System

Abstract

A system is provided.SOLUTION: Means for collecting job offer information in Japan, means for filtering the collected job offer information that does not require advanced Japanese skills and is expected to have a certain level of salary or more, means for translating the filtered job offer information into multiple languages, means for storing the translated job offer information in a database, means for collecting posting histories of users from a foreign social networking service, means for extracting users who are estimated to be interested in Japan from the collected posting histories, and means for analyzing academic record and work record information of the extracted users, A system comprising: means for filtering users who meet certain criteria; means for recommending appropriate job offer information to the filtered users; means for receiving application information from the users and notifying a company; means for coordinating an interview schedule between the company and the users; and means for tracking interview results and notifying matching completion when an adoption is determined.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Utilizing foreign talent is an effective solution to Japan's challenges of population decline and productivity improvement. However, public sentiment, including concerns about a worsening security situation, continues to spur resistance to accepting foreign talent. While securing talent with advanced specialized knowledge that does not require advanced Japanese language skills is particularly important, there is a lack of an appropriate matching system. To solve this problem, a system is needed that efficiently matches job information from Japanese companies with highly skilled foreign talent. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes the following means: First, a means for collecting job information in Japan is provided. Next, a means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain salary or higher is provided. Further, a means for translating the filtered job information into multiple languages ​​and storing the translated job information in a database is provided. In addition, a means for collecting user posting histories from foreign social networking services and extracting users who are likely to be interested in Japan from the collected posting histories is provided. A means for analyzing the educational and work history information of the extracted users, filtering users who meet certain criteria, and recommending appropriate job information to the filtered users is provided. Finally, a means for receiving application information from users and notifying companies, and a means for arranging interview dates between companies and users is provided. Finally, a means for tracking interview results and notifying companies of the completion of matching when a candidate is hired is provided. In this way, the system aims to quickly solve the population problem by efficiently matching job information from Japanese companies with highly skilled foreign personnel.

[0006] "Job information" refers to information such as job content, required qualifications, work location, and salary that a company provides when recruiting employees.

[0007] "Crawling" is a technique for automatically collecting information on the Internet.

[0008] "Filtering" is the process of removing unnecessary information and selecting only necessary information based on specific criteria.

[0009] "No need for advanced Japanese language skills" means that a high level of Japanese language skills is not required to carry out the job.

[0010] "Multilingual translation" refers to converting information expressed in one language into another language.

[0011] "Storing in a database" means storing collected and organized information in a digital format.

[0012] A "social networking service" refers to a web service that allows users to interact with each other online and share information.

[0013] "Posting history" refers to content that a user has posted in the past on a social networking service.

[0014] "Educational background" refers to information about which school the user graduated from and what degree they obtained.

[0015] "Work history" refers to information about what jobs a user has had and which companies they have worked for.

[0016] "Recommendation" refers to the individual recommendation of appropriate information, products, etc. based on specific criteria.

[0017] "Application information" refers to information such as a resume or curriculum vitae that a user submits when applying for a job.

[0018] "Notifying the company" means informing the company of the collected information.

[0019] "Adjusting interview dates" means setting a date and time for an interview between the company and the applicant.

[0020] "Completion of matching" refers to the establishment of an employment agreement between the job seeker and the hiring company. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] System Configuration

[0043] The system of the present invention is mainly composed of three entities: a "server," a "terminal," and a "user." The roles of each are as follows:

[0044] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; and notifying applicants when matching is complete.

[0045] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[0046] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[0047] Program processing flow

[0048] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[0049] 1. Collecting job information

[0050] The server periodically crawls major job sites in Japan (generally referred to as "job information sites") to collect the latest job information.

[0051] 2. Job Filtering

[0052] The server analyzes the collected job information and filters out job listings that do not require advanced Japanese language skills and are expected to pay a certain amount or more.

[0053] 3. Multilingual Translation

[0054] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and then stores the translated listings in a database.

[0055] 4. Extraction of foreign talent

[0056] The server crawls foreign social networking services, analyzes users' posting history, and extracts users who are likely to be interested in Japan.

[0057] 5. Filtering educational and work history information

[0058] The server collects the educational and work history information of the extracted users and filters out users who meet certain criteria (e.g., bachelor's degree or above, work experience of 5 years or more).

[0059] 6. Job Recommendations

[0060] The server sends appropriate job information to users who meet the criteria via push notifications or email.

[0061] 7. Receipt of application information

[0062] The user checks the received job information, and if they are interested, they enter the necessary information into the application form and submit it. The server stores this application information in a database.

[0063] 8. Notification of application information and interview arrangements

[0064] The server then notifies the necessary job information providers and companies of the saved application information, after which the company and the user can arrange an interview date.

[0065] 9. Matching completed

[0066] The server tracks the interview results, notifies the company and the user if a job offer is made, and updates the database to record successful matches.

[0067] Specific examples

[0068] Example 1: Providing job information for IT engineers

[0069] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[0070] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0071] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[0072] Analyze the extracted users' work history (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[0073] The server then emails the appropriate job listings to the filtered users.

[0074] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0075] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0076] Example 2: Recruiting biotechnology researchers

[0077] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0078] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0079] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[0080] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[0081] The server will email suitable job listings to the user.

[0082] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0083] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0084] This concludes the explanation of the specific system operation as a "mode for carrying out the invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign personnel.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to provide to job seekers.

[0088] Step 2:

[0089] The server analyzes the collected job listings and filters out those that do not require a high level of Japanese language ability and those that are expected to pay above a certain salary, thereby narrowing down the list of job listings that are easy for foreign talent to apply for.

[0090] Step 3:

[0091] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[0092] Step 4:

[0093] The server crawls major social networking services in foreign countries and collects users' posting history, thereby gathering data to extract users who are presumed to be interested in Japan.

[0094] Step 5:

[0095] The server analyzes the collected posting history and extracts users who have posted using keywords such as "Japan" and "Tokyo." This user extraction is important for identifying foreign talent who are interested in Japan.

[0096] Step 6:

[0097] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[0098] Step 7:

[0099] The server then recommends suitable job information to the filtered users via push notifications or emails, so that relevant job information is delivered directly to the users.

[0100] Step 8:

[0101] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[0102] Step 9:

[0103] The server receives the application information sent by the user and stores it in a database, which then notifies the relevant Japanese companies.

[0104] Step 10:

[0105] The server coordinates interview dates between the company and the user, and once the interview date and time are confirmed, the server notifies both the company and the user of the information.

[0106] Step 11:

[0107] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. Information about successful matches is updated in the database for future analysis and improvement.

[0108] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign personnel.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] Japanese companies face the challenge of finding it difficult to effectively recruit foreign talent. Foreign talent with advanced expertise in particular has limited access to job listings in Japan, making it difficult to match potential candidates with the right talent. Furthermore, language barriers and geographical restrictions mean that proper communication between companies and job seekers is often not possible, hindering smooth recruitment.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain salary or higher; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; analysis means using a multilingual translation API and natural language processing technology; and means for collecting job information and user postings using crawling technology. This makes it easier for foreign talent to access job information in Japan, enabling appropriate matching and a quick hiring process.

[0114] "Means for collecting job information within Japan" refers to technology for automatically obtaining data on job openings from various job information platforms within Japan.

[0115] "A means of filtering out jobs that do not require advanced Japanese language skills and are expected to have a salary above a certain level" is a technology for selecting, from the collected job information, job information that does not require a high level of specialized Japanese language skills and meets a specified salary standard.

[0116] "Multilingual translation means" refers to technology for translating filtered job listings into English and other major foreign languages.

[0117] "Means for storing in a database" refers to the technology used to store translated job information in an organized manner and in a database in a format that can be quickly accessed when needed.

[0118] "Means for collecting user posting history from foreign social networking services" refers to technology for automatically obtaining users' public posting history from foreign SNSs.

[0119] The "means of extracting users who are likely to be interested in Japan" is a technology that analyzes collected posting history and identifies users who are likely to be interested in Japan and its culture.

[0120] "Means for analyzing educational and work history information and filtering users who meet certain criteria" refers to technology for evaluating the educational and work history information of extracted users and selecting users who meet certain criteria (for example, a specific degree or years of work experience).

[0121] "Means for recommending appropriate job information" refers to technology for recommending suitable job information to users who meet criteria.

[0122] The "means for receiving application information and notifying companies" refers to a technique for receiving application information from users and notifying the relevant companies of that information.

[0123] "Means for coordinating interview dates between a company and a user" refers to a technique for coordinating the schedules of both the company and the user and setting an interview date.

[0124] "Means for tracking interview results and notifying the completion of matching when a candidate is hired" refers to technology for managing the results of interviews and informing both the company and the user when a candidate is hired.

[0125] "Analysis method using multilingual translation API and natural language processing technology" refers to technology for translating job postings and user posts into multiple languages ​​and analyzing them through natural language processing.

[0126] "Methods of collecting job information and user posts using crawling technology" refers to techniques for collecting necessary data from job information sites and social media using web scraping and crawling technology.

[0127] MODE FOR CARRYING OUT THE INVENTION

[0128] The purpose of this system is to collect job information in Japan and provide appropriate job information to highly skilled foreign professionals. The operation of the system consists of processing steps that are made up of three main entities: the server, the terminal, and the user.

[0129] The server executes the following processes in order.

[0130] 1. Collecting job information

[0131] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl the latest job listings from major job listing websites in Japan. Specifically, it retrieves job listings from sites such as Indeed and Rikunabi.

[0132] 2. Job Filtering

[0133] The server uses natural language processing (NLP) technology to analyze the collected job listings. By segmenting and tagging the listings using Python's NLTK and SpaCy libraries, it filters out listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more."

[0134] 3. Multilingual Translation

[0135] The server translates the filtered job listings using a multilingual translation API (e.g., Google Translate API, DeepL API), and the translated information is stored in a database (e.g., PostgreSQL, MySQL).

[0136] 4. Extraction of foreign talent

[0137] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. This process also uses NLP technology to extract keywords related to "Japan" and "Tokyo" from the posted content.

[0138] 5. Filtering educational and work history information

[0139] The server analyzes the extracted user profile information to see if they meet certain criteria (e.g., a computer science degree, more than five years of work experience), using the Python Pandas library for this filtering.

[0140] 6. Job Recommendations

[0141] The server recommends suitable job listings to users who meet the criteria, and sends these recommendations via push notifications or email delivery services (e.g., Firebase Cloud Messaging, SendGrid, Mailgun).

[0142] 7. Receipt of application information

[0143] The user checks the received job information, enters the necessary information in the application form, and submits it. The device (PC, tablet, smartphone) provides the UI for this application form and sends the entered information to the server. The server saves the application information in a database.

[0144] 8. Notification of application information and interview arrangements

[0145] The server notifies the appropriate companies of the saved application information and provides an interface for companies and users to arrange interview dates. Specifically, Google Calendar API or a schedule adjustment app can be used.

[0146] 9. Matching completed

[0147] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for later analysis.

[0148] Specific examples

[0149] Example 1: Providing job information for IT engineers

[0150] The server crawls information about the job title "IT engineer" from job information sites A and B in Japan.

[0151] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0152] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[0153] Analyze the extracted users' work history (e.g., computer science degree, 5+ years of work experience) and filter users who meet the criteria.

[0154] The server then emails the appropriate job listings to the filtered users.

[0155] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0156] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0157] Example prompt: "Please translate job postings for IT engineers with an annual salary of at least 5 million yen, no advanced Japanese language skills required, into English and Spanish, and email the job postings to suitable candidates."

[0158] Example 2: Recruiting biotechnology researchers

[0159] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0160] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0161] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[0162] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[0163] The server will email suitable job listings to the user.

[0164] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0165] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0166] Example prompt: "Please translate job postings for biotechnology researchers with an annual salary of at least 6 million yen and no advanced Japanese language skills required into multiple languages ​​and email suitable candidates."

[0167] This system effectively matches job information from Japanese companies with highly skilled foreign talent, ensuring a fast and smooth recruitment process.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1: Gather job information

[0170] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl job information from job information websites in Japan. The server periodically accesses a specified URL and downloads the HTML page of the job information. Next, it uses BeautifulSoup to analyze the HTML page and extract the necessary data (job type, salary, language requirements, etc.).

[0171] Input: URL list of job information sites

[0172] Output: A list of extracted jobs

[0173] Step 2: Filter Jobs

[0174] The server analyzes the collected job listings using natural language processing (NLP) technology. It uses Python's NLTK and SpaCy libraries to split and tag the text data. This allows it to filter out job listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more." The server then temporarily stores only the filtered job listings in order to proceed to the next step.

[0175] Input: A list of extracted job postings

[0176] Output: A filtered list of jobs

[0177] Step 3: Translate into multiple languages

[0178] The server uses the Google Translate API or DeepL API to translate the filtered job listings into multiple languages. Specifically, it sends the text data of the job listings to the API and receives the translated text. The translated information is then stored in a database (e.g., PostgreSQL, MySQL).

[0179] Input: A filtered list of jobs

[0180] Output: A list of translated job postings

[0181] Step 4: Identifying foreign talent

[0182] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. Using Python's Scrapy, it crawls the pages of specified social media accounts and collects posting data. From the collected posting history, it extracts keywords indicating interest in Japan and its culture (e.g., "Japan" and "Tokyo") and analyzes them using natural language processing technology.

[0183] Input: List of social media account URLs

[0184] Output: List of users interested in Japan

[0185] Step 5: Filtering education and work history information

[0186] The server parses the extracted user profile information (educational background and work experience). It uses the Python Pandas library to read the user data and filter users who meet certain criteria (e.g., computer science degree, 5+ years of work experience). The filtered user information is used in the next step.

[0187] Input: List of users interested in Japan and their profile information

[0188] Output: A list of users who meet the criteria

[0189] Step 6: Job Recommendations

[0190] The server recommends suitable job listings to filtered users. The server uses email delivery services such as SendGrid and Mailgun to send emails containing job listings to users. If push notifications are used, services such as Firebase Cloud Messaging can be used.

[0191] Input: A list of users who meet the criteria and a list of translated job postings

[0192] Output: Job notification sent to user

[0193] Step 7: Receiving your application information

[0194] The user checks the received job information, and if they are interested, they enter the required information in the application form and submit it. The device (PC, tablet, smartphone) provides the UI for the application form and sends the entered information to the server. The server receives the application information and stores it in a database.

[0195] Input: User's application information

[0196] Output: Application information stored in a database

[0197] Step 8: Notification of application information and interview arrangements

[0198] The server notifies the company of the saved application information, provides the application information to the company via automatic email or API integration, and provides an interface for coordinating interview dates between the company and the user. Scheduling can also be done using the Google Calendar API.

[0199] Input: Application information stored in the database

[0200] Output: Application information and interview schedule notified to the company

[0201] Step 9: Matching Complete

[0202] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for future analytics and reporting.

[0203] Input: Interview results from company

[0204] Output: Matching completion information notified to companies and users, and success stories recorded in the database

[0205] (Application example 1)

[0206] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0207] Currently, it is extremely difficult to effectively provide job information in Japan to foreign talent and recruit the right talent. In particular, there is a lack of efficient means to find foreign talent with advanced skills related to the development and operation of autonomous vehicles and match them with Japanese companies. As a result, there is a lack of a system that can centrally identify foreign talent, translate job information, and arrange interviews with companies.

[0208] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0209] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for job listings that do not require advanced language skills and are expected to offer salaries above a certain level; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign Internet services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' education and work history information and filtering users who meet certain criteria; means for recommending appropriate job listings to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; and means for recruiting foreign talent suitable for technologies and work related to autonomous vehicles and recommending talent according to the needs of companies. This enables centralized management of everything from identifying foreign talent to matching with companies, thereby enabling efficient recruitment of talent with advanced skills related to autonomous vehicles.

[0210] "Job Information" means information about employment opportunities provided to job seekers.

[0211] "Filtering means" refers to the function of removing unnecessary parts from collected information based on specific conditions and selecting only useful information.

[0212] "Means for translating into multiple languages" refers to the ability to convert specific information into multiple different languages.

[0213] "Means of storing information in a database" refers to a system that systematically organizes and stores information, making it easy to search and reference.

[0214] "Internet services" is a general term for various online services provided via the Internet.

[0215] "Posting history" refers to a record of posts and comments made by a user on the Internet.

[0216] "Means of extraction" refers to the work or process of extracting the necessary information based on specific conditions.

[0217] "Education and work history information" refers to data related to an individual's educational background and work history.

[0218] "Recommendation means" refers to the function of suggesting specific information or options to the user.

[0219] "Application information" refers to information such as personal information and resumes provided by job seekers in response to job offers.

[0220] An "interview schedule" is a scheduled date and time for an interview between a company and a job seeker.

[0221] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without driver operation.

[0222] An "enterprise" is a legal entity that operates a particular business and provides goods or services to the market.

[0223] To put this invention into practice, a system is constructed in which three entities, a server, a terminal, and a user, work in cooperation with each other. The specific operation of this system will be described below.

[0224] Server Features

[0225] 1. Collecting job information

[0226] The server crawls and collects job information from job information websites in Japan. The collected job information is stored in a database. The crawling technology used here is a Python library (Beautiful Soup and Scrapy).

[0227] 2. Job Filtering

[0228] From the collected job information, we filter job information that does not require advanced language skills and has a salary expected to be above a certain level. At this stage, we use natural language processing tools such as NLTK and spaCy to perform text analysis and filtering.

[0229] 3. Multilingual Translation

[0230] The filtered job listings are translated into multiple languages ​​using a multilingual translation API such as Microsoft Translator Text API. This API converts the job listings into multiple languages, including English, and stores them back in the database.

[0231] 4. Collection and Extraction of User Information

[0232] The server collects user posting history from foreign internet services. For example, it crawls the posting history using the APIs of Facebook and LinkedIn. From the collected posting history, it extracts posts containing keywords such as "Japan" and "Tokyo" to find users who are likely to be interested in Japan.

[0233] 5. Analysis of education and work history information

[0234] The extracted user's education and work history information is analyzed and users who meet certain criteria (e.g., bachelor's degree or higher, five or more years of work experience) are filtered out. This analysis uses a trained generative AI model to compare the user's skill set with job postings.

[0235] 6. Job Recommendations

[0236] It recommends suitable job listings for filtered users. For recommendations, it uses a machine learning model (e.g., scikit-learn's TfidfVectorizer and linear kernel) to calculate the degree of match between the user's skill set and job listings. Recommendation results are sent to the user's email address or via push notification.

[0237] 7. Receipt of application information and notification

[0238] Receives application information from users and notifies the company. Application information is sent from a device (such as a smartphone) and received by the server. The application information is then notified to the company.

[0239] 8. Arranging interview dates

[0240] The server coordinates interview dates between users and companies, using calendar integration functions such as the Google Calendar API.

[0241] 9. Notification of match completion

[0242] We track the interview results and notify the user and the company that the match is complete if a job offer is made. We also update the database to record the successful match.

[0243] 10. Recruiting for autonomous vehicles

[0244] The server will recruit foreign talent suited to the technology and work related to autonomous vehicles, and recommend talent that meets the company's needs. At this stage, the server will also use the generative AI model and translation API to evaluate the degree of match between the candidate's skills and the job information.

[0245] Device Features

[0246] The device (smartphone, tablet, PC) provides the interface for users to check job information and apply. In particular, the usability of the notification function, the function to submit application information, and the data entry form is important.

[0247] User Roles

[0248] Users access job information provided through their devices and apply for jobs they are interested in. If a job related to autonomous vehicles is recommended, users apply based on their own education and work history information.

[0249] Prompt Sentence Examples

[0250] "For a job information platform related to autonomous vehicles, we will collect job information that does not require Japanese language skills and offers an annual salary of 6 million yen or more, and extract job listings for specific occupations such as IT engineers and biotechnology researchers. We will then build a system that translates the job listings into multiple languages ​​and recommends them to suitable candidates."

[0251] This concludes the detailed explanation of the "Mode for Carrying Out the Invention." This system will enable effective matching of job information from Japanese companies with foreign talent possessing advanced skills.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1:

[0254] The server crawls and collects job information from job information websites in Japan. In this step, Python libraries (Beautiful Soup and Scrapy) are used to parse the HTML of the job information website and extract information. The input is the URL of the job information website, and the output is a list of the retrieved job information.

[0255] Step 2:

[0256] The server filters the collected job listings for those that do not require advanced language skills and are expected to pay above a certain salary. In this step, natural language processing tools such as NLTK and spaCy are used to perform text analysis and filtering of the collected job listings. The input is a list of collected job listings, and the output is a list of filtered job listings.

[0257] Step 3:

[0258] The server translates the filtered job listings into multiple languages. This step uses the Microsoft Translator Text API to translate the job listings into English and other languages. The input is the filtered list of job listings, and the output is the translated list of job listings.

[0259] Step 4:

[0260] The server stores the translated job postings in a database. This step uses an SQL database (PostgreSQL or MySQL) to organize and store the stored data for quick search and reference. The input is a list of translated job postings, and the output is a database entry for the stored job postings.

[0261] Step 5:

[0262] The server uses the API of a foreign internet service (such as Facebook or LinkedIn) to collect the user's posting history. In this step, it searches for and extracts content containing specific keywords (e.g., "Japan" or "Tokyo") from the user's posts. The input is the account information of the internet service, and the output is a list of posts containing the specific keywords.

[0263] Step 6:

[0264] The server extracts users who are presumed to be interested in Japan from the collected posting history. In this step, the content of the extracted posting history is analyzed, and users are selected based on the number and frequency of posts containing Japan-related keywords. The input is a list of posts containing the keywords, and the output is a list of users who are presumed to be interested in Japan.

[0265] Step 7:

[0266] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. In this step, the server analyzes the educational and work history data to determine whether they meet the criteria (bachelor's degree or higher, five or more years of work experience). The input is a list of users who are presumed to be interested in Japan, and the output is a list of users who meet the criteria.

[0267] Step 8:

[0268] The server recommends suitable job listings for the filtered users. In this step, a machine learning model using scikit-learn's TfidfVectorizer and a linear kernel is used to calculate the degree of match between the user's skills and job listings. The input is a list of users who meet the criteria and a list of job listings, and the output is a list of recommended job listings.

[0269] Step 9:

[0270] The server receives the application information from the user and notifies the company. In this step, the server receives the data entered in the application form and sends a notification email to the corresponding company. The input is the information entered by the user in the application form, and the output is the application notification email sent to the company.

[0271] Step 10:

[0272] The server coordinates interview dates between the company and the user. In this step, calendar integration functions such as the Google Calendar API are used to coordinate the schedules of both parties. The input is the desired interview date information of the user and the company, and the output is the confirmed interview date.

[0273] Step 11:

[0274] The server tracks the interview results and notifies the completion of the match when the candidate is hired. In this step, the input data of the interview results is recorded and a notification is sent to the corresponding user and company when the candidate is hired. The input is the interview result information and the output is a match completion notification.

[0275] Through the above processing steps, the system will effectively match foreign talent with advanced autonomous vehicle-related skills with Japanese companies.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0277] System Configuration

[0278] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining an emotion engine, it realizes recommendations based on the user's emotional state. The roles of each entity are as follows:

[0279] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; analyzing sentiment; and notifying applicants when matching is complete.

[0280] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[0281] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[0282] Program processing flow

[0283] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[0284] 1. Collecting job information

[0285] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to offer to job seekers.

[0286] 2. Job Filtering

[0287] The server analyzes the collected job information and filters out job listings that do not require a high level of Japanese language ability and are expected to offer a certain salary or higher, thereby narrowing down the job listings to those that are more likely to be suitable for foreign talent.

[0288] 3. Multilingual Translation

[0289] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[0290] 4. Extraction of foreign talent

[0291] The server crawls foreign social networking services and collects users' posting history, thereby gathering data to identify users who are presumed to be interested in Japan.

[0292] 5. Analysis of posting history using an emotion engine

[0293] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users, making it possible to identify users who have positive emotions, not just an interest in Japan.

[0294] 6. Filtering educational and work history information

[0295] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[0296] 7. Emotion-Based Job Recommendations

[0297] The server then recommends appropriate job information via push notification or email based on the filtered user's emotional state, allowing the server to provide job information that matches the user's current emotional state.

[0298] 8. Receipt of application information

[0299] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[0300] 9. Notification of application information and interview arrangements

[0301] The server receives the application information sent by the user and stores it in a database. The saved application information is then notified to the relevant Japanese companies. Interview dates are then arranged between the company and the user.

[0302] 10. Matching completed

[0303] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. It also updates the database with information about successful matches for future analysis and improvement.

[0304] Specific examples

[0305] Example 1: Providing job information for IT engineers

[0306] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[0307] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0308] The server crawls Facebook and LinkedIn, collecting the posting history of users in the United States and Spain.

[0309] The server uses a sentiment engine to analyze posting history and identify users who have positive sentiment toward Japan.

[0310] Check the work history of the extracted users (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[0311] The server recommends suitable job information to the user by email based on their emotional state.

[0312] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0313] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[0314] Example 2: Recruiting biotechnology researchers

[0315] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0316] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0317] The server uses LinkedIn to collect posting history of users who reside in Canada.

[0318] The server uses an emotion engine to perform sentiment analysis of posting history and identify users who have positive sentiment toward Japanese research.

[0319] The extracted users' educational background (e.g., PhD from the University of Toronto) and work experience (3 years of research experience) are checked, and users who meet the criteria are filtered out.

[0320] The server recommends suitable job information to the user by email based on their emotional state.

[0321] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0322] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[0323] This concludes the detailed explanation of the system's operation as part of the "Mode for Carrying Out the Invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign talent. By adding an emotion engine, more personalized recommendations based on the user's emotional state can be realized.

[0324] The processing flow will be explained below.

[0325] Step 1:

[0326] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing you to stay up to date with new job information.

[0327] Step 2:

[0328] The server analyzes the collected job listings and filters out those that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Specifically, the filtering is performed based on criteria that are important to job seekers, such as the "no Japanese language skills required" tag and "salary range."

[0329] Step 3:

[0330] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, such as Google Translate or DeepL, and stores the translated listings in a database.

[0331] Step 4:

[0332] The server crawls major foreign social networking services (SNS) and collects user posting history, including Facebook, LinkedIn, and Twitter.

[0333] Step 5:

[0334] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users. This allows it to identify posts that have "positive emotions toward Japan." For example, it extracts posts that contain positive phrases such as "I love Japan."

[0335] Step 6:

[0336] The server checks the profile information of the extracted users and collects their educational and work history information, such as whether they have a bachelor's degree or higher and whether they have more than five years of work experience, from the public profile provided by the users.

[0337] Step 7:

[0338] The server uses the collected information to filter users who meet certain criteria, such as educational background or work history, and only allows users who meet certain criteria to proceed to the next step.

[0339] Step 8:

[0340] The server then recommends appropriate job information via push notification or email based on the user's filtered emotional state. For example, users with positive emotions will be given priority in receiving job information in their desired occupation and location.

[0341] Step 9:

[0342] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied, they enter the necessary information in the application form and submit it.

[0343] Step 10:

[0344] The server receives the application information sent by the user, stores it in a database, and notifies the relevant Japanese companies of the saved application information.

[0345] Step 11:

[0346] The server coordinates interview dates between the company and the user, and finalizes the interview date after coordinating the schedule with the company.

[0347] Step 12:

[0348] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match has been completed. It also updates and stores information about successful matches in a database, which can be used for future analysis and improvement.

[0349] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign professionals. By adding an emotion engine, it becomes possible to provide more personalized job information based on the user's emotional state.

[0350] Example 2

[0351] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] Existing recruitment systems make it difficult for foreign talent to efficiently search for job listings in Japan and apply for suitable positions. Companies also lack the ability to match foreign talent based on their emotional state and interests, preventing an effective recruitment process. Furthermore, it is difficult to accurately filter foreign talent's educational and work history information to select the right candidates. These challenges reduce the efficiency of matching foreign talent with Japanese companies.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0354] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain level of salary; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for analyzing the collected posting histories with a sentiment analysis engine to extract users who are interested in Japan and show positive emotions; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users based on their emotional state; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; and means for tracking interview results and notifying companies of the completion of matching when a candidate is hired. This enables foreign talent to efficiently search for job information in Japan and receive appropriate job information, thereby enabling companies to implement an effective hiring process.

[0355] "Job Information" means detailed information about employment opportunities, including job types, salary, work location, required qualifications and experience, etc.

[0356] "Means of collection" refers to the methods and techniques used to obtain the required data from designated sources, including crawling, API calls, etc.

[0357] "Filtering means" refers to methods and techniques for eliminating unnecessary information from collected data based on specific conditions and extracting only data that meets the purpose.

[0358] "Means of translating into multiple languages" refers to methods and technologies for converting text written in one language into multiple other languages, including translation APIs.

[0359] "Database storage" refers to methods and technologies for securely managing and retaining data in a structured format, including SQL and NoSQL databases.

[0360] "Social networking service" means an internet-based platform that enables users to interact and share information with others online.

[0361] "Posting history" refers to data that records the content of posts that a user has made in the past on a social networking service.

[0362] "Sentiment analysis engine" refers to software or algorithms used to identify and analyze emotions from text data.

[0363] "Educational history" refers to information about the educational institutions an individual has attended and the degrees or qualifications they have obtained.

[0364] "Employment history" refers to the history of jobs and positions held by an individual in the past.

[0365] "Recommendation means" refers to methods and technologies for suggesting appropriate information and options to users based on collected and analyzed data.

[0366] "Means of receiving application information" refers to the methods and technologies for obtaining the application details submitted by job seekers, including web forms and APIs.

[0367] "Means of notifying companies" refers to the methods and techniques used to notify relevant companies of the received application information.

[0368] "Means for arranging interview dates" refers to methods and techniques for setting the date, time, and location of an interview between a company and a job seeker.

[0369] "Means for tracking interview results" refers to methods and techniques for monitoring and recording the progress and results of the interview.

[0370] "Means for notifying completion of matching" refers to the methods and technologies used to notify relevant parties of the results of the recruitment process once they have been determined.

[0371] MODE FOR CARRYING OUT THE INVENTION

[0372] System Configuration

[0373] The system of the present invention is mainly composed of three entities: a server, a terminal, and a user. The server is responsible for collecting, filtering, translating, and storing job information, collecting, analyzing, and making recommendations about foreign talent, managing application information, arranging interview dates, analyzing emotions, and notifying applicants when matching is complete. The terminal is a device through which users receive job information, check details, and enter application information. This typically includes PCs, tablets, and smartphones. The user is the entity that receives job information, applies if interested, and goes through the process of being hired after an interview.

[0374] Server Functions and Operations

[0375] The server periodically crawls job information websites in Japan to collect the latest job information. It uses natural language processing (NLP) technology and filtering algorithms to filter out jobs that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Python's Beautiful Soup and pandas libraries are particularly useful.

[0376] The filtered job listings are translated into English and other major languages ​​using multilingual translation APIs such as Google Translate API and DeepL API, and stored in a database, where the translated information is available for future searches and analysis.

[0377] Next, the server crawls foreign social networking services such as Facebook and LinkedIn to collect users' posting history, which is then analyzed using a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to identify users who have positive sentiment toward Japan.

[0378] The educational and work history information of the identified users is collected using the LinkedIn API, etc., and users who meet certain criteria (e.g., bachelor's degree or higher, five years or more of work experience) are filtered out. Appropriate job information is recommended to the filtered users based on their emotional state. This recommendation process uses an email service (e.g., SendGrid API).

[0379] User operations

[0380] The user checks the received job information, and if interested, clicks on the provided link to view more information. If satisfied with the details, the user can enter the necessary information in the application form and submit it.

[0381] Collaboration with companies

[0382] The server receives application information sent by users and stores it in a database. The stored application information is then notified to relevant Japanese companies. Interview dates are then arranged between the company and the user, and the server ultimately tracks the interview results. If the candidate is hired, the server notifies the company and the user that matching is complete. Information about successful matches is also updated in the database, allowing for future analysis and improvement.

[0383] Specific examples

[0384] As a concrete example, consider the provision of job information for IT engineers. The server crawls information on the job title "IT engineer" from job information providers A and B in Japan. From this information, it filters job listings that "do not require advanced Japanese language skills" and offer an annual salary of 5 million yen or more, and translates them into English and Spanish. Next, the server crawls Facebook and LinkedIn, collecting the posting history of users living in the United States and Spain. This posting history is analyzed using an emotion engine to identify users who have positive feelings toward Japan. The extracted users' work history and educational background are checked, and suitable job listings are recommended to users who meet the criteria based on their emotional state.

[0385] Prompt Sentence Examples

[0386] "Dear Generative AI Model, please explain in detail the processing flow of your recruitment system for foreign talent based on the following technical specifications."

[0387] By using this system, Japanese companies can effectively recruit highly skilled personnel from overseas, and foreign talent can easily find job information that suits them. The recommendation function based on the emotion engine is expected to provide more personalized job information and improve the success rate of matching.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Step 1:

[0390] Collecting job information

[0391] The server uses Python's Beautiful Soup and requests libraries to periodically crawl job information from job information websites in Japan. The input is the URL of the job information website, and the output is a list of collected job information. The crawled data is converted to JSON format and saved.

[0392] Step 2:

[0393] Job Filtering

[0394] The server filters the collected job information to those that do not require advanced Japanese language skills and have an annual salary of 5 million yen or more. The input is a list of collected job information, and the output is a list of job information after filtering. This process uses the Python pandas library to extract data that matches the conditions.

[0395] Step 3:

[0396] Multilingual translation

[0397] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API such as Google Translate API or DeepL API. The input is a list of filtered job listings, and the output is a list of translated job listings. The translation results are stored in a database for future searches and analysis.

[0398] Step 4:

[0399] Extraction of foreign talent

[0400] The server crawls social networking services such as Facebook and LinkedIn to collect the posting history of foreign users. The input is the search keywords on the social networking site and the user's posting history, and the output is a list of the collected posting history. This allows data to be obtained to identify users who may be interested in Japan.

[0401] Step 5:

[0402] Sentiment analysis of posting history

[0403] The server analyzes the collected posting history using IBM Watson's sentiment analysis API. The input is a list of posting history, and the output is an analysis of the emotions expressed by the user. Based on the analysis results, users with positive emotions toward Japan are identified.

[0404] Step 6:

[0405] Filtering educational and work history information

[0406] The server collects the identified users' educational and work history information using the LinkedIn API or similar, and filters users who meet certain criteria. The input is the user's profile information, and the output is a list of users who meet the criteria. The filtering criteria include a bachelor's degree or higher and five or more years of work experience.

[0407] Step 7:

[0408] Emotion-based job recommendations

[0409] The server recommends appropriate job listings based on the sentiment analysis results and filtered user information. The input is the filtered user information and translated job listings, and the output is a list of recommended job listings. The recommended job listings are sent to the user via an email service (SendGrid API).

[0410] Step 8:

[0411] Receiving application information

[0412] The user checks the received job information and, if they are interested, clicks to view the detailed information. The input is the email with the recommended job information, and the output is the application information entered by the user. When the user enters the necessary information in the application form and submits it, the application information is sent to the server.

[0413] Step 9:

[0414] Notification of application information and interview arrangements

[0415] The server receives the application information sent by the user and stores it in a database. It then notifies the relevant companies of the application information and arranges interview dates between the companies and the user. The input is the user's application information, and the output is the arranged interview schedule.

[0416] Step 10:

[0417] Matching completion and notification

[0418] The server tracks the interview results and notifies the company and user that the match is complete if a job offer is made. It also updates the database with information about successful matches for future analysis and improvement. The input is the interview results, and the output is a notification that the match is complete.

[0419] (Application example 2)

[0420] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0421] Conventional job information systems are primarily focused on matching job openings with applications. While effective in improving the efficiency of recruiting, they lack the elements necessary to recruit the right talent. In particular, they are unable to provide personalized recommendations that take into account the user's emotional state, creating a need for improved user experience. Furthermore, in customer service, they are unable to provide appropriate product recommendations based on the customer's emotional state, making it difficult to improve customer service quality.

[0422] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0423] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a salary above a certain level; means for translating the filtered job information into multiple languages; means for saving the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users who are presumed to be interested in Japan from the collected posting histories; means for analyzing the educational and work history information of the extracted users and filtering out users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying the completion of matching when a job is decided; means for analyzing the facial expressions of customers using an emotion engine and making recommendations based on their emotional state; and means having a multilingual display device for recommending products based on the emotional state of customers. This not only enables personalized recommendations that take into account the user's emotions, but also enables high-quality customer service based on the emotional state of customers.

[0424] "Job information in Japan" refers to information about employment and job changes that is published or posted within Japan.

[0425] "Advanced Japanese language skills not required" means that fluent reading, writing, or speaking Japanese is not required to apply for the job.

[0426] "Salary above a certain level" means that the compensation is above a set standard amount.

[0427] "Filtering" is the process of selecting only data or information that meets certain conditions from a large amount of data or information.

[0428] "Multilingual" means not limited to one language but includes multiple different languages.

[0429] A "social networking service" is an online platform that facilitates interaction between users via the Internet.

[0430] "Posting history" refers to the history of content that a user has posted on a social networking service in the past.

[0431] "Interested in Japan" refers to a state in which a user shows interest or curiosity about Japan.

[0432] An "emotion engine" is software that analyzes a user's emotional state and recommends appropriate actions based on the results.

[0433] "Facial expression analysis" is the process of analyzing facial expressions to determine the emotion behind them.

[0434] "Personalized recommendations" refers to customized suggestions based on the preferences and emotional state of individual users.

[0435] A "multilingual display device" is an electronic device that has the ability to display information in multiple languages.

[0436] "Customer service" refers to all work related to providing customer service and services in stores and other places.

[0437] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it realizes recommendations based on the user's emotional state.

[0438] Detailed system preparation

[0439] Server Roles

[0440] The server has a means of periodically collecting job information within Japan. This collection is done by crawling major job information websites. The collected job information is analyzed and filtered to determine whether it requires a high level of Japanese language proficiency and offers a certain salary or higher. Job information selected through this filtering process is translated into English and other major languages ​​using a multilingual translation API and stored in a database.

[0441] Next, the server has a means for collecting users' posting history from foreign social networking services. This posting history is used to extract posts that are presumed to indicate that the user is interested in Japan. From the collected posting history, the user's emotional state is analyzed using an emotion engine, and users who have not only an interest in Japan but also positive emotions are identified.

[0442] Device Role

[0443] The terminal is equipped with an interface that allows users to receive job information, check details, and enter application information. Mobile devices such as smartphones and tablets are the primary targets.

[0444] By using emotion analysis technology, it is possible to analyze the facial expressions of customers in real time through their devices and make recommendations based on their emotional state. Specifically, a device such as smart glasses is used to analyze the customer's facial expressions, and product recommendations are displayed based on the results.

[0445] User Roles

[0446] Users can receive job information provided, and if they are interested, they can view the details and enter the necessary information in the application form. Furthermore, in stores, they can receive product recommendations in real time using smart glasses.

[0447] Specific examples

[0448] Suppose a customer approaches a store clerk wearing smart glasses. The server analyzes the customer's facial expression through camera footage and detects "happiness." As a result, a recommendation for a "newly released coffee maker" is returned from the API. Based on the information displayed on the smart glasses, the store clerk suggests, "How about this new coffee maker?" The following is an example of a prompt sentence that can be used in this case:

[0449] "When the display shows 'New Coffee Maker,' we suggest, 'How about a new coffee maker?'"

[0450] Hardware and software used

[0451] Hardware: Smartphones, smart glasses, camera-equipped devices

[0452] Software: OpenCV, DeepFace, libraries for sending API requests (e.g. Requests)

[0453] The server uses OpenCV to detect faces from real-time video and DeepFace to analyze emotions from facial images. In response, an emotion engine using a generative AI model analyzes the user's emotions and can display recommendation results on the device's display.

[0454] As described above, the present invention makes it possible to provide personalized recommendations that take into account the emotional state of the user, and to realize high-quality service in customer service operations.

[0455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0456] Step 1:

[0457] The server crawls job information websites in Japan and collects the latest job information. As input, it receives a list of URLs for the specified job information websites and obtains HTML data from each website. To process the data, it uses a crawling tool to analyze the HTML data and extract the necessary job information. As output, it generates a list of the extracted job information.

[0458] Step 2:

[0459] The server filters the collected job information for those that do not require advanced Japanese language skills and are expected to offer a certain salary or higher. As input, it receives the list of job information obtained in step 1. As data calculation, it analyzes the content of the job information and executes logic to determine whether it matches the filter conditions. As output, it generates a list of filtered job information.

[0460] Step 3:

[0461] The server translates the filtered job postings using a multilingual translation API. As input, it receives the list of filtered job postings obtained in step 2. As data calculation, it sends a request for each job posting to the multilingual translation API and obtains the translation results. As output, it generates a list of translated job postings.

[0462] Step 4:

[0463] The server stores the translated job postings in the database. As input, it receives the list of translated job postings obtained in step 3. As data processing, it performs a data insertion operation into the database. As output, it generates the job posting data stored in the database.

[0464] Step 5:

[0465] The server collects user posting histories from foreign social networking services. As input, it receives the API key of the specified social networking service and a list of user IDs. As data processing, it obtains each user's posting history and analyzes the collected posting data. As output, it generates a list of the collected posting histories.

[0466] Step 6:

[0467] The server extracts users who are presumed to be interested in Japan from the collected posting history. As input, it receives the list of posting histories obtained in step 5. As data processing, it uses an emotion engine to perform sentiment analysis of each post and identifies posts that show interest or positive sentiment toward Japan. As output, it generates a list of users who are interested in Japan.

[0468] Step 7:

[0469] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. As input, it receives the list of users interested in Japan obtained in step 6. As data processing, it analyzes each user's social networking service profile information and extracts educational and work history information. It then executes logic to evaluate whether the criteria are met. As output, it generates a list of filtered users.

[0470] Step 8:

[0471] The server recommends appropriate job listings for the filtered users. As input, it receives the filtered user list obtained in step 7 and the job listing data saved in step 4. As data calculation, it selects appropriate job listings based on each user's emotional state and profile information and executes the recommendation algorithm. As output, it generates recommendation results for each user.

[0472] Step 9:

[0473] The terminal provides an interface for receiving application information from users. As input, it receives information entered by users into an application form. As data processing, it converts the application information into a specified format and sends it to the server. As output, it generates the application information sent to the server.

[0474] Step 10:

[0475] The server notifies companies of the application information and arranges interview dates between the relevant companies and the user. As input, it receives the application information received in step 9. As data processing, it generates and sends a notification message to the company. As output, it generates the application information notified to the company and arranges interview dates.

[0476] Step 11:

[0477] The server tracks the interview results and notifies the completion of matching when a candidate is hired. It receives interview result information from the company as input. It processes the data by updating the matching results in the database and notifying the user and company. It generates the notified matching completion information as output.

[0478] Step 12:

[0479] The device uses an emotion engine to analyze the facial expressions of customers and make recommendations based on their emotional state. Camera footage is acquired in real time as input. Face detection is performed using OpenCV and emotion analysis is performed using DeepFace for data processing. Emotion analysis results are generated as output.

[0480] Step 13:

[0481] The terminal uses a multilingual display device to recommend products based on the emotional state of the customer. As input, it receives the emotion analysis results obtained in step 12. As data processing, it executes an algorithm to select and display products appropriate for the customer's emotional state. As output, it displays the product recommendation information on a multilingual display.

[0482] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0483] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0484] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0488] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0489] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0490] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0491] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0492] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0493] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0494] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0495] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0496] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0497] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0498] System Configuration

[0499] The system of the present invention is mainly composed of three entities: a "server," a "terminal," and a "user." The roles of each are as follows:

[0500] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; and notifying applicants when matching is complete.

[0501] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[0502] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[0503] Program processing flow

[0504] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[0505] 1. Collecting job information

[0506] The server periodically crawls major job sites in Japan (generally referred to as "job information sites") to collect the latest job information.

[0507] 2. Job Filtering

[0508] The server analyzes the collected job information and filters out job listings that do not require advanced Japanese language skills and are expected to pay a certain amount or more.

[0509] 3. Multilingual Translation

[0510] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and then stores the translated listings in a database.

[0511] 4. Extraction of foreign talent

[0512] The server crawls foreign social networking services, analyzes users' posting history, and extracts users who are likely to be interested in Japan.

[0513] 5. Filtering educational and work history information

[0514] The server collects the educational and work history information of the extracted users and filters out users who meet certain criteria (e.g., bachelor's degree or above, work experience of 5 years or more).

[0515] 6. Job Recommendations

[0516] The server sends appropriate job information to users who meet the criteria via push notifications or email.

[0517] 7. Receipt of application information

[0518] The user checks the received job information, and if they are interested, they enter the necessary information into the application form and submit it. The server stores this application information in a database.

[0519] 8. Notification of application information and interview arrangements

[0520] The server then notifies the necessary job information providers and companies of the saved application information, after which the company and the user can arrange an interview date.

[0521] 9. Matching completed

[0522] The server tracks the interview results, notifies the company and the user if a job offer is made, and updates the database to record successful matches.

[0523] Specific examples

[0524] Example 1: Providing job information for IT engineers

[0525] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[0526] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0527] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[0528] Analyze the extracted users' work history (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[0529] The server then emails the appropriate job listings to the filtered users.

[0530] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0531] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0532] Example 2: Recruiting biotechnology researchers

[0533] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0534] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0535] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[0536] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[0537] The server will email suitable job listings to the user.

[0538] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0539] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0540] This concludes the explanation of the specific system operation as a "mode for carrying out the invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign personnel.

[0541] The processing flow will be explained below.

[0542] Step 1:

[0543] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to provide to job seekers.

[0544] Step 2:

[0545] The server analyzes the collected job listings and filters out those that do not require a high level of Japanese language ability and those that are expected to pay above a certain salary, thereby narrowing down the list of job listings that are easy for foreign talent to apply for.

[0546] Step 3:

[0547] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[0548] Step 4:

[0549] The server crawls major social networking services in foreign countries and collects users' posting history, thereby gathering data to extract users who are presumed to be interested in Japan.

[0550] Step 5:

[0551] The server analyzes the collected posting history and extracts users who have posted using keywords such as "Japan" and "Tokyo." This user extraction is important for identifying foreign talent who are interested in Japan.

[0552] Step 6:

[0553] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[0554] Step 7:

[0555] The server then recommends suitable job information to the filtered users via push notifications or emails, so that relevant job information is delivered directly to the users.

[0556] Step 8:

[0557] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[0558] Step 9:

[0559] The server receives the application information sent by the user and stores it in a database, which then notifies the relevant Japanese companies.

[0560] Step 10:

[0561] The server coordinates interview dates between the company and the user, and once the interview date and time are confirmed, the server notifies both the company and the user of the information.

[0562] Step 11:

[0563] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. Information about successful matches is updated in the database for future analysis and improvement.

[0564] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign personnel.

[0565] Example 1

[0566] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] Japanese companies face the challenge of finding it difficult to effectively recruit foreign talent. Foreign talent with advanced expertise in particular has limited access to job listings in Japan, making it difficult to match potential candidates with the right talent. Furthermore, language barriers and geographical restrictions mean that proper communication between companies and job seekers is often not possible, hindering smooth recruitment.

[0568] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0569] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain salary or higher; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; analysis means using a multilingual translation API and natural language processing technology; and means for collecting job information and user postings using crawling technology. This makes it easier for foreign talent to access job information in Japan, enabling appropriate matching and a quick hiring process.

[0570] "Means for collecting job information within Japan" refers to technology for automatically obtaining data on job openings from various job information platforms within Japan.

[0571] "A means of filtering out jobs that do not require advanced Japanese language skills and are expected to have a salary above a certain level" is a technology for selecting, from the collected job information, job information that does not require a high level of specialized Japanese language skills and meets a specified salary standard.

[0572] "Multilingual translation means" refers to technology for translating filtered job listings into English and other major foreign languages.

[0573] "Means for storing in a database" refers to the technology used to store translated job information in an organized manner and in a database in a format that can be quickly accessed when needed.

[0574] "Means for collecting user posting history from foreign social networking services" refers to technology for automatically obtaining users' public posting history from foreign SNSs.

[0575] The "means of extracting users who are likely to be interested in Japan" is a technology that analyzes collected posting history and identifies users who are likely to be interested in Japan and its culture.

[0576] "Means for analyzing educational and work history information and filtering users who meet certain criteria" refers to technology for evaluating the educational and work history information of extracted users and selecting users who meet certain criteria (for example, a specific degree or years of work experience).

[0577] "Means for recommending appropriate job information" refers to technology for recommending suitable job information to users who meet criteria.

[0578] The "means for receiving application information and notifying companies" refers to a technique for receiving application information from users and notifying the relevant companies of that information.

[0579] "Means for coordinating interview dates between a company and a user" refers to a technique for coordinating the schedules of both the company and the user and setting an interview date.

[0580] "Means for tracking interview results and notifying the completion of matching when a candidate is hired" refers to technology for managing the results of interviews and informing both the company and the user when a candidate is hired.

[0581] "Analysis method using multilingual translation API and natural language processing technology" refers to technology for translating job postings and user posts into multiple languages ​​and analyzing them through natural language processing.

[0582] "Methods of collecting job information and user posts using crawling technology" refers to techniques for collecting necessary data from job information sites and social media using web scraping and crawling technology.

[0583] MODE FOR CARRYING OUT THE INVENTION

[0584] The purpose of this system is to collect job information in Japan and provide appropriate job information to highly skilled foreign professionals. The operation of the system consists of processing steps that are made up of three main entities: the server, the terminal, and the user.

[0585] The server executes the following processes in order.

[0586] 1. Collecting job information

[0587] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl the latest job listings from major job listing websites in Japan. Specifically, it retrieves job listings from sites such as Indeed and Rikunabi.

[0588] 2. Job Filtering

[0589] The server uses natural language processing (NLP) technology to analyze the collected job listings. By segmenting and tagging the listings using Python's NLTK and SpaCy libraries, it filters out listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more."

[0590] 3. Multilingual Translation

[0591] The server translates the filtered job listings using a multilingual translation API (e.g., Google Translate API, DeepL API), and the translated information is stored in a database (e.g., PostgreSQL, MySQL).

[0592] 4. Extraction of foreign talent

[0593] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. This process also uses NLP technology to extract keywords related to "Japan" and "Tokyo" from the posted content.

[0594] 5. Filtering educational and work history information

[0595] The server analyzes the extracted user profile information to see if they meet certain criteria (e.g., a computer science degree, five or more years of work experience), using the Python Pandas library for this filtering.

[0596] 6. Job Recommendations

[0597] The server recommends suitable job listings to users who meet the criteria, and sends these recommendations via push notifications or email delivery services (e.g., Firebase Cloud Messaging, SendGrid, Mailgun).

[0598] 7. Receipt of application information

[0599] The user checks the received job information, enters the necessary information in the application form, and submits it. The device (PC, tablet, smartphone) provides the UI for this application form and sends the entered information to the server. The server saves the application information in a database.

[0600] 8. Notification of application information and interview arrangements

[0601] The server notifies the appropriate companies of the saved application information and provides an interface for companies and users to arrange interview dates. Specifically, Google Calendar API or a schedule adjustment app can be used.

[0602] 9. Matching completed

[0603] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for later analysis.

[0604] Specific examples

[0605] Example 1: Providing job information for IT engineers

[0606] The server crawls information about the job title "IT engineer" from job information sites A and B in Japan.

[0607] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0608] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[0609] Analyze the extracted users' work history (e.g., computer science degree, 5+ years of work experience) and filter users who meet the criteria.

[0610] The server then emails the appropriate job listings to the filtered users.

[0611] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0612] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0613] Example prompt: "Please translate job postings for IT engineers with an annual salary of at least 5 million yen, no advanced Japanese language skills required, into English and Spanish, and email the job postings to suitable candidates."

[0614] Example 2: Recruiting biotechnology researchers

[0615] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0616] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0617] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[0618] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[0619] The server will email suitable job listings to the user.

[0620] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0621] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0622] Example prompt: "Please translate job postings for biotechnology researchers with an annual salary of at least 6 million yen and no advanced Japanese language skills required into multiple languages ​​and email suitable candidates."

[0623] This system effectively matches job information from Japanese companies with highly skilled foreign talent, ensuring a fast and smooth recruitment process.

[0624] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0625] Step 1: Gather job information

[0626] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl job information from job information websites in Japan. The server periodically accesses a specified URL and downloads the HTML page of the job information. Next, it uses BeautifulSoup to analyze the HTML page and extract the necessary data (job type, salary, language requirements, etc.).

[0627] Input: URL list of job information sites

[0628] Output: A list of extracted jobs

[0629] Step 2: Filter Jobs

[0630] The server analyzes the collected job listings using natural language processing (NLP) technology. It uses Python's NLTK and SpaCy libraries to split and tag the text data. This allows it to filter out job listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more." The server then temporarily stores only the filtered job listings in order to proceed to the next step.

[0631] Input: A list of extracted job postings

[0632] Output: A filtered list of jobs

[0633] Step 3: Translate into multiple languages

[0634] The server uses the Google Translate API or DeepL API to translate the filtered job listings into multiple languages. Specifically, it sends the text data of the job listings to the API and receives the translated text. The translated information is then stored in a database (e.g., PostgreSQL, MySQL).

[0635] Input: A filtered list of jobs

[0636] Output: A list of translated job postings

[0637] Step 4: Identifying foreign talent

[0638] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. Using Python's Scrapy, it crawls the pages of specified social media accounts and collects posting data. From the collected posting history, it extracts keywords indicating interest in Japan and its culture (e.g., "Japan" and "Tokyo") and analyzes them using natural language processing technology.

[0639] Input: List of social media account URLs

[0640] Output: List of users interested in Japan

[0641] Step 5: Filtering education and work history information

[0642] The server parses the extracted user profile information (educational background and work experience). It uses the Python Pandas library to read the user data and filter users who meet certain criteria (e.g., computer science degree, 5+ years of work experience). The filtered user information is used in the next step.

[0643] Input: List of users interested in Japan and their profile information

[0644] Output: A list of users who meet the criteria

[0645] Step 6: Job Recommendations

[0646] The server recommends suitable job listings to filtered users. The server uses email delivery services such as SendGrid and Mailgun to send emails containing job listings to users. If push notifications are used, services such as Firebase Cloud Messaging can be used.

[0647] Input: A list of users who meet the criteria and a list of translated job postings

[0648] Output: Job notification sent to user

[0649] Step 7: Receiving your application information

[0650] The user checks the received job information, and if they are interested, they enter the required information in the application form and submit it. The device (PC, tablet, smartphone) provides the UI for the application form and sends the entered information to the server. The server receives the application information and stores it in a database.

[0651] Input: User's application information

[0652] Output: Application information stored in a database

[0653] Step 8: Notification of application information and interview arrangements

[0654] The server notifies the company of the saved application information, provides the application information to the company via automatic email or API integration, and provides an interface for coordinating interview dates between the company and the user. Scheduling can also be done using the Google Calendar API.

[0655] Input: Application information stored in the database

[0656] Output: Application information and interview schedule notified to the company

[0657] Step 9: Matching Complete

[0658] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for future analytics and reporting.

[0659] Input: Interview results from company

[0660] Output: Matching completion information notified to companies and users, and success stories recorded in the database

[0661] (Application example 1)

[0662] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] Currently, it is extremely difficult to effectively provide job information in Japan to foreign talent and recruit the right talent. In particular, there is a lack of efficient means to find foreign talent with advanced skills related to the development and operation of autonomous vehicles and match them with Japanese companies. As a result, there is a lack of a system that can centrally identify foreign talent, translate job information, and arrange interviews with companies.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0665] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for job listings that do not require advanced language skills and are expected to offer salaries above a certain level; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign Internet services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' education and work history information and filtering users who meet certain criteria; means for recommending appropriate job listings to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; and means for recruiting foreign talent suitable for technologies and work related to autonomous vehicles and recommending talent according to the needs of companies. This enables centralized management of everything from identifying foreign talent to matching with companies, thereby enabling efficient recruitment of talent with advanced skills related to autonomous vehicles.

[0666] "Job Information" means information about employment opportunities provided to job seekers.

[0667] "Filtering means" refers to the function of removing unnecessary parts from collected information based on specific conditions and selecting only useful information.

[0668] "Means for translating into multiple languages" refers to the ability to convert specific information into multiple different languages.

[0669] "Means of storing information in a database" refers to a system that systematically organizes and stores information, making it easy to search and reference.

[0670] "Internet services" is a general term for various online services provided via the Internet.

[0671] "Posting history" refers to a record of posts and comments made by a user on the Internet.

[0672] "Means of extraction" refers to the work or process of extracting the necessary information based on specific conditions.

[0673] "Education and work history information" refers to data related to an individual's educational background and work history.

[0674] "Recommendation means" refers to the function of suggesting specific information or options to the user.

[0675] "Application information" refers to information such as personal information and resumes provided by job seekers in response to job offers.

[0676] An "interview schedule" is a scheduled date and time for an interview between a company and a job seeker.

[0677] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without driver operation.

[0678] An "enterprise" is a legal entity that operates a particular business and provides goods or services to the market.

[0679] To put this invention into practice, a system is constructed in which three entities, a server, a terminal, and a user, work in cooperation with each other. The specific operation of this system will be described below.

[0680] Server Features

[0681] 1. Collecting job information

[0682] The server crawls and collects job information from job information websites in Japan. The collected job information is stored in a database. The crawling technology used here is a Python library (Beautiful Soup and Scrapy).

[0683] 2. Job Filtering

[0684] From the collected job information, we filter job information that does not require advanced language skills and has a salary expected to be above a certain level. At this stage, we use natural language processing tools such as NLTK and spaCy to perform text analysis and filtering.

[0685] 3. Multilingual Translation

[0686] The filtered job listings are translated into multiple languages ​​using a multilingual translation API such as Microsoft Translator Text API. This API converts the job listings into multiple languages, including English, and stores them back in the database.

[0687] 4. Collection and Extraction of User Information

[0688] The server collects user posting history from foreign internet services. For example, it crawls the posting history using the APIs of Facebook and LinkedIn. From the collected posting history, it extracts posts containing keywords such as "Japan" and "Tokyo" to find users who are likely to be interested in Japan.

[0689] 5. Analysis of education and work history information

[0690] The extracted user's education and work history information is analyzed and users who meet certain criteria (e.g., bachelor's degree or higher, five or more years of work experience) are filtered out. This analysis uses a trained generative AI model to compare the user's skill set with job postings.

[0691] 6. Job Recommendations

[0692] It recommends suitable job listings for filtered users. For recommendations, it uses a machine learning model (e.g., scikit-learn's TfidfVectorizer and linear kernel) to calculate the degree of match between the user's skill set and job listings. Recommendation results are sent to the user's email address or via push notification.

[0693] 7. Receipt of application information and notification

[0694] Receives application information from users and notifies the company. Application information is sent from a device (such as a smartphone) and received by the server. The application information is then notified to the company.

[0695] 8. Arranging interview dates

[0696] The server coordinates interview dates between users and companies, using calendar integration functions such as the Google Calendar API.

[0697] 9. Notification of match completion

[0698] We track the interview results and notify the user and the company that the match is complete if a job offer is made. We also update the database to record the successful match.

[0699] 10. Recruiting for autonomous vehicles

[0700] The server will recruit foreign talent suited to the technology and work related to autonomous vehicles, and recommend talent that meets the company's needs. At this stage, the server will also use the generative AI model and translation API to evaluate the degree of match between the candidate's skills and the job information.

[0701] Device Features

[0702] The device (smartphone, tablet, PC) provides the interface for users to check job information and apply. In particular, the usability of the notification function, the function to submit application information, and the data entry form is important.

[0703] User Roles

[0704] Users access job information provided through their devices and apply for jobs they are interested in. If a job related to autonomous vehicles is recommended, users apply based on their own education and work history information.

[0705] Prompt Sentence Examples

[0706] "For a job information platform related to autonomous vehicles, we will collect job information that does not require Japanese language skills and offers an annual salary of 6 million yen or more, and extract job listings for specific occupations such as IT engineers and biotechnology researchers. We will then build a system that translates the job listings into multiple languages ​​and recommends them to suitable candidates."

[0707] This concludes the detailed explanation of the "Mode for Carrying Out the Invention." This system will enable effective matching of job information from Japanese companies with foreign talent possessing advanced skills.

[0708] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0709] Step 1:

[0710] The server crawls and collects job information from job information websites in Japan. In this step, Python libraries (Beautiful Soup and Scrapy) are used to parse the HTML of the job information website and extract information. The input is the URL of the job information website, and the output is a list of the retrieved job information.

[0711] Step 2:

[0712] The server filters the collected job listings for those that do not require advanced language skills and are expected to pay above a certain salary. In this step, natural language processing tools such as NLTK and spaCy are used to perform text analysis and filtering of the collected job listings. The input is a list of collected job listings, and the output is a list of filtered job listings.

[0713] Step 3:

[0714] The server translates the filtered job listings into multiple languages. This step uses the Microsoft Translator Text API to translate the job listings into English and other languages. The input is the filtered list of job listings, and the output is the translated list of job listings.

[0715] Step 4:

[0716] The server stores the translated job postings in a database. This step uses an SQL database (PostgreSQL or MySQL) to organize and store the stored data for quick search and reference. The input is a list of translated job postings, and the output is a database entry for the stored job postings.

[0717] Step 5:

[0718] The server uses the API of a foreign internet service (such as Facebook or LinkedIn) to collect the user's posting history. In this step, it searches for and extracts content containing specific keywords (e.g., "Japan" or "Tokyo") from the user's posts. The input is the account information of the internet service, and the output is a list of posts containing the specific keywords.

[0719] Step 6:

[0720] The server extracts users who are presumed to be interested in Japan from the collected posting history. In this step, the content of the extracted posting history is analyzed, and users are selected based on the number and frequency of posts containing Japan-related keywords. The input is a list of posts containing the keywords, and the output is a list of users who are presumed to be interested in Japan.

[0721] Step 7:

[0722] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. In this step, the server analyzes the educational and work history data to determine whether they meet the criteria (bachelor's degree or higher, five or more years of work experience). The input is a list of users who are presumed to be interested in Japan, and the output is a list of users who meet the criteria.

[0723] Step 8:

[0724] The server recommends suitable job listings for the filtered users. In this step, a machine learning model using scikit-learn's TfidfVectorizer and a linear kernel is used to calculate the degree of match between the user's skills and job listings. The input is a list of users who meet the criteria and a list of job listings, and the output is a list of recommended job listings.

[0725] Step 9:

[0726] The server receives the application information from the user and notifies the company. In this step, the server receives the data entered in the application form and sends a notification email to the corresponding company. The input is the information entered by the user in the application form, and the output is the application notification email sent to the company.

[0727] Step 10:

[0728] The server coordinates interview dates between the company and the user. In this step, calendar integration functions such as the Google Calendar API are used to coordinate the schedules of both parties. The input is the desired interview date information of the user and the company, and the output is the confirmed interview date.

[0729] Step 11:

[0730] The server tracks the interview results and notifies the completion of the match when the candidate is hired. In this step, the input data of the interview results is recorded and a notification is sent to the corresponding user and company when the candidate is hired. The input is the interview result information and the output is a match completion notification.

[0731] Through the above processing steps, the system will effectively match foreign talent with advanced autonomous vehicle-related skills with Japanese companies.

[0732] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0733] System Configuration

[0734] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining an emotion engine, it realizes recommendations based on the user's emotional state. The roles of each entity are as follows:

[0735] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; analyzing sentiment; and notifying applicants when matching is complete.

[0736] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[0737] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[0738] Program processing flow

[0739] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[0740] 1. Collecting job information

[0741] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to offer to job seekers.

[0742] 2. Job Filtering

[0743] The server analyzes the collected job information and filters out job listings that do not require a high level of Japanese language ability and are expected to offer a certain salary or higher, thereby narrowing down the job listings to those that are more likely to be suitable for foreign talent.

[0744] 3. Multilingual Translation

[0745] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[0746] 4. Extraction of foreign talent

[0747] The server crawls foreign social networking services and collects users' posting history, thereby gathering data to identify users who are presumed to be interested in Japan.

[0748] 5. Analysis of posting history using an emotion engine

[0749] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users, making it possible to identify users who have positive emotions, not just an interest in Japan.

[0750] 6. Filtering educational and work history information

[0751] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[0752] 7. Emotion-Based Job Recommendations

[0753] The server then recommends appropriate job information via push notification or email based on the filtered user's emotional state, allowing the server to provide job information that matches the user's current emotional state.

[0754] 8. Receipt of application information

[0755] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[0756] 9. Notification of application information and interview arrangements

[0757] The server receives the application information sent by the user and stores it in a database. The saved application information is then notified to the relevant Japanese companies. Interview dates are then arranged between the company and the user.

[0758] 10. Matching completed

[0759] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. It also updates the database with information about successful matches for future analysis and improvement.

[0760] Specific examples

[0761] Example 1: Providing job information for IT engineers

[0762] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[0763] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0764] The server crawls Facebook and LinkedIn, collecting the posting history of users in the United States and Spain.

[0765] The server uses a sentiment engine to analyze posting history and identify users who have positive sentiment toward Japan.

[0766] Check the work history of the extracted users (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[0767] The server recommends suitable job information to the user by email based on their emotional state.

[0768] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0769] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[0770] Example 2: Recruiting biotechnology researchers

[0771] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0772] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0773] The server uses LinkedIn to collect posting history of users who reside in Canada.

[0774] The server uses an emotion engine to perform sentiment analysis of posting history and identify users who have positive sentiment toward Japanese research.

[0775] The extracted users' educational background (e.g., PhD from the University of Toronto) and work experience (3 years of research experience) are checked, and users who meet the criteria are filtered out.

[0776] The server recommends suitable job information to the user by email based on their emotional state.

[0777] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0778] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[0779] This concludes the detailed explanation of the system's operation as part of the "Mode for Carrying Out the Invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign talent. By adding an emotion engine, more personalized recommendations based on the user's emotional state can be realized.

[0780] The processing flow will be explained below.

[0781] Step 1:

[0782] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing you to stay up to date with new job information.

[0783] Step 2:

[0784] The server analyzes the collected job listings and filters out those that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Specifically, the filtering is performed based on criteria that are important to job seekers, such as the "no Japanese language skills required" tag and "salary range."

[0785] Step 3:

[0786] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, such as Google Translate or DeepL, and stores the translated listings in a database.

[0787] Step 4:

[0788] The server crawls major foreign social networking services (SNS) and collects user posting history, including Facebook, LinkedIn, and Twitter.

[0789] Step 5:

[0790] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users. This allows it to identify posts that have "positive emotions toward Japan." For example, it extracts posts that contain positive phrases such as "I love Japan."

[0791] Step 6:

[0792] The server checks the profile information of the extracted users and collects their educational and work history information, such as whether they have a bachelor's degree or higher and whether they have more than five years of work experience, from the public profile provided by the users.

[0793] Step 7:

[0794] The server uses the collected information to filter users who meet certain criteria, such as educational background or work history, and only allows users who meet certain criteria to proceed to the next step.

[0795] Step 8:

[0796] The server then recommends appropriate job information via push notification or email based on the user's filtered emotional state. For example, users with positive emotions will be given priority in receiving job information in their desired occupation and location.

[0797] Step 9:

[0798] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied, they enter the necessary information in the application form and submit it.

[0799] Step 10:

[0800] The server receives the application information sent by the user, stores it in a database, and notifies the relevant Japanese companies of the saved application information.

[0801] Step 11:

[0802] The server coordinates interview dates between the company and the user, and finalizes the interview date after coordinating the schedule with the company.

[0803] Step 12:

[0804] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match has been completed. It also updates and stores information about successful matches in a database, which can be used for future analysis and improvement.

[0805] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign professionals. By adding an emotion engine, it becomes possible to provide more personalized job information based on the user's emotional state.

[0806] Example 2

[0807] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0808] Existing recruitment systems make it difficult for foreign talent to efficiently search for job listings in Japan and apply for suitable positions. Companies also lack the ability to match foreign talent based on their emotional state and interests, preventing an effective recruitment process. Furthermore, it is difficult to accurately filter foreign talent's educational and work history information to select the right candidates. These challenges reduce the efficiency of matching foreign talent with Japanese companies.

[0809] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0810] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain level of salary; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for analyzing the collected posting histories with a sentiment analysis engine to extract users who are interested in Japan and show positive emotions; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users based on their emotional state; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; and means for tracking interview results and notifying companies of the completion of matching when a candidate is hired. This enables foreign talent to efficiently search for job information in Japan and receive appropriate job information, thereby enabling companies to implement an effective hiring process.

[0811] "Job Information" means detailed information about employment opportunities, including job types, salary, work location, required qualifications and experience, etc.

[0812] "Means of collection" refers to the methods and techniques used to obtain the required data from designated sources, including crawling, API calls, etc.

[0813] "Filtering means" refers to methods and techniques for eliminating unnecessary information from collected data based on specific conditions and extracting only data that meets the purpose.

[0814] "Means of translating into multiple languages" refers to methods and technologies for converting text written in one language into multiple other languages, including translation APIs.

[0815] "Database storage" refers to methods and technologies for securely managing and retaining data in a structured format, including SQL and NoSQL databases.

[0816] "Social networking service" means an internet-based platform that enables users to interact and share information with others online.

[0817] "Posting history" refers to data that records the content of posts that a user has made in the past on a social networking service.

[0818] "Sentiment analysis engine" refers to software or algorithms used to identify and analyze emotions from text data.

[0819] "Educational history" refers to information about the educational institutions an individual has attended and the degrees or qualifications they have obtained.

[0820] "Employment history" refers to the history of jobs and positions held by an individual in the past.

[0821] "Recommendation means" refers to methods and technologies for suggesting appropriate information and options to users based on collected and analyzed data.

[0822] "Means of receiving application information" refers to the methods and technologies for obtaining the application details submitted by job seekers, including web forms and APIs.

[0823] "Means of notifying companies" refers to the methods and techniques used to notify relevant companies of the received application information.

[0824] "Means for arranging interview dates" refers to methods and techniques for setting the date, time, and location of an interview between a company and a job seeker.

[0825] "Means for tracking interview results" refers to methods and techniques for monitoring and recording the progress and results of the interview.

[0826] "Means for notifying completion of matching" refers to the methods and technologies used to notify relevant parties of the results of the recruitment process once they have been determined.

[0827] MODE FOR CARRYING OUT THE INVENTION

[0828] System Configuration

[0829] The system of the present invention is mainly composed of three entities: a server, a terminal, and a user. The server is responsible for collecting, filtering, translating, and storing job information, collecting, analyzing, and making recommendations about foreign talent, managing application information, arranging interview dates, analyzing emotions, and notifying applicants when matching is complete. The terminal is a device through which users receive job information, check details, and enter application information. This typically includes PCs, tablets, and smartphones. The user is the entity that receives job information, applies if interested, and goes through the process of being hired after an interview.

[0830] Server Functions and Operations

[0831] The server periodically crawls job information websites in Japan to collect the latest job information. It uses natural language processing (NLP) technology and filtering algorithms to filter out jobs that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Python's Beautiful Soup and pandas libraries are particularly useful.

[0832] The filtered job listings are translated into English and other major languages ​​using multilingual translation APIs such as Google Translate API and DeepL API, and stored in a database, where the translated information is available for future searches and analysis.

[0833] Next, the server crawls foreign social networking services such as Facebook and LinkedIn to collect users' posting history, which is then analyzed using a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to identify users who have positive sentiment toward Japan.

[0834] The educational and work history information of the identified users is collected using the LinkedIn API, etc., and users who meet certain criteria (e.g., bachelor's degree or higher, five years or more of work experience) are filtered out. Appropriate job information is recommended to the filtered users based on their emotional state. This recommendation process uses an email service (e.g., SendGrid API).

[0835] User operations

[0836] The user checks the received job information, and if interested, clicks on the provided link to view more information. If satisfied with the details, the user can enter the necessary information in the application form and submit it.

[0837] Collaboration with companies

[0838] The server receives application information sent by users and stores it in a database. The stored application information is then notified to relevant Japanese companies. Interview dates are then arranged between the company and the user, and the server ultimately tracks the interview results. If the candidate is hired, the server notifies the company and the user that matching is complete. Information about successful matches is also updated in the database, allowing for future analysis and improvement.

[0839] Specific examples

[0840] As a concrete example, consider the provision of job information for IT engineers. The server crawls information on the job title "IT engineer" from job information providers A and B in Japan. From this information, it filters job listings that "do not require advanced Japanese language skills" and offer an annual salary of 5 million yen or more, and translates them into English and Spanish. Next, the server crawls Facebook and LinkedIn, collecting the posting history of users living in the United States and Spain. This posting history is analyzed using an emotion engine to identify users who have positive feelings toward Japan. The extracted users' work history and educational background are checked, and suitable job listings are recommended to users who meet the criteria based on their emotional state.

[0841] Prompt Sentence Examples

[0842] "Dear Generative AI Model, please explain in detail the processing flow of your recruitment system for foreign talent based on the following technical specifications."

[0843] By using this system, Japanese companies can effectively recruit highly skilled personnel from overseas, and foreign talent can easily find job information that suits them. The recommendation function based on the emotion engine is expected to provide more personalized job information and improve the success rate of matching.

[0844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] Collecting job information

[0847] The server uses Python's Beautiful Soup and requests libraries to periodically crawl job information from job information websites in Japan. The input is the URL of the job information website, and the output is a list of collected job information. The crawled data is converted to JSON format and saved.

[0848] Step 2:

[0849] Job Filtering

[0850] The server filters the collected job information to those that do not require advanced Japanese language skills and have an annual salary of 5 million yen or more. The input is a list of collected job information, and the output is a list of job information after filtering. This process uses the Python pandas library to extract data that matches the conditions.

[0851] Step 3:

[0852] Multilingual translation

[0853] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API such as Google Translate API or DeepL API. The input is a list of filtered job listings, and the output is a list of translated job listings. The translation results are stored in a database for future searches and analysis.

[0854] Step 4:

[0855] Extraction of foreign talent

[0856] The server crawls social networking services such as Facebook and LinkedIn to collect the posting history of foreign users. The input is the search keywords on the social networking site and the user's posting history, and the output is a list of the collected posting history. This allows data to be obtained to identify users who may be interested in Japan.

[0857] Step 5:

[0858] Sentiment analysis of posting history

[0859] The server analyzes the collected posting history using IBM Watson's sentiment analysis API. The input is a list of posting history, and the output is an analysis of the emotions expressed by the user. Based on the analysis results, users with positive emotions toward Japan are identified.

[0860] Step 6:

[0861] Filtering educational and work history information

[0862] The server collects the identified users' educational and work history information using the LinkedIn API or similar, and filters users who meet certain criteria. The input is the user's profile information, and the output is a list of users who meet the criteria. The filtering criteria include a bachelor's degree or higher and five or more years of work experience.

[0863] Step 7:

[0864] Emotion-based job recommendations

[0865] The server recommends appropriate job listings based on the sentiment analysis results and filtered user information. The input is the filtered user information and translated job listings, and the output is a list of recommended job listings. The recommended job listings are sent to the user via an email service (SendGrid API).

[0866] Step 8:

[0867] Receiving application information

[0868] The user checks the received job information and, if they are interested, clicks to view the detailed information. The input is the email with the recommended job information, and the output is the application information entered by the user. When the user enters the necessary information in the application form and submits it, the application information is sent to the server.

[0869] Step 9:

[0870] Notification of application information and interview arrangements

[0871] The server receives the application information sent by the user and stores it in a database. It then notifies the relevant companies of the application information and arranges interview dates between the companies and the user. The input is the user's application information, and the output is the arranged interview schedule.

[0872] Step 10:

[0873] Matching completion and notification

[0874] The server tracks the interview results and notifies the company and user that the match is complete if a job offer is made. It also updates the database with information about successful matches for future analysis and improvement. The input is the interview results, and the output is a notification that the match is complete.

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] Conventional job information systems are primarily focused on matching job openings with applications. While effective in improving the efficiency of recruiting, they lack the elements necessary to recruit the right talent. In particular, they are unable to provide personalized recommendations that take into account the user's emotional state, creating a need for improved user experience. Furthermore, in customer service, they are unable to provide appropriate product recommendations based on the customer's emotional state, making it difficult to improve customer service quality.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0879] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a salary above a certain level; means for translating the filtered job information into multiple languages; means for saving the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users who are presumed to be interested in Japan from the collected posting histories; means for analyzing the educational and work history information of the extracted users and filtering out users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying the completion of matching when a job is decided; means for analyzing the facial expressions of customers using an emotion engine and making recommendations based on their emotional state; and means having a multilingual display device for recommending products based on the emotional state of customers. This not only enables personalized recommendations that take into account the user's emotions, but also enables high-quality customer service based on the emotional state of customers.

[0880] "Job information in Japan" refers to information about employment and job changes that is published or posted within Japan.

[0881] "Advanced Japanese language skills not required" means that fluent reading, writing, or speaking Japanese is not required to apply for the job.

[0882] "Salary above a certain level" means that the compensation is above a set standard amount.

[0883] "Filtering" is the process of selecting only data or information that meets certain conditions from a large amount of data or information.

[0884] "Multilingual" means not limited to one language but includes multiple different languages.

[0885] A "social networking service" is an online platform that facilitates interaction between users via the Internet.

[0886] "Posting history" refers to the history of content that a user has posted on a social networking service in the past.

[0887] "Interested in Japan" refers to a state in which a user shows interest or curiosity about Japan.

[0888] An "emotion engine" is software that analyzes a user's emotional state and recommends appropriate actions based on the results.

[0889] "Facial expression analysis" is the process of analyzing facial expressions to determine the emotion behind them.

[0890] "Personalized recommendations" refers to customized suggestions based on the preferences and emotional state of individual users.

[0891] A "multilingual display device" is an electronic device that has the ability to display information in multiple languages.

[0892] "Customer service" refers to all work related to providing customer service and services in stores and other places.

[0893] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it realizes recommendations based on the user's emotional state.

[0894] Detailed system preparation

[0895] Server Roles

[0896] The server has a means of periodically collecting job information within Japan. This collection is done by crawling major job information websites. The collected job information is analyzed and filtered to determine whether it requires a high level of Japanese language proficiency and offers a certain salary or higher. Job information selected through this filtering process is translated into English and other major languages ​​using a multilingual translation API and stored in a database.

[0897] Next, the server has a means for collecting users' posting history from foreign social networking services. This posting history is used to extract posts that are presumed to indicate that the user is interested in Japan. From the collected posting history, the user's emotional state is analyzed using an emotion engine, and users who have not only an interest in Japan but also positive emotions are identified.

[0898] Device Role

[0899] The terminal is equipped with an interface that allows users to receive job information, check details, and enter application information. Mobile devices such as smartphones and tablets are the primary targets.

[0900] By using emotion analysis technology, it is possible to analyze the facial expressions of customers in real time through their devices and make recommendations based on their emotional state. Specifically, a device such as smart glasses is used to analyze the customer's facial expressions, and product recommendations are displayed based on the results.

[0901] User Roles

[0902] Users can receive job information provided, and if they are interested, they can view the details and enter the necessary information in the application form. Furthermore, in stores, they can receive product recommendations in real time using smart glasses.

[0903] Specific examples

[0904] Suppose a customer approaches a store clerk wearing smart glasses. The server analyzes the customer's facial expression through camera footage and detects "happiness." As a result, a recommendation for a "newly released coffee maker" is returned from the API. Based on the information displayed on the smart glasses, the store clerk suggests, "How about this new coffee maker?" The following is an example of a prompt sentence that can be used in this case:

[0905] "When the display shows 'New Coffee Maker,' we suggest, 'How about a new coffee maker?'"

[0906] Hardware and software used

[0907] Hardware: Smartphones, smart glasses, camera-equipped devices

[0908] Software: OpenCV, DeepFace, libraries for sending API requests (e.g. Requests)

[0909] The server uses OpenCV to detect faces from real-time video and DeepFace to analyze emotions from facial images. In response, an emotion engine using a generative AI model analyzes the user's emotions and can display recommendation results on the device's display.

[0910] As described above, the present invention makes it possible to provide personalized recommendations that take into account the emotional state of the user, and to realize high-quality service in customer service operations.

[0911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0912] Step 1:

[0913] The server crawls job information websites in Japan and collects the latest job information. As input, it receives a list of URLs for the specified job information websites and obtains HTML data from each website. To process the data, it uses a crawling tool to analyze the HTML data and extract the necessary job information. As output, it generates a list of the extracted job information.

[0914] Step 2:

[0915] The server filters the collected job information for those that do not require advanced Japanese language skills and are expected to offer a certain salary or higher. As input, it receives the list of job information obtained in step 1. As data calculation, it analyzes the content of the job information and executes logic to determine whether it matches the filter conditions. As output, it generates a list of filtered job information.

[0916] Step 3:

[0917] The server translates the filtered job postings using a multilingual translation API. As input, it receives the list of filtered job postings obtained in step 2. As data calculation, it sends a request for each job posting to the multilingual translation API and obtains the translation results. As output, it generates a list of translated job postings.

[0918] Step 4:

[0919] The server stores the translated job postings in the database. As input, it receives the list of translated job postings obtained in step 3. As data processing, it performs a data insertion operation into the database. As output, it generates the job posting data stored in the database.

[0920] Step 5:

[0921] The server collects user posting histories from foreign social networking services. As input, it receives the API key of the specified social networking service and a list of user IDs. As data processing, it obtains each user's posting history and analyzes the collected posting data. As output, it generates a list of the collected posting histories.

[0922] Step 6:

[0923] The server extracts users who are presumed to be interested in Japan from the collected posting history. As input, it receives the list of posting histories obtained in step 5. As data processing, it uses an emotion engine to perform sentiment analysis of each post and identifies posts that show interest or positive sentiment toward Japan. As output, it generates a list of users who are interested in Japan.

[0924] Step 7:

[0925] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. As input, it receives the list of users interested in Japan obtained in step 6. As data processing, it analyzes each user's social networking service profile information and extracts educational and work history information. It then executes logic to evaluate whether the criteria are met. As output, it generates a list of filtered users.

[0926] Step 8:

[0927] The server recommends appropriate job listings for the filtered users. As input, it receives the filtered user list obtained in step 7 and the job listing data saved in step 4. As data calculation, it selects appropriate job listings based on each user's emotional state and profile information and executes the recommendation algorithm. As output, it generates recommendation results for each user.

[0928] Step 9:

[0929] The terminal provides an interface for receiving application information from users. As input, it receives information entered by users into an application form. As data processing, it converts the application information into a specified format and sends it to the server. As output, it generates the application information sent to the server.

[0930] Step 10:

[0931] The server notifies companies of the application information and arranges interview dates between the relevant companies and the user. As input, it receives the application information received in step 9. As data processing, it generates and sends a notification message to the company. As output, it generates the application information notified to the company and arranges interview dates.

[0932] Step 11:

[0933] The server tracks the interview results and notifies the completion of matching when a candidate is hired. It receives interview result information from the company as input. It processes the data by updating the matching results in the database and notifying the user and company. It generates the notified matching completion information as output.

[0934] Step 12:

[0935] The device uses an emotion engine to analyze the facial expressions of customers and make recommendations based on their emotional state. Camera footage is acquired in real time as input. Face detection is performed using OpenCV and emotion analysis is performed using DeepFace for data processing. Emotion analysis results are generated as output.

[0936] Step 13:

[0937] The terminal uses a multilingual display device to recommend products based on the emotional state of the customer. As input, it receives the emotion analysis results obtained in step 12. As data processing, it executes an algorithm to select and display products appropriate for the customer's emotional state. As output, it displays the product recommendation information on a multilingual display.

[0938] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0939] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0940] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0941] [Third embodiment]

[0942] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0943] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0944] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0945] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0946] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0947] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0948] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0949] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0950] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0951] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0952] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0953] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0954] System Configuration

[0955] The system of the present invention is mainly composed of three entities: a "server," a "terminal," and a "user." The roles of each are as follows:

[0956] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; and notifying applicants when matching is complete.

[0957] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[0958] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[0959] Program processing flow

[0960] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[0961] 1. Collecting job information

[0962] The server periodically crawls major job sites in Japan (generally referred to as "job information sites") to collect the latest job information.

[0963] 2. Job Filtering

[0964] The server analyzes the collected job information and filters out job listings that do not require advanced Japanese language skills and are expected to pay a certain amount or more.

[0965] 3. Multilingual Translation

[0966] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and then stores the translated listings in a database.

[0967] 4. Extraction of foreign talent

[0968] The server crawls foreign social networking services, analyzes users' posting history, and extracts users who are likely to be interested in Japan.

[0969] 5. Filtering educational and work history information

[0970] The server collects the educational and work history information of the extracted users and filters out users who meet certain criteria (e.g., bachelor's degree or above, work experience of 5 years or more).

[0971] 6. Job Recommendations

[0972] The server sends appropriate job information to users who meet the criteria via push notifications or email.

[0973] 7. Receipt of application information

[0974] The user checks the received job information, and if they are interested, they enter the necessary information into the application form and submit it. The server stores this application information in a database.

[0975] 8. Notification of application information and interview arrangements

[0976] The server then notifies the necessary job information providers and companies of the saved application information, after which the company and the user can arrange an interview date.

[0977] 9. Matching completed

[0978] The server tracks the interview results, notifies the company and the user if a job offer is made, and updates the database to record successful matches.

[0979] Specific examples

[0980] Example 1: Providing job information for IT engineers

[0981] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[0982] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[0983] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[0984] Analyze the extracted users' work history (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[0985] The server then emails the appropriate job listings to the filtered users.

[0986] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0987] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0988] Example 2: Recruiting biotechnology researchers

[0989] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[0990] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[0991] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[0992] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[0993] The server will email suitable job listings to the user.

[0994] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[0995] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[0996] This concludes the explanation of the specific system operation as a "mode for carrying out the invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign personnel.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to provide to job seekers.

[1000] Step 2:

[1001] The server analyzes the collected job listings and filters out those that do not require a high level of Japanese language ability and those that are expected to pay above a certain salary, thereby narrowing down the list of job listings that are easy for foreign talent to apply for.

[1002] Step 3:

[1003] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[1004] Step 4:

[1005] The server crawls major social networking services in foreign countries and collects users' posting history, thereby gathering data to extract users who are presumed to be interested in Japan.

[1006] Step 5:

[1007] The server analyzes the collected posting history and extracts users who have posted using keywords such as "Japan" and "Tokyo." This user extraction is important for identifying foreign talent who are interested in Japan.

[1008] Step 6:

[1009] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[1010] Step 7:

[1011] The server then recommends suitable job information to the filtered users via push notifications or emails, so that relevant job information is delivered directly to the users.

[1012] Step 8:

[1013] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[1014] Step 9:

[1015] The server receives the application information sent by the user and stores it in a database, which then notifies the relevant Japanese companies.

[1016] Step 10:

[1017] The server coordinates interview dates between the company and the user, and once the interview date and time are confirmed, the server notifies both the company and the user of the information.

[1018] Step 11:

[1019] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. Information about successful matches is updated in the database for future analysis and improvement.

[1020] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign personnel.

[1021] Example 1

[1022] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1023] Japanese companies face the challenge of finding it difficult to effectively recruit foreign talent. Foreign talent with advanced expertise in particular has limited access to job listings in Japan, making it difficult to match potential candidates with the right talent. Furthermore, language barriers and geographical restrictions mean that proper communication between companies and job seekers is often not possible, hindering smooth recruitment.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1025] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain salary or higher; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; analysis means using a multilingual translation API and natural language processing technology; and means for collecting job information and user postings using crawling technology. This makes it easier for foreign talent to access job information in Japan, enabling appropriate matching and a quick hiring process.

[1026] "Means for collecting job information within Japan" refers to technology for automatically obtaining data on job openings from various job information platforms within Japan.

[1027] "A means of filtering out jobs that do not require advanced Japanese language skills and are expected to have a salary above a certain level" is a technology for selecting, from the collected job information, job information that does not require a high level of specialized Japanese language skills and meets a specified salary standard.

[1028] "Multilingual translation means" refers to technology for translating filtered job listings into English and other major foreign languages.

[1029] "Means for storing in a database" refers to the technology used to store translated job information in an organized manner and in a database in a format that can be quickly accessed when needed.

[1030] "Means for collecting user posting history from foreign social networking services" refers to technology for automatically obtaining users' public posting history from foreign SNSs.

[1031] The "means of extracting users who are likely to be interested in Japan" is a technology that analyzes collected posting history and identifies users who are likely to be interested in Japan and its culture.

[1032] "Means for analyzing educational and work history information and filtering users who meet certain criteria" refers to technology for evaluating the educational and work history information of extracted users and selecting users who meet certain criteria (for example, a specific degree or years of work experience).

[1033] "Means for recommending appropriate job information" refers to technology for recommending suitable job information to users who meet criteria.

[1034] The "means for receiving application information and notifying companies" refers to a technique for receiving application information from users and notifying the relevant companies of that information.

[1035] "Means for coordinating interview dates between a company and a user" refers to a technique for coordinating the schedules of both the company and the user and setting an interview date.

[1036] "Means for tracking interview results and notifying the completion of matching when a candidate is hired" refers to technology for managing the results of interviews and informing both the company and the user when a candidate is hired.

[1037] "Analysis method using multilingual translation API and natural language processing technology" refers to technology for translating job postings and user posts into multiple languages ​​and analyzing them through natural language processing.

[1038] "Methods of collecting job information and user posts using crawling technology" refers to techniques for collecting necessary data from job information sites and social media using web scraping and crawling technology.

[1039] MODE FOR CARRYING OUT THE INVENTION

[1040] The purpose of this system is to collect job information in Japan and provide appropriate job information to highly skilled foreign professionals. The operation of the system consists of processing steps that are made up of three main entities: the server, the terminal, and the user.

[1041] The server executes the following processes in order.

[1042] 1. Collecting job information

[1043] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl the latest job listings from major job listing websites in Japan. Specifically, it retrieves job listings from sites such as Indeed and Rikunabi.

[1044] 2. Job Filtering

[1045] The server uses natural language processing (NLP) technology to analyze the collected job listings. By segmenting and tagging the listings using Python's NLTK and SpaCy libraries, it filters out listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more."

[1046] 3. Multilingual Translation

[1047] The server translates the filtered job listings using a multilingual translation API (e.g., Google Translate API, DeepL API), and the translated information is stored in a database (e.g., PostgreSQL, MySQL).

[1048] 4. Extraction of foreign talent

[1049] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. This process also uses NLP technology to extract keywords related to "Japan" and "Tokyo" from the posted content.

[1050] 5. Filtering educational and work history information

[1051] The server analyzes the extracted user profile information to see if they meet certain criteria (e.g., a computer science degree, more than five years of work experience), using the Python Pandas library for this filtering.

[1052] 6. Job Recommendations

[1053] The server recommends suitable job listings to users who meet the criteria, and sends these recommendations via push notifications or email delivery services (e.g., Firebase Cloud Messaging, SendGrid, Mailgun).

[1054] 7. Receipt of application information

[1055] The user checks the received job information, enters the necessary information in the application form, and submits it. The device (PC, tablet, smartphone) provides the UI for this application form and sends the entered information to the server. The server saves the application information in a database.

[1056] 8. Notification of application information and interview arrangements

[1057] The server notifies the appropriate companies of the saved application information and provides an interface for companies and users to arrange interview dates. Specifically, Google Calendar API or a schedule adjustment app can be used.

[1058] 9. Matching completed

[1059] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for later analysis.

[1060] Specific examples

[1061] Example 1: Providing job information for IT engineers

[1062] The server crawls information about the job title "IT engineer" from job information sites A and B in Japan.

[1063] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[1064] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[1065] Analyze the extracted users' work history (e.g., computer science degree, 5+ years of work experience) and filter users who meet the criteria.

[1066] The server then emails the appropriate job listings to the filtered users.

[1067] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1068] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1069] Example prompt: "Please translate job postings for IT engineers with an annual salary of at least 5 million yen, no advanced Japanese language skills required, into English and Spanish, and email the job postings to suitable candidates."

[1070] Example 2: Recruiting biotechnology researchers

[1071] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[1072] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[1073] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[1074] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[1075] The server will email suitable job listings to the user.

[1076] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1077] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1078] Example prompt: "Please translate job postings for biotechnology researchers with an annual salary of at least 6 million yen and no advanced Japanese language skills required into multiple languages ​​and email suitable candidates."

[1079] This system effectively matches job information from Japanese companies with highly skilled foreign talent, ensuring a fast and smooth recruitment process.

[1080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1081] Step 1: Gather job information

[1082] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl job information from job information websites in Japan. The server periodically accesses a specified URL and downloads the HTML page of the job information. Next, it uses BeautifulSoup to analyze the HTML page and extract the necessary data (job type, salary, language requirements, etc.).

[1083] Input: URL list of job information sites

[1084] Output: A list of extracted jobs

[1085] Step 2: Filter Jobs

[1086] The server analyzes the collected job listings using natural language processing (NLP) technology. It uses Python's NLTK and SpaCy libraries to split and tag the text data. This allows it to filter out job listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more." The server then temporarily stores only the filtered job listings in order to proceed to the next step.

[1087] Input: A list of extracted job postings

[1088] Output: A filtered list of jobs

[1089] Step 3: Translate into multiple languages

[1090] The server uses the Google Translate API or DeepL API to translate the filtered job listings into multiple languages. Specifically, it sends the text data of the job listings to the API and receives the translated text. The translated information is then stored in a database (e.g., PostgreSQL, MySQL).

[1091] Input: A filtered list of jobs

[1092] Output: A list of translated job postings

[1093] Step 4: Identifying foreign talent

[1094] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. Using Python's Scrapy, it crawls the pages of specified social media accounts and collects posting data. From the collected posting history, it extracts keywords indicating interest in Japan and its culture (e.g., "Japan" and "Tokyo") and analyzes them using natural language processing technology.

[1095] Input: List of social media account URLs

[1096] Output: List of users interested in Japan

[1097] Step 5: Filtering education and work history information

[1098] The server parses the extracted user profile information (educational background and work experience). It uses the Python Pandas library to read the user data and filter users who meet certain criteria (e.g., computer science degree, 5+ years of work experience). The filtered user information is used in the next step.

[1099] Input: List of users interested in Japan and their profile information

[1100] Output: A list of users who meet the criteria

[1101] Step 6: Job Recommendations

[1102] The server recommends suitable job listings to filtered users. The server uses email delivery services such as SendGrid and Mailgun to send emails containing job listings to users. If push notifications are used, services such as Firebase Cloud Messaging can be used.

[1103] Input: A list of users who meet the criteria and a list of translated job postings

[1104] Output: Job notification sent to user

[1105] Step 7: Receiving your application information

[1106] The user checks the received job information, and if they are interested, they enter the required information in the application form and submit it. The device (PC, tablet, smartphone) provides the UI for the application form and sends the entered information to the server. The server receives the application information and stores it in a database.

[1107] Input: User's application information

[1108] Output: Application information stored in a database

[1109] Step 8: Notification of application information and interview arrangements

[1110] The server notifies the company of the saved application information, provides the application information to the company via automatic email or API integration, and provides an interface for coordinating interview dates between the company and the user. Scheduling can also be done using the Google Calendar API.

[1111] Input: Application information stored in the database

[1112] Output: Application information and interview schedule notified to the company

[1113] Step 9: Matching Complete

[1114] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for future analytics and reporting.

[1115] Input: Interview results from company

[1116] Output: Matching completion information notified to companies and users, and success stories recorded in the database

[1117] (Application example 1)

[1118] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] Currently, it is extremely difficult to effectively provide job information in Japan to foreign talent and recruit the right talent. In particular, there is a lack of efficient means to find foreign talent with advanced skills related to the development and operation of autonomous vehicles and match them with Japanese companies. As a result, there is a lack of a system that can centrally identify foreign talent, translate job information, and arrange interviews with companies.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1121] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for job listings that do not require advanced language skills and are expected to offer salaries above a certain level; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign Internet services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' education and work history information and filtering users who meet certain criteria; means for recommending appropriate job listings to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; and means for recruiting foreign talent suitable for technologies and work related to autonomous vehicles and recommending talent according to the needs of companies. This enables centralized management of everything from identifying foreign talent to matching with companies, thereby enabling efficient recruitment of talent with advanced skills related to autonomous vehicles.

[1122] "Job Information" means information about employment opportunities provided to job seekers.

[1123] "Filtering means" refers to the function of removing unnecessary parts from collected information based on specific conditions and selecting only useful information.

[1124] "Means for translating into multiple languages" refers to the ability to convert specific information into multiple different languages.

[1125] "Means of storing information in a database" refers to a system that systematically organizes and stores information, making it easy to search and reference.

[1126] "Internet services" is a general term for various online services provided via the Internet.

[1127] "Posting history" refers to a record of posts and comments made by a user on the Internet.

[1128] "Means of extraction" refers to the work or process of extracting the necessary information based on specific conditions.

[1129] "Education and work history information" refers to data related to an individual's educational background and work history.

[1130] "Recommendation means" refers to the function of suggesting specific information or options to the user.

[1131] "Application information" refers to information such as personal information and resumes provided by job seekers in response to job offers.

[1132] An "interview schedule" is a scheduled date and time for an interview between a company and a job seeker.

[1133] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without driver operation.

[1134] An "enterprise" is a legal entity that operates a particular business and provides goods or services to the market.

[1135] To put this invention into practice, a system is constructed in which three entities, a server, a terminal, and a user, work in cooperation with each other. The specific operation of this system will be described below.

[1136] Server Features

[1137] 1. Collecting job information

[1138] The server crawls and collects job information from job information websites in Japan. The collected job information is stored in a database. The crawling technology used here is a Python library (Beautiful Soup and Scrapy).

[1139] 2. Job Filtering

[1140] From the collected job information, we filter job information that does not require advanced language skills and has a salary expected to be above a certain level. At this stage, we use natural language processing tools such as NLTK and spaCy to perform text analysis and filtering.

[1141] 3. Multilingual Translation

[1142] The filtered job listings are translated into multiple languages ​​using a multilingual translation API such as Microsoft Translator Text API. This API converts the job listings into multiple languages, including English, and stores them back in the database.

[1143] 4. Collection and Extraction of User Information

[1144] The server collects user posting history from foreign internet services. For example, it crawls the posting history using the APIs of Facebook and LinkedIn. From the collected posting history, it extracts posts containing keywords such as "Japan" and "Tokyo" to find users who are likely to be interested in Japan.

[1145] 5. Analysis of education and work history information

[1146] The extracted user's education and work history information is analyzed and users who meet certain criteria (e.g., bachelor's degree or higher, five or more years of work experience) are filtered out. This analysis uses a trained generative AI model to compare the user's skill set with job postings.

[1147] 6. Job Recommendations

[1148] It recommends suitable job listings for filtered users. For recommendations, it uses a machine learning model (e.g., scikit-learn's TfidfVectorizer and linear kernel) to calculate the degree of match between the user's skill set and job listings. Recommendation results are sent to the user's email address or via push notification.

[1149] 7. Receipt of application information and notification

[1150] Receives application information from users and notifies the company. Application information is sent from a device (such as a smartphone) and received by the server. The application information is then notified to the company.

[1151] 8. Arranging interview dates

[1152] The server coordinates interview dates between users and companies, using calendar integration functions such as the Google Calendar API.

[1153] 9. Notification of match completion

[1154] We track the interview results and notify the user and the company that the match is complete if a job offer is made. We also update the database to record the successful match.

[1155] 10. Recruiting for autonomous vehicles

[1156] The server will recruit foreign talent suited to the technology and work related to autonomous vehicles, and recommend talent that meets the company's needs. At this stage, the server will also use the generative AI model and translation API to evaluate the degree of match between the candidate's skills and the job information.

[1157] Device Features

[1158] The device (smartphone, tablet, PC) provides the interface for users to check job information and apply. In particular, the usability of the notification function, the function to submit application information, and the data entry form is important.

[1159] User Roles

[1160] Users access job information provided through their devices and apply for jobs they are interested in. If a job related to autonomous vehicles is recommended, users apply based on their own education and work history information.

[1161] Prompt Sentence Examples

[1162] "For a job information platform related to autonomous vehicles, we will collect job information that does not require Japanese language skills and offers an annual salary of 6 million yen or more, and extract job listings for specific occupations such as IT engineers and biotechnology researchers. We will then build a system that translates the job listings into multiple languages ​​and recommends them to suitable candidates."

[1163] This concludes the detailed explanation of the "Mode for Carrying Out the Invention." This system will enable effective matching of job information from Japanese companies with foreign talent possessing advanced skills.

[1164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1165] Step 1:

[1166] The server crawls and collects job information from job information websites in Japan. In this step, Python libraries (Beautiful Soup and Scrapy) are used to parse the HTML of the job information website and extract information. The input is the URL of the job information website, and the output is a list of the retrieved job information.

[1167] Step 2:

[1168] The server filters the collected job listings for those that do not require advanced language skills and are expected to pay above a certain salary. In this step, natural language processing tools such as NLTK and spaCy are used to perform text analysis and filtering of the collected job listings. The input is a list of collected job listings, and the output is a list of filtered job listings.

[1169] Step 3:

[1170] The server translates the filtered job listings into multiple languages. This step uses the Microsoft Translator Text API to translate the job listings into English and other languages. The input is the filtered list of job listings, and the output is the translated list of job listings.

[1171] Step 4:

[1172] The server stores the translated job postings in a database. This step uses an SQL database (PostgreSQL or MySQL) to organize and store the stored data for quick search and reference. The input is a list of translated job postings, and the output is a database entry for the stored job postings.

[1173] Step 5:

[1174] The server uses the API of a foreign internet service (such as Facebook or LinkedIn) to collect the user's posting history. In this step, it searches for and extracts content containing specific keywords (e.g., "Japan" or "Tokyo") from the user's posts. The input is the account information of the internet service, and the output is a list of posts containing the specific keywords.

[1175] Step 6:

[1176] The server extracts users who are presumed to be interested in Japan from the collected posting history. In this step, the content of the extracted posting history is analyzed, and users are selected based on the number and frequency of posts containing Japan-related keywords. The input is a list of posts containing the keywords, and the output is a list of users who are presumed to be interested in Japan.

[1177] Step 7:

[1178] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. In this step, the server analyzes the educational and work history data to determine whether they meet the criteria (bachelor's degree or higher, five or more years of work experience). The input is a list of users who are presumed to be interested in Japan, and the output is a list of users who meet the criteria.

[1179] Step 8:

[1180] The server recommends suitable job listings for the filtered users. In this step, a machine learning model using scikit-learn's TfidfVectorizer and a linear kernel is used to calculate the degree of match between the user's skills and job listings. The input is a list of users who meet the criteria and a list of job listings, and the output is a list of recommended job listings.

[1181] Step 9:

[1182] The server receives the application information from the user and notifies the company. In this step, the server receives the data entered in the application form and sends a notification email to the corresponding company. The input is the information entered by the user in the application form, and the output is the application notification email sent to the company.

[1183] Step 10:

[1184] The server coordinates interview dates between the company and the user. In this step, calendar integration functions such as the Google Calendar API are used to coordinate the schedules of both parties. The input is the desired interview date information of the user and the company, and the output is the confirmed interview date.

[1185] Step 11:

[1186] The server tracks the interview results and notifies the completion of the match when the candidate is hired. In this step, the input data of the interview results is recorded and a notification is sent to the corresponding user and company when the candidate is hired. The input is the interview result information and the output is a match completion notification.

[1187] Through the above processing steps, the system will effectively match foreign talent with advanced autonomous vehicle-related skills with Japanese companies.

[1188] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1189] System Configuration

[1190] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining an emotion engine, it realizes recommendations based on the user's emotional state. The roles of each entity are as follows:

[1191] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; analyzing sentiment; and notifying applicants when matching is complete.

[1192] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[1193] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[1194] Program processing flow

[1195] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[1196] 1. Collecting job information

[1197] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to offer to job seekers.

[1198] 2. Job Filtering

[1199] The server analyzes the collected job information and filters out job listings that do not require a high level of Japanese language ability and are expected to offer a certain salary or higher, thereby narrowing down the job listings to those that are more likely to be suitable for foreign talent.

[1200] 3. Multilingual Translation

[1201] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[1202] 4. Extraction of foreign talent

[1203] The server crawls foreign social networking services and collects users' posting history, thereby gathering data to identify users who are presumed to be interested in Japan.

[1204] 5. Analysis of posting history using an emotion engine

[1205] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users, making it possible to identify users who have positive emotions, not just an interest in Japan.

[1206] 6. Filtering educational and work history information

[1207] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[1208] 7. Emotion-Based Job Recommendations

[1209] The server then recommends appropriate job information via push notification or email based on the filtered user's emotional state, allowing the server to provide job information that matches the user's current emotional state.

[1210] 8. Receipt of application information

[1211] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[1212] 9. Notification of application information and interview arrangements

[1213] The server receives the application information sent by the user and stores it in a database. The saved application information is then notified to the relevant Japanese companies. Interview dates are then arranged between the company and the user.

[1214] 10. Matching completed

[1215] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. It also updates the database with information about successful matches for future analysis and improvement.

[1216] Specific examples

[1217] Example 1: Providing job information for IT engineers

[1218] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[1219] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[1220] The server crawls Facebook and LinkedIn, collecting the posting history of users in the United States and Spain.

[1221] The server uses a sentiment engine to analyze posting history and identify users who have positive sentiment toward Japan.

[1222] Check the work history of the extracted users (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[1223] The server recommends suitable job information to the user by email based on their emotional state.

[1224] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1225] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[1226] Example 2: Recruiting biotechnology researchers

[1227] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[1228] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[1229] The server uses LinkedIn to collect posting history of users who reside in Canada.

[1230] The server uses an emotion engine to perform sentiment analysis of posting history and identify users who have positive sentiment toward Japanese research.

[1231] The extracted users' educational background (e.g., PhD from the University of Toronto) and work experience (3 years of research experience) are checked, and users who meet the criteria are filtered out.

[1232] The server recommends suitable job information to the user by email based on their emotional state.

[1233] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1234] The server receives the application information, notifies the relevant Japanese companies, and arranges interview dates between the companies and the users to complete the matching.

[1235] This concludes the detailed explanation of the system's operation as part of the "Mode for Carrying Out the Invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign talent. By adding an emotion engine, more personalized recommendations based on the user's emotional state can be realized.

[1236] The processing flow will be explained below.

[1237] Step 1:

[1238] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing you to stay up to date with new job information.

[1239] Step 2:

[1240] The server analyzes the collected job listings and filters out those that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Specifically, the filtering is performed based on criteria that are important to job seekers, such as the "no Japanese language skills required" tag and "salary range."

[1241] Step 3:

[1242] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, such as Google Translate or DeepL, and stores the translated listings in a database.

[1243] Step 4:

[1244] The server crawls major foreign social networking services (SNS) and collects user posting history, including Facebook, LinkedIn, and Twitter.

[1245] Step 5:

[1246] The server uses an emotion engine to analyze the collected posting history and recognize the emotions expressed by users. This allows it to identify posts that have "positive emotions toward Japan." For example, it extracts posts that contain positive phrases such as "I love Japan."

[1247] Step 6:

[1248] The server checks the profile information of the extracted users and collects their educational and work history information, such as whether they have a bachelor's degree or higher and whether they have more than five years of work experience, from the public profile provided by the users.

[1249] Step 7:

[1250] The server uses the collected information to filter users who meet certain criteria, such as educational background or work history, and only allows users who meet certain criteria to proceed to the next step.

[1251] Step 8:

[1252] The server then recommends appropriate job information via push notification or email based on the user's filtered emotional state. For example, users with positive emotions will be given priority in receiving job information in their desired occupation and location.

[1253] Step 9:

[1254] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied, they enter the necessary information in the application form and submit it.

[1255] Step 10:

[1256] The server receives the application information sent by the user, stores it in a database, and notifies the relevant Japanese companies of the saved application information.

[1257] Step 11:

[1258] The server coordinates interview dates between the company and the user, and finalizes the interview date after coordinating the schedule with the company.

[1259] Step 12:

[1260] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match has been completed. It also updates and stores information about successful matches in a database, which can be used for future analysis and improvement.

[1261] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign professionals. By adding an emotion engine, it becomes possible to provide more personalized job information based on the user's emotional state.

[1262] Example 2

[1263] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1264] Existing recruitment systems make it difficult for foreign talent to efficiently search for job listings in Japan and apply for suitable positions. Companies also lack the ability to match foreign talent based on their emotional state and interests, preventing an effective recruitment process. Furthermore, it is difficult to accurately filter foreign talent's educational and work history information to select the right candidates. These challenges reduce the efficiency of matching foreign talent with Japanese companies.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1266] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain level of salary; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for analyzing the collected posting histories with a sentiment analysis engine to extract users who are interested in Japan and show positive emotions; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users based on their emotional state; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; and means for tracking interview results and notifying companies of the completion of matching when a candidate is hired. This enables foreign talent to efficiently search for job information in Japan and receive appropriate job information, thereby enabling companies to implement an effective hiring process.

[1267] "Job Information" means detailed information about employment opportunities, including job types, salary, work location, required qualifications and experience, etc.

[1268] "Means of collection" refers to the methods and techniques used to obtain the required data from designated sources, including crawling, API calls, etc.

[1269] "Filtering means" refers to methods and techniques for eliminating unnecessary information from collected data based on specific conditions and extracting only data that meets the purpose.

[1270] "Means of translating into multiple languages" refers to methods and technologies for converting text written in one language into multiple other languages, including translation APIs.

[1271] "Database storage" refers to methods and technologies for securely managing and retaining data in a structured format, including SQL and NoSQL databases.

[1272] "Social networking service" means an internet-based platform that enables users to interact and share information with others online.

[1273] "Posting history" refers to data that records the content of posts that a user has made in the past on a social networking service.

[1274] "Sentiment analysis engine" refers to software or algorithms used to identify and analyze emotions from text data.

[1275] "Educational history" refers to information about the educational institutions an individual has attended and the degrees or qualifications they have obtained.

[1276] "Employment history" refers to the history of jobs and positions held by an individual in the past.

[1277] "Recommendation means" refers to methods and technologies for suggesting appropriate information and options to users based on collected and analyzed data.

[1278] "Means of receiving application information" refers to the methods and technologies for obtaining the application details submitted by job seekers, including web forms and APIs.

[1279] "Means of notifying companies" refers to the methods and techniques used to notify relevant companies of the received application information.

[1280] "Means for arranging interview dates" refers to methods and techniques for setting the date, time, and location of an interview between a company and a job seeker.

[1281] "Means for tracking interview results" refers to methods and techniques for monitoring and recording the progress and results of the interview.

[1282] "Means for notifying completion of matching" refers to the methods and technologies used to notify relevant parties of the results of the recruitment process once they have been determined.

[1283] MODE FOR CARRYING OUT THE INVENTION

[1284] System Configuration

[1285] The system of the present invention is mainly composed of three entities: a server, a terminal, and a user. The server is responsible for collecting, filtering, translating, and storing job information, collecting, analyzing, and making recommendations about foreign talent, managing application information, arranging interview dates, analyzing emotions, and notifying applicants when matching is complete. The terminal is a device through which users receive job information, check details, and enter application information. This typically includes PCs, tablets, and smartphones. The user is the entity that receives job information, applies if interested, and goes through the process of being hired after an interview.

[1286] Server Functions and Operations

[1287] The server periodically crawls job information websites in Japan to collect the latest job information. It uses natural language processing (NLP) technology and filtering algorithms to filter out jobs that do not require advanced Japanese language skills and are expected to offer salaries above a certain level. Python's Beautiful Soup and pandas libraries are particularly useful.

[1288] The filtered job listings are translated into English and other major languages ​​using multilingual translation APIs such as Google Translate API and DeepL API, and stored in a database, where the translated information is available for future searches and analysis.

[1289] Next, the server crawls foreign social networking services such as Facebook and LinkedIn to collect users' posting history, which is then analyzed using a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to identify users who have positive sentiment toward Japan.

[1290] The educational and work history information of the identified users is collected using the LinkedIn API, etc., and users who meet certain criteria (e.g., bachelor's degree or higher, five years or more of work experience) are filtered out. Appropriate job information is recommended to the filtered users based on their emotional state. This recommendation process uses an email service (e.g., SendGrid API).

[1291] User operations

[1292] The user checks the received job information, and if interested, clicks on the provided link to view more information. If satisfied with the details, the user can enter the necessary information in the application form and submit it.

[1293] Collaboration with companies

[1294] The server receives application information sent by users and stores it in a database. The stored application information is then notified to relevant Japanese companies. Interview dates are then arranged between the company and the user, and the server ultimately tracks the interview results. If the candidate is hired, the server notifies the company and the user that matching is complete. Information about successful matches is also updated in the database, allowing for future analysis and improvement.

[1295] Specific examples

[1296] As a concrete example, consider the provision of job information for IT engineers. The server crawls information on the job title "IT engineer" from job information providers A and B in Japan. From this information, it filters job listings that "do not require advanced Japanese language skills" and offer an annual salary of 5 million yen or more, and translates them into English and Spanish. Next, the server crawls Facebook and LinkedIn, collecting the posting history of users living in the United States and Spain. This posting history is analyzed using an emotion engine to identify users who have positive feelings toward Japan. The extracted users' work history and educational background are checked, and suitable job listings are recommended to users who meet the criteria based on their emotional state.

[1297] Prompt Sentence Examples

[1298] "Dear Generative AI Model, please explain in detail the processing flow of your recruitment system for foreign talent based on the following technical specifications."

[1299] By using this system, Japanese companies can effectively recruit highly skilled personnel from overseas, and foreign talent can easily find job information that suits them. The recommendation function based on the emotion engine is expected to provide more personalized job information and improve the success rate of matching.

[1300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1301] Step 1:

[1302] Collecting job information

[1303] The server uses Python's Beautiful Soup and requests libraries to periodically crawl job information from job information websites in Japan. The input is the URL of the job information website, and the output is a list of collected job information. The crawled data is converted to JSON format and saved.

[1304] Step 2:

[1305] Job Filtering

[1306] The server filters the collected job information to those that do not require advanced Japanese language skills and have an annual salary of 5 million yen or more. The input is a list of collected job information, and the output is a list of job information after filtering. This process uses the Python pandas library to extract data that matches the conditions.

[1307] Step 3:

[1308] Multilingual translation

[1309] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API such as Google Translate API or DeepL API. The input is a list of filtered job listings, and the output is a list of translated job listings. The translation results are stored in a database for future searches and analysis.

[1310] Step 4:

[1311] Extraction of foreign talent

[1312] The server crawls social networking services such as Facebook and LinkedIn to collect the posting history of foreign users. The input is the search keywords on the social networking site and the user's posting history, and the output is a list of the collected posting history. This allows data to be obtained to identify users who may be interested in Japan.

[1313] Step 5:

[1314] Sentiment analysis of posting history

[1315] The server analyzes the collected posting history using IBM Watson's sentiment analysis API. The input is a list of posting history, and the output is an analysis of the emotions expressed by the user. Based on the analysis results, users with positive emotions toward Japan are identified.

[1316] Step 6:

[1317] Filtering educational and work history information

[1318] The server collects the identified users' educational and work history information using the LinkedIn API or similar, and filters users who meet certain criteria. The input is the user's profile information, and the output is a list of users who meet the criteria. The filtering criteria include a bachelor's degree or higher and five or more years of work experience.

[1319] Step 7:

[1320] Emotion-based job recommendations

[1321] The server recommends appropriate job listings based on the sentiment analysis results and filtered user information. The input is the filtered user information and translated job listings, and the output is a list of recommended job listings. The recommended job listings are sent to the user via an email service (SendGrid API).

[1322] Step 8:

[1323] Receiving application information

[1324] The user checks the received job information and, if they are interested, clicks to view the detailed information. The input is the email with the recommended job information, and the output is the application information entered by the user. When the user enters the necessary information in the application form and submits it, the application information is sent to the server.

[1325] Step 9:

[1326] Notification of application information and interview arrangements

[1327] The server receives the application information sent by the user and stores it in a database. It then notifies the relevant companies of the application information and arranges interview dates between the companies and the user. The input is the user's application information, and the output is the arranged interview schedule.

[1328] Step 10:

[1329] Matching completion and notification

[1330] The server tracks the interview results and notifies the company and user that the match is complete if a job offer is made. It also updates the database with information about successful matches for future analysis and improvement. The input is the interview results, and the output is a notification that the match is complete.

[1331] (Application example 2)

[1332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Conventional job information systems are primarily focused on matching job openings with applications. While effective in improving the efficiency of recruiting, they lack the elements necessary to recruit the right talent. In particular, they are unable to provide personalized recommendations that take into account the user's emotional state, creating a need for improved user experience. Furthermore, in customer service, they are unable to provide appropriate product recommendations based on the customer's emotional state, making it difficult to improve customer service quality.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1335] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a salary above a certain level; means for translating the filtered job information into multiple languages; means for saving the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users who are presumed to be interested in Japan from the collected posting histories; means for analyzing the educational and work history information of the extracted users and filtering out users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying the completion of matching when a job is decided; means for analyzing the facial expressions of customers using an emotion engine and making recommendations based on their emotional state; and means having a multilingual display device for recommending products based on the emotional state of customers. This not only enables personalized recommendations that take into account the user's emotions, but also enables high-quality customer service based on the emotional state of customers.

[1336] "Job information in Japan" refers to information about employment and job changes that is published or posted within Japan.

[1337] "Advanced Japanese language skills not required" means that fluent reading, writing, or speaking Japanese is not required to apply for the job.

[1338] "Salary above a certain level" means that the compensation is above a set standard amount.

[1339] "Filtering" is the process of selecting only data or information that meets certain conditions from a large amount of data or information.

[1340] "Multilingual" means not limited to one language but includes multiple different languages.

[1341] A "social networking service" is an online platform that facilitates interaction between users via the Internet.

[1342] "Posting history" refers to the history of content that a user has posted on a social networking service in the past.

[1343] "Interested in Japan" refers to a state in which a user shows interest or curiosity about Japan.

[1344] An "emotion engine" is software that analyzes a user's emotional state and recommends appropriate actions based on the results.

[1345] "Facial expression analysis" is the process of analyzing facial expressions to determine the emotion behind them.

[1346] "Personalized recommendations" refers to customized suggestions based on the preferences and emotional state of individual users.

[1347] A "multilingual display device" is an electronic device that has the ability to display information in multiple languages.

[1348] "Customer service" refers to all work related to providing customer service and services in stores and other places.

[1349] The system of this invention is mainly composed of three entities: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it realizes recommendations based on the user's emotional state.

[1350] Detailed system preparation

[1351] Server Roles

[1352] The server has a means of periodically collecting job information within Japan. This collection is done by crawling major job information websites. The collected job information is analyzed and filtered to determine whether it requires a high level of Japanese language proficiency and offers a certain salary or higher. Job information selected through this filtering process is translated into English and other major languages ​​using a multilingual translation API and stored in a database.

[1353] Next, the server has a means for collecting users' posting history from foreign social networking services. This posting history is used to extract posts that are presumed to indicate that the user is interested in Japan. From the collected posting history, the user's emotional state is analyzed using an emotion engine, and users who have not only an interest in Japan but also positive emotions are identified.

[1354] Device Role

[1355] The terminal is equipped with an interface that allows users to receive job information, check details, and enter application information. Mobile devices such as smartphones and tablets are the primary targets.

[1356] By using emotion analysis technology, it is possible to analyze the facial expressions of customers in real time through their devices and make recommendations based on their emotional state. Specifically, a device such as smart glasses is used to analyze the customer's facial expressions, and product recommendations are displayed based on the results.

[1357] User Roles

[1358] Users can receive job information provided, and if they are interested, they can view the details and enter the necessary information in the application form. Furthermore, in stores, they can receive product recommendations in real time using smart glasses.

[1359] Specific examples

[1360] Suppose a customer approaches a store clerk wearing smart glasses. The server analyzes the customer's facial expression through camera footage and detects "happiness." As a result, a recommendation for a "newly released coffee maker" is returned from the API. Based on the information displayed on the smart glasses, the store clerk suggests, "How about this new coffee maker?" The following is an example of a prompt sentence that can be used in this case:

[1361] "When the display shows 'New Coffee Maker,' we suggest, 'How about a new coffee maker?'"

[1362] Hardware and software used

[1363] Hardware: Smartphones, smart glasses, camera-equipped devices

[1364] Software: OpenCV, DeepFace, libraries for sending API requests (e.g. Requests)

[1365] The server uses OpenCV to detect faces from real-time video and DeepFace to analyze emotions from facial images. In response, an emotion engine using a generative AI model analyzes the user's emotions and can display recommendation results on the device's display.

[1366] As described above, the present invention makes it possible to provide personalized recommendations that take into account the emotional state of the user, and to realize high-quality service in customer service operations.

[1367] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1368] Step 1:

[1369] The server crawls job information websites in Japan and collects the latest job information. As input, it receives a list of URLs for the specified job information websites and obtains HTML data from each website. To process the data, it uses a crawling tool to analyze the HTML data and extract the necessary job information. As output, it generates a list of the extracted job information.

[1370] Step 2:

[1371] The server filters the collected job information for those that do not require advanced Japanese language skills and are expected to offer a certain salary or higher. As input, it receives the list of job information obtained in step 1. As data calculation, it analyzes the content of the job information and executes logic to determine whether it matches the filter conditions. As output, it generates a list of filtered job information.

[1372] Step 3:

[1373] The server translates the filtered job postings using a multilingual translation API. As input, it receives the list of filtered job postings obtained in step 2. As data calculation, it sends a request for each job posting to the multilingual translation API and obtains the translation results. As output, it generates a list of translated job postings.

[1374] Step 4:

[1375] The server stores the translated job postings in the database. As input, it receives the list of translated job postings obtained in step 3. As data processing, it performs a data insertion operation into the database. As output, it generates the job posting data stored in the database.

[1376] Step 5:

[1377] The server collects user posting histories from foreign social networking services. As input, it receives the API key of the specified social networking service and a list of user IDs. As data processing, it obtains each user's posting history and analyzes the collected posting data. As output, it generates a list of the collected posting histories.

[1378] Step 6:

[1379] The server extracts users who are presumed to be interested in Japan from the collected posting history. As input, it receives the list of posting histories obtained in step 5. As data processing, it uses an emotion engine to perform sentiment analysis of each post and identifies posts that show interest or positive sentiment toward Japan. As output, it generates a list of users who are interested in Japan.

[1380] Step 7:

[1381] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. As input, it receives the list of users interested in Japan obtained in step 6. As data processing, it analyzes each user's social networking service profile information and extracts educational and work history information. It then executes logic to evaluate whether the criteria are met. As output, it generates a list of filtered users.

[1382] Step 8:

[1383] The server recommends appropriate job listings for the filtered users. As input, it receives the filtered user list obtained in step 7 and the job listing data saved in step 4. As data calculation, it selects appropriate job listings based on each user's emotional state and profile information and executes the recommendation algorithm. As output, it generates recommendation results for each user.

[1384] Step 9:

[1385] The terminal provides an interface for receiving application information from users. As input, it receives information entered by users into an application form. As data processing, it converts the application information into a specified format and sends it to the server. As output, it generates the application information sent to the server.

[1386] Step 10:

[1387] The server notifies companies of the application information and arranges interview dates between the relevant companies and the user. As input, it receives the application information received in step 9. As data processing, it generates and sends a notification message to the company. As output, it generates the application information notified to the company and arranges interview dates.

[1388] Step 11:

[1389] The server tracks the interview results and notifies the completion of matching when a candidate is hired. It receives interview result information from the company as input. It processes the data by updating the matching results in the database and notifying the user and company. It generates the notified matching completion information as output.

[1390] Step 12:

[1391] The device uses an emotion engine to analyze the facial expressions of customers and make recommendations based on their emotional state. Camera footage is acquired in real time as input. Face detection is performed using OpenCV and emotion analysis is performed using DeepFace for data processing. Emotion analysis results are generated as output.

[1392] Step 13:

[1393] The terminal uses a multilingual display device to recommend products based on the emotional state of the customer. As input, it receives the emotion analysis results obtained in step 12. As data processing, it executes an algorithm to select and display products appropriate for the customer's emotional state. As output, it displays the product recommendation information on a multilingual display.

[1394] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1395] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1396] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1397] [Fourth embodiment]

[1398] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1399] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1400] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1401] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1402] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1404] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1405] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1406] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1407] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1408] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1409] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1410] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1411] System Configuration

[1412] The system of the present invention is mainly composed of three entities: a "server," a "terminal," and a "user." The roles of each are as follows:

[1413] 1. Server: Responsible for collecting, filtering, translating, and storing job information; collecting, analyzing, and recommending foreign talent; managing application information; arranging interview schedules; and notifying applicants when matching is complete.

[1414] 2. Terminal: This is the device on which users receive job information, check details, and enter application information. This mainly includes PCs, tablets, and smartphones.

[1415] 3. Users: These are individuals who receive job information, and if interested, apply, go through interviews, and are hired. Specifically, these individuals are people who live abroad, have an interest in Japan, and have advanced specialized knowledge.

[1416] Program processing flow

[1417] The operation of this system consists of several processing steps. The specific processing flow for each step is shown below.

[1418] 1. Collecting job information

[1419] The server periodically crawls major job sites in Japan (generally referred to as "job information sites") to collect the latest job information.

[1420] 2. Job Filtering

[1421] The server analyzes the collected job information and filters out job listings that do not require advanced Japanese language skills and are expected to pay a certain amount or more.

[1422] 3. Multilingual Translation

[1423] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and then stores the translated listings in a database.

[1424] 4. Extraction of foreign talent

[1425] The server crawls foreign social networking services, analyzes users' posting history, and extracts users who are likely to be interested in Japan.

[1426] 5. Filtering educational and work history information

[1427] The server collects the educational and work history information of the extracted users and filters out users who meet certain criteria (e.g., bachelor's degree or above, work experience of 5 years or more).

[1428] 6. Job Recommendations

[1429] The server sends appropriate job information to users who meet the criteria via push notifications or email.

[1430] 7. Receipt of application information

[1431] The user checks the received job information, and if they are interested, they enter the necessary information into the application form and submit it. The server stores this application information in a database.

[1432] 8. Notification of application information and interview arrangements

[1433] The server then notifies the necessary job information providers and companies of the saved application information, after which the company and the user can arrange an interview date.

[1434] 9. Matching completed

[1435] The server tracks the interview results, notifies the company and the user if a job offer is made, and updates the database to record successful matches.

[1436] Specific examples

[1437] Example 1: Providing job information for IT engineers

[1438] The server crawls information about the job title "IT engineer" from "job information sites" A and B in Japan.

[1439] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[1440] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[1441] Analyze the extracted users' work history (e.g., computer science degree, 5 years of work experience) and filter users who meet the criteria.

[1442] The server then emails the appropriate job listings to the filtered users.

[1443] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1444] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1445] Example 2: Recruiting biotechnology researchers

[1446] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[1447] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[1448] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[1449] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[1450] The server will email suitable job listings to the user.

[1451] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1452] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1453] This concludes the explanation of the specific system operation as a "mode for carrying out the invention." This system makes it possible to effectively match job information from Japanese companies with highly skilled foreign personnel.

[1454] The processing flow will be explained below.

[1455] Step 1:

[1456] The server periodically crawls major job information websites in Japan to collect the latest job information, allowing it to constantly keep track of new job information to provide to job seekers.

[1457] Step 2:

[1458] The server analyzes the collected job listings and filters out those that do not require a high level of Japanese language ability and those that are expected to pay above a certain salary, thereby narrowing down the list of job listings that are easy for foreign talent to apply for.

[1459] Step 3:

[1460] The server translates the filtered job listings into English and other major languages ​​using a multilingual translation API, and the translated listings are stored in a database.

[1461] Step 4:

[1462] The server crawls major social networking services in foreign countries and collects users' posting history, thereby gathering data to extract users who are presumed to be interested in Japan.

[1463] Step 5:

[1464] The server analyzes the collected posting history and extracts users who have posted using keywords such as "Japan" and "Tokyo." This user extraction is important for identifying foreign talent who are interested in Japan.

[1465] Step 6:

[1466] The server checks the profile information of the extracted users in the social networking service to collect their educational and work history information, and based on this information, filters users who meet certain criteria (e.g., a bachelor's degree or higher, five or more years of work experience).

[1467] Step 7:

[1468] The server then recommends suitable job information to the filtered users via push notifications or emails, so that relevant job information is delivered directly to the users.

[1469] Step 8:

[1470] The user checks the received job information, and if they are interested, they click on the details to view the content of the job information. If they are satisfied with the content, they enter the necessary information in the application form and submit it.

[1471] Step 9:

[1472] The server receives the application information sent by the user and stores it in a database, which then notifies the relevant Japanese companies.

[1473] Step 10:

[1474] The server coordinates interview dates between the company and the user, and once the interview date and time are confirmed, the server notifies both the company and the user of the information.

[1475] Step 11:

[1476] The server tracks the interview results and, if a job offer is made, notifies the company and the user that the match is complete. Information about successful matches is updated in the database for future analysis and improvement.

[1477] The above processing steps realize the operation of a system that effectively matches job information in Japan with highly skilled foreign personnel.

[1478] Example 1

[1479] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] Japanese companies face the challenge of finding it difficult to effectively recruit foreign talent. Foreign talent with advanced expertise in particular has limited access to job listings in Japan, making it difficult to match potential candidates with the right talent. Furthermore, language barriers and geographical restrictions mean that proper communication between companies and job seekers is often not possible, hindering smooth recruitment.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1482] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for jobs that do not require advanced Japanese language skills and are expected to offer a certain salary or higher; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign social networking services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' educational and work history information and filtering users who meet certain criteria; means for recommending appropriate job information to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; analysis means using a multilingual translation API and natural language processing technology; and means for collecting job information and user postings using crawling technology. This makes it easier for foreign talent to access job information in Japan, enabling appropriate matching and a quick hiring process.

[1483] "Means for collecting job information within Japan" refers to technology for automatically obtaining data on job openings from various job information platforms within Japan.

[1484] "A means of filtering out jobs that do not require advanced Japanese language skills and are expected to have a salary above a certain level" is a technology for selecting, from the collected job information, job information that does not require a high level of specialized Japanese language skills and meets a specified salary standard.

[1485] "Multilingual translation means" refers to technology for translating filtered job listings into English and other major foreign languages.

[1486] "Means for storing in a database" refers to the technology used to store translated job information in an organized manner and in a database in a format that can be quickly accessed when needed.

[1487] "Means for collecting user posting history from foreign social networking services" refers to technology for automatically obtaining users' public posting history from foreign SNSs.

[1488] The "means of extracting users who are likely to be interested in Japan" is a technology that analyzes collected posting history and identifies users who are likely to be interested in Japan and its culture.

[1489] "Means for analyzing educational and work history information and filtering users who meet certain criteria" refers to technology for evaluating the educational and work history information of extracted users and selecting users who meet certain criteria (for example, a specific degree or years of work experience).

[1490] "Means for recommending appropriate job information" refers to technology for recommending suitable job information to users who meet criteria.

[1491] The "means for receiving application information and notifying companies" refers to a technique for receiving application information from users and notifying the relevant companies of that information.

[1492] "Means for coordinating interview dates between a company and a user" refers to a technique for coordinating the schedules of both the company and the user and setting an interview date.

[1493] "Means for tracking interview results and notifying the completion of matching when a candidate is hired" refers to technology for managing the results of interviews and informing both the company and the user when a candidate is hired.

[1494] "Analysis method using multilingual translation API and natural language processing technology" refers to technology for translating job postings and user posts into multiple languages ​​and analyzing them through natural language processing.

[1495] "Methods of collecting job information and user posts using crawling technology" refers to techniques for collecting necessary data from job information sites and social media using web scraping and crawling technology.

[1496] MODE FOR CARRYING OUT THE INVENTION

[1497] The purpose of this system is to collect job information in Japan and provide appropriate job information to highly skilled foreign professionals. The operation of the system consists of processing steps that are made up of three main entities: the server, the terminal, and the user.

[1498] The server executes the following processes in order.

[1499] 1. Collecting job information

[1500] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl the latest job listings from major job listing websites in Japan. Specifically, it retrieves job listings from sites such as Indeed and Rikunabi.

[1501] 2. Job Filtering

[1502] The server uses natural language processing (NLP) technology to analyze the collected job listings. By segmenting and tagging the listings using Python's NLTK and SpaCy libraries, it filters out listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more."

[1503] 3. Multilingual Translation

[1504] The server translates the filtered job listings using a multilingual translation API (e.g., Google Translate API, DeepL API), and the translated information is stored in a database (e.g., PostgreSQL, MySQL).

[1505] 4. Extraction of foreign talent

[1506] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. This process also uses NLP technology to extract keywords related to "Japan" and "Tokyo" from the posted content.

[1507] 5. Filtering educational and work history information

[1508] The server analyzes the extracted user profile information to see if they meet certain criteria (e.g., a computer science degree, more than five years of work experience), using the Python Pandas library for this filtering.

[1509] 6. Job Recommendations

[1510] The server recommends suitable job listings to users who meet the criteria, and sends these recommendations via push notifications or email delivery services (e.g., Firebase Cloud Messaging, SendGrid, Mailgun).

[1511] 7. Receipt of application information

[1512] The user checks the received job information, enters the necessary information in the application form, and submits it. The device (PC, tablet, smartphone) provides the UI for this application form and sends the entered information to the server. The server saves the application information in a database.

[1513] 8. Notification of application information and interview arrangements

[1514] The server notifies the appropriate companies of the saved application information and provides an interface for companies and users to arrange interview dates. Specifically, Google Calendar API or a schedule adjustment app can be used.

[1515] 9. Matching completed

[1516] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for later analysis.

[1517] Specific examples

[1518] Example 1: Providing job information for IT engineers

[1519] The server crawls information about the job title "IT engineer" from job information sites A and B in Japan.

[1520] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 5 million yen or more, translating them into English and Spanish.

[1521] The server crawls Facebook and LinkedIn, extracting posts from the posting history of users in the United States and Spain that show interest in "Japan" or "Tokyo."

[1522] Analyze the extracted users' work history (e.g., computer science degree, 5+ years of work experience) and filter users who meet the criteria.

[1523] The server then emails the appropriate job listings to the filtered users.

[1524] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1525] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1526] Example prompt: "Please translate job postings for IT engineers with an annual salary of at least 5 million yen, no advanced Japanese language skills required, into English and Spanish, and email the job postings to suitable candidates."

[1527] Example 2: Recruiting biotechnology researchers

[1528] The server crawls information about the job title "biotechnology researcher" from "job information sites" C and D.

[1529] The server does not require advanced Japanese language skills and filters job listings with annual salaries of 6 million yen or more, translating them into multiple languages.

[1530] The server uses LinkedIn to crawl the posting history of users living in Canada and extracts posts that show interest in "Japanese research."

[1531] Analyze the extracted users' educational background (e.g., PhD from the University of Toronto) and work history (3 years of research experience) and filter users who meet the criteria.

[1532] The server will email suitable job listings to the user.

[1533] Users receive job information and, if they are interested, enter their information into an application form and submit it.

[1534] The server receives the application information and notifies the company, then arranges an interview date between the company and the user, completing the matching process.

[1535] Example prompt: "Please translate job postings for biotechnology researchers with an annual salary of at least 6 million yen and no advanced Japanese language skills required into multiple languages ​​and email suitable candidates."

[1536] This system effectively matches job information from Japanese companies with highly skilled foreign talent, ensuring a fast and smooth recruitment process.

[1537] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1538] Step 1: Gather job information

[1539] The server uses a Python scraping library (e.g., BeautifulSoup or Scrapy) to crawl job information from job information websites in Japan. The server periodically accesses a specified URL and downloads the HTML page of the job information. Next, it uses BeautifulSoup to analyze the HTML page and extract the necessary data (job type, salary, language requirements, etc.).

[1540] Input: URL list of job information sites

[1541] Output: A list of extracted jobs

[1542] Step 2: Filter Jobs

[1543] The server analyzes the collected job listings using natural language processing (NLP) technology. It uses Python's NLTK and SpaCy libraries to split and tag the text data. This allows it to filter out job listings that meet the criteria of "no advanced Japanese language skills required" and "annual salary of 5 million yen or more." The server then temporarily stores only the filtered job listings in order to proceed to the next step.

[1544] Input: A list of extracted job postings

[1545] Output: A filtered list of jobs

[1546] Step 3: Translate into multiple languages

[1547] The server uses the Google Translate API or DeepL API to translate the filtered job listings into multiple languages. Specifically, it sends the text data of the job listings to the API and receives the translated text. The translated information is then stored in a database (e.g., PostgreSQL, MySQL).

[1548] Input: A filtered list of jobs

[1549] Output: A list of translated job postings

[1550] Step 4: Identifying foreign talent

[1551] The server crawls social networking services such as Facebook and LinkedIn, collecting and analyzing users' public posting history. Using Python's Scrapy, it crawls the pages of specified social media accounts and collects posting data. From the collected posting history, it extracts keywords indicating interest in Japan and its culture (e.g., "Japan" and "Tokyo") and analyzes them using natural language processing technology.

[1552] Input: List of social media account URLs

[1553] Output: List of users interested in Japan

[1554] Step 5: Filtering education and work history information

[1555] The server parses the extracted user profile information (educational background and work experience). It uses the Python Pandas library to read the user data and filter users who meet certain criteria (e.g., computer science degree, 5+ years of work experience). The filtered user information is used in the next step.

[1556] Input: List of users interested in Japan and their profile information

[1557] Output: A list of users who meet the criteria

[1558] Step 6: Job Recommendations

[1559] The server recommends suitable job listings to filtered users. The server uses email delivery services such as SendGrid and Mailgun to send emails containing job listings to users. If push notifications are used, services such as Firebase Cloud Messaging can be used.

[1560] Input: A list of users who meet the criteria and a list of translated job postings

[1561] Output: Job notification sent to user

[1562] Step 7: Receiving your application information

[1563] The user checks the received job information, and if they are interested, they enter the required information in the application form and submit it. The device (PC, tablet, smartphone) provides the UI for the application form and sends the entered information to the server. The server receives the application information and stores it in a database.

[1564] Input: User's application information

[1565] Output: Application information stored in a database

[1566] Step 8: Notification of application information and interview arrangements

[1567] The server notifies the company of the saved application information, provides the application information to the company via automatic email or API integration, and provides an interface for coordinating interview dates between the company and the user. Scheduling can also be done using the Google Calendar API.

[1568] Input: Application information stored in the database

[1569] Output: Application information and interview schedule notified to the company

[1570] Step 9: Matching Complete

[1571] The server tracks the interview results and notifies both the company and the user if a job offer is made. Successful matches are recorded in a database for future analytics and reporting.

[1572] Input: Interview results from company

[1573] Output: Matching completion information notified to companies and users, and success stories recorded in the database

[1574] (Application example 1)

[1575] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1576] Currently, it is extremely difficult to effectively provide job information in Japan to foreign talent and recruit the right talent. In particular, there is a lack of efficient means to find foreign talent with advanced skills related to the development and operation of autonomous vehicles and match them with Japanese companies. As a result, there is a lack of a system that can centrally identify foreign talent, translate job information, and arrange interviews with companies.

[1577] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1578] In this invention, the server includes: means for collecting job information in Japan; means for filtering the collected job information for job listings that do not require advanced language skills and are expected to offer salaries above a certain level; means for translating the filtered job information into multiple languages; means for storing the translated job information in a database; means for collecting user posting histories from foreign Internet services; means for extracting users presumed to be interested in Japan from the collected posting histories; means for analyzing the extracted users' education and work history information and filtering users who meet certain criteria; means for recommending appropriate job listings to the filtered users; means for receiving application information from users and notifying companies; means for arranging interview dates between companies and users; means for tracking interview results and notifying companies of the completion of matching when a candidate is hired; and means for recruiting foreign talent suitable for technologies and work related to autonomous vehicles and recommending talent according to the needs of companies. This enables centralized management of everything from identifying foreign talent to matching with companies, thereby enabling efficient recruitment of talent with advanced skills related to autonomous vehicles.

[1579] "Job Information" means information about employment opportunities provided to job seekers.

[1580] "Filtering means" refers to the function of removing unnecessary parts from collected information based on specific conditions and selecting only useful information.

[1581] "Means for translating into multiple languages" refers to the ability to convert specific information into multiple different languages.

[1582] "Means of storing information in a database" refers to a system that systematically organizes and stores information, making it easy to search and reference.

[1583] "Internet services" is a general term for various online services provided via the Internet.

[1584] "Posting history" refers to a record of posts and comments made by a user on the Internet.

[1585] "Means of extraction" refers to the work or process of extracting the necessary information based on specific conditions.

[1586] "Education and work history information" refers to data related to an individual's educational background and work history.

[1587] "Recommendation means" refers to the function of suggesting specific information or options to the user.

[1588] "Application information" refers to information such as personal information and resumes provided by job seekers in response to job offers.

[1589] An "interview schedule" is a scheduled date and time for an interview between a company and a job seeker.

[1590] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without driver operation.

[1591] An "enterprise" is a legal entity that operates a particular business and provides goods or services to the market.

[1592] To put this invention into practice, a system is constructed in which three entities, a server, a terminal, and a user, work in cooperation with each other. The specific operation of this system will be described below.

[1593] Server Features

[1594] 1. Collecting job information

[1595] The server crawls and collects job information from job information websites in Japan. The collected job information is stored in a database. The crawling technology used here is a Python library (Beautiful Soup and Scrapy).

[1596] 2. Job Filtering

[1597] From the collected job information, we filter job information that does not require advanced language skills and has a salary expected to be above a certain level. At this stage, we use natural language processing tools such as NLTK and spaCy to perform text analysis and filtering.

[1598] 3. Multilingual Translation

[1599] The filtered job listings are translated into multiple languages ​​using a multilingual translation API such as Microsoft Translator Text API. This API converts the job listings into multiple languages, including English, and stores them back in the database.

[1600] 4. Collection and Extraction of User Information

[1601] The server collects user posting history from foreign internet services. For example, it crawls the posting history using the APIs of Facebook and LinkedIn. From the collected posting history, it extracts posts containing keywords such as "Japan" and "Tokyo" to find users who are likely to be interested in Japan.

[1602] 5. Analysis of education and work history information

[1603] The extracted user's education and work history information is analyzed and users who meet certain criteria (e.g., bachelor's degree or higher, five or more years of work experience) are filtered out. This analysis uses a trained generative AI model to compare the user's skill set with job postings.

[1604] 6. Job Recommendations

[1605] It recommends suitable job listings for filtered users. For recommendations, it uses a machine learning model (e.g., scikit-learn's TfidfVectorizer and linear kernel) to calculate the degree of match between the user's skill set and job listings. Recommendation results are sent to the user's email address or via push notification.

[1606] 7. Receipt of application information and notification

[1607] Receives application information from users and notifies the company. Application information is sent from a device (such as a smartphone) and received by the server. The application information is then notified to the company.

[1608] 8. Arranging interview dates

[1609] The server coordinates interview dates between users and companies, using calendar integration functions such as the Google Calendar API.

[1610] 9. Notification of match completion

[1611] We track the interview results and notify the user and the company that the match is complete if a job offer is made. We also update the database to record the successful match.

[1612] 10. Recruiting for autonomous vehicles

[1613] The server will recruit foreign talent suited to the technology and work related to autonomous vehicles, and recommend talent that meets the company's needs. At this stage, the server will also use the generative AI model and translation API to evaluate the degree of match between the candidate's skills and the job information.

[1614] Device Features

[1615] The device (smartphone, tablet, PC) provides the interface for users to check job information and apply. In particular, the usability of the notification function, the function to submit application information, and the data entry form is important.

[1616] User Roles

[1617] Users access job information provided through their devices and apply for jobs they are interested in. If a job related to autonomous vehicles is recommended, users apply based on their own education and work history information.

[1618] Prompt Sentence Examples

[1619] "For a job information platform related to autonomous vehicles, we will collect job information that does not require Japanese language skills and offers an annual salary of 6 million yen or more, and extract job listings for specific occupations such as IT engineers and biotechnology researchers. We will then build a system that translates the job listings into multiple languages ​​and recommends them to suitable candidates."

[1620] This concludes the detailed explanation of the "Mode for Carrying Out the Invention." This system will enable effective matching of job information from Japanese companies with foreign talent possessing advanced skills.

[1621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1622] Step 1:

[1623] The server crawls and collects job information from job information websites in Japan. In this step, Python libraries (Beautiful Soup and Scrapy) are used to parse the HTML of the job information website and extract information. The input is the URL of the job information website, and the output is a list of the retrieved job information.

[1624] Step 2:

[1625] The server filters the collected job listings for those that do not require advanced language skills and are expected to pay above a certain salary. In this step, natural language processing tools such as NLTK and spaCy are used to perform text analysis and filtering of the collected job listings. The input is a list of collected job listings, and the output is a list of filtered job listings.

[1626] Step 3:

[1627] The server translates the filtered job listings into multiple languages. This step uses the Microsoft Translator Text API to translate the job listings into English and other languages. The input is the filtered list of job listings, and the output is the translated list of job listings.

[1628] Step 4:

[1629] The server stores the translated job postings in a database. This step uses an SQL database (PostgreSQL or MySQL) to organize and store the stored data for quick search and reference. The input is a list of translated job postings, and the output is a database entry for the stored job postings.

[1630] Step 5:

[1631] The server uses the API of a foreign internet service (such as Facebook or LinkedIn) to collect the user's posting history. In this step, it searches for and extracts content containing specific keywords (e.g., "Japan" or "Tokyo") from the user's posts. The input is the account information of the internet service, and the output is a list of posts containing the specific keywords.

[1632] Step 6:

[1633] The server extracts users who are presumed to be interested in Japan from the collected posting history. In this step, the content of the extracted posting history is analyzed, and users are selected based on the number and frequency of posts containing Japan-related keywords. The input is a list of posts containing the keywords, and the output is a list of users who are presumed to be interested in Japan.

[1634] Step 7:

[1635] The server analyzes the extracted users' educational and work history information and filters out users who meet certain criteria. In this step, the server analyzes the educational and work history data to determine whether they meet the criteria (bachelor's degree or higher, five or more years of work experience). The input is a list of users who are presumed to be interested in Japan, and the output is a list of users who meet the criteria.

[1636] Step 8:

[1637] The server recommends suitable job listings for the filtered users. In this step, a machine learning model using scikit-learn's TfidfVectorizer and a linear kernel is used to calculate th...

Claims

1. A means of collecting job information in Japan, A means of filtering out the collected job information that does not require advanced Japanese language skills and is expected to pay above a certain level, A means to translate filtered job listings into multiple languages; A means of storing the translated job listings in a database; A means for collecting user posting history from a foreign social networking service; A means of extracting users who are presumed to be interested in Japan from the collected posting history, and A means for analyzing the extracted users' educational and work history information and filtering out users who meet certain criteria; A means for recommending appropriate job information to the filtered user; A means for receiving application information from users and notifying companies; A means to arrange interview dates between companies and users, A way to track interview results and notify the successful candidate when a match is made. A system including:

2. 2. The system according to claim 1, further comprising means for analyzing posting histories collected from foreign social networking services and extracting users who are presumed to be interested in Japan based on keywords.

3. 2. The system according to claim 1, further comprising means for processing job information and educational and employment history information of foreign users in multiple languages.

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