system

Abstract

We provide the system. [Solution] A means of receiving input data from job seekers, Methods for obtaining data on past successes from a database, A method for identifying suitable occupations for job seekers by analyzing job seeker input data and successful candidate data using machine learning algorithms, Means of presenting suitable occupations to job seekers, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern job seekers have the problem that it is difficult to find the most suitable occupation for themselves and they have limited opportunities to receive accurate career selection support. With conventional career selection means, it is difficult to present occupations that suit an individual's characteristics, and there is no statistical and data-driven career proposal based on the cases of successful people. Therefore, the support for job seekers to find occupations that can make the most of their characteristics and skills is insufficient.

Means for Solving the Problems

[0005] To address these challenges, the present invention provides a system utilizing an AI agent that receives input data from job seekers and compares it with data from past successful candidates. This system has means to analyze the characteristics and skills of job seekers using machine learning algorithms and to make statistically accurate occupational aptitude judgments based on the data. Furthermore, by including functions to support job seekers in preparing to apply for selected occupations and means to provide occupational information through an interactive interface, it enables more effective career selection and preparation.

[0006] A "job seeker" refers to an individual who is trying to find a new job.

[0007] "Input data" refers to information provided by job seekers for self-assessment, and includes skills, interests, values, and experience.

[0008] "Success data" refers to information about the career history, skill sets, and characteristics of individuals who have achieved success in a particular profession.

[0009] A "database" refers to a management system that efficiently stores and searches large amounts of information and data.

[0010] A "machine learning algorithm" refers to a mathematical method used by computers to automatically learn patterns and rules from data.

[0011] "Analysis" refers to the process of systematically examining data and finding meaning and trends.

[0012] "Occupation" refers to a specific type of work or role that an individual engages in in order to earn a living.

[0013] An "interactive interface" refers to a two-way system screen that allows users to manipulate or input information.

[0014] "Statistical" refers to methods of performing numerical analysis and inference based on data.

[0015] "Occupation suggestion" refers to the act of indicating occupation options suitable for job seekers based on specific criteria.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The system according to the present invention provides support to job seekers in finding the most suitable occupation, and is implemented as follows.

[0038] The server maintains a database that collects and manages a large amount of data on the career histories, skill sets, and characteristics of individuals who have achieved success in the past. This data is categorized by occupation and includes the career paths and personal traits of successful individuals.

[0039] Users access this system to input information about their talents and interests. Specifically, they provide input data on their skills, experience, interests, and values ​​through surveys and skill tests.

[0040] The terminal sends data entered by the user to the server, which stores it in a central database. The server then uses an AI agent to process this data. The AI ​​agent analyzes the user's input data using machine learning algorithms and compares it with data from past successful users.

[0041] Based on this analysis, the server identifies occupations that match the user's characteristics based on statistical analysis. A list of identified occupations is generated along with the user's aptitude score and presented to the user via the terminal. For the presented occupations, the user can interactively view detailed information and receive assistance with the specific application procedures for the selected occupation.

[0042] For example, if a user uses the system and inputs their skills and interests, and the system suggests that a data science career is suitable, the server will provide detailed information, including success stories and career paths related to that profession. In this specific example, the user can use the provided information to consider a career in the field of data science. Furthermore, they can use the resume creation support function to prepare for applications. In this way, the present invention fully supports job seekers from efficiently finding and selecting a job that suits their characteristics to preparing for applications.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users create an account and log in to the system they access. Next, they answer specific questionnaires and skill tests to collect input data about their interests and skills.

[0046] Step 2:

[0047] The terminal transmits data entered by the user to the server in real time. This data includes information that forms the job seeker's profile, such as skills, experience, interests, and values.

[0048] Step 3:

[0049] The server accesses the database to store input data and retrieves occupational data about past successes. This includes their career paths, skills, and personal characteristics.

[0050] Step 4:

[0051] The server activates an AI agent and uses machine learning algorithms to perform comparative analysis of the user's input data against a dataset of successful individuals. Here, it identifies occupational patterns that match the user's characteristics.

[0052] Step 5:

[0053] Based on the analysis results, the server generates a list of occupations best suited to the job seeker's characteristics and calculates an aptitude score for each occupation.

[0054] Step 6:

[0055] The terminal displays a list of occupations and aptitude scores received from the server to the user. Based on this information, the user interactively displays detailed information about each occupation and selects their favorite or most interesting occupation.

[0056] Step 7:

[0057] The server will begin assisting users with job application preparation related to their chosen occupation. This includes generating resume templates and providing guidance on writing cover letters.

[0058] Step 8:

[0059] The terminal displays the provided application preparation information to the user, allowing the user to use it to create application documents and prepare for interviews.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Modern job seekers face the challenge of finding the most suitable occupation from a diverse range of options. In particular, the burden of selecting the right job to maximize their experience and skills, and the preparation of applications, are increasing. In this context, there is a need for support to help job seekers efficiently select occupations and prepare their applications.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for receiving input information from job seekers, means for obtaining information on past successful candidates from a recording device, and means for analyzing the job seeker's input information and the information on successful candidates using machine learning techniques to identify the most suitable job for the job seeker. This enables job seekers to efficiently find the job that best suits them and to smoothly proceed with preparing their application.

[0065] A "job seeker" refers to an individual who is looking for employment, specifically someone who is searching for the most suitable job based on their own abilities and interests.

[0066] "Input information" refers to data that job seekers provide to the system, summarizing personal characteristics related to the occupation, such as skills, experience, interests, and values.

[0067] A "successful person" refers to an individual who has already achieved results in a particular profession and whose experience and skills in that profession can serve as a reference for other job seekers.

[0068] The term "recording device" refers to any device used to store information, and in this invention, it specifically refers to electronically managed systems such as databases.

[0069] "Machine learning techniques" refer to a series of technologies and algorithms that enable computers to automatically learn patterns from data and use that knowledge to analyze and predict new data.

[0070] "Analysis" refers to the process of examining data in detail and finding meaning based on a specific purpose. In this invention, it refers to the analytical work aimed at comparing information on job seekers and successful individuals to identify the most suitable occupation.

[0071] "Job duties" refers to a professional position or responsibility that has a specific function or role, and is a category of work that a job seeker may be able to engage in.

[0072] This invention provides a support system for job seekers to find the most suitable occupation. The specific method for implementing the invention, primarily involving a server, terminal, and user, is described below.

[0073] The server plays a central role in aggregating and managing detailed information about occupations. Specifically, the server maintains a database, collecting, classifying, and storing information about the occupations of past successful individuals. This allows the database to store the career paths and personal characteristics of successful individuals categorized by occupation. The server regularly updates this data to keep it up-to-date.

[0074] Users access this system and enter information about their skills and interests. This data is sent to the server via web forms and applications. The input data includes skills, experience, interests, values, and more.

[0075] The terminal's role is to send user input to the server. This ensures that data entered by the user is stored on the server in real time, maintaining data integrity.

[0076] The server processes the received data using an AI agent. The AI ​​agent leverages powerful machine learning algorithms to analyze the characteristics of job seekers. This process uses techniques such as neural networks and decision trees to analyze the job seeker's data and compare it with information on past successful candidates. This analysis identifies the most suitable occupation for the job seeker and generates an aptitude assessment.

[0077] The results presented to the user include a list of the most suitable occupations for the job seeker, along with detailed information related to those occupations. For example, if the user's input into the system suggests that a data science occupation is suitable, the server will provide career information and success stories related to that occupation. Based on this information, the user can consider a career in the data science field and utilize features that support resume creation and application preparation.

[0078] As a concrete example of a prompt for the generative AI model, it is possible to input a question such as, "I am interested in data science, but what skills and career paths would lead to success?" Based on this prompt, the AI ​​model will assist the user by providing relevant information and advice.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users input information about their skills and interests. Specifically, they use a web form or application to answer various questions about their skills, experience, interests, and values, and send that information to their device. The input information is collected on the device and ready to be sent to the server.

[0082] Step 2:

[0083] The terminal sends the information entered by the user to the server. The transmitted data is sent in real time over the internet. The server verifies the integrity of the data upon receipt and checks for errors. The output of this step is the input information whose integrity has been verified.

[0084] Step 3:

[0085] The server stores the received input information in a recording device. This adds new data to the database, which is then used for subsequent processing. At this point, the output is the job seeker's input information stored in the database.

[0086] Step 4:

[0087] The server analyzes the stored input information using an AI agent. The AI ​​agent utilizes a generative AI model to analyze the received user data. This includes recognizing data patterns using a neural network and comparing information on past successful candidates with the job seeker's data. The output of this process is a job suggestion tailored to the job seeker, based on the analysis results.

[0088] Step 5:

[0089] The server generates an aptitude assessment based on the results obtained from the analysis. This assessment is generated using statistical methods and creates a list of occupations that best match the job seeker. The output is a list of occupations accompanied by the job seeker's aptitude score.

[0090] Step 6:

[0091] The server transfers the occupation list and aptitude assessment to the terminal. This presents the user with occupational information. The terminal interactively displays detailed information for each occupation presented to the user, allowing the user to view detailed information about their selected occupation. The output of this step is the occupation list and aptitude assessment displayed to the user.

[0092] Step 7:

[0093] Users view detailed information about presented occupations through interactive displays. They can browse success stories and required skills to deepen their understanding of their chosen occupation. At this stage, users can also utilize resume creation tools. The output of this step represents the user's acquired occupational knowledge and progress in application preparation.

[0094] Step 8:

[0095] Users prepare their applications through the system. They can utilize the resume creation support function to prepare application documents for their chosen occupation. This step leads to concrete application actions, ultimately preparing the job seeker to actually apply for the job. The output of this step is the completion and preparation of application documents.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] There is a need for methods that enable job seekers to efficiently find suitable occupations and smoothly prepare for applications. However, existing methods fail to adequately analyze the characteristics of job seekers, resulting in insufficient support for occupational selection and application.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for receiving input data from job seekers, means for obtaining data from past successful candidates from a storage device, and means for analyzing the job seeker's input data and the data from successful candidates using a machine learning algorithm to identify suitable occupations for the job seekers. This makes it possible to quickly identify appropriate occupations based on the characteristics of job seekers and efficiently support them from occupation selection to application preparation.

[0101] A "job seeker" refers to an individual who is looking for employment and is seeking information and support to find a suitable job.

[0102] "Means for receiving input data" refers to a device or function for taking in information about skills, experience, and interests provided by job seekers.

[0103] "Data on past successes" refers to information about the work history, skill sets, and characteristics of individuals who have achieved success in a particular profession, and is stored in a database.

[0104] A "storage device" refers to a storage device or system that continuously stores data and allows this data to be retrieved as needed.

[0105] A "machine learning algorithm" refers to a mathematical method that allows computers to learn from data and make predictions and decisions about the future.

[0106] "Analysis means" refers to a process or device for processing received input data from job seekers and data from past successful candidates to calculate the degree of match to a specific occupation.

[0107] "Means of identifying occupations" refers to functions and methods that use machine learning algorithms to determine the most suitable occupation for a job seeker.

[0108] "Display means" refers to a device or interface used to visually present information such as analysis results and job suggestions to job seekers.

[0109] An "interactive information terminal" refers to a digital device designed to allow job seekers to input information and respond to received information.

[0110] To implement this invention, the server must first receive input data from job seekers, retrieve data on past successful candidates from its storage device, and analyze it. The server is equipped with receiving means to receive information on the skills, experience, and interests provided by the user. The received data, along with the data on past successful candidates stored in the storage device, is analyzed using a machine learning algorithm. The TENSORFLOW® library can be used for this algorithm.

[0111] The AI ​​agent automatically analyzes the data, identifying the most suitable occupation for each job seeker through statistical analysis. The identified occupations are then calculated as an aptitude score and presented to the user. Users can visually receive this information on their smartphones or other interactive devices.

[0112] Furthermore, the platform provides features to help users view detailed information about occupations they are interested in and to assist them in the process of selecting an occupation. As a result, job seekers can find occupations that suit their characteristics and proceed efficiently from selection to application preparation.

[0113] For example, within a payment service app that users use daily, they can select a career assessment function and enter a prompt such as, "Based on my skills and interests, what kind of job would be suitable for me?" to find a suitable career.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] Users use a device to input data about their skills, experience, and interests. This input data provides detailed information about the user's characteristics and serves as an important basis for identifying their occupation in subsequent processing.

[0117] Step 2:

[0118] The terminal sends the entered user data to the server. The server stores the received data in its storage device and simultaneously retrieves data from past successful users from its storage device. This ensures that all the data necessary for analysis is prepared on the server side.

[0119] Step 3:

[0120] The server activates a machine learning algorithm to compare and analyze the received user data with data from past successful users. The TensorFlow library is used for data analysis, statistically matching patterns from past successful users with user characteristics. This process generates a list of suitable occupations for the user, along with an aptitude score for each occupation.

[0121] Step 4:

[0122] The server sends a list of generated occupations and aptitude scores to the user's device. The user's device displays the received information through an interactive user interface, allowing the user to visually determine which occupations are suitable for them.

[0123] Step 5:

[0124] Users can use their devices to view details about occupations that interest them. The server, in response to these requests, provides data to display detailed occupational information and success stories. Ultimately, this allows users to receive concrete support in preparing their applications for their chosen occupations.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] The system according to the present invention improves accuracy by considering the user's emotions in the process of suggesting the most suitable occupation to job seekers. This system incorporates an emotion engine to recognize the user's emotional state in real time and reflects it in job suggestions and application support.

[0127] The server receives input data from job seekers and stores it in a database. Next, the server utilizes an AI agent to identify the most suitable occupation for the job seeker based on data from past successful candidates. During this process, the emotion engine analyzes the user's facial expressions and voice tone through the device to understand their emotional state.

[0128] By recognizing the various emotions users experience during their job search, the system tailors the list of jobs it presents to their psychological needs. For example, if a user is feeling stressed, the server can prioritize suggesting jobs with lower stress levels.

[0129] The terminal presents the user with a list of occupations sent from the server, along with the results of the emotion engine's analysis. Through an interactive interface, the user can immediately view detailed information about the presented occupations. Furthermore, the emotion data is saved as user feedback and used to improve the accuracy of future suggestions.

[0130] For example, when a user is considering a career as a "creative director," if the program detects anxiety from the user's facial expressions or voice, it will provide a detailed explanation of the common challenges associated with that profession. In this way, the present invention makes it possible to provide nuanced career selection support that responds to the user's emotions.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user logs into the system and answers a self-assessment questionnaire. During this process, they turn on their camera and microphone and select settings that allow for the collection of emotional data.

[0134] Step 2:

[0135] The device sends the user's responses, along with facial expression data captured by the camera and audio data acquired from the microphone, to the server. This data is then processed by an emotion engine.

[0136] Step 3:

[0137] The server uses an AI agent to match the user's input data with a database of past success stories and generates a list of the most suitable occupations. Meanwhile, the emotion engine analyzes the transmitted emotion data and recognizes the user's emotional state in real time.

[0138] Step 4:

[0139] Based on the analyzed emotional data, the server adjusts job suggestions to take into account the user's mental stress level and interests. For example, if negative emotions are detected, it prioritizes presenting supportive information and low-stress occupations.

[0140] Step 5:

[0141] The terminal displays a list of occupations and related information received from the server to the user. An interactive interface allows the user to view details about each occupation and obtain further information about their selected occupation.

[0142] Step 6:

[0143] Based on user selections, the device sends emotional feedback back to the server to improve subsequent job suggestions and optimize application support. The emotional engine also provides information to enhance user motivation during the application preparation process.

[0144] Through this series of steps, the system can provide users with highly accurate career support that takes their emotions into account.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In conventional job placement systems, the job lists provided to job seekers were based solely on past data and lacked consideration for the job seeker's emotional state. As a result, it was difficult to provide appropriate job suggestions that met the job seeker's needs and psychological state, leading to decreased accuracy and convenience of suggestions.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for collecting characteristic information of job seekers, means for acquiring emotional information, and means for extracting information on past applicants from a data storage device. This enables highly accurate job recommendations based on the characteristics and emotions of job seekers.

[0150] A "job seeker" refers to an individual who is looking for employment and has specific career and job-related preferences.

[0151] "Characteristic information" refers to a collection of personal data about job seekers, including resume information, skills, and work history.

[0152] "Emotional information" refers to data that indicates the psychological state of a job seeker, and is extracted from things like facial expressions and tone of voice.

[0153] An "experienced professional" refers to an individual who has achieved success in a specific occupation in the past, and information about that individual's work history is stored in a data storage device.

[0154] A "data storage device" refers to a recording medium that stores information and allows data to be read out as needed.

[0155] "Machine learning techniques" refer to algorithms and methods that enable computers to learn patterns from data and perform predictions and classifications.

[0156] "Occupation" refers to the duties or job types that a job seeker may be able to perform, and the content of these duties will be specified by the information provided.

[0157] An "interactive display device" refers to a device that allows users to input information through an interface and receive results.

[0158] The present invention's system utilizes a combination of multiple components to provide job seekers with career suggestions that take their emotional information into account. This system aims to suggest suitable occupations to job seekers using a server, terminals, and AI technology.

[0159] The server collects characteristic information about job seekers in the initial stages. This characteristic information includes resume data, skills information, and past work history. The server also retrieves and analyzes data on past experienced individuals from its data storage device. Based on this data, the server uses machine learning techniques to identify suitable occupations for job seekers. In this process, the server's generative AI models are used to achieve highly accurate analysis.

[0160] The terminal is equipped with a device to collect the user's facial expressions and voice information in real time and transmit it to the server as emotional information. This uses a computer or smartphone with a camera and microphone. The terminal communicates data bidirectionally between the job seeker and the server, providing job suggestions that take emotional information into account.

[0161] As a concrete example, suppose a user is searching for a job and the device's camera detects anxiety from the user's facial expression. When this information is transmitted to the server, the server uses a generative AI model to present a list of jobs that take this emotional state into account. This list prioritizes jobs that are less stressful and more emotionally resonant.

[0162] An example of a prompt message would be, "Based on your current mental state, the following occupations are considered suitable for you. Would you like to view more information?" This allows job seekers to obtain occupational information that aligns with their emotions and needs.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server receives job seeker profile information as input. This is done when users enter resume data, skills, work history, etc., through an online form. The received data is immediately stored in a database. This data is used for subsequent processing.

[0166] Step 2:

[0167] The device collects user emotional information. This process utilizes the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time. The analyzed emotional data is sent to a server. Input is facial and voice data, while output is numerical data or labels indicating the emotional state.

[0168] Step 3:

[0169] The server extracts information on past applicants from its data storage. The extracted data is then analyzed using a machine learning algorithm. Here, the applicant's characteristic and emotional information are combined to create input data for recommending the most suitable occupation. The output is a list of suitable occupations.

[0170] Step 4:

[0171] The server uses a generative AI model to evaluate job suitability based on past success stories in the database and the characteristics and emotional information of job seekers. This algorithm takes into account the stress level and required skills for each job. As a result of the processing, a filtered list of jobs is generated and sent to the terminal.

[0172] Step 5:

[0173] The terminal presents the user with a list of occupations received from the server. It generates prompts based on the user's emotional state and provides an interactive display to help the user select the most suitable occupation. The user can then view details of the occupations based on the presented information. The terminal's output consists of the prompts and the occupation list interface.

[0174] Step 6:

[0175] Users provide feedback on the provided job information. This feedback is then sent back to the server and stored in the server's database. This information is used to improve the accuracy of future job suggestions. The output is the addition of the feedback to the database.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Conventional job suggestion systems propose jobs without considering the emotions or psychological state of job seekers, resulting in low emotional satisfaction and a lack of acceptance of the suggested jobs. Furthermore, because they cannot provide flexible job suggestions that utilize emotional data, it is difficult to match job seekers with appropriate jobs that meet their latent needs.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for receiving input data from job seekers, means for acquiring data from past successful candidates from a storage device, means for analyzing the input data from job seekers and the data from successful candidates using a machine learning algorithm to identify suitable occupations for job seekers, means for collecting and analyzing the emotional data of job seekers using an emotion analysis engine and reflecting the results in job suggestions, and means for presenting suitable occupations for job seekers. This enables flexible and highly accurate job suggestions that take into account the emotional state of job seekers.

[0181] "Job seeker input data" refers to information provided by individuals who are actively seeking employment, such as their work history and desired job type.

[0182] "Data on past successes" refers to information about the work history and experience of people who previously received job placement services and achieved success.

[0183] A "storage device" is a device that stores information over a long period of time and makes it possible to search and retrieve it as needed.

[0184] A "machine learning algorithm" is a computational method that allows a computer to recognize patterns based on provided data and make predictions and judgments about new data.

[0185] An "emotion analysis engine" is a software module that uses voice and image data to recognize and analyze a person's emotional state.

[0186] "Job recommendation" is a process that recommends suitable jobs to job seekers based on analyzed data.

[0187] An "interactive information processing screen" refers to a device that users can operate visually or tactilely, and that enables the provision and reception of information in real time.

[0188] "Facilitation function" refers to the functional characteristics of a system designed to facilitate specific activities or operations.

[0189] The server receives input data from job seekers and stores it in a database. The database contains data on past successful candidates, which the AI ​​agent utilizes. The AI ​​agent uses machine learning algorithms to analyze the job seeker's input data and the data on past successful candidates to identify the most suitable occupation for the job seeker. In this process, an emotion analysis engine monitors the job seeker's facial expressions and voice tone, collecting and analyzing emotional data. The resulting emotional state information is incorporated into job suggestions, generating a personalized list.

[0190] The terminal incorporates the results of sentiment analysis when presenting job seekers with a list of occupations received from the server. Users can view detailed occupation information through the terminal's interactive information processing screen. Furthermore, the job seeker's sentiment data is stored in the system as feedback and used to improve the accuracy of future job suggestions.

[0191] For example, when a user is considering a job as a "creative director," if the emotion analysis engine detects anxiety, the device will provide a detailed explanation of the common challenges of that profession. This is a function that allows the system to understand the user's choices and provide more in-depth information as needed.

[0192] An example of a prompt message is, "If the user's emotional state is stressed, please enhance job suggestions that involve low workplace pressure." This prompt helps to enhance job suggestions using a generative AI model.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server receives input data from job seekers. This input data includes information such as work history, desired job type, and skills. The received data is stored in a database and used in the next analysis step.

[0196] Step 2:

[0197] The server retrieves data on past successes from a database. This data includes the work history and suitable job types of the successful individuals. Based on this dataset, the server prepares to perform analysis using machine learning algorithms.

[0198] Step 3:

[0199] The machine learning algorithm installed on the server analyzes the job seeker's input data and past success data. Based on the acquired data, it performs pattern recognition and calculations to identify suitable occupations for the job seeker. As output, it generates a list of occupations with a high degree of fit.

[0200] Step 4:

[0201] The device captures the user's facial expressions and voice using a camera and microphone, and analyzes them in real time using an emotion analysis engine. The emotion data obtained through this process indicates the user's psychological state, which influences the career suggestions in the next step.

[0202] Step 5:

[0203] The server takes the analyzed sentiment data into account and uses a generative AI model to adjust job suggestions based on the prompt text. In this process, it flexibly adjusts suggestions by combining sentiment data with the job list. For example, if the user is feeling stressed, it prioritizes listing jobs with lower stress levels.

[0204] Step 6:

[0205] The terminal presents the user with a customized list of occupations on an interactive information processing screen. The user can view detailed information from the list and select a suitable occupation. An example of a prompt message is: "If the user's emotional state is stressed, please enhance suggestions for jobs with low workplace pressure."

[0206] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0222] The system according to the present invention provides support to job seekers in finding the most suitable occupation, and is implemented as follows.

[0223] The server maintains a database that collects and manages a large amount of data on the career histories, skill sets, and characteristics of individuals who have achieved success in the past. This data is categorized by occupation and includes the career paths and personal traits of successful individuals.

[0224] Users access this system to input information about their talents and interests. Specifically, they provide input data on their skills, experience, interests, and values ​​through surveys and skill tests.

[0225] The terminal sends data entered by the user to the server, which stores it in a central database. The server then uses an AI agent to process this data. The AI ​​agent analyzes the user's input data using machine learning algorithms and compares it with data from past successful users.

[0226] Based on this analysis, the server identifies occupations that match the user's characteristics based on statistical analysis. A list of identified occupations is generated along with the user's aptitude score and presented to the user via the terminal. For the presented occupations, the user can interactively view detailed information and receive assistance with the specific application procedures for the selected occupation.

[0227] For example, if a user uses the system and inputs their skills and interests, and the system suggests that a data science career is suitable, the server will provide detailed information, including success stories and career paths related to that profession. In this specific example, the user can use the provided information to consider a career in the field of data science. Furthermore, they can use the resume creation support function to prepare for applications. In this way, the present invention fully supports job seekers from efficiently finding and selecting a job that suits their characteristics to preparing for applications.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] Users create an account and log in to the system they access. Next, they answer specific questionnaires and skill tests to collect input data about their interests and skills.

[0231] Step 2:

[0232] The terminal transmits data entered by the user to the server in real time. This data includes information that forms the job seeker's profile, such as skills, experience, interests, and values.

[0233] Step 3:

[0234] The server accesses the database to store input data and retrieves occupational data about past successes. This includes their career paths, skills, and personal characteristics.

[0235] Step 4:

[0236] The server activates an AI agent and uses machine learning algorithms to perform comparative analysis of the user's input data against a dataset of successful individuals. Here, it identifies occupational patterns that match the user's characteristics.

[0237] Step 5:

[0238] Based on the analysis results, the server generates a list of occupations best suited to the job seeker's characteristics and calculates an aptitude score for each occupation.

[0239] Step 6:

[0240] The terminal displays a list of occupations and aptitude scores received from the server to the user. Based on this information, the user interactively displays detailed information about each occupation and selects their favorite or most interesting occupation.

[0241] Step 7:

[0242] The server will begin assisting users with job application preparation related to their chosen occupation. This includes generating resume templates and providing guidance on writing cover letters.

[0243] Step 8:

[0244] The terminal displays the provided application preparation information to the user, allowing the user to use it to create application documents and prepare for interviews.

[0245] (Example 1)

[0246] Next, we will describe Example 1. 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."

[0247] Modern job seekers face the challenge of finding the most suitable occupation from a diverse range of options. In particular, the burden of selecting the right job to maximize their experience and skills, and the preparation of applications, are increasing. In this context, there is a need for support to help job seekers efficiently select occupations and prepare their applications.

[0248] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0249] In this invention, the server includes means for receiving input information from job seekers, means for obtaining information on past successful candidates from a recording device, and means for analyzing the job seeker's input information and the information on successful candidates using machine learning techniques to identify the most suitable job for the job seeker. This enables job seekers to efficiently find the job that best suits them and to smoothly proceed with preparing their application.

[0250] A "job seeker" refers to an individual who is looking for employment, specifically someone who is searching for the most suitable job based on their own abilities and interests.

[0251] "Input information" refers to data that job seekers provide to the system, summarizing personal characteristics related to the occupation, such as skills, experience, interests, and values.

[0252] A "successful person" refers to an individual who has already achieved results in a particular profession and whose experience and skills in that profession can serve as a reference for other job seekers.

[0253] The term "recording device" refers to any device used to store information, and in this invention, it specifically refers to electronically managed systems such as databases.

[0254] "Machine learning techniques" refer to a series of technologies and algorithms that enable computers to automatically learn patterns from data and use that knowledge to analyze and predict new data.

[0255] "Analysis" refers to the process of examining data in detail and finding meaning based on a specific purpose. In this invention, it refers to the analytical work aimed at comparing information on job seekers and successful individuals to identify the most suitable occupation.

[0256] "Job duties" refers to a professional position or responsibility that has a specific function or role, and is a category of work that a job seeker may be able to engage in.

[0257] This invention provides a support system for job seekers to find the most suitable occupation. The specific method for implementing the invention, primarily involving a server, terminal, and user, is described below.

[0258] The server plays a central role in aggregating and managing detailed information about occupations. Specifically, the server maintains a database, collecting, classifying, and storing information about the occupations of past successful individuals. This allows the database to store the career paths and personal characteristics of successful individuals categorized by occupation. The server regularly updates this data to keep it up-to-date.

[0259] Users access this system and enter information about their skills and interests. This data is sent to the server via web forms and applications. The input data includes skills, experience, interests, values, and more.

[0260] The terminal's role is to send user input to the server. This ensures that data entered by the user is stored on the server in real time, maintaining data integrity.

[0261] The server processes the received data using an AI agent. The AI ​​agent leverages powerful machine learning algorithms to analyze the characteristics of job seekers. This process uses techniques such as neural networks and decision trees to analyze the job seeker's data and compare it with information on past successful candidates. This analysis identifies the most suitable occupation for the job seeker and generates an aptitude assessment.

[0262] The results presented to the user include a list of the most suitable occupations for the job seeker, along with detailed information related to those occupations. For example, if the user's input into the system suggests that a data science occupation is suitable, the server will provide career information and success stories related to that occupation. Based on this information, the user can consider a career in the data science field and utilize features that support resume creation and application preparation.

[0263] As a concrete example of a prompt for the generative AI model, it is possible to input a question such as, "I am interested in data science, but what skills and career paths would lead to success?" Based on this prompt, the AI ​​model will assist the user by providing relevant information and advice.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] Users input information about their skills and interests. Specifically, they use a web form or application to answer various questions about their skills, experience, interests, and values, and send that information to their device. The input information is collected on the device and ready to be sent to the server.

[0267] Step 2:

[0268] The terminal sends the information entered by the user to the server. The transmitted data is sent in real time over the internet. The server verifies the integrity of the data upon receipt and checks for errors. The output of this step is the input information whose integrity has been verified.

[0269] Step 3:

[0270] The server stores the received input information in a recording device. This adds new data to the database, which is then used for subsequent processing. At this point, the output is the job seeker's input information stored in the database.

[0271] Step 4:

[0272] The server analyzes the stored input information using an AI agent. The AI ​​agent utilizes a generative AI model to analyze the received user data. This includes recognizing data patterns using a neural network and comparing information on past successful candidates with the job seeker's data. The output of this process is a job suggestion tailored to the job seeker, based on the analysis results.

[0273] Step 5:

[0274] The server generates an aptitude assessment based on the results obtained from the analysis. This assessment is generated using statistical methods and creates a list of occupations that best match the job seeker. The output is a list of occupations accompanied by the job seeker's aptitude score.

[0275] Step 6:

[0276] The server transfers the occupation list and aptitude evaluation to the terminal. As a result, occupation information is presented to the user. The terminal interactively displays detailed information about each occupation presented to the user, and the user can view the detailed information regarding the selected occupation. The output of this step is the occupation list and aptitude evaluation presented to the user.

[0277] Step 7:

[0278] The user refers to the detailed information about the presented occupations through the interactive display device. The user browses the details of success stories and required skills to deepen the understanding of the selected occupation. At this time, the user can further utilize the resume creation tool. The output of this step is the knowledge obtained by the user regarding the occupation and the progress of application preparation.

[0279] Step 8:

[0280] The user proceeds with application preparation through the system. It is possible to utilize the resume creation support function to prepare application documents for the selected occupation. This step leads to specific application actions and ultimately prepares the job seeker to actually apply for a job opening. The output of this step is the completion of the application documents and the readiness for application.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] There is a need for a means for job seekers to efficiently find occupations suitable for themselves and smoothly proceed with application preparation. However, existing methods have the problem that they cannot appropriately analyze the characteristics of job seekers, and the support for occupation selection and application is insufficient.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for receiving input data of job seekers, means for obtaining data of past successful candidates from a storage device, and means for analyzing the input data of job seekers and the data of successful candidates using a machine learning algorithm to identify occupations suitable for the job seekers. As a result, it becomes possible to quickly identify appropriate occupations based on the characteristics of job seekers and efficiently support from occupation selection to application preparation.

[0286] A "job seeker" refers to an individual who is looking for a job and seeks information and support to find an occupation suitable for themselves.

[0287] The "means for receiving input data" refers to a device or function for capturing information on skills, experience, and interests provided by job seekers.

[0288] The "data of past successful candidates" refers to information on the work history, skill set, and characteristics of individuals who have achieved success in a specific occupation, which is information accumulated in a database.

[0289] The "storage device" refers to a storage device or system that continuously stores data and can obtain this data as needed.

[0290] The "machine learning algorithm" refers to a mathematical method for a computer to learn based on data and make future predictions and judgments.

[0291] The "analysis means" refers to a process or device for processing the received input data of job seekers and the data of past successful candidates and calculating the degree of match to a specific occupation.

[0292] The "means for identifying an occupation" refers to a function or method for determining the occupation most suitable for a job seeker by a machine learning algorithm.

[0293] The "display means" refers to a device or interface for visually presenting information such as analysis results and occupation proposals to job seekers.

[0294] An "interactive information terminal" refers to a digital device designed to allow job seekers to input information and respond to received information.

[0295] To implement this invention, the server must first receive input data from job seekers, retrieve data on past successful candidates from its storage device, and analyze it. The server is equipped with a receiving means to receive information about the skills, experience, and interests provided by the user. The received data, along with the data on past successful candidates stored in the storage device, is analyzed using a machine learning algorithm. The TensorFlow library can be used for this algorithm.

[0296] The AI ​​agent automatically analyzes the data, identifying the most suitable occupation for each job seeker through statistical analysis. The identified occupations are then calculated as an aptitude score and presented to the user. Users can visually receive this information on their smartphones or other interactive devices.

[0297] Furthermore, the platform provides features to help users view detailed information about occupations they are interested in and to assist them in the process of selecting an occupation. As a result, job seekers can find occupations that suit their characteristics and proceed efficiently from selection to application preparation.

[0298] For example, within a payment service app that users use daily, they can select a career assessment function and enter a prompt such as, "Based on my skills and interests, what kind of job would be suitable for me?" to find a suitable career.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user uses the terminal to input data regarding their skills, experience, and interests. This input data is information that details the user's characteristics and serves as an important basis for identifying occupations in subsequent processing.

[0302] Step 2:

[0303] The terminal sends the input user data to the server. The server stores the received data in a storage device and simultaneously retrieves data of past successful candidates from the storage device. In this way, all the data necessary for analysis is prepared on the server side.

[0304] Step 3:

[0305] The server activates a machine learning algorithm to compare and analyze the received user data with the data of past successful candidates. For data analysis, the TensorFlow library is used to statistically match the patterns of past successful candidates with user characteristics. Through this process, a list of occupations suitable for the user is generated, and at the same time, the suitability score for each occupation is calculated.

[0306] Step 4:

[0307] The server sends the generated list of occupations and suitability scores to the terminal. The user's terminal displays the received information through an interactive user interface. In this way, the user can visually confirm which occupations are suitable for themselves.

[0308] Step 5:

[0309] The user can use the terminal to view the details of the occupations they are interested in. The server provides data for displaying occupation details and success stories in response to this request. Finally, through this, the user can receive specific support for preparing to apply for the selected occupation.

[0310] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0311] The system according to the present invention improves accuracy by considering the user's emotions in the process of suggesting the most suitable occupation to job seekers. This system incorporates an emotion engine to recognize the user's emotional state in real time and reflects it in job suggestions and application support.

[0312] The server receives input data from job seekers and stores it in a database. Next, the server utilizes an AI agent to identify the most suitable occupation for the job seeker based on data from past successful candidates. During this process, the emotion engine analyzes the user's facial expressions and voice tone through the device to understand their emotional state.

[0313] By recognizing the various emotions users experience during their job search, the system tailors the list of jobs it presents to their psychological needs. For example, if a user is feeling stressed, the server can prioritize suggesting jobs with lower stress levels.

[0314] The terminal presents the user with a list of occupations sent from the server, along with the results of the emotion engine's analysis. Through an interactive interface, the user can immediately view detailed information about the presented occupations. Furthermore, the emotion data is saved as user feedback and used to improve the accuracy of future suggestions.

[0315] For example, when a user is considering a career as a "creative director," if the program detects anxiety from the user's facial expressions or voice, it will provide a detailed explanation of the common challenges associated with that profession. In this way, the present invention makes it possible to provide nuanced career selection support that responds to the user's emotions.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user logs into the system and answers a self-assessment questionnaire. During this process, they turn on their camera and microphone and select settings that allow for the collection of emotional data.

[0319] Step 2:

[0320] The device sends the user's responses, along with facial expression data captured by the camera and audio data acquired from the microphone, to the server. This data is then processed by an emotion engine.

[0321] Step 3:

[0322] The server uses an AI agent to match the user's input data with a database of past success stories and generates a list of the most suitable occupations. Meanwhile, the emotion engine analyzes the transmitted emotion data and recognizes the user's emotional state in real time.

[0323] Step 4:

[0324] Based on the analyzed emotional data, the server adjusts job suggestions to take into account the user's mental stress level and interests. For example, if negative emotions are detected, it prioritizes presenting supportive information and low-stress occupations.

[0325] Step 5:

[0326] The terminal displays a list of occupations and related information received from the server to the user. An interactive interface allows the user to view details about each occupation and obtain further information about their selected occupation.

[0327] Step 6:

[0328] Based on user selections, the device sends emotional feedback back to the server to improve subsequent job suggestions and optimize application support. The emotional engine also provides information to enhance user motivation during the application preparation process.

[0329] Through this series of steps, the system can provide users with highly accurate career support that takes their emotions into account.

[0330] (Example 2)

[0331] Next, we will describe Example 2. 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".

[0332] In conventional job placement systems, the job lists provided to job seekers were based solely on past data and lacked consideration for the job seeker's emotional state. As a result, it was difficult to provide appropriate job suggestions that met the job seeker's needs and psychological state, leading to decreased accuracy and convenience of suggestions.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for collecting characteristic information of job seekers, means for acquiring emotional information, and means for extracting information on past applicants from a data storage device. This enables highly accurate job recommendations based on the characteristics and emotions of job seekers.

[0335] A "job seeker" refers to an individual who is looking for employment and has specific career and job-related preferences.

[0336] "Characteristic information" refers to a collection of personal data about job seekers, including resume information, skills, and work history.

[0337] "Emotional information" refers to data that indicates the psychological state of a job seeker, and is extracted from things like facial expressions and tone of voice.

[0338] An "experienced professional" refers to an individual who has achieved success in a specific occupation in the past, and information about that individual's work history is stored in a data storage device.

[0339] A "data storage device" refers to a recording medium that stores information and allows data to be read out as needed.

[0340] "Machine learning techniques" refer to algorithms and methods that enable computers to learn patterns from data and perform predictions and classifications.

[0341] "Occupation" refers to the duties or job types that a job seeker may be able to perform, and the content of these duties will be specified by the information provided.

[0342] An "interactive display device" refers to a device that allows users to input information through an interface and receive results.

[0343] The present invention's system utilizes a combination of multiple components to provide job seekers with career suggestions that take their emotional information into account. This system aims to suggest suitable occupations to job seekers using a server, terminals, and AI technology.

[0344] The server collects characteristic information about job seekers in the initial stages. This characteristic information includes resume data, skills information, and past work history. The server also retrieves and analyzes data on past experienced individuals from its data storage device. Based on this data, the server uses machine learning techniques to identify suitable occupations for job seekers. In this process, the server's generative AI models are used to achieve highly accurate analysis.

[0345] The terminal is equipped with a device to collect the user's facial expressions and voice information in real time and transmit it to the server as emotional information. This uses a computer or smartphone with a camera and microphone. The terminal communicates data bidirectionally between the job seeker and the server, providing job suggestions that take emotional information into account.

[0346] As a concrete example, suppose a user is searching for a job and the device's camera detects anxiety from the user's facial expression. When this information is transmitted to the server, the server uses a generative AI model to present a list of jobs that take this emotional state into account. This list prioritizes jobs that are less stressful and more emotionally resonant.

[0347] An example of a prompt message would be, "Based on your current mental state, the following occupations are considered suitable for you. Would you like to view more information?" This allows job seekers to obtain occupational information that aligns with their emotions and needs.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server receives job seeker profile information as input. This is done when users enter resume data, skills, work history, etc., through an online form. The received data is immediately stored in a database. This data is used for subsequent processing.

[0351] Step 2:

[0352] The device collects user emotional information. This process utilizes the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time. The analyzed emotional data is sent to a server. Input is facial and voice data, while output is numerical data or labels indicating the emotional state.

[0353] Step 3:

[0354] The server extracts information on past applicants from its data storage. The extracted data is then analyzed using a machine learning algorithm. Here, the applicant's characteristic and emotional information are combined to create input data for recommending the most suitable occupation. The output is a list of suitable occupations.

[0355] Step 4:

[0356] The server uses a generative AI model to evaluate job suitability based on past success stories in the database and the characteristics and emotional information of job seekers. This algorithm takes into account the stress level and required skills for each job. As a result of the processing, a filtered list of jobs is generated and sent to the terminal.

[0357] Step 5:

[0358] The terminal presents the user with a list of occupations received from the server. It generates prompts based on the user's emotional state and provides an interactive display to help the user select the most suitable occupation. The user can then view details of the occupations based on the presented information. The terminal's output consists of the prompts and the occupation list interface.

[0359] Step 6:

[0360] Users provide feedback on the provided job information. This feedback is then sent back to the server and stored in the server's database. This information is used to improve the accuracy of future job suggestions. The output is the addition of the feedback to the database.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] Conventional job suggestion systems propose jobs without considering the emotions or psychological state of job seekers, resulting in low emotional satisfaction and a lack of acceptance of the suggested jobs. Furthermore, because they cannot provide flexible job suggestions that utilize emotional data, it is difficult to match job seekers with appropriate jobs that meet their latent needs.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for receiving input data from job seekers, means for acquiring data from past successful candidates from a storage device, means for analyzing the input data from job seekers and the data from successful candidates using a machine learning algorithm to identify suitable occupations for job seekers, means for collecting and analyzing the emotional data of job seekers using an emotion analysis engine and reflecting the results in job suggestions, and means for presenting suitable occupations for job seekers. This enables flexible and highly accurate job suggestions that take into account the emotional state of job seekers.

[0366] "Job seeker input data" refers to information provided by individuals who are actively seeking employment, such as their work history and desired job type.

[0367] "Data on past successes" refers to information about the work history and experience of people who previously received job placement services and achieved success.

[0368] A "storage device" is a device that stores information over a long period of time and makes it possible to search and retrieve it as needed.

[0369] A "machine learning algorithm" is a computational method that allows a computer to recognize patterns based on provided data and make predictions and judgments about new data.

[0370] An "emotion analysis engine" is a software module that uses voice and image data to recognize and analyze a person's emotional state.

[0371] "Job recommendation" is a process that recommends suitable jobs to job seekers based on analyzed data.

[0372] An "interactive information processing screen" refers to a device that users can operate visually or tactilely, and that enables the provision and reception of information in real time.

[0373] "Facilitation function" refers to the functional characteristics of a system designed to facilitate specific activities or operations.

[0374] The server receives input data from job seekers and stores it in a database. The database contains data on past successful candidates, which the AI ​​agent utilizes. The AI ​​agent uses machine learning algorithms to analyze the job seeker's input data and the data on past successful candidates to identify the most suitable occupation for the job seeker. In this process, an emotion analysis engine monitors the job seeker's facial expressions and voice tone, collecting and analyzing emotional data. The resulting emotional state information is incorporated into job suggestions, generating a personalized list.

[0375] The terminal incorporates the results of sentiment analysis when presenting job seekers with a list of occupations received from the server. Users can view detailed occupation information through the terminal's interactive information processing screen. Furthermore, the job seeker's sentiment data is stored in the system as feedback and used to improve the accuracy of future job suggestions.

[0376] For example, when a user is considering a job as a "creative director," if the emotion analysis engine detects anxiety, the device will provide a detailed explanation of the common challenges of that profession. This is a function that allows the system to understand the user's choices and provide more in-depth information as needed.

[0377] An example of a prompt message is, "If the user's emotional state is stressed, please enhance job suggestions that involve low workplace pressure." This prompt helps to enhance job suggestions using a generative AI model.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The server receives input data from job seekers. This input data includes information such as work history, desired job type, and skills. The received data is stored in a database and used in the next analysis step.

[0381] Step 2:

[0382] The server retrieves data on past successes from a database. This data includes the work history and suitable job types of the successful individuals. Based on this dataset, the server prepares to perform analysis using machine learning algorithms.

[0383] Step 3:

[0384] The machine learning algorithm installed on the server analyzes the job seeker's input data and past success data. Based on the acquired data, it performs pattern recognition and calculations to identify suitable occupations for the job seeker. As output, it generates a list of occupations with a high degree of fit.

[0385] Step 4:

[0386] The device captures the user's facial expressions and voice using a camera and microphone, and analyzes them in real time using an emotion analysis engine. The emotion data obtained through this process indicates the user's psychological state, which influences the career suggestions in the next step.

[0387] Step 5:

[0388] The server takes the analyzed sentiment data into account and uses a generative AI model to adjust job suggestions based on the prompt text. In this process, it flexibly adjusts suggestions by combining sentiment data with the job list. For example, if the user is feeling stressed, it prioritizes listing jobs with lower stress levels.

[0389] Step 6:

[0390] The terminal presents the user with a customized list of occupations on an interactive information processing screen. The user can view detailed information from the list and select a suitable occupation. An example of a prompt message is: "If the user's emotional state is stressed, please enhance suggestions for jobs with low workplace pressure."

[0391] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] The system according to the present invention provides support to job seekers in finding the most suitable occupation, and is implemented as follows.

[0408] The server maintains a database that collects and manages a large amount of data on the career histories, skill sets, and characteristics of individuals who have achieved success in the past. This data is categorized by occupation and includes the career paths and personal traits of successful individuals.

[0409] Users access this system to input information about their talents and interests. Specifically, they provide input data on their skills, experience, interests, and values ​​through surveys and skill tests.

[0410] The terminal sends data entered by the user to the server, which stores it in a central database. The server then uses an AI agent to process this data. The AI ​​agent analyzes the user's input data using machine learning algorithms and compares it with data from past successful users.

[0411] Based on this analysis, the server identifies occupations that match the user's characteristics based on statistical analysis. A list of identified occupations is generated along with the user's aptitude score and presented to the user via the terminal. For the presented occupations, the user can interactively view detailed information and receive assistance with the specific application procedures for the selected occupation.

[0412] For example, if a user uses the system and inputs their skills and interests, and the system suggests that a data science career is suitable, the server will provide detailed information, including success stories and career paths related to that profession. In this specific example, the user can use the provided information to consider a career in the field of data science. Furthermore, they can use the resume creation support function to prepare for applications. In this way, the present invention fully supports job seekers from efficiently finding and selecting a job that suits their characteristics to preparing for applications.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] Users create an account and log in to the system they access. Next, they answer specific questionnaires and skill tests to collect input data about their interests and skills.

[0416] Step 2:

[0417] The terminal transmits data entered by the user to the server in real time. This data includes information that forms the job seeker's profile, such as skills, experience, interests, and values.

[0418] Step 3:

[0419] The server accesses the database to store input data and retrieves occupational data about past successes. This includes their career paths, skills, and personal characteristics.

[0420] Step 4:

[0421] The server activates an AI agent and uses machine learning algorithms to perform comparative analysis of the user's input data against a dataset of successful individuals. Here, it identifies occupational patterns that match the user's characteristics.

[0422] Step 5:

[0423] Based on the analysis results, the server generates a list of occupations best suited to the job seeker's characteristics and calculates an aptitude score for each occupation.

[0424] Step 6:

[0425] The terminal displays a list of occupations and aptitude scores received from the server to the user. Based on this information, the user interactively displays detailed information about each occupation and selects their favorite or most interesting occupation.

[0426] Step 7:

[0427] The server will begin assisting users with job application preparation related to their chosen occupation. This includes generating resume templates and providing guidance on writing cover letters.

[0428] Step 8:

[0429] The terminal displays the provided application preparation information to the user, allowing the user to use it to create application documents and prepare for interviews.

[0430] (Example 1)

[0431] Next, we will describe Example 1. 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."

[0432] Modern job seekers face the challenge of finding the most suitable occupation from a diverse range of options. In particular, the burden of selecting the right job to maximize their experience and skills, and the preparation of applications, are increasing. In this context, there is a need for support to help job seekers efficiently select occupations and prepare their applications.

[0433] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0434] In this invention, the server includes means for receiving input information from job seekers, means for obtaining information on past successful candidates from a recording device, and means for analyzing the job seeker's input information and the information on successful candidates using machine learning techniques to identify the most suitable job for the job seeker. This enables job seekers to efficiently find the job that best suits them and to smoothly proceed with preparing their application.

[0435] A "job seeker" refers to an individual who is looking for employment, specifically someone who is searching for the most suitable job based on their own abilities and interests.

[0436] "Input information" refers to data that job seekers provide to the system, summarizing personal characteristics related to the occupation, such as skills, experience, interests, and values.

[0437] A "successful person" refers to an individual who has already achieved results in a particular profession and whose experience and skills in that profession can serve as a reference for other job seekers.

[0438] The term "recording device" refers to any device used to store information, and in this invention, it specifically refers to electronically managed systems such as databases.

[0439] "Machine learning techniques" refer to a series of technologies and algorithms that enable computers to automatically learn patterns from data and use that knowledge to analyze and predict new data.

[0440] "Analysis" refers to the process of examining data in detail and finding meaning based on a specific purpose. In this invention, it refers to the analytical work aimed at comparing information on job seekers and successful individuals to identify the most suitable occupation.

[0441] "Job duties" refers to a professional position or responsibility that has a specific function or role, and is a category of work that a job seeker may be able to engage in.

[0442] This invention provides a support system for job seekers to find the most suitable occupation. The specific method for implementing the invention, primarily involving a server, terminal, and user, is described below.

[0443] The server plays a central role in aggregating and managing detailed information about occupations. Specifically, the server maintains a database, collecting, classifying, and storing information about the occupations of past successful individuals. This allows the database to store the career paths and personal characteristics of successful individuals categorized by occupation. The server regularly updates this data to keep it up-to-date.

[0444] Users access this system and enter information about their skills and interests. This data is sent to the server via web forms and applications. The input data includes skills, experience, interests, values, and more.

[0445] The terminal's role is to send user input to the server. This ensures that data entered by the user is stored on the server in real time, maintaining data integrity.

[0446] The server processes the received data using an AI agent. The AI ​​agent leverages powerful machine learning algorithms to analyze the characteristics of job seekers. This process uses techniques such as neural networks and decision trees to analyze the job seeker's data and compare it with information on past successful candidates. This analysis identifies the most suitable occupation for the job seeker and generates an aptitude assessment.

[0447] The results presented to the user include a list of the most suitable occupations for the job seeker, along with detailed information related to those occupations. For example, if the user's input into the system suggests that a data science occupation is suitable, the server will provide career information and success stories related to that occupation. Based on this information, the user can consider a career in the data science field and utilize features that support resume creation and application preparation.

[0448] As a concrete example of a prompt for the generative AI model, it is possible to input a question such as, "I am interested in data science, but what skills and career paths would lead to success?" Based on this prompt, the AI ​​model will assist the user by providing relevant information and advice.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] Users input information about their skills and interests. Specifically, they use a web form or application to answer various questions about their skills, experience, interests, and values, and send that information to their device. The input information is collected on the device and ready to be sent to the server.

[0452] Step 2:

[0453] The terminal sends the information entered by the user to the server. The transmitted data is sent in real time over the internet. The server verifies the integrity of the data upon receipt and checks for errors. The output of this step is the input information whose integrity has been verified.

[0454] Step 3:

[0455] The server stores the received input information in a recording device. This adds new data to the database, which is then used for subsequent processing. At this point, the output is the job seeker's input information stored in the database.

[0456] Step 4:

[0457] The server analyzes the stored input information using an AI agent. The AI ​​agent utilizes a generative AI model to analyze the received user data. This includes recognizing data patterns using a neural network and comparing information on past successful candidates with the job seeker's data. The output of this process is a job suggestion tailored to the job seeker, based on the analysis results.

[0458] Step 5:

[0459] The server generates an aptitude assessment based on the results obtained from the analysis. This assessment is generated using statistical methods and creates a list of occupations that best match the job seeker. The output is a list of occupations accompanied by the job seeker's aptitude score.

[0460] Step 6:

[0461] The server transfers the occupation list and aptitude assessment to the terminal. This presents the user with occupational information. The terminal interactively displays detailed information for each occupation presented to the user, allowing the user to view detailed information about their selected occupation. The output of this step is the occupation list and aptitude assessment displayed to the user.

[0462] Step 7:

[0463] Users view detailed information about presented occupations through interactive displays. They can browse success stories and required skills to deepen their understanding of their chosen occupation. At this stage, users can also utilize resume creation tools. The output of this step represents the user's acquired occupational knowledge and progress in application preparation.

[0464] Step 8:

[0465] Users prepare their applications through the system. They can utilize the resume creation support function to prepare application documents for their chosen occupation. This step leads to concrete application actions, ultimately preparing the job seeker to actually apply for the job. The output of this step is the completion and preparation of application documents.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, 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."

[0468] There is a need for methods that enable job seekers to efficiently find suitable occupations and smoothly prepare for applications. However, existing methods fail to adequately analyze the characteristics of job seekers, resulting in insufficient support for occupational selection and application.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for receiving input data from job seekers, means for obtaining data from past successful candidates from a storage device, and means for analyzing the job seeker's input data and the data from successful candidates using a machine learning algorithm to identify suitable occupations for the job seekers. This makes it possible to quickly identify appropriate occupations based on the characteristics of job seekers and efficiently support them from occupation selection to application preparation.

[0471] A "job seeker" refers to an individual who is looking for employment and is seeking information and support to find a suitable job.

[0472] "Means for receiving input data" refers to a device or function for taking in information about skills, experience, and interests provided by job seekers.

[0473] "Data on past successes" refers to information about the work history, skill sets, and characteristics of individuals who have achieved success in a particular profession, and is stored in a database.

[0474] A "storage device" refers to a storage device or system that continuously stores data and allows this data to be retrieved as needed.

[0475] A "machine learning algorithm" refers to a mathematical method that allows computers to learn from data and make predictions and decisions about the future.

[0476] "Analysis means" refers to a process or device for processing received input data from job seekers and data from past successful candidates to calculate the degree of match to a specific occupation.

[0477] "Means of identifying occupations" refers to functions and methods that use machine learning algorithms to determine the most suitable occupation for a job seeker.

[0478] "Display means" refers to a device or interface used to visually present information such as analysis results and job suggestions to job seekers.

[0479] An "interactive information terminal" refers to a digital device designed to allow job seekers to input information and respond to received information.

[0480] To implement this invention, the server must first receive input data from job seekers, retrieve data on past successful candidates from its storage device, and analyze it. The server is equipped with a receiving means to receive information about the skills, experience, and interests provided by the user. The received data, along with the data on past successful candidates stored in the storage device, is analyzed using a machine learning algorithm. The TensorFlow library can be used for this algorithm.

[0481] The AI ​​agent automatically analyzes the data, identifying the most suitable occupation for each job seeker through statistical analysis. The identified occupations are then calculated as an aptitude score and presented to the user. Users can visually receive this information on their smartphones or other interactive devices.

[0482] Furthermore, the platform provides features to help users view detailed information about occupations they are interested in and to assist them in the process of selecting an occupation. As a result, job seekers can find occupations that suit their characteristics and proceed efficiently from selection to application preparation.

[0483] For example, within a payment service app that users use daily, they can select a career assessment function and enter a prompt such as, "Based on my skills and interests, what kind of job would be suitable for me?" to find a suitable career.

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] Users use a device to input data about their skills, experience, and interests. This input data provides detailed information about the user's characteristics and serves as an important basis for identifying their occupation in subsequent processing.

[0487] Step 2:

[0488] The terminal sends the entered user data to the server. The server stores the received data in its storage device and simultaneously retrieves data from past successful users from its storage device. This ensures that all the data necessary for analysis is prepared on the server side.

[0489] Step 3:

[0490] The server activates a machine learning algorithm to compare and analyze the received user data with data from past successful users. The TensorFlow library is used for data analysis, statistically matching patterns from past successful users with user characteristics. This process generates a list of suitable occupations for the user, along with an aptitude score for each occupation.

[0491] Step 4:

[0492] The server sends a list of generated occupations and aptitude scores to the user's device. The user's device displays the received information through an interactive user interface, allowing the user to visually determine which occupations are suitable for them.

[0493] Step 5:

[0494] Users can use their devices to view details about occupations that interest them. The server, in response to these requests, provides data to display detailed occupational information and success stories. Ultimately, this allows users to receive concrete support in preparing their applications for their chosen occupations.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] The system according to the present invention improves accuracy by considering the user's emotions in the process of suggesting the most suitable occupation to job seekers. This system incorporates an emotion engine to recognize the user's emotional state in real time and reflects it in job suggestions and application support.

[0497] The server receives input data from job seekers and stores it in a database. Next, the server utilizes an AI agent to identify the most suitable occupation for the job seeker based on data from past successful candidates. During this process, the emotion engine analyzes the user's facial expressions and voice tone through the device to understand their emotional state.

[0498] By recognizing the various emotions users experience during their job search, the system tailors the list of jobs it presents to their psychological needs. For example, if a user is feeling stressed, the server can prioritize suggesting jobs with lower stress levels.

[0499] The terminal presents the user with a list of occupations sent from the server, along with the results of the emotion engine's analysis. Through an interactive interface, the user can immediately view detailed information about the presented occupations. Furthermore, the emotion data is saved as user feedback and used to improve the accuracy of future suggestions.

[0500] For example, when a user is considering a career as a "creative director," if the program detects anxiety from the user's facial expressions or voice, it will provide a detailed explanation of the common challenges associated with that profession. In this way, the present invention makes it possible to provide nuanced career selection support that responds to the user's emotions.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The user logs into the system and answers a self-assessment questionnaire. During this process, they turn on their camera and microphone and select settings that allow for the collection of emotional data.

[0504] Step 2:

[0505] The device sends the user's responses, along with facial expression data captured by the camera and audio data acquired from the microphone, to the server. This data is then processed by an emotion engine.

[0506] Step 3:

[0507] The server uses an AI agent to match the user's input data with a database of past success stories and generates a list of the most suitable occupations. Meanwhile, the emotion engine analyzes the transmitted emotion data and recognizes the user's emotional state in real time.

[0508] Step 4:

[0509] Based on the analyzed emotional data, the server adjusts job suggestions to take into account the user's mental stress level and interests. For example, if negative emotions are detected, it prioritizes presenting supportive information and low-stress occupations.

[0510] Step 5:

[0511] The terminal displays a list of occupations and related information received from the server to the user. An interactive interface allows the user to view details about each occupation and obtain further information about their selected occupation.

[0512] Step 6:

[0513] Based on user selections, the device sends emotional feedback back to the server to improve subsequent job suggestions and optimize application support. The emotional engine also provides information to enhance user motivation during the application preparation process.

[0514] Through this series of steps, the system can provide users with highly accurate career support that takes their emotions into account.

[0515] (Example 2)

[0516] Next, we will describe Example 2. 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."

[0517] In conventional job placement systems, the job lists provided to job seekers were based solely on past data and lacked consideration for the job seeker's emotional state. As a result, it was difficult to provide appropriate job suggestions that met the job seeker's needs and psychological state, leading to decreased accuracy and convenience of suggestions.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0519] In this invention, the server includes means for collecting characteristic information of job seekers, means for acquiring emotional information, and means for extracting information on past applicants from a data storage device. This enables highly accurate job recommendations based on the characteristics and emotions of job seekers.

[0520] A "job seeker" refers to an individual who is looking for employment and has specific career and job-related preferences.

[0521] "Characteristic information" refers to a collection of personal data about job seekers, including resume information, skills, and work history.

[0522] "Emotional information" refers to data that indicates the psychological state of a job seeker, and is extracted from things like facial expressions and tone of voice.

[0523] An "experienced professional" refers to an individual who has achieved success in a specific occupation in the past, and information about that individual's work history is stored in a data storage device.

[0524] A "data storage device" refers to a recording medium that stores information and allows data to be read out as needed.

[0525] "Machine learning techniques" refer to algorithms and methods that enable computers to learn patterns from data and perform predictions and classifications.

[0526] "Occupation" refers to the duties or job types that a job seeker may be able to perform, and the content of these duties will be specified by the information provided.

[0527] An "interactive display device" refers to a device that allows users to input information through an interface and receive results.

[0528] The present invention's system utilizes a combination of multiple components to provide job seekers with career suggestions that take their emotional information into account. This system aims to suggest suitable occupations to job seekers using a server, terminals, and AI technology.

[0529] The server collects characteristic information about job seekers in the initial stages. This characteristic information includes resume data, skills information, and past work history. The server also retrieves and analyzes data on past experienced individuals from its data storage device. Based on this data, the server uses machine learning techniques to identify suitable occupations for job seekers. In this process, the server's generative AI models are used to achieve highly accurate analysis.

[0530] The terminal is equipped with a device to collect the user's facial expressions and voice information in real time and transmit it to the server as emotional information. This uses a computer or smartphone with a camera and microphone. The terminal communicates data bidirectionally between the job seeker and the server, providing job suggestions that take emotional information into account.

[0531] As a concrete example, suppose a user is searching for a job and the device's camera detects anxiety from the user's facial expression. When this information is transmitted to the server, the server uses a generative AI model to present a list of jobs that take this emotional state into account. This list prioritizes jobs that are less stressful and more emotionally resonant.

[0532] An example of a prompt message would be, "Based on your current mental state, the following occupations are considered suitable for you. Would you like to view more information?" This allows job seekers to obtain occupational information that aligns with their emotions and needs.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The server receives job seeker profile information as input. This is done when users enter resume data, skills, work history, etc., through an online form. The received data is immediately stored in a database. This data is used for subsequent processing.

[0536] Step 2:

[0537] The device collects user emotional information. This process utilizes the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time. The analyzed emotional data is sent to a server. Input is facial and voice data, while output is numerical data or labels indicating the emotional state.

[0538] Step 3:

[0539] The server extracts information on past applicants from its data storage. The extracted data is then analyzed using a machine learning algorithm. Here, the applicant's characteristic and emotional information are combined to create input data for recommending the most suitable occupation. The output is a list of suitable occupations.

[0540] Step 4:

[0541] The server uses a generative AI model to evaluate job suitability based on past success stories in the database and the characteristics and emotional information of job seekers. This algorithm takes into account the stress level and required skills for each job. As a result of the processing, a filtered list of jobs is generated and sent to the terminal.

[0542] Step 5:

[0543] The terminal presents the user with a list of occupations received from the server. It generates prompts based on the user's emotional state and provides an interactive display to help the user select the most suitable occupation. The user can then view details of the occupations based on the presented information. The terminal's output consists of the prompts and the occupation list interface.

[0544] Step 6:

[0545] Users provide feedback on the provided job information. This feedback is then sent back to the server and stored in the server's database. This information is used to improve the accuracy of future job suggestions. The output is the addition of the feedback to the database.

[0546] (Application Example 2)

[0547] Next, we will explain application example 2. In the following explanation, 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."

[0548] Conventional job suggestion systems propose jobs without considering the emotions or psychological state of job seekers, resulting in low emotional satisfaction and a lack of acceptance of the suggested jobs. Furthermore, because they cannot provide flexible job suggestions that utilize emotional data, it is difficult to match job seekers with appropriate jobs that meet their latent needs.

[0549] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0550] In this invention, the server includes means for receiving input data from job seekers, means for acquiring data from past successful candidates from a storage device, means for analyzing the input data from job seekers and the data from successful candidates using a machine learning algorithm to identify suitable occupations for job seekers, means for collecting and analyzing the emotional data of job seekers using an emotion analysis engine and reflecting the results in job suggestions, and means for presenting suitable occupations for job seekers. This enables flexible and highly accurate job suggestions that take into account the emotional state of job seekers.

[0551] "Job seeker input data" refers to information provided by individuals who are actively seeking employment, such as their work history and desired job type.

[0552] "Data on past successes" refers to information about the work history and experience of people who previously received job placement services and achieved success.

[0553] A "storage device" is a device that stores information over a long period of time and makes it possible to search and retrieve it as needed.

[0554] A "machine learning algorithm" is a computational method that allows a computer to recognize patterns based on provided data and make predictions and judgments about new data.

[0555] An "emotion analysis engine" is a software module that uses voice and image data to recognize and analyze a person's emotional state.

[0556] "Job recommendation" is a process that recommends suitable jobs to job seekers based on analyzed data.

[0557] An "interactive information processing screen" refers to a device that users can operate visually or tactilely, and that enables the provision and reception of information in real time.

[0558] "Facilitation function" refers to the functional characteristics of a system designed to facilitate specific activities or operations.

[0559] The server receives input data from job seekers and stores it in a database. The database contains data on past successful candidates, which the AI ​​agent utilizes. The AI ​​agent uses machine learning algorithms to analyze the job seeker's input data and the data on past successful candidates to identify the most suitable occupation for the job seeker. In this process, an emotion analysis engine monitors the job seeker's facial expressions and voice tone, collecting and analyzing emotional data. The resulting emotional state information is incorporated into job suggestions, generating a personalized list.

[0560] The terminal incorporates the results of sentiment analysis when presenting job seekers with a list of occupations received from the server. Users can view detailed occupation information through the terminal's interactive information processing screen. Furthermore, the job seeker's sentiment data is stored in the system as feedback and used to improve the accuracy of future job suggestions.

[0561] For example, when a user is considering a job as a "creative director," if the emotion analysis engine detects anxiety, the device will provide a detailed explanation of the common challenges of that profession. This is a function that allows the system to understand the user's choices and provide more in-depth information as needed.

[0562] An example of a prompt message is, "If the user's emotional state is stressed, please enhance job suggestions that involve low workplace pressure." This prompt helps to enhance job suggestions using a generative AI model.

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The server receives input data from job seekers. This input data includes information such as work history, desired job type, and skills. The received data is stored in a database and used in the next analysis step.

[0566] Step 2:

[0567] The server retrieves data on past successes from a database. This data includes the work history and suitable job types of the successful individuals. Based on this dataset, the server prepares to perform analysis using machine learning algorithms.

[0568] Step 3:

[0569] The machine learning algorithm installed on the server analyzes the job seeker's input data and past success data. Based on the acquired data, it performs pattern recognition and calculations to identify suitable occupations for the job seeker. As output, it generates a list of occupations with a high degree of fit.

[0570] Step 4:

[0571] The device captures the user's facial expressions and voice using a camera and microphone, and analyzes them in real time using an emotion analysis engine. The emotion data obtained through this process indicates the user's psychological state, which influences the career suggestions in the next step.

[0572] Step 5:

[0573] The server takes the analyzed sentiment data into account and uses a generative AI model to adjust job suggestions based on the prompt text. In this process, it flexibly adjusts suggestions by combining sentiment data with the job list. For example, if the user is feeling stressed, it prioritizes listing jobs with lower stress levels.

[0574] Step 6:

[0575] The terminal presents the user with a customized list of occupations on an interactive information processing screen. The user can view detailed information from the list and select a suitable occupation. An example of a prompt message is: "If the user's emotional state is stressed, please enhance suggestions for jobs with low workplace pressure."

[0576] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0577] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0578] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] As shown in Figure 7, the 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.

[0582] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0583] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0584] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0585] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0586] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0587] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0588] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0589] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0590] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0591] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0592] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0593] The system according to the present invention provides support to job seekers in finding the most suitable occupation, and is implemented as follows.

[0594] The server maintains a database that collects and manages a large amount of data on the career histories, skill sets, and characteristics of individuals who have achieved success in the past. This data is categorized by occupation and includes the career paths and personal traits of successful individuals.

[0595] Users access this system to input information about their talents and interests. Specifically, they provide input data on their skills, experience, interests, and values ​​through surveys and skill tests.

[0596] The terminal sends data entered by the user to the server, which stores it in a central database. The server then uses an AI agent to process this data. The AI ​​agent analyzes the user's input data using machine learning algorithms and compares it with data from past successful users.

[0597] Based on this analysis, the server identifies occupations that match the user's characteristics based on statistical analysis. A list of identified occupations is generated along with the user's aptitude score and presented to the user via the terminal. For the presented occupations, the user can interactively view detailed information and receive assistance with the specific application procedures for the selected occupation.

[0598] For example, if a user uses the system and inputs their skills and interests, and the system suggests that a data science career is suitable, the server will provide detailed information, including success stories and career paths related to that profession. In this specific example, the user can use the provided information to consider a career in the field of data science. Furthermore, they can use the resume creation support function to prepare for applications. In this way, the present invention fully supports job seekers from efficiently finding and selecting a job that suits their characteristics to preparing for applications.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] Users create an account and log in to the system they access. Next, they answer specific questionnaires and skill tests to collect input data about their interests and skills.

[0602] Step 2:

[0603] The terminal transmits data entered by the user to the server in real time. This data includes information that forms the job seeker's profile, such as skills, experience, interests, and values.

[0604] Step 3:

[0605] The server accesses the database to store input data and retrieves occupational data about past successes. This includes their career paths, skills, and personal characteristics.

[0606] Step 4:

[0607] The server activates an AI agent and uses machine learning algorithms to perform comparative analysis of the user's input data against a dataset of successful individuals. Here, it identifies occupational patterns that match the user's characteristics.

[0608] Step 5:

[0609] Based on the analysis results, the server generates a list of occupations best suited to the job seeker's characteristics and calculates an aptitude score for each occupation.

[0610] Step 6:

[0611] The terminal displays a list of occupations and aptitude scores received from the server to the user. Based on this information, the user interactively displays detailed information about each occupation and selects their favorite or most interesting occupation.

[0612] Step 7:

[0613] The server will begin assisting users with job application preparation related to their chosen occupation. This includes generating resume templates and providing guidance on writing cover letters.

[0614] Step 8:

[0615] The terminal displays the provided application preparation information to the user, allowing the user to use it to create application documents and prepare for interviews.

[0616] (Example 1)

[0617] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] Modern job seekers face the challenge of finding the most suitable occupation from a diverse range of options. In particular, the burden of selecting the right job to maximize their experience and skills, and the preparation of applications, are increasing. In this context, there is a need for support to help job seekers efficiently select occupations and prepare their applications.

[0619] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0620] In this invention, the server includes means for receiving input information from job seekers, means for obtaining information on past successful candidates from a recording device, and means for analyzing the job seeker's input information and the information on successful candidates using machine learning techniques to identify the most suitable job for the job seeker. This enables job seekers to efficiently find the job that best suits them and to smoothly proceed with preparing their application.

[0621] A "job seeker" refers to an individual who is looking for employment, specifically someone who is searching for the most suitable job based on their own abilities and interests.

[0622] "Input information" refers to data that job seekers provide to the system, summarizing personal characteristics related to the occupation, such as skills, experience, interests, and values.

[0623] A "successful person" refers to an individual who has already achieved results in a particular profession and whose experience and skills in that profession can serve as a reference for other job seekers.

[0624] The term "recording device" refers to any device used to store information, and in this invention, it specifically refers to electronically managed systems such as databases.

[0625] "Machine learning techniques" refer to a series of technologies and algorithms that enable computers to automatically learn patterns from data and use that knowledge to analyze and predict new data.

[0626] "Analysis" refers to the process of examining data in detail and finding meaning based on a specific purpose. In this invention, it refers to the analytical work aimed at comparing information on job seekers and successful individuals to identify the most suitable occupation.

[0627] "Job duties" refers to a professional position or responsibility that has a specific function or role, and is a category of work that a job seeker may be able to engage in.

[0628] This invention provides a support system for job seekers to find the most suitable occupation. The specific method for implementing the invention, primarily involving a server, terminal, and user, is described below.

[0629] The server plays a central role in aggregating and managing detailed information about occupations. Specifically, the server maintains a database, collecting, classifying, and storing information about the occupations of past successful individuals. This allows the database to store the career paths and personal characteristics of successful individuals categorized by occupation. The server regularly updates this data to keep it up-to-date.

[0630] Users access this system and enter information about their skills and interests. This data is sent to the server via web forms and applications. The input data includes skills, experience, interests, values, and more.

[0631] The terminal's role is to send user input to the server. This ensures that data entered by the user is stored on the server in real time, maintaining data integrity.

[0632] The server processes the received data using an AI agent. The AI ​​agent leverages powerful machine learning algorithms to analyze the characteristics of job seekers. This process uses techniques such as neural networks and decision trees to analyze the job seeker's data and compare it with information on past successful candidates. This analysis identifies the most suitable occupation for the job seeker and generates an aptitude assessment.

[0633] The results presented to the user include a list of the most suitable occupations for the job seeker, along with detailed information related to those occupations. For example, if the user's input into the system suggests that a data science occupation is suitable, the server will provide career information and success stories related to that occupation. Based on this information, the user can consider a career in the data science field and utilize features that support resume creation and application preparation.

[0634] As a concrete example of a prompt for the generative AI model, it is possible to input a question such as, "I am interested in data science, but what skills and career paths would lead to success?" Based on this prompt, the AI ​​model will assist the user by providing relevant information and advice.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] Users input information about their skills and interests. Specifically, they use a web form or application to answer various questions about their skills, experience, interests, and values, and send that information to their device. The input information is collected on the device and ready to be sent to the server.

[0638] Step 2:

[0639] The terminal sends the information entered by the user to the server. The transmitted data is sent in real time over the internet. The server verifies the integrity of the data upon receipt and checks for errors. The output of this step is the input information whose integrity has been verified.

[0640] Step 3:

[0641] The server stores the received input information in a recording device. This adds new data to the database, which is then used for subsequent processing. At this point, the output is the job seeker's input information stored in the database.

[0642] Step 4:

[0643] The server analyzes the stored input information using an AI agent. The AI ​​agent utilizes a generative AI model to analyze the received user data. This includes recognizing data patterns using a neural network and comparing information on past successful candidates with the job seeker's data. The output of this process is a job suggestion tailored to the job seeker, based on the analysis results.

[0644] Step 5:

[0645] The server generates an aptitude assessment based on the results obtained from the analysis. This assessment is generated using statistical methods and creates a list of occupations that best match the job seeker. The output is a list of occupations accompanied by the job seeker's aptitude score.

[0646] Step 6:

[0647] The server transfers the occupation list and aptitude assessment to the terminal. This presents the user with occupational information. The terminal interactively displays detailed information for each occupation presented to the user, allowing the user to view detailed information about their selected occupation. The output of this step is the occupation list and aptitude assessment displayed to the user.

[0648] Step 7:

[0649] Users view detailed information about presented occupations through interactive displays. They can browse success stories and required skills to deepen their understanding of their chosen occupation. At this stage, users can also utilize resume creation tools. The output of this step represents the user's acquired occupational knowledge and progress in application preparation.

[0650] Step 8:

[0651] Users prepare their applications through the system. They can utilize the resume creation support function to prepare application documents for their chosen occupation. This step leads to concrete application actions, ultimately preparing the job seeker to actually apply for the job. The output of this step is the completion and preparation of application documents.

[0652] (Application Example 1)

[0653] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] There is a need for methods that enable job seekers to efficiently find suitable occupations and smoothly prepare for applications. However, existing methods fail to adequately analyze the characteristics of job seekers, resulting in insufficient support for occupational selection and application.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0656] In this invention, the server includes means for receiving input data from job seekers, means for obtaining data from past successful candidates from a storage device, and means for analyzing the job seeker's input data and the data from successful candidates using a machine learning algorithm to identify suitable occupations for the job seekers. This makes it possible to quickly identify appropriate occupations based on the characteristics of job seekers and efficiently support them from occupation selection to application preparation.

[0657] A "job seeker" refers to an individual who is looking for employment and is seeking information and support to find a suitable job.

[0658] "Means for receiving input data" refers to a device or function for taking in information about skills, experience, and interests provided by job seekers.

[0659] "Data on past successes" refers to information about the work history, skill sets, and characteristics of individuals who have achieved success in a particular profession, and is stored in a database.

[0660] A "storage device" refers to a storage device or system that continuously stores data and allows this data to be retrieved as needed.

[0661] A "machine learning algorithm" refers to a mathematical method that allows computers to learn from data and make predictions and decisions about the future.

[0662] "Analysis means" refers to a process or device for processing received input data from job seekers and data from past successful candidates to calculate the degree of match to a specific occupation.

[0663] "Means of identifying occupations" refers to functions and methods that use machine learning algorithms to determine the most suitable occupation for a job seeker.

[0664] "Display means" refers to a device or interface used to visually present information such as analysis results and job suggestions to job seekers.

[0665] An "interactive information terminal" refers to a digital device designed to allow job seekers to input information and respond to received information.

[0666] To implement this invention, the server must first receive input data from job seekers, retrieve data on past successful candidates from its storage device, and analyze it. The server is equipped with a receiving means to receive information about the skills, experience, and interests provided by the user. The received data, along with the data on past successful candidates stored in the storage device, is analyzed using a machine learning algorithm. The TensorFlow library can be used for this algorithm.

[0667] The AI ​​agent automatically analyzes the data, identifying the most suitable occupation for each job seeker through statistical analysis. The identified occupations are then calculated as an aptitude score and presented to the user. Users can visually receive this information on their smartphones or other interactive devices.

[0668] Furthermore, the platform provides features to help users view detailed information about occupations they are interested in and to assist them in the process of selecting an occupation. As a result, job seekers can find occupations that suit their characteristics and proceed efficiently from selection to application preparation.

[0669] For example, within a payment service app that users use daily, they can select a career assessment function and enter a prompt such as, "Based on my skills and interests, what kind of job would be suitable for me?" to find a suitable career.

[0670] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0671] Step 1:

[0672] Users use a device to input data about their skills, experience, and interests. This input data provides detailed information about the user's characteristics and serves as an important basis for identifying their occupation in subsequent processing.

[0673] Step 2:

[0674] The terminal sends the entered user data to the server. The server stores the received data in its storage device and simultaneously retrieves data from past successful users from its storage device. This ensures that all the data necessary for analysis is prepared on the server side.

[0675] Step 3:

[0676] The server activates a machine learning algorithm to compare and analyze the received user data with data from past successful users. The TensorFlow library is used for data analysis, statistically matching patterns from past successful users with user characteristics. This process generates a list of suitable occupations for the user, along with an aptitude score for each occupation.

[0677] Step 4:

[0678] The server sends a list of generated occupations and aptitude scores to the user's device. The user's device displays the received information through an interactive user interface, allowing the user to visually determine which occupations are suitable for them.

[0679] Step 5:

[0680] Users can use their devices to view details about occupations that interest them. The server, in response to these requests, provides data to display detailed occupational information and success stories. Ultimately, this allows users to receive concrete support in preparing their applications for their chosen occupations.

[0681] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0682] The system according to the present invention improves accuracy by considering the user's emotions in the process of suggesting the most suitable occupation to job seekers. This system incorporates an emotion engine to recognize the user's emotional state in real time and reflects it in job suggestions and application support.

[0683] The server receives input data from job seekers and stores it in a database. Next, the server utilizes an AI agent to identify the most suitable occupation for the job seeker based on data from past successful candidates. During this process, the emotion engine analyzes the user's facial expressions and voice tone through the device to understand their emotional state.

[0684] By recognizing the various emotions users experience during their job search, the system tailors the list of jobs it presents to their psychological needs. For example, if a user is feeling stressed, the server can prioritize suggesting jobs with lower stress levels.

[0685] The terminal presents the user with a list of occupations sent from the server, along with the results of the emotion engine's analysis. Through an interactive interface, the user can immediately view detailed information about the presented occupations. Furthermore, the emotion data is saved as user feedback and used to improve the accuracy of future suggestions.

[0686] For example, when a user is considering a career as a "creative director," if the program detects anxiety from the user's facial expressions or voice, it will provide a detailed explanation of the common challenges associated with that profession. In this way, the present invention makes it possible to provide nuanced career selection support that responds to the user's emotions.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The user logs into the system and answers a self-assessment questionnaire. During this process, they turn on their camera and microphone and select settings that allow for the collection of emotional data.

[0690] Step 2:

[0691] The device sends the user's responses, along with facial expression data captured by the camera and audio data acquired from the microphone, to the server. This data is then processed by an emotion engine.

[0692] Step 3:

[0693] The server uses an AI agent to match the user's input data with a database of past success stories and generates a list of the most suitable occupations. Meanwhile, the emotion engine analyzes the transmitted emotion data and recognizes the user's emotional state in real time.

[0694] Step 4:

[0695] Based on the analyzed emotional data, the server adjusts job suggestions to take into account the user's mental stress level and interests. For example, if negative emotions are detected, it prioritizes presenting supportive information and low-stress occupations.

[0696] Step 5:

[0697] The terminal displays a list of occupations and related information received from the server to the user. An interactive interface allows the user to view details about each occupation and obtain further information about their selected occupation.

[0698] Step 6:

[0699] Based on user selections, the device sends emotional feedback back to the server to improve subsequent job suggestions and optimize application support. The emotional engine also provides information to enhance user motivation during the application preparation process.

[0700] Through this series of steps, the system can provide users with highly accurate career support that takes their emotions into account.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In conventional job placement systems, the job lists provided to job seekers were based solely on past data and lacked consideration for the job seeker's emotional state. As a result, it was difficult to provide appropriate job suggestions that met the job seeker's needs and psychological state, leading to decreased accuracy and convenience of suggestions.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for collecting characteristic information of job seekers, means for acquiring emotional information, and means for extracting information on past applicants from a data storage device. This enables highly accurate job recommendations based on the characteristics and emotions of job seekers.

[0706] A "job seeker" refers to an individual who is looking for employment and has specific career and job-related preferences.

[0707] "Characteristic information" refers to a collection of personal data about job seekers, including resume information, skills, and work history.

[0708] "Emotional information" refers to data that indicates the psychological state of a job seeker, and is extracted from things like facial expressions and tone of voice.

[0709] An "experienced professional" refers to an individual who has achieved success in a specific occupation in the past, and information about that individual's work history is stored in a data storage device.

[0710] A "data storage device" refers to a recording medium that stores information and allows data to be read out as needed.

[0711] "Machine learning techniques" refer to algorithms and methods that enable computers to learn patterns from data and perform predictions and classifications.

[0712] "Occupation" refers to the duties or job types that a job seeker may be able to perform, and the content of these duties will be specified by the information provided.

[0713] An "interactive display device" refers to a device that allows users to input information through an interface and receive results.

[0714] The present invention's system utilizes a combination of multiple components to provide job seekers with career suggestions that take their emotional information into account. This system aims to suggest suitable occupations to job seekers using a server, terminals, and AI technology.

[0715] The server collects characteristic information about job seekers in the initial stages. This characteristic information includes resume data, skills information, and past work history. The server also retrieves and analyzes data on past experienced individuals from its data storage device. Based on this data, the server uses machine learning techniques to identify suitable occupations for job seekers. In this process, the server's generative AI models are used to achieve highly accurate analysis.

[0716] The terminal is equipped with a device to collect the user's facial expressions and voice information in real time and transmit it to the server as emotional information. This uses a computer or smartphone with a camera and microphone. The terminal communicates data bidirectionally between the job seeker and the server, providing job suggestions that take emotional information into account.

[0717] As a concrete example, suppose a user is searching for a job and the device's camera detects anxiety from the user's facial expression. When this information is transmitted to the server, the server uses a generative AI model to present a list of jobs that take this emotional state into account. This list prioritizes jobs that are less stressful and more emotionally resonant.

[0718] An example of a prompt message would be, "Based on your current mental state, the following occupations are considered suitable for you. Would you like to view more information?" This allows job seekers to obtain occupational information that aligns with their emotions and needs.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The server receives job seeker profile information as input. This is done when users enter resume data, skills, work history, etc., through an online form. The received data is immediately stored in a database. This data is used for subsequent processing.

[0722] Step 2:

[0723] The device collects user emotional information. This process utilizes the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time. The analyzed emotional data is sent to a server. Input is facial and voice data, while output is numerical data or labels indicating the emotional state.

[0724] Step 3:

[0725] The server extracts information on past applicants from its data storage. The extracted data is then analyzed using a machine learning algorithm. Here, the applicant's characteristic and emotional information are combined to create input data for recommending the most suitable occupation. The output is a list of suitable occupations.

[0726] Step 4:

[0727] The server uses a generative AI model to evaluate job suitability based on past success stories in the database and the characteristics and emotional information of job seekers. This algorithm takes into account the stress level and required skills for each job. As a result of the processing, a filtered list of jobs is generated and sent to the terminal.

[0728] Step 5:

[0729] The terminal presents the user with a list of occupations received from the server. It generates prompts based on the user's emotional state and provides an interactive display to help the user select the most suitable occupation. The user can then view details of the occupations based on the presented information. The terminal's output consists of the prompts and the occupation list interface.

[0730] Step 6:

[0731] Users provide feedback on the provided job information. This feedback is then sent back to the server and stored in the server's database. This information is used to improve the accuracy of future job suggestions. The output is the addition of the feedback to the database.

[0732] (Application Example 2)

[0733] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Conventional job suggestion systems propose jobs without considering the emotions or psychological state of job seekers, resulting in low emotional satisfaction and a lack of acceptance of the suggested jobs. Furthermore, because they cannot provide flexible job suggestions that utilize emotional data, it is difficult to match job seekers with appropriate jobs that meet their latent needs.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0736] In this invention, the server includes means for receiving input data from job seekers, means for acquiring data from past successful candidates from a storage device, means for analyzing the input data from job seekers and the data from successful candidates using a machine learning algorithm to identify suitable occupations for job seekers, means for collecting and analyzing the emotional data of job seekers using an emotion analysis engine and reflecting the results in job suggestions, and means for presenting suitable occupations for job seekers. This enables flexible and highly accurate job suggestions that take into account the emotional state of job seekers.

[0737] "Job seeker input data" refers to information provided by individuals who are actively seeking employment, such as their work history and desired job type.

[0738] "Data on past successes" refers to information about the work history and experience of people who previously received job placement services and achieved success.

[0739] A "storage device" is a device that stores information over a long period of time and makes it possible to search and retrieve it as needed.

[0740] A "machine learning algorithm" is a computational method that allows a computer to recognize patterns based on provided data and make predictions and judgments about new data.

[0741] An "emotion analysis engine" is a software module that uses voice and image data to recognize and analyze a person's emotional state.

[0742] "Job recommendation" is a process that recommends suitable jobs to job seekers based on analyzed data.

[0743] An "interactive information processing screen" refers to a device that users can operate visually or tactilely, and that enables the provision and reception of information in real time.

[0744] "Facilitation function" refers to the functional characteristics of a system designed to facilitate specific activities or operations.

[0745] The server receives input data from job seekers and stores it in a database. The database contains data on past successful candidates, which the AI ​​agent utilizes. The AI ​​agent uses machine learning algorithms to analyze the job seeker's input data and the data on past successful candidates to identify the most suitable occupation for the job seeker. In this process, an emotion analysis engine monitors the job seeker's facial expressions and voice tone, collecting and analyzing emotional data. The resulting emotional state information is incorporated into job suggestions, generating a personalized list.

[0746] The terminal incorporates the results of sentiment analysis when presenting job seekers with a list of occupations received from the server. Users can view detailed occupation information through the terminal's interactive information processing screen. Furthermore, the job seeker's sentiment data is stored in the system as feedback and used to improve the accuracy of future job suggestions.

[0747] For example, when a user is considering a job as a "creative director," if the emotion analysis engine detects anxiety, the device will provide a detailed explanation of the common challenges of that profession. This is a function that allows the system to understand the user's choices and provide more in-depth information as needed.

[0748] An example of a prompt message is, "If the user's emotional state is stressed, please enhance job suggestions that involve low workplace pressure." This prompt helps to enhance job suggestions using a generative AI model.

[0749] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0750] Step 1:

[0751] The server receives input data from job seekers. This input data includes information such as work history, desired job type, and skills. The received data is stored in a database and used in the next analysis step.

[0752] Step 2:

[0753] The server retrieves data on past successes from a database. This data includes the work history and suitable job types of the successful individuals. Based on this dataset, the server prepares to perform analysis using machine learning algorithms.

[0754] Step 3:

[0755] The machine learning algorithm installed on the server analyzes the job seeker's input data and past success data. Based on the acquired data, it performs pattern recognition and calculations to identify suitable occupations for the job seeker. As output, it generates a list of occupations with a high degree of fit.

[0756] Step 4:

[0757] The device captures the user's facial expressions and voice using a camera and microphone, and analyzes them in real time using an emotion analysis engine. The emotion data obtained through this process indicates the user's psychological state, which influences the career suggestions in the next step.

[0758] Step 5:

[0759] The server takes the analyzed sentiment data into account and uses a generative AI model to adjust job suggestions based on the prompt text. In this process, it flexibly adjusts suggestions by combining sentiment data with the job list. For example, if the user is feeling stressed, it prioritizes listing jobs with lower stress levels.

[0760] Step 6:

[0761] The terminal presents the user with a customized list of occupations on an interactive information processing screen. The user can view detailed information from the list and select a suitable occupation. An example of a prompt message is: "If the user's emotional state is stressed, please enhance suggestions for jobs with low workplace pressure."

[0762] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0763] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0765] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0766] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0767] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0768] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0769] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0770] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0771] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0772] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0773] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0774] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0775] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0776] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0777] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0778] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0779] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0780] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0781] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0782] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means of receiving input data from job seekers,

[0786] Methods for obtaining data on past successes from a database,

[0787] A method for identifying suitable occupations for job seekers by analyzing job seeker input data and successful candidate data using machine learning algorithms,

[0788] Means of presenting suitable occupations to job seekers,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, further comprising means for assisting a job seeker in preparing to apply for an occupation they have selected.

[0792] (Claim 3)

[0793] The system according to claim 1, further comprising means for presenting occupations via an interactive interface.

[0794] "Example 1"

[0795] (Claim 1)

[0796] A means of receiving information entered by job seekers,

[0797] A means of obtaining information on past successful individuals from a recording device,

[0798] A method to identify the most suitable job for a job seeker by analyzing the input information of job seekers and the information of successful candidates using machine learning techniques,

[0799] A means of providing job seekers with the most suitable job,

[0800] A means for storing the received input information of job seekers in a recording device,

[0801] A means of performing analysis using an AI agent,

[0802] A means for generating an aptitude assessment based on the analysis results,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for assisting a job seeker in preparing to apply for a position of their choice.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for providing services via an interactive display device.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of receiving input data from job seekers,

[0811] A means of obtaining data on past successes from storage devices,

[0812] A method for identifying suitable occupations for job seekers by analyzing job seeker input data and successful candidate data using machine learning algorithms,

[0813] Means of presenting suitable occupations to job seekers,

[0814] A display means that provides user interaction,

[0815] Data analysis tools,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for assisting a job seeker in preparing to apply for a selected occupation.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising means for presenting occupations via an interactive information terminal.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] Means for collecting information on the characteristics of job seekers,

[0824] Means of acquiring emotional information,

[0825] A means of extracting information from past experienced individuals from data storage devices,

[0826] A means of identifying suitable occupations by analyzing the characteristic information and emotional information of job seekers and the information of experienced individuals using machine learning technology,

[0827] A method for prioritizing and presenting suitable occupations while considering emotional information,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which includes a function to support job seekers in preparing to apply for jobs they have selected.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising a function for presenting occupations via an interactive display device.

[0833] "Application example 2 when combining with an emotional engine"

[0834] (Claim 1)

[0835] A means of receiving input data from job seekers,

[0836] A means of obtaining data on past successful individuals from a storage device,

[0837] A method for identifying suitable occupations for job seekers by analyzing job seeker input data and successful candidate data using machine learning algorithms,

[0838] A means of collecting and analyzing job seekers' emotional data using an emotion analysis engine and reflecting the results in job recommendations,

[0839] Means of presenting suitable occupations to job seekers,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for assisting job seekers in preparing to apply for selected occupations and providing detailed information based on sentiment data.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for presenting occupations via an interactive information processing screen and having a promotion function that reflects the results of emotion analysis. [Explanation of Symbols]

[0845] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving input data from job seekers, Methods for obtaining data on past successes from a database, A method for identifying suitable occupations for job seekers by analyzing job seeker input data and successful candidate data using machine learning algorithms, Means of presenting suitable occupations to job seekers, A system that includes this.

2. The system according to claim 1, further comprising means for assisting a job seeker in preparing to apply for an occupation they have selected.

3. The system according to claim 1, further comprising means for presenting occupations via an interactive interface.

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