative model to suggest careers based on the user's interests, abilities, and values, means for analyzing the user's responses using a generative AI model and suggesting appropriate careers, means for generating a study plan related to the skills and qualifications required when the user requests career advice, and means for analyzing collected data on industry and market trends and generating trend information. This allows the user to receive career suggestions based on their individual abilities and interests, receive specific career advice and career plans, and obtain information that reflects the latest industry and market trends.

[0655] A "generative model" is a type of artificial intelligence that makes predictions and suggestions based on user input data, and has functions such as document generation, data analysis, and job recommendations.

[0656] "Interest" refers to a user's interest or curiosity in a particular thing or activity.

[0657] "Ability" refers to the skills, knowledge, and abilities that a user possesses to perform a specific task or work.

[0658] "Values" refers to the basic ideas and standards that serve as the basis for users' beliefs and choices of behavior.

[0659] "Means" refer to the methods or processes used to achieve a particular goal.

[0660] "Job Opportunities" refers to job postings and employment opportunities related to occupations that interest users.

[0661] A "career plan" is a plan that specifies the steps and actions a user needs to take to reach a specific career.

[0662] "Competence development" refers to the process by which clients improve the skills and knowledge required for a particular occupation.

[0663] "Support" refers to the support and assistance provided to users to achieve their goals.

[0664] An "industry" is a part of economic activity that produces or provides a particular product or service.

[0665] A "market" refers to an economic venue or area where goods and services are bought and sold.

[0666] "Trends" refers to the current situation and future direction in a particular field or area.

[0667] "Data" refers to information that is collected, stored and processed for a specific purpose.

[0668] "Analysis" refers to the investigation or processing of data for the purpose of detailed examination or evaluation.

[0669] "User interface" refers to the means or method by which a user and a system interact with each other to exchange information.

[0670] A "learning plan" refers to a set of learning activities and schedules designed to acquire specific skills or knowledge.

[0671] "Trend information" refers to information about the latest developments and trends in a particular industry or market.

[0672] "Answer" refers to the information a user enters in response to a question or prompt from the system.

[0673] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[0674] System Overview

[0675] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[0676] Career diagnosis chatbot

[0677] When a user logs in from their device and selects the career diagnosis function, the server connects to a generative AI model (e.g., OpenAI GPT-4) and generates a chatbot interface. The user inputs information about their interests, abilities, and values ​​in response to questions from the chatbot. The server then proceeds with a dialogue with the user as follows:

[0678] Example prompt sentence:

[0679] User: "I want to find a new career."

[0680] Server: "What are your hobbies?"

[0681] User: "I like reading and programming."

[0682] The server then uses a generative AI model to analyze the user's answers and suggest "software developer" as the most suitable occupation. It also retrieves related job information from a database and displays it to the user.

[0683] Career Advice and Planning

[0684] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a study plan based on that information. For example, if the user asks, "What should I do to become a software developer?" the server will respond, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information on online courses and training programs. The server then compiles the generated study plan into a specific career plan and presents it in the form of a timeline. The career plan will specify the goals and necessary resources for each step.

[0685] Industry trends and market demand analysis

[0686] The server regularly collects data on industry and market trends and analyzes them using a proprietary analytical algorithm. The data is extracted from publicly available market reports and statistical information. When a user wants to research trend information for a specific industry or occupation, the server generates analytical results and provides them to the user. For example, if a user requests, "I want to know the future outlook for the IT industry," the server will provide the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path toward becoming an AI engineer.

[0687] Hardware or software used

[0688] The server uses high-performance hardware such as NVIDIA GPUs and generative AI models. The terminal provides a user interface through a browser or dedicated application. It also uses database management systems and online education platform APIs such as MySQL, PostgreSQL, Coursera, and Udemy to obtain information. Furthermore, it uses scraping tools and big data processing platforms (e.g., Beautiful Soup, Scrapy, Apache Hadoop, and Spark) for data collection and analysis.

[0689] In this way, the server, terminal, and user each play their own role, and the entire system works together to provide comprehensive career selection support.

[0690] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0691] Step 1:

[0692] A user accesses the system from a terminal and logs in.

[0693] Input: User login information (username, password).

[0694] Data processing and calculation: The terminal sends the login information to the server, which then checks it against a database for authentication.

[0695] Output: Login success message, access to occupational diagnostic function.

[0696] Specific operation: The device launches a website or app, enters a username and password on the login screen, and after successful authentication, the menu screen is displayed.

[0697] Step 2:

[0698] The user selects the career assessment function.

[0699] Input: User's choice of occupational diagnostic function.

[0700] Data processing and calculation: The device sends the selected information to the server, which connects it to the generated AI model and generates the chatbot interface.

[0701] Output: Initial message from the career assessment chatbot.

[0702] What happens: The user clicks the "Start Career Assessment" button and the chatbot asks, "What are your hobbies?"

[0703] Step 3:

[0704] The user answers the chatbot's questions.

[0705] Input: Information about the user's interests, abilities, and values.

[0706] Data processing and calculation: The server collects user input and inputs it as a prompt to the generative AI model, which then performs analysis.

[0707] Output: Career suggestions and related job listings.

[0708] Specific behavior: The user responds, "I like reading and programming," and the server suggests "Software Developer" and also displays related job listings.

[0709] Step 4:

[0710] A user seeks career advice regarding a suggested occupation.

[0711] Input: User's career advice question.

[0712] Data processing and calculation: The server retrieves the necessary skills and qualifications from the database, analyzes them using a generative AI model, and generates a learning plan.

[0713] Output: Learning plan and training program information.

[0714] What it does: A user types, "What do I need to do to become a software developer?" The server responds, "You need Python or Java skills," and provides a link to an online course.

[0715] Step 5:

[0716] The server compiles the user's study plan into a career plan.

[0717] Enter: Study Plan.

[0718] Data processing and calculation: The server organizes the learning plan in a timeline format, specifying the goals and required resources for each step.

[0719] Output: A timeline of your career plan.

[0720] What it does: The server displays a timeline like "Learn Python within 3 months, then learn Java."

[0721] Step 6:

[0722] The server periodically collects and analyzes data on industry and market trends.

[0723] Input: Public market reports and statistics.

[0724] Data processing and calculation: The server collects data using a scraping tool and analyzes it on the big data processing platform.

[0725] Output: Industry and market trend information.

[0726] What it does: The server collects public market reports every month, analyzes them using Apache Spark, and stores the trend information in a database.

[0727] Step 7:

[0728] A user requests trending information for a particular industry or occupation.

[0729] Input: User's request for trend information.

[0730] Data processing and calculation: The server retrieves the analysis results from the database and generates the necessary information.

[0731] Output: Analysis results and trend information.

[0732] Specific operation: The user requests, "I want to know the future outlook for the IT industry," and the server presents the analysis result, such as, "The fields of AI and machine learning will grow."

[0733] In this way, the entire system works together according to the user's needs, providing comprehensive support for career choices, career advice, career planning, and industry trend analysis.

[0734] (Application example 1)

[0735] 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."

[0736] In modern society, many people face the challenge of finding the most suitable occupation based on their aptitudes and interests. Furthermore, it is difficult to obtain information on appropriate career plans and market demand, creating many barriers to career development. Furthermore, existing career diagnosis systems have limited user interfaces and lack a way to visually display career diagnosis information and career plans received in real time.

[0737] 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.

[0738] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing industry and market trends and providing the analysis results to the user, and means for displaying the career diagnosis results and career plans in an interactive format using smart glasses, thereby enabling the user to select an occupation according to their aptitude and obtain specific career plans and market trend information in real time.

[0739] A "generative model" is an artificial intelligence algorithm that generates new data and predictions based on user-provided information.

[0740] "Career suggestions" are the act of determining the most suitable occupation based on the user's interests, abilities, and values, and presenting it to the user.

[0741] "Job opportunities" refers to job information provided to users for employment or career changes.

[0742] A "career plan" is a plan that includes specific steps and a timeline for how a client will reach their chosen career.

[0743] "Support for capacity building" refers to the provision of learning paths and training programs that enable users to acquire the skills and knowledge required for a specific occupation.

[0744] "Industry and Market Trends" is information about current and future market trends and demands in various industries.

[0745] "Analysis results" refer to the specific conclusions and findings of the analysis conducted based on the collected data.

[0746] "Smart glasses" are wearable devices used to display information, and are eyeglass-type devices that have the ability to display visual data in an interactive format.

[0747] "Dialogue" refers to a method of communication in which the user and the system exchange information in a two-way manner.

[0748] "Career diagnosis results" refer to career suggestions provided based on the user's characteristics through a generative model.

[0749] In order to implement the present invention, it is necessary to construct a system including the following steps.

[0750] System Overview

[0751] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, career advice and path planning functions, and analysis of industry trends and market demands. These functions are realized through server-side processing and a user interface using smart glasses.

[0752] Program processing flow and hardware used

[0753] Career diagnosis chatbot

[0754] User access and interaction initiation:

[0755] The user uses smart glasses to access the employment and career change support service and selects the career diagnosis function. Using the smart glasses' HUD (head-up display), the server displays a chatbot interface based on the generative AI model and begins a dialogue with the user. The user inputs information about their interests, abilities, and values ​​by answering the chatbot's questions.

[0756] Analysis and career suggestions using generative models:

[0757] The server analyzes the user's answers using a generative model and suggests the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[0758] Career Advice and Planning

[0759] Providing career advice:

[0760] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. This learning plan includes information about online courses and training programs.

[0761] Implementing your career plan:

[0762] The server compiles the user's learning plan into a detailed course plan, which is displayed in a timeline format on the smart glasses' HUD. The course plan specifies the goals and required resources for each step.

[0763] Industry trends and market demand analysis

[0764] Data collection and analysis:

[0765] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[0766] Providing analysis results:

[0767] When a user wants to find out trend information for a particular industry or occupation, the server will generate analysis results and provide them to the user through the smart glasses, allowing the user to consider careers based on market demand.

[0768] Specific examples of functions

[0769] Career Assessment Chatbot Use Cases:

[0770] When a user types "I want to find a new career" into the smart glasses, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes the answer with a generative model and suggests "software developer." It also retrieves related job listings from a database and displays them to the user.

[0771] Examples of career advice provided:

[0772] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[0773] Industry trends and market demand analysis examples:

[0774] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[0775] Examples of prompt statements

[0776] Suggest the best career for you based on your interests, abilities, and values:

[0777] Interests: I like reading and programming

[0778] Ability to use Python and Java

[0779] Values: I want to do creative work

[0780] In this way, the system provides users with comprehensive career selection assistance and allows real-time feedback through the smart glasses.

[0781] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0782] Step 1:

[0783] To begin using the service, users access the employment and career change support service using smart glasses and select the career diagnosis function. The career diagnosis chatbot interface will then be displayed on the smart glasses' HUD.

[0784] Step 2:

[0785] By answering questions from the chatbot, users input information about their interests, abilities, and values, which is then sent to a server and used to analyze the generative AI model.

[0786] Step 3:

[0787] The server uses a generative AI model to analyze data entered by the user. Specifically, it analyzes information on interests, abilities, and values, and inputs prompts to the generative AI model to suggest the most suitable occupation. For example, a prompt such as "Interests: I like reading and programming. Abilities: I can use Python and Java. Values: I want to work in a creative field" is passed to the generative AI model. Based on this, the model generates data suggesting appropriate occupations. The output is the suggested occupations and related job information.

[0788] Step 4:

[0789] The server presents the user with suggested occupations derived from the generative AI model. The suggested occupation, for example, "Software Developer," is displayed on the smart glasses' HUD. Related job listings are also displayed simultaneously.

[0790] Step 5:

[0791] If the user is interested in the suggested careers, they can ask for career advice through the smart glasses. The user can ask, "What should I do to become a software developer?" and the question is sent to the server.

[0792] Step 6:

[0793] The server retrieves the required skills and qualifications from a database based on the user's questions, and generates a learning plan based on that information. For example, it prepares an answer such as "The required skills are programming languages ​​(Python, Java) and understanding of algorithms," and displays it on the smart glasses' HUD. It also provides information on related online courses and training programs.

[0794] Step 7:

[0795] The server then compiles the generated learning plan into a specific course plan, which is displayed in timeline format on the smart glasses' HUD. The plan specifies the goals and necessary resources for each step, allowing the user to learn and gain experience accordingly.

[0796] Step 8:

[0797] When a user wants to find out trend information about a particular industry or occupation, they send a request to the server through the smart glasses. For example, they might request, "I want to know the future outlook for the IT industry."

[0798] Step 9:

[0799] The server analyzes industry and market trend data collected periodically and generates analysis results based on the request, such as "The fields of AI and machine learning are expected to continue to grow," which is displayed on the smart glasses' HUD.

[0800] This will realize a system that allows users to select a career that suits their aptitude and obtain specific career plans and market trend information in real time.

[0801] 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.

[0802] The present invention uses a system that combines a generative model and an emotion engine to provide a unified service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. Specific embodiments for implementing the present invention are described below.

[0803] System Overview

[0804] This system is composed of a generative model, an emotion engine, a user interface, and a database. The generative model is responsible for suggesting the most suitable occupation based on the user's interests, abilities, and values, while the emotion engine analyzes the user's emotional state and reflects it in each function of the system.

[0805] Program processing

[0806] Career diagnosis chatbot

[0807] User access and interaction initiation:

[0808] The user accesses the employment and career change support service from their device and logs in. The server authenticates the user and displays the home screen. The user selects the "career diagnosis" function and begins a dialogue through the chatbot interface. The generative model collects information about the user's interests, abilities, and values, and gathers data to determine suggested careers.

[0809] Analysis and career suggestions using generative models

[0810] Analysis by generative model:

[0811] The server passes the user's input data to a generative model that then analyzes the user's responses using natural language processing techniques to generate a list of the most suitable occupations based on the user's interests, abilities, and values.

[0812] The role of the Emotion Engine:

[0813] The emotion engine analyzes the user's dialogue in real time to determine their emotional state. The results of the emotion engine's analysis are reflected in the generative model's analysis results, resulting in more precise career suggestions.

[0814] Job information provided by:

[0815] The server retrieves job opportunities (job information) related to the proposed occupation from the database and presents them to the user, who can use them to make further career choices.

[0816] Career Advice and Planning

[0817] Providing career advice:

[0818] If the user is interested in the suggested career, they can request detailed career advice. The server retrieves the skills and qualifications required for the user's career choice from a database. The emotion engine adjusts the advice content according to the user's emotional state.

[0819] Implementing your career plan:

[0820] Based on the acquired information, the server generates a specific career plan (study plan, training program list, etc.) and presents it to the user in a timeline format. The information provided by the emotion engine allows the server to provide the optimal plan according to the user's emotional state.

[0821] Industry trends and market demand analysis

[0822] Data collection and analysis:

[0823] The server periodically collects industry and market trend data, including publicly available market reports and statistics, and analyzes the trends using proprietary analytical algorithms.

[0824] Providing analysis results:

[0825] When a user wants to find trend information for a particular industry or profession, the server generates and provides the latest analysis results to the user, and the emotion engine adjusts the presentation of the analysis results based on the user's emotional state.

[0826] Specific examples

[0827] Career Assessment Chatbot Use Cases:

[0828] When a user types "I want to find a new career" into the chatbot, the server asks, "Tell me about your interests and skills." If the user responds, "I like reading and programming," the server analyzes the information using a generative model and suggests "software developer." At this point, an emotion engine distinguishes between the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly. Related job information is also retrieved from the database and displayed.

[0829] Examples of career advice provided:

[0830] When a user asks, "What should I do to become a software developer?", the server provides the necessary skills (e.g., programming languages, understanding algorithms, etc.). The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy or anxiety. For example, if the user is feeling anxious, it presents a more detailed plan.

[0831] Industry trends and market demand analysis examples:

[0832] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." The emotion engine analyzes the user's emotions at that time and adjusts the presentation method, such as emphasizing positive information.

[0833] In this way, the system provides users with comprehensive career selection support and concrete means to support their career success. Furthermore, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[0834] The processing flow will be explained below.

[0835] Specific processing flow of the career diagnosis chatbot program

[0836] Processing flow

[0837] Step 1:

[0838] The user accesses the employment and career change support service from their device and enters their login information.

[0839] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[0840] Step 2:

[0841] The server authenticates the user and displays the home screen.

[0842] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[0843] Step 3:

[0844] The user selects the "Career Diagnosis" function on the home screen.

[0845] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[0846] Step 4:

[0847] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[0848] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[0849] Step 5:

[0850] The user answers the chatbot's question (e.g., "I like reading and programming").

[0851] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[0852] Step 6:

[0853] The server analyzes the user's responses and emotional data using a generative AI model and emotion engine.

[0854] How it works: The server inputs the user's response into the natural language processing engine, passes the analysis results to the generative model, and then uses the emotion engine to analyze the user's emotional state and reflects it in the analysis results of the generative model.

[0855] Step 7:

[0856] The server will suggest the best career aptitudes.

[0857] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[0858] Step 8:

[0859] The server displays job listings related to the proposed occupation.

[0860] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[0861] Career Advice and Planning Program Process

[0862] Processing flow

[0863] Step 1:

[0864] If the user is interested in the careers presented, they will ask for detailed career advice.

[0865] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[0866] Step 2:

[0867] The server retrieves the skills and qualifications required for the user's career choice from a database.

[0868] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[0869] Step 3:

[0870] The server generates the lesson plan.

[0871] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[0872] Step 4:

[0873] The server will then concretely present the course plan.

[0874] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[0875] Specific processing flow of the industry trend and market demand analysis program

[0876] Processing flow

[0877] Step 1:

[0878] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[0879] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[0880] Step 2:

[0881] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[0882] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[0883] Step 3:

[0884] The server generates the analysis results.

[0885] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result. The emotion engine takes the user's emotional state into account to generate a customized analysis result.

[0886] Step 4:

[0887] The server presents the analysis results to the user.

[0888] Operation: The generated analysis results are compiled into a report, and HTML code for displaying them in a user interface is generated and sent to the device. The content and method of presentation are adjusted depending on the user's emotional state.

[0889] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[0890] Example 2

[0891] 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."

[0892] While conventional career suggestion systems suggest careers based on users' interests and abilities, they are inadequate in taking into account their emotional state and adjusting their career plans. Furthermore, they struggle to analyze and provide market and industry trends, making it difficult for users to obtain specific future predictions. This makes it difficult for users to make appropriate career choices and develop career plans.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0894] In this invention, the server includes means for using a generative model to suggest appropriate occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and ability improvement support to reach the user's selected occupation, means for analyzing industry and market trends and providing the user with the analysis results, and means for analyzing the user's emotional state using an emotion engine and adjusting the content of the career suggestions and career plans made by the generative model. This allows users to not only receive career suggestions based on their own interests, abilities, and values, but also receive optimal advice and career plans according to their emotional state at the time, and further enables them to obtain future predictions based on an understanding of market and industry trends.

[0895] A "generative model" is the part of the system that uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[0896] The "emotion engine" is a technology that analyzes the user's emotional state in real time and reflects the results of that analysis in career suggestions and career planning.

[0897] A "career plan" is a specific action plan that includes a list of study plans and training programs to help the user reach their chosen career.

[0898] "Job Opportunities" means job postings or employment opportunities related to the proposed occupation.

[0899] "Industry and Market Trends" is information that indicates current and future changes and trends in a particular industry or market.

[0900] "Analysis Results" refers to the data and conclusions derived by generative models, emotion engines, and other analytical algorithms.

[0901] "User" refers to an individual who uses the System to receive career suggestions and career plans.

[0902] The present invention is a system that combines a generative model and an emotion engine to provide career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state in an integrated manner.

[0903] System Configuration

[0904] The system includes the following components:

[0905] 1. Generative Model:

[0906] It uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[0907] 2. Emotion Engine:

[0908] The user's emotional state is analyzed in real time, and the results of that analysis are reflected in career suggestions and career planning.

[0909] 3. User Interface:

[0910] The interface is designed to make it easy for users to interact with the system, allowing them to input and retrieve information in chatbot format.

[0911] 4. Database:

[0912] Store data on occupational information, job openings, skill acquisition information, and industry and market trends, and provide this data as needed.

[0913] Hardware and software used

[0914] Hardware: Servers, terminals

[0915] The servers use a cloud-based infrastructure.

[0916] Terminals include PCs and mobile devices.

[0917] Software: Generative AI models, natural language processing technology, sentiment analysis technology, database management systems

[0918] Use TensorFlow or PyTorch for generative models.

[0919] Use a natural language processing library such as the Natural Language Toolkit (NLTK) for your sentiment engine.

[0920] Use MySQL or PostgreSQL as your database management system.

[0921] Program processing

[0922] Specific examples

[0923] 1. Use cases for the career assessment chatbot:

[0924] A user types into a chatbot, "I want to find a new career."

[0925] The server asks, "Tell me about your interests and skills."

[0926] The user responds, "I like reading and programming."

[0927] The server performs analysis using a generative model and suggests a "software developer."

[0928] The emotion engine determines the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly.

[0929] Related job information is also retrieved from the database and displayed.

[0930] 2. Examples of career advice provided:

[0931] A user asks, "How do I become a software developer?"

[0932] The server provides the necessary skills (e.g., programming languages, understanding of algorithms, etc.).

[0933] The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy and anxiety.

[0934] For example, if the user is feeling anxious, the plan will be presented in more detail.

[0935] 3. Industry Trends and Market Demand Analysis Examples:

[0936] A user requests, "I want to know about the future outlook for the IT industry."

[0937] The server provides the analysis results, stating, "The fields of AI and machine learning are expected to continue to grow."

[0938] The emotion engine analyzes the user's emotions at that time and adjusts the presentation method by, for example, emphasizing positive information.

[0939] Prompt Sentence Examples

[0940] "I want to find a new career"

[0941] "Tell me about your interests and skills"

[0942] I like reading and programming.

[0943] "How do I become a software developer?"

[0944] "I want to know the future outlook for the IT industry."

[0945] As described above, this system provides comprehensive career selection support to users and offers concrete means to support their career success. In addition, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[0946] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0947] Step 1:

[0948] The user accesses the employment and career change support service from the terminal and logs in.

[0949] Input: User ID and password.

[0950] Data processing / calculation: The server passes the received user ID and password to the authentication system and performs hash matching.

[0951] Output: Authentication result (success / failure).

[0952] Specific operation: If authentication is successful, the server generates a home screen and sends it to the device. If authentication fails, an error message is displayed.

[0953] Step 2:

[0954] The user selects the "Career Diagnosis" feature on the home screen and begins a dialogue through the chatbot interface.

[0955] Input: User's selection of the "Career Assessment" feature.

[0956] Data processing / computation: The server receives the user's selection and initializes the chatbot interface.

[0957] Output: Display of the chatbot interface.

[0958] Specific operation: The server sends the UI component to the device, and the chatbot displays a message prompting the user to "Tell us about your interests and skills."

[0959] Step 3:

[0960] Users input their interests, abilities, and values ​​into the chatbot.

[0961] Input: User's answer (e.g., "I like reading" or "I have programming experience").

[0962] Data processing / calculation: The server passes the response data to the generative model and performs natural language processing.

[0963] Output: Analysis results (profile of user interests, abilities, and values).

[0964] Specific operation: The server temporarily stores the analysis results obtained from the generative model, and the chatbot displays the next question.

[0965] Step 4:

[0966] A generative model analyzes the user's answers and makes career suggestions.

[0967] Input: User response data passed to the generative model.

[0968] Data processing / computation: The generative model applies natural language processing techniques and analyzes the answers based on the career suggestion algorithm.

[0969] Output: A list of occupations based on the analysis results.

[0970] Specific operation: The server receives the analysis results of the generative model and presents a list of occupations to the user through the chatbot.

[0971] Step 5:

[0972] The emotion engine analyzes the user's emotional state and feeds the analysis results back to the generative model.

[0973] Input: User interaction.

[0974] Data processing / calculation: The emotion engine analyzes the dialogue content and determines the emotional state (e.g., joy, anxiety).

[0975] Output: Emotion analysis results.

[0976] Specific operation: The server feeds back the emotion analysis results to the generative model and adjusts it to optimal career suggestions.

[0977] Step 6:

[0978] Providing relevant job information based on career suggestions.

[0979] Input: Best Occupation List.

[0980] Data processing / calculation: The server extracts relevant job information from the database.

[0981] Output:Related job listings.

[0982] Specific operation: The server sends the job information to the terminal and displays it to the user.

[0983] Step 7:

[0984] When users seek career advice, they provide detailed skills and qualifications.

[0985] Input: User's career advice request.

[0986] Data processing / calculation: The server retrieves the required skills and qualifications from the database.

[0987] Output: Skills and qualifications.

[0988] Specific operation: The server adjusts the acquired information based on the emotion engine and displays appropriate advice to the user.

[0989] Step 8:

[0990] The career plan is concretized and presented to the user.

[0991] Input: Information needed to choose a career.

[0992] Data processing / calculation: The server generates a list of learning plans and training programs and organizes them into a timeline format.

[0993] Output: A concrete career plan.

[0994] Specific operation: The server sends the course plan to the terminal and displays it to the user. The emotion engine also adjusts it.

[0995] Step 9:

[0996] Provides analysis of industry trends and market demand.

[0997] Input: User request for trend information.

[0998] Data processing / calculation: The server collects and analyzes market reports and statistical information to predict future supply and demand.

[0999] Output: Market trend analysis results.

[1000] Specific operation: The server generates the latest analysis results, adjusts them using the emotion engine, sends them to the device, and displays them to the user.

[1001] (Application example 2)

[1002] 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."

[1003] Modern society requires diverse career choices and career support, but there is a lack of systems that make appropriate career suggestions that are tailored to each user's interests, abilities, and values. There is also a need for services that take into account the user's emotional state, but current systems do not adequately achieve this. Furthermore, there are few systems that provide comprehensive, specific career plans and market trend analysis linked to career suggestions. Therefore, there is a need for a system that analyzes users' daily behavioral data, such as their purchasing history and product browsing history, to provide more personalized, emotionally sensitive career support.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1005] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job-seeking opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing the user's interests, abilities, and values ​​based on purchase history and product browsing history, and means for analyzing the user's emotional state and adjusting the content of suggestions and screen display according to the emotional state. This makes it possible to make occupation suggestions and career support based on the user's personal data, and to provide detailed consulting that takes the user's emotional state into consideration.

[1006] A "generative model" refers to an algorithm that suggests suitable occupations based on information such as a user's interests, abilities, and values.

[1007] "Career suggestions" refers to presenting appropriate occupations and career paths to users based on the results of analysis by the generative model.

[1008] "Job Opportunities" refers to the provision of job postings or employment opportunities related to the proposed occupation.

[1009] "Career planning" refers to the specific learning plan or training program that will lead a user to their chosen career.

[1010] "Support for capacity building" refers to providing assistance to users to acquire the skills and knowledge necessary for their chosen occupation.

[1011] "Industry and Market Trends" refers to trends and changes in specific industry sectors and markets.

[1012] "Purchase history" refers to a record of products and services purchased by a user in the past.

[1013] "Product browsing history" refers to the record of products a user has viewed on an online shopping site or app.

[1014] "Emotional state" refers to the psychological state, such as joy, anxiety, or sadness, that a user feels at a given moment.

[1015] "Analysis" refers to the process of analyzing collected data using generative models and sentiment analysis engines to derive meaning from it.

[1016] "Screen display" refers to visually presenting the analyzed results to the user through a user interface.

[1017] "System" refers to the computer-based infrastructure that integrates and operates the various means mentioned above.

[1018] This invention is a system that provides an integrated service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. This system is realized by combining a generative model, an emotion engine, a user interface, and a database.

[1019] System Configuration

[1020] The system consists of the following elements:

[1021] Generative Model

[1022] Emotion Engine

[1023] User Interface

[1024] Database

[1025] Program processing

[1026] User Access and Data Collection

[1027] Users access the system using a smartphone. When the user logs in, the application on the device collects their purchase history and product browsing history and sends it to the server. This data collection is done using a REST API.

[1028] Analysis using generative models

[1029] The server inputs the collected data into a generative model to analyze the user's interests, abilities, and values. This analysis is performed using natural language processing technology, specifically using Hugging Face's Transformers. The generative model then uses the analysis results to suggest the most suitable occupation.

[1030] Analysis by emotion engine

[1031] Furthermore, the emotion engine analyzes the user's emotional state in real time, using tools such as Luxand's Face SDK. Based on the user's emotional state, the suggestions and screen display are adjusted. For example, if the user is feeling anxious, more detailed and thoughtful explanations are displayed.

[1032] Providing job opportunities and career planning

[1033] Job information related to the occupations suggested by the generative model is retrieved from a database and displayed to the user. The learning paths and training programs required for the occupation selected by the user are also presented, along with specific career planning and skill development support.

[1034] Industry and market trend analysis

[1035] The server periodically collects market data and analyzes industry and market trends. The analysis results are provided to users to help them make career choices. This analysis utilizes statistical information and publicly available market reports.

[1036] Specific examples

[1037] For example, if a user "frequently purchases tech-related products," the generative model analyzes that data and suggests the occupation "software developer." The emotion engine analyzes the user's emotions at the time and adjusts the suggestions based on their level of joy or anxiety.

[1038] Example prompt sentence:

[1039] Purchase history: I frequently purchase tech-related products, programming books, gadgets, etc. What is your suitable occupation?

[1040] This system allows users to receive career suggestions based on their personal data, and also provides detailed consulting that takes into account their emotional state.

[1041] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1042] Step 1:

[1043] The user accesses the system from a smartphone.

[1044] Input: User login information (ID, password)

[1045] Output: User authentication result

[1046] The server receives the user's login information and performs authentication. If authentication is successful, the home screen is displayed.

[1047] Step 2:

[1048] The user's purchase history and product browsing history are collected and sent to the server.

[1049] Input: Purchase history and product browsing history data

[1050] Output: Send data to the server

[1051] The device collects data on products the user has previously purchased or viewed and sends it to the server. Specifically, it creates a data set containing information such as product category, purchase date, and viewing time, and sends it using a REST API.

[1052] Step 3:

[1053] The server inputs the submitted data into a generative model to analyze the user's interests, abilities, and values.

[1054] Input: Purchase history and product browsing history data

[1055] Output: Analysis results based on the user's interests, abilities, and values

[1056] The server inputs the received data into a generative model (such as Hugging Face's Transformers) and processes the data using natural language processing techniques. As a result of the analysis, it generates a list of suitable occupations.

[1057] Step 4:

[1058] An emotion engine is used to analyze the user's emotional state.

[1059] Input: User's facial image or voice data

[1060] Output: Emotional state analysis results

[1061] To analyze the user's emotional state in real time, the device uses a camera and microphone to capture facial images or voice data and sends them to a server. The server then analyzes the emotional state using software such as Luxand Face SDK and outputs the degree of emotion as a numerical value.

[1062] Step 5:

[1063] The server integrates the analysis results from the generative model and the results from the emotion engine to make appropriate career suggestions.

[1064] Input: Analysis results of the generative model, analysis results of the emotion engine

[1065] Output: Adjusted list of career suggestions

[1066] The server integrates data from the generative model and the emotion engine to generate a list of career suggestions based on the user's emotional state. For example, if the user feels anxious, the server adjusts the list by adding more specific and detailed descriptions.

[1067] Step 6:

[1068] Job listings related to the suggested occupation are retrieved from a database and displayed to the user.

[1069] Input: Career suggestion list

[1070] Output: List of job postings

[1071] The server searches the database for job listings related to the proposed occupation and transmits the acquired job listings to the terminal, which visually displays them to the user.

[1072] Step 7:

[1073] Provide users with the learning paths and specific career plans required for their chosen career.

[1074] Input: User's occupation choice

[1075] Output: Learning path, career plan

[1076] The server retrieves the skills and qualifications required for the job selected by the user from a database and generates a learning path or training program, which is displayed in a timeline format.

[1077] Step 8:

[1078] Analyze industry and market trends and provide the results to users.

[1079] Input: Market Data

[1080] Output: Market trend analysis results

[1081] The server analyzes industry and market trends based on regularly collected market data, using statistical information and publicly available market reports, and presents the analysis results to users to help them make career choices.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] [Third embodiment]

[1086] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1087] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1088] 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).

[1089] 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.

[1090] 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.

[1091] 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).

[1092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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.

[1097] 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."

[1098] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[1099] System Overview

[1100] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[1101] Program processing

[1102] Career diagnosis chatbot

[1103] User access and interaction initiation:

[1104] The user accesses the employment and career change support service from their device, logs in, and selects the career diagnosis function. The server connects to the generative AI model, generates a chatbot interface, and presents it to the user. The user inputs information about their interests, abilities, and values ​​in response to the chatbot's questions.

[1105] Analysis and career suggestions using generative models:

[1106] The server analyzes the user's answers using a generative AI model to suggest the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[1107] Career Advice and Planning

[1108] Providing career advice:

[1109] If the user is interested in the suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. The learning plan includes information about online courses and training programs.

[1110] Implementing your career plan:

[1111] The server compiles the user's learning plan into a detailed course plan and presents it in the form of a timeline, which specifies the goals and necessary resources for each step.

[1112] Industry trends and market demand analysis

[1113] Data collection and analysis:

[1114] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[1115] Providing analysis results:

[1116] If a user wants to find trend information for a particular industry or occupation, the server generates and provides the analysis results to the user, allowing the user to consider careers based on market demand.

[1117] Specific examples

[1118] Career Assessment Chatbot Use Cases:

[1119] When a user types "I want to find a new career" into the chatbot, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes this using a generative AI model and suggests "software developer." It also retrieves related job information from a database and displays it to the user.

[1120] Examples of career advice provided:

[1121] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[1122] Industry trends and market demand analysis examples:

[1123] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[1124] In this way, the system provides users with comprehensive career choice support and provides specific means to support career success.

[1125] The processing flow will be explained below.

[1126] Specific processing flow of the career diagnosis chatbot program

[1127] Processing flow

[1128] Step 1:

[1129] The user accesses the employment and career change support service from their device and enters their login information.

[1130] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[1131] Step 2:

[1132] The server authenticates the user and displays the home screen.

[1133] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[1134] Step 3:

[1135] The user selects the "Career Diagnosis" function on the home screen.

[1136] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[1137] Step 4:

[1138] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[1139] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[1140] Step 5:

[1141] The user answers the chatbot's question (e.g., "I like reading and programming").

[1142] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[1143] Step 6:

[1144] The server analyzes the user's answers using a generative AI model.

[1145] How it works: The server inputs the user's answers into a natural language processing engine, and passes the analysis results to a generative model, which then calculates the best possible job candidates.

[1146] Step 7:

[1147] The server will suggest the best career aptitudes.

[1148] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[1149] Step 8:

[1150] The server displays job listings related to the proposed occupation.

[1151] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[1152] Career Advice and Planning Program Process

[1153] Processing flow

[1154] Step 1:

[1155] If the user is interested in the careers presented, they will ask for detailed career advice.

[1156] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[1157] Step 2:

[1158] The server retrieves the skills and qualifications required for the user's career choice from a database.

[1159] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[1160] Step 3:

[1161] The server generates the lesson plan.

[1162] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[1163] Step 4:

[1164] The server will then concretely present the course plan.

[1165] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[1166] Specific processing flow of the industry trend and market demand analysis program

[1167] Processing flow

[1168] Step 1:

[1169] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[1170] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[1171] Step 2:

[1172] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[1173] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[1174] Step 3:

[1175] The server generates the analysis results.

[1176] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result.

[1177] Step 4:

[1178] The server presents the analysis results to the user.

[1179] Operation: The generated analysis results are compiled into a report, HTML code is generated to display in the user interface, and the report is sent to the terminal.

[1180] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[1181] Example 1

[1182] 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."

[1183] Conventional career selection support systems have had difficulty suggesting appropriate careers based on the user's interests, abilities, and values. They also have had issues with providing effective career advice, specific career plans, and demand forecasts that reflect industry and market trends. The purpose of this invention is to solve these issues and provide optimal career selection support for users.

[1184] 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.

[1185] In this invention, the server includes means for using a generative model to suggest careers based on the user's interests, abilities, and values, means for analyzing the user's responses using a generative AI model and suggesting appropriate careers, means for generating a study plan related to the skills and qualifications required when the user requests career advice, and means for analyzing collected data on industry and market trends and generating trend information. This allows the user to receive career suggestions based on their individual abilities and interests, receive specific career advice and career plans, and obtain information that reflects the latest industry and market trends.

[1186] A "generative model" is a type of artificial intelligence that makes predictions and suggestions based on user input data, and has functions such as document generation, data analysis, and job recommendations.

[1187] "Interest" refers to a user's interest or curiosity in a particular thing or activity.

[1188] "Ability" refers to the skills, knowledge, and abilities that a user possesses to perform a specific task or work.

[1189] "Values" refers to the basic ideas and standards that serve as the basis for users' beliefs and choices of behavior.

[1190] "Means" refer to the methods or processes used to achieve a particular goal.

[1191] "Job Opportunities" refers to job postings and employment opportunities related to occupations that interest users.

[1192] A "career plan" is a plan that specifies the steps and actions a user needs to take to reach a specific career.

[1193] "Competence development" refers to the process by which clients improve the skills and knowledge required for a particular occupation.

[1194] "Support" refers to the support and assistance provided to users to achieve their goals.

[1195] An "industry" is a part of economic activity that produces or provides a particular product or service.

[1196] A "market" refers to an economic venue or area where goods and services are bought and sold.

[1197] "Trends" refers to the current situation and future direction in a particular field or area.

[1198] "Data" refers to information that is collected, stored and processed for a specific purpose.

[1199] "Analysis" refers to the investigation or processing of data for the purpose of detailed examination or evaluation.

[1200] "User interface" refers to the means or method by which a user and a system interact with each other to exchange information.

[1201] A "learning plan" refers to a set of learning activities and schedules designed to acquire specific skills or knowledge.

[1202] "Trend information" refers to information about the latest developments and trends in a particular industry or market.

[1203] "Answer" refers to the information a user enters in response to a question or prompt from the system.

[1204] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[1205] System Overview

[1206] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[1207] Career diagnosis chatbot

[1208] When a user logs in from their device and selects the career diagnosis function, the server connects to a generative AI model (e.g., OpenAI GPT-4) and generates a chatbot interface. The user inputs information about their interests, abilities, and values ​​in response to questions from the chatbot. The server then proceeds with a dialogue with the user as follows:

[1209] Example prompt sentence:

[1210] User: "I want to find a new career."

[1211] Server: "What are your hobbies?"

[1212] User: "I like reading and programming."

[1213] The server then uses a generative AI model to analyze the user's answers and suggest "software developer" as the most suitable occupation. It also retrieves related job information from a database and displays it to the user.

[1214] Career Advice and Planning

[1215] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a study plan based on that information. For example, if the user asks, "What should I do to become a software developer?" the server will respond, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information on online courses and training programs. The server then compiles the generated study plan into a specific career plan and presents it in the form of a timeline. The career plan will specify the goals and necessary resources for each step.

[1216] Industry trends and market demand analysis

[1217] The server regularly collects data on industry and market trends and analyzes them using a proprietary analytical algorithm. The data is extracted from publicly available market reports and statistical information. When a user wants to research trend information for a specific industry or occupation, the server generates analytical results and provides them to the user. For example, if a user requests, "I want to know the future outlook for the IT industry," the server will provide the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path toward becoming an AI engineer.

[1218] Hardware or software used

[1219] The server uses high-performance hardware such as NVIDIA GPUs and generative AI models. The terminal provides a user interface through a browser or dedicated application. It also uses database management systems and online education platform APIs such as MySQL, PostgreSQL, Coursera, and Udemy to obtain information. Furthermore, it uses scraping tools and big data processing platforms (e.g., Beautiful Soup, Scrapy, Apache Hadoop, and Spark) for data collection and analysis.

[1220] In this way, the server, terminal, and user each play their own role, and the entire system works together to provide comprehensive career selection support.

[1221] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1222] Step 1:

[1223] A user accesses the system from a terminal and logs in.

[1224] Input: User login information (username, password).

[1225] Data processing and calculation: The terminal sends the login information to the server, which then checks it against a database for authentication.

[1226] Output: Login success message, access to occupational diagnostic function.

[1227] Specific operation: The device launches a website or app, enters a username and password on the login screen, and after successful authentication, the menu screen is displayed.

[1228] Step 2:

[1229] The user selects the career assessment function.

[1230] Input: User's choice of occupational diagnostic function.

[1231] Data processing and calculation: The device sends the selected information to the server, which connects it to the generated AI model and generates the chatbot interface.

[1232] Output: Initial message from the career assessment chatbot.

[1233] What happens: The user clicks the "Start Career Assessment" button and the chatbot asks, "What are your hobbies?"

[1234] Step 3:

[1235] The user answers the chatbot's questions.

[1236] Input: Information about the user's interests, abilities, and values.

[1237] Data processing and calculation: The server collects user input and inputs it as a prompt to the generative AI model, which then performs analysis.

[1238] Output: Career suggestions and related job listings.

[1239] Specific behavior: The user responds, "I like reading and programming," and the server suggests "Software Developer" and also displays related job listings.

[1240] Step 4:

[1241] A user seeks career advice regarding a suggested occupation.

[1242] Input: User's career advice question.

[1243] Data processing and calculation: The server retrieves the necessary skills and qualifications from the database, analyzes them using a generative AI model, and generates a learning plan.

[1244] Output: Learning plan and training program information.

[1245] What it does: A user types, "What do I need to do to become a software developer?" The server responds, "You need Python or Java skills," and provides a link to an online course.

[1246] Step 5:

[1247] The server compiles the user's study plan into a career plan.

[1248] Enter: Study Plan.

[1249] Data processing and calculation: The server organizes the learning plan in a timeline format, specifying the goals and required resources for each step.

[1250] Output: A timeline of your career plan.

[1251] What it does: The server displays a timeline like "Learn Python within 3 months, then learn Java."

[1252] Step 6:

[1253] The server periodically collects and analyzes data on industry and market trends.

[1254] Input: Public market reports and statistics.

[1255] Data processing and calculation: The server collects data using a scraping tool and analyzes it on the big data processing platform.

[1256] Output: Industry and market trend information.

[1257] What it does: The server collects public market reports every month, analyzes them using Apache Spark, and stores the trend information in a database.

[1258] Step 7:

[1259] A user requests trending information for a particular industry or occupation.

[1260] Input: User's request for trend information.

[1261] Data processing and calculation: The server retrieves the analysis results from the database and generates the necessary information.

[1262] Output: Analysis results and trend information.

[1263] Specific operation: The user requests, "I want to know the future outlook for the IT industry," and the server presents the analysis result, such as, "The fields of AI and machine learning will grow."

[1264] In this way, the entire system works together according to the user's needs, providing comprehensive support for career choices, career advice, career planning, and industry trend analysis.

[1265] (Application example 1)

[1266] 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."

[1267] In modern society, many people face the challenge of finding the most suitable occupation based on their aptitudes and interests. Furthermore, it is difficult to obtain information on appropriate career plans and market demand, creating many barriers to career development. Furthermore, existing career diagnosis systems have limited user interfaces and lack a way to visually display career diagnosis information and career plans received in real time.

[1268] 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.

[1269] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing industry and market trends and providing the analysis results to the user, and means for displaying the career diagnosis results and career plans in an interactive format using smart glasses, thereby enabling the user to select an occupation according to their aptitude and obtain specific career plans and market trend information in real time.

[1270] A "generative model" is an artificial intelligence algorithm that generates new data and predictions based on user-provided information.

[1271] "Career suggestions" are the act of determining the most suitable occupation based on the user's interests, abilities, and values, and presenting it to the user.

[1272] "Job opportunities" refers to job information provided to users for employment or career changes.

[1273] A "career plan" is a plan that includes specific steps and a timeline for how a client will reach their chosen career.

[1274] "Support for capacity building" refers to the provision of learning paths and training programs that enable users to acquire the skills and knowledge required for a specific occupation.

[1275] "Industry and Market Trends" is information about current and future market trends and demands in various industries.

[1276] "Analysis results" refer to the specific conclusions and findings of the analysis conducted based on the collected data.

[1277] "Smart glasses" are wearable devices used to display information, and are eyeglass-type devices that have the ability to display visual data in an interactive format.

[1278] "Dialogue" refers to a method of communication in which the user and the system exchange information in a two-way manner.

[1279] "Career diagnosis results" refer to career suggestions provided based on the user's characteristics through a generative model.

[1280] In order to implement the present invention, it is necessary to construct a system including the following steps.

[1281] System Overview

[1282] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, career advice and path planning functions, and analysis of industry trends and market demands. These functions are realized through server-side processing and a user interface using smart glasses.

[1283] Program processing flow and hardware used

[1284] Career diagnosis chatbot

[1285] User access and interaction initiation:

[1286] The user uses smart glasses to access the employment and career change support service and selects the career diagnosis function. Using the smart glasses' HUD (head-up display), the server displays a chatbot interface based on the generative AI model and begins a dialogue with the user. The user inputs information about their interests, abilities, and values ​​by answering the chatbot's questions.

[1287] Analysis and career suggestions using generative models:

[1288] The server analyzes the user's answers using a generative model and suggests the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[1289] Career Advice and Planning

[1290] Providing career advice:

[1291] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. This learning plan includes information about online courses and training programs.

[1292] Implementing your career plan:

[1293] The server compiles the user's learning plan into a detailed course plan, which is displayed in a timeline format on the smart glasses' HUD. The course plan specifies the goals and required resources for each step.

[1294] Industry trends and market demand analysis

[1295] Data collection and analysis:

[1296] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[1297] Providing analysis results:

[1298] When a user wants to find out trend information for a particular industry or occupation, the server will generate analysis results and provide them to the user through the smart glasses, allowing the user to consider careers based on market demand.

[1299] Specific examples of functions

[1300] Career Assessment Chatbot Use Cases:

[1301] When a user types "I want to find a new career" into the smart glasses, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes the answer with a generative model and suggests "software developer." It also retrieves related job listings from a database and displays them to the user.

[1302] Examples of career advice provided:

[1303] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[1304] Industry trends and market demand analysis examples:

[1305] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[1306] Examples of prompt statements

[1307] Suggest the best career for you based on your interests, abilities, and values:

[1308] Interests: I like reading and programming

[1309] Ability to use Python and Java

[1310] Values: I want to do creative work

[1311] In this way, the system provides users with comprehensive career selection assistance and allows real-time feedback through the smart glasses.

[1312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1313] Step 1:

[1314] To begin using the service, users access the employment and career change support service using smart glasses and select the career diagnosis function. The career diagnosis chatbot interface will then be displayed on the smart glasses' HUD.

[1315] Step 2:

[1316] By answering questions from the chatbot, users input information about their interests, abilities, and values, which is then sent to a server and used to analyze the generative AI model.

[1317] Step 3:

[1318] The server uses a generative AI model to analyze data entered by the user. Specifically, it analyzes information on interests, abilities, and values, and inputs prompts to the generative AI model to suggest the most suitable occupation. For example, a prompt such as "Interests: I like reading and programming. Abilities: I can use Python and Java. Values: I want to work in a creative field" is passed to the generative AI model. Based on this, the model generates data suggesting appropriate occupations. The output is the suggested occupations and related job information.

[1319] Step 4:

[1320] The server presents the user with suggested occupations derived from the generative AI model. The suggested occupation, for example, "Software Developer," is displayed on the smart glasses' HUD. Related job listings are also displayed simultaneously.

[1321] Step 5:

[1322] If the user is interested in the suggested careers, they can ask for career advice through the smart glasses. The user can ask, "What should I do to become a software developer?" and the question is sent to the server.

[1323] Step 6:

[1324] The server retrieves the required skills and qualifications from a database based on the user's questions, and generates a learning plan based on that information. For example, it prepares an answer such as "The required skills are programming languages ​​(Python, Java) and understanding of algorithms," and displays it on the smart glasses' HUD. It also provides information on related online courses and training programs.

[1325] Step 7:

[1326] The server then compiles the generated learning plan into a specific course plan, which is displayed in timeline format on the smart glasses' HUD. The plan specifies the goals and necessary resources for each step, allowing the user to learn and gain experience accordingly.

[1327] Step 8:

[1328] When a user wants to find out trend information about a particular industry or occupation, they send a request to the server through the smart glasses. For example, they might request, "I want to know the future outlook for the IT industry."

[1329] Step 9:

[1330] The server analyzes industry and market trend data collected periodically and generates analysis results based on the request, such as "The fields of AI and machine learning are expected to continue to grow," which is displayed on the smart glasses' HUD.

[1331] This will realize a system that allows users to select a career that suits their aptitude and obtain specific career plans and market trend information in real time.

[1332] 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.

[1333] The present invention uses a system that combines a generative model and an emotion engine to provide a unified service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. Specific embodiments for implementing the present invention are described below.

[1334] System Overview

[1335] This system is composed of a generative model, an emotion engine, a user interface, and a database. The generative model is responsible for suggesting the most suitable occupation based on the user's interests, abilities, and values, while the emotion engine analyzes the user's emotional state and reflects it in each function of the system.

[1336] Program processing

[1337] Career diagnosis chatbot

[1338] User access and interaction initiation:

[1339] The user accesses the employment and career change support service from their device and logs in. The server authenticates the user and displays the home screen. The user selects the "career diagnosis" function and begins a dialogue through the chatbot interface. The generative model collects information about the user's interests, abilities, and values, and gathers data to determine suggested careers.

[1340] Analysis and career suggestions using generative models

[1341] Analysis by generative model:

[1342] The server passes the user's input data to a generative model that then analyzes the user's responses using natural language processing techniques to generate a list of the most suitable occupations based on the user's interests, abilities, and values.

[1343] The role of the Emotion Engine:

[1344] The emotion engine analyzes the user's dialogue in real time to determine their emotional state. The results of the emotion engine's analysis are reflected in the generative model's analysis results, resulting in more precise career suggestions.

[1345] Job information provided by:

[1346] The server retrieves job opportunities (job information) related to the proposed occupation from the database and presents them to the user, who can use them to make further career choices.

[1347] Career Advice and Planning

[1348] Providing career advice:

[1349] If the user is interested in the suggested career, they can request detailed career advice. The server retrieves the skills and qualifications required for the user's career choice from a database. The emotion engine adjusts the advice content according to the user's emotional state.

[1350] Implementing your career plan:

[1351] Based on the acquired information, the server generates a specific career plan (study plan, training program list, etc.) and presents it to the user in a timeline format. The information provided by the emotion engine allows the server to provide the optimal plan according to the user's emotional state.

[1352] Industry trends and market demand analysis

[1353] Data collection and analysis:

[1354] The server periodically collects industry and market trend data, including publicly available market reports and statistics, and analyzes the trends using proprietary analytical algorithms.

[1355] Providing analysis results:

[1356] When a user wants to find trend information for a particular industry or profession, the server generates and provides the latest analysis results to the user, and the emotion engine adjusts the presentation of the analysis results based on the user's emotional state.

[1357] Specific examples

[1358] Career Assessment Chatbot Use Cases:

[1359] When a user types "I want to find a new career" into the chatbot, the server asks, "Tell me about your interests and skills." If the user responds, "I like reading and programming," the server analyzes the information using a generative model and suggests "software developer." At this point, an emotion engine distinguishes between the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly. Related job information is also retrieved from the database and displayed.

[1360] Examples of career advice provided:

[1361] When a user asks, "What should I do to become a software developer?", the server provides the necessary skills (e.g., programming languages, understanding algorithms, etc.). The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy or anxiety. For example, if the user is feeling anxious, it presents a more detailed plan.

[1362] Industry trends and market demand analysis examples:

[1363] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." The emotion engine analyzes the user's emotions at that time and adjusts the presentation method, such as emphasizing positive information.

[1364] In this way, the system provides users with comprehensive career selection support and concrete means to support their career success. Furthermore, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[1365] The processing flow will be explained below.

[1366] Specific processing flow of the career diagnosis chatbot program

[1367] Processing flow

[1368] Step 1:

[1369] The user accesses the employment and career change support service from their device and enters their login information.

[1370] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[1371] Step 2:

[1372] The server authenticates the user and displays the home screen.

[1373] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[1374] Step 3:

[1375] The user selects the "Career Diagnosis" function on the home screen.

[1376] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[1377] Step 4:

[1378] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[1379] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[1380] Step 5:

[1381] The user answers the chatbot's question (e.g., "I like reading and programming").

[1382] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[1383] Step 6:

[1384] The server analyzes the user's responses and emotional data using a generative AI model and emotion engine.

[1385] How it works: The server inputs the user's response into the natural language processing engine, passes the analysis results to the generative model, and then uses the emotion engine to analyze the user's emotional state and reflects it in the analysis results of the generative model.

[1386] Step 7:

[1387] The server will suggest the best career aptitudes.

[1388] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[1389] Step 8:

[1390] The server displays job listings related to the proposed occupation.

[1391] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[1392] Career Advice and Planning Program Process

[1393] Processing flow

[1394] Step 1:

[1395] If the user is interested in the careers presented, they will ask for detailed career advice.

[1396] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[1397] Step 2:

[1398] The server retrieves the skills and qualifications required for the user's career choice from a database.

[1399] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[1400] Step 3:

[1401] The server generates the lesson plan.

[1402] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[1403] Step 4:

[1404] The server will then concretely present the course plan.

[1405] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[1406] Specific processing flow of the industry trend and market demand analysis program

[1407] Processing flow

[1408] Step 1:

[1409] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[1410] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[1411] Step 2:

[1412] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[1413] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[1414] Step 3:

[1415] The server generates the analysis results.

[1416] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result. The emotion engine takes the user's emotional state into account to generate a customized analysis result.

[1417] Step 4:

[1418] The server presents the analysis results to the user.

[1419] Operation: The generated analysis results are compiled into a report, and HTML code for displaying them in a user interface is generated and sent to the device. The content and method of presentation are adjusted depending on the user's emotional state.

[1420] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[1421] Example 2

[1422] 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."

[1423] While conventional career suggestion systems suggest careers based on users' interests and abilities, they are inadequate in taking into account their emotional state and adjusting their career plans. Furthermore, they struggle to analyze and provide market and industry trends, making it difficult for users to obtain specific future predictions. This makes it difficult for users to make appropriate career choices and develop career plans.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1425] In this invention, the server includes means for using a generative model to suggest appropriate occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and ability improvement support to reach the user's selected occupation, means for analyzing industry and market trends and providing the user with the analysis results, and means for analyzing the user's emotional state using an emotion engine and adjusting the content of the career suggestions and career plans made by the generative model. This allows users to not only receive career suggestions based on their own interests, abilities, and values, but also receive optimal advice and career plans according to their emotional state at the time, and further enables them to obtain future predictions based on an understanding of market and industry trends.

[1426] A "generative model" is the part of the system that uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[1427] The "emotion engine" is a technology that analyzes the user's emotional state in real time and reflects the results of that analysis in career suggestions and career planning.

[1428] A "career plan" is a specific action plan that includes a list of study plans and training programs to help the user reach their chosen career.

[1429] "Job Opportunities" means job postings or employment opportunities related to the proposed occupation.

[1430] "Industry and Market Trends" is information that indicates current and future changes and trends in a particular industry or market.

[1431] "Analysis Results" refers to the data and conclusions derived by generative models, emotion engines, and other analytical algorithms.

[1432] "User" refers to an individual who uses the System to receive career suggestions and career plans.

[1433] The present invention is a system that combines a generative model and an emotion engine to provide career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state in an integrated manner.

[1434] System Configuration

[1435] The system includes the following components:

[1436] 1. Generative Model:

[1437] It uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[1438] 2. Emotion Engine:

[1439] The user's emotional state is analyzed in real time, and the results of that analysis are reflected in career suggestions and career planning.

[1440] 3. User Interface:

[1441] The interface is designed to make it easy for users to interact with the system, allowing them to input and retrieve information in chatbot format.

[1442] 4. Database:

[1443] Store data on occupational information, job openings, skill acquisition information, and industry and market trends, and provide this data as needed.

[1444] Hardware and software used

[1445] Hardware: Servers, terminals

[1446] The servers use a cloud-based infrastructure.

[1447] Terminals include PCs and mobile devices.

[1448] Software: Generative AI models, natural language processing technology, sentiment analysis technology, database management systems

[1449] Use TensorFlow or PyTorch for generative models.

[1450] Use a natural language processing library such as the Natural Language Toolkit (NLTK) for your sentiment engine.

[1451] Use MySQL or PostgreSQL as your database management system.

[1452] Program processing

[1453] Specific examples

[1454] 1. Use cases for the career assessment chatbot:

[1455] A user types into a chatbot, "I want to find a new career."

[1456] The server asks, "Tell me about your interests and skills."

[1457] The user responds, "I like reading and programming."

[1458] The server performs analysis using a generative model and suggests a "software developer."

[1459] The emotion engine determines the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly.

[1460] Related job information is also retrieved from the database and displayed.

[1461] 2. Examples of career advice provided:

[1462] A user asks, "How do I become a software developer?"

[1463] The server provides the necessary skills (e.g., programming languages, understanding of algorithms, etc.).

[1464] The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy and anxiety.

[1465] For example, if the user is feeling anxious, the plan will be presented in more detail.

[1466] 3. Industry Trends and Market Demand Analysis Examples:

[1467] A user requests, "I want to know about the future outlook for the IT industry."

[1468] The server provides the analysis results, stating, "The fields of AI and machine learning are expected to continue to grow."

[1469] The emotion engine analyzes the user's emotions at that time and adjusts the presentation method by, for example, emphasizing positive information.

[1470] Prompt Sentence Examples

[1471] "I want to find a new career"

[1472] "Tell me about your interests and skills"

[1473] I like reading and programming.

[1474] "How do I become a software developer?"

[1475] "I want to know the future outlook for the IT industry."

[1476] As described above, this system provides comprehensive career selection support to users and offers concrete means to support their career success. In addition, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[1477] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1478] Step 1:

[1479] The user accesses the employment and career change support service from the terminal and logs in.

[1480] Input: User ID and password.

[1481] Data processing / calculation: The server passes the received user ID and password to the authentication system and performs hash matching.

[1482] Output: Authentication result (success / failure).

[1483] Specific operation: If authentication is successful, the server generates a home screen and sends it to the device. If authentication fails, an error message is displayed.

[1484] Step 2:

[1485] The user selects the "Career Diagnosis" feature on the home screen and begins a dialogue through the chatbot interface.

[1486] Input: User's selection of the "Career Assessment" feature.

[1487] Data processing / computation: The server receives the user's selection and initializes the chatbot interface.

[1488] Output: Display of the chatbot interface.

[1489] Specific operation: The server sends the UI component to the device, and the chatbot displays a message prompting the user to "Tell us about your interests and skills."

[1490] Step 3:

[1491] Users input their interests, abilities, and values ​​into the chatbot.

[1492] Input: User's answer (e.g., "I like reading" or "I have programming experience").

[1493] Data processing / calculation: The server passes the response data to the generative model and performs natural language processing.

[1494] Output: Analysis results (profile of user interests, abilities, and values).

[1495] Specific operation: The server temporarily stores the analysis results obtained from the generative model, and the chatbot displays the next question.

[1496] Step 4:

[1497] A generative model analyzes the user's answers and makes career suggestions.

[1498] Input: User response data passed to the generative model.

[1499] Data processing / computation: The generative model applies natural language processing techniques and analyzes the answers based on the career suggestion algorithm.

[1500] Output: A list of occupations based on the analysis results.

[1501] Specific operation: The server receives the analysis results of the generative model and presents a list of occupations to the user through the chatbot.

[1502] Step 5:

[1503] The emotion engine analyzes the user's emotional state and feeds the analysis results back to the generative model.

[1504] Input: User interaction.

[1505] Data processing / calculation: The emotion engine analyzes the dialogue content and determines the emotional state (e.g., joy, anxiety).

[1506] Output: Emotion analysis results.

[1507] Specific operation: The server feeds back the emotion analysis results to the generative model and adjusts it to optimal career suggestions.

[1508] Step 6:

[1509] Providing relevant job information based on career suggestions.

[1510] Input: Best Occupation List.

[1511] Data processing / calculation: The server extracts relevant job information from the database.

[1512] Output:Related job listings.

[1513] Specific operation: The server sends the job information to the terminal and displays it to the user.

[1514] Step 7:

[1515] When users seek career advice, they provide detailed skills and qualifications.

[1516] Input: User's career advice request.

[1517] Data processing / calculation: The server retrieves the required skills and qualifications from the database.

[1518] Output: Skills and qualifications.

[1519] Specific operation: The server adjusts the acquired information based on the emotion engine and displays appropriate advice to the user.

[1520] Step 8:

[1521] The career plan is concretized and presented to the user.

[1522] Input: Information needed to choose a career.

[1523] Data processing / calculation: The server generates a list of learning plans and training programs and organizes them into a timeline format.

[1524] Output: A concrete career plan.

[1525] Specific operation: The server sends the course plan to the terminal and displays it to the user. The emotion engine also adjusts it.

[1526] Step 9:

[1527] Provides analysis of industry trends and market demand.

[1528] Input: User request for trend information.

[1529] Data processing / calculation: The server collects and analyzes market reports and statistical information to predict future supply and demand.

[1530] Output: Market trend analysis results.

[1531] Specific operation: The server generates the latest analysis results, adjusts them using the emotion engine, sends them to the device, and displays them to the user.

[1532] (Application example 2)

[1533] 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."

[1534] Modern society requires diverse career choices and career support, but there is a lack of systems that make appropriate career suggestions that are tailored to each user's interests, abilities, and values. There is also a need for services that take into account the user's emotional state, but current systems do not adequately achieve this. Furthermore, there are few systems that provide comprehensive, specific career plans and market trend analysis linked to career suggestions. Therefore, there is a need for a system that analyzes users' daily behavioral data, such as their purchasing history and product browsing history, to provide more personalized, emotionally sensitive career support.

[1535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1536] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job-seeking opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing the user's interests, abilities, and values ​​based on purchase history and product browsing history, and means for analyzing the user's emotional state and adjusting the content of suggestions and screen display according to the emotional state. This makes it possible to make occupation suggestions and career support based on the user's personal data, and to provide detailed consulting that takes the user's emotional state into consideration.

[1537] A "generative model" refers to an algorithm that suggests suitable occupations based on information such as a user's interests, abilities, and values.

[1538] "Career suggestions" refers to presenting appropriate occupations and career paths to users based on the results of analysis by the generative model.

[1539] "Job Opportunities" refers to the provision of job postings or employment opportunities related to the proposed occupation.

[1540] "Career planning" refers to the specific learning plan or training program that will lead a user to their chosen career.

[1541] "Support for capacity building" refers to providing assistance to users to acquire the skills and knowledge necessary for their chosen occupation.

[1542] "Industry and Market Trends" refers to trends and changes in specific industry sectors and markets.

[1543] "Purchase history" refers to a record of products and services purchased by a user in the past.

[1544] "Product browsing history" refers to the record of products a user has viewed on an online shopping site or app.

[1545] "Emotional state" refers to the psychological state, such as joy, anxiety, or sadness, that a user feels at a given moment.

[1546] "Analysis" refers to the process of analyzing collected data using generative models and sentiment analysis engines to derive meaning from it.

[1547] "Screen display" refers to visually presenting the analyzed results to the user through a user interface.

[1548] "System" refers to the computer-based infrastructure that integrates and operates the various means mentioned above.

[1549] This invention is a system that provides an integrated service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. This system is realized by combining a generative model, an emotion engine, a user interface, and a database.

[1550] System Configuration

[1551] The system consists of the following elements:

[1552] Generative Model

[1553] Emotion Engine

[1554] User Interface

[1555] Database

[1556] Program processing

[1557] User Access and Data Collection

[1558] Users access the system using a smartphone. When the user logs in, the application on the device collects their purchase history and product browsing history and sends it to the server. This data collection is done using a REST API.

[1559] Analysis using generative models

[1560] The server inputs the collected data into a generative model to analyze the user's interests, abilities, and values. This analysis is performed using natural language processing technology, specifically using Hugging Face's Transformers. The generative model then uses the analysis results to suggest the most suitable occupation.

[1561] Analysis by emotion engine

[1562] Furthermore, the emotion engine analyzes the user's emotional state in real time, using tools such as Luxand's Face SDK. Based on the user's emotional state, the suggestions and screen display are adjusted. For example, if the user is feeling anxious, more detailed and thoughtful explanations are displayed.

[1563] Providing job opportunities and career planning

[1564] Job information related to the occupations suggested by the generative model is retrieved from a database and displayed to the user. The learning paths and training programs required for the occupation selected by the user are also presented, along with specific career planning and skill development support.

[1565] Industry and market trend analysis

[1566] The server periodically collects market data and analyzes industry and market trends. The analysis results are provided to users to help them make career choices. This analysis utilizes statistical information and publicly available market reports.

[1567] Specific examples

[1568] For example, if a user "frequently purchases tech-related products," the generative model analyzes that data and suggests the occupation "software developer." The emotion engine analyzes the user's emotions at the time and adjusts the suggestions based on their level of joy or anxiety.

[1569] Example prompt sentence:

[1570] Purchase history: I frequently purchase tech-related products, programming books, gadgets, etc. What is your suitable occupation?

[1571] This system allows users to receive career suggestions based on their personal data, and also provides detailed consulting that takes into account their emotional state.

[1572] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1573] Step 1:

[1574] The user accesses the system from a smartphone.

[1575] Input: User login information (ID, password)

[1576] Output: User authentication result

[1577] The server receives the user's login information and performs authentication. If authentication is successful, the home screen is displayed.

[1578] Step 2:

[1579] The user's purchase history and product browsing history are collected and sent to the server.

[1580] Input: Purchase history and product browsing history data

[1581] Output: Send data to the server

[1582] The device collects data on products the user has previously purchased or viewed and sends it to the server. Specifically, it creates a data set containing information such as product category, purchase date, and viewing time, and sends it using a REST API.

[1583] Step 3:

[1584] The server inputs the submitted data into a generative model to analyze the user's interests, abilities, and values.

[1585] Input: Purchase history and product browsing history data

[1586] Output: Analysis results based on the user's interests, abilities, and values

[1587] The server inputs the received data into a generative model (such as Hugging Face's Transformers) and processes the data using natural language processing techniques. As a result of the analysis, it generates a list of suitable occupations.

[1588] Step 4:

[1589] An emotion engine is used to analyze the user's emotional state.

[1590] Input: User's facial image or voice data

[1591] Output: Emotional state analysis results

[1592] To analyze the user's emotional state in real time, the device uses a camera and microphone to capture facial images or voice data and sends them to a server. The server then analyzes the emotional state using software such as Luxand Face SDK and outputs the degree of emotion as a numerical value.

[1593] Step 5:

[1594] The server integrates the analysis results from the generative model and the results from the emotion engine to make appropriate career suggestions.

[1595] Input: Analysis results of the generative model, analysis results of the emotion engine

[1596] Output: Adjusted list of career suggestions

[1597] The server integrates data from the generative model and the emotion engine to generate a list of career suggestions based on the user's emotional state. For example, if the user feels anxious, the server adjusts the list by adding more specific and detailed descriptions.

[1598] Step 6:

[1599] Job listings related to the suggested occupation are retrieved from a database and displayed to the user.

[1600] Input: Career suggestion list

[1601] Output: List of job postings

[1602] The server searches the database for job listings related to the proposed occupation and transmits the acquired job listings to the terminal, which visually displays them to the user.

[1603] Step 7:

[1604] Provide users with the learning paths and specific career plans required for their chosen career.

[1605] Input: User's occupation choice

[1606] Output: Learning path, career plan

[1607] The server retrieves the skills and qualifications required for the job selected by the user from a database and generates a learning path or training program, which is displayed in a timeline format.

[1608] Step 8:

[1609] Analyze industry and market trends and provide the results to users.

[1610] Input: Market Data

[1611] Output: Market trend analysis results

[1612] The server analyzes industry and market trends based on regularly collected market data, using statistical information and publicly available market reports, and presents the analysis results to users to help them make career choices.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] [Fourth embodiment]

[1617] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1618] 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.

[1619] 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).

[1620] 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.

[1621] 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.

[1622] 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).

[1623] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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."

[1630] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[1631] System Overview

[1632] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[1633] Program processing

[1634] Career diagnosis chatbot

[1635] User access and interaction initiation:

[1636] The user accesses the employment and career change support service from their device, logs in, and selects the career diagnosis function. The server connects to the generative AI model, generates a chatbot interface, and presents it to the user. The user inputs information about their interests, abilities, and values ​​in response to the chatbot's questions.

[1637] Analysis and career suggestions using generative models:

[1638] The server analyzes the user's answers using a generative AI model to suggest the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[1639] Career Advice and Planning

[1640] Providing career advice:

[1641] If the user is interested in the suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. The learning plan includes information about online courses and training programs.

[1642] Implementing your career plan:

[1643] The server compiles the user's learning plan into a detailed course plan and presents it in the form of a timeline, which specifies the goals and necessary resources for each step.

[1644] Industry trends and market demand analysis

[1645] Data collection and analysis:

[1646] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[1647] Providing analysis results:

[1648] If a user wants to find trend information for a particular industry or occupation, the server generates and provides the analysis results to the user, allowing the user to consider careers based on market demand.

[1649] Specific examples

[1650] Career Assessment Chatbot Use Cases:

[1651] When a user types "I want to find a new career" into the chatbot, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes this using a generative AI model and suggests "software developer." It also retrieves related job information from a database and displays it to the user.

[1652] Examples of career advice provided:

[1653] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[1654] Industry trends and market demand analysis examples:

[1655] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[1656] In this way, the system provides users with comprehensive career choice support and provides specific means to support career success.

[1657] The processing flow will be explained below.

[1658] Specific processing flow of the career diagnosis chatbot program

[1659] Processing flow

[1660] Step 1:

[1661] The user accesses the employment and career change support service from their device and enters their login information.

[1662] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[1663] Step 2:

[1664] The server authenticates the user and displays the home screen.

[1665] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[1666] Step 3:

[1667] The user selects the "Career Diagnosis" function on the home screen.

[1668] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[1669] Step 4:

[1670] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[1671] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[1672] Step 5:

[1673] The user answers the chatbot's question (e.g., "I like reading and programming").

[1674] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[1675] Step 6:

[1676] The server analyzes the user's answers using a generative AI model.

[1677] How it works: The server inputs the user's answers into a natural language processing engine, and passes the analysis results to a generative model, which then calculates the best possible job candidates.

[1678] Step 7:

[1679] The server will suggest the best career aptitudes.

[1680] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[1681] Step 8:

[1682] The server displays job listings related to the proposed occupation.

[1683] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[1684] Career Advice and Planning Program Process

[1685] Processing flow

[1686] Step 1:

[1687] If the user is interested in the careers presented, they will ask for detailed career advice.

[1688] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[1689] Step 2:

[1690] The server retrieves the skills and qualifications required for the user's career choice from a database.

[1691] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[1692] Step 3:

[1693] The server generates the lesson plan.

[1694] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[1695] Step 4:

[1696] The server will then concretely present the course plan.

[1697] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[1698] Specific processing flow of the industry trend and market demand analysis program

[1699] Processing flow

[1700] Step 1:

[1701] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[1702] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[1703] Step 2:

[1704] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[1705] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[1706] Step 3:

[1707] The server generates the analysis results.

[1708] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result.

[1709] Step 4:

[1710] The server presents the analysis results to the user.

[1711] Operation: The generated analysis results are compiled into a report, HTML code is generated to display in the user interface, and the report is sent to the terminal.

[1712] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[1713] Example 1

[1714] 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."

[1715] Conventional career selection support systems have had difficulty suggesting appropriate careers based on the user's interests, abilities, and values. They also have had issues with providing effective career advice, specific career plans, and demand forecasts that reflect industry and market trends. The purpose of this invention is to solve these issues and provide optimal career selection support for users.

[1716] 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.

[1717] In this invention, the server includes means for using a generative model to suggest careers based on the user's interests, abilities, and values, means for analyzing the user's responses using a generative AI model and suggesting appropriate careers, means for generating a study plan related to the skills and qualifications required when the user requests career advice, and means for analyzing collected data on industry and market trends and generating trend information. This allows the user to receive career suggestions based on their individual abilities and interests, receive specific career advice and career plans, and obtain information that reflects the latest industry and market trends.

[1718] A "generative model" is a type of artificial intelligence that makes predictions and suggestions based on user input data, and has functions such as document generation, data analysis, and job recommendations.

[1719] "Interest" refers to a user's interest or curiosity in a particular thing or activity.

[1720] "Ability" refers to the skills, knowledge, and abilities that a user possesses to perform a specific task or work.

[1721] "Values" refers to the basic ideas and standards that serve as the basis for users' beliefs and choices of behavior.

[1722] "Means" refer to the methods or processes used to achieve a particular goal.

[1723] "Job Opportunities" refers to job postings and employment opportunities related to occupations that interest users.

[1724] A "career plan" is a plan that specifies the steps and actions a user needs to take to reach a specific career.

[1725] "Competence development" refers to the process by which clients improve the skills and knowledge required for a particular occupation.

[1726] "Support" refers to the support and assistance provided to users to achieve their goals.

[1727] An "industry" is a part of economic activity that produces or provides a particular product or service.

[1728] A "market" refers to an economic venue or area where goods and services are bought and sold.

[1729] "Trends" refers to the current situation and future direction in a particular field or area.

[1730] "Data" refers to information that is collected, stored and processed for a specific purpose.

[1731] "Analysis" refers to the investigation or processing of data for the purpose of detailed examination or evaluation.

[1732] "User interface" refers to the means or method by which a user and a system interact with each other to exchange information.

[1733] A "learning plan" refers to a set of learning activities and schedules designed to acquire specific skills or knowledge.

[1734] "Trend information" refers to information about the latest developments and trends in a particular industry or market.

[1735] "Answer" refers to the information a user enters in response to a question or prompt from the system.

[1736] The present invention relates to a system that uses generative models to suggest optimal occupations based on a user's interests, abilities, and values, provides job information, provides career advice and path planning, and analyzes industry and market trends. Specific embodiments for implementing the present invention are described below.

[1737] System Overview

[1738] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, a career advice and path planning function, and an analysis function for industry trends and market demand. These functions are realized through server-side processing and a user interface on the terminal side.

[1739] Career diagnosis chatbot

[1740] When a user logs in from their device and selects the career diagnosis function, the server connects to a generative AI model (e.g., OpenAI GPT-4) and generates a chatbot interface. The user inputs information about their interests, abilities, and values ​​in response to questions from the chatbot. The server then proceeds with a dialogue with the user as follows:

[1741] Example prompt sentence:

[1742] User: "I want to find a new career."

[1743] Server: "What are your hobbies?"

[1744] User: "I like reading and programming."

[1745] The server then uses a generative AI model to analyze the user's answers and suggest "software developer" as the most suitable occupation. It also retrieves related job information from a database and displays it to the user.

[1746] Career Advice and Planning

[1747] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a study plan based on that information. For example, if the user asks, "What should I do to become a software developer?" the server will respond, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information on online courses and training programs. The server then compiles the generated study plan into a specific career plan and presents it in the form of a timeline. The career plan will specify the goals and necessary resources for each step.

[1748] Industry trends and market demand analysis

[1749] The server regularly collects data on industry and market trends and analyzes them using a proprietary analytical algorithm. The data is extracted from publicly available market reports and statistical information. When a user wants to research trend information for a specific industry or occupation, the server generates analytical results and provides them to the user. For example, if a user requests, "I want to know the future outlook for the IT industry," the server will provide the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path toward becoming an AI engineer.

[1750] Hardware or software used

[1751] The server uses high-performance hardware such as NVIDIA GPUs and generative AI models. The terminal provides a user interface through a browser or dedicated application. It also uses database management systems and online education platform APIs such as MySQL, PostgreSQL, Coursera, and Udemy to obtain information. Furthermore, it uses scraping tools and big data processing platforms (e.g., Beautiful Soup, Scrapy, Apache Hadoop, and Spark) for data collection and analysis.

[1752] In this way, the server, terminal, and user each play their own role, and the entire system works together to provide comprehensive career selection support.

[1753] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1754] Step 1:

[1755] A user accesses the system from a terminal and logs in.

[1756] Input: User login information (username, password).

[1757] Data processing and calculation: The terminal sends the login information to the server, which then checks it against a database for authentication.

[1758] Output: Login success message, access to occupational diagnostic function.

[1759] Specific operation: The device launches a website or app, enters a username and password on the login screen, and after successful authentication, the menu screen is displayed.

[1760] Step 2:

[1761] The user selects the career assessment function.

[1762] Input: User's choice of occupational diagnostic function.

[1763] Data processing and calculation: The device sends the selected information to the server, which connects it to the generated AI model and generates the chatbot interface.

[1764] Output: Initial message from the career assessment chatbot.

[1765] What happens: The user clicks the "Start Career Assessment" button and the chatbot asks, "What are your hobbies?"

[1766] Step 3:

[1767] The user answers the chatbot's questions.

[1768] Input: Information about the user's interests, abilities, and values.

[1769] Data processing and calculation: The server collects user input and inputs it as a prompt to the generative AI model, which then performs analysis.

[1770] Output: Career suggestions and related job listings.

[1771] Specific behavior: The user responds, "I like reading and programming," and the server suggests "Software Developer" and also displays related job listings.

[1772] Step 4:

[1773] A user seeks career advice regarding a suggested occupation.

[1774] Input: User's career advice question.

[1775] Data processing and calculation: The server retrieves the necessary skills and qualifications from the database, analyzes them using a generative AI model, and generates a learning plan.

[1776] Output: Learning plan and training program information.

[1777] What it does: A user types, "What do I need to do to become a software developer?" The server responds, "You need Python or Java skills," and provides a link to an online course.

[1778] Step 5:

[1779] The server compiles the user's study plan into a career plan.

[1780] Enter: Study Plan.

[1781] Data processing and calculation: The server organizes the learning plan in a timeline format, specifying the goals and required resources for each step.

[1782] Output: A timeline of your career plan.

[1783] What it does: The server displays a timeline like "Learn Python within 3 months, then learn Java."

[1784] Step 6:

[1785] The server periodically collects and analyzes data on industry and market trends.

[1786] Input: Public market reports and statistics.

[1787] Data processing and calculation: The server collects data using a scraping tool and analyzes it on the big data processing platform.

[1788] Output: Industry and market trend information.

[1789] What it does: The server collects public market reports every month, analyzes them using Apache Spark, and stores the trend information in a database.

[1790] Step 7:

[1791] A user requests trending information for a particular industry or occupation.

[1792] Input: User's request for trend information.

[1793] Data processing and calculation: The server retrieves the analysis results from the database and generates the necessary information.

[1794] Output: Analysis results and trend information.

[1795] Specific operation: The user requests, "I want to know the future outlook for the IT industry," and the server presents the analysis result, such as, "The fields of AI and machine learning will grow."

[1796] In this way, the entire system works together according to the user's needs, providing comprehensive support for career choices, career advice, career planning, and industry trend analysis.

[1797] (Application example 1)

[1798] 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."

[1799] In modern society, many people face the challenge of finding the most suitable occupation based on their aptitudes and interests. Furthermore, it is difficult to obtain information on appropriate career plans and market demand, creating many barriers to career development. Furthermore, existing career diagnosis systems have limited user interfaces and lack a way to visually display career diagnosis information and career plans received in real time.

[1800] 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.

[1801] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing industry and market trends and providing the analysis results to the user, and means for displaying the career diagnosis results and career plans in an interactive format using smart glasses, thereby enabling the user to select an occupation according to their aptitude and obtain specific career plans and market trend information in real time.

[1802] A "generative model" is an artificial intelligence algorithm that generates new data and predictions based on user-provided information.

[1803] "Career suggestions" are the act of determining the most suitable occupation based on the user's interests, abilities, and values, and presenting it to the user.

[1804] "Job opportunities" refers to job information provided to users for employment or career changes.

[1805] A "career plan" is a plan that includes specific steps and a timeline for how a client will reach their chosen career.

[1806] "Support for capacity building" refers to the provision of learning paths and training programs that enable users to acquire the skills and knowledge required for a specific occupation.

[1807] "Industry and Market Trends" is information about current and future market trends and demands in various industries.

[1808] "Analysis results" refer to the specific conclusions and findings of the analysis conducted based on the collected data.

[1809] "Smart glasses" are wearable devices used to display information, and are eyeglass-type devices that have the ability to display visual data in an interactive format.

[1810] "Dialogue" refers to a method of communication in which the user and the system exchange information in a two-way manner.

[1811] "Career diagnosis results" refer to career suggestions provided based on the user's characteristics through a generative model.

[1812] In order to implement the present invention, it is necessary to construct a system including the following steps.

[1813] System Overview

[1814] The system includes a career diagnosis chatbot that uses generative models to suggest careers based on the user's characteristics, career advice and path planning functions, and analysis of industry trends and market demands. These functions are realized through server-side processing and a user interface using smart glasses.

[1815] Program processing flow and hardware used

[1816] Career diagnosis chatbot

[1817] User access and interaction initiation:

[1818] The user uses smart glasses to access the employment and career change support service and selects the career diagnosis function. Using the smart glasses' HUD (head-up display), the server displays a chatbot interface based on the generative AI model and begins a dialogue with the user. The user inputs information about their interests, abilities, and values ​​by answering the chatbot's questions.

[1819] Analysis and career suggestions using generative models:

[1820] The server analyzes the user's answers using a generative model and suggests the most suitable occupation for the user. For each occupation, the server also provides related job opportunities.

[1821] Career Advice and Planning

[1822] Providing career advice:

[1823] If the user is interested in a suggested career, they can request career advice. The server retrieves the skills and qualifications required for the user's career choice from a database and generates a learning plan based on that information. This learning plan includes information about online courses and training programs.

[1824] Implementing your career plan:

[1825] The server compiles the user's learning plan into a detailed course plan, which is displayed in a timeline format on the smart glasses' HUD. The course plan specifies the goals and required resources for each step.

[1826] Industry trends and market demand analysis

[1827] Data collection and analysis:

[1828] The server periodically collects data on industry and market trends and analyzes them using proprietary analytical algorithms. The data is extracted from publicly available market reports, statistics, and other sources.

[1829] Providing analysis results:

[1830] When a user wants to find out trend information for a particular industry or occupation, the server will generate analysis results and provide them to the user through the smart glasses, allowing the user to consider careers based on market demand.

[1831] Specific examples of functions

[1832] Career Assessment Chatbot Use Cases:

[1833] When a user types "I want to find a new career" into the smart glasses, the server asks, "What are your hobbies?" If the user answers, "I like reading and programming," the server analyzes the answer with a generative model and suggests "software developer." It also retrieves related job listings from a database and displays them to the user.

[1834] Examples of career advice provided:

[1835] When a user asks, "What do I need to do to become a software developer?", the server responds, "The skills required are programming languages ​​(Python, Java) and an understanding of algorithms." It also provides information about online courses and training programs.

[1836] Industry trends and market demand analysis examples:

[1837] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." Based on this, the user can plan their career path to become an AI engineer.

[1838] Examples of prompt statements

[1839] Suggest the best career for you based on your interests, abilities, and values:

[1840] Interests: I like reading and programming

[1841] Ability to use Python and Java

[1842] Values: I want to do creative work

[1843] In this way, the system provides users with comprehensive career selection assistance and allows real-time feedback through the smart glasses.

[1844] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1845] Step 1:

[1846] To begin using the service, users access the employment and career change support service using smart glasses and select the career diagnosis function. The career diagnosis chatbot interface will then be displayed on the smart glasses' HUD.

[1847] Step 2:

[1848] By answering questions from the chatbot, users input information about their interests, abilities, and values, which is then sent to a server and used to analyze the generative AI model.

[1849] Step 3:

[1850] The server uses a generative AI model to analyze data entered by the user. Specifically, it analyzes information on interests, abilities, and values, and inputs prompts to the generative AI model to suggest the most suitable occupation. For example, a prompt such as "Interests: I like reading and programming. Abilities: I can use Python and Java. Values: I want to work in a creative field" is passed to the generative AI model. Based on this, the model generates data suggesting appropriate occupations. The output is the suggested occupations and related job information.

[1851] Step 4:

[1852] The server presents the user with suggested occupations derived from the generative AI model. The suggested occupation, for example, "Software Developer," is displayed on the smart glasses' HUD. Related job listings are also displayed simultaneously.

[1853] Step 5:

[1854] If the user is interested in the suggested careers, they can ask for career advice through the smart glasses. The user can ask, "What should I do to become a software developer?" and the question is sent to the server.

[1855] Step 6:

[1856] The server retrieves the required skills and qualifications from a database based on the user's questions, and generates a learning plan based on that information. For example, it prepares an answer such as "The required skills are programming languages ​​(Python, Java) and understanding of algorithms," and displays it on the smart glasses' HUD. It also provides information on related online courses and training programs.

[1857] Step 7:

[1858] The server then compiles the generated learning plan into a specific course plan, which is displayed in timeline format on the smart glasses' HUD. The plan specifies the goals and necessary resources for each step, allowing the user to learn and gain experience accordingly.

[1859] Step 8:

[1860] When a user wants to find out trend information about a particular industry or occupation, they send a request to the server through the smart glasses. For example, they might request, "I want to know the future outlook for the IT industry."

[1861] Step 9:

[1862] The server analyzes industry and market trend data collected periodically and generates analysis results based on the request, such as "The fields of AI and machine learning are expected to continue to grow," which is displayed on the smart glasses' HUD.

[1863] This will realize a system that allows users to select a career that suits their aptitude and obtain specific career plans and market trend information in real time.

[1864] 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.

[1865] The present invention uses a system that combines a generative model and an emotion engine to provide a unified service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. Specific embodiments for implementing the present invention are described below.

[1866] System Overview

[1867] This system is composed of a generative model, an emotion engine, a user interface, and a database. The generative model is responsible for suggesting the most suitable occupation based on the user's interests, abilities, and values, while the emotion engine analyzes the user's emotional state and reflects it in each function of the system.

[1868] Program processing

[1869] Career diagnosis chatbot

[1870] User access and interaction initiation:

[1871] The user accesses the employment and career change support service from their device and logs in. The server authenticates the user and displays the home screen. The user selects the "career diagnosis" function and begins a dialogue through the chatbot interface. The generative model collects information about the user's interests, abilities, and values, and gathers data to determine suggested careers.

[1872] Analysis and career suggestions using generative models

[1873] Analysis by generative model:

[1874] The server passes the user's input data to a generative model that then analyzes the user's responses using natural language processing techniques to generate a list of the most suitable occupations based on the user's interests, abilities, and values.

[1875] The role of the Emotion Engine:

[1876] The emotion engine analyzes the user's dialogue in real time to determine their emotional state. The results of the emotion engine's analysis are reflected in the generative model's analysis results, resulting in more precise career suggestions.

[1877] Job information provided by:

[1878] The server retrieves job opportunities (job information) related to the proposed occupation from the database and presents them to the user, who can use them to make further career choices.

[1879] Career Advice and Planning

[1880] Providing career advice:

[1881] If the user is interested in the suggested career, they can request detailed career advice. The server retrieves the skills and qualifications required for the user's career choice from a database. The emotion engine adjusts the advice content according to the user's emotional state.

[1882] Implementing your career plan:

[1883] Based on the acquired information, the server generates a specific career plan (study plan, training program list, etc.) and presents it to the user in a timeline format. The information provided by the emotion engine allows the server to provide the optimal plan according to the user's emotional state.

[1884] Industry trends and market demand analysis

[1885] Data collection and analysis:

[1886] The server periodically collects industry and market trend data, including publicly available market reports and statistics, and analyzes the trends using proprietary analytical algorithms.

[1887] Providing analysis results:

[1888] When a user wants to find trend information for a particular industry or profession, the server generates and provides the latest analysis results to the user, and the emotion engine adjusts the presentation of the analysis results based on the user's emotional state.

[1889] Specific examples

[1890] Career Assessment Chatbot Use Cases:

[1891] When a user types "I want to find a new career" into the chatbot, the server asks, "Tell me about your interests and skills." If the user responds, "I like reading and programming," the server analyzes the information using a generative model and suggests "software developer." At this point, an emotion engine distinguishes between the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly. Related job information is also retrieved from the database and displayed.

[1892] Examples of career advice provided:

[1893] When a user asks, "What should I do to become a software developer?", the server provides the necessary skills (e.g., programming languages, understanding algorithms, etc.). The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy or anxiety. For example, if the user is feeling anxious, it presents a more detailed plan.

[1894] Industry trends and market demand analysis examples:

[1895] When a user requests, "I want to know the future outlook for the IT industry," the server provides the analysis result, "The fields of AI and machine learning are expected to continue to grow." The emotion engine analyzes the user's emotions at that time and adjusts the presentation method, such as emphasizing positive information.

[1896] In this way, the system provides users with comprehensive career selection support and concrete means to support their career success. Furthermore, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[1897] The processing flow will be explained below.

[1898] Specific processing flow of the career diagnosis chatbot program

[1899] Processing flow

[1900] Step 1:

[1901] The user accesses the employment and career change support service from their device and enters their login information.

[1902] How it works: A user accesses a service's website using a browser and attempts to authenticate by entering their username and password on the login screen.

[1903] Step 2:

[1904] The server authenticates the user and displays the home screen.

[1905] How it works: The server checks the username and password against a database and, if authentication is successful, generates the HTML content for the home screen and sends it to the user's device.

[1906] Step 3:

[1907] The user selects the "Career Diagnosis" function on the home screen.

[1908] How it works: Click the "Career Assessment" button on the home screen. The browser detects the click event and sends the information to the server.

[1909] Step 4:

[1910] The server connects to the generative AI model, generates a chatbot interface, and presents it to the user.

[1911] How it works: The server calls the generative model's API to start a chatbot session, generating an initial message saying "Tell us about your interests and skills," and sending HTML code to display in the user interface.

[1912] Step 5:

[1913] The user answers the chatbot's question (e.g., "I like reading and programming").

[1914] How it works: The user types a response into the chat box and clicks the submit button. The browser sends the response data to the server.

[1915] Step 6:

[1916] The server analyzes the user's responses and emotional data using a generative AI model and emotion engine.

[1917] How it works: The server inputs the user's response into the natural language processing engine, passes the analysis results to the generative model, and then uses the emotion engine to analyze the user's emotional state and reflects it in the analysis results of the generative model.

[1918] Step 7:

[1919] The server will suggest the best career aptitudes.

[1920] How it works: Based on the occupational aptitude information obtained from the generative model, a text message suggesting suitable occupations for the user is generated and sent along with HTML code to be displayed in the user interface.

[1921] Step 8:

[1922] The server displays job listings related to the proposed occupation.

[1923] How it works: The server searches a database for job listings related to the proposed occupation, generates the results as HTML code to display in a user interface, and sends it.

[1924] Career Advice and Planning Program Process

[1925] Processing flow

[1926] Step 1:

[1927] If the user is interested in the careers presented, they will ask for detailed career advice.

[1928] How it works: The user clicks the "Get Career Advice" button on the suggested careers screen. The browser detects the click event and sends a request to the server.

[1929] Step 2:

[1930] The server retrieves the skills and qualifications required for the user's career choice from a database.

[1931] How it works: The server searches a database for and retrieves the skills and qualifications associated with the job.

[1932] Step 3:

[1933] The server generates the lesson plan.

[1934] What it does: Creates a learning plan based on information retrieved from the database, generates HTML code to display in the user interface, including information about online courses and training programs, and sends it.

[1935] Step 4:

[1936] The server will then concretely present the course plan.

[1937] What it does: It organizes the created learning plan into a timeline format and generates and sends HTML code to display the course plan in a user interface, including goals for each step and required resources.

[1938] Specific processing flow of the industry trend and market demand analysis program

[1939] Processing flow

[1940] Step 1:

[1941] The server regularly collects data on industry and market trends and analyzes the trends using proprietary analytical algorithms.

[1942] How it works: A server downloads data from data sources such as market reports and statistics, then feeds it into analytical algorithms to extract trend information.

[1943] Step 2:

[1944] When a user wants to find trending information for a particular industry or occupation, they submit a request.

[1945] How it works: A user enters the industry or occupation they want to research on the trend information search screen and clicks the "Search" button. The browser then sends that information to the server.

[1946] Step 3:

[1947] The server generates the analysis results.

[1948] How it works: Based on the user's request, the server extracts relevant information from the collected data and creates an analysis result. The emotion engine takes the user's emotional state into account to generate a customized analysis result.

[1949] Step 4:

[1950] The server presents the analysis results to the user.

[1951] Operation: The generated analysis results are compiled into a report, and HTML code for displaying them in a user interface is generated and sent to the device. The content and method of presentation are adjusted depending on the user's emotional state.

[1952] In this way, by describing the specific operations in detail at each processing step, it becomes easier for users to understand the flow of the system.

[1953] Example 2

[1954] 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."

[1955] While conventional career suggestion systems suggest careers based on users' interests and abilities, they are inadequate in taking into account their emotional state and adjusting their career plans. Furthermore, they struggle to analyze and provide market and industry trends, making it difficult for users to obtain specific future predictions. This makes it difficult for users to make appropriate career choices and develop career plans.

[1956] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1957] In this invention, the server includes means for using a generative model to suggest appropriate occupations based on the user's interests, abilities, and values, means for providing job opportunities related to the suggested occupations, means for providing specific career plans and ability improvement support to reach the user's selected occupation, means for analyzing industry and market trends and providing the user with the analysis results, and means for analyzing the user's emotional state using an emotion engine and adjusting the content of the career suggestions and career plans made by the generative model. This allows users to not only receive career suggestions based on their own interests, abilities, and values, but also receive optimal advice and career plans according to their emotional state at the time, and further enables them to obtain future predictions based on an understanding of market and industry trends.

[1958] A "generative model" is the part of the system that uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[1959] The "emotion engine" is a technology that analyzes the user's emotional state in real time and reflects the results of that analysis in career suggestions and career planning.

[1960] A "career plan" is a specific action plan that includes a list of study plans and training programs to help the user reach their chosen career.

[1961] "Job Opportunities" means job postings or employment opportunities related to the proposed occupation.

[1962] "Industry and Market Trends" is information that indicates current and future changes and trends in a particular industry or market.

[1963] "Analysis Results" refers to the data and conclusions derived by generative models, emotion engines, and other analytical algorithms.

[1964] "User" refers to an individual who uses the System to receive career suggestions and career plans.

[1965] The present invention is a system that combines a generative model and an emotion engine to provide career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state in an integrated manner.

[1966] System Configuration

[1967] The system includes the following components:

[1968] 1. Generative Model:

[1969] It uses machine learning algorithms to analyze user input data and suggest suitable careers based on interests, abilities, and values.

[1970] 2. Emotion Engine:

[1971] The user's emotional state is analyzed in real time, and the results of that analysis are reflected in career suggestions and career planning.

[1972] 3. User Interface:

[1973] The interface is designed to make it easy for users to interact with the system, allowing them to input and retrieve information in chatbot format.

[1974] 4. Database:

[1975] Store data on occupational information, job openings, skill acquisition information, and industry and market trends, and provide this data as needed.

[1976] Hardware and software used

[1977] Hardware: Servers, terminals

[1978] The servers use a cloud-based infrastructure.

[1979] Terminals include PCs and mobile devices.

[1980] Software: Generative AI models, natural language processing technology, sentiment analysis technology, database management systems

[1981] Use TensorFlow or PyTorch for generative models.

[1982] Use a natural language processing library such as the Natural Language Toolkit (NLTK) for your sentiment engine.

[1983] Use MySQL or PostgreSQL as your database management system.

[1984] Program processing

[1985] Specific examples

[1986] 1. Use cases for the career assessment chatbot:

[1987] A user types into a chatbot, "I want to find a new career."

[1988] The server asks, "Tell me about your interests and skills."

[1989] The user responds, "I like reading and programming."

[1990] The server performs analysis using a generative model and suggests a "software developer."

[1991] The emotion engine determines the user's emotions, such as joy or anxiety, and adjusts the suggestions accordingly.

[1992] Related job information is also retrieved from the database and displayed.

[1993] 2. Examples of career advice provided:

[1994] A user asks, "How do I become a software developer?"

[1995] The server provides the necessary skills (e.g., programming languages, understanding of algorithms, etc.).

[1996] The emotion engine analyzes the user's emotions and adjusts the advice content taking into account joy and anxiety.

[1997] For example, if the user is feeling anxious, the plan will be presented in more detail.

[1998] 3. Industry Trends and Market Demand Analysis Examples:

[1999] A user requests, "I want to know about the future outlook for the IT industry."

[2000] The server provides the analysis results, stating, "The fields of AI and machine learning are expected to continue to grow."

[2001] The emotion engine analyzes the user's emotions at that time and adjusts the presentation method by, for example, emphasizing positive information.

[2002] Prompt Sentence Examples

[2003] "I want to find a new career"

[2004] "Tell me about your interests and skills"

[2005] I like reading and programming.

[2006] "How do I become a software developer?"

[2007] "I want to know the future outlook for the IT industry."

[2008] As described above, this system provides comprehensive career selection support to users and offers concrete means to support their career success. In addition, the emotion engine analyzes and reflects the user's emotional state, providing services that are more tailored to individual needs.

[2009] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2010] Step 1:

[2011] The user accesses the employment and career change support service from the terminal and logs in.

[2012] Input: User ID and password.

[2013] Data processing / calculation: The server passes the received user ID and password to the authentication system and performs hash matching.

[2014] Output: Authentication result (success / failure).

[2015] Specific operation: If authentication is successful, the server generates a home screen and sends it to the device. If authentication fails, an error message is displayed.

[2016] Step 2:

[2017] The user selects the "Career Diagnosis" feature on the home screen and begins a dialogue through the chatbot interface.

[2018] Input: User's selection of the "Career Assessment" feature.

[2019] Data processing / computation: The server receives the user's selection and initializes the chatbot interface.

[2020] Output: Display of the chatbot interface.

[2021] Specific operation: The server sends the UI component to the device, and the chatbot displays a message prompting the user to "Tell us about your interests and skills."

[2022] Step 3:

[2023] Users input their interests, abilities, and values ​​into the chatbot.

[2024] Input: User's answer (e.g., "I like reading" or "I have programming experience").

[2025] Data processing / calculation: The server passes the response data to the generative model and performs natural language processing.

[2026] Output: Analysis results (profile of user interests, abilities, and values).

[2027] Specific operation: The server temporarily stores the analysis results obtained from the generative model, and the chatbot displays the next question.

[2028] Step 4:

[2029] A generative model analyzes the user's answers and makes career suggestions.

[2030] Input: User response data passed to the generative model.

[2031] Data processing / computation: The generative model applies natural language processing techniques and analyzes the answers based on the career suggestion algorithm.

[2032] Output: A list of occupations based on the analysis results.

[2033] Specific operation: The server receives the analysis results of the generative model and presents a list of occupations to the user through the chatbot.

[2034] Step 5:

[2035] The emotion engine analyzes the user's emotional state and feeds the analysis results back to the generative model.

[2036] Input: User interaction.

[2037] Data processing / calculation: The emotion engine analyzes the dialogue content and determines the emotional state (e.g., joy, anxiety).

[2038] Output: Emotion analysis results.

[2039] Specific operation: The server feeds back the emotion analysis results to the generative model and adjusts it to optimal career suggestions.

[2040] Step 6:

[2041] Providing relevant job information based on career suggestions.

[2042] Input: Best Occupation List.

[2043] Data processing / calculation: The server extracts relevant job information from the database.

[2044] Output:Related job listings.

[2045] Specific operation: The server sends the job information to the terminal and displays it to the user.

[2046] Step 7:

[2047] When users seek career advice, they provide detailed skills and qualifications.

[2048] Input: User's career advice request.

[2049] Data processing / calculation: The server retrieves the required skills and qualifications from the database.

[2050] Output: Skills and qualifications.

[2051] Specific operation: The server adjusts the acquired information based on the emotion engine and displays appropriate advice to the user.

[2052] Step 8:

[2053] The career plan is concretized and presented to the user.

[2054] Input: Information needed to choose a career.

[2055] Data processing / calculation: The server generates a list of learning plans and training programs and organizes them into a timeline format.

[2056] Output: A concrete career plan.

[2057] Specific operation: The server sends the course plan to the terminal and displays it to the user. The emotion engine also adjusts it.

[2058] Step 9:

[2059] Provides analysis of industry trends and market demand.

[2060] Input: User request for trend information.

[2061] Data processing / calculation: The server collects and analyzes market reports and statistical information to predict future supply and demand.

[2062] Output: Market trend analysis results.

[2063] Specific operation: The server generates the latest analysis results, adjusts them using the emotion engine, sends them to the device, and displays them to the user.

[2064] (Application example 2)

[2065] 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."

[2066] Modern society requires diverse career choices and career support, but there is a lack of systems that make appropriate career suggestions that are tailored to each user's interests, abilities, and values. There is also a need for services that take into account the user's emotional state, but current systems do not adequately achieve this. Furthermore, there are few systems that provide comprehensive, specific career plans and market trend analysis linked to career suggestions. Therefore, there is a need for a system that analyzes users' daily behavioral data, such as their purchasing history and product browsing history, to provide more personalized, emotionally sensitive career support.

[2067] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2068] In this invention, the server includes means for using a generative model to suggest suitable occupations based on the user's interests, abilities, and values, means for providing job-seeking opportunities related to the suggested occupations, means for providing specific career plans and support for improving skills to reach the occupation selected by the user, means for analyzing the user's interests, abilities, and values ​​based on purchase history and product browsing history, and means for analyzing the user's emotional state and adjusting the content of suggestions and screen display according to the emotional state. This makes it possible to make occupation suggestions and career support based on the user's personal data, and to provide detailed consulting that takes the user's emotional state into consideration.

[2069] A "generative model" refers to an algorithm that suggests suitable occupations based on information such as a user's interests, abilities, and values.

[2070] "Career suggestions" refers to presenting appropriate occupations and career paths to users based on the results of analysis by the generative model.

[2071] "Job Opportunities" refers to the provision of job postings or employment opportunities related to the proposed occupation.

[2072] "Career planning" refers to the specific learning plan or training program that will lead a user to their chosen career.

[2073] "Support for capacity building" refers to providing assistance to users to acquire the skills and knowledge necessary for their chosen occupation.

[2074] "Industry and Market Trends" refers to trends and changes in specific industry sectors and markets.

[2075] "Purchase history" refers to a record of products and services purchased by a user in the past.

[2076] "Product browsing history" refers to the record of products a user has viewed on an online shopping site or app.

[2077] "Emotional state" refers to the psychological state, such as joy, anxiety, or sadness, that a user feels at a given moment.

[2078] "Analysis" refers to the process of analyzing collected data using generative models and sentiment analysis engines to derive meaning from it.

[2079] "Screen display" refers to visually presenting the analyzed results to the user through a user interface.

[2080] "System" refers to the computer-based infrastructure that integrates and operates the various means mentioned above.

[2081] This invention is a system that provides an integrated service that includes career suggestions based on the user's interests, abilities, and values, job information, career advice, career planning, and consulting that takes into account the user's emotional state. This system is realized by combining a generative model, an emotion engine, a user interface, and a database.

[2082] System Configuration

[2083] The system consists of the following elements:

[2084] Generative Model

[2085] Emotion Engine

[2086] User Interface

[2087] Database

[2088] Program processing

[2089] User Access and Data Collection

[2090] Users access the system using a smartphone. When the user logs in, the application on the device collects their purchase history and product browsing history and sends it to the server. This data collection is done using a REST API.

[2091] Analysis using generative models

[2092] The server inputs the collected data into a generative model to analyze the user's interests, abilities, and values. This analysis is performed using natural language processing technology, specifically using Hugging Face's Transformers. The generative model then uses the analysis results to suggest the most suitable occupation.

[2093] Analysis by emotion engine

[2094] Furthermore, the emotion engine analyzes the user's emotional state in real time, using tools such as Luxand's Face SDK. Based on the user's emotional state, the suggestions and screen display are adjusted. For example, if the user is feeling anxious, more detailed and thoughtful explanations are displayed.

[2095] Providing job opportunities and career planning

[2096] Job information related to the occupations suggested by the generative model is retrieved from a database and displayed to the user. The learning paths and training programs required for the occupation selected by the user are also presented, along with specific career planning and skill development support.

[2097] Industry and market trend analysis

[2098] The server periodically collects market data and analyzes industry and market trends. The analysis results are provided to users to help them make career choices. This analysis utilizes statistical information and publicly available market reports.

[2099] Specific examples

[2100] For example, if a user "frequently purchases tech-related products," the generative model analyzes that data and suggests the occupation "software developer." The emotion engine analyzes the user's emotions at the time and adjusts the suggestions based on their level of joy or anxiety.

[2101] Example prompt sentence:

[2102] Purchase history: I frequently purchase tech-related products, programming books, gadgets, etc. What is your suitable occupation?

[2103] This system allows users to receive career suggestions based on their personal data, and also provides detailed consulting that takes into account their emotional state.

[2104] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2105] Step 1:

[2106] The user accesses the system from a smartphone.

[2107] Input: User login information (ID, password)

[2108] Output: User authentication result

[2109] The server receives the user's login information and performs authentication. If authentication is successful, the home screen is displayed.

[2110] Step 2:

[2111] The user's purchase history and product browsing history are collected and sent to the server.

[2112] Input: Purchase history and product browsing history data

[2113] Output: Send data to the server

[2114] The device collects data on products the user has previously purchased or viewed and sends it to the server. Specifically, it creates a data set containing information such as product category, purchase date, and viewing time, and sends it using a REST API.

[2115] Step 3:

[2116] The server inputs the submitted data into a generative model to analyze the user's interests, abilities, and values.

[2117] Input: Purchase history and product browsing history data

[2118] Output: Analysis results based on the user's interests, abilities, and values

[2119] The server inputs the received data into a generative model (such as Hugging Face's Transformers) and processes the data using natural language processing techniques. As a result of the analysis, it generates a list of suitable occupations.

[2120] Step 4:

[2121] An emotion engine is used to analyze the user's emotional state.

[2122] Input: User's facial image or voice data

[2123] Output: Emotional state analysis results

[2124] To analyze the user's emotional state in real time, the device uses a camera and microphone to capture facial images or voice data and sends them to a server. The server then analyzes the emotional state using software such as Luxand Face SDK and outputs the degree of emotion as a numerical value.

[2125] Step 5:

[2126] The server integrates the analysis results from the generative model and the results from the emotion engine to make appropriate career suggestions.

[2127] Input: Analysis results of the generative model, analysis results of the emotion engine

[2128] Output: Adjusted list of career suggestions

[2129] The server integrates data from the generative model and the emotion engine to generate a list of career suggestions based on the user's emotional state. For example, if the user feels anxious, the server adjusts the list by adding more specific and detailed descriptions.

[2130] Step 6:

[2131] Job listings related to the suggested occupation are retrieved from a database and displayed to the user.

[2132] Input: Career suggestion list

[2133] Output: List of job postings

[2134] The server searches the database for job listings related to the proposed occupation and transmits the acquired job listings to the terminal, which visually displays them to the user.

[2135] Step 7:

[2136] Provide users with the learning paths and specific career plans required for their chosen career.

[2137] Input: User's occupation choice

[2138] Output: Learning path, career plan

[2139] The server retrieves the skills and qualifications required for the job selected by the user from a database and generates a learning path or training program, which is displayed in a timeline format.

[2140] Step 8:

[2141] Analyze industry and market trends and provide the results to users.

[2142] Input: Market Data

[2143] Output: Market trend analysis results

[2144] The server analyzes industry and market trends based on regularly collected market data, using statistical information and publicly available market reports, and presents the analysis results to users to help them make career choices.

[2145] 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.

[2146] 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.

[2147] 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.

[2148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2149] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotio...

Claims

1. A method for using generative models to suggest suitable occupations based on the user's interests, abilities, and values; means for providing job search opportunities related to said proposed occupation; A means for providing specific career plans and support for improving skills to reach the career of the user; means for analyzing industry and market trends and providing the results of said analysis to users; A system including:

2. The system of claim 1 , wherein the generative model analyzes the user's responses and generates learning paths and career-related information appropriate for the proposed career.

3. The system of claim 1 , further comprising: providing an analysis result indicating a future demand forecast based on the industry and market trends.

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