system

Abstract

A system is provided.SOLUTION: A means for a user to upload curriculum vitae and a skill sheet, a means for an end company to input detailed matter information, and a means for comparing the curriculum vitae and the skill sheet with the detailed matter information by a AI algorithm; Means for generating a matching talent list, means for notifying an end company and a user of the generated matching candidate list, means for generating a skill check test based on detailed matter information, means for transmitting the skill check test to the user and receiving a test result of the user, means for transmitting the received skill check test result to the end company, means for uploading a self-PR moving image by the user, and means for analyzing the uploaded moving image by an AI algorithm, this system includes a means for evaluating a non-technical aspect, a means for transmitting an evaluation result to an end enterprise, a means for allowing the end enterprise to select the optimal personnel on the basis of collected information, and a means for notifying a user of a final selection result.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] The multi-tiered subcontracting structure in the IT industry hinders direct matching between talent and companies, resulting in inefficient employment practices. As a result, the value of IT talent is not properly assessed, making it difficult for end companies to quickly find suitable talent. Furthermore, matching through agent companies incurs high fees, increasing costs and reducing matching accuracy. Furthermore, traditional skill assessment methods present challenges, such as the difficulty of properly assessing not only technical skills but also non-technical aspects and personalities. [Means for solving the problem]

[0005] The present invention provides a means for users to upload resumes and skill sheets, and for end companies to input detailed job information. It also includes a means for comparing the uploaded resumes and skill sheets with the detailed job information using an AI algorithm to generate a list of matching candidates. The generated list of matching candidates is notified to the end company and the user. It also includes a means for generating a skill check test based on the detailed job information, sending the test to the user, and receiving the test results from the user. The received skill check test results are sent to the end company. It also provides a means for users to upload self-promotional videos, analyze the videos using an AI algorithm, and evaluate non-technical aspects. The evaluation results are sent to the end company. Finally, these issues are solved by including a means for the end company to select the most suitable personnel based on the collected information and notify the user of the final selection results.

[0006] "User" refers to an individual or engineer working as an IT freelancer, who is responsible for uploading resumes and skill sheets to the system.

[0007] An "end company" is a company that provides projects to IT freelancers and is responsible for entering detailed project information into the system and selecting appropriate personnel.

[0008] A "resume" is a document that describes a user's past work experience and achievements, and is information that is uploaded to the system.

[0009] A "skill sheet" is a document that lists the technical skills and qualifications possessed by a user, and is information that is uploaded to the system.

[0010] "Project details" refers to information entered by the end company, such as the project details, required skill set, years of experience, and project duration.

[0011] The "AI algorithm" is a calculation method using artificial intelligence to compare resumes, skill sheets, and detailed job information to select the appropriate candidate.

[0012] A "matching candidate list" is a list of users who are suitable for an end company's project, generated by an AI algorithm.

[0013] A "skill check test" is a test for evaluating technical skills that is generated based on the case detailed information and is sent to the user.

[0014] A "self-promotion video" is a one-minute video that a user films and uploads to the system to showcase their skills and personality.

[0015] "Non-technical aspects" refer to user characteristics and abilities other than technology, such as communication skills and teamwork abilities.

[0016] "Evaluation results" are information obtained as a result of the AI ​​algorithm analyzing the self-promotional video and skill check test. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that allows users to upload their resumes and skill sheets, and end companies to input detailed information about the job, efficiently matching the most suitable personnel. The program for realizing this system includes multiple operations consisting of a server, a terminal, and a user.

[0039] Uploading user resumes and skill sheets

[0040] The user logs in to the system's portal site using their own terminal.

[0041] The user selects the resume and skill sheet and clicks the upload button.

[0042] The terminal transmits the selected file to the server.

[0043] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0044] End company inputs project details

[0045] End companies log in to the system's portal site using their own devices.

[0046] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0047] The terminal transmits the input information to the server.

[0048] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0049] Checking resumes and skill sheets against project details

[0050] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0051] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0052] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[0053] Creating and administering simple skill check tests

[0054] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0055] The generation AI automatically generates skill check tests based on the project.

[0056] The server sends the generated skill check test to the corresponding user.

[0057] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0058] The server stores the received skill check test results in a database and sends them to the end company.

[0059] Submission and evaluation of one-minute promotional videos

[0060] Users film a self-promotional video and upload it to the system via their device.

[0061] The device sends the uploaded video to the server.

[0062] The server sends the received video to an AI algorithm that evaluates non-technical aspects.

[0063] The AI ​​algorithm analyzes the video and returns the evaluation results to the server.

[0064] The server notifies the end company of the evaluation results.

[0065] Final selection by end company

[0066] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[0067] The end company selects the most suitable personnel based on the information presented.

[0068] The server notifies the corresponding user of the selection result of the end company.

[0069] This will enable quick and intuitive matching between end companies and IT freelancers, solving the problem of multiple subcontracting and maximizing the value of Japan's IT talent.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] A user uses a terminal to log in to the system's portal site.

[0073] Step 2:

[0074] The user selects the resume and skill sheet and clicks the upload button.

[0075] Step 3:

[0076] The terminal transmits the selected file to the server.

[0077] Step 4:

[0078] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0079] Step 5:

[0080] The end company uses a terminal to log in to the system's portal site.

[0081] Step 6:

[0082] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0083] Step 7:

[0084] The terminal transmits the input information to the server.

[0085] Step 8:

[0086] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0087] Step 9:

[0088] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0089] Step 10:

[0090] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0091] Step 11:

[0092] The server notifies the end company and the user of the generated matching candidate list.

[0093] Step 12:

[0094] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0095] Step 13:

[0096] The generation AI automatically generates skill check tests based on the project.

[0097] Step 14:

[0098] The server sends the generated skill check test to the corresponding user.

[0099] Step 15:

[0100] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0101] Step 16:

[0102] The server stores the received skill check test results in a database and sends them to the end company.

[0103] Step 17:

[0104] Users film a self-promotional video and upload it to the system via their device.

[0105] Step 18:

[0106] The device sends the uploaded video to the server.

[0107] Step 19:

[0108] The server sends the received video to the AI ​​algorithm.

[0109] Step 20:

[0110] AI algorithms analyze the video and evaluate non-technical aspects.

[0111] Step 21:

[0112] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0113] Step 22:

[0114] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[0115] Step 23:

[0116] The end company selects the most suitable personnel based on the information presented.

[0117] Step 24:

[0118] The server notifies the corresponding user of the selection result of the end company.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In today's labor market, quickly and efficiently matching companies with the right talent is crucial. However, current systems require a lot of time and effort, making it difficult to effectively select talent. Furthermore, there is a lack of a system for comprehensively evaluating candidates' skills and non-technical aspects beyond simply referencing their resumes and skill sheets, which often results in a mismatch between the talent companies are looking for and the actual candidates. To solve this problem, a comprehensive matching system is needed that evaluates not only technical skills but also non-technical aspects such as communication skills and teamwork abilities.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for companies to input job details; a means for using an AI algorithm to compare the uploaded resumes and skill sheets with the job details and generate a list of matching candidates; a means for notifying companies and users of the generated matching candidate list; a means for requesting an AI model to generate a skill check test based on the job details; a means for sending the skill check test to users and receiving the user's test results; a means for users to upload self-promotional videos; a means for using an AI algorithm to analyze the uploaded videos and evaluate non-technical aspects; a means for sending the evaluation results to companies; a means for companies to select the most suitable candidates based on the collected information; a means for notifying users of the final selection results; and a means for integrating data such as resumes, skill sheets, skill check results, and evaluations of the self-promotional videos. This enables fast and efficient talent matching and minimizes mismatches between companies and job seekers.

[0124] A "user" is an individual or entity that uploads a resume and skill sheet to the system.

[0125] "Company" refers to an organization or legal entity that inputs detailed project information into the system and selects appropriate personnel.

[0126] A "resume" is a document that details a user's work history, experience, and skills.

[0127] A "skill sheet" is a document that details a user's professional skills and qualifications.

[0128] "Project details" refers to data that companies enter into the system, such as the required skill set, years of experience, and project duration.

[0129] An "AI algorithm" is a series of calculation methods and models that allow the system to compare and match resumes, skill sheets, and detailed job information.

[0130] A "generative AI model" is an algorithm and model that automatically generates skill check tests based on detailed project information.

[0131] A "skill check test" is a test that a user takes to assess a specific technical skill.

[0132] A "self-promotion video" is a video that a user shoots and uploads to showcase their abilities and charms.

[0133] "Non-technical aspects" refer to aspects of human characteristics other than technical skills, such as communication skills and teamwork abilities.

[0134] The "optimal candidate" is the user who best meets the company's requirements based on their resume, skill sheet, skill check results, and evaluation of their self-promotional video.

[0135] A "matching candidate list" is a list of users who meet a company's project requirements, generated by an AI algorithm.

[0136] A "server" is a device that centrally manages system operations and processes and stores data such as resumes, skill sheets, detailed project information, test results, and self-promotional videos.

[0137] A "terminal" is a device that allows a user or company to access the system and perform various operations.

[0138] This invention is a system that allows users to upload their resumes and skill sheets, and companies to input detailed information about the job, efficiently matching the most suitable personnel. To specifically implement this system, the following steps and configuration are required.

[0139] First, the user logs in to the system's portal site using their own device (PC, smartphone, etc.). The user selects their resume and skill sheet and clicks the upload button. At this time, the device encodes the selected files and sends them to the server. The server verifies the validity of the format of the received files and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[0140] Next, the company's employee logs in to the system's portal site using their own device. The employee enters project details such as the skill set required for the project, years of experience, and project duration. The device encodes the entered information and sends it to the server. The server verifies the validity of the format of the received project details and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[0141] Next, the server sends the user's resume and skill sheet, along with the company's job details, from the database to an AI algorithm (e.g., a natural language processing model or machine learning algorithm). The AI ​​algorithm compares and analyzes this information and generates a list of candidates who match. The server then notifies the company and the user of the generated list of candidates.

[0142] If a skill check test is required, the server requests a generative AI model (e.g., OpenAI GPT-4 or Google BERT) to generate a skill check test based on the job details. The generative AI model automatically generates a skill check test based on the job requirements. The server sends the generated skill check test to the corresponding user. The user takes the skill check test on their own device and sends the results to the server. The server stores the received test results in a database and sends them to the company.

[0143] Users can also film a self-promotional video and upload it to the system via their device. The device then sends the uploaded video file to the server. The server then requests an AI algorithm to analyze the received video. The AI ​​algorithm analyzes the video and evaluates non-technical aspects (e.g., communication skills, teamwork ability, etc.). The server then notifies the company of the evaluation results.

[0144] Finally, the server presents all collected information (resumes, skill sheets, skill check test results, evaluations of self-promotional videos, etc.) to companies, helping them select the most suitable candidates. Once the company's selection results have been determined, the server notifies the user of the results.

[0145] Here are some examples of prompts:

[0146] Please upload your resume and skills sheet.

[0147] Enter project details, including required skills, years of experience, and project duration.

[0148] Generate a Python skills test based on the following criteria: writing functions, analyzing data, using basic libraries, etc.

[0149] Rate the uploaded 1-minute promotional video and provide feedback on non-technical aspects.

[0150] In this way, the server, terminal, and user each have a clear role within the system, and they can work together to efficiently match personnel.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] A user logs in to the portal site

[0154] Input: Portal site URL, user name, password

[0155] Action: A user accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[0156] Output: The user is shown a successful login message and is redirected to the main page.

[0157] Step 2:

[0158] Users upload their resumes and skill sheets

[0159] Input: Resume file, Skill sheet file

[0160] How it works: The user selects the resume and skill sheet files on their device and clicks the upload button.

[0161] Output: The device encodes the selected file and generates a request to send it to the server.

[0162] Step 3:

[0163] The server receives the file and stores it in the database

[0164] Input: Encoded resume file, skill sheet file

[0165] Operation: The server verifies the format of the received file and saves it in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[0166] Output: The resume and skill sheet are saved in the database and a confirmation message is displayed on the user's terminal.

[0167] Step 4:

[0168] A company representative logs in to the portal site

[0169] Input: Portal site URL, user name, password

[0170] How it works: A company employee accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[0171] Output: Company representative will see a login success message and be redirected to the main page.

[0172] Step 5:

[0173] Company personnel enter detailed project information

[0174] Input: Details of the job, such as required skill set, years of experience, and project duration

[0175] Operation: A company representative enters the details of the case and clicks the send button.

[0176] Output: The terminal generates a request to encode the entered job details information and send it to the server.

[0177] Step 6:

[0178] The server receives the job details and stores them in the database

[0179] Input: Encoded job details

[0180] Operation: The server verifies the format of the received case details and saves them in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[0181] Output: The case details are saved in the database and a confirmation message is displayed on the company's terminal.

[0182] Step 7:

[0183] The server sends the database information to the AI ​​algorithm.

[0184] Input: Resume, skill sheet, and detailed job information obtained from the database

[0185] How it works: The server sends the user's resume and skill sheet, as well as the company's job details, from the database to an AI algorithm, which then analyzes this information.

[0186] Output: A list of possible matches is generated.

[0187] Step 8:

[0188] The server notifies the list of possible matches

[0189] Input: Generated list of matching candidates

[0190] How it works: The server sends a list of potential matches to businesses and users.

[0191] Output: Notifications of potential matches are displayed on the company and user devices.

[0192] Step 9:

[0193] The server requests the AI ​​model to generate a skill check test.

[0194] Input: Case details information

[0195] Operation: The server requests the generation AI model to generate a skill check test based on the detailed job information. The generation AI model generates the skill check test based on the instructions.

[0196] Output: The generated skill check test is returned to the server.

[0197] Step 10:

[0198] The server sends the generated skill check test to the user.

[0199] Input: Generated skill check test

[0200] Action: The server sends the generated skill check test to the corresponding user.

[0201] Output: The skill check test is displayed on the user's device.

[0202] Step 11:

[0203] The user takes the skill check test and sends the results to the server.

[0204] Input: Skill check test result

[0205] How it works: The user takes a skill check test on their device and sends the results to the server.

[0206] Output: The received skill check test results are saved on the server.

[0207] Step 12:

[0208] The server stores the skill check test results in a database and sends them to the company.

[0209] Input: User skill check test results

[0210] Operation: The server stores the received skill check test results in a database and sends them to the company.

[0211] Output: The skill check test results are displayed on the company's terminal.

[0212] Step 13:

[0213] Users film and upload their own promotional videos

[0214] Input: Self-promotion video file

[0215] How it works: A user shoots a self-promotional video and uploads it from a portal site.

[0216] Output: The device generates a request to send the uploaded video file to the server.

[0217] Step 14:

[0218] The server receives the video and sends it to the AI ​​algorithm.

[0219] Input: Uploaded self-promotion video file

[0220] How it works: A server receives the video and sends it to an AI algorithm for analysis. The AI ​​algorithm analyzes the video and evaluates non-technical aspects.

[0221] Output: The results of the evaluation of non-technical aspects are returned to the server.

[0222] Step 15:

[0223] The server notifies the company of the evaluation results.

[0224] Input: Non-technical aspects evaluation results

[0225] How it works: The server sends the evaluation results to the company.

[0226] Output: The evaluation results are displayed on the company's terminal.

[0227] Step 16:

[0228] The server presents the collected information to the company.

[0229] Input: Resume, skill sheet, skill check test results, evaluation of self-promotion video

[0230] How it works: The server presents all the information it has collected to the company.

[0231] Output: The collected information is displayed on the company's terminal.

[0232] Step 17:

[0233] Companies select the best talent

[0234] Input: All information provided

[0235] How it works: The company selects the best candidate based on the information provided.

[0236] Output: The final selection results of the companies are determined.

[0237] Step 18:

[0238] The server notifies the user of the selection result.

[0239] Input: Final selection results of companies

[0240] Operation: The server notifies the user of the final selection of companies.

[0241] Output: The final selection result is displayed on the user's terminal.

[0242] (Application example 1)

[0243] 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."

[0244] In modern industry, it is extremely important to quickly and efficiently match robot engineers. However, traditional methods often involve manual matching, which is time-consuming and labor-intensive. It is also difficult to accurately evaluate the skills and experience of engineers and select the most suitable candidates. Given this background, there is a need for a system that can properly match robot engineers with end companies and efficiently recruit engineers.

[0245] 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.

[0246] In this invention, the server includes: a means for a user to upload a resume and skill sheet; a means for an end company to input job details; a means for comparing the uploaded resume and skill sheet with the job details using an AI algorithm to generate a list of matching personnel; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details; a means for sending the skill check test to the user and receiving the user's test results; a means for a user to upload a self-promotional video; a means for analyzing the uploaded video using an AI algorithm to evaluate non-technical aspects; a means for sending the evaluation results to the end company; a means for the end company to select the most suitable personnel based on the collected information; a means for notifying the user of the final selection result; a means for matching a robot engineer based on the user's resume and skill sheet with the job details; a means for the end company to evaluate the self-promotional video uploaded by the robot engineer using an AI algorithm and analyze the results based on the job details; and a means for the end company to generate a skill check test optimized for the user based on the job details, thereby enabling fast and efficient matching of robot engineers.

[0247] "User" refers to an individual who uses the system to upload a resume and skill sheet.

[0248] "End companies" refer to companies that use the system to input detailed project information and select the most suitable personnel.

[0249] A "resume" is a document in which a user details their past employment history and work experience.

[0250] A "skill sheet" is a document that contains information about the skills and techniques possessed by a user.

[0251] "Project details" refers to information entered by the end company, such as the skill set required for the project, years of experience, and project duration.

[0252] The "AI algorithm" is an artificial intelligence algorithm that compares and analyzes resumes and skill sheets with detailed project information.

[0253] A "matching candidate list" is a list of users that matches end company projects, generated by an AI algorithm.

[0254] A "skill check test" is a test generated based on detailed case information to evaluate a user's skills.

[0255] A "self-promotion video" is a video that a user shoots to promote themselves and uploads to the system.

[0256] "Non-technical aspects" refer to factors other than technical skills, such as communication skills and teamwork abilities.

[0257] A "working robot" is an automated industrial machine, such as a robot used in a factory.

[0258] "Engineers" refer to experts in charge of the design, operation, and maintenance of work robots.

[0259] This invention is a system for quickly and efficiently matching robot engineers. The system aims to select the most suitable personnel by uploading the resume and skill sheet of a specific engineer and having the end company input detailed information about the project.

[0260] This system uses the following hardware and software:

[0261] Hardware: Servers, user devices (PCs, smartphones), and end-company devices

[0262] Software: Python, Flask, OpenAI API, scikit-learn

[0263] Database: JSON file (simple example)

[0264] Machine learning: scikit-learn (TF-IDF vectorizer and cosine similarity)

[0265] Users log in to the system's portal site and upload their resumes and skill sheets. The terminal then sends the selected files to the server, which then stores them in a database. This data is later analyzed by AI algorithms.

[0266] The end company enters the job details, and the terminal sends the information to the server. The server stores the received job details in a database and returns a receipt confirmation message to the terminal. The server then sends the user's resume and skill sheet, along with the end company's job details, from the database to an AI algorithm, which generates a list of matching candidates.

[0267] The AI ​​algorithm encodes the resume and skill sheet using a TF-IDF vectorizer, compares them with the job details, calculates the cosine similarity, and generates a list of matching candidates by generating the most similar candidates.

[0268] The server notifies the end company and the user of the generated matching candidate list. The end company then requests the AI ​​to generate a skill check test based on the job details, and the skill check test is automatically generated via the OpenAI API. This skill check test includes questions that evaluate specific technical skills.

[0269] The generated skill check test is sent to the user, who takes the test and sends the results to the server, which stores the received skill check test results in a database and sends them to the end company.

[0270] Users can also film a self-promotional video and upload it to the system via their device. The server then sends the video to an AI algorithm that evaluates non-technical aspects (such as communication skills and teamwork abilities). The evaluation results are then sent to the end company.

[0271] Based on all the collected information (work history, skill check results, self-promotion video evaluation), the end company selects the most suitable candidate. The server notifies the end company's selection results to the corresponding user. This enables quick and efficient matching of work robot engineers.

[0272] As a concrete example, consider a factory looking for an engineer for a project to introduce a new robot control system. This engineer needs to have specific programming skills and knowledge of specific robots. The engineer uploads their skill sheet and resume, and the factory enters the details of the project to perform matching. Suitable engineers are found, and they are evaluated through a simple skill check test and a self-promotional video. Finally, the best engineer is selected.

[0273] An example of a prompt is as follows:

[0274] We are looking for a skill check test for a technician required for the installation of a new robot control system at a factory. The details of the job are as follows: programming skills, robot hardware knowledge, project duration 3 months. Knowledge of Python and C++ is especially essential.

[0275] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0276] Step 1:

[0277] The user uploads their resume and skill sheet. The user logs in to the system's portal site using their own device, selects their resume and skill sheet, and clicks the upload button. The input is the user's resume and skill sheet files, and the output is the file sent to the server. The server receives the files and stores them in a database.

[0278] Step 2:

[0279] The end company enters the project details. The end company uses their own terminal to log in to the system's portal site and enters the project details (required skill set, years of experience, project duration, etc.). The input is the project details, and the output is the information sent to the server. The server stores this in a database and returns a receipt confirmation message to the terminal.

[0280] Step 3:

[0281] Matching is performed by comparing resumes and skill sheets with job details. The server retrieves the user's resume and skill sheets and the end company's job details from the database and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The input is the user's resume and skill sheets, and the job details, and the output is a list of matching candidates.

[0282] Step 4:

[0283] The generated list of match candidates is notified. The server notifies the end company and the user of the generated list of match candidates. The input is the list of match candidates, and the output is a notification to the end company and the user.

[0284] Step 5:

[0285] Generate a skill check test. The server requests the generation AI to generate a skill check test based on the case details. The AI ​​generates a skill check test related to the case based on the prompt text. The input is the case details, and the output is the generated skill check test.

[0286] Step 6:

[0287] Send a skill check test and receive the results. The server sends the generated skill check test to the corresponding user. The user uses their terminal to take the received skill check test and sends the results to the server. The input is the skill check test result, and the output is the result sent to the server.

[0288] Step 7:

[0289] The skill check test results are sent to the end company. The server stores the received skill check test results in a database and sends them to the end company. The input is the skill check test results, and the output is a notification to the end company.

[0290] Step 8:

[0291] The user uploads a self-promotional video. The user films the self-promotional video and uploads it to the system via their device. The input is the self-promotional video file, and the output is the video sent to the server. The server receives it and stores it in a database.

[0292] Step 9:

[0293] The self-promotional video is analyzed using an AI algorithm to evaluate non-technical aspects. The server sends the received video to the AI ​​algorithm, which evaluates non-technical aspects (such as communication skills and teamwork skills). The input is the self-promotional video, and the output is the evaluation results.

[0294] Step 10:

[0295] The evaluation results of non-technical aspects are notified to the end company. The server notifies the end company of the evaluation results. The input is the evaluation results, and the output is a notification to the end company.

[0296] Step 11:

[0297] The end company selects the most suitable candidate based on the collected information. The server presents all collected information (work history, skill check results, self-promotion video evaluation) to the end company, who then selects the most suitable candidate. The input is the collected information, and the output is the end company's selection result.

[0298] Step 12:

[0299] The final selection result is notified to the user. The server notifies the corresponding user of the selection result of the end company. The input is the selection result of the end company, and the output is a notification to the user.

[0300] 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.

[0301] This system allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, allowing for a more detailed evaluation of the user's non-technical aspects. The program for implementing this system includes multiple operations consisting of a server, a terminal, and a user.

[0302] Uploading user resumes and skill sheets

[0303] The user logs in to the system's portal site using their own terminal.

[0304] The user selects the resume and skill sheet and clicks the upload button.

[0305] The terminal transmits the selected file to the server.

[0306] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0307] End company inputs project details

[0308] End companies log in to the system's portal site using their own devices.

[0309] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0310] The terminal transmits the input information to the server.

[0311] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0312] Checking resumes and skill sheets against project details

[0313] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0314] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0315] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[0316] Creating and administering simple skill check tests

[0317] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0318] The generation AI automatically generates skill check tests based on the project.

[0319] The server sends the generated skill check test to the corresponding user.

[0320] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0321] The server stores the received skill check test results in a database and sends them to the end company.

[0322] Submission and evaluation of one-minute promotional videos

[0323] Users film a self-promotional video and upload it to the system via their device.

[0324] The device sends the uploaded video to the server.

[0325] The server sends the received video to the AI ​​algorithm.

[0326] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[0327] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0328] Emotion recognition by emotion engine

[0329] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotion video in real time to identify the user's emotional state.

[0330] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[0331] Final selection by end company

[0332] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[0333] The end company selects the most suitable personnel based on the information presented.

[0334] The server notifies the corresponding user of the selection result of the end company.

[0335] Specific examples

[0336] For example, assume that User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system, and enters the job details required by End Company B (e.g., skills in that programming language and more than 5 years of experience). The server sends both pieces of information to the AI ​​algorithm, which finds that User A is suitable for the job.

[0337] Furthermore, since the project from End Company B requires database management skills, the server sends this information to the generation AI, which automatically generates a test consisting of questions related to database management. User A takes the test and receives a pass / fail result.

[0338] Next, User A uploads a self-promotional video, highlighting his or her communication skills and teamwork abilities. The server sends the video to an AI algorithm, which then reports the analysis results to End Company B. At the same time, the emotion engine identifies User A's emotional state, which is then added to the evaluation. End Company B then selects the most suitable candidate based on User A's technical skills, as well as non-technical aspects and emotional state.

[0339] In this way, the present invention eliminates the multi-tiered subcontracting structure and realizes quick and accurate matching between end companies and IT freelancers.

[0340] The processing flow will be explained below.

[0341] Step 1:

[0342] The user logs in to the system's portal site using their own terminal.

[0343] Step 2:

[0344] The user selects the resume and skill sheet and clicks the upload button.

[0345] Step 3:

[0346] The terminal transmits the selected file to the server.

[0347] Step 4:

[0348] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0349] Step 5:

[0350] End companies log in to the system's portal site using their own devices.

[0351] Step 6:

[0352] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0353] Step 7:

[0354] The terminal transmits the input information to the server.

[0355] Step 8:

[0356] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0357] Step 9:

[0358] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0359] Step 10:

[0360] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0361] Step 11:

[0362] The server notifies the end company and the user of the generated matching candidate list.

[0363] Step 12:

[0364] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0365] Step 13:

[0366] The generation AI automatically generates skill check tests based on the project.

[0367] Step 14:

[0368] The server sends the generated skill check test to the corresponding user.

[0369] Step 15:

[0370] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0371] Step 16:

[0372] The server stores the received skill check test results in a database and sends them to the end company.

[0373] Step 17:

[0374] Users film a self-promotional video and upload it to the system via their device.

[0375] Step 18:

[0376] The device sends the uploaded video to the server.

[0377] Step 19:

[0378] The server sends the received video to the AI ​​algorithm.

[0379] Step 20:

[0380] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[0381] Step 21:

[0382] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0383] Step 22:

[0384] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotional video to identify their emotional state.

[0385] Step 23:

[0386] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[0387] Step 24:

[0388] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[0389] Step 25:

[0390] The end company selects the most suitable personnel based on the information presented.

[0391] Step 26:

[0392] The server notifies the corresponding user of the selection result of the end company.

[0393] Step 27:

[0394] The server provides support for facilitating formal contracts between end companies and users, establishing new employment relationships.

[0395] Example 2

[0396] 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."

[0397] Current talent matching systems are based solely on work history and skill sheet information, and are unable to incorporate non-technical aspects or emotional states of candidates into their evaluation. This means that end companies may end up selecting candidates who are technically suitable but lack non-technical abilities. Furthermore, current systems often generate skill check tests manually, creating a fast and efficient matching process is crucial.

[0398] 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.

[0399] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for end companies to input job details; a means for comparing the uploaded resumes and skill sheets with the job details using an AI algorithm to generate a list of matching candidates; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details using a generative AI model; a means for sending the skill check test to the user and receiving the user's test results; a means for sending the received skill check test results to the end company; a means for users to upload self-promotional videos; a means for analyzing the uploaded videos using an AI algorithm to evaluate non-technical aspects; a means for identifying the user's emotional state using an emotion engine while analyzing the self-promotional videos; a means for end companies to select the most suitable personnel based on the collected information; and a means for notifying the user of the final selection results. This enables end companies to select the most suitable personnel based on a comprehensive evaluation that takes into account not only technical skills but also the candidate's non-technical aspects and emotional state. Furthermore, automatic generation of skill check tests using a generative AI model enables fast and efficient matching.

[0400] "User" refers to an individual who uses the system to upload a resume and skill sheet and provide their skills and experience to end companies.

[0401] An "end company" is a company or organization that uses the system to input project details and search for the best person with the required skill set.

[0402] A "resume" is a document in which a user lists his or her work experience, project experience, skills, qualifications, etc.

[0403] A "skill sheet" is a document in which a user provides detailed information about a particular skill, technique, or tool.

[0404] "Project details" refers to information entered by the end company, such as the required skill set, years of experience, and project duration.

[0405] The "AI algorithm" is an artificial intelligence technology that compares resumes and skill sheets with detailed job information to make the best possible match.

[0406] A "matching candidate list" is a list of people who are compatible with a job, generated by an AI algorithm.

[0407] A "generative AI model" is an artificial intelligence technology that automatically generates skill check tests based on specific requirements.

[0408] A "skill check test" is a test generated based on detailed project information to assess specific technical skills.

[0409] A "Self-Promotional Video" is a video that a user shoots and uploads to showcase their strengths, skills, and experience.

[0410] The "Emotion Engine" is a technology that analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state.

[0411] "Non-technical aspects" refer to a user's communication skills, teamwork abilities, and other non-technical attributes.

[0412] "Evaluation results" are information about a user's skills and emotional state obtained as a result of analysis by AI algorithms and emotion engines.

[0413] The "final selection result" is the end company's selection of the most suitable personnel based on all collected information.

[0414] The system for implementing this invention allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. It also incorporates an emotion engine that recognizes the user's emotions, allowing for more detailed evaluation of non-technical aspects.

[0415] Hardware or software used

[0416] 1. Server:

[0417] Uses servers for data processing and database management, such as Amazon Web Services (AWS) EC2 instances and Google Cloud Platform's Compute Engine.

[0418] For the database, database management software such as MongoDB or MySQL is used.

[0419] 2. Terminal:

[0420] Devices such as PCs and tablets used by users and end companies. These devices access the system's portal site using a web browser.

[0421] 3. AI algorithms:

[0422] It uses artificial intelligence technology to generate match candidates and analyze self-promotional videos, using machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.

[0423] 4. Generative AI Models:

[0424] It is used to automatically generate skill check tests based on detailed job information, using language models such as OpenAI's GPT (Generative Pre-trained Transformer) and Google's BERT (Bidirectional Encoder Representations from Transformers).

[0425] 5. Emotion Engine:

[0426] This technology analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state. It can utilize Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[0427] Specific processing of the program

[0428] Uploading user resumes and skill sheets

[0429] A user logs in to the system's portal site and uploads their resume and skill sheet. The terminal sends the selected files to the server, which then stores the received resume and skill sheet in storage and registers the metadata in the database.

[0430] End company inputs project details

[0431] The end company logs in to the system's portal site and enters detailed project information (required skill set, years of experience, project duration, etc.). The terminal sends the entered information to the server, which then stores it in a database.

[0432] Matching by collation

[0433] The server extracts the user's resume and skill sheet and the company's job details and sends them to the AI ​​algorithm. The AI ​​algorithm compares this information and generates a list of matching candidates. The server notifies the company and the user of this list.

[0434] Creating and administering simple skill check tests

[0435] The server generates prompt text based on the detailed project information and sends it to the generative AI model. The generative AI model automatically generates a skill check test, which the server sends to the user. The user takes the test and sends the results to the server. The server stores the results in a database and notifies the end company.

[0436] Submission and evaluation of one-minute promotional videos

[0437] Users film a self-promotional video and upload it to the system via their device. The server receives the video and sends it to an AI algorithm. The AI ​​algorithm analyzes the video, evaluates the user's non-technical aspects, and identifies the user's emotional state using an emotion engine. The server then notifies the end company of the evaluation results.

[0438] Prompt Sentence Examples

[0439] Below are some example prompts for the generative AI model:

[0440] "Generate a skills check test based on the skills required by the end company: database management, years of experience: 5+ years."

[0441] Specific examples

[0442] For example, suppose User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system and enters the detailed job information required by the end company. The server sends both pieces of information to an AI algorithm and finds that User A is the right candidate. Next, the server sends the detailed job information to a generation AI model, which automatically generates a skill check test. User A takes the test and is deemed to have passed. User A then uploads a self-promotional video, which the server analyzes using an AI algorithm and emotion engine and sends the results to the end company. The end company uses this information to select User A as the best candidate.

[0443] In this way, the system of the present invention enables comprehensive talent evaluation that takes into account not only technical skills but also non-technical aspects and emotional states, achieving fast and accurate matching.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Step 1: User logs in.

[0446] A user accesses the system's portal site, enters their user ID and password, and clicks the login button. The server receives the user ID and password as input, collates the authentication information with the database, and if authentication is successful, the user is redirected to their personal page with a success message.

[0447] Step 2: User uploads resume and skill sheet.

[0448] After logging in, the user goes to the page to upload their resume and skill sheet, selects the resume and skill sheet files from the file selection dialog, and clicks the upload button. The selected files are obtained as input, and the terminal sends them to the server as an HTTP POST request. The server saves the received files in storage, registers the metadata in the database, and returns a success confirmation message to the terminal.

[0449] Step 3: The end company logs in.

[0450] The end company accesses the system's portal site, enters their company ID and password, and clicks the login button. The server receives the company ID and password as input, collates the authentication information with the database, and if authentication is successful, the end company is redirected to their personal page with a success message.

[0451] Step 4: The end company enters the project details.

[0452] After logging in, the end company goes to the page for entering project details, enters the required skill set, years of experience, project duration, etc. into the form, and clicks the submit button. The form data containing the required information is received as input, and the terminal sends it to the server as an HTTP POST request. The server saves the received information in a database and returns a success confirmation message to the terminal.

[0453] Step 5: Compare your resume and skills sheet with the job details.

[0454] The server extracts the user's resume and skill sheet, as well as the end company's job details, from the database and sends this to the AI ​​algorithm. Data based on the user and end company information is obtained as input. The server passes this data to the AI ​​algorithm, which processes it and generates a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[0455] Step 6: Generate a quick skills check test.

[0456] The server generates a prompt sentence based on the end company's detailed project information and sends it to the generative AI model. The prompt sentence containing the detailed project information is obtained as input. The generative AI model generates a skill check test based on the prompt sentence and returns the result to the server. The server sends the generated skill check test to the user.

[0457] Step 7: The user takes the skill check test.

[0458] The user takes the skill check test on a specified web page and sends the results to the server after completion. The results of the skill check test answered by the user are received as input. The server receives the results and stores them in a database.

[0459] Step 8: Notify the end company of the skill check test results.

[0460] Based on the skill check test results received by the server, a notification is sent to the end company as an HTTP POST request. The analysis results of the skill check test are obtained as input. The server notifies the end company of the results.

[0461] Step 9: The user uploads a promotional video.

[0462] The user shoots a self-promotional video, accesses the specified upload page, selects the video file, and clicks the upload button. The selected video file is obtained as input. The device sends it to the server as an HTTP POST request. The server saves the received video file in storage.

[0463] Step 10: The AI ​​algorithm analyzes the video.

[0464] The server sends the received video file to the AI ​​algorithm for analysis. The uploaded video file is taken as input. The AI ​​algorithm analyzes the video and identifies the user's emotional state using non-technical aspects and an emotion engine. The analysis results are returned to the server.

[0465] Step 11: Notify the end company of the evaluation results.

[0466] The server notifies the end company's account of the evaluation result based on the analysis result returned by the AI ​​algorithm. The analysis result is obtained as input. The server sends the evaluation result to the end company.

[0467] Step 12: The end company selects the best talent.

[0468] The server presents the collected information to the end company, which then selects the most suitable candidate based on the information presented. Work history, skill check test results, self-promotion video evaluation, and emotional information are obtained as input. The end company selects a candidate on the management screen and clicks the select button. The server notifies the corresponding user of the selection results.

[0469] (Application example 2)

[0470] 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."

[0471] Conventional talent matching systems have difficulty efficiently evaluating not only users' resumes and skill sheets, but also their non-technical aspects and emotional state. Furthermore, there is a need for rapid and precise matching of talent specialized in specific fields, especially security services. This creates challenges for end companies, making it difficult to select the best talent, and it takes a lot of time and money.

[0472] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for users to upload resumes and skill sheets, a means for end companies to input detailed job information, and a means for comparing the uploaded resumes and skill sheets with the detailed job information using an AI algorithm and an emotion recognition engine to generate a list of matching personnel. This makes it possible to efficiently select personnel particularly suited to security services. Furthermore, the ability to evaluate the user's non-technical aspects and emotional state allows end companies to quickly find the best overall personnel.

[0473] "User" refers to an individual who uploads a resume and skill sheet, or a job seeker who provides the information necessary to apply for a job at an end company.

[0474] "End company" refers to a company or organization that inputs detailed project information and selects the most suitable candidate based on the results of a skill check test and the evaluation of a self-promotional video.

[0475] A "resume" refers to a document that describes a user's past work history and experience.

[0476] A "skill sheet" refers to a document that lists the specific skills and techniques that a user possesses.

[0477] "Project details" refers to information entered by the end company, such as the desired skill set, years of experience, and project details.

[0478] "AI algorithm" refers to an artificial intelligence method that compares and analyzes uploaded resumes, skill sheets, and job details to generate optimal matching candidates.

[0479] An "emotion recognition engine" refers to a program or system that analyzes a user's self-promotional video and identifies their emotional state based on their voice and facial expressions.

[0480] "Skill Check Test" refers to a test generated based on the end company's project details to assess a user's specific technical skills.

[0481] "Self-promotional videos" refer to videos that users shoot and upload to promote themselves.

[0482] "Non-technical aspects" refer to aspects of a user's skills other than technical skills, such as communication skills and teamwork abilities.

[0483] "Emotional state" refers to the emotional state of the user that is identified by the emotion recognition engine, such as happiness, sadness, tension, etc.

[0484] This invention is a system that efficiently matches suitable personnel by utilizing AI algorithms and emotion recognition engines, with users uploading resumes and skill sheets and end companies inputting detailed job information. The system includes multiple operations consisting of a server, terminals, and users. Specific embodiments are described below.

[0485] Uploading user resumes and skill sheets

[0486] A user logs in to the system's portal site using their own device and uploads their resume and skill sheet. The device sends the selected files to the server, which then stores the received files in a database. When the upload is complete, the server returns a receipt confirmation message to the device.

[0487] End company inputs project details

[0488] End companies log in to the system's portal site using their own devices and enter detailed information about the job (required skill set, years of experience, working hours, etc.). The device sends the entered information to the server, which then stores the received information in a database.

[0489] Checking resumes and skill sheets against project details

[0490] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[0491] Creating and administering simple skill check tests

[0492] The server requests the generation AI to generate a skill check test based on the detailed project information, and the generation AI automatically generates a skill check test based on the project. The server sends the generated skill check test to the corresponding user, and the results of the user's test are sent to the server. The server stores the received test results in a database and sends them to the end company.

[0493] Submission and evaluation of one-minute promotional videos

[0494] Users film a self-promotional video and upload it to the system via their device. The server then sends the received video to an AI algorithm and emotion recognition engine, which analyzes the video and evaluates the user's non-technical aspects and emotional state. The evaluation results are then sent to the end company via the server.

[0495] Emotion recognition by emotion engine

[0496] The emotion engine analyzes the voice and facial expressions in the user's self-promotion video in real time to identify their emotional state, which is then fed back to the AI ​​algorithm for further evaluation of non-technical aspects.

[0497] Final selection by end company

[0498] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company, which selects the most suitable candidate based on the information presented and notifies the corresponding user of the selection results via the server.

[0499] As a concrete example, consider a security services company recruiting for a "night security guard" and inputting the required skill set (e.g., experience working night shifts, operating surveillance cameras, etc.). The user uploads a resume and a self-promotion video, and the system uses AI algorithms and an emotion recognition engine to select the most suitable candidate. An example of a prompt from a generative AI model is as follows:

[0500] Select candidates with the required skill set, night shift experience, and CCTV camera operation experience for the "Night Security Guard" position. Also, analyze the candidates' self-promotion videos and evaluate their emotional state to recommend the best candidates.

[0501] In this way, the present invention can efficiently match end companies with personnel specialized in security services, thereby reducing time and costs.

[0502] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0503] Step 1:

[0504] The user uploads their resume and skill sheet to the system's portal site using their own device. The file selected by the user as input is sent to the server by the device, and the server stores the received file in the database. This registers the user's skills and career information in the system.

[0505] Step 2:

[0506] End companies use their own devices to log in to the system's portal site and enter detailed information about the job (such as the required skill set, years of experience, and working hours). The device sends the entered information to the server, which then stores it in a database. This allows the end company's requirements to be registered in the system.

[0507] Step 3:

[0508] The server retrieves the user's resume and skill sheet, as well as the end company's job details from the database, and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes the work history and job details as input, generating a list of matching candidates. The generated list of matching candidates is obtained as output.

[0509] Step 4:

[0510] The server notifies the end company and the user of the generated matching candidate list. The server receives the matching candidate list as input and sends it to the end company and the corresponding user. This allows each user and the end company to check the matching results.

[0511] Step 5:

[0512] The server requests the generative AI model to generate a skill check test based on the detailed job information. The detailed job information is input, and the generative AI model automatically generates a skill check test based on that information. The generated skill check test is output.

[0513] Step 6:

[0514] The server sends the generated skill check test to the corresponding user. The user takes the skill check test using a terminal and sends the result to the server. The skill check test result is received as input by the server, which stores it in a database. This allows the user to be evaluated as to whether they possess a specific technical skill.

[0515] Step 7:

[0516] The server sends the skill check test results to the end company. The skill check test results are input, and the server sends them to the end company, allowing the end company to verify the user's technical skills.

[0517] Step 8:

[0518] Users film a self-promotional video and upload it to the system via their device. The device sends the video as input to the server, which then sends it to the AI ​​algorithm and emotion recognition engine, which then prepares the system to evaluate the user's non-technical aspects and emotional state.

[0519] Step 9:

[0520] An AI algorithm and emotion recognition engine analyze uploaded self-promotion videos and evaluate non-technical aspects and emotional states. The input is the self-promotion video, the AI ​​algorithm analyzes the content of the video, and the emotion recognition engine identifies the emotional state based on voice and facial expressions. The output is an evaluation result.

[0521] Step 10:

[0522] The server receives the evaluation results from the AI ​​algorithm and emotion recognition engine and sends them to the end company. The evaluation results are input, and the server sends them to the end company.

[0523] Step 11:

[0524] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company. The collected information is used as input, and the end company selects the most suitable candidate based on that information. This allows the end company to make a comprehensive judgment and select the appropriate candidate.

[0525] Step 12:

[0526] The server notifies the corresponding user of the selection result of the end company. The server receives the selection result of the end company as input and sends it to the user, allowing the user to confirm whether they have been selected.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] [Second embodiment]

[0531] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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."

[0543] The present invention is a system that allows users to upload their resumes and skill sheets, and end companies to input detailed information about the job, efficiently matching the most suitable personnel. The program for realizing this system includes multiple operations consisting of a server, a terminal, and a user.

[0544] Uploading user resumes and skill sheets

[0545] The user logs in to the system's portal site using their own terminal.

[0546] The user selects the resume and skill sheet and clicks the upload button.

[0547] The terminal transmits the selected file to the server.

[0548] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0549] End company inputs project details

[0550] End companies log in to the system's portal site using their own devices.

[0551] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0552] The terminal transmits the input information to the server.

[0553] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0554] Checking resumes and skill sheets against project details

[0555] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0556] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0557] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[0558] Creating and administering simple skill check tests

[0559] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0560] The generation AI automatically generates skill check tests based on the project.

[0561] The server sends the generated skill check test to the corresponding user.

[0562] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0563] The server stores the received skill check test results in a database and sends them to the end company.

[0564] Submission and evaluation of one-minute promotional videos

[0565] Users film a self-promotional video and upload it to the system via their device.

[0566] The device sends the uploaded video to the server.

[0567] The server sends the received video to an AI algorithm that evaluates non-technical aspects.

[0568] The AI ​​algorithm analyzes the video and returns the evaluation results to the server.

[0569] The server notifies the end company of the evaluation results.

[0570] Final selection by end company

[0571] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[0572] The end company selects the most suitable personnel based on the information presented.

[0573] The server notifies the corresponding user of the selection result of the end company.

[0574] This will enable quick and intuitive matching between end companies and IT freelancers, solving the problem of multiple subcontracting and maximizing the value of Japan's IT talent.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] A user uses a terminal to log in to the system's portal site.

[0578] Step 2:

[0579] The user selects the resume and skill sheet and clicks the upload button.

[0580] Step 3:

[0581] The terminal transmits the selected file to the server.

[0582] Step 4:

[0583] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0584] Step 5:

[0585] The end company uses a terminal to log in to the system's portal site.

[0586] Step 6:

[0587] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0588] Step 7:

[0589] The terminal transmits the input information to the server.

[0590] Step 8:

[0591] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0592] Step 9:

[0593] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0594] Step 10:

[0595] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0596] Step 11:

[0597] The server notifies the end company and the user of the generated matching candidate list.

[0598] Step 12:

[0599] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0600] Step 13:

[0601] The generation AI automatically generates skill check tests based on the project.

[0602] Step 14:

[0603] The server sends the generated skill check test to the corresponding user.

[0604] Step 15:

[0605] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0606] Step 16:

[0607] The server stores the received skill check test results in a database and sends them to the end company.

[0608] Step 17:

[0609] Users film a self-promotional video and upload it to the system via their device.

[0610] Step 18:

[0611] The device sends the uploaded video to the server.

[0612] Step 19:

[0613] The server sends the received video to the AI ​​algorithm.

[0614] Step 20:

[0615] AI algorithms analyze the video and evaluate non-technical aspects.

[0616] Step 21:

[0617] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0618] Step 22:

[0619] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[0620] Step 23:

[0621] The end company selects the most suitable personnel based on the information presented.

[0622] Step 24:

[0623] The server notifies the corresponding user of the selection result of the end company.

[0624] Example 1

[0625] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0626] In today's labor market, quickly and efficiently matching companies with the right talent is crucial. However, current systems require a lot of time and effort, making it difficult to effectively select talent. Furthermore, there is a lack of a system for comprehensively evaluating candidates' skills and non-technical aspects beyond simply referencing their resumes and skill sheets, which often results in a mismatch between the talent companies are looking for and the actual candidates. To solve this problem, a comprehensive matching system is needed that evaluates not only technical skills but also non-technical aspects such as communication skills and teamwork abilities.

[0627] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0628] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for companies to input job details; a means for using an AI algorithm to compare the uploaded resumes and skill sheets with the job details and generate a list of matching candidates; a means for notifying companies and users of the generated matching candidate list; a means for requesting an AI model to generate a skill check test based on the job details; a means for sending the skill check test to users and receiving the user's test results; a means for users to upload self-promotional videos; a means for using an AI algorithm to analyze the uploaded videos and evaluate non-technical aspects; a means for sending the evaluation results to companies; a means for companies to select the most suitable candidates based on the collected information; a means for notifying users of the final selection results; and a means for integrating data such as resumes, skill sheets, skill check results, and evaluations of the self-promotional videos. This enables fast and efficient talent matching and minimizes mismatches between companies and job seekers.

[0629] A "user" is an individual or entity that uploads a resume and skill sheet to the system.

[0630] "Company" refers to an organization or legal entity that inputs detailed project information into the system and selects appropriate personnel.

[0631] A "resume" is a document that details a user's work history, experience, and skills.

[0632] A "skill sheet" is a document that details a user's professional skills and qualifications.

[0633] "Project details" refers to data that companies enter into the system, such as the required skill set, years of experience, and project duration.

[0634] An "AI algorithm" is a series of calculation methods and models that allow the system to compare and match resumes, skill sheets, and detailed job information.

[0635] A "generative AI model" is an algorithm and model that automatically generates skill check tests based on detailed project information.

[0636] A "skill check test" is a test that a user takes to assess a specific technical skill.

[0637] A "self-promotion video" is a video that a user shoots and uploads to showcase their abilities and charms.

[0638] "Non-technical aspects" refer to aspects of human characteristics other than technical skills, such as communication skills and teamwork abilities.

[0639] The "optimal candidate" is the user who best meets the company's requirements based on their resume, skill sheet, skill check results, and evaluation of their self-promotional video.

[0640] A "matching candidate list" is a list of users who meet a company's project requirements, generated by an AI algorithm.

[0641] A "server" is a device that centrally manages system operations and processes and stores data such as resumes, skill sheets, detailed project information, test results, and self-promotional videos.

[0642] A "terminal" is a device that allows a user or company to access the system and perform various operations.

[0643] This invention is a system that allows users to upload their resumes and skill sheets, and companies to input detailed information about the job, efficiently matching the most suitable personnel. To specifically implement this system, the following steps and configuration are required.

[0644] First, the user logs in to the system's portal site using their own device (PC, smartphone, etc.). The user selects their resume and skill sheet and clicks the upload button. At this time, the device encodes the selected files and sends them to the server. The server verifies the validity of the format of the received files and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[0645] Next, the company's employee logs in to the system's portal site using their own device. The employee enters project details such as the skill set required for the project, years of experience, and project duration. The device encodes the entered information and sends it to the server. The server verifies the validity of the format of the received project details and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[0646] Next, the server sends the user's resume and skill sheet, along with the company's job details, from the database to an AI algorithm (e.g., a natural language processing model or machine learning algorithm). The AI ​​algorithm compares and analyzes this information and generates a list of candidates who match. The server then notifies the company and the user of the generated list of candidates.

[0647] If a skill check test is required, the server requests a generative AI model (e.g., OpenAI GPT-4 or Google BERT) to generate a skill check test based on the job details. The generative AI model automatically generates a skill check test based on the job requirements. The server sends the generated skill check test to the corresponding user. The user takes the skill check test on their own device and sends the results to the server. The server stores the received test results in a database and sends them to the company.

[0648] Users can also film a self-promotional video and upload it to the system via their device. The device then sends the uploaded video file to the server. The server then requests an AI algorithm to analyze the received video. The AI ​​algorithm analyzes the video and evaluates non-technical aspects (e.g., communication skills, teamwork ability, etc.). The server then notifies the company of the evaluation results.

[0649] Finally, the server presents all collected information (resumes, skill sheets, skill check test results, evaluations of self-promotional videos, etc.) to companies, helping them select the most suitable candidates. Once the company's selection results have been determined, the server notifies the user of the results.

[0650] Here are some examples of prompts:

[0651] Please upload your resume and skills sheet.

[0652] Enter project details, including required skills, years of experience, and project duration.

[0653] Generate a Python skills test based on the following criteria: writing functions, analyzing data, using basic libraries, etc.

[0654] Rate the uploaded 1-minute promotional video and provide feedback on non-technical aspects.

[0655] In this way, the server, terminal, and user each have a clear role within the system, and they can work together to efficiently match personnel.

[0656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0657] Step 1:

[0658] A user logs in to the portal site

[0659] Input: Portal site URL, user name, password

[0660] Action: A user accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[0661] Output: The user is shown a successful login message and is redirected to the main page.

[0662] Step 2:

[0663] Users upload their resumes and skill sheets

[0664] Input: Resume file, Skill sheet file

[0665] How it works: The user selects the resume and skill sheet files on their device and clicks the upload button.

[0666] Output: The device encodes the selected file and generates a request to send it to the server.

[0667] Step 3:

[0668] The server receives the file and stores it in the database

[0669] Input: Encoded resume file, skill sheet file

[0670] Operation: The server verifies the format of the received file and saves it in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[0671] Output: The resume and skill sheet are saved in the database and a confirmation message is displayed on the user's terminal.

[0672] Step 4:

[0673] A company representative logs in to the portal site

[0674] Input: Portal site URL, user name, password

[0675] How it works: A company employee accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[0676] Output: Company representative will see a login success message and be redirected to the main page.

[0677] Step 5:

[0678] Company personnel enter detailed project information

[0679] Input: Details of the job, such as required skill set, years of experience, and project duration

[0680] Operation: A company representative enters the details of the case and clicks the send button.

[0681] Output: The terminal generates a request to encode the entered job details information and send it to the server.

[0682] Step 6:

[0683] The server receives the job details and stores them in the database

[0684] Input: Encoded job details

[0685] Operation: The server verifies the format of the received case details and saves them in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[0686] Output: The case details are saved in the database and a confirmation message is displayed on the company's terminal.

[0687] Step 7:

[0688] The server sends the database information to the AI ​​algorithm.

[0689] Input: Resume, skill sheet, and detailed job information obtained from the database

[0690] How it works: The server sends the user's resume and skill sheet, as well as the company's job details, from the database to an AI algorithm, which then analyzes this information.

[0691] Output: A list of possible matches is generated.

[0692] Step 8:

[0693] The server notifies the list of possible matches

[0694] Input: Generated list of matching candidates

[0695] How it works: The server sends a list of potential matches to businesses and users.

[0696] Output: Notifications of potential matches are displayed on the company and user devices.

[0697] Step 9:

[0698] The server requests the AI ​​model to generate a skill check test.

[0699] Input: Case details information

[0700] Operation: The server requests the generation AI model to generate a skill check test based on the detailed job information. The generation AI model generates the skill check test based on the instructions.

[0701] Output: The generated skill check test is returned to the server.

[0702] Step 10:

[0703] The server sends the generated skill check test to the user.

[0704] Input: Generated skill check test

[0705] Action: The server sends the generated skill check test to the corresponding user.

[0706] Output: The skill check test is displayed on the user's device.

[0707] Step 11:

[0708] The user takes the skill check test and sends the results to the server.

[0709] Input: Skill check test result

[0710] How it works: The user takes a skill check test on their device and sends the results to the server.

[0711] Output: The received skill check test results are saved on the server.

[0712] Step 12:

[0713] The server stores the skill check test results in a database and sends them to the company.

[0714] Input: User skill check test results

[0715] Operation: The server stores the received skill check test results in a database and sends them to the company.

[0716] Output: The skill check test results are displayed on the company's terminal.

[0717] Step 13:

[0718] Users film and upload their own promotional videos

[0719] Input: Self-promotion video file

[0720] How it works: A user shoots a self-promotional video and uploads it from a portal site.

[0721] Output: The device generates a request to send the uploaded video file to the server.

[0722] Step 14:

[0723] The server receives the video and sends it to the AI ​​algorithm.

[0724] Input: Uploaded self-promotion video file

[0725] How it works: A server receives the video and sends it to an AI algorithm for analysis. The AI ​​algorithm analyzes the video and evaluates non-technical aspects.

[0726] Output: The results of the evaluation of non-technical aspects are returned to the server.

[0727] Step 15:

[0728] The server notifies the company of the evaluation results.

[0729] Input: Non-technical aspects evaluation results

[0730] How it works: The server sends the evaluation results to the company.

[0731] Output: The evaluation results are displayed on the company's terminal.

[0732] Step 16:

[0733] The server presents the collected information to the company.

[0734] Input: Resume, skill sheet, skill check test results, evaluation of self-promotion video

[0735] How it works: The server presents all the information it has collected to the company.

[0736] Output: The collected information is displayed on the company's terminal.

[0737] Step 17:

[0738] Companies select the best talent

[0739] Input: All information provided

[0740] How it works: The company selects the best candidate based on the information provided.

[0741] Output: The final selection results of the companies are determined.

[0742] Step 18:

[0743] The server notifies the user of the selection result.

[0744] Input: Final selection results of companies

[0745] Operation: The server notifies the user of the final selection of companies.

[0746] Output: The final selection result is displayed on the user's terminal.

[0747] (Application example 1)

[0748] 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."

[0749] In modern industry, it is extremely important to quickly and efficiently match robot engineers. However, traditional methods often involve manual matching, which is time-consuming and labor-intensive. It is also difficult to accurately evaluate the skills and experience of engineers and select the most suitable candidates. Given this background, there is a need for a system that can properly match robot engineers with end companies and efficiently recruit engineers.

[0750] 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.

[0751] In this invention, the server includes: a means for a user to upload a resume and skill sheet; a means for an end company to input job details; a means for comparing the uploaded resume and skill sheet with the job details using an AI algorithm to generate a list of matching personnel; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details; a means for sending the skill check test to the user and receiving the user's test results; a means for a user to upload a self-promotional video; a means for analyzing the uploaded video using an AI algorithm to evaluate non-technical aspects; a means for sending the evaluation results to the end company; a means for the end company to select the most suitable personnel based on the collected information; a means for notifying the user of the final selection result; a means for matching a robot engineer based on the user's resume and skill sheet with the job details; a means for the end company to evaluate the self-promotional video uploaded by the robot engineer using an AI algorithm and analyze the results based on the job details; and a means for the end company to generate a skill check test optimized for the user based on the job details, thereby enabling fast and efficient matching of robot engineers.

[0752] "User" refers to an individual who uses the system to upload a resume and skill sheet.

[0753] "End companies" refer to companies that use the system to input detailed project information and select the most suitable personnel.

[0754] A "resume" is a document in which a user details their past employment history and work experience.

[0755] A "skill sheet" is a document that contains information about the skills and techniques possessed by a user.

[0756] "Project details" refers to information entered by the end company, such as the skill set required for the project, years of experience, and project duration.

[0757] The "AI algorithm" is an artificial intelligence algorithm that compares and analyzes resumes and skill sheets with detailed project information.

[0758] A "matching candidate list" is a list of users that matches end company projects, generated by an AI algorithm.

[0759] A "skill check test" is a test generated based on detailed case information to evaluate a user's skills.

[0760] A "self-promotion video" is a video that a user shoots to promote themselves and uploads to the system.

[0761] "Non-technical aspects" refer to factors other than technical skills, such as communication skills and teamwork abilities.

[0762] A "working robot" is an automated industrial machine, such as a robot used in a factory.

[0763] "Engineers" refer to experts in charge of the design, operation, and maintenance of work robots.

[0764] This invention is a system for quickly and efficiently matching robot engineers. The system aims to select the most suitable personnel by uploading the resume and skill sheet of a specific engineer and having the end company input detailed information about the project.

[0765] This system uses the following hardware and software:

[0766] Hardware: Servers, user devices (PCs, smartphones), and end-company devices

[0767] Software: Python, Flask, OpenAI API, scikit-learn

[0768] Database: JSON file (simple example)

[0769] Machine learning: scikit-learn (TF-IDF vectorizer and cosine similarity)

[0770] Users log in to the system's portal site and upload their resumes and skill sheets. The terminal then sends the selected files to the server, which then stores them in a database. This data is later analyzed by AI algorithms.

[0771] The end company enters the job details, and the terminal sends the information to the server. The server stores the received job details in a database and returns a receipt confirmation message to the terminal. The server then sends the user's resume and skill sheet, along with the end company's job details, from the database to an AI algorithm, which generates a list of matching candidates.

[0772] The AI ​​algorithm encodes the resume and skill sheet using a TF-IDF vectorizer, compares them with the job details, calculates the cosine similarity, and generates a list of matching candidates by generating the most similar candidates.

[0773] The server notifies the end company and the user of the generated matching candidate list. The end company then requests the AI ​​to generate a skill check test based on the job details, and the skill check test is automatically generated via the OpenAI API. This skill check test includes questions that evaluate specific technical skills.

[0774] The generated skill check test is sent to the user, who takes the test and sends the results to the server, which stores the received skill check test results in a database and sends them to the end company.

[0775] Users can also film a self-promotional video and upload it to the system via their device. The server then sends the video to an AI algorithm that evaluates non-technical aspects (such as communication skills and teamwork abilities). The evaluation results are then sent to the end company.

[0776] Based on all the collected information (work history, skill check results, self-promotion video evaluation), the end company selects the most suitable candidate. The server notifies the end company's selection results to the corresponding user. This enables quick and efficient matching of work robot engineers.

[0777] As a concrete example, consider a factory looking for an engineer for a project to introduce a new robot control system. This engineer needs to have specific programming skills and knowledge of specific robots. The engineer uploads their skill sheet and resume, and the factory enters the details of the project to perform matching. Suitable engineers are found, and they are evaluated through a simple skill check test and a self-promotional video. Finally, the best engineer is selected.

[0778] An example of a prompt is as follows:

[0779] We are looking for a skill check test for a technician required for the installation of a new robot control system at a factory. The details of the job are as follows: programming skills, robot hardware knowledge, project duration 3 months. Knowledge of Python and C++ is especially essential.

[0780] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0781] Step 1:

[0782] The user uploads their resume and skill sheet. The user logs in to the system's portal site using their own device, selects their resume and skill sheet, and clicks the upload button. The input is the user's resume and skill sheet files, and the output is the file sent to the server. The server receives the files and stores them in a database.

[0783] Step 2:

[0784] The end company enters the project details. The end company uses their own terminal to log in to the system's portal site and enters the project details (required skill set, years of experience, project duration, etc.). The input is the project details, and the output is the information sent to the server. The server stores this in a database and returns a receipt confirmation message to the terminal.

[0785] Step 3:

[0786] Matching is performed by comparing resumes and skill sheets with job details. The server retrieves the user's resume and skill sheets and the end company's job details from the database and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The input is the user's resume and skill sheets, and the job details, and the output is a list of matching candidates.

[0787] Step 4:

[0788] The generated list of match candidates is notified. The server notifies the end company and the user of the generated list of match candidates. The input is the list of match candidates, and the output is a notification to the end company and the user.

[0789] Step 5:

[0790] Generate a skill check test. The server requests the generation AI to generate a skill check test based on the case details. The AI ​​generates a skill check test related to the case based on the prompt text. The input is the case details, and the output is the generated skill check test.

[0791] Step 6:

[0792] Send a skill check test and receive the results. The server sends the generated skill check test to the corresponding user. The user uses their terminal to take the received skill check test and sends the results to the server. The input is the skill check test result, and the output is the result sent to the server.

[0793] Step 7:

[0794] The skill check test results are sent to the end company. The server stores the received skill check test results in a database and sends them to the end company. The input is the skill check test results, and the output is a notification to the end company.

[0795] Step 8:

[0796] The user uploads a self-promotional video. The user films the self-promotional video and uploads it to the system via their device. The input is the self-promotional video file, and the output is the video sent to the server. The server receives it and stores it in a database.

[0797] Step 9:

[0798] The self-promotional video is analyzed using an AI algorithm to evaluate non-technical aspects. The server sends the received video to the AI ​​algorithm, which evaluates non-technical aspects (such as communication skills and teamwork skills). The input is the self-promotional video, and the output is the evaluation results.

[0799] Step 10:

[0800] The evaluation results of non-technical aspects are notified to the end company. The server notifies the end company of the evaluation results. The input is the evaluation results, and the output is a notification to the end company.

[0801] Step 11:

[0802] The end company selects the most suitable candidate based on the collected information. The server presents all collected information (work history, skill check results, self-promotion video evaluation) to the end company, who then selects the most suitable candidate. The input is the collected information, and the output is the end company's selection result.

[0803] Step 12:

[0804] The final selection result is notified to the user. The server notifies the corresponding user of the selection result of the end company. The input is the selection result of the end company, and the output is a notification to the user.

[0805] 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.

[0806] This system allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, allowing for a more detailed evaluation of the user's non-technical aspects. The program for implementing this system includes multiple operations consisting of a server, a terminal, and a user.

[0807] Uploading user resumes and skill sheets

[0808] The user logs in to the system's portal site using their own terminal.

[0809] The user selects the resume and skill sheet and clicks the upload button.

[0810] The terminal transmits the selected file to the server.

[0811] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0812] End company inputs project details

[0813] End companies log in to the system's portal site using their own devices.

[0814] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0815] The terminal transmits the input information to the server.

[0816] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0817] Checking resumes and skill sheets against project details

[0818] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0819] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0820] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[0821] Creating and administering simple skill check tests

[0822] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0823] The generation AI automatically generates skill check tests based on the project.

[0824] The server sends the generated skill check test to the corresponding user.

[0825] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0826] The server stores the received skill check test results in a database and sends them to the end company.

[0827] Submission and evaluation of one-minute promotional videos

[0828] Users film a self-promotional video and upload it to the system via their device.

[0829] The device sends the uploaded video to the server.

[0830] The server sends the received video to the AI ​​algorithm.

[0831] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[0832] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0833] Emotion recognition by emotion engine

[0834] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotion video in real time to identify the user's emotional state.

[0835] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[0836] Final selection by end company

[0837] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[0838] The end company selects the most suitable personnel based on the information presented.

[0839] The server notifies the corresponding user of the selection result of the end company.

[0840] Specific examples

[0841] For example, assume that User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system, and enters the job details required by End Company B (e.g., skills in that programming language and more than 5 years of experience). The server sends both pieces of information to the AI ​​algorithm, which finds that User A is suitable for the job.

[0842] Furthermore, since the project from End Company B requires database management skills, the server sends this information to the generation AI, which automatically generates a test consisting of questions related to database management. User A takes the test and receives a pass / fail result.

[0843] Next, User A uploads a self-promotional video, highlighting his or her communication skills and teamwork abilities. The server sends the video to an AI algorithm, which then reports the analysis results to End Company B. At the same time, the emotion engine identifies User A's emotional state, which is then added to the evaluation. End Company B then selects the most suitable candidate based on User A's technical skills, as well as non-technical aspects and emotional state.

[0844] In this way, the present invention eliminates the multi-tiered subcontracting structure and realizes quick and accurate matching between end companies and IT freelancers.

[0845] The processing flow will be explained below.

[0846] Step 1:

[0847] The user logs in to the system's portal site using their own terminal.

[0848] Step 2:

[0849] The user selects the resume and skill sheet and clicks the upload button.

[0850] Step 3:

[0851] The terminal transmits the selected file to the server.

[0852] Step 4:

[0853] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[0854] Step 5:

[0855] End companies log in to the system's portal site using their own devices.

[0856] Step 6:

[0857] The end company enters project details (required skill set, years of experience, project duration, etc.).

[0858] Step 7:

[0859] The terminal transmits the input information to the server.

[0860] Step 8:

[0861] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[0862] Step 9:

[0863] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[0864] Step 10:

[0865] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[0866] Step 11:

[0867] The server notifies the end company and the user of the generated matching candidate list.

[0868] Step 12:

[0869] The server requests the generation AI to generate a skill check test based on the detailed project information.

[0870] Step 13:

[0871] The generation AI automatically generates skill check tests based on the project.

[0872] Step 14:

[0873] The server sends the generated skill check test to the corresponding user.

[0874] Step 15:

[0875] The user uses the terminal to take the received skill check test and transmits the result to the server.

[0876] Step 16:

[0877] The server stores the received skill check test results in a database and sends them to the end company.

[0878] Step 17:

[0879] Users film a self-promotional video and upload it to the system via their device.

[0880] Step 18:

[0881] The device sends the uploaded video to the server.

[0882] Step 19:

[0883] The server sends the received video to the AI ​​algorithm.

[0884] Step 20:

[0885] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[0886] Step 21:

[0887] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[0888] Step 22:

[0889] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotional video to identify their emotional state.

[0890] Step 23:

[0891] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[0892] Step 24:

[0893] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[0894] Step 25:

[0895] The end company selects the most suitable personnel based on the information presented.

[0896] Step 26:

[0897] The server notifies the corresponding user of the selection result of the end company.

[0898] Step 27:

[0899] The server provides support for facilitating formal contracts between end companies and users, establishing new employment relationships.

[0900] Example 2

[0901] 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."

[0902] Current talent matching systems are based solely on work history and skill sheet information, and are unable to incorporate non-technical aspects or emotional states of candidates into their evaluation. This means that end companies may end up selecting candidates who are technically suitable but lack non-technical abilities. Furthermore, current systems often generate skill check tests manually, creating a fast and efficient matching process is crucial.

[0903] 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.

[0904] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for end companies to input job details; a means for comparing the uploaded resumes and skill sheets with the job details using an AI algorithm to generate a list of matching candidates; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details using a generative AI model; a means for sending the skill check test to the user and receiving the user's test results; a means for sending the received skill check test results to the end company; a means for users to upload self-promotional videos; a means for analyzing the uploaded videos using an AI algorithm to evaluate non-technical aspects; a means for identifying the user's emotional state using an emotion engine while analyzing the self-promotional videos; a means for end companies to select the most suitable personnel based on the collected information; and a means for notifying the user of the final selection results. This enables end companies to select the most suitable personnel based on a comprehensive evaluation that takes into account not only technical skills but also the candidate's non-technical aspects and emotional state. Furthermore, automatic generation of skill check tests using a generative AI model enables fast and efficient matching.

[0905] "User" refers to an individual who uses the system to upload a resume and skill sheet and provide their skills and experience to end companies.

[0906] An "end company" is a company or organization that uses the system to input project details and search for the best person with the required skill set.

[0907] A "resume" is a document in which a user lists his or her work experience, project experience, skills, qualifications, etc.

[0908] A "skill sheet" is a document in which a user provides detailed information about a particular skill, technique, or tool.

[0909] "Project details" refers to information entered by the end company, such as the required skill set, years of experience, and project duration.

[0910] The "AI algorithm" is an artificial intelligence technology that compares resumes and skill sheets with detailed job information to make the best possible match.

[0911] A "matching candidate list" is a list of people who are compatible with a job, generated by an AI algorithm.

[0912] A "generative AI model" is an artificial intelligence technology that automatically generates skill check tests based on specific requirements.

[0913] A "skill check test" is a test generated based on detailed project information to assess specific technical skills.

[0914] A "Self-Promotional Video" is a video that a user shoots and uploads to showcase their strengths, skills, and experience.

[0915] The "Emotion Engine" is a technology that analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state.

[0916] "Non-technical aspects" refer to a user's communication skills, teamwork abilities, and other non-technical attributes.

[0917] "Evaluation results" are information about a user's skills and emotional state obtained as a result of analysis by AI algorithms and emotion engines.

[0918] The "final selection result" is the end company's selection of the most suitable personnel based on all collected information.

[0919] The system for implementing this invention allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. It also incorporates an emotion engine that recognizes the user's emotions, allowing for more detailed evaluation of non-technical aspects.

[0920] Hardware or software used

[0921] 1. Server:

[0922] Uses servers for data processing and database management, such as Amazon Web Services (AWS) EC2 instances and Google Cloud Platform's Compute Engine.

[0923] For the database, database management software such as MongoDB or MySQL is used.

[0924] 2. Terminal:

[0925] Devices such as PCs and tablets used by users and end companies. These devices access the system's portal site using a web browser.

[0926] 3. AI algorithms:

[0927] It uses artificial intelligence technology to generate match candidates and analyze self-promotional videos, using machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.

[0928] 4. Generative AI Models:

[0929] It is used to automatically generate skill check tests based on detailed job information, using language models such as OpenAI's GPT (Generative Pre-trained Transformer) and Google's BERT (Bidirectional Encoder Representations from Transformers).

[0930] 5. Emotion Engine:

[0931] This technology analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state. It can utilize Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[0932] Specific processing of the program

[0933] Uploading user resumes and skill sheets

[0934] A user logs in to the system's portal site and uploads their resume and skill sheet. The terminal sends the selected files to the server, which then stores the received resume and skill sheet in storage and registers the metadata in the database.

[0935] End company inputs project details

[0936] The end company logs in to the system's portal site and enters detailed project information (required skill set, years of experience, project duration, etc.). The terminal sends the entered information to the server, which then stores it in a database.

[0937] Matching by collation

[0938] The server extracts the user's resume and skill sheet and the company's job details and sends them to the AI ​​algorithm. The AI ​​algorithm compares this information and generates a list of matching candidates. The server notifies the company and the user of this list.

[0939] Creating and administering simple skill check tests

[0940] The server generates prompt text based on the detailed project information and sends it to the generative AI model. The generative AI model automatically generates a skill check test, which the server sends to the user. The user takes the test and sends the results to the server. The server stores the results in a database and notifies the end company.

[0941] Submission and evaluation of one-minute promotional videos

[0942] Users film a self-promotional video and upload it to the system via their device. The server receives the video and sends it to an AI algorithm. The AI ​​algorithm analyzes the video, evaluates the user's non-technical aspects, and identifies the user's emotional state using an emotion engine. The server then notifies the end company of the evaluation results.

[0943] Prompt Sentence Examples

[0944] Below are some example prompts for the generative AI model:

[0945] "Generate a skills check test based on the skills required by the end company: database management, years of experience: 5+ years."

[0946] Specific examples

[0947] For example, suppose User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system and enters the detailed job information required by the end company. The server sends both pieces of information to an AI algorithm and finds that User A is the right candidate. Next, the server sends the detailed job information to a generation AI model, which automatically generates a skill check test. User A takes the test and is deemed to have passed. User A then uploads a self-promotional video, which the server analyzes using an AI algorithm and emotion engine and sends the results to the end company. The end company uses this information to select User A as the best candidate.

[0948] In this way, the system of the present invention enables comprehensive talent evaluation that takes into account not only technical skills but also non-technical aspects and emotional states, achieving fast and accurate matching.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1: User logs in.

[0951] A user accesses the system's portal site, enters their user ID and password, and clicks the login button. The server receives the user ID and password as input, collates the authentication information with the database, and if authentication is successful, the user is redirected to their personal page with a success message.

[0952] Step 2: User uploads resume and skill sheet.

[0953] After logging in, the user goes to the page to upload their resume and skill sheet, selects the resume and skill sheet files from the file selection dialog, and clicks the upload button. The selected files are obtained as input, and the terminal sends them to the server as an HTTP POST request. The server saves the received files in storage, registers the metadata in the database, and returns a success confirmation message to the terminal.

[0954] Step 3: The end company logs in.

[0955] The end company accesses the system's portal site, enters their company ID and password, and clicks the login button. The server receives the company ID and password as input, collates the authentication information with the database, and if authentication is successful, the end company is redirected to their personal page with a success message.

[0956] Step 4: The end company enters the project details.

[0957] After logging in, the end company goes to the page for entering project details, enters the required skill set, years of experience, project duration, etc. into the form, and clicks the submit button. The form data containing the required information is received as input, and the terminal sends it to the server as an HTTP POST request. The server saves the received information in a database and returns a success confirmation message to the terminal.

[0958] Step 5: Compare your resume and skills sheet with the job details.

[0959] The server extracts the user's resume and skill sheet, as well as the end company's job details, from the database and sends this to the AI ​​algorithm. Data based on the user and end company information is obtained as input. The server passes this data to the AI ​​algorithm, which processes it and generates a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[0960] Step 6: Generate a quick skills check test.

[0961] The server generates a prompt sentence based on the end company's detailed project information and sends it to the generative AI model. The prompt sentence containing the detailed project information is obtained as input. The generative AI model generates a skill check test based on the prompt sentence and returns the result to the server. The server sends the generated skill check test to the user.

[0962] Step 7: The user takes the skill check test.

[0963] The user takes the skill check test on a specified web page and sends the results to the server after completion. The results of the skill check test answered by the user are received as input. The server receives the results and stores them in a database.

[0964] Step 8: Notify the end company of the skill check test results.

[0965] Based on the skill check test results received by the server, a notification is sent to the end company as an HTTP POST request. The analysis results of the skill check test are obtained as input. The server notifies the end company of the results.

[0966] Step 9: The user uploads a promotional video.

[0967] The user shoots a self-promotional video, accesses the specified upload page, selects the video file, and clicks the upload button. The selected video file is obtained as input. The device sends it to the server as an HTTP POST request. The server saves the received video file in storage.

[0968] Step 10: The AI ​​algorithm analyzes the video.

[0969] The server sends the received video file to the AI ​​algorithm for analysis. The uploaded video file is taken as input. The AI ​​algorithm analyzes the video and identifies the user's emotional state using non-technical aspects and an emotion engine. The analysis results are returned to the server.

[0970] Step 11: Notify the end company of the evaluation results.

[0971] The server notifies the end company's account of the evaluation result based on the analysis result returned by the AI ​​algorithm. The analysis result is obtained as input. The server sends the evaluation result to the end company.

[0972] Step 12: The end company selects the best talent.

[0973] The server presents the collected information to the end company, which then selects the most suitable candidate based on the information presented. Work history, skill check test results, self-promotion video evaluation, and emotional information are obtained as input. The end company selects a candidate on the management screen and clicks the select button. The server notifies the corresponding user of the selection results.

[0974] (Application example 2)

[0975] 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."

[0976] Conventional talent matching systems have difficulty efficiently evaluating not only users' resumes and skill sheets, but also their non-technical aspects and emotional state. Furthermore, there is a need for rapid and precise matching of talent specialized in specific fields, especially security services. This creates challenges for end companies, making it difficult to select the best talent, and it takes a lot of time and money.

[0977] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for users to upload resumes and skill sheets, a means for end companies to input detailed job information, and a means for comparing the uploaded resumes and skill sheets with the detailed job information using an AI algorithm and an emotion recognition engine to generate a list of matching personnel. This makes it possible to efficiently select personnel particularly suited to security services. Furthermore, the ability to evaluate the user's non-technical aspects and emotional state allows end companies to quickly find the best overall personnel.

[0978] "User" refers to an individual who uploads a resume and skill sheet, or a job seeker who provides the information necessary to apply for a job at an end company.

[0979] "End company" refers to a company or organization that inputs detailed project information and selects the most suitable candidate based on the results of a skill check test and the evaluation of a self-promotional video.

[0980] A "resume" refers to a document that describes a user's past work history and experience.

[0981] A "skill sheet" refers to a document that lists the specific skills and techniques that a user possesses.

[0982] "Project details" refers to information entered by the end company, such as the desired skill set, years of experience, and project details.

[0983] "AI algorithm" refers to an artificial intelligence method that compares and analyzes uploaded resumes, skill sheets, and job details to generate optimal matching candidates.

[0984] An "emotion recognition engine" refers to a program or system that analyzes a user's self-promotional video and identifies their emotional state based on their voice and facial expressions.

[0985] "Skill Check Test" refers to a test generated based on the end company's project details to assess a user's specific technical skills.

[0986] "Self-promotional videos" refer to videos that users shoot and upload to promote themselves.

[0987] "Non-technical aspects" refer to aspects of a user's skills other than technical skills, such as communication skills and teamwork abilities.

[0988] "Emotional state" refers to the emotional state of the user that is identified by the emotion recognition engine, such as happiness, sadness, tension, etc.

[0989] This invention is a system that efficiently matches suitable personnel by utilizing AI algorithms and emotion recognition engines, with users uploading resumes and skill sheets and end companies inputting detailed job information. The system includes multiple operations consisting of a server, terminals, and users. Specific embodiments are described below.

[0990] Uploading user resumes and skill sheets

[0991] A user logs in to the system's portal site using their own device and uploads their resume and skill sheet. The device sends the selected files to the server, which then stores the received files in a database. When the upload is complete, the server returns a receipt confirmation message to the device.

[0992] End company inputs project details

[0993] End companies log in to the system's portal site using their own devices and enter detailed information about the job (required skill set, years of experience, working hours, etc.). The device sends the entered information to the server, which then stores the received information in a database.

[0994] Checking resumes and skill sheets against project details

[0995] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[0996] Creating and administering simple skill check tests

[0997] The server requests the generation AI to generate a skill check test based on the detailed project information, and the generation AI automatically generates a skill check test based on the project. The server sends the generated skill check test to the corresponding user, and the results of the user's test are sent to the server. The server stores the received test results in a database and sends them to the end company.

[0998] Submission and evaluation of one-minute promotional videos

[0999] Users film a self-promotional video and upload it to the system via their device. The server then sends the received video to an AI algorithm and emotion recognition engine, which analyzes the video and evaluates the user's non-technical aspects and emotional state. The evaluation results are then sent to the end company via the server.

[1000] Emotion recognition by emotion engine

[1001] The emotion engine analyzes the voice and facial expressions in the user's self-promotion video in real time to identify their emotional state, which is then fed back to the AI ​​algorithm for further evaluation of non-technical aspects.

[1002] Final selection by end company

[1003] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company, which selects the most suitable candidate based on the information presented and notifies the corresponding user of the selection results via the server.

[1004] As a concrete example, consider a security services company recruiting for a "night security guard" and inputting the required skill set (e.g., experience working night shifts, operating surveillance cameras, etc.). The user uploads a resume and a self-promotion video, and the system uses AI algorithms and an emotion recognition engine to select the most suitable candidate. An example of a prompt from a generative AI model is as follows:

[1005] Select candidates with the required skill set, night shift experience, and CCTV camera operation experience for the "Night Security Guard" position. Also, analyze the candidates' self-promotion videos and evaluate their emotional state to recommend the best candidates.

[1006] In this way, the present invention can efficiently match end companies with personnel specialized in security services, thereby reducing time and costs.

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

[1008] Step 1:

[1009] The user uploads their resume and skill sheet to the system's portal site using their own device. The file selected by the user as input is sent to the server by the device, and the server stores the received file in the database. This registers the user's skills and career information in the system.

[1010] Step 2:

[1011] End companies use their own devices to log in to the system's portal site and enter detailed information about the job (such as the required skill set, years of experience, and working hours). The device sends the entered information to the server, which then stores it in a database. This allows the end company's requirements to be registered in the system.

[1012] Step 3:

[1013] The server retrieves the user's resume and skill sheet, as well as the end company's job details from the database, and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes the work history and job details as input, generating a list of matching candidates. The generated list of matching candidates is obtained as output.

[1014] Step 4:

[1015] The server notifies the end company and the user of the generated matching candidate list. The server receives the matching candidate list as input and sends it to the end company and the corresponding user. This allows each user and the end company to check the matching results.

[1016] Step 5:

[1017] The server requests the generative AI model to generate a skill check test based on the detailed job information. The detailed job information is input, and the generative AI model automatically generates a skill check test based on that information. The generated skill check test is output.

[1018] Step 6:

[1019] The server sends the generated skill check test to the corresponding user. The user takes the skill check test using a terminal and sends the result to the server. The skill check test result is received as input by the server, which stores it in a database. This allows the user to be evaluated as to whether they possess a specific technical skill.

[1020] Step 7:

[1021] The server sends the skill check test results to the end company. The skill check test results are input, and the server sends them to the end company, allowing the end company to verify the user's technical skills.

[1022] Step 8:

[1023] Users film a self-promotional video and upload it to the system via their device. The device sends the video as input to the server, which then sends it to the AI ​​algorithm and emotion recognition engine, which then prepares the system to evaluate the user's non-technical aspects and emotional state.

[1024] Step 9:

[1025] An AI algorithm and emotion recognition engine analyze uploaded self-promotion videos and evaluate non-technical aspects and emotional states. The input is the self-promotion video, the AI ​​algorithm analyzes the content of the video, and the emotion recognition engine identifies the emotional state based on voice and facial expressions. The output is an evaluation result.

[1026] Step 10:

[1027] The server receives the evaluation results from the AI ​​algorithm and emotion recognition engine and sends them to the end company. The evaluation results are input, and the server sends them to the end company.

[1028] Step 11:

[1029] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company. The collected information is used as input, and the end company selects the most suitable candidate based on that information. This allows the end company to make a comprehensive judgment and select the appropriate candidate.

[1030] Step 12:

[1031] The server notifies the corresponding user of the selection result of the end company. The server receives the selection result of the end company as input and sends it to the user, allowing the user to confirm whether they have been selected.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] [Third embodiment]

[1036] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1037] 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.

[1038] 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).

[1039] 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.

[1040] 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.

[1041] 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).

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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."

[1048] The present invention is a system that allows users to upload their resumes and skill sheets, and end companies to input detailed information about the job, efficiently matching the most suitable personnel. The program for realizing this system includes multiple operations consisting of a server, a terminal, and a user.

[1049] Uploading user resumes and skill sheets

[1050] The user logs in to the system's portal site using their own terminal.

[1051] The user selects the resume and skill sheet and clicks the upload button.

[1052] The terminal transmits the selected file to the server.

[1053] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1054] End company inputs project details

[1055] End companies log in to the system's portal site using their own devices.

[1056] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1057] The terminal transmits the input information to the server.

[1058] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1059] Checking resumes and skill sheets against project details

[1060] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1061] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1062] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[1063] Creating and administering simple skill check tests

[1064] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1065] The generation AI automatically generates skill check tests based on the project.

[1066] The server sends the generated skill check test to the corresponding user.

[1067] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1068] The server stores the received skill check test results in a database and sends them to the end company.

[1069] Submission and evaluation of one-minute promotional videos

[1070] Users film a self-promotional video and upload it to the system via their device.

[1071] The device sends the uploaded video to the server.

[1072] The server sends the received video to an AI algorithm that evaluates non-technical aspects.

[1073] The AI ​​algorithm analyzes the video and returns the evaluation results to the server.

[1074] The server notifies the end company of the evaluation results.

[1075] Final selection by end company

[1076] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[1077] The end company selects the most suitable personnel based on the information presented.

[1078] The server notifies the corresponding user of the selection result of the end company.

[1079] This will enable quick and intuitive matching between end companies and IT freelancers, solving the problem of multiple subcontracting and maximizing the value of Japan's IT talent.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] A user uses a terminal to log in to the system's portal site.

[1083] Step 2:

[1084] The user selects the resume and skill sheet and clicks the upload button.

[1085] Step 3:

[1086] The terminal transmits the selected file to the server.

[1087] Step 4:

[1088] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1089] Step 5:

[1090] The end company uses a terminal to log in to the system's portal site.

[1091] Step 6:

[1092] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1093] Step 7:

[1094] The terminal transmits the input information to the server.

[1095] Step 8:

[1096] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1097] Step 9:

[1098] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1099] Step 10:

[1100] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1101] Step 11:

[1102] The server notifies the end company and the user of the generated matching candidate list.

[1103] Step 12:

[1104] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1105] Step 13:

[1106] The generation AI automatically generates skill check tests based on the project.

[1107] Step 14:

[1108] The server sends the generated skill check test to the corresponding user.

[1109] Step 15:

[1110] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1111] Step 16:

[1112] The server stores the received skill check test results in a database and sends them to the end company.

[1113] Step 17:

[1114] Users film a self-promotional video and upload it to the system via their device.

[1115] Step 18:

[1116] The device sends the uploaded video to the server.

[1117] Step 19:

[1118] The server sends the received video to the AI ​​algorithm.

[1119] Step 20:

[1120] AI algorithms analyze the video and evaluate non-technical aspects.

[1121] Step 21:

[1122] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1123] Step 22:

[1124] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[1125] Step 23:

[1126] The end company selects the most suitable personnel based on the information presented.

[1127] Step 24:

[1128] The server notifies the corresponding user of the selection result of the end company.

[1129] Example 1

[1130] 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."

[1131] In today's labor market, quickly and efficiently matching companies with the right talent is crucial. However, current systems require a lot of time and effort, making it difficult to effectively select talent. Furthermore, there is a lack of a system for comprehensively evaluating candidates' skills and non-technical aspects beyond simply referencing their resumes and skill sheets, which often results in a mismatch between the talent companies are looking for and the actual candidates. To solve this problem, a comprehensive matching system is needed that evaluates not only technical skills but also non-technical aspects such as communication skills and teamwork abilities.

[1132] 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.

[1133] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for companies to input job details; a means for using an AI algorithm to compare the uploaded resumes and skill sheets with the job details and generate a list of matching candidates; a means for notifying companies and users of the generated matching candidate list; a means for requesting an AI model to generate a skill check test based on the job details; a means for sending the skill check test to users and receiving the user's test results; a means for users to upload self-promotional videos; a means for using an AI algorithm to analyze the uploaded videos and evaluate non-technical aspects; a means for sending the evaluation results to companies; a means for companies to select the most suitable candidates based on the collected information; a means for notifying users of the final selection results; and a means for integrating data such as resumes, skill sheets, skill check results, and evaluations of the self-promotional videos. This enables fast and efficient talent matching and minimizes mismatches between companies and job seekers.

[1134] A "user" is an individual or entity that uploads a resume and skill sheet to the system.

[1135] "Company" refers to an organization or legal entity that inputs detailed project information into the system and selects appropriate personnel.

[1136] A "resume" is a document that details a user's work history, experience, and skills.

[1137] A "skill sheet" is a document that details a user's professional skills and qualifications.

[1138] "Project details" refers to data that companies enter into the system, such as the required skill set, years of experience, and project duration.

[1139] An "AI algorithm" is a series of calculation methods and models that allow the system to compare and match resumes, skill sheets, and detailed job information.

[1140] A "generative AI model" is an algorithm and model that automatically generates skill check tests based on detailed project information.

[1141] A "skill check test" is a test that a user takes to assess a specific technical skill.

[1142] A "self-promotion video" is a video that a user shoots and uploads to showcase their abilities and charms.

[1143] "Non-technical aspects" refer to aspects of human characteristics other than technical skills, such as communication skills and teamwork abilities.

[1144] The "optimal candidate" is the user who best meets the company's requirements based on their resume, skill sheet, skill check results, and evaluation of their self-promotional video.

[1145] A "matching candidate list" is a list of users who meet a company's project requirements, generated by an AI algorithm.

[1146] A "server" is a device that centrally manages system operations and processes and stores data such as resumes, skill sheets, detailed project information, test results, and self-promotional videos.

[1147] A "terminal" is a device that allows a user or company to access the system and perform various operations.

[1148] This invention is a system that allows users to upload their resumes and skill sheets, and companies to input detailed information about the job, efficiently matching the most suitable personnel. To specifically implement this system, the following steps and configuration are required.

[1149] First, the user logs in to the system's portal site using their own device (PC, smartphone, etc.). The user selects their resume and skill sheet and clicks the upload button. At this time, the device encodes the selected files and sends them to the server. The server verifies the validity of the format of the received files and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[1150] Next, the company's employee logs in to the system's portal site using their own device. The employee enters project details such as the skill set required for the project, years of experience, and project duration. The device encodes the entered information and sends it to the server. The server verifies the validity of the format of the received project details and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[1151] Next, the server sends the user's resume and skill sheet, along with the company's job details, from the database to an AI algorithm (e.g., a natural language processing model or machine learning algorithm). The AI ​​algorithm compares and analyzes this information and generates a list of candidates who match. The server then notifies the company and the user of the generated list of candidates.

[1152] If a skill check test is required, the server requests a generative AI model (e.g., OpenAI GPT-4 or Google BERT) to generate a skill check test based on the job details. The generative AI model automatically generates a skill check test based on the job requirements. The server sends the generated skill check test to the corresponding user. The user takes the skill check test on their own device and sends the results to the server. The server stores the received test results in a database and sends them to the company.

[1153] Users can also film a self-promotional video and upload it to the system via their device. The device then sends the uploaded video file to the server. The server then requests an AI algorithm to analyze the received video. The AI ​​algorithm analyzes the video and evaluates non-technical aspects (e.g., communication skills, teamwork ability, etc.). The server then notifies the company of the evaluation results.

[1154] Finally, the server presents all collected information (resumes, skill sheets, skill check test results, evaluations of self-promotional videos, etc.) to companies, helping them select the most suitable candidates. Once the company's selection results have been determined, the server notifies the user of the results.

[1155] Here are some examples of prompts:

[1156] Please upload your resume and skills sheet.

[1157] Enter project details, including required skills, years of experience, and project duration.

[1158] Generate a Python skills test based on the following criteria: writing functions, analyzing data, using basic libraries, etc.

[1159] Rate the uploaded 1-minute promotional video and provide feedback on non-technical aspects.

[1160] In this way, the server, terminal, and user each have a clear role within the system, and they can work together to efficiently match personnel.

[1161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1162] Step 1:

[1163] A user logs in to the portal site

[1164] Input: Portal site URL, user name, password

[1165] Action: A user accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[1166] Output: The user is shown a successful login message and is redirected to the main page.

[1167] Step 2:

[1168] Users upload their resumes and skill sheets

[1169] Input: Resume file, Skill sheet file

[1170] How it works: The user selects the resume and skill sheet files on their device and clicks the upload button.

[1171] Output: The device encodes the selected file and generates a request to send it to the server.

[1172] Step 3:

[1173] The server receives the file and stores it in the database

[1174] Input: Encoded resume file, skill sheet file

[1175] Operation: The server verifies the format of the received file and saves it in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[1176] Output: The resume and skill sheet are saved in the database and a confirmation message is displayed on the user's terminal.

[1177] Step 4:

[1178] A company representative logs in to the portal site

[1179] Input: Portal site URL, user name, password

[1180] How it works: A company employee accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[1181] Output: Company representative will see a login success message and be redirected to the main page.

[1182] Step 5:

[1183] Company personnel enter detailed project information

[1184] Input: Details of the job, such as required skill set, years of experience, and project duration

[1185] Operation: A company representative enters the details of the case and clicks the send button.

[1186] Output: The terminal generates a request to encode the entered job details information and send it to the server.

[1187] Step 6:

[1188] The server receives the job details and stores them in the database

[1189] Input: Encoded job details

[1190] Operation: The server verifies the format of the received case details and saves them in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[1191] Output: The case details are saved in the database and a confirmation message is displayed on the company's terminal.

[1192] Step 7:

[1193] The server sends the database information to the AI ​​algorithm.

[1194] Input: Resume, skill sheet, and detailed job information obtained from the database

[1195] How it works: The server sends the user's resume and skill sheet, as well as the company's job details, from the database to an AI algorithm, which then analyzes this information.

[1196] Output: A list of possible matches is generated.

[1197] Step 8:

[1198] The server notifies the list of possible matches

[1199] Input: Generated list of matching candidates

[1200] How it works: The server sends a list of potential matches to businesses and users.

[1201] Output: Notifications of potential matches are displayed on the company and user devices.

[1202] Step 9:

[1203] The server requests the AI ​​model to generate a skill check test.

[1204] Input: Case details information

[1205] Operation: The server requests the generation AI model to generate a skill check test based on the detailed job information. The generation AI model generates the skill check test based on the instructions.

[1206] Output: The generated skill check test is returned to the server.

[1207] Step 10:

[1208] The server sends the generated skill check test to the user.

[1209] Input: Generated skill check test

[1210] Action: The server sends the generated skill check test to the corresponding user.

[1211] Output: The skill check test is displayed on the user's device.

[1212] Step 11:

[1213] The user takes the skill check test and sends the results to the server.

[1214] Input: Skill check test result

[1215] How it works: The user takes a skill check test on their device and sends the results to the server.

[1216] Output: The received skill check test results are saved on the server.

[1217] Step 12:

[1218] The server stores the skill check test results in a database and sends them to the company.

[1219] Input: User skill check test results

[1220] Operation: The server stores the received skill check test results in a database and sends them to the company.

[1221] Output: The skill check test results are displayed on the company's terminal.

[1222] Step 13:

[1223] Users film and upload their own promotional videos

[1224] Input: Self-promotion video file

[1225] How it works: A user shoots a self-promotional video and uploads it from a portal site.

[1226] Output: The device generates a request to send the uploaded video file to the server.

[1227] Step 14:

[1228] The server receives the video and sends it to the AI ​​algorithm.

[1229] Input: Uploaded self-promotion video file

[1230] How it works: A server receives the video and sends it to an AI algorithm for analysis. The AI ​​algorithm analyzes the video and evaluates non-technical aspects.

[1231] Output: The results of the evaluation of non-technical aspects are returned to the server.

[1232] Step 15:

[1233] The server notifies the company of the evaluation results.

[1234] Input: Non-technical aspects evaluation results

[1235] How it works: The server sends the evaluation results to the company.

[1236] Output: The evaluation results are displayed on the company's terminal.

[1237] Step 16:

[1238] The server presents the collected information to the company.

[1239] Input: Resume, skill sheet, skill check test results, evaluation of self-promotion video

[1240] How it works: The server presents all the information it has collected to the company.

[1241] Output: The collected information is displayed on the company's terminal.

[1242] Step 17:

[1243] Companies select the best talent

[1244] Input: All information provided

[1245] How it works: The company selects the best candidate based on the information provided.

[1246] Output: The final selection results of the companies are determined.

[1247] Step 18:

[1248] The server notifies the user of the selection result.

[1249] Input: Final selection results of companies

[1250] Operation: The server notifies the user of the final selection of companies.

[1251] Output: The final selection result is displayed on the user's terminal.

[1252] (Application example 1)

[1253] 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."

[1254] In modern industry, it is extremely important to quickly and efficiently match robot engineers. However, traditional methods often involve manual matching, which is time-consuming and labor-intensive. It is also difficult to accurately evaluate the skills and experience of engineers and select the most suitable candidates. Given this background, there is a need for a system that can properly match robot engineers with end companies and efficiently recruit engineers.

[1255] 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.

[1256] In this invention, the server includes: a means for a user to upload a resume and skill sheet; a means for an end company to input job details; a means for comparing the uploaded resume and skill sheet with the job details using an AI algorithm to generate a list of matching personnel; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details; a means for sending the skill check test to the user and receiving the user's test results; a means for a user to upload a self-promotional video; a means for analyzing the uploaded video using an AI algorithm to evaluate non-technical aspects; a means for sending the evaluation results to the end company; a means for the end company to select the most suitable personnel based on the collected information; a means for notifying the user of the final selection result; a means for matching a robot engineer based on the user's resume and skill sheet with the job details; a means for the end company to evaluate the self-promotional video uploaded by the robot engineer using an AI algorithm and analyze the results based on the job details; and a means for the end company to generate a skill check test optimized for the user based on the job details, thereby enabling fast and efficient matching of robot engineers.

[1257] "User" refers to an individual who uses the system to upload a resume and skill sheet.

[1258] "End companies" refer to companies that use the system to input detailed project information and select the most suitable personnel.

[1259] A "resume" is a document in which a user details their past employment history and work experience.

[1260] A "skill sheet" is a document that contains information about the skills and techniques possessed by a user.

[1261] "Project details" refers to information entered by the end company, such as the skill set required for the project, years of experience, and project duration.

[1262] The "AI algorithm" is an artificial intelligence algorithm that compares and analyzes resumes and skill sheets with detailed project information.

[1263] A "matching candidate list" is a list of users that matches end company projects, generated by an AI algorithm.

[1264] A "skill check test" is a test generated based on detailed case information to evaluate a user's skills.

[1265] A "self-promotion video" is a video that a user shoots to promote themselves and uploads to the system.

[1266] "Non-technical aspects" refer to factors other than technical skills, such as communication skills and teamwork abilities.

[1267] A "working robot" is an automated industrial machine, such as a robot used in a factory.

[1268] "Engineers" refer to experts in charge of the design, operation, and maintenance of work robots.

[1269] This invention is a system for quickly and efficiently matching robot engineers. The system aims to select the most suitable personnel by uploading the resume and skill sheet of a specific engineer and having the end company input detailed information about the project.

[1270] This system uses the following hardware and software:

[1271] Hardware: Servers, user devices (PCs, smartphones), and end-company devices

[1272] Software: Python, Flask, OpenAI API, scikit-learn

[1273] Database: JSON file (simple example)

[1274] Machine learning: scikit-learn (TF-IDF vectorizer and cosine similarity)

[1275] Users log in to the system's portal site and upload their resumes and skill sheets. The terminal then sends the selected files to the server, which then stores them in a database. This data is later analyzed by AI algorithms.

[1276] The end company enters the job details, and the terminal sends the information to the server. The server stores the received job details in a database and returns a receipt confirmation message to the terminal. The server then sends the user's resume and skill sheet, along with the end company's job details, from the database to an AI algorithm, which generates a list of matching candidates.

[1277] The AI ​​algorithm encodes the resume and skill sheet using a TF-IDF vectorizer, compares them with the job details, calculates the cosine similarity, and generates a list of matching candidates by generating the most similar candidates.

[1278] The server notifies the end company and the user of the generated matching candidate list. The end company then requests the AI ​​to generate a skill check test based on the job details, and the skill check test is automatically generated via the OpenAI API. This skill check test includes questions that evaluate specific technical skills.

[1279] The generated skill check test is sent to the user, who takes the test and sends the results to the server, which stores the received skill check test results in a database and sends them to the end company.

[1280] Users can also film a self-promotional video and upload it to the system via their device. The server then sends the video to an AI algorithm that evaluates non-technical aspects (such as communication skills and teamwork abilities). The evaluation results are then sent to the end company.

[1281] Based on all the collected information (work history, skill check results, self-promotion video evaluation), the end company selects the most suitable candidate. The server notifies the end company's selection results to the corresponding user. This enables quick and efficient matching of work robot engineers.

[1282] As a concrete example, consider a factory looking for an engineer for a project to introduce a new robot control system. This engineer needs to have specific programming skills and knowledge of specific robots. The engineer uploads their skill sheet and resume, and the factory enters the details of the project to perform matching. Suitable engineers are found, and they are evaluated through a simple skill check test and a self-promotional video. Finally, the best engineer is selected.

[1283] An example of a prompt is as follows:

[1284] We are looking for a skill check test for a technician required for the installation of a new robot control system at a factory. The details of the job are as follows: programming skills, robot hardware knowledge, project duration 3 months. Knowledge of Python and C++ is especially essential.

[1285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1286] Step 1:

[1287] The user uploads their resume and skill sheet. The user logs in to the system's portal site using their own device, selects their resume and skill sheet, and clicks the upload button. The input is the user's resume and skill sheet files, and the output is the file sent to the server. The server receives the files and stores them in a database.

[1288] Step 2:

[1289] The end company enters the project details. The end company uses their own terminal to log in to the system's portal site and enters the project details (required skill set, years of experience, project duration, etc.). The input is the project details, and the output is the information sent to the server. The server stores this in a database and returns a receipt confirmation message to the terminal.

[1290] Step 3:

[1291] Matching is performed by comparing resumes and skill sheets with job details. The server retrieves the user's resume and skill sheets and the end company's job details from the database and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The input is the user's resume and skill sheets, and the job details, and the output is a list of matching candidates.

[1292] Step 4:

[1293] The generated list of match candidates is notified. The server notifies the end company and the user of the generated list of match candidates. The input is the list of match candidates, and the output is a notification to the end company and the user.

[1294] Step 5:

[1295] Generate a skill check test. The server requests the generation AI to generate a skill check test based on the case details. The AI ​​generates a skill check test related to the case based on the prompt text. The input is the case details, and the output is the generated skill check test.

[1296] Step 6:

[1297] Send a skill check test and receive the results. The server sends the generated skill check test to the corresponding user. The user uses their terminal to take the received skill check test and sends the results to the server. The input is the skill check test result, and the output is the result sent to the server.

[1298] Step 7:

[1299] The skill check test results are sent to the end company. The server stores the received skill check test results in a database and sends them to the end company. The input is the skill check test results, and the output is a notification to the end company.

[1300] Step 8:

[1301] The user uploads a self-promotional video. The user films the self-promotional video and uploads it to the system via their device. The input is the self-promotional video file, and the output is the video sent to the server. The server receives it and stores it in a database.

[1302] Step 9:

[1303] The self-promotional video is analyzed using an AI algorithm to evaluate non-technical aspects. The server sends the received video to the AI ​​algorithm, which evaluates non-technical aspects (such as communication skills and teamwork skills). The input is the self-promotional video, and the output is the evaluation results.

[1304] Step 10:

[1305] The evaluation results of non-technical aspects are notified to the end company. The server notifies the end company of the evaluation results. The input is the evaluation results, and the output is a notification to the end company.

[1306] Step 11:

[1307] The end company selects the most suitable candidate based on the collected information. The server presents all collected information (work history, skill check results, self-promotion video evaluation) to the end company, who then selects the most suitable candidate. The input is the collected information, and the output is the end company's selection result.

[1308] Step 12:

[1309] The final selection result is notified to the user. The server notifies the corresponding user of the selection result of the end company. The input is the selection result of the end company, and the output is a notification to the user.

[1310] 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.

[1311] This system allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, allowing for a more detailed evaluation of the user's non-technical aspects. The program for implementing this system includes multiple operations consisting of a server, a terminal, and a user.

[1312] Uploading user resumes and skill sheets

[1313] The user logs in to the system's portal site using their own terminal.

[1314] The user selects the resume and skill sheet and clicks the upload button.

[1315] The terminal transmits the selected file to the server.

[1316] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1317] End company inputs project details

[1318] End companies log in to the system's portal site using their own devices.

[1319] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1320] The terminal transmits the input information to the server.

[1321] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1322] Checking resumes and skill sheets against project details

[1323] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1324] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1325] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[1326] Creating and administering simple skill check tests

[1327] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1328] The generation AI automatically generates skill check tests based on the project.

[1329] The server sends the generated skill check test to the corresponding user.

[1330] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1331] The server stores the received skill check test results in a database and sends them to the end company.

[1332] Submission and evaluation of one-minute promotional videos

[1333] Users film a self-promotional video and upload it to the system via their device.

[1334] The device sends the uploaded video to the server.

[1335] The server sends the received video to the AI ​​algorithm.

[1336] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[1337] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1338] Emotion recognition by emotion engine

[1339] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotion video in real time to identify the user's emotional state.

[1340] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[1341] Final selection by end company

[1342] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[1343] The end company selects the most suitable personnel based on the information presented.

[1344] The server notifies the corresponding user of the selection result of the end company.

[1345] Specific examples

[1346] For example, assume that User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system, and enters the job details required by End Company B (e.g., skills in that programming language and more than 5 years of experience). The server sends both pieces of information to the AI ​​algorithm, which finds that User A is suitable for the job.

[1347] Furthermore, since the project from End Company B requires database management skills, the server sends this information to the generation AI, which automatically generates a test consisting of questions related to database management. User A takes the test and receives a pass / fail result.

[1348] Next, User A uploads a self-promotional video, highlighting his or her communication skills and teamwork abilities. The server sends the video to an AI algorithm, which then reports the analysis results to End Company B. At the same time, the emotion engine identifies User A's emotional state, which is then added to the evaluation. End Company B then selects the most suitable candidate based on User A's technical skills, as well as non-technical aspects and emotional state.

[1349] In this way, the present invention eliminates the multi-tiered subcontracting structure and realizes quick and accurate matching between end companies and IT freelancers.

[1350] The processing flow will be explained below.

[1351] Step 1:

[1352] The user logs in to the system's portal site using their own terminal.

[1353] Step 2:

[1354] The user selects the resume and skill sheet and clicks the upload button.

[1355] Step 3:

[1356] The terminal transmits the selected file to the server.

[1357] Step 4:

[1358] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1359] Step 5:

[1360] End companies log in to the system's portal site using their own devices.

[1361] Step 6:

[1362] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1363] Step 7:

[1364] The terminal transmits the input information to the server.

[1365] Step 8:

[1366] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1367] Step 9:

[1368] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1369] Step 10:

[1370] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1371] Step 11:

[1372] The server notifies the end company and the user of the generated matching candidate list.

[1373] Step 12:

[1374] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1375] Step 13:

[1376] The generation AI automatically generates skill check tests based on the project.

[1377] Step 14:

[1378] The server sends the generated skill check test to the corresponding user.

[1379] Step 15:

[1380] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1381] Step 16:

[1382] The server stores the received skill check test results in a database and sends them to the end company.

[1383] Step 17:

[1384] Users film a self-promotional video and upload it to the system via their device.

[1385] Step 18:

[1386] The device sends the uploaded video to the server.

[1387] Step 19:

[1388] The server sends the received video to the AI ​​algorithm.

[1389] Step 20:

[1390] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[1391] Step 21:

[1392] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1393] Step 22:

[1394] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotional video to identify their emotional state.

[1395] Step 23:

[1396] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[1397] Step 24:

[1398] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[1399] Step 25:

[1400] The end company selects the most suitable personnel based on the information presented.

[1401] Step 26:

[1402] The server notifies the corresponding user of the selection result of the end company.

[1403] Step 27:

[1404] The server provides support for facilitating formal contracts between end companies and users, establishing new employment relationships.

[1405] Example 2

[1406] 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."

[1407] Current talent matching systems are based solely on work history and skill sheet information, and are unable to incorporate non-technical aspects or emotional states of candidates into their evaluation. This means that end companies may end up selecting candidates who are technically suitable but lack non-technical abilities. Furthermore, current systems often generate skill check tests manually, creating a fast and efficient matching process is crucial.

[1408] 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.

[1409] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for end companies to input job details; a means for comparing the uploaded resumes and skill sheets with the job details using an AI algorithm to generate a list of matching candidates; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details using a generative AI model; a means for sending the skill check test to the user and receiving the user's test results; a means for sending the received skill check test results to the end company; a means for users to upload self-promotional videos; a means for analyzing the uploaded videos using an AI algorithm to evaluate non-technical aspects; a means for identifying the user's emotional state using an emotion engine while analyzing the self-promotional videos; a means for end companies to select the most suitable personnel based on the collected information; and a means for notifying the user of the final selection results. This enables end companies to select the most suitable personnel based on a comprehensive evaluation that takes into account not only technical skills but also the candidate's non-technical aspects and emotional state. Furthermore, automatic generation of skill check tests using a generative AI model enables fast and efficient matching.

[1410] "User" refers to an individual who uses the system to upload a resume and skill sheet and provide their skills and experience to end companies.

[1411] An "end company" is a company or organization that uses the system to input project details and search for the best person with the required skill set.

[1412] A "resume" is a document in which a user lists his or her work experience, project experience, skills, qualifications, etc.

[1413] A "skill sheet" is a document in which a user provides detailed information about a particular skill, technique, or tool.

[1414] "Project details" refers to information entered by the end company, such as the required skill set, years of experience, and project duration.

[1415] The "AI algorithm" is an artificial intelligence technology that compares resumes and skill sheets with detailed job information to make the best possible match.

[1416] A "matching candidate list" is a list of people who are compatible with a job, generated by an AI algorithm.

[1417] A "generative AI model" is an artificial intelligence technology that automatically generates skill check tests based on specific requirements.

[1418] A "skill check test" is a test generated based on detailed project information to assess specific technical skills.

[1419] A "Self-Promotional Video" is a video that a user shoots and uploads to showcase their strengths, skills, and experience.

[1420] The "Emotion Engine" is a technology that analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state.

[1421] "Non-technical aspects" refer to a user's communication skills, teamwork abilities, and other non-technical attributes.

[1422] "Evaluation results" are information about a user's skills and emotional state obtained as a result of analysis by AI algorithms and emotion engines.

[1423] The "final selection result" is the end company's selection of the most suitable personnel based on all collected information.

[1424] The system for implementing this invention allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. It also incorporates an emotion engine that recognizes the user's emotions, allowing for more detailed evaluation of non-technical aspects.

[1425] Hardware or software used

[1426] 1. Server:

[1427] Uses servers for data processing and database management, such as Amazon Web Services (AWS) EC2 instances and Google Cloud Platform's Compute Engine.

[1428] For the database, database management software such as MongoDB or MySQL is used.

[1429] 2. Terminal:

[1430] Devices such as PCs and tablets used by users and end companies. These devices access the system's portal site using a web browser.

[1431] 3. AI algorithms:

[1432] It uses artificial intelligence technology to generate match candidates and analyze self-promotional videos, using machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.

[1433] 4. Generative AI Models:

[1434] It is used to automatically generate skill check tests based on detailed job information, using language models such as OpenAI's GPT (Generative Pre-trained Transformer) and Google's BERT (Bidirectional Encoder Representations from Transformers).

[1435] 5. Emotion Engine:

[1436] This technology analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state. It can utilize Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[1437] Specific processing of the program

[1438] Uploading user resumes and skill sheets

[1439] A user logs in to the system's portal site and uploads their resume and skill sheet. The terminal sends the selected files to the server, which then stores the received resume and skill sheet in storage and registers the metadata in the database.

[1440] End company inputs project details

[1441] The end company logs in to the system's portal site and enters detailed project information (required skill set, years of experience, project duration, etc.). The terminal sends the entered information to the server, which then stores it in a database.

[1442] Matching by collation

[1443] The server extracts the user's resume and skill sheet and the company's job details and sends them to the AI ​​algorithm. The AI ​​algorithm compares this information and generates a list of matching candidates. The server notifies the company and the user of this list.

[1444] Creating and administering simple skill check tests

[1445] The server generates prompt text based on the detailed project information and sends it to the generative AI model. The generative AI model automatically generates a skill check test, which the server sends to the user. The user takes the test and sends the results to the server. The server stores the results in a database and notifies the end company.

[1446] Submission and evaluation of one-minute promotional videos

[1447] Users film a self-promotional video and upload it to the system via their device. The server receives the video and sends it to an AI algorithm. The AI ​​algorithm analyzes the video, evaluates the user's non-technical aspects, and identifies the user's emotional state using an emotion engine. The server then notifies the end company of the evaluation results.

[1448] Prompt Sentence Examples

[1449] Below are some example prompts for the generative AI model:

[1450] "Generate a skills check test based on the skills required by the end company: database management, years of experience: 5+ years."

[1451] Specific examples

[1452] For example, suppose User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system and enters the detailed job information required by the end company. The server sends both pieces of information to an AI algorithm and finds that User A is the right candidate. Next, the server sends the detailed job information to a generation AI model, which automatically generates a skill check test. User A takes the test and is deemed to have passed. User A then uploads a self-promotional video, which the server analyzes using an AI algorithm and emotion engine and sends the results to the end company. The end company uses this information to select User A as the best candidate.

[1453] In this way, the system of the present invention enables comprehensive talent evaluation that takes into account not only technical skills but also non-technical aspects and emotional states, achieving fast and accurate matching.

[1454] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1455] Step 1: User logs in.

[1456] A user accesses the system's portal site, enters their user ID and password, and clicks the login button. The server receives the user ID and password as input, collates the authentication information with the database, and if authentication is successful, the user is redirected to their personal page with a success message.

[1457] Step 2: User uploads resume and skill sheet.

[1458] After logging in, the user goes to the page to upload their resume and skill sheet, selects the resume and skill sheet files from the file selection dialog, and clicks the upload button. The selected files are obtained as input, and the terminal sends them to the server as an HTTP POST request. The server saves the received files in storage, registers the metadata in the database, and returns a success confirmation message to the terminal.

[1459] Step 3: The end company logs in.

[1460] The end company accesses the system's portal site, enters their company ID and password, and clicks the login button. The server receives the company ID and password as input, collates the authentication information with the database, and if authentication is successful, the end company is redirected to their personal page with a success message.

[1461] Step 4: The end company enters the project details.

[1462] After logging in, the end company goes to the page for entering project details, enters the required skill set, years of experience, project duration, etc. into the form, and clicks the submit button. The form data containing the required information is received as input, and the terminal sends it to the server as an HTTP POST request. The server saves the received information in a database and returns a success confirmation message to the terminal.

[1463] Step 5: Compare your resume and skills sheet with the job details.

[1464] The server extracts the user's resume and skill sheet, as well as the end company's job details, from the database and sends this to the AI ​​algorithm. Data based on the user and end company information is obtained as input. The server passes this data to the AI ​​algorithm, which processes it and generates a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[1465] Step 6: Generate a quick skills check test.

[1466] The server generates a prompt sentence based on the end company's detailed project information and sends it to the generative AI model. The prompt sentence containing the detailed project information is obtained as input. The generative AI model generates a skill check test based on the prompt sentence and returns the result to the server. The server sends the generated skill check test to the user.

[1467] Step 7: The user takes the skill check test.

[1468] The user takes the skill check test on a specified web page and sends the results to the server after completion. The results of the skill check test answered by the user are received as input. The server receives the results and stores them in a database.

[1469] Step 8: Notify the end company of the skill check test results.

[1470] Based on the skill check test results received by the server, a notification is sent to the end company as an HTTP POST request. The analysis results of the skill check test are obtained as input. The server notifies the end company of the results.

[1471] Step 9: The user uploads a promotional video.

[1472] The user shoots a self-promotional video, accesses the specified upload page, selects the video file, and clicks the upload button. The selected video file is obtained as input. The device sends it to the server as an HTTP POST request. The server saves the received video file in storage.

[1473] Step 10: The AI ​​algorithm analyzes the video.

[1474] The server sends the received video file to the AI ​​algorithm for analysis. The uploaded video file is taken as input. The AI ​​algorithm analyzes the video and identifies the user's emotional state using non-technical aspects and an emotion engine. The analysis results are returned to the server.

[1475] Step 11: Notify the end company of the evaluation results.

[1476] The server notifies the end company's account of the evaluation result based on the analysis result returned by the AI ​​algorithm. The analysis result is obtained as input. The server sends the evaluation result to the end company.

[1477] Step 12: The end company selects the best talent.

[1478] The server presents the collected information to the end company, which then selects the most suitable candidate based on the information presented. Work history, skill check test results, self-promotion video evaluation, and emotional information are obtained as input. The end company selects a candidate on the management screen and clicks the select button. The server notifies the corresponding user of the selection results.

[1479] (Application example 2)

[1480] 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."

[1481] Conventional talent matching systems have difficulty efficiently evaluating not only users' resumes and skill sheets, but also their non-technical aspects and emotional state. Furthermore, there is a need for rapid and precise matching of talent specialized in specific fields, especially security services. This creates challenges for end companies, making it difficult to select the best talent, and it takes a lot of time and money.

[1482] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for users to upload resumes and skill sheets, a means for end companies to input detailed job information, and a means for comparing the uploaded resumes and skill sheets with the detailed job information using an AI algorithm and an emotion recognition engine to generate a list of matching personnel. This makes it possible to efficiently select personnel particularly suited to security services. Furthermore, the ability to evaluate the user's non-technical aspects and emotional state allows end companies to quickly find the best overall personnel.

[1483] "User" refers to an individual who uploads a resume and skill sheet, or a job seeker who provides the information necessary to apply for a job at an end company.

[1484] "End company" refers to a company or organization that inputs detailed project information and selects the most suitable candidate based on the results of a skill check test and the evaluation of a self-promotional video.

[1485] A "resume" refers to a document that describes a user's past work history and experience.

[1486] A "skill sheet" refers to a document that lists the specific skills and techniques that a user possesses.

[1487] "Project details" refers to information entered by the end company, such as the desired skill set, years of experience, and project details.

[1488] "AI algorithm" refers to an artificial intelligence method that compares and analyzes uploaded resumes, skill sheets, and job details to generate optimal matching candidates.

[1489] An "emotion recognition engine" refers to a program or system that analyzes a user's self-promotional video and identifies their emotional state based on their voice and facial expressions.

[1490] "Skill Check Test" refers to a test generated based on the end company's project details to assess a user's specific technical skills.

[1491] "Self-promotional videos" refer to videos that users shoot and upload to promote themselves.

[1492] "Non-technical aspects" refer to aspects of a user's skills other than technical skills, such as communication skills and teamwork abilities.

[1493] "Emotional state" refers to the emotional state of the user that is identified by the emotion recognition engine, such as happiness, sadness, tension, etc.

[1494] This invention is a system that efficiently matches suitable personnel by utilizing AI algorithms and emotion recognition engines, with users uploading resumes and skill sheets and end companies inputting detailed job information. The system includes multiple operations consisting of a server, terminals, and users. Specific embodiments are described below.

[1495] Uploading user resumes and skill sheets

[1496] A user logs in to the system's portal site using their own device and uploads their resume and skill sheet. The device sends the selected files to the server, which then stores the received files in a database. When the upload is complete, the server returns a receipt confirmation message to the device.

[1497] End company inputs project details

[1498] End companies log in to the system's portal site using their own devices and enter detailed information about the job (required skill set, years of experience, working hours, etc.). The device sends the entered information to the server, which then stores the received information in a database.

[1499] Checking resumes and skill sheets against project details

[1500] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[1501] Creating and administering simple skill check tests

[1502] The server requests the generation AI to generate a skill check test based on the detailed project information, and the generation AI automatically generates a skill check test based on the project. The server sends the generated skill check test to the corresponding user, and the results of the user's test are sent to the server. The server stores the received test results in a database and sends them to the end company.

[1503] Submission and evaluation of one-minute promotional videos

[1504] Users film a self-promotional video and upload it to the system via their device. The server then sends the received video to an AI algorithm and emotion recognition engine, which analyzes the video and evaluates the user's non-technical aspects and emotional state. The evaluation results are then sent to the end company via the server.

[1505] Emotion recognition by emotion engine

[1506] The emotion engine analyzes the voice and facial expressions in the user's self-promotion video in real time to identify their emotional state, which is then fed back to the AI ​​algorithm for further evaluation of non-technical aspects.

[1507] Final selection by end company

[1508] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company, which selects the most suitable candidate based on the information presented and notifies the corresponding user of the selection results via the server.

[1509] As a concrete example, consider a security services company recruiting for a "night security guard" and inputting the required skill set (e.g., experience working night shifts, operating surveillance cameras, etc.). The user uploads a resume and a self-promotion video, and the system uses AI algorithms and an emotion recognition engine to select the most suitable candidate. An example of a prompt from a generative AI model is as follows:

[1510] Select candidates with the required skill set, night shift experience, and CCTV camera operation experience for the "Night Security Guard" position. Also, analyze the candidates' self-promotion videos and evaluate their emotional state to recommend the best candidates.

[1511] In this way, the present invention can efficiently match end companies with personnel specialized in security services, thereby reducing time and costs.

[1512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1513] Step 1:

[1514] The user uploads their resume and skill sheet to the system's portal site using their own device. The file selected by the user as input is sent to the server by the device, and the server stores the received file in the database. This registers the user's skills and career information in the system.

[1515] Step 2:

[1516] End companies use their own devices to log in to the system's portal site and enter detailed information about the job (such as the required skill set, years of experience, and working hours). The device sends the entered information to the server, which then stores it in a database. This allows the end company's requirements to be registered in the system.

[1517] Step 3:

[1518] The server retrieves the user's resume and skill sheet, as well as the end company's job details from the database, and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes the work history and job details as input, generating a list of matching candidates. The generated list of matching candidates is obtained as output.

[1519] Step 4:

[1520] The server notifies the end company and the user of the generated matching candidate list. The server receives the matching candidate list as input and sends it to the end company and the corresponding user. This allows each user and the end company to check the matching results.

[1521] Step 5:

[1522] The server requests the generative AI model to generate a skill check test based on the detailed job information. The detailed job information is input, and the generative AI model automatically generates a skill check test based on that information. The generated skill check test is output.

[1523] Step 6:

[1524] The server sends the generated skill check test to the corresponding user. The user takes the skill check test using a terminal and sends the result to the server. The skill check test result is received as input by the server, which stores it in a database. This allows the user to be evaluated as to whether they possess a specific technical skill.

[1525] Step 7:

[1526] The server sends the skill check test results to the end company. The skill check test results are input, and the server sends them to the end company, allowing the end company to verify the user's technical skills.

[1527] Step 8:

[1528] Users film a self-promotional video and upload it to the system via their device. The device sends the video as input to the server, which then sends it to the AI ​​algorithm and emotion recognition engine, which then prepares the system to evaluate the user's non-technical aspects and emotional state.

[1529] Step 9:

[1530] An AI algorithm and emotion recognition engine analyze uploaded self-promotion videos and evaluate non-technical aspects and emotional states. The input is the self-promotion video, the AI ​​algorithm analyzes the content of the video, and the emotion recognition engine identifies the emotional state based on voice and facial expressions. The output is an evaluation result.

[1531] Step 10:

[1532] The server receives the evaluation results from the AI ​​algorithm and emotion recognition engine and sends them to the end company. The evaluation results are input, and the server sends them to the end company.

[1533] Step 11:

[1534] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company. The collected information is used as input, and the end company selects the most suitable candidate based on that information. This allows the end company to make a comprehensive judgment and select the appropriate candidate.

[1535] Step 12:

[1536] The server notifies the corresponding user of the selection result of the end company. The server receives the selection result of the end company as input and sends it to the user, allowing the user to confirm whether they have been selected.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] [Fourth embodiment]

[1541] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1542] 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.

[1543] 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).

[1544] 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.

[1545] 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.

[1546] 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).

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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."

[1554] The present invention is a system that allows users to upload their resumes and skill sheets, and end companies to input detailed information about the job, efficiently matching the most suitable personnel. The program for realizing this system includes multiple operations consisting of a server, a terminal, and a user.

[1555] Uploading user resumes and skill sheets

[1556] The user logs in to the system's portal site using their own terminal.

[1557] The user selects the resume and skill sheet and clicks the upload button.

[1558] The terminal transmits the selected file to the server.

[1559] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1560] End company inputs project details

[1561] End companies log in to the system's portal site using their own devices.

[1562] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1563] The terminal transmits the input information to the server.

[1564] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1565] Checking resumes and skill sheets against project details

[1566] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1567] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1568] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[1569] Creating and administering simple skill check tests

[1570] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1571] The generation AI automatically generates skill check tests based on the project.

[1572] The server sends the generated skill check test to the corresponding user.

[1573] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1574] The server stores the received skill check test results in a database and sends them to the end company.

[1575] Submission and evaluation of one-minute promotional videos

[1576] Users film a self-promotional video and upload it to the system via their device.

[1577] The device sends the uploaded video to the server.

[1578] The server sends the received video to an AI algorithm that evaluates non-technical aspects.

[1579] The AI ​​algorithm analyzes the video and returns the evaluation results to the server.

[1580] The server notifies the end company of the evaluation results.

[1581] Final selection by end company

[1582] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[1583] The end company selects the most suitable personnel based on the information presented.

[1584] The server notifies the corresponding user of the selection result of the end company.

[1585] This will enable quick and intuitive matching between end companies and IT freelancers, solving the problem of multiple subcontracting and maximizing the value of Japan's IT talent.

[1586] The processing flow will be explained below.

[1587] Step 1:

[1588] A user uses a terminal to log in to the system's portal site.

[1589] Step 2:

[1590] The user selects the resume and skill sheet and clicks the upload button.

[1591] Step 3:

[1592] The terminal transmits the selected file to the server.

[1593] Step 4:

[1594] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1595] Step 5:

[1596] The end company uses a terminal to log in to the system's portal site.

[1597] Step 6:

[1598] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1599] Step 7:

[1600] The terminal transmits the input information to the server.

[1601] Step 8:

[1602] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1603] Step 9:

[1604] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1605] Step 10:

[1606] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1607] Step 11:

[1608] The server notifies the end company and the user of the generated matching candidate list.

[1609] Step 12:

[1610] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1611] Step 13:

[1612] The generation AI automatically generates skill check tests based on the project.

[1613] Step 14:

[1614] The server sends the generated skill check test to the corresponding user.

[1615] Step 15:

[1616] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1617] Step 16:

[1618] The server stores the received skill check test results in a database and sends them to the end company.

[1619] Step 17:

[1620] Users film a self-promotional video and upload it to the system via their device.

[1621] Step 18:

[1622] The device sends the uploaded video to the server.

[1623] Step 19:

[1624] The server sends the received video to the AI ​​algorithm.

[1625] Step 20:

[1626] AI algorithms analyze the video and evaluate non-technical aspects.

[1627] Step 21:

[1628] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1629] Step 22:

[1630] The server presents all collected information (work history, skill check results, and self-promotional video evaluation) to the end company.

[1631] Step 23:

[1632] The end company selects the most suitable personnel based on the information presented.

[1633] Step 24:

[1634] The server notifies the corresponding user of the selection result of the end company.

[1635] Example 1

[1636] 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."

[1637] In today's labor market, quickly and efficiently matching companies with the right talent is crucial. However, current systems require a lot of time and effort, making it difficult to effectively select talent. Furthermore, there is a lack of a system for comprehensively evaluating candidates' skills and non-technical aspects beyond simply referencing their resumes and skill sheets, which often results in a mismatch between the talent companies are looking for and the actual candidates. To solve this problem, a comprehensive matching system is needed that evaluates not only technical skills but also non-technical aspects such as communication skills and teamwork abilities.

[1638] 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.

[1639] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for companies to input job details; a means for using an AI algorithm to compare the uploaded resumes and skill sheets with the job details and generate a list of matching candidates; a means for notifying companies and users of the generated matching candidate list; a means for requesting an AI model to generate a skill check test based on the job details; a means for sending the skill check test to users and receiving the user's test results; a means for users to upload self-promotional videos; a means for using an AI algorithm to analyze the uploaded videos and evaluate non-technical aspects; a means for sending the evaluation results to companies; a means for companies to select the most suitable candidates based on the collected information; a means for notifying users of the final selection results; and a means for integrating data such as resumes, skill sheets, skill check results, and evaluations of the self-promotional videos. This enables fast and efficient talent matching and minimizes mismatches between companies and job seekers.

[1640] A "user" is an individual or entity that uploads a resume and skill sheet to the system.

[1641] "Company" refers to an organization or legal entity that inputs detailed project information into the system and selects appropriate personnel.

[1642] A "resume" is a document that details a user's work history, experience, and skills.

[1643] A "skill sheet" is a document that details a user's professional skills and qualifications.

[1644] "Project details" refers to data that companies enter into the system, such as the required skill set, years of experience, and project duration.

[1645] An "AI algorithm" is a series of calculation methods and models that allow the system to compare and match resumes, skill sheets, and detailed job information.

[1646] A "generative AI model" is an algorithm and model that automatically generates skill check tests based on detailed project information.

[1647] A "skill check test" is a test that a user takes to assess a specific technical skill.

[1648] A "self-promotion video" is a video that a user shoots and uploads to showcase their abilities and charms.

[1649] "Non-technical aspects" refer to aspects of human characteristics other than technical skills, such as communication skills and teamwork abilities.

[1650] The "optimal candidate" is the user who best meets the company's requirements based on their resume, skill sheet, skill check results, and evaluation of their self-promotional video.

[1651] A "matching candidate list" is a list of users who meet a company's project requirements, generated by an AI algorithm.

[1652] A "server" is a device that centrally manages system operations and processes and stores data such as resumes, skill sheets, detailed project information, test results, and self-promotional videos.

[1653] A "terminal" is a device that allows a user or company to access the system and perform various operations.

[1654] This invention is a system that allows users to upload their resumes and skill sheets, and companies to input detailed information about the job, efficiently matching the most suitable personnel. To specifically implement this system, the following steps and configuration are required.

[1655] First, the user logs in to the system's portal site using their own device (PC, smartphone, etc.). The user selects their resume and skill sheet and clicks the upload button. At this time, the device encodes the selected files and sends them to the server. The server verifies the validity of the format of the received files and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[1656] Next, the company's employee logs in to the system's portal site using their own device. The employee enters project details such as the skill set required for the project, years of experience, and project duration. The device encodes the entered information and sends it to the server. The server verifies the validity of the format of the received project details and saves them in a database. The server confirms that the save was successful and returns a receipt confirmation message to the device.

[1657] Next, the server sends the user's resume and skill sheet, along with the company's job details, from the database to an AI algorithm (e.g., a natural language processing model or machine learning algorithm). The AI ​​algorithm compares and analyzes this information and generates a list of candidates who match. The server then notifies the company and the user of the generated list of candidates.

[1658] If a skill check test is required, the server requests a generative AI model (e.g., OpenAI GPT-4 or Google BERT) to generate a skill check test based on the job details. The generative AI model automatically generates a skill check test based on the job requirements. The server sends the generated skill check test to the corresponding user. The user takes the skill check test on their own device and sends the results to the server. The server stores the received test results in a database and sends them to the company.

[1659] Users can also film a self-promotional video and upload it to the system via their device. The device then sends the uploaded video file to the server. The server then requests an AI algorithm to analyze the received video. The AI ​​algorithm analyzes the video and evaluates non-technical aspects (e.g., communication skills, teamwork ability, etc.). The server then notifies the company of the evaluation results.

[1660] Finally, the server presents all collected information (resumes, skill sheets, skill check test results, evaluations of self-promotional videos, etc.) to companies, helping them select the most suitable candidates. Once the company's selection results have been determined, the server notifies the user of the results.

[1661] Here are some examples of prompts:

[1662] Please upload your resume and skills sheet.

[1663] Enter project details, including required skills, years of experience, and project duration.

[1664] Generate a Python skills test based on the following criteria: writing functions, analyzing data, using basic libraries, etc.

[1665] Rate the uploaded 1-minute promotional video and provide feedback on non-technical aspects.

[1666] In this way, the server, terminal, and user each have a clear role within the system, and they can work together to efficiently match personnel.

[1667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1668] Step 1:

[1669] A user logs in to the portal site

[1670] Input: Portal site URL, user name, password

[1671] Action: A user accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[1672] Output: The user is shown a successful login message and is redirected to the main page.

[1673] Step 2:

[1674] Users upload their resumes and skill sheets

[1675] Input: Resume file, Skill sheet file

[1676] How it works: The user selects the resume and skill sheet files on their device and clicks the upload button.

[1677] Output: The device encodes the selected file and generates a request to send it to the server.

[1678] Step 3:

[1679] The server receives the file and stores it in the database

[1680] Input: Encoded resume file, skill sheet file

[1681] Operation: The server verifies the format of the received file and saves it in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[1682] Output: The resume and skill sheet are saved in the database and a confirmation message is displayed on the user's terminal.

[1683] Step 4:

[1684] A company representative logs in to the portal site

[1685] Input: Portal site URL, user name, password

[1686] How it works: A company employee accesses the portal site URL in a browser, enters their username and password, and clicks the login button.

[1687] Output: Company representative will see a login success message and be redirected to the main page.

[1688] Step 5:

[1689] Company personnel enter detailed project information

[1690] Input: Details of the job, such as required skill set, years of experience, and project duration

[1691] Operation: A company representative enters the details of the case and clicks the send button.

[1692] Output: The terminal generates a request to encode the entered job details information and send it to the server.

[1693] Step 6:

[1694] The server receives the job details and stores them in the database

[1695] Input: Encoded job details

[1696] Operation: The server verifies the format of the received case details and saves them in the database. If the save is successful, the server returns a receipt confirmation message to the terminal.

[1697] Output: The case details are saved in the database and a confirmation message is displayed on the company's terminal.

[1698] Step 7:

[1699] The server sends the database information to the AI ​​algorithm.

[1700] Input: Resume, skill sheet, and detailed job information obtained from the database

[1701] How it works: The server sends the user's resume and skill sheet, as well as the company's job details, from the database to an AI algorithm, which then analyzes this information.

[1702] Output: A list of possible matches is generated.

[1703] Step 8:

[1704] The server notifies the list of possible matches

[1705] Input: Generated list of matching candidates

[1706] How it works: The server sends a list of potential matches to businesses and users.

[1707] Output: Notifications of potential matches are displayed on the company and user devices.

[1708] Step 9:

[1709] The server requests the AI ​​model to generate a skill check test.

[1710] Input: Case details information

[1711] Operation: The server requests the generation AI model to generate a skill check test based on the detailed job information. The generation AI model generates the skill check test based on the instructions.

[1712] Output: The generated skill check test is returned to the server.

[1713] Step 10:

[1714] The server sends the generated skill check test to the user.

[1715] Input: Generated skill check test

[1716] Action: The server sends the generated skill check test to the corresponding user.

[1717] Output: The skill check test is displayed on the user's device.

[1718] Step 11:

[1719] The user takes the skill check test and sends the results to the server.

[1720] Input: Skill check test result

[1721] How it works: The user takes a skill check test on their device and sends the results to the server.

[1722] Output: The received skill check test results are saved on the server.

[1723] Step 12:

[1724] The server stores the skill check test results in a database and sends them to the company.

[1725] Input: User skill check test results

[1726] Operation: The server stores the received skill check test results in a database and sends them to the company.

[1727] Output: The skill check test results are displayed on the company's terminal.

[1728] Step 13:

[1729] Users film and upload their own promotional videos

[1730] Input: Self-promotion video file

[1731] How it works: A user shoots a self-promotional video and uploads it from a portal site.

[1732] Output: The device generates a request to send the uploaded video file to the server.

[1733] Step 14:

[1734] The server receives the video and sends it to the AI ​​algorithm.

[1735] Input: Uploaded self-promotion video file

[1736] How it works: A server receives the video and sends it to an AI algorithm for analysis. The AI ​​algorithm analyzes the video and evaluates non-technical aspects.

[1737] Output: The results of the evaluation of non-technical aspects are returned to the server.

[1738] Step 15:

[1739] The server notifies the company of the evaluation results.

[1740] Input: Non-technical aspects evaluation results

[1741] How it works: The server sends the evaluation results to the company.

[1742] Output: The evaluation results are displayed on the company's terminal.

[1743] Step 16:

[1744] The server presents the collected information to the company.

[1745] Input: Resume, skill sheet, skill check test results, evaluation of self-promotion video

[1746] How it works: The server presents all the information it has collected to the company.

[1747] Output: The collected information is displayed on the company's terminal.

[1748] Step 17:

[1749] Companies select the best talent

[1750] Input: All information provided

[1751] How it works: The company selects the best candidate based on the information provided.

[1752] Output: The final selection results of the companies are determined.

[1753] Step 18:

[1754] The server notifies the user of the selection result.

[1755] Input: Final selection results of companies

[1756] Operation: The server notifies the user of the final selection of companies.

[1757] Output: The final selection result is displayed on the user's terminal.

[1758] (Application example 1)

[1759] 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."

[1760] In modern industry, it is extremely important to quickly and efficiently match robot engineers. However, traditional methods often involve manual matching, which is time-consuming and labor-intensive. It is also difficult to accurately evaluate the skills and experience of engineers and select the most suitable candidates. Given this background, there is a need for a system that can properly match robot engineers with end companies and efficiently recruit engineers.

[1761] 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.

[1762] In this invention, the server includes: a means for a user to upload a resume and skill sheet; a means for an end company to input job details; a means for comparing the uploaded resume and skill sheet with the job details using an AI algorithm to generate a list of matching personnel; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details; a means for sending the skill check test to the user and receiving the user's test results; a means for a user to upload a self-promotional video; a means for analyzing the uploaded video using an AI algorithm to evaluate non-technical aspects; a means for sending the evaluation results to the end company; a means for the end company to select the most suitable personnel based on the collected information; a means for notifying the user of the final selection result; a means for matching a robot engineer based on the user's resume and skill sheet with the job details; a means for the end company to evaluate the self-promotional video uploaded by the robot engineer using an AI algorithm and analyze the results based on the job details; and a means for the end company to generate a skill check test optimized for the user based on the job details, thereby enabling fast and efficient matching of robot engineers.

[1763] "User" refers to an individual who uses the system to upload a resume and skill sheet.

[1764] "End companies" refer to companies that use the system to input detailed project information and select the most suitable personnel.

[1765] A "resume" is a document in which a user details their past employment history and work experience.

[1766] A "skill sheet" is a document that contains information about the skills and techniques possessed by a user.

[1767] "Project details" refers to information entered by the end company, such as the skill set required for the project, years of experience, and project duration.

[1768] The "AI algorithm" is an artificial intelligence algorithm that compares and analyzes resumes and skill sheets with detailed project information.

[1769] A "matching candidate list" is a list of users that matches end company projects, generated by an AI algorithm.

[1770] A "skill check test" is a test generated based on detailed case information to evaluate a user's skills.

[1771] A "self-promotion video" is a video that a user shoots to promote themselves and uploads to the system.

[1772] "Non-technical aspects" refer to factors other than technical skills, such as communication skills and teamwork abilities.

[1773] A "working robot" is an automated industrial machine, such as a robot used in a factory.

[1774] "Engineers" refer to experts in charge of the design, operation, and maintenance of work robots.

[1775] This invention is a system for quickly and efficiently matching robot engineers. The system aims to select the most suitable personnel by uploading the resume and skill sheet of a specific engineer and having the end company input detailed information about the project.

[1776] This system uses the following hardware and software:

[1777] Hardware: Servers, user devices (PCs, smartphones), and end-company devices

[1778] Software: Python, Flask, OpenAI API, scikit-learn

[1779] Database: JSON file (simple example)

[1780] Machine learning: scikit-learn (TF-IDF vectorizer and cosine similarity)

[1781] Users log in to the system's portal site and upload their resumes and skill sheets. The terminal then sends the selected files to the server, which then stores them in a database. This data is later analyzed by AI algorithms.

[1782] The end company enters the job details, and the terminal sends the information to the server. The server stores the received job details in a database and returns a receipt confirmation message to the terminal. The server then sends the user's resume and skill sheet, along with the end company's job details, from the database to an AI algorithm, which generates a list of matching candidates.

[1783] The AI ​​algorithm encodes the resume and skill sheet using a TF-IDF vectorizer, compares them with the job details, calculates the cosine similarity, and generates a list of matching candidates by generating the most similar candidates.

[1784] The server notifies the end company and the user of the generated matching candidate list. The end company then requests the AI ​​to generate a skill check test based on the job details, and the skill check test is automatically generated via the OpenAI API. This skill check test includes questions that evaluate specific technical skills.

[1785] The generated skill check test is sent to the user, who takes the test and sends the results to the server, which stores the received skill check test results in a database and sends them to the end company.

[1786] Users can also film a self-promotional video and upload it to the system via their device. The server then sends the video to an AI algorithm that evaluates non-technical aspects (such as communication skills and teamwork abilities). The evaluation results are then sent to the end company.

[1787] Based on all the collected information (work history, skill check results, self-promotion video evaluation), the end company selects the most suitable candidate. The server notifies the end company's selection results to the corresponding user. This enables quick and efficient matching of work robot engineers.

[1788] As a concrete example, consider a factory looking for an engineer for a project to introduce a new robot control system. This engineer needs to have specific programming skills and knowledge of specific robots. The engineer uploads their skill sheet and resume, and the factory enters the details of the project to perform matching. Suitable engineers are found, and they are evaluated through a simple skill check test and a self-promotional video. Finally, the best engineer is selected.

[1789] An example of a prompt is as follows:

[1790] We are looking for a skill check test for a technician required for the installation of a new robot control system at a factory. The details of the job are as follows: programming skills, robot hardware knowledge, project duration 3 months. Knowledge of Python and C++ is especially essential.

[1791] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1792] Step 1:

[1793] The user uploads their resume and skill sheet. The user logs in to the system's portal site using their own device, selects their resume and skill sheet, and clicks the upload button. The input is the user's resume and skill sheet files, and the output is the file sent to the server. The server receives the files and stores them in a database.

[1794] Step 2:

[1795] The end company enters the project details. The end company uses their own terminal to log in to the system's portal site and enters the project details (required skill set, years of experience, project duration, etc.). The input is the project details, and the output is the information sent to the server. The server stores this in a database and returns a receipt confirmation message to the terminal.

[1796] Step 3:

[1797] Matching is performed by comparing resumes and skill sheets with job details. The server retrieves the user's resume and skill sheets and the end company's job details from the database and sends them to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The input is the user's resume and skill sheets, and the job details, and the output is a list of matching candidates.

[1798] Step 4:

[1799] The generated list of match candidates is notified. The server notifies the end company and the user of the generated list of match candidates. The input is the list of match candidates, and the output is a notification to the end company and the user.

[1800] Step 5:

[1801] Generate a skill check test. The server requests the generation AI to generate a skill check test based on the case details. The AI ​​generates a skill check test related to the case based on the prompt text. The input is the case details, and the output is the generated skill check test.

[1802] Step 6:

[1803] Send a skill check test and receive the results. The server sends the generated skill check test to the corresponding user. The user uses their terminal to take the received skill check test and sends the results to the server. The input is the skill check test result, and the output is the result sent to the server.

[1804] Step 7:

[1805] The skill check test results are sent to the end company. The server stores the received skill check test results in a database and sends them to the end company. The input is the skill check test results, and the output is a notification to the end company.

[1806] Step 8:

[1807] The user uploads a self-promotional video. The user films the self-promotional video and uploads it to the system via their device. The input is the self-promotional video file, and the output is the video sent to the server. The server receives it and stores it in a database.

[1808] Step 9:

[1809] The self-promotional video is analyzed using an AI algorithm to evaluate non-technical aspects. The server sends the received video to the AI ​​algorithm, which evaluates non-technical aspects (such as communication skills and teamwork skills). The input is the self-promotional video, and the output is the evaluation results.

[1810] Step 10:

[1811] The evaluation results of non-technical aspects are notified to the end company. The server notifies the end company of the evaluation results. The input is the evaluation results, and the output is a notification to the end company.

[1812] Step 11:

[1813] The end company selects the most suitable candidate based on the collected information. The server presents all collected information (work history, skill check results, self-promotion video evaluation) to the end company, who then selects the most suitable candidate. The input is the collected information, and the output is the end company's selection result.

[1814] Step 12:

[1815] The final selection result is notified to the user. The server notifies the corresponding user of the selection result of the end company. The input is the selection result of the end company, and the output is a notification to the user.

[1816] 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.

[1817] This system allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, allowing for a more detailed evaluation of the user's non-technical aspects. The program for implementing this system includes multiple operations consisting of a server, a terminal, and a user.

[1818] Uploading user resumes and skill sheets

[1819] The user logs in to the system's portal site using their own terminal.

[1820] The user selects the resume and skill sheet and clicks the upload button.

[1821] The terminal transmits the selected file to the server.

[1822] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1823] End company inputs project details

[1824] End companies log in to the system's portal site using their own devices.

[1825] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1826] The terminal transmits the input information to the server.

[1827] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1828] Checking resumes and skill sheets against project details

[1829] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1830] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1831] The server notifies the end company of the generated matching candidate list, and also notifies the user.

[1832] Creating and administering simple skill check tests

[1833] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1834] The generation AI automatically generates skill check tests based on the project.

[1835] The server sends the generated skill check test to the corresponding user.

[1836] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1837] The server stores the received skill check test results in a database and sends them to the end company.

[1838] Submission and evaluation of one-minute promotional videos

[1839] Users film a self-promotional video and upload it to the system via their device.

[1840] The device sends the uploaded video to the server.

[1841] The server sends the received video to the AI ​​algorithm.

[1842] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[1843] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1844] Emotion recognition by emotion engine

[1845] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotion video in real time to identify the user's emotional state.

[1846] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[1847] Final selection by end company

[1848] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[1849] The end company selects the most suitable personnel based on the information presented.

[1850] The server notifies the corresponding user of the selection result of the end company.

[1851] Specific examples

[1852] For example, assume that User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system, and enters the job details required by End Company B (e.g., skills in that programming language and more than 5 years of experience). The server sends both pieces of information to the AI ​​algorithm, which finds that User A is suitable for the job.

[1853] Furthermore, since the project from End Company B requires database management skills, the server sends this information to the generation AI, which automatically generates a test consisting of questions related to database management. User A takes the test and receives a pass / fail result.

[1854] Next, User A uploads a self-promotional video, highlighting his or her communication skills and teamwork abilities. The server sends the video to an AI algorithm, which then reports the analysis results to End Company B. At the same time, the emotion engine identifies User A's emotional state, which is then added to the evaluation. End Company B then selects the most suitable candidate based on User A's technical skills, as well as non-technical aspects and emotional state.

[1855] In this way, the present invention eliminates the multi-tiered subcontracting structure and realizes quick and accurate matching between end companies and IT freelancers.

[1856] The processing flow will be explained below.

[1857] Step 1:

[1858] The user logs in to the system's portal site using their own terminal.

[1859] Step 2:

[1860] The user selects the resume and skill sheet and clicks the upload button.

[1861] Step 3:

[1862] The terminal transmits the selected file to the server.

[1863] Step 4:

[1864] The server stores the received resume and skill sheet in a database and returns a reception confirmation message to the terminal.

[1865] Step 5:

[1866] End companies log in to the system's portal site using their own devices.

[1867] Step 6:

[1868] The end company enters project details (required skill set, years of experience, project duration, etc.).

[1869] Step 7:

[1870] The terminal transmits the input information to the server.

[1871] Step 8:

[1872] The server stores the received case details in a database and returns a reception confirmation message to the terminal.

[1873] Step 9:

[1874] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm.

[1875] Step 10:

[1876] An AI algorithm compares and analyzes this information to generate a list of potential matches.

[1877] Step 11:

[1878] The server notifies the end company and the user of the generated matching candidate list.

[1879] Step 12:

[1880] The server requests the generation AI to generate a skill check test based on the detailed project information.

[1881] Step 13:

[1882] The generation AI automatically generates skill check tests based on the project.

[1883] Step 14:

[1884] The server sends the generated skill check test to the corresponding user.

[1885] Step 15:

[1886] The user uses the terminal to take the received skill check test and transmits the result to the server.

[1887] Step 16:

[1888] The server stores the received skill check test results in a database and sends them to the end company.

[1889] Step 17:

[1890] Users film a self-promotional video and upload it to the system via their device.

[1891] Step 18:

[1892] The device sends the uploaded video to the server.

[1893] Step 19:

[1894] The server sends the received video to the AI ​​algorithm.

[1895] Step 20:

[1896] AI algorithms analyze videos and assess the user's non-technical aspects, as well as their emotional state using an emotion engine.

[1897] Step 21:

[1898] The server receives the evaluation results from the AI ​​algorithm and sends them to the end company.

[1899] Step 22:

[1900] The emotion engine analyzes the voice and facial expressions contained in the user's self-promotional video to identify their emotional state.

[1901] Step 23:

[1902] The emotion engine returns the identified emotional information to the AI ​​algorithm, which uses it to further evaluate non-technical aspects.

[1903] Step 24:

[1904] The server presents all collected information (work history, skill check results, self-promotion video evaluation, emotional information) to the end company.

[1905] Step 25:

[1906] The end company selects the most suitable personnel based on the information presented.

[1907] Step 26:

[1908] The server notifies the corresponding user of the selection result of the end company.

[1909] Step 27:

[1910] The server provides support for facilitating formal contracts between end companies and users, establishing new employment relationships.

[1911] Example 2

[1912] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1913] Current talent matching systems are based solely on work history and skill sheet information, and are unable to incorporate non-technical aspects or emotional states of candidates into their evaluation. This means that end companies may end up selecting candidates who are technically suitable but lack non-technical abilities. Furthermore, current systems often generate skill check tests manually, creating a fast and efficient matching process is crucial.

[1914] 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.

[1915] In this invention, the server includes: a means for users to upload resumes and skill sheets; a means for end companies to input job details; a means for comparing the uploaded resumes and skill sheets with the job details using an AI algorithm to generate a list of matching candidates; a means for notifying the end company and the user of the generated matching candidate list; a means for generating a skill check test based on the job details using a generative AI model; a means for sending the skill check test to the user and receiving the user's test results; a means for sending the received skill check test results to the end company; a means for users to upload self-promotional videos; a means for analyzing the uploaded videos using an AI algorithm to evaluate non-technical aspects; a means for identifying the user's emotional state using an emotion engine while analyzing the self-promotional videos; a means for end companies to select the most suitable personnel based on the collected information; and a means for notifying the user of the final selection results. This enables end companies to select the most suitable personnel based on a comprehensive evaluation that takes into account not only technical skills but also the candidate's non-technical aspects and emotional state. Furthermore, automatic generation of skill check tests using a generative AI model enables fast and efficient matching.

[1916] "User" refers to an individual who uses the system to upload a resume and skill sheet and provide their skills and experience to end companies.

[1917] An "end company" is a company or organization that uses the system to input project details and search for the best person with the required skill set.

[1918] A "resume" is a document in which a user lists his or her work experience, project experience, skills, qualifications, etc.

[1919] A "skill sheet" is a document in which a user provides detailed information about a particular skill, technique, or tool.

[1920] "Project details" refers to information entered by the end company, such as the required skill set, years of experience, and project duration.

[1921] The "AI algorithm" is an artificial intelligence technology that compares resumes and skill sheets with detailed job information to make the best possible match.

[1922] A "matching candidate list" is a list of people who are compatible with a job, generated by an AI algorithm.

[1923] A "generative AI model" is an artificial intelligence technology that automatically generates skill check tests based on specific requirements.

[1924] A "skill check test" is a test generated based on detailed project information to assess specific technical skills.

[1925] A "Self-Promotional Video" is a video that a user shoots and uploads to showcase their strengths, skills, and experience.

[1926] The "Emotion Engine" is a technology that analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state.

[1927] "Non-technical aspects" refer to a user's communication skills, teamwork abilities, and other non-technical attributes.

[1928] "Evaluation results" are information about a user's skills and emotional state obtained as a result of analysis by AI algorithms and emotion engines.

[1929] The "final selection result" is the end company's selection of the most suitable personnel based on all collected information.

[1930] The system for implementing this invention allows users to upload their resumes and skill sheets, and end companies to input detailed job information, efficiently matching the most suitable candidates. It also incorporates an emotion engine that recognizes the user's emotions, allowing for more detailed evaluation of non-technical aspects.

[1931] Hardware or software used

[1932] 1. Server:

[1933] Uses servers for data processing and database management, such as Amazon Web Services (AWS) EC2 instances and Google Cloud Platform's Compute Engine.

[1934] For the database, database management software such as MongoDB or MySQL is used.

[1935] 2. Terminal:

[1936] Devices such as PCs and tablets used by users and end companies. These devices access the system's portal site using a web browser.

[1937] 3. AI algorithms:

[1938] It uses artificial intelligence technology to generate match candidates and analyze self-promotional videos, using machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.

[1939] 4. Generative AI Models:

[1940] It is used to automatically generate skill check tests based on detailed job information, using language models such as OpenAI's GPT (Generative Pre-trained Transformer) and Google's BERT (Bidirectional Encoder Representations from Transformers).

[1941] 5. Emotion Engine:

[1942] This technology analyzes the voice and facial expressions in a self-promotional video to identify the user's emotional state. It can utilize Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[1943] Specific processing of the program

[1944] Uploading user resumes and skill sheets

[1945] A user logs in to the system's portal site and uploads their resume and skill sheet. The terminal sends the selected files to the server, which then stores the received resume and skill sheet in storage and registers the metadata in the database.

[1946] End company inputs project details

[1947] The end company logs in to the system's portal site and enters detailed project information (required skill set, years of experience, project duration, etc.). The terminal sends the entered information to the server, which then stores it in a database.

[1948] Matching by collation

[1949] The server extracts the user's resume and skill sheet and the company's job details and sends them to the AI ​​algorithm. The AI ​​algorithm compares this information and generates a list of matching candidates. The server notifies the company and the user of this list.

[1950] Creating and administering simple skill check tests

[1951] The server generates prompt text based on the detailed project information and sends it to the generative AI model. The generative AI model automatically generates a skill check test, which the server sends to the user. The user takes the test and sends the results to the server. The server stores the results in a database and notifies the end company.

[1952] Submission and evaluation of one-minute promotional videos

[1953] Users film a self-promotional video and upload it to the system via their device. The server receives the video and sends it to an AI algorithm. The AI ​​algorithm analyzes the video, evaluates the user's non-technical aspects, and identifies the user's emotional state using an emotion engine. The server then notifies the end company of the evaluation results.

[1954] Prompt Sentence Examples

[1955] Below are some example prompts for the generative AI model:

[1956] "Generate a skills check test based on the skills required by the end company: database management, years of experience: 5+ years."

[1957] Specific examples

[1958] For example, suppose User A has 10 years of software development experience and skills in a specific programming language. User A uploads his / her resume and skill sheet to the system and enters the detailed job information required by the end company. The server sends both pieces of information to an AI algorithm and finds that User A is the right candidate. Next, the server sends the detailed job information to a generation AI model, which automatically generates a skill check test. User A takes the test and is deemed to have passed. User A then uploads a self-promotional video, which the server analyzes using an AI algorithm and emotion engine and sends the results to the end company. The end company uses this information to select User A as the best candidate.

[1959] In this way, the system of the present invention enables comprehensive talent evaluation that takes into account not only technical skills but also non-technical aspects and emotional states, achieving fast and accurate matching.

[1960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1961] Step 1: User logs in.

[1962] A user accesses the system's portal site, enters their user ID and password, and clicks the login button. The server receives the user ID and password as input, collates the authentication information with the database, and if authentication is successful, the user is redirected to their personal page with a success message.

[1963] Step 2: User uploads resume and skill sheet.

[1964] After logging in, the user goes to the page to upload their resume and skill sheet, selects the resume and skill sheet files from the file selection dialog, and clicks the upload button. The selected files are obtained as input, and the terminal sends them to the server as an HTTP POST request. The server saves the received files in storage, registers the metadata in the database, and returns a success confirmation message to the terminal.

[1965] Step 3: The end company logs in.

[1966] The end company accesses the system's portal site, enters their company ID and password, and clicks the login button. The server receives the company ID and password as input, collates the authentication information with the database, and if authentication is successful, the end company is redirected to their personal page with a success message.

[1967] Step 4: The end company enters the project details.

[1968] After logging in, the end company goes to the page for entering project details, enters the required skill set, years of experience, project duration, etc. into the form, and clicks the submit button. The form data containing the required information is received as input, and the terminal sends it to the server as an HTTP POST request. The server saves the received information in a database and returns a success confirmation message to the terminal.

[1969] Step 5: Compare your resume and skills sheet with the job details.

[1970] The server extracts the user's resume and skill sheet, as well as the end company's job details, from the database and sends this to the AI ​​algorithm. Data based on the user and end company information is obtained as input. The server passes this data to the AI ​​algorithm, which processes it and generates a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[1971] Step 6: Generate a quick skills check test.

[1972] The server generates a prompt sentence based on the end company's detailed project information and sends it to the generative AI model. The prompt sentence containing the detailed project information is obtained as input. The generative AI model generates a skill check test based on the prompt sentence and returns the result to the server. The server sends the generated skill check test to the user.

[1973] Step 7: The user takes the skill check test.

[1974] The user takes the skill check test on a specified web page and sends the results to the server after completion. The results of the skill check test answered by the user are received as input. The server receives the results and stores them in a database.

[1975] Step 8: Notify the end company of the skill check test results.

[1976] Based on the skill check test results received by the server, a notification is sent to the end company as an HTTP POST request. The analysis results of the skill check test are obtained as input. The server notifies the end company of the results.

[1977] Step 9: The user uploads a promotional video.

[1978] The user shoots a self-promotional video, accesses the specified upload page, selects the video file, and clicks the upload button. The selected video file is obtained as input. The device sends it to the server as an HTTP POST request. The server saves the received video file in storage.

[1979] Step 10: The AI ​​algorithm analyzes the video.

[1980] The server sends the received video file to the AI ​​algorithm for analysis. The uploaded video file is taken as input. The AI ​​algorithm analyzes the video and identifies the user's emotional state using non-technical aspects and an emotion engine. The analysis results are returned to the server.

[1981] Step 11: Notify the end company of the evaluation results.

[1982] The server notifies the end company's account of the evaluation result based on the analysis result returned by the AI ​​algorithm. The analysis result is obtained as input. The server sends the evaluation result to the end company.

[1983] Step 12: The end company selects the best talent.

[1984] The server presents the collected information to the end company, which then selects the most suitable candidate based on the information presented. Work history, skill check test results, self-promotion video evaluation, and emotional information are obtained as input. The end company selects a candidate on the management screen and clicks the select button. The server notifies the corresponding user of the selection results.

[1985] (Application example 2)

[1986] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1987] Conventional talent matching systems have difficulty efficiently evaluating not only users' resumes and skill sheets, but also their non-technical aspects and emotional state. Furthermore, there is a need for rapid and precise matching of talent specialized in specific fields, especially security services. This creates challenges for end companies, making it difficult to select the best talent, and it takes a lot of time and money.

[1988] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for users to upload resumes and skill sheets, a means for end companies to input detailed job information, and a means for comparing the uploaded resumes and skill sheets with the detailed job information using an AI algorithm and an emotion recognition engine to generate a list of matching personnel. This makes it possible to efficiently select personnel particularly suited to security services. Furthermore, the ability to evaluate the user's non-technical aspects and emotional state allows end companies to quickly find the best overall personnel.

[1989] "User" refers to an individual who uploads a resume and skill sheet, or a job seeker who provides the information necessary to apply for a job at an end company.

[1990] "End company" refers to a company or organization that inputs detailed project information and selects the most suitable candidate based on the results of a skill check test and the evaluation of a self-promotional video.

[1991] A "resume" refers to a document that describes a user's past work history and experience.

[1992] A "skill sheet" refers to a document that lists the specific skills and techniques that a user possesses.

[1993] "Project details" refers to information entered by the end company, such as the desired skill set, years of experience, and project details.

[1994] "AI algorithm" refers to an artificial intelligence method that compares and analyzes uploaded resumes, skill sheets, and job details to generate optimal matching candidates.

[1995] An "emotion recognition engine" refers to a program or system that analyzes a user's self-promotional video and identifies their emotional state based on their voice and facial expressions.

[1996] "Skill Check Test" refers to a test generated based on the end company's project details to assess a user's specific technical skills.

[1997] "Self-promotional videos" refer to videos that users shoot and upload to promote themselves.

[1998] "Non-technical aspects" refer to aspects of a user's skills other than technical skills, such as communication skills and teamwork abilities.

[1999] "Emotional state" refers to the emotional state of the user that is identified by the emotion recognition engine, such as happiness, sadness, tension, etc.

[2000] This invention is a system that efficiently matches suitable personnel by utilizing AI algorithms and emotion recognition engines, with users uploading resumes and skill sheets and end companies inputting detailed job information. The system includes multiple operations consisting of a server, terminals, and users. Specific embodiments are described below.

[2001] Uploading user resumes and skill sheets

[2002] A user logs in to the system's portal site using their own device and uploads their resume and skill sheet. The device sends the selected files to the server, which then stores the received files in a database. When the upload is complete, the server returns a receipt confirmation message to the device.

[2003] End company inputs project details

[2004] End companies log in to the system's portal site using their own devices and enter detailed information about the job (required skill set, years of experience, working hours, etc.). The device sends the entered information to the server, which then stores the received information in a database.

[2005] Checking resumes and skill sheets against project details

[2006] The server sends the user's resume and skill sheet, as well as the end company's job details, from the database to the AI ​​algorithm. The AI ​​algorithm compares and analyzes this information to generate a list of matching candidates. The server notifies the end company and the user of the generated list of matching candidates.

[2007] Creating and administering simple skill check tests

[2008] The server requests the generation AI to generate a skill check test based on the detailed project information, and the generation AI automatically generates a skill check test based on the project. The server sends the generated skill check test to the corresponding us...

Claims

1. A means for users to upload their resumes and skill sheets; A means for end companies to input project details, A method to compare uploaded resumes and skill sheets with detailed job information using an AI algorithm and generate a list of matching candidates; A means for notifying the end company and the user of the generated matching candidate list; A means for generating skill check tests based on detailed project information; means for sending a skill check test to a user and receiving the user's test results; A means for transmitting the received skill check test results to the end company; A means for users to upload self-promotional videos, A method to analyze uploaded videos using AI algorithms and evaluate non-technical aspects, a means for transmitting the evaluation results to the end company; A means for end companies to select the most suitable personnel based on the collected information, The system includes a means for notifying the user of the final selection result.

2. 2. The system according to claim 1, wherein the skill check test generated based on the detailed case information includes a means for including questions that evaluate specific technical skills.

3. The system of claim 1, further comprising means for evaluating communication skills and teamwork abilities when the AI ​​algorithm analyzes the user's self-promotional video.

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