system

Abstract

We provide the system. [Solution] An input method for entering individual skills information of job seekers, An analysis means that analyzes the skill information input by the input means and generates a skill profile, An information selection means for selecting job postings based on the skill profile generated by the aforementioned analysis means, A means for displaying job information selected by the aforementioned information selection means to the user, A content generation means that generates learning content based on the skills that are lacking, Means for providing the user with the learning content generated by the content generation means, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the labor market, there is a problem that job seekers cannot access appropriate employment opportunities due to geographical constraints and language barriers. In addition, since there is a lack of means for job seekers to quickly acquire skills that meet market requirements, the unemployment problem among young people and job seekers who wish to change careers is particularly serious. In such a situation, there is a need for a system that allows job seekers to quickly and accurately access job information and quickly supplement the lacking skills.

Means for Solving the Problems

[0005] This invention provides a multilingual AI matching platform that includes an analysis means for analyzing skill information entered by job seekers to generate skill profiles. Furthermore, it selects job postings based on the analyzed skill information and displays the most suitable job postings to job seekers. In addition, it uses generational AI technology to instantly generate learning content for skills that are lacking and provides it to job seekers, thereby promoting their skill improvement. In this way, the system enables job seekers to overcome geographical constraints and language barriers to quickly access appropriate employment opportunities and acquire the necessary skills.

[0006] An "input method" is an interface that allows job seekers to input information such as their skills, work history, and desired conditions into the system.

[0007] "Analysis means" refers to methods and techniques for processing information about job seekers obtained through input means and generating individual skill profiles.

[0008] "Information selection means" refers to the process of searching a job database based on skill profiles generated by analysis means and selecting the most suitable job information.

[0009] "Means of display" refers to methods or devices for presenting selected job information to job seekers in an easily understandable format.

[0010] "Content generation methods" refer to AI technologies and algorithms that instantly create learning content to compensate for the lack of skills among job seekers. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the 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), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the 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, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] As an embodiment of this invention, a multilingual AI matching platform is provided. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits this information to a server. The server analyzes the received data and generates individual skill profiles using natural language processing technology.

[0033] The generated skill profile is then matched by the server against a database of company job postings. The server matches this profile with job information and selects the most suitable job postings based on the user's skill set and desired conditions. The selected job postings are then presented to the user via their terminal.

[0034] Furthermore, the server utilizes generative AI technology to create learning content to compensate for users' skill deficiencies. For example, if a user wishes to improve a specific technical skill, the server can immediately generate and provide corresponding learning materials, allowing users to quickly improve their skills.

[0035] As a concrete example, suppose a user desires a job in "data analysis" and has beginner-level Python skills. The server recognizes this information through analysis and provides relevant job postings. Furthermore, if it determines that the user needs to improve their Python data analysis skills, the server generates and provides online Python tutorials and practice problems as content. This allows the user to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. In this way, the invention realizes a platform that enables job seekers to leverage their skill profiles to obtain employment opportunities in an international and multilingual labor market.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The user uses the device to input information such as their skills, work history, desired job type, and work location. The device then retrieves this input data.

[0039] Step 2:

[0040] The terminal sends user input data to the server. The data sent includes text-formatted resume information and selected skills information.

[0041] Step 3:

[0042] The server analyzes the received data. Using natural language processing techniques, it analyzes the user's skill information and generates a skill profile. This profile identifies skill categories and levels.

[0043] Step 4:

[0044] The server searches the job database based on the skill profile and selects suitable job postings. Keyword matching and scoring algorithms are used to select highly relevant job postings.

[0045] Step 5:

[0046] The server sends the selected job postings to the terminal. The information sent includes company name, job title, required skills, work location, and salary information.

[0047] Step 6:

[0048] The device displays the job postings it has received to the user. The user can review the displayed job postings and select those that interest them.

[0049] Step 7:

[0050] To compensate for the lack of skills on the server, we utilize generative AI to generate instant learning content. For example, if a user needs data analysis skills, we create appropriate online tutorials and learning materials.

[0051] Step 8:

[0052] The server generates learning content and sends it to the device, which then provides it to the user. The user can then use the provided content to improve their skills.

[0053] (Example 1)

[0054] 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."

[0055] In today's labor market, job seekers often struggle to find suitable positions by leveraging their own skills and knowledge. Furthermore, a lack of effective learning materials to address job seekers' skill deficiencies hinders rapid skill development. This situation needs to be improved to enable more efficient and accurate skills matching and learning support.

[0056] 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.

[0057] In this invention, the server includes data input means for inputting individual skill information of job seekers, information analysis means for analyzing the input skill information and generating a skill profile, and data selection means for selecting job information based on the generated skill profile. This makes it possible for job seekers to efficiently find job information that suits them. Furthermore, it is possible to quickly support skill improvement by generating and providing learning materials based on the skills that are lacking.

[0058] "Data entry means" refers to a device or method for job seekers to input individual skills information.

[0059] "Information analysis means" refers to a device or method for analyzing input skill information and generating a skill profile.

[0060] "Data selection means" refers to a device or method for selecting the most suitable job information based on the generated skill profile.

[0061] "Display means" refers to a device or method for presenting selected job information to the user.

[0062] "Material generation means" refers to an apparatus or method for generating learning materials based on the skills that are lacking.

[0063] "Means of provision" refers to a device or method for providing generated learning materials to users.

[0064] "Natural language processing methods" refer to technologies that mechanically understand, analyze, and generate human language.

[0065] "Generative AI technology" refers to technology that uses artificial intelligence to automatically generate new data and content.

[0066] This invention constructs a multilingual AI matching platform system to help job seekers maximize their skills and find the most suitable job. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits the entered information to the server using the HTTPS protocol.

[0067] The server analyzes the received data and generates individual skill profiles using Google® Cloud Natural Language API and other natural language processing software. The generated skill profiles are compared with job postings from each company registered in the cloud database to ensure the best possible match. The server uses SQL queries to search the database for the most suitable job postings and creates a ranking.

[0068] The selected job postings are sent to the terminal in JSON format and presented to the user. This allows the user to efficiently obtain job information that matches their skill set.

[0069] Furthermore, the server utilizes generative AI models (e.g., widely used language models) to generate learning content to complement the user's lacking skills. In this process, the user inputs prompts tailored to the area in which they wish to improve their skills into the generative AI. For example, if a user desires a job in "data analysis" and has beginner-level Python skills, they might use a prompt such as "Generate intermediate-level learning materials on data analysis using Python." Based on this prompt, the generative AI creates learning materials and provides them to the user.

[0070] This allows users to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. The system provides job seekers with concrete ways to leverage their skill profiles and effectively secure employment opportunities in the international and multilingual labor market.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users input information such as skills, work experience, and desired job type using a terminal. This information is structured by the terminal and sent to the server using the HTTPS protocol. The input data includes individual pieces of information written in text format.

[0074] Step 2:

[0075] The server receives information sent from the terminal. The received data is temporarily stored in a database. Next, the server uses natural language processing methods to analyze the received information. This analysis generates a personal skill profile. The input is raw text data, and the output is a structured skill profile.

[0076] Step 3:

[0077] The server compares the generated skill profile with the job information in the job database. Here, an SQL query is used to query for the most suitable job postings based on the skill profile. The input is the skill profile, and the output is the matching job postings in the database.

[0078] Step 4:

[0079] The server ranks the searched job postings and selects the most suitable one. This selection result is converted to JSON format, ready to be sent to the terminal. The input is a list of search results, and the output is the ranked job postings presented to the user.

[0080] Step 5:

[0081] The terminal receives job postings in JSON format sent by the server and presents them visually to the user. The user then decides whether or not to apply based on the information provided. The input is job postings in JSON format, and the output is the visual information displayed to the user.

[0082] Step 6:

[0083] The server utilizes generative AI technology to generate learning content that addresses the user's skill gaps. Prompt statements are input to the generative AI, which then creates specific learning materials as a result. The input is a prompt statement, and the output is customized learning content.

[0084] Step 7:

[0085] The server sends the generated learning content to the terminal. The terminal displays this content to the user, who then uses it to improve their skills. The input is the generated learning content, and the output is the visual content provided to the user.

[0086] (Application Example 1)

[0087] 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."

[0088] Modern electronic payment systems lack sufficient methods for users to easily select financial products that suit their purchasing behavior. Furthermore, there are insufficient means to efficiently improve users' financial literacy. Therefore, there is a need to provide financial products and learning opportunities optimized for individual users.

[0089] 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.

[0090] In this invention, the server includes data acquisition means for inputting information on purchasing activities, analysis means for analyzing the acquired purchasing information to generate individual user profiles, and information selection means for selecting financial product information based on the generated user profiles. This makes it possible to provide users with optimal financial products and learning content that match their purchasing behavior.

[0091] "Purchasing activity information" refers to data about a user's shopping history and spending patterns, which is obtained through electronic payments.

[0092] "Data acquisition means" refers to hardware and software elements for collecting information on users' purchasing activities, such as sensors or APIs that have the function of collecting data.

[0093] "Analysis means" refers to a system that analyzes purchase information collected by data acquisition means, extracts user behavior and trends, and generates individual profiles.

[0094] A "user profile" is an individual dataset generated by analytical methods that reflects a user's purchasing behavior and preferences, and is used for purposes such as recommending financial products.

[0095] An "information selection tool" is a system that has the function of identifying and providing the most suitable financial products and services based on the generated user profile.

[0096] "Financial product information" refers to data about financial services and benefits that can be offered to users, such as credit card and debit card promotions, loans, and investment products.

[0097] "Educational content" refers to learning programs and materials provided to improve users' financial knowledge, and is created using generative AI technology.

[0098] The system for realizing this invention consists of three parties: a user, a terminal, and a server. The user makes electronic payments using a smartphone or other device. The terminal collects information on the user's purchasing activities and transmits it to the server.

[0099] The server first uses data acquisition tools to receive information about the user's purchasing activities. This information includes the user's purchase history and spending trends. The received data is then used with analysis tools to generate a user profile. For this profile generation, algorithms known as natural language processing techniques, such as spaCy, are used. This allows for a detailed analysis of the user's purchasing patterns.

[0100] Next, based on the generated user profile, the information selection system selects financial product information. The selected information is compared with a database of various financial services and promotions stored on the server, and the most suitable financial product is suggested to the user. During this process, the selected information is displayed to the user via the terminal.

[0101] Furthermore, the server uses content generation tools to generate educational content related to the financial knowledge that users need. This process utilizes generative AI technology; for example, OpenAI's GPT model instantly generates educational content. This allows users to effectively enhance their financial knowledge.

[0102] For example, if a user frequently shops online, a cashback promotion for a specific credit card will be suggested based on their analyzed profile. Furthermore, for users who wish to improve their financial knowledge, an online course on the fundamentals of personal financial management will be recommended as part of the generated educational content.

[0103] Examples of prompts include, "Generate the best credit card promotions based on this user's recent online shopping activity," and "Create learning content based on the financial knowledge information this user desires."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] A user makes an electronic payment using their smartphone. The smartphone collects data on the user's purchase activity and sends it to a server via the device. The input is the user's purchase history data, and the output is the transmission of data to the server.

[0107] Step 2:

[0108] The server receives information about transmitted purchasing activities using data acquisition methods. This data includes the user's transaction amount, category, frequency, etc. The input is purchase information data from the terminal, and the output is data storage within the server. In this step, the received data is organized into a database.

[0109] Step 3:

[0110] The server uses analysis tools to analyze acquired purchase information and generate user profiles. It utilizes natural language processing algorithms (e.g., spaCy) to extract user purchasing patterns and preferences from the data. The input is accumulated purchase information, and the output is individual user profiles.

[0111] Step 4:

[0112] The server selects financial product information based on the generated user profile through an information filtering mechanism. It searches the database for products and promotions that match the profile analysis results and selects the most suitable ones. The input is the user profile, and the output is the selected financial product information.

[0113] Step 5:

[0114] The server presents the selected financial product information to the user via the terminal. This allows the user to check financial products and promotions that are suitable for them. The input is the selected product information, and the output is what is presented to the user.

[0115] Step 6:

[0116] The server uses content generation tools to generate educational material related to missing financial knowledge. It uses a generation AI model (e.g., OpenAI GPT) to create learning materials and resources tailored to the user. The input is information about missing financial knowledge, and the output is the generated educational content.

[0117] Step 7:

[0118] The server provides the generated educational content to the user via the terminal. This enables the user to learn quickly through the presented educational materials. The input is the generated educational content, and the output is the delivery of the content to the user.

[0119] 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.

[0120] As an embodiment of this invention, a system is provided that incorporates an emotion engine into a multilingual AI matching platform. First, the user uses a terminal to input information such as work history, skill set, and desired job type. The terminal collects this data and sends it to the server.

[0121] The server analyzes the received data and generates a user skill profile using a natural language processing algorithm. Then, based on this skill profile, it searches the company's job database and selects suitable job postings.

[0122] Furthermore, the server utilizes an emotion engine to assess the user's emotional state. It analyzes the user's input and actions on the device to understand their current emotional needs. This emotional information also influences the selection and order of job postings presented. For example, if the server assesses that the user is feeling stressed, it can prioritize presenting job postings from companies that offer a more comfortable work environment.

[0123] The emotion engine is also used to provide learning content using generative AI. Based on the user's emotional state, it generates easy-to-learn and motivating content and delivers it to the user through their device.

[0124] As a concrete example, suppose a user desires a job in "software development" and has intermediate-level artificial intelligence skills. In this case, if the user's emotional state is elevated, the server will proactively provide job postings that include challenging roles and advanced-level learning materials. On the other hand, if the emotion engine detects that the user is tired or stressed, the server will prioritize presenting job postings from companies that offer flexible working hours and learning content with less work pressure.

[0125] In this way, the present invention aims to provide more appropriate employment opportunities and learning experiences by adapting not only to the skill improvement of job seekers but also to their emotional state.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user enters information about their work history, skills, and desired job type and location via a device. The device collects this information and sends it to the server.

[0129] Step 2:

[0130] The server analyzes the information it receives. It uses natural language processing techniques to evaluate the user's skill set and generate a skill profile. This profile accurately reflects the user's abilities and experience.

[0131] Step 3:

[0132] The server searches the job database based on the generated skill profile and selects job postings that match the user's desired conditions. Selection is based on keyword matching and the degree of match in required skills.

[0133] Step 4:

[0134] The device collects emotional information based on user input data and analyzes the device's operation and input patterns. This data is sent to a server and used to infer the user's emotional state.

[0135] Step 5:

[0136] The server uses an emotion engine to evaluate the user's emotional state from the data they send. For example, it can detect stress or euphoria from input speed, frequency of use, and word choice patterns.

[0137] Step 6:

[0138] Based on the evaluation of the user's emotional state, the server adjusts the order in which job postings are presented. Depending on the emotional state indicated by the evaluation, the server prioritizes presenting the user with more appropriate job postings.

[0139] Step 7:

[0140] To compensate for the server's lack of skills, it utilizes generative AI technology to generate instant learning content. It takes emotional information into account to create content that allows users to learn in a more relaxed manner, as well as content that is challenging.

[0141] Step 8:

[0142] The server generates learning content and sends it to the device. The device then provides the received content to the user, allowing the user to efficiently improve their skills according to their mood and mental state.

[0143] (Example 2)

[0144] 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".

[0145] There is a need to provide a matching platform that not only offers job postings based on skills information to job seekers with multiple languages ​​and diverse skill sets, but also takes into account the emotional state of the job seekers. Furthermore, in order to support job seekers' skill development, it is necessary to provide appropriate learning content tailored to their emotional state. However, conventional systems have lacked a comprehensive means to achieve these goals.

[0146] 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.

[0147] In this invention, the server includes input means for inputting individual skill information and emotional state of job seekers, analysis means for analyzing the information collected by the input means to generate a skill profile, and information selection means for selecting job postings based on the skill profile and emotional state. This makes it possible to provide job postings and learning content tailored to the skills and emotional state of each individual job seeker.

[0148] "Job seeker" refers to an individual or information about someone who is looking for work.

[0149] "Skills information" refers to data about an individual's professional abilities and experience.

[0150] "Emotional state" refers to information that indicates the psychological state of a job seeker, such as stress or excitement.

[0151] "Input means" refers to an interface or device for a user to provide information to a system.

[0152] "Analysis means" refers to a process or device that has the function of processing collected data and extracting or generating specific information.

[0153] A "skill profile" is a systematic record of a job seeker's abilities, generated based on individual skill information.

[0154] "Information selection means" refers to a process or device for selecting data that meets specific criteria.

[0155] "Job postings" refer to a dataset of employment opportunities, consisting of information about job types and conditions provided by companies.

[0156] "Display means" refers to a device or interface for visually presenting selected information to a user.

[0157] "Content generation means" refers to a process or device for automatically generating necessary information and educational materials.

[0158] "Learning content" refers to educational materials and information provided for the purpose of improving skills and acquiring knowledge.

[0159] To implement this invention, the user first inputs data on their skills and emotional state using a terminal. This terminal is equipped with a web browser or mobile application, allowing for easy collection of job seeker information through the user interface. For example, the user inputs their work history, skills, and desired job type into a web form. Furthermore, it is envisioned that emotional state will be input using a simple questionnaire or facial recognition technology.

[0160] The terminal sends the collected information to the server. The server is located on a platform for analyzing the received data and generates skill profiles using advanced natural language processing algorithms and generative AI models. This utilizes existing open-source natural language processing libraries and commercial AI models.

[0161] Based on the generated skill profile, the server searches the company's job database and selects the most suitable job postings, taking into account the user's emotional state. Analytics software is used to quickly process the collected data. The selected job postings are returned to the terminal and presented to the user visually.

[0162] Furthermore, the server instantly generates learning content using generative AI based on the user's skill profile and emotional state. The generated content serves as learning material to improve the user's skills and is provided to the user via their device.

[0163] As a concrete example, suppose a user is searching for a "data scientist" job and their AI skills are at an intermediate level. If the emotion engine detects that the user's current emotional state is elevated, the server will prioritize presenting the user with job postings that include challenging projects or advanced-level learning materials.

[0164] An example of a prompt would be, "Suggest job postings and learning content based on emotional state for a data scientist with intermediate AI skills." In this way, the system can provide job postings and learning opportunities that correspond to the user's skills and emotional state.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users begin by using a device to input their skills information and emotional state. They enter information such as their work history, skill set, and desired job into an input form, and register their emotional state through a short questionnaire and sensor data. This input is temporarily stored on the device and then prepared to be sent to the server.

[0168] Step 2:

[0169] The device transmits user input data to the server using a secure protocol. During this process, the data is converted to a standardized format. The transmitted data includes work history, skill set, desired job type, and emotional state. After transmission is complete, the device displays a notification to the user confirming that the data has been successfully sent.

[0170] Step 3:

[0171] The server runs a natural language processing algorithm to analyze the received data. The analysis algorithm processes the work history and skill set data received as input, generating an appropriate skill profile as output. This analysis systematically organizes the user's skills, which are then temporarily stored on the server.

[0172] Step 4:

[0173] The server searches the job database using the generated skill profile and the user's emotional state. An information filtering algorithm filters the job postings to match the skill profile and emotional state. The output of this process is a list of the most suitable job postings, which the server sends to the terminal.

[0174] Step 5:

[0175] The terminal displays job postings received from the server to the user. The displayed job postings include work styles and environments that are suitable for the user's current emotional state. The user can review the presented information and apply for suitable jobs.

[0176] Step 6:

[0177] The server uses generative AI to create learning content based on the user's skill profile and emotional state. Using the prompt "Provide emotional state-based content for data scientists with intermediate AI skills," the generative AI algorithm generates specific learning materials. This ensures that learning content is tailored to improve the user's skills.

[0178] Step 7:

[0179] The device provides users with generated learning content. Users can learn from this content through the device and improve their skills. This provides a foundation for preparing for better career opportunities.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] Traditional job-seeker matching systems can perform basic matching based on users' skills and work experience, but they have a problem in that they cannot provide job information or educational content that takes into account the emotional state of the users. Because the emotional state of the users is not taken into consideration, appropriate measures are not taken to address stress levels or decreased motivation, and effective support for job-seeking activities and skill improvement is difficult to provide.

[0183] 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.

[0184] In this invention, the server includes information input means, information analysis means, information selection means, display means, content provision means, information provision means, emotion analysis means, and information presentation means. This makes it possible to evaluate the emotional state of the user and provide more personalized job information and educational content.

[0185] An "information input device" is a device that provides an interface for users to input their skills information, work history, and other relevant data.

[0186] An "information analysis tool" is a system for processing input skill information and generating a user's ability profile.

[0187] An "information selection tool" is a device that extracts suitable job postings based on the generated competency profile.

[0188] A "display means" is a device used to visually present selected job information to users.

[0189] A "content delivery device" is a device that has the function of generating educational content that addresses skills that are lacking.

[0190] An "information provision means" is a device that provides generated educational content to users.

[0191] An "emotion analysis tool" is a system for evaluating a user's emotional state based on their input and actions.

[0192] An "information presentation means" is a device for providing users with information that has been adjusted through emotion analysis.

[0193] In order to implement this invention, it is necessary to construct a system in which a server and a user's terminal work together. The server has information input means, information analysis means, information selection means, display means, content provision means, information provision means, sentiment analysis means, and information presentation means.

[0194] The user uses a terminal to transmit their skills information and work history to the server through an information input device. This input information is analyzed on the server side using a natural language processing algorithm by an information analysis device and stored in a database as the user's competency profile. Next, an information selection device extracts suitable job postings from the database based on this competency profile and presents them to the user's terminal through a display device.

[0195] Furthermore, the server uses emotion analysis tools to evaluate the user's emotional state. Based on this emotional evaluation, the information presentation tools select adjusted job postings and educational content, providing appropriate content according to the user's emotions. For example, if the user is feeling stressed, the information presentation tools may prioritize displaying job postings that offer flexible working hours or present educational content with a relaxing effect.

[0196] The content delivery method utilizes generative artificial intelligence technology to instantly generate educational content to improve users' abilities. An example of a prompt given to the generative AI model would be, "Please generate educational content with a relaxing effect."

[0197] The entire system aims to provide more effective job search support and skill development by offering personalized job information and content tailored to the user's work history and emotional state.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] Users use their devices to send individual data, such as skills information and work history, to the server via input methods. This input data is then imported into the server as basic information for creating user profiles.

[0201] Step 2:

[0202] The server processes the received skills information using information analysis tools. Using natural language processing algorithms, it analyzes the input data and generates a user's skills profile. This profile is stored in a database as structured data about the user's skills and desired job type.

[0203] Step 3:

[0204] The server uses information filtering methods to select job postings based on the generated competency profiles. It employs algorithms that match each item in the competency profile with job postings in the database, extracting and outputting highly suitable job postings. These output postings are then listed in order of priority.

[0205] Step 4:

[0206] The server uses an emotion analysis tool after an information analysis tool to evaluate the user's emotional state based on their terminal operations and input. The evaluated emotional state is stored in the database as the user's emotional information and influences the selection results of job postings.

[0207] Step 5:

[0208] Based on the user's emotional state, the server uses information presentation tools to adjust the display order and content of job postings. For example, a user experiencing stress will be prioritized to see job postings for relaxing workplaces. This adjusted list of job postings is then sent to and output to the user's terminal.

[0209] Step 6:

[0210] The content delivery method utilizes generative AI technology to generate educational content based on evaluated emotional states and ability profiles. For example, a prompt message such as "Generate educational content with a relaxing effect" is sent to the AI ​​model, and the resulting content is provided to the device.

[0211] Step 7:

[0212] The server displays the final output—adjusted job postings and generated educational content—on the user's device through an information delivery system. This allows users to effectively receive information tailored to their emotional needs and engage in job-seeking activities and self-development.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] As an embodiment of this invention, a multilingual AI matching platform is provided. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits this information to a server. The server analyzes the received data and generates individual skill profiles using natural language processing technology.

[0230] The generated skill profile is then matched by the server against a database of company job postings. The server matches this profile with job information and selects the most suitable job postings based on the user's skill set and desired conditions. The selected job postings are then presented to the user via their terminal.

[0231] Furthermore, the server utilizes generative AI technology to create learning content to compensate for users' skill deficiencies. For example, if a user wishes to improve a specific technical skill, the server can immediately generate and provide corresponding learning materials, allowing users to quickly improve their skills.

[0232] As a concrete example, suppose a user desires a job in "data analysis" and has beginner-level Python skills. The server recognizes this information through analysis and provides relevant job postings. Furthermore, if it determines that the user needs to improve their Python data analysis skills, the server generates and provides online Python tutorials and practice problems as content. This allows the user to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. In this way, the invention realizes a platform that enables job seekers to leverage their skill profiles to obtain employment opportunities in an international and multilingual labor market.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The user uses the device to input information such as their skills, work history, desired job type, and work location. The device then retrieves this input data.

[0236] Step 2:

[0237] The terminal sends user input data to the server. The data sent includes text-formatted resume information and selected skills information.

[0238] Step 3:

[0239] The server analyzes the received data. Using natural language processing techniques, it analyzes the user's skill information and generates a skill profile. This profile identifies skill categories and levels.

[0240] Step 4:

[0241] The server searches the job database based on the skill profile and selects suitable job postings. Keyword matching and scoring algorithms are used to select highly relevant job postings.

[0242] Step 5:

[0243] The server sends the selected job postings to the terminal. The information sent includes company name, job title, required skills, work location, and salary information.

[0244] Step 6:

[0245] The device displays the job postings it has received to the user. The user can review the displayed job postings and select those that interest them.

[0246] Step 7:

[0247] To compensate for the lack of skills on the server, we utilize generative AI to generate instant learning content. For example, if a user needs data analysis skills, we create appropriate online tutorials and learning materials.

[0248] Step 8:

[0249] The server generates learning content and sends it to the device, which then provides it to the user. The user can then use the provided content to improve their skills.

[0250] (Example 1)

[0251] 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".

[0252] In today's labor market, job seekers often struggle to find suitable positions by leveraging their own skills and knowledge. Furthermore, a lack of effective learning materials to address job seekers' skill deficiencies hinders rapid skill development. This situation needs to be improved to enable more efficient and accurate skills matching and learning support.

[0253] 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.

[0254] In this invention, the server includes data input means for inputting individual skill information of job seekers, information analysis means for analyzing the input skill information and generating a skill profile, and data selection means for selecting job information based on the generated skill profile. This makes it possible for job seekers to efficiently find job information that suits them. Furthermore, it is possible to quickly support skill improvement by generating and providing learning materials based on the skills that are lacking.

[0255] "Data entry means" refers to a device or method for job seekers to input individual skills information.

[0256] "Information analysis means" refers to a device or method for analyzing input skill information and generating a skill profile.

[0257] "Data selection means" refers to a device or method for selecting the most suitable job information based on the generated skill profile.

[0258] "Display means" refers to a device or method for presenting selected job information to the user.

[0259] "Material generation means" refers to an apparatus or method for generating learning materials based on the skills that are lacking.

[0260] "Means of provision" refers to a device or method for providing generated learning materials to users.

[0261] "Natural language processing methods" refer to technologies that mechanically understand, analyze, and generate human language.

[0262] "Generative AI technology" refers to technology that uses artificial intelligence to automatically generate new data and content.

[0263] This invention constructs a multilingual AI matching platform system to help job seekers maximize their skills and find the most suitable job. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits the entered information to the server using the HTTPS protocol.

[0264] The server analyzes the received data and uses the Google Cloud Natural Language API and other natural language processing software to generate individual skill profiles. The generated skill profiles are compared with job postings from each company registered in the cloud database to ensure the best possible match. The server uses SQL queries to search the database for the most suitable job postings and creates a ranking.

[0265] The selected job postings are sent to the terminal in JSON format and presented to the user. This allows the user to efficiently obtain job information that matches their skill set.

[0266] Furthermore, the server utilizes generative AI models (e.g., widely used language models) to generate learning content to complement the user's lacking skills. In this process, the user inputs prompts tailored to the area in which they wish to improve their skills into the generative AI. For example, if a user desires a job in "data analysis" and has beginner-level Python skills, they might use a prompt such as "Generate intermediate-level learning materials on data analysis using Python." Based on this prompt, the generative AI creates learning materials and provides them to the user.

[0267] This allows users to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. The system provides job seekers with concrete ways to leverage their skill profiles and effectively secure employment opportunities in the international and multilingual labor market.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users input information such as skills, work experience, and desired job type using a terminal. This information is structured by the terminal and sent to the server using the HTTPS protocol. The input data includes individual pieces of information written in text format.

[0271] Step 2:

[0272] The server receives information sent from the terminal. The received data is temporarily stored in a database. Next, the server uses natural language processing methods to analyze the received information. This analysis generates a personal skill profile. The input is raw text data, and the output is a structured skill profile.

[0273] Step 3:

[0274] The server compares the generated skill profile with the job information in the job database. Here, an SQL query is used to query for the most suitable job postings based on the skill profile. The input is the skill profile, and the output is the matching job postings in the database.

[0275] Step 4:

[0276] The server ranks the searched job postings and selects the most suitable one. This selection result is converted to JSON format, ready to be sent to the terminal. The input is a list of search results, and the output is the ranked job postings presented to the user.

[0277] Step 5:

[0278] The terminal receives job postings in JSON format sent by the server and presents them visually to the user. The user then decides whether or not to apply based on the information provided. The input is job postings in JSON format, and the output is the visual information displayed to the user.

[0279] Step 6:

[0280] The server utilizes generative AI technology to generate learning content that addresses the user's skill gaps. Prompt statements are input to the generative AI, which then creates specific learning materials as a result. The input is a prompt statement, and the output is customized learning content.

[0281] Step 7:

[0282] The server sends the generated learning content to the terminal. The terminal displays this content to the user, who then uses it to improve their skills. The input is the generated learning content, and the output is the visual content provided to the user.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0285] In modern electronic payment systems, there is a lack of a method that enables users to easily select financial products suitable for their purchasing behavior. Also, there are not enough means to efficiently improve users' financial knowledge. Therefore, there is a need to provide financial products and learning opportunities optimized for individual users.

[0286] 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.

[0287] In this invention, the server includes data acquisition means for inputting information on purchasing activities, analysis means for analyzing the acquired purchase information to generate an individual user profile, and information selection means for selecting financial product information based on the generated user profile. As a result, it becomes possible to provide users with optimal financial products and learning content according to their purchasing behavior.

[0288] "Information on purchasing activities" refers to data regarding a user's shopping history and expenditure pattern, which is acquired through electronic payment.

[0289] "Data acquisition means" refers to hardware and software elements for collecting information on a user's purchasing activities, and has a function of collecting data using, for example, sensors and APIs.

[0290] "Analysis means" has a function of analyzing the purchase information collected by the data acquisition means, extracting the behavior and tendencies of the user, and generating an individual profile.

[0291] "User profile" refers to an individual dataset generated by the analysis means that reflects a user's purchasing behavior and preferences, and is used for, among other things, the proposal of financial products.

[0292] An "information selection tool" is a tool that has the function of identifying and providing the most suitable financial products and services based on the generated user profile.

[0293] "Financial product information" refers to data about financial services and benefits that can be offered to users, such as credit card and debit card promotions, loans, and investment products.

[0294] "Educational content" refers to learning programs and materials provided to improve users' financial knowledge, and is created using generative AI technology.

[0295] The system for realizing this invention consists of three parties: a user, a terminal, and a server. The user makes electronic payments using a smartphone or other device. The terminal collects information on the user's purchasing activities and transmits it to the server.

[0296] The server first uses data acquisition tools to receive information about the user's purchasing activities. This information includes the user's purchase history and spending trends. The received data is then used with analysis tools to generate a user profile. For this profile generation, algorithms known as natural language processing techniques, such as spaCy, are used. This allows for a detailed analysis of the user's purchasing patterns.

[0297] Next, based on the generated user profile, the information selection system selects financial product information. The selected information is compared with a database of various financial services and promotions stored on the server, and the most suitable financial product is suggested to the user. During this process, the selected information is displayed to the user via the terminal.

[0298] Furthermore, the server uses content generation tools to generate educational content related to the financial knowledge that users need. This process utilizes generative AI technology; for example, OpenAI's GPT model instantly generates educational content. This allows users to effectively enhance their financial knowledge.

[0299] For example, if a user frequently shops online, a cashback promotion for a specific credit card will be suggested based on their analyzed profile. Furthermore, for users who wish to improve their financial knowledge, an online course on the fundamentals of personal financial management will be recommended as generated educational content.

[0300] Examples of prompts include, "Generate the best credit card promotions based on this user's recent online shopping activity," and "Create learning content based on the financial knowledge information this user desires."

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] A user makes an electronic payment using their smartphone. The smartphone collects data on the user's purchase activity and sends it to a server via the device. The input is the user's purchase history data, and the output is the transmission of data to the server.

[0304] Step 2:

[0305] The server receives information about transmitted purchasing activities using data acquisition methods. This data includes the user's transaction amount, category, frequency, etc. The input is purchase information data from the terminal, and the output is data storage within the server. In this step, the received data is organized into a database.

[0306] Step 3:

[0307] The server uses analysis means to analyze the acquired purchase information and generate a user profile. It utilizes natural language processing algorithms (e.g., spaCy) to extract the user's purchase patterns and preferences from the data. The input is the accumulated purchase information, and the output is an individual user profile.

[0308] Step 4:

[0309] The server selects financial product information based on the generated user profile through information screening means. It searches the database for products and promotions that match the profile analysis results and selects the optimal ones. The input is the user profile, and the output is the selected financial product information.

[0310] Step 5:

[0311] The server presents the selected financial product information to the user via the terminal. As a result, the user can check the content of financial products and promotions suitable for themselves. The input is the selected product information, and the output is the presentation to the user.

[0312] Step 6:

[0313] The server uses content generation means to generate educational content regarding the lacking financial knowledge. It uses a generation AI model (e.g., OpenAI GPT) to create learning materials and resources suitable for the user. The input is the lack information of financial knowledge, and the output is the generated educational content.

[0314] Step 7:

[0315] The server provides the generated educational content to the user via the terminal. As a result, the user can learn quickly through the presented educational materials. The input is the generated educational content, and the output is the content distribution to the user.

[0316] 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.

[0317] As an embodiment of this invention, a system is provided that incorporates an emotion engine into a multilingual AI matching platform. First, the user uses a terminal to input information such as work history, skill set, and desired job type. The terminal collects this data and sends it to the server.

[0318] The server analyzes the received data and generates a user skill profile using a natural language processing algorithm. Then, based on this skill profile, it searches the company's job database and selects suitable job postings.

[0319] Furthermore, the server utilizes an emotion engine to assess the user's emotional state. It analyzes the user's input and actions on the device to understand their current emotional needs. This emotional information also influences the selection and order of job postings presented. For example, if the server assesses that the user is feeling stressed, it can prioritize presenting job postings from companies that offer a more comfortable work environment.

[0320] The emotion engine is also used to provide learning content using generative AI. Based on the user's emotional state, it generates easy-to-learn and motivating content and delivers it to the user through their device.

[0321] As a concrete example, suppose a user desires a job in "software development" and has intermediate-level artificial intelligence skills. In this case, if the user's emotional state is elevated, the server will proactively provide job postings that include challenging roles and advanced-level learning materials. On the other hand, if the emotion engine detects that the user is tired or stressed, the server will prioritize presenting job postings from companies that offer flexible working hours and learning content with less work pressure.

[0322] In this way, the present invention aims to provide more appropriate employment opportunities and learning experiences by adapting not only to the skill improvement of job seekers but also to their emotional state.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The user enters information about their work history, skills, and desired job type and location via a device. The device collects this information and sends it to the server.

[0326] Step 2:

[0327] The server analyzes the information it receives. It uses natural language processing techniques to evaluate the user's skill set and generate a skill profile. This profile accurately reflects the user's abilities and experience.

[0328] Step 3:

[0329] The server searches the job database based on the generated skill profile and selects job postings that match the user's desired conditions. Selection is based on keyword matching and the degree of match in required skills.

[0330] Step 4:

[0331] The device collects emotional information based on user input data and analyzes the device's operation and input patterns. This data is sent to a server and used to infer the user's emotional state.

[0332] Step 5:

[0333] The server uses an emotion engine to evaluate the user's emotional state from the data they send. For example, it can detect stress or euphoria from input speed, frequency of use, and word choice patterns.

[0334] Step 6:

[0335] Based on the evaluation of the user's emotional state, the server adjusts the order in which job postings are presented. Depending on the emotional state indicated by the evaluation, the server prioritizes presenting the user with more appropriate job postings.

[0336] Step 7:

[0337] To compensate for the server's lack of skills, it utilizes generative AI technology to generate instant learning content. It takes emotional information into account to create content that allows users to learn in a more relaxed manner, as well as content that is challenging.

[0338] Step 8:

[0339] The server generates learning content and sends it to the device. The device then provides the received content to the user, allowing the user to efficiently improve their skills according to their mood and mental state.

[0340] (Example 2)

[0341] 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".

[0342] There is a need to provide a matching platform that not only offers job postings based on skills information to job seekers with multiple languages ​​and diverse skill sets, but also takes into account the emotional state of the job seekers. Furthermore, in order to support job seekers' skill development, it is necessary to provide appropriate learning content tailored to their emotional state. However, conventional systems have lacked a comprehensive means to achieve these goals.

[0343] 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.

[0344] In this invention, the server includes input means for inputting individual skill information and emotional state of job seekers, analysis means for analyzing the information collected by the input means to generate a skill profile, and information selection means for selecting job postings based on the skill profile and emotional state. This makes it possible to provide job postings and learning content tailored to the skills and emotional state of each individual job seeker.

[0345] "Job seeker" refers to an individual or information about someone who is looking for work.

[0346] "Skills information" refers to data about an individual's professional abilities and experience.

[0347] "Emotional state" refers to information that indicates the psychological state of a job seeker, such as stress or excitement.

[0348] "Input means" refers to an interface or device for a user to provide information to a system.

[0349] "Analysis means" refers to a process or device that has the function of processing collected data and extracting or generating specific information.

[0350] A "skill profile" is a systematic record of a job seeker's abilities, generated based on individual skill information.

[0351] "Information selection means" refers to a process or device for selecting data that meets specific criteria.

[0352] "Job postings" refer to a dataset of employment opportunities, consisting of information about job types and conditions provided by companies.

[0353] "Display means" refers to a device or interface for visually presenting selected information to a user.

[0354] "Content generation means" refers to a process or device for automatically generating necessary information and educational materials.

[0355] "Learning content" refers to educational materials and information provided for the purpose of improving skills and acquiring knowledge.

[0356] To implement this invention, the user first inputs data on their skills and emotional state using a terminal. This terminal is equipped with a web browser or mobile application, allowing for easy collection of job seeker information through the user interface. For example, the user inputs their work history, skills, and desired job type into a web form. Furthermore, it is envisioned that emotional state will be input using a simple questionnaire or facial recognition technology.

[0357] The terminal sends the collected information to the server. The server is located on a platform for analyzing the received data and generates skill profiles using advanced natural language processing algorithms and generative AI models. This utilizes existing open-source natural language processing libraries and commercial AI models.

[0358] Based on the generated skill profile, the server searches the company's job database and selects the most suitable job postings, taking into account the user's emotional state. Analytics software is used to quickly process the collected data. The selected job postings are returned to the terminal and presented to the user visually.

[0359] Furthermore, the server instantly generates learning content using generative AI based on the user's skill profile and emotional state. The generated content serves as learning material to improve the user's skills and is provided to the user via their device.

[0360] As a concrete example, suppose a user is searching for a "data scientist" job and their AI skills are at an intermediate level. If the emotion engine detects that the user's current emotional state is elevated, the server will prioritize presenting the user with job postings that include challenging projects or advanced-level learning materials.

[0361] An example of a prompt would be, "Suggest job postings and learning content based on emotional state for a data scientist with intermediate AI skills." In this way, the system can provide job postings and learning opportunities that correspond to the user's skills and emotional state.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] Users begin by using a device to input their skills information and emotional state. They enter information such as their work history, skill set, and desired job into an input form, and register their emotional state through a short questionnaire and sensor data. This input is temporarily stored on the device and then prepared to be sent to the server.

[0365] Step 2:

[0366] The device transmits user input data to the server using a secure protocol. During this process, the data is converted to a standardized format. The transmitted data includes work history, skill set, desired job type, and emotional state. After transmission is complete, the device displays a notification to the user confirming that the data has been successfully sent.

[0367] Step 3:

[0368] The server runs a natural language processing algorithm to analyze the received data. The analysis algorithm processes the work history and skill set data received as input, generating an appropriate skill profile as output. This analysis systematically organizes the user's skills, which are then temporarily stored on the server.

[0369] Step 4:

[0370] The server searches the job database using the generated skill profile and the user's emotional state. An information filtering algorithm filters the job postings to match the skill profile and emotional state. The output of this process is a list of the most suitable job postings, which the server sends to the terminal.

[0371] Step 5:

[0372] The terminal displays job postings received from the server to the user. The displayed job postings include work styles and environments that are suitable for the user's current emotional state. The user can review the presented information and apply for suitable jobs.

[0373] Step 6:

[0374] The server uses generative AI to create learning content based on the user's skill profile and emotional state. Using the prompt "Provide emotional state-based content for data scientists with intermediate AI skills," the generative AI algorithm generates specific learning materials. This ensures that learning content is tailored to improve the user's skills.

[0375] Step 7:

[0376] The device provides users with generated learning content. Users can learn from this content through the device and improve their skills. This provides a foundation for preparing for better career opportunities.

[0377] (Application Example 2)

[0378] 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."

[0379] Traditional job-seeker matching systems can perform basic matching based on users' skills and work experience, but they have a problem in that they cannot provide job information or educational content that takes into account the emotional state of the users. Because the emotional state of the users is not taken into consideration, appropriate measures are not taken to address stress levels or decreased motivation, and effective support for job-seeking activities and skill improvement is difficult to provide.

[0380] 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.

[0381] In this invention, the server includes information input means, information analysis means, information selection means, display means, content provision means, information provision means, emotion analysis means, and information presentation means. This makes it possible to evaluate the emotional state of the user and provide more personalized job information and educational content.

[0382] An "information input device" is a device that provides an interface for users to input their skills information, work history, and other relevant data.

[0383] An "information analysis tool" is a system for processing input skill information and generating a user's ability profile.

[0384] An "information selection tool" is a device that extracts suitable job postings based on the generated competency profile.

[0385] A "display means" is a device used to visually present selected job information to users.

[0386] A "content delivery device" is a device that has the function of generating educational content that addresses skills that are lacking.

[0387] An "information provision means" is a device that provides generated educational content to users.

[0388] An "emotion analysis tool" is a system for evaluating a user's emotional state based on their input and actions.

[0389] An "information presentation means" is a device for providing users with information that has been adjusted through emotion analysis.

[0390] In order to implement this invention, it is necessary to construct a system in which a server and a user's terminal work together. The server has information input means, information analysis means, information selection means, display means, content provision means, information provision means, sentiment analysis means, and information presentation means.

[0391] The user uses a terminal to transmit their skills information and work history to the server through an information input device. This input information is analyzed on the server side using a natural language processing algorithm by an information analysis device and stored in a database as the user's competency profile. Next, an information selection device extracts suitable job postings from the database based on this competency profile and presents them to the user's terminal through a display device.

[0392] Furthermore, the server uses emotion analysis tools to evaluate the user's emotional state. Based on this emotional evaluation, the information presentation tools select adjusted job postings and educational content, providing appropriate content according to the user's emotions. For example, if the user is feeling stressed, the information presentation tools may prioritize displaying job postings that offer flexible working hours or present educational content with a relaxing effect.

[0393] The content delivery method utilizes generative artificial intelligence technology to instantly generate educational content to improve users' abilities. An example of a prompt given to the generative AI model would be, "Please generate educational content with a relaxing effect."

[0394] The entire system aims to provide more effective job search support and skill development by offering personalized job information and content tailored to the user's work history and emotional state.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] Users use their devices to send individual data, such as skills information and work history, to the server via input methods. This input data is then imported into the server as basic information for creating user profiles.

[0398] Step 2:

[0399] The server processes the received skills information using information analysis tools. Using natural language processing algorithms, it analyzes the input data and generates a user's skills profile. This profile is stored in a database as structured data about the user's skills and desired job type.

[0400] Step 3:

[0401] The server uses information filtering methods to select job postings based on the generated competency profiles. It employs algorithms that match each item in the competency profile with job postings in the database, extracting and outputting highly suitable job postings. These output postings are then listed in order of priority.

[0402] Step 4:

[0403] The server uses an emotion analysis tool after an information analysis tool to evaluate the user's emotional state based on their terminal operations and input. The evaluated emotional state is stored in the database as the user's emotional information and influences the selection results of job postings.

[0404] Step 5:

[0405] Based on the user's emotional state, the server uses information presentation tools to adjust the display order and content of job postings. For example, a user experiencing stress will be prioritized to see job postings for relaxing workplaces. This adjusted list of job postings is then sent to and output to the user's terminal.

[0406] Step 6:

[0407] The content delivery method utilizes generative AI technology to generate educational content based on evaluated emotional states and ability profiles. For example, a prompt message such as "Generate educational content with a relaxing effect" is sent to the AI ​​model, and the output content is provided to the device.

[0408] Step 7:

[0409] The server displays the final output—adjusted job postings and generated educational content—on the user's device through the information delivery system. This allows users to effectively receive information tailored to their emotional needs and engage in job-seeking activities and self-development.

[0410] 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.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0412] 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.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] As an embodiment of this invention, a multilingual AI matching platform is provided. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits this information to a server. The server analyzes the received data and generates individual skill profiles using natural language processing technology.

[0427] The generated skill profile is then matched by the server against a database of company job postings. The server matches this profile with job information and selects the most suitable job postings based on the user's skill set and desired conditions. The selected job postings are then presented to the user via their terminal.

[0428] Furthermore, the server utilizes generative AI technology to create learning content to compensate for users' skill deficiencies. For example, if a user wishes to improve a specific technical skill, the server can immediately generate and provide corresponding learning materials, allowing users to quickly improve their skills.

[0429] As a concrete example, suppose a user desires a job in "data analysis" and has beginner-level Python skills. The server recognizes this information through analysis and provides relevant job postings. Furthermore, if it determines that the user needs to improve their Python data analysis skills, the server generates and provides online Python tutorials and practice problems as content. This allows the user to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. In this way, the invention realizes a platform that enables job seekers to leverage their skill profiles to obtain employment opportunities in an international and multilingual labor market.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The user uses the device to input information such as their skills, work history, desired job type, and work location. The device then retrieves this input data.

[0433] Step 2:

[0434] The terminal sends user input data to the server. The data sent includes text-formatted resume information and selected skills information.

[0435] Step 3:

[0436] The server analyzes the received data. Using natural language processing techniques, it analyzes the user's skill information and generates a skill profile. This profile identifies skill categories and levels.

[0437] Step 4:

[0438] The server searches the job database based on the skill profile and selects suitable job postings. Keyword matching and scoring algorithms are used to select highly relevant job postings.

[0439] Step 5:

[0440] The server sends the selected job postings to the terminal. The information sent includes company name, job title, required skills, work location, and salary information.

[0441] Step 6:

[0442] The device displays the job postings it has received to the user. The user can review the displayed job postings and select those that interest them.

[0443] Step 7:

[0444] To compensate for the lack of skills on the server, we utilize generative AI to generate instant learning content. For example, if a user needs data analysis skills, we create appropriate online tutorials and learning materials.

[0445] Step 8:

[0446] The server generates learning content and sends it to the device, which then provides it to the user. The user can then use the provided content to improve their skills.

[0447] (Example 1)

[0448] 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."

[0449] In today's labor market, job seekers often struggle to find suitable positions by leveraging their own skills and knowledge. Furthermore, a lack of effective learning materials to address job seekers' skill deficiencies hinders rapid skill development. This situation needs to be improved to enable more efficient and accurate skills matching and learning support.

[0450] 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.

[0451] In this invention, the server includes data input means for inputting individual skill information of job seekers, information analysis means for analyzing the input skill information and generating a skill profile, and data selection means for selecting job information based on the generated skill profile. This makes it possible for job seekers to efficiently find job information that suits them. Furthermore, it is possible to quickly support skill improvement by generating and providing learning materials based on the skills that are lacking.

[0452] "Data entry means" refers to a device or method for job seekers to input individual skills information.

[0453] "Information analysis means" refers to a device or method for analyzing input skill information and generating a skill profile.

[0454] "Data selection means" refers to a device or method for selecting the most suitable job information based on the generated skill profile.

[0455] "Display means" refers to a device or method for presenting selected job information to the user.

[0456] "Material generation means" refers to an apparatus or method for generating learning materials based on the skills that are lacking.

[0457] "Means of provision" refers to a device or method for providing generated learning materials to users.

[0458] "Natural language processing methods" refer to technologies that mechanically understand, analyze, and generate human language.

[0459] "Generative AI technology" refers to technology that uses artificial intelligence to automatically generate new data and content.

[0460] This invention constructs a multilingual AI matching platform system to help job seekers maximize their skills and find the most suitable job. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits the entered information to the server using the HTTPS protocol.

[0461] The server analyzes the received data and uses the Google Cloud Natural Language API and other natural language processing software to generate individual skill profiles. The generated skill profiles are compared with job postings from each company registered in the cloud database to ensure the best possible match. The server uses SQL queries to search the database for the most suitable job postings and creates a ranking.

[0462] The selected job postings are sent to the terminal in JSON format and presented to the user. This allows the user to efficiently obtain job information that matches their skill set.

[0463] Furthermore, the server utilizes generative AI models (e.g., widely used language models) to generate learning content to complement the user's lacking skills. In this process, the user inputs prompts tailored to the area in which they wish to improve their skills into the generative AI. For example, if a user desires a job in "data analysis" and has beginner-level Python skills, they might use a prompt such as "Generate intermediate-level learning materials on data analysis using Python." Based on this prompt, the generative AI creates learning materials and provides them to the user.

[0464] This allows users to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. The system provides job seekers with concrete ways to leverage their skill profiles and effectively secure employment opportunities in the international and multilingual labor market.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] Users input information such as skills, work experience, and desired job type using a terminal. This information is structured by the terminal and sent to the server using the HTTPS protocol. The input data includes individual pieces of information written in text format.

[0468] Step 2:

[0469] The server receives information sent from the terminal. The received data is temporarily stored in a database. Next, the server uses natural language processing methods to analyze the received information. This analysis generates a personal skill profile. The input is raw text data, and the output is a structured skill profile.

[0470] Step 3:

[0471] The server compares the generated skill profile with the job information in the job database. Here, an SQL query is used to query for the most suitable job postings based on the skill profile. The input is the skill profile, and the output is the matching job postings in the database.

[0472] Step 4:

[0473] The server ranks the searched job postings and selects the most suitable one. This selection result is converted to JSON format, ready to be sent to the terminal. The input is a list of search results, and the output is the ranked job postings presented to the user.

[0474] Step 5:

[0475] The terminal receives job postings in JSON format sent by the server and presents them visually to the user. The user then decides whether or not to apply based on the information provided. The input is job postings in JSON format, and the output is the visual information displayed to the user.

[0476] Step 6:

[0477] The server utilizes generative AI technology to generate learning content that addresses the user's skill gaps. Prompt statements are input to the generative AI, which then creates specific learning materials as a result. The input is a prompt statement, and the output is customized learning content.

[0478] Step 7:

[0479] The server sends the generated learning content to the terminal. The terminal displays this content to the user, who then uses it to improve their skills. The input is the generated learning content, and the output is the visual content provided to the user.

[0480] (Application Example 1)

[0481] 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."

[0482] Modern electronic payment systems lack sufficient methods for users to easily select financial products that suit their purchasing behavior. Furthermore, there are insufficient means to efficiently improve users' financial literacy. Therefore, there is a need to provide financial products and learning opportunities optimized for individual users.

[0483] 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.

[0484] In this invention, the server includes data acquisition means for inputting information on purchasing activities, analysis means for analyzing the acquired purchasing information to generate individual user profiles, and information selection means for selecting financial product information based on the generated user profiles. This makes it possible to provide users with optimal financial products and learning content that match their purchasing behavior.

[0485] "Purchasing activity information" refers to data about a user's shopping history and spending patterns, which is obtained through electronic payments.

[0486] "Data acquisition means" refers to hardware and software elements for collecting information on users' purchasing activities, such as sensors or APIs that have the function of collecting data.

[0487] "Analysis means" refers to a system that analyzes purchase information collected by data acquisition means, extracts user behavior and trends, and generates individual profiles.

[0488] A "user profile" is an individual dataset generated by analytical methods that reflects a user's purchasing behavior and preferences, and is used for purposes such as recommending financial products.

[0489] An "information selection tool" is a tool that has the function of identifying and providing the most suitable financial products and services based on the generated user profile.

[0490] "Financial product information" refers to data about financial services and benefits that can be offered to users, such as credit card and debit card promotions, loans, and investment products.

[0491] "Educational content" refers to learning programs and materials provided to improve users' financial knowledge, and is created using generative AI technology.

[0492] The system for realizing this invention consists of three parties: a user, a terminal, and a server. The user makes electronic payments using a smartphone or other device. The terminal collects information on the user's purchasing activities and transmits it to the server.

[0493] The server first uses data acquisition tools to receive information about the user's purchasing activities. This information includes the user's purchase history and spending trends. The received data is then used with analysis tools to generate a user profile. For this profile generation, algorithms known as natural language processing techniques, such as spaCy, are used. This allows for a detailed analysis of the user's purchasing patterns.

[0494] Next, based on the generated user profile, the information selection system selects financial product information. The selected information is compared with a database of various financial services and promotions stored on the server, and the most suitable financial product is suggested to the user. During this process, the selected information is displayed to the user via the terminal.

[0495] Furthermore, the server uses content generation tools to generate educational content related to the financial knowledge that users need. This process utilizes generative AI technology; for example, OpenAI's GPT model instantly generates educational content. This allows users to effectively enhance their financial knowledge.

[0496] For example, if a user frequently shops online, a cashback promotion for a specific credit card will be suggested based on their analyzed profile. Furthermore, for users who wish to improve their financial knowledge, an online course on the fundamentals of personal financial management will be recommended as generated educational content.

[0497] Examples of prompts include, "Generate the best credit card promotions based on this user's recent online shopping activity," and "Create learning content based on the financial knowledge information this user desires."

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] A user makes an electronic payment using their smartphone. The smartphone collects data on the user's purchase activity and sends it to a server via the device. The input is the user's purchase history data, and the output is the transmission of data to the server.

[0501] Step 2:

[0502] The server receives information about transmitted purchasing activities using data acquisition methods. This data includes the user's transaction amount, category, frequency, etc. The input is purchase information data from the terminal, and the output is data storage within the server. In this step, the received data is organized into a database.

[0503] Step 3:

[0504] The server uses analysis tools to analyze acquired purchase information and generate user profiles. It utilizes natural language processing algorithms (e.g., spaCy) to extract user purchasing patterns and preferences from the data. The input is accumulated purchase information, and the output is individual user profiles.

[0505] Step 4:

[0506] The server selects financial product information based on the generated user profile through an information filtering mechanism. It searches the database for products and promotions that match the profile analysis results and selects the most suitable ones. The input is the user profile, and the output is the selected financial product information.

[0507] Step 5:

[0508] The server presents the selected financial product information to the user via the terminal. This allows the user to check financial products and promotions that are suitable for them. The input is the selected product information, and the output is what is presented to the user.

[0509] Step 6:

[0510] The server uses content generation tools to generate educational material related to missing financial knowledge. It uses a generation AI model (e.g., OpenAI GPT) to create learning materials and resources tailored to the user. The input is information about missing financial knowledge, and the output is the generated educational content.

[0511] Step 7:

[0512] The server provides the generated educational content to the user via the terminal. This enables the user to learn quickly through the presented educational materials. The input is the generated educational content, and the output is the delivery of the content to the user.

[0513] 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.

[0514] As an embodiment of this invention, a system is provided that incorporates an emotion engine into a multilingual AI matching platform. First, the user uses a terminal to input information such as work history, skill set, and desired job type. The terminal collects this data and sends it to the server.

[0515] The server analyzes the received data and generates a user skill profile using a natural language processing algorithm. Then, based on this skill profile, it searches the company's job database and selects suitable job postings.

[0516] Furthermore, the server utilizes an emotion engine to assess the user's emotional state. It analyzes the user's input and actions on the device to understand their current emotional needs. This emotional information also influences the selection and order of job postings presented. For example, if the server assesses that the user is feeling stressed, it can prioritize presenting job postings from companies that offer a more comfortable work environment.

[0517] The emotion engine is also used to provide learning content using generative AI. Based on the user's emotional state, it generates easy-to-learn and motivating content and delivers it to the user through their device.

[0518] As a concrete example, suppose a user desires a job in "software development" and has intermediate-level artificial intelligence skills. In this case, if the user's emotional state is elevated, the server will proactively provide job postings that include challenging roles and advanced-level learning materials. On the other hand, if the emotion engine detects that the user is tired or stressed, the server will prioritize presenting job postings from companies that offer flexible working hours and learning content with less work pressure.

[0519] In this way, the present invention aims to provide more appropriate employment opportunities and learning experiences by adapting not only to the skill improvement of job seekers but also to their emotional state.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The user enters information about their work history, skills, and desired job type and location via a device. The device collects this information and sends it to the server.

[0523] Step 2:

[0524] The server analyzes the information it receives. It uses natural language processing techniques to evaluate the user's skill set and generate a skill profile. This profile accurately reflects the user's abilities and experience.

[0525] Step 3:

[0526] The server searches the job database based on the generated skill profile and selects job postings that match the user's desired conditions. Selection is based on keyword matching and the degree of match in required skills.

[0527] Step 4:

[0528] The device collects emotional information based on user input data and analyzes the device's operation and input patterns. This data is sent to a server and used to infer the user's emotional state.

[0529] Step 5:

[0530] The server uses an emotion engine to evaluate the user's emotional state from the data they send. For example, it can detect stress or euphoria from input speed, frequency of use, and word choice patterns.

[0531] Step 6:

[0532] Based on the evaluation of the user's emotional state, the server adjusts the order in which job postings are presented. Depending on the emotional state indicated by the evaluation, the server prioritizes presenting the user with more appropriate job postings.

[0533] Step 7:

[0534] To compensate for the server's lack of skills, it utilizes generative AI technology to generate instant learning content. It takes emotional information into account to create content that allows users to learn in a more relaxed manner, as well as content that is challenging.

[0535] Step 8:

[0536] The server generates learning content and sends it to the device. The device then provides the received content to the user, allowing the user to efficiently improve their skills according to their mood and mental state.

[0537] (Example 2)

[0538] 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."

[0539] There is a need to provide a matching platform that not only offers job postings based on skills information to job seekers with multiple languages ​​and diverse skill sets, but also takes into account the emotional state of the job seekers. Furthermore, in order to support job seekers' skill development, it is necessary to provide appropriate learning content tailored to their emotional state. However, conventional systems have lacked a comprehensive means to achieve these goals.

[0540] 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.

[0541] In this invention, the server includes input means for inputting individual skill information and emotional state of job seekers, analysis means for analyzing the information collected by the input means to generate a skill profile, and information selection means for selecting job postings based on the skill profile and emotional state. This makes it possible to provide job postings and learning content tailored to the skills and emotional state of each individual job seeker.

[0542] "Job seeker" refers to an individual or information about someone who is looking for work.

[0543] "Skills information" refers to data about an individual's professional abilities and experience.

[0544] "Emotional state" refers to information that indicates the psychological state of a job seeker, such as stress or excitement.

[0545] "Input means" refers to an interface or device for a user to provide information to a system.

[0546] "Analysis means" refers to a process or device that has the function of processing collected data and extracting or generating specific information.

[0547] A "skill profile" is a systematic record of a job seeker's abilities, generated based on individual skill information.

[0548] "Information selection means" refers to a process or device for selecting data that meets specific criteria.

[0549] "Job postings" refer to a dataset of employment opportunities, consisting of information about job types and conditions provided by companies.

[0550] "Display means" refers to a device or interface for visually presenting selected information to a user.

[0551] "Content generation means" refers to a process or device for automatically generating necessary information and educational materials.

[0552] "Learning content" refers to educational materials and information provided for the purpose of improving skills and acquiring knowledge.

[0553] To implement this invention, the user first inputs data on their skills and emotional state using a terminal. This terminal is equipped with a web browser or mobile application, allowing for easy collection of job seeker information through the user interface. For example, the user inputs their work history, skills, and desired job type into a web form. Furthermore, it is envisioned that emotional state will be input using a simple questionnaire or facial recognition technology.

[0554] The terminal sends the collected information to the server. The server is located on a platform for analyzing the received data and generates skill profiles using advanced natural language processing algorithms and generative AI models. This utilizes existing open-source natural language processing libraries and commercial AI models.

[0555] Based on the generated skill profile, the server searches the company's job database and selects the most suitable job postings, taking into account the user's emotional state. Analytics software is used to quickly process the collected data. The selected job postings are returned to the terminal and presented to the user visually.

[0556] Furthermore, the server instantly generates learning content using generative AI based on the user's skill profile and emotional state. The generated content serves as learning material to improve the user's skills and is provided to the user via their device.

[0557] As a concrete example, suppose a user is searching for a "data scientist" job and their AI skills are at an intermediate level. If the emotion engine detects that the user's current emotional state is elevated, the server will prioritize presenting the user with job postings that include challenging projects or advanced-level learning materials.

[0558] An example of a prompt would be, "Suggest job postings and learning content based on emotional state for a data scientist with intermediate AI skills." In this way, the system can provide job postings and learning opportunities that correspond to the user's skills and emotional state.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] Users begin by using a device to input their skills information and emotional state. They enter information such as their work history, skill set, and desired job into an input form, and register their emotional state through a short questionnaire and sensor data. This input is temporarily stored on the device and then prepared to be sent to the server.

[0562] Step 2:

[0563] The device transmits user input data to the server using a secure protocol. During this process, the data is converted to a standardized format. The transmitted data includes work history, skill set, desired job type, and emotional state. After transmission is complete, the device displays a notification to the user confirming that the data has been successfully sent.

[0564] Step 3:

[0565] The server runs a natural language processing algorithm to analyze the received data. The analysis algorithm processes the work history and skill set data received as input, generating an appropriate skill profile as output. This analysis systematically organizes the user's skills, which are then temporarily stored on the server.

[0566] Step 4:

[0567] The server searches the job database using the generated skill profile and the user's emotional state. An information filtering algorithm filters the job postings to match the skill profile and emotional state. The output of this process is a list of the most suitable job postings, which the server sends to the terminal.

[0568] Step 5:

[0569] The terminal displays job postings received from the server to the user. The displayed job postings include work styles and environments that are suitable for the user's current emotional state. The user can review the presented information and apply for suitable jobs.

[0570] Step 6:

[0571] The server uses generative AI to create learning content based on the user's skill profile and emotional state. Using the prompt "Provide emotional state-based content for data scientists with intermediate AI skills," the generative AI algorithm generates specific learning materials. This ensures that learning content is tailored to improve the user's skills.

[0572] Step 7:

[0573] The device provides users with generated learning content. Users can learn from this content through the device and improve their skills. This provides a foundation for preparing for better career opportunities.

[0574] (Application Example 2)

[0575] 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."

[0576] Traditional job-seeker matching systems can perform basic matching based on users' skills and work experience, but they have a problem in that they cannot provide job information or educational content that takes into account the emotional state of the users. Because the emotional state of the users is not taken into consideration, appropriate measures are not taken to address stress levels or decreased motivation, and effective support for job-seeking activities and skill improvement is difficult to provide.

[0577] 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.

[0578] In this invention, the server includes information input means, information analysis means, information selection means, display means, content provision means, information provision means, emotion analysis means, and information presentation means. This makes it possible to evaluate the emotional state of the user and provide more personalized job information and educational content.

[0579] An "information input device" is a device that provides an interface for users to input their skills information, work history, and other relevant data.

[0580] An "information analysis tool" is a system for processing input skill information and generating a user's ability profile.

[0581] An "information selection tool" is a device that extracts suitable job postings based on the generated competency profile.

[0582] A "display means" is a device used to visually present selected job information to users.

[0583] A "content delivery device" is a device that has the function of generating educational content that addresses skills that are lacking.

[0584] An "information provision means" is a device that provides generated educational content to users.

[0585] An "emotion analysis tool" is a system for evaluating a user's emotional state based on their input and actions.

[0586] An "information presentation means" is a device for providing users with information that has been adjusted through emotion analysis.

[0587] In order to implement this invention, it is necessary to construct a system in which a server and a user's terminal cooperate. The server has information input means, information analysis means, information selection means, display means, content provision means, information provision means, sentiment analysis means, and information presentation means.

[0588] The user uses a terminal to transmit their skills information and work history to the server through an information input device. This input information is analyzed on the server side using a natural language processing algorithm by an information analysis device and stored in a database as the user's competency profile. Next, an information selection device extracts suitable job postings from the database based on this competency profile and presents them to the user's terminal through a display device.

[0589] Furthermore, the server uses emotion analysis tools to evaluate the user's emotional state. Based on this emotional evaluation, the information presentation tools select adjusted job postings and educational content, providing appropriate content according to the user's emotions. For example, if the user is feeling stressed, the information presentation tools may prioritize displaying job postings that offer flexible working hours or present educational content with a relaxing effect.

[0590] The content delivery method utilizes generative artificial intelligence technology to instantly generate educational content to improve users' abilities. An example of a prompt given to the generative AI model would be, "Please generate educational content with a relaxing effect."

[0591] The entire system aims to provide more effective job search support and skill development by offering personalized job information and content tailored to the user's work history and emotional state.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] Users use their devices to send individual data, such as skills information and work history, to the server via input methods. This input data is then imported into the server as basic information for creating user profiles.

[0595] Step 2:

[0596] The server processes the received skills information using information analysis tools. Using natural language processing algorithms, it analyzes the input data and generates a user's skills profile. This profile is stored in a database as structured data about the user's skills and desired job type.

[0597] Step 3:

[0598] The server uses information filtering methods to select job postings based on the generated competency profiles. It employs algorithms that match each item in the competency profile with job postings in the database, extracting and outputting highly suitable job postings. These output postings are then listed in order of priority.

[0599] Step 4:

[0600] The server uses an emotion analysis tool after an information analysis tool to evaluate the user's emotional state based on their terminal operations and input. The evaluated emotional state is stored in the database as the user's emotional information and influences the selection results of job postings.

[0601] Step 5:

[0602] Based on the user's emotional state, the server uses information presentation tools to adjust the display order and content of job postings. For example, a user experiencing stress will be prioritized to see job postings for relaxing workplaces. This adjusted list of job postings is then sent to and output to the user's terminal.

[0603] Step 6:

[0604] The content delivery method utilizes generative AI technology to generate educational content based on evaluated emotional states and ability profiles. For example, a prompt message such as "Generate educational content with a relaxing effect" is sent to the AI ​​model, and the output content is provided to the device.

[0605] Step 7:

[0606] The server displays the final output—adjusted job postings and generated educational content—on the user's device through the information delivery system. This allows users to effectively receive information tailored to their emotional needs and engage in job-seeking activities and self-development.

[0607] 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.

[0608] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] As an embodiment of this invention, a multilingual AI matching platform is provided. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits this information to a server. The server analyzes the received data and generates individual skill profiles using natural language processing technology.

[0625] The generated skill profile is then matched by the server against a database of company job postings. The server matches this profile with job information and selects the most suitable job postings based on the user's skill set and desired conditions. The selected job postings are then presented to the user via their terminal.

[0626] Furthermore, the server utilizes generative AI technology to create learning content to compensate for users' skill deficiencies. For example, if a user wishes to improve a specific technical skill, the server can immediately generate and provide corresponding learning materials, allowing users to quickly improve their skills.

[0627] As a concrete example, suppose a user desires a job in "data analysis" and has beginner-level Python skills. The server recognizes this information through analysis and provides relevant job postings. Furthermore, if it determines that the user needs to improve their Python data analysis skills, the server generates and provides online Python tutorials and practice problems as content. This allows the user to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. In this way, the invention realizes a platform that enables job seekers to leverage their skill profiles to obtain employment opportunities in an international and multilingual labor market.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user uses the device to input information such as their skills, work history, desired job type, and work location. The device then retrieves this input data.

[0631] Step 2:

[0632] The terminal sends user input data to the server. The data sent includes text-formatted resume information and selected skills information.

[0633] Step 3:

[0634] The server analyzes the received data. Using natural language processing techniques, it analyzes the user's skill information and generates a skill profile. This profile identifies skill categories and levels.

[0635] Step 4:

[0636] The server searches the job database based on the skill profile and selects suitable job postings. Keyword matching and scoring algorithms are used to select highly relevant job postings.

[0637] Step 5:

[0638] The server sends the selected job postings to the terminal. The information sent includes company name, job title, required skills, work location, and salary information.

[0639] Step 6:

[0640] The device displays the job postings it has received to the user. The user can review the displayed job postings and select those that interest them.

[0641] Step 7:

[0642] To compensate for the lack of skills on the server, we utilize generative AI to generate instant learning content. For example, if a user needs data analysis skills, we create appropriate online tutorials and learning materials.

[0643] Step 8:

[0644] The server generates learning content and sends it to the device, which then provides it to the user. The user can then use the provided content to improve their skills.

[0645] (Example 1)

[0646] 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".

[0647] In today's labor market, job seekers often struggle to find suitable positions by leveraging their own skills and knowledge. Furthermore, a lack of effective learning materials to address job seekers' skill deficiencies hinders rapid skill development. This situation needs to be improved to enable more efficient and accurate skills matching and learning support.

[0648] 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.

[0649] In this invention, the server includes data input means for inputting individual skill information of job seekers, information analysis means for analyzing the input skill information and generating a skill profile, and data selection means for selecting job information based on the generated skill profile. This makes it possible for job seekers to efficiently find job information that suits them. Furthermore, it is possible to quickly support skill improvement by generating and providing learning materials based on the skills that are lacking.

[0650] "Data entry means" refers to a device or method for job seekers to input individual skills information.

[0651] "Information analysis means" refers to a device or method for analyzing input skill information and generating a skill profile.

[0652] "Data selection means" refers to a device or method for selecting the most suitable job information based on the generated skill profile.

[0653] "Display means" refers to a device or method for presenting selected job information to the user.

[0654] "Material generation means" refers to an apparatus or method for generating learning materials based on the skills that are lacking.

[0655] "Means of provision" refers to a device or method for providing generated learning materials to users.

[0656] "Natural language processing methods" refer to technologies that mechanically understand, analyze, and generate human language.

[0657] "Generative AI technology" refers to technology that uses artificial intelligence to automatically generate new data and content.

[0658] This invention constructs a multilingual AI matching platform system to help job seekers maximize their skills and find the most suitable job. Users input information such as their skills, work history, and desired job type via a terminal. The terminal transmits the entered information to the server using the HTTPS protocol.

[0659] The server analyzes the received data and uses the Google Cloud Natural Language API and other natural language processing software to generate individual skill profiles. The generated skill profiles are compared with job postings from each company registered in the cloud database to ensure the best possible match. The server uses SQL queries to search the database for the most suitable job postings and creates a ranking.

[0660] The selected job postings are sent to the terminal in JSON format and presented to the user. This allows the user to efficiently obtain job information that matches their skill set.

[0661] Furthermore, the server utilizes generative AI models (e.g., widely used language models) to generate learning content to complement the user's lacking skills. In this process, the user inputs prompts tailored to the area in which they wish to improve their skills into the generative AI. For example, if a user desires a job in "data analysis" and has beginner-level Python skills, they might use a prompt such as "Generate intermediate-level learning materials on data analysis using Python." Based on this prompt, the generative AI creates learning materials and provides them to the user.

[0662] This allows users to efficiently acquire the necessary skills and prepare for more suitable employment opportunities. The system provides job seekers with concrete ways to leverage their skill profiles and effectively secure employment opportunities in the international and multilingual labor market.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] Users input information such as skills, work experience, and desired job type using a terminal. This information is structured by the terminal and sent to the server using the HTTPS protocol. The input data includes individual pieces of information written in text format.

[0666] Step 2:

[0667] The server receives information sent from the terminal. The received data is temporarily stored in a database. Next, the server uses natural language processing methods to analyze the received information. This analysis generates a personal skill profile. The input is raw text data, and the output is a structured skill profile.

[0668] Step 3:

[0669] The server compares the generated skill profile with the job information in the job database. Here, an SQL query is used to query for the most suitable job postings based on the skill profile. The input is the skill profile, and the output is the matching job postings in the database.

[0670] Step 4:

[0671] The server ranks the searched job postings and selects the most suitable one. This selection result is converted to JSON format, ready to be sent to the terminal. The input is a list of search results, and the output is the ranked job postings presented to the user.

[0672] Step 5:

[0673] The terminal receives job postings in JSON format sent by the server and presents them visually to the user. The user then decides whether or not to apply based on the information provided. The input is job postings in JSON format, and the output is the visual information displayed to the user.

[0674] Step 6:

[0675] The server utilizes generative AI technology to generate learning content that addresses the user's skill gaps. Prompt statements are input to the generative AI, which then creates specific learning materials as a result. The input is a prompt statement, and the output is customized learning content.

[0676] Step 7:

[0677] The server sends the generated learning content to the terminal. The terminal displays this content to the user, who then uses it to improve their skills. The input is the generated learning content, and the output is the visual content provided to the user.

[0678] (Application Example 1)

[0679] 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".

[0680] Modern electronic payment systems lack sufficient methods for users to easily select financial products that suit their purchasing behavior. Furthermore, there are insufficient means to efficiently improve users' financial literacy. Therefore, there is a need to provide financial products and learning opportunities optimized for individual users.

[0681] 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.

[0682] In this invention, the server includes data acquisition means for inputting information on purchasing activities, analysis means for analyzing the acquired purchasing information to generate individual user profiles, and information selection means for selecting financial product information based on the generated user profiles. This makes it possible to provide users with optimal financial products and learning content that match their purchasing behavior.

[0683] "Purchasing activity information" refers to data about a user's shopping history and spending patterns, which is obtained through electronic payments.

[0684] "Data acquisition means" refers to hardware and software elements for collecting information on users' purchasing activities, such as sensors or APIs that have the function of collecting data.

[0685] "Analysis means" refers to a system that analyzes purchase information collected by data acquisition means, extracts user behavior and trends, and generates individual profiles.

[0686] A "user profile" is an individual dataset generated by analytical methods that reflects a user's purchasing behavior and preferences, and is used for purposes such as recommending financial products.

[0687] An "information selection tool" is a tool that has the function of identifying and providing the most suitable financial products and services based on the generated user profile.

[0688] "Financial product information" refers to data about financial services and benefits that can be offered to users, such as credit card and debit card promotions, loans, and investment products.

[0689] "Educational content" refers to learning programs and materials provided to improve users' financial knowledge, and is created using generative AI technology.

[0690] The system for realizing this invention consists of three parties: a user, a terminal, and a server. The user makes electronic payments using a smartphone or other device. The terminal collects information on the user's purchasing activities and transmits it to the server.

[0691] The server first uses data acquisition tools to receive information about the user's purchasing activities. This information includes the user's purchase history and spending trends. The received data is then used with analysis tools to generate a user profile. For this profile generation, algorithms known as natural language processing techniques, such as spaCy, are used. This allows for a detailed analysis of the user's purchasing patterns.

[0692] Next, based on the generated user profile, the information selection system selects financial product information. The selected information is compared with a database of various financial services and promotions stored on the server, and the most suitable financial product is suggested to the user. During this process, the selected information is displayed to the user via the terminal.

[0693] Furthermore, the server uses content generation tools to generate educational content related to the financial knowledge that users need. This process utilizes generative AI technology; for example, OpenAI's GPT model instantly generates educational content. This allows users to effectively enhance their financial knowledge.

[0694] For example, if a user frequently shops online, a cashback promotion for a specific credit card will be suggested based on their analyzed profile. Furthermore, for users who wish to improve their financial knowledge, an online course on the fundamentals of personal financial management will be recommended as generated educational content.

[0695] Examples of prompts include, "Generate the best credit card promotions based on this user's recent online shopping activity," and "Create learning content based on the financial knowledge information this user desires."

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] A user makes an electronic payment using their smartphone. The smartphone collects data on the user's purchase activity and sends it to a server via the device. The input is the user's purchase history data, and the output is the transmission of data to the server.

[0699] Step 2:

[0700] The server receives information about transmitted purchasing activities using data acquisition methods. This data includes the user's transaction amount, category, frequency, etc. The input is purchase information data from the terminal, and the output is data storage within the server. In this step, the received data is organized into a database.

[0701] Step 3:

[0702] The server uses analysis tools to analyze acquired purchase information and generate user profiles. It utilizes natural language processing algorithms (e.g., spaCy) to extract user purchasing patterns and preferences from the data. The input is accumulated purchase information, and the output is individual user profiles.

[0703] Step 4:

[0704] The server selects financial product information based on the generated user profile through an information filtering mechanism. It searches the database for products and promotions that match the profile analysis results and selects the most suitable ones. The input is the user profile, and the output is the selected financial product information.

[0705] Step 5:

[0706] The server presents the selected financial product information to the user via the terminal. This allows the user to check financial products and promotions that are suitable for them. The input is the selected product information, and the output is what is presented to the user.

[0707] Step 6:

[0708] The server uses content generation tools to generate educational material related to missing financial knowledge. It uses a generation AI model (e.g., OpenAI GPT) to create learning materials and resources tailored to the user. The input is information about missing financial knowledge, and the output is the generated educational content.

[0709] Step 7:

[0710] The server provides the generated educational content to the user via the terminal. This enables the user to learn quickly through the presented educational materials. The input is the generated educational content, and the output is the delivery of the content to the user.

[0711] 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.

[0712] As an embodiment of this invention, a system is provided that incorporates an emotion engine into a multilingual AI matching platform. First, the user uses a terminal to input information such as work history, skill set, and desired job type. The terminal collects this data and sends it to the server.

[0713] The server analyzes the received data and generates a user skill profile using a natural language processing algorithm. Then, based on this skill profile, it searches the company's job database and selects suitable job postings.

[0714] Furthermore, the server utilizes an emotion engine to assess the user's emotional state. It analyzes the user's input and actions on the device to understand their current emotional needs. This emotional information also influences the selection and order of job postings presented. For example, if the server assesses that the user is feeling stressed, it can prioritize presenting job postings from companies that offer a more comfortable work environment.

[0715] The emotion engine is also used to provide learning content using generative AI. Based on the user's emotional state, it generates easy-to-learn and motivating content and delivers it to the user through their device.

[0716] As a concrete example, suppose a user desires a job in "software development" and has intermediate-level artificial intelligence skills. In this case, if the user's emotional state is elevated, the server will proactively provide job postings that include challenging roles and advanced-level learning materials. On the other hand, if the emotion engine detects that the user is tired or stressed, the server will prioritize presenting job postings from companies that offer flexible working hours and learning content with less work pressure.

[0717] In this way, the present invention aims to provide more appropriate employment opportunities and learning experiences by adapting not only to the skill improvement of job seekers but also to their emotional state.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user enters information about their work history, skills, and desired job type and location via a device. The device collects this information and sends it to the server.

[0721] Step 2:

[0722] The server analyzes the information it receives. It uses natural language processing techniques to evaluate the user's skill set and generate a skill profile. This profile accurately reflects the user's abilities and experience.

[0723] Step 3:

[0724] The server searches the job database based on the generated skill profile and selects job postings that match the user's desired conditions. Selection is based on keyword matching and the degree of match in required skills.

[0725] Step 4:

[0726] The device collects emotional information based on user input data and analyzes the device's operation and input patterns. This data is sent to a server and used to infer the user's emotional state.

[0727] Step 5:

[0728] The server uses an emotion engine to evaluate the user's emotional state from the data they send. For example, it can detect stress or euphoria from input speed, frequency of use, and word choice patterns.

[0729] Step 6:

[0730] Based on the evaluation of the user's emotional state, the server adjusts the order in which job postings are presented. Depending on the emotional state indicated by the evaluation, the server prioritizes presenting the user with more appropriate job postings.

[0731] Step 7:

[0732] To compensate for the server's lack of skills, it utilizes generative AI technology to generate instant learning content. It takes emotional information into account to create content that allows users to learn in a more relaxed manner, as well as content that is challenging.

[0733] Step 8:

[0734] The server generates learning content and sends it to the device. The device then provides the received content to the user, allowing the user to efficiently improve their skills according to their mood and mental state.

[0735] (Example 2)

[0736] 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".

[0737] There is a need to provide a matching platform that not only offers job postings based on skills information to job seekers with multiple languages ​​and diverse skill sets, but also takes into account the emotional state of the job seekers. Furthermore, in order to support job seekers' skill development, it is necessary to provide appropriate learning content tailored to their emotional state. However, conventional systems have lacked a comprehensive means to achieve these goals.

[0738] 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.

[0739] In this invention, the server includes input means for inputting individual skill information and emotional state of job seekers, analysis means for analyzing the information collected by the input means to generate a skill profile, and information selection means for selecting job postings based on the skill profile and emotional state. This makes it possible to provide job postings and learning content tailored to the skills and emotional state of each individual job seeker.

[0740] "Job seeker" refers to an individual or information about someone who is looking for work.

[0741] "Skills information" refers to data about an individual's professional abilities and experience.

[0742] "Emotional state" refers to information that indicates the psychological state of a job seeker, such as stress or excitement.

[0743] "Input means" refers to an interface or device for a user to provide information to a system.

[0744] "Analysis means" refers to a process or device that has the function of processing collected data and extracting or generating specific information.

[0745] A "skill profile" is a systematic record of a job seeker's abilities, generated based on individual skill information.

[0746] "Information selection means" refers to a process or device for selecting data that meets specific criteria.

[0747] "Job postings" refer to a dataset of employment opportunities, consisting of information about job types and conditions provided by companies.

[0748] "Display means" refers to a device or interface for visually presenting selected information to a user.

[0749] "Content generation means" refers to a process or device for automatically generating necessary information and educational materials.

[0750] "Learning content" refers to educational materials and information provided for the purpose of improving skills and acquiring knowledge.

[0751] To implement this invention, the user first inputs data on their skills and emotional state using a terminal. This terminal is equipped with a web browser or mobile application, allowing for easy collection of job seeker information through the user interface. For example, the user inputs their work history, skills, and desired job type into a web form. Furthermore, it is envisioned that emotional state will be input using a simple questionnaire or facial recognition technology.

[0752] The terminal sends the collected information to the server. The server is located on a platform for analyzing the received data and generates skill profiles using advanced natural language processing algorithms and generative AI models. This utilizes existing open-source natural language processing libraries and commercial AI models.

[0753] Based on the generated skill profile, the server searches the company's job database and selects the most suitable job postings, taking into account the user's emotional state. Analytics software is used to quickly process the collected data. The selected job postings are returned to the terminal and presented to the user visually.

[0754] Furthermore, the server instantly generates learning content using generative AI based on the user's skill profile and emotional state. The generated content serves as learning material to improve the user's skills and is provided to the user via their device.

[0755] As a concrete example, suppose a user is searching for a "data scientist" job and their AI skills are at an intermediate level. If the emotion engine detects that the user's current emotional state is elevated, the server will prioritize presenting the user with job postings that include challenging projects or advanced-level learning materials.

[0756] An example of a prompt would be, "Suggest job postings and learning content based on emotional state for a data scientist with intermediate AI skills." In this way, the system can provide job postings and learning opportunities that correspond to the user's skills and emotional state.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] Users begin by using a device to input their skills information and emotional state. They enter information such as their work history, skill set, and desired job into an input form, and register their emotional state through a short questionnaire and sensor data. This input is temporarily stored on the device and then prepared to be sent to the server.

[0760] Step 2:

[0761] The device transmits user input data to the server using a secure protocol. During this process, the data is converted to a standardized format. The transmitted data includes work history, skill set, desired job type, and emotional state. After transmission is complete, the device displays a notification to the user confirming that the data has been successfully sent.

[0762] Step 3:

[0763] The server runs a natural language processing algorithm to analyze the received data. The analysis algorithm processes the work history and skill set data received as input, generating an appropriate skill profile as output. This analysis systematically organizes the user's skills, which are then temporarily stored on the server.

[0764] Step 4:

[0765] The server searches the job database using the generated skill profile and the user's emotional state. An information filtering algorithm filters the job postings to match the skill profile and emotional state. The output of this process is a list of the most suitable job postings, which the server sends to the terminal.

[0766] Step 5:

[0767] The terminal displays job postings received from the server to the user. The displayed job postings include work styles and environments that are suitable for the user's current emotional state. The user can review the presented information and apply for suitable jobs.

[0768] Step 6:

[0769] The server uses generative AI to create learning content based on the user's skill profile and emotional state. Using the prompt "Provide emotional state-based content for data scientists with intermediate AI skills," the generative AI algorithm generates specific learning materials. This ensures that learning content is tailored to improve the user's skills.

[0770] Step 7:

[0771] The device provides users with generated learning content. Users can learn from this content through the device and improve their skills. This provides a foundation for preparing for better career opportunities.

[0772] (Application Example 2)

[0773] 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".

[0774] Traditional job-seeker matching systems can perform basic matching based on users' skills and work experience, but they have a problem in that they cannot provide job information or educational content that takes into account the emotional state of the users. Because the emotional state of the users is not taken into consideration, appropriate measures are not taken to address stress levels or decreased motivation, and effective support for job-seeking activities and skill improvement is difficult to provide.

[0775] 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.

[0776] In this invention, the server includes information input means, information analysis means, information selection means, display means, content provision means, information provision means, emotion analysis means, and information presentation means. This makes it possible to evaluate the emotional state of the user and provide more personalized job information and educational content.

[0777] An "information input device" is a device that provides an interface for users to input their skills information, work history, and other relevant data.

[0778] An "information analysis tool" is a system for processing input skill information and generating a user's ability profile.

[0779] An "information selection tool" is a device that extracts suitable job postings based on the generated competency profile.

[0780] A "display means" is a device used to visually present selected job information to users.

[0781] A "content delivery device" is a device that has the function of generating educational content that addresses skills that are lacking.

[0782] An "information provision means" is a device that provides generated educational content to users.

[0783] An "emotion analysis tool" is a system for evaluating a user's emotional state based on their input and actions.

[0784] An "information presentation means" is a device for providing users with information that has been adjusted through emotion analysis.

[0785] In order to implement this invention, it is necessary to construct a system in which a server and a user's terminal cooperate. The server has information input means, information analysis means, information selection means, display means, content provision means, information provision means, sentiment analysis means, and information presentation means.

[0786] The user uses a terminal to transmit their skills information and work history to the server through an information input device. This input information is analyzed on the server side using a natural language processing algorithm by an information analysis device and stored in a database as the user's competency profile. Next, an information selection device extracts suitable job postings from the database based on this competency profile and presents them to the user's terminal through a display device.

[0787] Furthermore, the server uses emotion analysis tools to evaluate the user's emotional state. Based on this emotional evaluation, the information presentation tools select adjusted job postings and educational content, providing appropriate content according to the user's emotions. For example, if the user is feeling stressed, the information presentation tools may prioritize displaying job postings that offer flexible working hours or present educational content with a relaxing effect.

[0788] The content delivery method utilizes generative artificial intelligence technology to instantly generate educational content to improve users' abilities. An example of a prompt given to the generative AI model would be, "Please generate educational content with a relaxing effect."

[0789] The entire system aims to provide more effective job search support and skill development by offering personalized job information and content tailored to the user's work history and emotional state.

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] Users use their devices to send individual data, such as skills information and work history, to the server via input methods. This input data is then imported into the server as basic information for creating user profiles.

[0793] Step 2:

[0794] The server processes the received skills information using information analysis tools. Using natural language processing algorithms, it analyzes the input data and generates a user's skills profile. This profile is stored in a database as structured data about the user's skills and desired job type.

[0795] Step 3:

[0796] The server uses information filtering methods to select job postings based on the generated competency profiles. It employs algorithms that match each item in the competency profile with job postings in the database, extracting and outputting highly suitable job postings. These output postings are then listed in order of priority.

[0797] Step 4:

[0798] The server uses an emotion analysis tool after an information analysis tool to evaluate the user's emotional state based on their terminal operations and input. The evaluated emotional state is stored in the database as the user's emotional information and influences the selection results of job postings.

[0799] Step 5:

[0800] Based on the user's emotional state, the server uses information presentation tools to adjust the display order and content of job postings. For example, a user experiencing stress will be prioritized to see job postings for relaxing workplaces. This adjusted list of job postings is then sent to and output to the user's terminal.

[0801] Step 6:

[0802] The content delivery method utilizes generative AI technology to generate educational content based on evaluated emotional states and ability profiles. For example, a prompt message such as "Generate educational content with a relaxing effect" is sent to the AI ​​model, and the output content is provided to the device.

[0803] Step 7:

[0804] The server displays the final output—adjusted job postings and generated educational content—on the user's device through the information delivery system. This allows users to effectively receive information tailored to their emotional needs and engage in job-seeking activities and self-development.

[0805] 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.

[0806] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] An input method for entering individual skills information of job seekers,

[0829] An analysis means that analyzes the skill information input by the input means and generates a skill profile,

[0830] An information selection means for selecting job postings based on the skill profile generated by the aforementioned analysis means,

[0831] A means for displaying job information selected by the aforementioned information selection means to the user,

[0832] A content generation means that generates learning content based on the skills that are lacking,

[0833] Means for providing the user with the learning content generated by the content generation means,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, characterized in that the analysis means uses a natural language processing algorithm to analyze the input skill information.

[0837] (Claim 3)

[0838] The system according to claim 1, characterized in that the content generation means generates learning content instantly using generation AI technology.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A data entry method for inputting individual skills information of job seekers,

[0842] Information analysis means that analyzes the skill information input by the data input means and generates a skill profile,

[0843] A data selection means for selecting job information based on the skill profile generated by the information analysis means,

[0844] A display means for displaying job information selected by the data selection means to the user,

[0845] A means for generating learning materials based on the skills that are lacking,

[0846] A means for providing learning materials generated by the aforementioned material generation means to users,

[0847] An information processing system that includes this.

[0848] (Claim 2)

[0849] The information processing system according to claim 1, characterized in that the analysis means uses a natural language processing method to analyze the input skill information.

[0850] (Claim 3)

[0851] The information processing system according to claim 1, characterized in that the data generation means instantly creates learning materials using generation AI technology.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A means of acquiring data for inputting information on purchasing activities,

[0855] An analysis means that analyzes the purchase information acquired by the data acquisition means and generates individual user profiles,

[0856] Information selection means for selecting financial product information based on the user profile generated by the analysis means,

[0857] A means for displaying financial product information selected by the aforementioned information selection means to the licensee,

[0858] A content generation method for generating educational content based on insufficient financial knowledge,

[0859] Means for providing the educational content generated by the content generation means to the licensor,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, characterized in that the analysis means analyzes the acquired purchase information using a natural language processing algorithm.

[0863] (Claim 3)

[0864] The system according to claim 1, characterized in that the content generation means instantly generates educational content using generation AI technology.

[0865] "Example 2 of combining an emotion engine"

[0866] (Claim 1)

[0867] An input method for entering the individual skills information and emotional state of job seekers,

[0868] An analysis means that analyzes the skill information input by the input means and generates a skill profile,

[0869] Information selection means for selecting job postings based on the skill profile and emotional state generated by the analysis means,

[0870] A means for displaying job information selected by the aforementioned information selection means to the user,

[0871] A content generation means that generates learning content based on the lacking skills and emotional state,

[0872] Means for providing the user with the learning content generated by the content generation means,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, characterized in that the analysis means uses a natural language processing algorithm to analyze the input skill information.

[0876] (Claim 3)

[0877] The system according to claim 1, characterized in that the content generation means instantly generates learning content using generation AI technology and further provides information that takes into account emotional states.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] An information input method for entering individual skills information of job seekers,

[0881] Information analysis means for analyzing skill information input by the information input means and generating a capability profile,

[0882] An information selection means for selecting job postings based on the ability profile generated by the aforementioned information analysis means,

[0883] A display means for displaying job information selected by the aforementioned information selection means to the user,

[0884] A content delivery method that generates educational content based on the skills that are lacking,

[0885] Information provision means for providing users with educational content generated by the aforementioned content provision means,

[0886] An emotion analysis tool that evaluates emotional state and adjusts information based on the user's emotions,

[0887] Information presentation means that presents information adjusted by the emotion analysis means to the user,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, characterized in that the information analysis means analyzes input skill information using a natural language processing algorithm.

[0891] (Claim 3)

[0892] The system according to claim 1, characterized in that the content provision means instantly generates educational content using generative artificial intelligence technology. [Explanation of symbols]

[0893] 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. An input method for entering individual skills information of job seekers, An analysis means that analyzes the skill information input by the input means and generates a skill profile, An information selection means for selecting job postings based on the skill profile generated by the aforementioned analysis means, A means for displaying job information selected by the aforementioned information selection means to the user, A content generation means that generates learning content based on the skills that are lacking, Means for providing the user with the learning content generated by the content generation means, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means uses a natural language processing algorithm to analyze the input skill information.

3. The system according to claim 1, characterized in that the content generation means generates learning content instantly using generation AI technology.

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