system

Abstract

Provide a system. [Solution] a means for job seekers to input job search information; A way for companies to enter job information, a means for storing the job search information and the recruitment information; a means for matching the job-seeking information with the recruitment information; A means for providing the matching results to job seekers and companies; a means for collecting feedback from said job seekers and businesses; means for improving matching accuracy based on the feedback; A system including:

Description

[Technical Field]

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

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

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

[0004] In conventional employment and re-employment systems, the skills and conditions required by companies often did not match well with the desires and skills of job seekers, resulting in mismatches for both companies and job seekers. As a result, companies were unable to hire suitable personnel, and job seekers had difficulty finding a job that suited them. In addition, there was a lack of a mechanism for incorporating feedback after interviews into the system to improve the accuracy of matching in future cases. This made it a challenge to improve the accuracy of matching. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] The system includes a means for job seekers to input job search information, a means for companies to input job recruitment information, a means for saving the job search information and the job recruitment information, a means for matching the job search information with the job recruitment information, a means for providing the matching results to job seekers and companies, a means for collecting feedback from the job seekers and companies, and a means for improving matching accuracy based on the feedback.

[0007] Specifically, after job seekers and companies input their respective conditions, the information is saved in a database and a score is generated based on the company's recruitment conditions and the job seeker's desired conditions, generating candidates with high matching accuracy. Based on the scoring results, companies are provided with a list of suitable job seekers, and job seekers are provided with a list of suitable job openings. In addition, a system will be put in place to improve future matching accuracy by inputting interview results and post-hiring feedback into the system. This will enable satisfactory matches for both companies and job seekers, and reduce mismatches.

[0008] "Job Seeker" refers to an individual who is looking for work and is a user of the system who enters their job search information.

[0009] "Company" refers to the organization that inputs job information and searches for suitable candidates.

[0010] "Job search information" refers to information entered by job seekers, such as work experience, skills, desired job type, work location, and salary.

[0011] "Job information" refers to information entered by companies, such as the type of job being recruited, required skills, work location, salary, and working style.

[0012] "Storage means" refers to a mechanism for storing information entered by job seekers and companies in a database.

[0013] "Matching" refers to the process of comparing job information provided by companies with job information provided by job seekers, calculating the degree of compatibility, and listing mutually suitable candidates.

[0014] "Matching Results" refers to the list of highly compatible job seekers and job offers generated through the matching process.

[0015] "Feedback" refers to evaluations and opinions provided by companies and job seekers, such as interview results and post-hire satisfaction.

[0016] "Scoring" refers to the process of quantifying compatibility based on the job requirements and the job seeker's desired conditions.

[0017] "Means of provision" refers to the mechanism for presenting matching results to companies and job seekers.

[0018] "Accuracy improvement means" refers to a mechanism for improving the accuracy of the matching algorithm based on collected feedback. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] As an embodiment of the present invention, the specific configuration and operation of a system for reducing mismatches between job seekers and companies will be described.

[0041] User registration and information entry

[0042] 1. Entering company information using a company terminal

[0043] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, and then enter details such as job type, required skills, work location, salary, and work style as job information.

[0044] 2. Entering job search information using job seeker terminal

[0045] Job seekers access the system using their job seeker terminal and create a new account. They enter basic information such as their name, contact details, and career history, and then enter details such as their educational background, work experience, skills, desired job type, desired work location, and desired salary as resume information.

[0046] Information stored in a database

[0047] 3. Receipt and storage of data by the server

[0048] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases.

[0049] Executing the matching algorithm

[0050] 4. Server-based matching criteria setting and scoring

[0051] The server sets matching conditions based on the company's job information and the job seeker's job information, and then scores the candidates based on the set conditions, taking into account the degree of match in skills, work location, salary range, etc.

[0052] 5. Server calculates relevance and lists candidates

[0053] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[0054] Providing matching results

[0055] 6. Server Generation and Provision of Matching Results

[0056] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[0057] 7. Check job applicant list and request interviews via company terminal

[0058] The company's human resources personnel use the company terminal to check the provided list of job seekers and send interview requests to interested job seekers.

[0059] 8. Check job listings and apply using job seeker terminals

[0060] Job seekers use their job seeker terminals to check the job listings provided and apply to companies that interest them.

[0061] Feedback collection and post-processing

[0062] 9. Post-interview feedback input via company and job seeker terminals

[0063] After the interview, the company and job seeker each input the interview results and post-employment feedback, including whether or not they passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[0064] 10. Storage and analysis of feedback by our server

[0065] The server stores the collected feedback in a database and analyzes the data, making adjustments to improve the accuracy of the matching algorithm.

[0066] Specific examples

[0067] Example 1: Corporate Use

[0068] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0069] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0070] 3. Server: Searches the job applicant database for applicants who match the criteria and scores them.

[0071] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0072] Example 2: Job Seeker Use

[0073] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java (registered trademark), Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0074] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0075] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[0076] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0077] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] Entering company information using a company terminal

[0081] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0082] Step 2:

[0083] Job seeker entering job search information on a terminal

[0084] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[0085] Step 3:

[0086] Receiving and storing data by the server

[0087] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[0088] Step 4:

[0089] Server-based matching criteria

[0090] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[0091] Step 5:

[0092] Server-based scoring

[0093] The server scores job seekers and companies based on the matching criteria that have been set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score.

[0094] Step 6:

[0095] Server calculates relevance and lists candidates

[0096] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[0097] Step 7:

[0098] Server generates and distributes matching results

[0099] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[0100] Step 8:

[0101] Checking job seeker lists on company terminals

[0102] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[0103] Step 9:

[0104] Job seeker checking job listings on their device

[0105] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[0106] Step 10:

[0107] Feedback Input

[0108] Both companies and job seekers input the results of their interviews via their own devices, and this feedback includes whether or not the candidate passed the interview and their intention to join the company.

[0109] Step 11:

[0110] Server storage and analysis of feedback

[0111] The server receives the input feedback information, stores it in a database, analyzes the feedback data, and adjusts the algorithm to improve the matching accuracy next time.

[0112] Through these steps, the system can improve the accuracy of matching between companies and job seekers, and support efficient recruitment and job-seeking activities.

[0113] Example 1

[0114] 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."

[0115] Reducing the mismatch between job information and recruitment information that occurs between job seekers and companies has been a long-standing challenge. Conventional systems have made it difficult to efficiently match the needs of both parties, and have not adequately utilized feedback to improve matching accuracy. As a result, many job seekers are unable to find the perfect workplace for them, and companies are unable to quickly hire the right talent.

[0116] 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.

[0117] In this invention, the server includes a means for inputting and validating job search information and recruitment information, a means including a generative AI model that sets matching conditions and performs scoring, a means for providing matching results, and a means for collecting and saving feedback to improve the accuracy of the algorithm. This enables highly accurate matching of information on job seekers and companies, improving satisfaction on both sides and realizing optimal job search and recruitment activities.

[0118] "Job Seeker" means an individual who is searching for employment.

[0119] "Company" refers to a legal entity or organization that provides job information to hire job seekers.

[0120] "Job search information" refers to information provided by job seekers, such as work history, skills, and desired conditions.

[0121] "Job information" refers to information provided by companies, such as job type, required skills, work location, salary, etc.

[0122] "Input means" refers to an interface through which a user inputs job search information and recruitment information into the system.

[0123] "Storage means" refers to a database or storage device for storing input information.

[0124] "Matching conditions" refer to the criteria for evaluating the degree of match between job information and employment information.

[0125] "Scoring" refers to the process of quantifying the compatibility between a job seeker and a job offer based on matching criteria.

[0126] "Generative AI model" refers to the artificial intelligence model used in the scoring algorithm.

[0127] "Means of provision" refers to the interface for displaying matching results to job seekers and companies.

[0128] "Feedback" refers to evaluation information collected from job seekers and companies after interviews and hiring.

[0129] "Measures to improve accuracy" refers to the process of adjusting the matching algorithm based on collected feedback.

[0130] "Means for sending interview requests" refers to a function that allows a company to send interview invitations to job seekers.

[0131] "Means of applying for a job" refers to the function that allows job seekers to apply for a job posting from a company.

[0132] MODE FOR CARRYING OUT THE INVENTION

[0133] System Overview

[0134] This invention is a system for reducing mismatches between job seekers and companies and improving matching accuracy. It mainly uses a server, company terminals, and job seeker terminals to efficiently manage and match job search information and recruitment information. It also uses a generative AI model for scoring and improves the algorithm based on feedback.

[0135] Specific examples of hardware and software used

[0136] Server: Use an EC2 instance from Amazon Web Services (AWS (registered trademark)).

[0137] Company and job seeker devices: Devices such as PCs, tablets, and smartphones.

[0138] Database: Use Amazon RDS (Relational Database Service).

[0139] Generative AI models: Use models generated using TENSORFLOW® or PyTorch.

[0140] User Interface: Uses an interface provided through a web browser.

[0141] Program processing

[0142] This system operates in the following manner.

[0143] User registration and information entry

[0144] 1. User: A company's human resources officer accesses the system using a company terminal. A new account creation screen appears, and the employee enters information such as the company name, location, industry, job type, required skills, work location, salary, and working style.

[0145] 2. User: The job seeker accesses the system using their job seeker terminal. A new account creation screen is displayed, and they enter information such as their name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0146] 3. Server: Validates the format of the entered information and checks for invalid data.

[0147] Information stored in a database

[0148] 4. Server: Stores information received from company and job seeker terminals in a dedicated database that supports high-speed searches and filtering.

[0149] Executing the matching algorithm

[0150] 5. Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[0151] 6. Server: Uses a generative AI model to score the suitability of each job seeker with a company based on the set criteria.

[0152] Providing matching results

[0153] 7. Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to the company terminal and the job seeker terminal.

[0154] 8. User: The company's human resources staff checks the provided list and sends interview requests to interested job seekers. Job seekers check the provided job listings and apply to companies they are interested in.

[0155] Feedback collection and post-processing

[0156] 9. User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[0157] 10. Server: The collected feedback is stored in a database and analyzed. The results of this analysis are used to improve the accuracy of the matching algorithm.

[0158] Specific examples

[0159] Example 1: Corporate Use

[0160] 1. Corporate terminal: A company creates a corporate account to recruit new data scientists and enters job information. The required skills are set to "Python, machine learning," the work location to "Osaka," and the salary to "annual salary of 7 million to 9 million yen."

[0161] 2. Server: Stores company job information in a database and searches for suitable job seekers.

[0162] 3. Server: Searches for job applicants who match the criteria from the job applicant database and scores them.

[0163] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0164] Example 2: Job Seeker Use

[0165] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0166] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0167] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[0168] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0169] Example prompt for a generative AI model:

[0170] "A new job seeker has registered. They have the skills and qualifications to be a Data Scientist. Get the job listings that fit you."

[0171] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[0172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0173] Step 1:

[0174] User: A company's HR staff member accesses the system using a company terminal. A new account creation screen is displayed.

[0175] Input: Enter information such as company name, location, industry, job type, required skills, work location, salary, and working style.

[0176] Server: Validates the format of the information entered and checks for invalid data.

[0177] Output: Verified company information data.

[0178] Step 2:

[0179] User: A job seeker accesses the system using a job seeker terminal. A new account creation screen is displayed.

[0180] Input: Enter information such as your name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0181] Server: Validates the format of the information entered and checks for invalid data.

[0182] Output: Verified job applicant information data.

[0183] Step 3:

[0184] Server: Stores information received from company terminals and job seeker terminals in a dedicated database.

[0185] Input: Verified company and job applicant information data.

[0186] Output: Company and job candidate information stored in a database.

[0187] Step 4:

[0188] Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[0189] Input: Company and job candidate information stored in a database.

[0190] Processing: Generative AI models are used to analyze company and job seeker information to generate matching criteria such as skill match, location match, and salary range.

[0191] Output: The matching conditions that have been set.

[0192] Step 5:

[0193] Server: Based on the set matching criteria, the server scores the compatibility between each job seeker and the company.

[0194] Input: The matching criteria you set and the company and job seeker information stored in the database.

[0195] Processing: A generative AI model is used to calculate a relevance score for each item.

[0196] Output: Scoring results.

[0197] Step 6:

[0198] Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to each terminal.

[0199] Input: Scoring results.

[0200] Output: Matching list (list of job seekers and job offers).

[0201] Step 7:

[0202] User (company): The company's human resources personnel checks the provided list and sends interview requests to interested job seekers.

[0203] Input: Job candidate information based on the matching list.

[0204] Action: Send interview request.

[0205] Output: Interview request status.

[0206] Step 8:

[0207] User (job seeker): Job seekers check the provided job listings and apply to companies that interest them.

[0208] Input: Job information based on the matching list.

[0209] Processing: Job applications.

[0210] Output: Application status.

[0211] Step 9:

[0212] User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[0213] Input: Interview results and post-hire feedback.

[0214] Processing: Enter and save feedback.

[0215] Output: Saved feedback data.

[0216] Step 10:

[0217] Server: Stores the collected feedback in a database and analyzes it. The results of this analysis are used to improve the accuracy of the matching algorithm.

[0218] Input: Saved feedback data.

[0219] Processing: Data analysis and algorithm tuning.

[0220] Output: Improved matching algorithm.

[0221] In this way, by performing specific operations at each processing step, validating the format of input data, saving information, setting conditions, scoring, providing results, and obtaining and analyzing feedback, highly accurate matching between job seekers and companies is possible.

[0222] (Application example 1)

[0223] 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."

[0224] Conventional matching systems often result in mismatches between job seekers and companies, resulting in high turnover rates after hiring. Furthermore, for jobs requiring specific skills, such as security work, quick on-site matching is required, but this is difficult to achieve with existing systems. In addition, there is a lack of efficient and accurate matching methods using smart devices, making it impossible to select personnel in real time.

[0225] 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.

[0226] In this invention, the server includes means for job seekers to input job search information, means for companies to input job recruitment information, means for saving the job search information and the job recruitment information, means for matching the job search information with the job recruitment information, means for providing the matching results to job seekers and companies, means for collecting feedback from the job seekers and companies, means for improving matching accuracy based on the feedback, means for scoring skill matches using cosine similarity, and means installed on a smart device. This makes it possible to quickly and accurately match personnel with appropriate skills in real time.

[0227] "Means for job seekers to enter job search information" refers to an interface that allows job seekers to enter their work history, skills, desired conditions, etc. online or on a device.

[0228] "Means for companies to enter job information" refers to an interface for companies to enter job information, such as job type, required skills, work location, salary, etc., online or on a device.

[0229] The "means for saving the job-seeking information and the recruitment information" is a function for saving the input information on job seekers and companies in a database, cloud storage, etc.

[0230] The "means for matching the job search information with the employment information" refers to an algorithm or system that evaluates whether the conditions of the two match based on the stored job search and employment information, and performs matching.

[0231] "Means for providing the matching results to job seekers and companies" refers to a mechanism for notifying the matching results to the terminals and systems of job seekers and companies.

[0232] The "means for collecting feedback from the job seekers and companies" refers to an interface or system for collecting interview results and post-employment evaluations from job seekers and companies.

[0233] The "means for improving matching accuracy based on the feedback" is a function for analyzing collected feedback data and improving the accuracy of the matching algorithm or system.

[0234] "Means for scoring skill matches using cosine similarity" refers to an algorithm or system for evaluating the similarity between the skill sets of job seekers and those required by companies using cosine similarity calculations and performing scoring.

[0235] "Means installed on a smart device" refers to applications or software that can be installed and used on smart devices such as smartphones, smart glasses, and head-mounted displays.

[0236] As an embodiment of this invention, we will explain a personnel matching system specialized for security work. This system aims to reduce mismatches between job seekers and companies, and has a function to collect feedback from job seekers and companies and improve matching accuracy.

[0237] Entering information and saving it to the database

[0238] 1. Entering company information using a company terminal

[0239] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, as well as detailed job information such as "job type," "required skills," "work location," "salary," and "work style."

[0240] 2. Entering job search information using job seeker terminal

[0241] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, and career history, as well as detailed job search information such as their educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0242] 3. Receipt and storage of data by the server

[0243] The server receives the information entered by companies and job seekers and stores it in their own dedicated databases.

[0244] Matching and Scoring

[0245] 4. Server-based matching criteria setting and scoring

[0246] The server sets matching conditions based on the company's job information and the job seeker's job information. According to the set conditions, scoring is performed taking into account the degree of match of skills, work location, salary range, etc. In particular, cosine similarity is used to evaluate the degree of match of skills and score the compatibility.

[0247] 5. Server calculates relevance and lists candidates

[0248] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[0249] Providing matching results

[0250] 6. Server Generation and Provision of Matching Results

[0251] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[0252] 7. On-site use

[0253] Through an application installed on a smart device (e.g., smart glasses, smartphone, etc.) used on-site, security personnel can receive real-time matching results, enabling them to quickly and accurately find the right personnel.

[0254] Feedback collection and post-processing

[0255] 8. Post-interview feedback input via company and job seeker terminals

[0256] After the interview, both the company and the job seeker will input the results of the interview and post-employment feedback, including whether or not the candidate passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[0257] 9. Storage and analysis of feedback by the server

[0258] The server stores the collected feedback in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[0259] Hardware and software used

[0260] The system utilizes the following hardware and software:

[0261] Server: A central server for receiving, storing and processing data, and implementing the appropriate algorithm to perform the cosine similarity calculation.

[0262] Company and Candidate Devices: Personal computers or mobile devices used to enter information, collect feedback, and review match results.

[0263] Smart devices: Applications installed on smart glasses, smartphones, head-mounted displays, etc.

[0264] Software: Skill vectorization was performed using TfidfVectorizer, and Python was used to calculate cosine similarity.

[0265] Examples of concrete examples and prompts

[0266] For example, when a security company is looking to hire new security guards, they input the job information into the system. The system scores job applicants who match the criteria and generates a list of suitable candidates. Using smart glasses on-site, they can quickly find the right candidates.

[0267] Example prompt sentence:

[0268] Describe the design of an application installed on smart glasses to quickly match personnel with the required skills in real time for security services. This application uses TfidfVectorizer to calculate the degree of skill matching using cosine similarity to generate a ranking. Please explain with a concrete example.

[0269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0270] Step 1:

[0271] Entering company and job search information

[0272] A company's human resources staff uses a company terminal to enter job information. A job seeker uses a job seeker terminal to enter job information. The entered information includes detailed information such as the company name, location, job type, required skills, work location, and salary. The entered information is sent to a data server.

[0273] Step 2:

[0274] Data storage

[0275] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases. At this stage, company information and job seeker information are stored in the database in a structured format. The stored data includes the entered text data and numerical data.

[0276] Step 3:

[0277] Matching condition settings and scoring

[0278] The server sets matching conditions based on the saved job information of companies and job seekers. Specifically, it uses TfidfVectorizer to vectorize the skills required by companies and the skills possessed by job seekers, and scores the degree of skill match using cosine similarity. The scoring results are saved as compatibility.

[0279] Step 4:

[0280] Calculating precision and generating candidate lists

[0281] The server calculates the matching rate for each job seeker and company based on the scoring results. Based on the calculation results, it generates a list of candidates suitable for each company and job seeker in descending order of score. This list is saved on the server and formatted in an appropriate format.

[0282] Step 5:

[0283] Providing matching results

[0284] The server provides the generated candidate list to the company's human resources personnel and job seekers. The list includes matching scores and other related information. The company and job seekers receive and check the list through their own devices.

[0285] Step 6:

[0286] Enter your feedback

[0287] After the interview, both the company and the job seeker enter feedback using their respective terminals. The feedback includes the outcome of the interview, the intention to join the company, the level of satisfaction after joining the company, etc. This feedback is sent to the server.

[0288] Step 7:

[0289] Feedback storage and analysis

[0290] The server stores the received feedback in a database. The stored feedback data is analyzed and used to adjust the matching algorithm to improve its accuracy. Based on the analysis results, the system is adjusted to continuously improve matching accuracy.

[0291] Step 8:

[0292] Real-time matching on smart devices

[0293] Security personnel receive real-time matching results through an application installed on smart devices (such as smart glasses or smartphones) used on-site, enabling them to quickly find the right personnel.

[0294] 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.

[0295] As an embodiment of the present invention, the specific configuration and operation of a system that combines an emotion engine to reduce mismatches between job seekers and companies will be described.

[0296] User registration and information entry

[0297] 1. Entering company information using a company terminal

[0298] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0299] 2. Entering job search information using job seeker terminal

[0300] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[0301] Information stored in a database

[0302] 3. Receipt and storage of data by the server

[0303] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[0304] Feedback collection and analysis using emotion engines

[0305] 4. Server-based emotion engine

[0306] The system is configured to install an emotion engine on the server and enable the engine to recognize the emotions of users (companies and job seekers), making it possible to collect user emotions when entering feedback.

[0307] 5. Feedback input via company and job seeker terminals

[0308] After the interview, the company and job seeker each input feedback on the interview results and the hiring process, including the user's emotions collected by the emotion engine.

[0309] 6. Storage and analysis of emotion data by the server

[0310] The server stores the collected feedback and emotion data in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[0311] Executing the matching algorithm

[0312] 7. Server-based matching criteria

[0313] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[0314] 8. Server-based scoring

[0315] The server scores job seekers and companies based on the matching criteria set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score. Emotional data is also taken into account.

[0316] 9. Server calculates relevance and lists candidates

[0317] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[0318] Providing matching results

[0319] 10. Server generation and distribution of matching results

[0320] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[0321] 11. Checking job applicant list via company terminal

[0322] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[0323] 12. Checking job listings on job seeker devices

[0324] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[0325] Specific examples

[0326] Example 1: Corporate Use

[0327] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0328] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0329] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0330] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0331] Example 2: Job Seeker Use

[0332] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0333] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0334] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0335] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0336] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[0337] The processing flow will be explained below.

[0338] Step 1:

[0339] Entering company information using a company terminal

[0340] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0341] Step 2:

[0342] Job seeker entering job search information on a terminal

[0343] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience. They also enter information such as their desired job type, desired work location, desired salary, and skills.

[0344] Step 3:

[0345] Receiving and storing data by the server

[0346] The server receives all information entered by companies and job seekers, and stores the received information in dedicated databases for companies and job seekers.

[0347] Step 4:

[0348] Server-based matching criteria

[0349] The server automatically sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match in skills, work location, salary range, etc.

[0350] Step 5:

[0351] Server-based scoring

[0352] The server performs scoring between job seekers and companies using the set matching conditions. For each job seeker, the degree of suitability is quantified for each company's job information and saved as a score in the database.

[0353] Step 6:

[0354] Server calculates relevance and lists candidates

[0355] The server calculates the matching rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of matching rate, which are provided to companies and job seekers, respectively.

[0356] Step 7:

[0357] Check job applicant list and request interviews via company terminal

[0358] A company's human resources personnel uses a company terminal to check the job applicant list provided by the server, and sends interview requests to interested job applicants through the system.

[0359] Step 8:

[0360] Job seekers can check job listings and apply using their device

[0361] The job seeker uses the job seeker terminal to check the job listings provided by the server, and sends a request to apply for the jobs in the list that interest them.

[0362] Step 9:

[0363] Server-based emotion engine implementation

[0364] An emotion engine is installed on the server, which can recognize the user's emotions when inputting feedback, and emotional data is also collected.

[0365] Step 10:

[0366] Feedback input via company and job seeker terminals

[0367] After the interview, the company and job seeker use their respective devices to input the interview results and post-hire feedback, which includes emotional data collected by the emotion engine.

[0368] Step 11:

[0369] Emotion data storage and analysis by server

[0370] The server stores the input feedback and emotion data in a database, analyzes all stored data, and makes adjustments to improve the accuracy of the matching algorithm.

[0371] Specific examples

[0372] Example 1: Corporate Use

[0373] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0374] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0375] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0376] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0377] Example 2: Job Seeker Use

[0378] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0379] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0380] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0381] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0382] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[0383] Example 2

[0384] 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."

[0385] Conventional systems for matching job seekers and companies lacked sufficient matching accuracy, resulting in frequent mismatches between job seekers and companies. Furthermore, there was no way to fully utilize user feedback, making it difficult to improve matching accuracy. Furthermore, there was no mechanism for taking user emotions into consideration, making it difficult to achieve appropriate matching.

[0386] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for a job seeker to input job search information, a means for a company to input job search information, a means for saving the job search information and the job search information, a means for matching the job search information with the job search information, a means for providing the matching results to the job seeker and the company, a means for collecting feedback from the job seeker and the company, a means for improving matching accuracy based on the feedback, a means for collecting and analyzing emotional data, and a means for adjusting the matching algorithm using the collected emotional data. This enables improved matching accuracy based on user feedback and more appropriate matching that takes emotional data into consideration.

[0387] "Job search information" refers to information that job seekers enter into the system, such as name, contact information, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[0388] "Job information" refers to information that companies enter into the system, such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[0389] "Feedback" refers to information such as interview results, post-employment evaluations, and impressions that job seekers and companies enter into the system after an interview.

[0390] "Emotional Data" refers to data collected using an emotion engine that represents a user's emotional state.

[0391] A "matching algorithm" refers to a calculation method for calculating the compatibility between job search information and employment information to achieve optimal matching.

[0392] "Scoring" refers to the process of quantifying information about job seekers and companies based on set matching conditions and expressing the degree of compatibility numerically.

[0393] "Relevance rate" refers to the percentage of the score results that indicate the degree of match between job seekers and job information.

[0394] "Emotion engine" refers to a software module that analyzes and collects emotions from user input data and feedback.

[0395] "Storage means" refers to a function for storing input information in a storage device such as a database.

[0396] "Providing means" refers to the function of presenting matching results and other information to users.

[0397] The present invention relates to a system for reducing mismatches between job seekers and companies, and in particular to improving matching accuracy by using an emotion engine. This system is implemented using the following hardware and software.

[0398] User registration and information entry

[0399] First, companies and job seekers access the system using their respective terminals and create accounts.

[0400] Ordinary personal computers (PCs) and tablet devices are used as corporate terminals.

[0401] Job seekers also use PCs and smartphones as their devices.

[0402] A company's human resources staff enters information such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[0403] Job seekers enter information such as their name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[0404] Information stored in a database

[0405] The server receives the entered company and job seeker information and stores it in a database.

[0406] This database is a general relational database system (RDBMS), such as MySQL (registered trademark) or PostgreSQL.

[0407] The stored data is validated to ensure consistency.

[0408] Feedback collection and analysis using emotion engines

[0409] The server can implement an emotion engine to collect user emotion data during feedback.

[0410] This emotion engine uses, for example, Google (registered trademark) emotion analysis API or IBM Watson (registered trademark) emotion analysis service.

[0411] After the interview, the company and job seeker enter their impressions and evaluations as feedback, which also includes emotional data collected by the emotion engine.

[0412] The server stores this feedback and emotion data in a database for later analysis.

[0413] Executing the matching algorithm

[0414] The server sets matching conditions based on the company's job information and the job seeker's job information. The conditions set include the degree of match in skills, work location, salary range, etc. The server performs scoring based on the set conditions and quantifies the suitability of each job seeker. Emotional data is also taken into account here.

[0415] Based on the scoring results, a list of candidates and job offers is generated in descending order of relevance.

[0416] Providing matching results

[0417] The server provides the generated matching results to job seekers and companies.

[0418] Corporate human resources personnel can review the list of job applicants provided and send interview requests to those who interest them.

[0419] Job seekers can also check the job listings provided and apply for the jobs that interest them.

[0420] Specific examples

[0421] Example 1: Corporate Use

[0422] 1. Company terminal: A company is looking to hire a new data scientist, so they create a company account and enter their job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0423] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0424] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0425] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0426] Example 2: Job Seeker Use

[0427] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0428] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0429] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0430] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0431] Prompt Sentence Examples

[0432] Please enter your company information.

[0433] Please enter your job search information.

[0434] "Please provide feedback and emotional state."

[0435] In this way, the system supports appropriate matching between job seekers and companies, and can achieve more satisfying matching by taking emotional data into account.

[0436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0437] Step 1: User registration and information entry

[0438] 1-1. Entering company information using a company terminal:

[0439] Input: Company name, address, industry, contact information, job type, required skills, work location, salary, and working conditions.

[0440] Specific operation: A company's human resources officer accesses the system using a company terminal, enters the required information in the input form, and clicks the "Submit" button.

[0441] Output: Company details and job listings are sent to the server.

[0442] 1-2. Entering job search information using job seeker terminal:

[0443] Input: Name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[0444] Specific operation: The job seeker accesses the system using the job seeker terminal, enters the required information in the input form, and clicks the "Submit" button.

[0445] Output: The job seeker's basic information and job application information are sent to the server.

[0446] Step 2: Store the information in a database

[0447] 2-1. Receipt and storage of data by the server:

[0448] Input: Information submitted by companies and job seekers.

[0449] Specific operation: The server receives information sent by companies and job seekers, stores it separately in each database, and validates it to ensure data consistency.

[0450] Output: Company and job search information stored in a database.

[0451] Step 3: Collect and analyze feedback using the emotion engine

[0452] 3-1. Emotion engine settings:

[0453] Input: Configuration information for the Emotion Engine API.

[0454] Specific operation: The server calls the emotion engine's API and configures it to analyze the user's input data and feedback.

[0455] Output: The emotion engine is ready to run.

[0456] 3-2. Gathering feedback:

[0457] Input: Feedback information such as post-interview evaluations and impressions.

[0458] Specific operation: Companies and job seekers enter feedback after the interview, and the emotion engine automatically collects users' emotion data. Enter information in the feedback form and click the "Submit" button.

[0459] Output: Feedback information including emotion data is sent to the server and stored in a database.

[0460] 3-3. Emotional Data Analysis:

[0461] Input: Collected feedback and sentiment data.

[0462] Specific operation: The server analyzes the data collected through the emotion engine and adjusts the parameters of the matching algorithm.

[0463] Output: The analyzed emotion data and adjusted algorithm parameters are saved.

[0464] Step 4: Run the matching algorithm

[0465] 4-1. Matching condition settings:

[0466] Input: Company job information, job seeker job information.

[0467] Specific operation: The server sets matching conditions based on criteria such as skill match, work location match, and salary range match.

[0468] Output: The matching criteria that have been set.

[0469] 4-2. Scoring execution:

[0470] Input: Matching criteria, company and job seeker information.

[0471] How it works: The server scores job seekers and companies based on matching criteria, taking into account emotional data. It then quantifies the degree of compatibility for each criterion and generates a total score.

[0472] Output: The scoring results are stored in a database.

[0473] 4-3. Calculating and listing precision:

[0474] Input: Scoring results.

[0475] Specific operation: The server calculates the relevance rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of relevance rate.

[0476] Output: A generated list of candidates and job offers based on the match rate.

[0477] Step 5: Providing matching results

[0478] 5-1. Generation and distribution of matching results:

[0479] Input: A list based on precision.

[0480] Specific operation: The server generates a list of highly suitable candidates and job offers and distributes them to each company and job seeker.

[0481] Output: List of candidates and jobs provided, notified to the user via their dashboard or email.

[0482] 5-2. Check the job seeker list:

[0483] Input: The provided job seeker list.

[0484] Specific operation: The company's human resources staff uses the company terminal to check the list and select the job seekers to whom they want to send an interview request. They check the list on the dashboard and click the "Request an interview" button.

[0485] Output: An interview request is sent to the job candidate.

[0486] 5-3. Check the job listings:

[0487] Input: The provided job listing.

[0488] Specific operation: The job seeker uses the job seeker terminal to check the list and select the job they want to apply for. They check the list on the dashboard and click the "Apply" button.

[0489] Output: A job application is sent to the company.

[0490] These are the specific processing steps of the system. By using this system, it is possible to improve the accuracy of matching between companies and job seekers and achieve appropriate matching that takes emotional data into account.

[0491] (Application example 2)

[0492] 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."

[0493] Conventional systems for matching job seekers and companies do not fully utilize feedback and emotional data between the two parties, resulting in low matching accuracy and insufficient user satisfaction. Furthermore, advertisements are delivered without taking into account the user's emotional state, resulting in insufficient effectiveness. By resolving these issues, there is a need to improve the accuracy of matching job seekers and companies while also enabling advertisement delivery based on the user's emotional state.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0495] In this invention, the server includes means for a job seeker to input job search information, means for a company to input job information, means for saving the job search information and the job information, means for matching the job search information with the job information, means for providing the matching results to the job seeker and the company, means for collecting feedback from the job seeker and the company, means for improving matching accuracy based on the feedback, means for collecting user emotion data, means for personalizing advertisements based on the emotion data, and means for delivering personalized advertisements to the user.

[0496] This will not only make it possible to improve matching accuracy by utilizing feedback and emotional data between job seekers and companies, but also enable appropriate advertisement delivery based on the user's emotional state.

[0497] "Job search information" refers to information entered by job seekers, such as work experience, skills, desired job type, and work location.

[0498] "Job information" refers to information about recruitment entered by companies, such as job type, required skills, work location, and salary.

[0499] The "means for saving" is a function for saving the entered job search information and recruitment information in a database while maintaining consistency.

[0500] The "means of matching" is a function that calculates compatibility based on job search information and employment information, and extracts matching candidates.

[0501] "Means for providing matching results" refers to the function of presenting matching results to job seekers and companies.

[0502] "Means for collecting feedback" is a function for collecting opinions and feedback from job seekers and companies.

[0503] "Means for improving matching accuracy" refers to a function that analyzes collected feedback and optimizes the matching algorithm based on that.

[0504] "Means for collecting emotional data" refers to a function for collecting the user's emotional state in real time through facial expression recognition and voice analysis.

[0505] "Means for personalizing ads" refers to a function that selects the most suitable ads for users based on collected emotional data.

[0506] "Means for delivering personalized advertisements" refers to a function for delivering selected personalized advertisements to users.

[0507] The following system configuration and operation will be described as an embodiment of the present invention.

[0508] System Configuration

[0509] This system consists of the following main components:

[0510] User device: A device where job seekers and companies enter information and runs a smartphone app.

[0511] Server: Stores data, performs matching, collects feedback, and performs analytics.

[0512] Database: A database for storing job applications, job postings, feedback, and sentiment data.

[0513] Emotion Engine: An engine that collects and analyzes user emotion data in real time.

[0514] Ad management system: A system that selects and delivers advertisements appropriate for users based on emotional data.

[0515] Hardware and Software

[0516] Hardware: Smartphones, servers

[0517] Software: Emotion engines (e.g., EmotionAI SDK), database management systems (e.g., MySQL), matching algorithms, ad management systems (e.g., Google Ad API, Facebook Ads API), mobile application frameworks (e.g., React Native, Flutter (registered trademark))

[0518] Operational Overview

[0519] 1. User registration and information entry

[0520] The server receives information entered by job seekers and companies via a smartphone app (such as name, contact details, skills, and desired conditions) and stores it in a database.

[0521] The user device provides an intuitive interface when registering job search information or recruitment information (e.g., React Native app).

[0522] 2. Collecting Emotional Data

[0523] The emotion engine collects the user's emotional state in real time through facial expression recognition and voice analysis, and sends it to the server, where the emotional data is stored in a database.

[0524] 3. Matching and feedback collection and analysis

[0525] The server runs a matching algorithm based on the job search information and job information, calculates the degree of compatibility, and provides the results to job seekers and companies.

[0526] After the interview or after the hire, the server collects feedback from the job seeker and the company and stores it in a database.

[0527] The feedback data is analyzed to improve the accuracy of the matching algorithm.

[0528] 4. Personalizing and delivering ads

[0529] The ad management system uses the collected emotional data to select the most suitable ad for the user, and the ad is then personalized and delivered to the user.

[0530] The smartphone app displays appropriate advertisements as notifications depending on your emotional state.

[0531] Specific examples

[0532] Example 1: Recruitment advertisement example

[0533] When a job seeker requests a "software engineer" position on a user device, they enter their desired job title into a smartphone app.

[0534] The server takes into account feedback and sentiment data to display the most suitable job listings.

[0535] Example 2: Emotion-based advertising example

[0536] The emotion engine detects the user's state of relaxation.

[0537] The advertising management system selects advertisements for products with relaxing effects and sends notifications to smartphones saying, "How about some aromas that are perfect for relaxation?"

[0538] Prompt Sentence Examples

[0539] Below are some example prompts for ad selection based on emotional state:

[0540] Mechanism for collecting user relaxation data and generating appropriate advertisements

[0541] The emotion engine detects the user's state of relaxation. Based on that, it selects ads for products with the most relaxing effects and sends notifications to the user. Specifically, please explain the mechanism for sending aroma ads when the user is relaxed.

[0542] In this way, the system improves the accuracy of matching job seekers with companies and delivers personalized advertisements based on the user's emotional state.

[0543] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0544] Step 1:

[0545] User registration and information entry

[0546] Users (job seekers and companies) use a smartphone app to enter the necessary information. Job seekers enter their name, contact details, work experience, skills, desired job type, and desired work location, while companies enter their company name, location, industry, job type, required skills, work location, salary, etc. This information is sent to the server and saved in a database.

[0547] Input: Job seeker information, job information

[0548] Output: Job applicant information and job postings stored in a database

[0549] Step 2:

[0550] Collecting Emotional Data

[0551] While a user is using the smartphone app, the emotion engine collects the user's emotional data (facial expressions, voice, etc.) in real time. The emotional data is sent to a server and stored in an emotion database.

[0552] Input: Real-time user emotion data

[0553] Output: Emotion data stored in a database

[0554] Step 3:

[0555] Performing matching

[0556] The server runs a matching algorithm based on the job search information and job information stored in the database. The matching algorithm calculates the degree of compatibility by taking into account conditions such as "skill compatibility," "work location compatibility," and "salary range compatibility," and generates matching results based on this.

[0557] Input: Job search information, recruitment information

[0558] Output: Matching result (relevance score)

[0559] Step 4:

[0560] Providing matching results

[0561] The server generates a list of the most suitable job seekers and companies based on the suitability scores and provides it to the job seekers' and companies' terminals. Job seekers check the job listings, and companies check the job seeker listings.

[0562] Input: Matching result (relevance score)

[0563] Output: Job seeker list, job listing

[0564] Step 5:

[0565] Collecting feedback

[0566] Job seekers and companies enter feedback through the system after an interview or after being hired. Feedback includes impressions of the interview and level of satisfaction after being hired. The server collects this feedback and stores it in a database.

[0567] Input: Feedback from job seekers and companies

[0568] Output: Feedback stored in a database

[0569] Step 6:

[0570] Feedback analysis and algorithm optimization

[0571] The server analyzes the collected feedback data and makes necessary adjustments to improve the accuracy of the matching algorithm, which will result in better matching in the future.

[0572] Input: Feedback data

[0573] Output: Optimized matching algorithm

[0574] Step 7:

[0575] Ad personalization and delivery

[0576] The ad management system analyzes the collected emotional data and selects the advertisement that best suits the user's emotional state. The selected advertisement is then delivered to the user's smartphone as a notification.

[0577] Input: Emotion data

[0578] Output: Personalized advertising notifications

[0579] As a specific example of how it works, if the emotion engine detects that a job seeker is relaxed, the advertising management system will select an advertisement for a product with a relaxing effect and send a notification to the job seeker's smartphone saying, "Thank you for your hard work. How about an aroma with a perfect scent for relaxation?"

[0580] 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.

[0581] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0582] 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.

[0583] [Second embodiment]

[0584] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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."

[0596] As an embodiment of the present invention, the specific configuration and operation of a system for reducing mismatches between job seekers and companies will be described.

[0597] User registration and information entry

[0598] 1. Entering company information using a company terminal

[0599] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, and then enter details such as job type, required skills, work location, salary, and work style as job information.

[0600] 2. Entering job search information using job seeker terminal

[0601] Job seekers access the system using their job seeker terminal and create a new account. They enter basic information such as their name, contact details, and career history, and then enter details such as their educational background, work experience, skills, desired job type, desired work location, and desired salary as resume information.

[0602] Information stored in a database

[0603] 3. Receipt and storage of data by the server

[0604] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases.

[0605] Executing the matching algorithm

[0606] 4. Server-based matching criteria setting and scoring

[0607] The server sets matching conditions based on the company's job information and the job seeker's job information, and then scores the candidates based on the set conditions, taking into account the degree of match in skills, work location, salary range, etc.

[0608] 5. Server calculates relevance and lists candidates

[0609] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[0610] Providing matching results

[0611] 6. Server Generation and Provision of Matching Results

[0612] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[0613] 7. Check job applicant list and request interviews via company terminal

[0614] The company's human resources personnel use the company terminal to check the provided list of job seekers and send interview requests to interested job seekers.

[0615] 8. Check job listings and apply using job seeker terminals

[0616] Job seekers use their job seeker terminals to check the job listings provided and apply to companies that interest them.

[0617] Feedback collection and post-processing

[0618] 9. Post-interview feedback input via company and job seeker terminals

[0619] After the interview, the company and job seeker each input the interview results and post-employment feedback, including whether or not they passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[0620] 10. Storage and analysis of feedback by our server

[0621] The server stores the collected feedback in a database and analyzes the data, making adjustments to improve the accuracy of the matching algorithm.

[0622] Specific examples

[0623] Example 1: Corporate Use

[0624] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0625] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0626] 3. Server: Searches the job applicant database for applicants who match the criteria and scores them.

[0627] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0628] Example 2: Job Seeker Use

[0629] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0630] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0631] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[0632] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0633] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[0634] The processing flow will be explained below.

[0635] Step 1:

[0636] Entering company information using a company terminal

[0637] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0638] Step 2:

[0639] Job seeker entering job search information on a terminal

[0640] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[0641] Step 3:

[0642] Receiving and storing data by the server

[0643] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[0644] Step 4:

[0645] Server-based matching criteria

[0646] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[0647] Step 5:

[0648] Server-based scoring

[0649] The server scores job seekers and companies based on the matching criteria that have been set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score.

[0650] Step 6:

[0651] Server calculates relevance and lists candidates

[0652] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[0653] Step 7:

[0654] Server generates and distributes matching results

[0655] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[0656] Step 8:

[0657] Checking job seeker lists on company terminals

[0658] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[0659] Step 9:

[0660] Job seeker checking job listings on their device

[0661] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[0662] Step 10:

[0663] Feedback Input

[0664] Both companies and job seekers input the results of their interviews via their own devices, and this feedback includes whether or not the candidate passed the interview and their intention to join the company.

[0665] Step 11:

[0666] Server storage and analysis of feedback

[0667] The server receives the input feedback information, stores it in a database, analyzes the feedback data, and adjusts the algorithm to improve the matching accuracy next time.

[0668] Through these steps, the system can improve the accuracy of matching between companies and job seekers, and support efficient recruitment and job-seeking activities.

[0669] Example 1

[0670] 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."

[0671] Reducing the mismatch between job information and recruitment information that occurs between job seekers and companies has been a long-standing challenge. Conventional systems have made it difficult to efficiently match the needs of both parties, and have not adequately utilized feedback to improve matching accuracy. As a result, many job seekers are unable to find the perfect workplace for them, and companies are unable to quickly hire the right talent.

[0672] 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.

[0673] In this invention, the server includes a means for inputting and validating job search information and recruitment information, a means including a generative AI model that sets matching conditions and performs scoring, a means for providing matching results, and a means for collecting and saving feedback to improve the accuracy of the algorithm. This enables highly accurate matching of information on job seekers and companies, improving satisfaction on both sides and realizing optimal job search and recruitment activities.

[0674] "Job Seeker" means an individual who is searching for employment.

[0675] "Company" refers to a legal entity or organization that provides job information to hire job seekers.

[0676] "Job search information" refers to information provided by job seekers, such as work history, skills, and desired conditions.

[0677] "Job information" refers to information provided by companies, such as job type, required skills, work location, salary, etc.

[0678] "Input means" refers to an interface through which a user inputs job search information and recruitment information into the system.

[0679] "Storage means" refers to a database or storage device for storing input information.

[0680] "Matching conditions" refer to the criteria for evaluating the degree of match between job information and employment information.

[0681] "Scoring" refers to the process of quantifying the compatibility between a job seeker and a job offer based on matching criteria.

[0682] "Generative AI model" refers to the artificial intelligence model used in the scoring algorithm.

[0683] "Means of provision" refers to the interface for displaying matching results to job seekers and companies.

[0684] "Feedback" refers to evaluation information collected from job seekers and companies after interviews and hiring.

[0685] "Measures to improve accuracy" refers to the process of adjusting the matching algorithm based on collected feedback.

[0686] "Means for sending interview requests" refers to a function that allows a company to send interview invitations to job seekers.

[0687] "Means of applying for a job" refers to the function that allows job seekers to apply for a job posting from a company.

[0688] MODE FOR CARRYING OUT THE INVENTION

[0689] System Overview

[0690] This invention is a system for reducing mismatches between job seekers and companies and improving matching accuracy. It mainly uses a server, company terminals, and job seeker terminals to efficiently manage and match job search information and recruitment information. It also uses a generative AI model for scoring and improves the algorithm based on feedback.

[0691] Specific examples of hardware and software used

[0692] Server: Use an EC2 instance from Amazon Web Services (AWS).

[0693] Company and job seeker devices: Devices such as PCs, tablets, and smartphones.

[0694] Database: Use Amazon RDS (Relational Database Service).

[0695] Generative AI models: Use models generated using TensorFlow or PyTorch.

[0696] User Interface: Uses an interface provided through a web browser.

[0697] Program processing

[0698] This system operates in the following manner.

[0699] User registration and information entry

[0700] 1. User: A company's human resources officer accesses the system using a company terminal. A new account creation screen appears, and the employee enters information such as the company name, location, industry, job type, required skills, work location, salary, and working style.

[0701] 2. User: The job seeker accesses the system using their job seeker terminal. A new account creation screen is displayed, and they enter information such as their name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0702] 3. Server: Validates the format of the entered information and checks for invalid data.

[0703] Information stored in a database

[0704] 4. Server: Stores information received from company and job seeker terminals in a dedicated database that supports high-speed searches and filtering.

[0705] Executing the matching algorithm

[0706] 5. Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[0707] 6. Server: Uses a generative AI model to score the suitability of each job seeker with a company based on the set criteria.

[0708] Providing matching results

[0709] 7. Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to the company terminal and the job seeker terminal.

[0710] 8. User: The company's human resources staff checks the provided list and sends interview requests to interested job seekers. Job seekers check the provided job listings and apply to companies they are interested in.

[0711] Feedback collection and post-processing

[0712] 9. User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[0713] 10. Server: The collected feedback is stored in a database and analyzed. The results of this analysis are used to improve the accuracy of the matching algorithm.

[0714] Specific examples

[0715] Example 1: Corporate Use

[0716] 1. Corporate terminal: A company creates a corporate account to recruit new data scientists and enters job information. The required skills are set to "Python, machine learning," the work location to "Osaka," and the salary to "annual salary of 7 million to 9 million yen."

[0717] 2. Server: Stores company job information in a database and searches for suitable job seekers.

[0718] 3. Server: Searches for job applicants who match the criteria from the job applicant database and scores them.

[0719] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0720] Example 2: Job Seeker Use

[0721] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0722] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0723] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[0724] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0725] Example prompt for a generative AI model:

[0726] "A new job seeker has registered. They have the skills and qualifications to be a Data Scientist. Get the job listings that fit you."

[0727] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[0728] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] User: A company's HR staff member accesses the system using a company terminal. A new account creation screen is displayed.

[0731] Input: Enter information such as company name, location, industry, job type, required skills, work location, salary, and working style.

[0732] Server: Validates the format of the information entered and checks for invalid data.

[0733] Output: Verified company information data.

[0734] Step 2:

[0735] User: A job seeker accesses the system using a job seeker terminal. A new account creation screen is displayed.

[0736] Input: Enter information such as your name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0737] Server: Validates the format of the information entered and checks for invalid data.

[0738] Output: Verified job applicant information data.

[0739] Step 3:

[0740] Server: Stores information received from company terminals and job seeker terminals in a dedicated database.

[0741] Input: Verified company and job applicant information data.

[0742] Output: Company and job candidate information stored in a database.

[0743] Step 4:

[0744] Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[0745] Input: Company and job candidate information stored in a database.

[0746] Processing: Generative AI models are used to analyze company and job seeker information to generate matching criteria such as skill match, location match, and salary range.

[0747] Output: The matching conditions that have been set.

[0748] Step 5:

[0749] Server: Based on the set matching criteria, the server scores the compatibility between each job seeker and the company.

[0750] Input: The matching criteria you set and the company and job seeker information stored in the database.

[0751] Processing: A generative AI model is used to calculate a relevance score for each item.

[0752] Output: Scoring results.

[0753] Step 6:

[0754] Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to each terminal.

[0755] Input: Scoring results.

[0756] Output: Matching list (list of job seekers and job offers).

[0757] Step 7:

[0758] User (company): The company's human resources personnel checks the provided list and sends interview requests to interested job seekers.

[0759] Input: Job candidate information based on the matching list.

[0760] Action: Send interview request.

[0761] Output: Interview request status.

[0762] Step 8:

[0763] User (job seeker): Job seekers check the provided job listings and apply to companies that interest them.

[0764] Input: Job information based on the matching list.

[0765] Processing: Job applications.

[0766] Output: Application status.

[0767] Step 9:

[0768] User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[0769] Input: Interview results and post-hire feedback.

[0770] Processing: Enter and save feedback.

[0771] Output: Saved feedback data.

[0772] Step 10:

[0773] Server: Stores the collected feedback in a database and analyzes it. The results of this analysis are used to improve the accuracy of the matching algorithm.

[0774] Input: Saved feedback data.

[0775] Processing: Data analysis and algorithm tuning.

[0776] Output: Improved matching algorithm.

[0777] In this way, by performing specific operations at each processing step, validating the format of input data, saving information, setting conditions, scoring, providing results, and obtaining and analyzing feedback, highly accurate matching between job seekers and companies is possible.

[0778] (Application example 1)

[0779] 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."

[0780] Conventional matching systems often result in mismatches between job seekers and companies, resulting in high turnover rates after hiring. Furthermore, for jobs requiring specific skills, such as security work, quick on-site matching is required, but this is difficult to achieve with existing systems. In addition, there is a lack of efficient and accurate matching methods using smart devices, making it impossible to select personnel in real time.

[0781] 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.

[0782] In this invention, the server includes means for job seekers to input job search information, means for companies to input job recruitment information, means for saving the job search information and the job recruitment information, means for matching the job search information with the job recruitment information, means for providing the matching results to job seekers and companies, means for collecting feedback from the job seekers and companies, means for improving matching accuracy based on the feedback, means for scoring skill matches using cosine similarity, and means installed on a smart device. This makes it possible to quickly and accurately match personnel with appropriate skills in real time.

[0783] "Means for job seekers to enter job search information" refers to an interface that allows job seekers to enter their work history, skills, desired conditions, etc. online or on a device.

[0784] "Means for companies to enter job information" refers to an interface for companies to enter job information, such as job type, required skills, work location, salary, etc., online or on a device.

[0785] The "means for saving the job-seeking information and the recruitment information" is a function for saving the input information on job seekers and companies in a database, cloud storage, etc.

[0786] The "means for matching the job search information with the employment information" refers to an algorithm or system that evaluates whether the conditions of the two match based on the stored job search and employment information, and performs matching.

[0787] "Means for providing the matching results to job seekers and companies" refers to a mechanism for notifying the matching results to the terminals and systems of job seekers and companies.

[0788] The "means for collecting feedback from the job seekers and companies" refers to an interface or system for collecting interview results and post-employment evaluations from job seekers and companies.

[0789] The "means for improving matching accuracy based on the feedback" is a function for analyzing collected feedback data and improving the accuracy of the matching algorithm or system.

[0790] "Means for scoring skill matches using cosine similarity" refers to an algorithm or system for evaluating the similarity between the skill sets of job seekers and those required by companies using cosine similarity calculations and performing scoring.

[0791] "Means installed on a smart device" refers to applications or software that can be installed and used on smart devices such as smartphones, smart glasses, and head-mounted displays.

[0792] As an embodiment of this invention, we will explain a personnel matching system specialized for security work. This system aims to reduce mismatches between job seekers and companies, and has a function to collect feedback from job seekers and companies and improve matching accuracy.

[0793] Entering information and saving it to the database

[0794] 1. Entering company information using a company terminal

[0795] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, as well as detailed job information such as "job type," "required skills," "work location," "salary," and "work style."

[0796] 2. Entering job search information using job seeker terminal

[0797] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, and career history, as well as detailed job search information such as their educational background, work experience, skills, desired job type, desired work location, and desired salary.

[0798] 3. Receipt and storage of data by the server

[0799] The server receives the information entered by companies and job seekers and stores it in their own dedicated databases.

[0800] Matching and Scoring

[0801] 4. Server-based matching criteria setting and scoring

[0802] The server sets matching conditions based on the company's job information and the job seeker's job information. According to the set conditions, scoring is performed taking into account the degree of match of skills, work location, salary range, etc. In particular, cosine similarity is used to evaluate the degree of match of skills and score the compatibility.

[0803] 5. Server calculates relevance and lists candidates

[0804] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[0805] Providing matching results

[0806] 6. Server Generation and Provision of Matching Results

[0807] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[0808] 7. On-site use

[0809] Through an application installed on a smart device (e.g., smart glasses, smartphone, etc.) used on-site, security personnel can receive real-time matching results, enabling them to quickly and accurately find the right personnel.

[0810] Feedback collection and post-processing

[0811] 8. Post-interview feedback input via company and job seeker terminals

[0812] After the interview, both the company and the job seeker will input the results of the interview and post-employment feedback, including whether or not the candidate passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[0813] 9. Storage and analysis of feedback by the server

[0814] The server stores the collected feedback in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[0815] Hardware and software used

[0816] The system utilizes the following hardware and software:

[0817] Server: A central server for receiving, storing and processing data, and implementing the appropriate algorithm to perform the cosine similarity calculation.

[0818] Company and Candidate Devices: Personal computers or mobile devices used to enter information, collect feedback, and review match results.

[0819] Smart devices: Applications installed on smart glasses, smartphones, head-mounted displays, etc.

[0820] Software: Skill vectorization was performed using TfidfVectorizer, and Python was used to calculate cosine similarity.

[0821] Examples of concrete examples and prompts

[0822] For example, when a security company is looking to hire new security guards, they input the job information into the system. The system scores job applicants who match the criteria and generates a list of suitable candidates. Using smart glasses on-site, they can quickly find the right candidates.

[0823] Example prompt sentence:

[0824] Describe the design of an application installed on smart glasses to quickly match personnel with the required skills in real time for security services. This application uses TfidfVectorizer to calculate the degree of skill matching using cosine similarity to generate a ranking. Please explain with a concrete example.

[0825] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0826] Step 1:

[0827] Entering company and job search information

[0828] A company's human resources staff uses a company terminal to enter job information. A job seeker uses a job seeker terminal to enter job information. The entered information includes detailed information such as the company name, location, job type, required skills, work location, and salary. The entered information is sent to a data server.

[0829] Step 2:

[0830] Data storage

[0831] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases. At this stage, company information and job seeker information are stored in the database in a structured format. The stored data includes the entered text data and numerical data.

[0832] Step 3:

[0833] Matching condition settings and scoring

[0834] The server sets matching conditions based on the saved job information of companies and job seekers. Specifically, it uses TfidfVectorizer to vectorize the skills required by companies and the skills possessed by job seekers, and scores the degree of skill match using cosine similarity. The scoring results are saved as compatibility.

[0835] Step 4:

[0836] Calculating precision and generating candidate lists

[0837] The server calculates the matching rate for each job seeker and company based on the scoring results. Based on the calculation results, it generates a list of candidates suitable for each company and job seeker in descending order of score. This list is saved on the server and formatted in an appropriate format.

[0838] Step 5:

[0839] Providing matching results

[0840] The server provides the generated candidate list to the company's human resources personnel and job seekers. The list includes matching scores and other related information. The company and job seekers receive and check the list through their own devices.

[0841] Step 6:

[0842] Enter your feedback

[0843] After the interview, both the company and the job seeker enter feedback using their respective terminals. The feedback includes the outcome of the interview, the intention to join the company, the level of satisfaction after joining the company, etc. This feedback is sent to the server.

[0844] Step 7:

[0845] Feedback storage and analysis

[0846] The server stores the received feedback in a database. The stored feedback data is analyzed and used to adjust the matching algorithm to improve its accuracy. Based on the analysis results, the system is adjusted to continuously improve matching accuracy.

[0847] Step 8:

[0848] Real-time matching on smart devices

[0849] Security personnel receive real-time matching results through an application installed on smart devices (such as smart glasses or smartphones) used on-site, enabling them to quickly find the right personnel.

[0850] 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.

[0851] As an embodiment of the present invention, the specific configuration and operation of a system that combines an emotion engine to reduce mismatches between job seekers and companies will be described.

[0852] User registration and information entry

[0853] 1. Entering company information using a company terminal

[0854] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0855] 2. Entering job search information using job seeker terminal

[0856] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[0857] Information stored in a database

[0858] 3. Receipt and storage of data by the server

[0859] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[0860] Feedback collection and analysis using emotion engines

[0861] 4. Server-based emotion engine

[0862] The system is configured to install an emotion engine on the server and enable the engine to recognize the emotions of users (companies and job seekers), making it possible to collect user emotions when entering feedback.

[0863] 5. Feedback input via company and job seeker terminals

[0864] After the interview, the company and job seeker each input feedback on the interview results and the hiring process, including the user's emotions collected by the emotion engine.

[0865] 6. Storage and analysis of emotion data by the server

[0866] The server stores the collected feedback and emotion data in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[0867] Executing the matching algorithm

[0868] 7. Server-based matching criteria

[0869] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[0870] 8. Server-based scoring

[0871] The server scores job seekers and companies based on the matching criteria set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score. Emotional data is also taken into account.

[0872] 9. Server calculates relevance and lists candidates

[0873] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[0874] Providing matching results

[0875] 10. Server generation and distribution of matching results

[0876] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[0877] 11. Checking job applicant list via company terminal

[0878] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[0879] 12. Checking job listings on job seeker devices

[0880] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[0881] Specific examples

[0882] Example 1: Corporate Use

[0883] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0884] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0885] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0886] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0887] Example 2: Job Seeker Use

[0888] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0889] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0890] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0891] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0892] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[0893] The processing flow will be explained below.

[0894] Step 1:

[0895] Entering company information using a company terminal

[0896] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[0897] Step 2:

[0898] Job seeker entering job search information on a terminal

[0899] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience. They also enter information such as their desired job type, desired work location, desired salary, and skills.

[0900] Step 3:

[0901] Receiving and storing data by the server

[0902] The server receives all information entered by companies and job seekers, and stores the received information in dedicated databases for companies and job seekers.

[0903] Step 4:

[0904] Server-based matching criteria

[0905] The server automatically sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match in skills, work location, salary range, etc.

[0906] Step 5:

[0907] Server-based scoring

[0908] The server performs scoring between job seekers and companies using the set matching conditions. For each job seeker, the degree of suitability is quantified for each company's job information and saved as a score in the database.

[0909] Step 6:

[0910] Server calculates relevance and lists candidates

[0911] The server calculates the matching rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of matching rate, which are provided to companies and job seekers, respectively.

[0912] Step 7:

[0913] Check job applicant list and request interviews via company terminal

[0914] A company's human resources personnel uses a company terminal to check the job applicant list provided by the server, and sends interview requests to interested job applicants through the system.

[0915] Step 8:

[0916] Job seekers can check job listings and apply using their device

[0917] The job seeker uses the job seeker terminal to check the job listings provided by the server, and sends a request to apply for the jobs in the list that interest them.

[0918] Step 9:

[0919] Server-based emotion engine implementation

[0920] An emotion engine is installed on the server, which can recognize the user's emotions when inputting feedback, and emotional data is also collected.

[0921] Step 10:

[0922] Feedback input via company and job seeker terminals

[0923] After the interview, the company and job seeker use their respective devices to input the interview results and post-hire feedback, which includes emotional data collected by the emotion engine.

[0924] Step 11:

[0925] Emotion data storage and analysis by server

[0926] The server stores the input feedback and emotion data in a database, analyzes all stored data, and makes adjustments to improve the accuracy of the matching algorithm.

[0927] Specific examples

[0928] Example 1: Corporate Use

[0929] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0930] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0931] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0932] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0933] Example 2: Job Seeker Use

[0934] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0935] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0936] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0937] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0938] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[0939] Example 2

[0940] 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."

[0941] Conventional systems for matching job seekers and companies lacked sufficient matching accuracy, resulting in frequent mismatches between job seekers and companies. Furthermore, there was no way to fully utilize user feedback, making it difficult to improve matching accuracy. Furthermore, there was no mechanism for taking user emotions into consideration, making it difficult to achieve appropriate matching.

[0942] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for a job seeker to input job search information, a means for a company to input job search information, a means for saving the job search information and the job search information, a means for matching the job search information with the job search information, a means for providing the matching results to the job seeker and the company, a means for collecting feedback from the job seeker and the company, a means for improving matching accuracy based on the feedback, a means for collecting and analyzing emotional data, and a means for adjusting the matching algorithm using the collected emotional data. This enables improved matching accuracy based on user feedback and more appropriate matching that takes emotional data into consideration.

[0943] "Job search information" refers to information that job seekers enter into the system, such as name, contact information, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[0944] "Job information" refers to information that companies enter into the system, such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[0945] "Feedback" refers to information such as interview results, post-employment evaluations, and impressions that job seekers and companies enter into the system after an interview.

[0946] "Emotional Data" refers to data collected using an emotion engine that represents a user's emotional state.

[0947] A "matching algorithm" refers to a calculation method for calculating the compatibility between job search information and employment information to achieve optimal matching.

[0948] "Scoring" refers to the process of quantifying information about job seekers and companies based on set matching conditions and expressing the degree of compatibility numerically.

[0949] "Relevance rate" refers to the percentage of the score results that indicate the degree of match between job seekers and job information.

[0950] "Emotion engine" refers to a software module that analyzes and collects emotions from user input data and feedback.

[0951] "Storage means" refers to a function for storing input information in a storage device such as a database.

[0952] "Providing means" refers to the function of presenting matching results and other information to users.

[0953] The present invention relates to a system for reducing mismatches between job seekers and companies, and in particular to improving matching accuracy by using an emotion engine. This system is implemented using the following hardware and software.

[0954] User registration and information entry

[0955] First, companies and job seekers access the system using their respective terminals and create accounts.

[0956] Ordinary personal computers (PCs) and tablet devices are used as corporate terminals.

[0957] Job seekers also use PCs and smartphones as their devices.

[0958] A company's human resources staff enters information such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[0959] Job seekers enter information such as their name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[0960] Information stored in a database

[0961] The server receives the entered company and job seeker information and stores it in a database.

[0962] This database is a general relational database system (RDBMS), such as MySQL or PostgreSQL.

[0963] The stored data is validated to ensure consistency.

[0964] Feedback collection and analysis using emotion engines

[0965] The server can implement an emotion engine to collect user emotion data during feedback.

[0966] This emotion engine uses, for example, Google's emotion analysis API and IBM Watson's emotion analysis service.

[0967] After the interview, the company and job seeker enter their impressions and evaluations as feedback, which also includes emotional data collected by the emotion engine.

[0968] The server stores this feedback and emotion data in a database for later analysis.

[0969] Executing the matching algorithm

[0970] The server sets matching conditions based on the company's job information and the job seeker's job information. The conditions set include the degree of match in skills, work location, salary range, etc. The server performs scoring based on the set conditions and quantifies the suitability of each job seeker. Emotional data is also taken into account here.

[0971] Based on the scoring results, a list of candidates and job offers is generated in descending order of relevance.

[0972] Providing matching results

[0973] The server provides the generated matching results to job seekers and companies.

[0974] Corporate human resources personnel can review the list of job applicants provided and send interview requests to those who interest them.

[0975] Job seekers can also check the job listings provided and apply for the jobs that interest them.

[0976] Specific examples

[0977] Example 1: Corporate Use

[0978] 1. Company terminal: A company is looking to hire a new data scientist, so they create a company account and enter their job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[0979] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[0980] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0981] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[0982] Example 2: Job Seeker Use

[0983] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[0984] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[0985] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[0986] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[0987] Prompt Sentence Examples

[0988] Please enter your company information.

[0989] Please enter your job search information.

[0990] "Please provide feedback and emotional state."

[0991] In this way, the system supports appropriate matching between job seekers and companies, and can achieve more satisfying matching by taking emotional data into account.

[0992] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0993] Step 1: User registration and information entry

[0994] 1-1. Entering company information using a company terminal:

[0995] Input: Company name, address, industry, contact information, job type, required skills, work location, salary, and working conditions.

[0996] Specific operation: A company's human resources officer accesses the system using a company terminal, enters the required information in the input form, and clicks the "Submit" button.

[0997] Output: Company details and job listings are sent to the server.

[0998] 1-2. Entering job search information using job seeker terminal:

[0999] Input: Name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[1000] Specific operation: The job seeker accesses the system using the job seeker terminal, enters the required information in the input form, and clicks the "Submit" button.

[1001] Output: The job seeker's basic information and job application information are sent to the server.

[1002] Step 2: Store the information in a database

[1003] 2-1. Receipt and storage of data by the server:

[1004] Input: Information submitted by companies and job seekers.

[1005] Specific operation: The server receives information sent by companies and job seekers, stores it separately in each database, and validates it to ensure data consistency.

[1006] Output: Company and job search information stored in a database.

[1007] Step 3: Collect and analyze feedback using the emotion engine

[1008] 3-1. Emotion engine settings:

[1009] Input: Configuration information for the Emotion Engine API.

[1010] Specific operation: The server calls the emotion engine's API and configures it to analyze the user's input data and feedback.

[1011] Output: The emotion engine is ready to run.

[1012] 3-2. Gathering feedback:

[1013] Input: Feedback information such as post-interview evaluations and impressions.

[1014] Specific operation: Companies and job seekers enter feedback after the interview, and the emotion engine automatically collects users' emotion data. Enter information in the feedback form and click the "Submit" button.

[1015] Output: Feedback information including emotion data is sent to the server and stored in a database.

[1016] 3-3. Emotional Data Analysis:

[1017] Input: Collected feedback and sentiment data.

[1018] Specific operation: The server analyzes the data collected through the emotion engine and adjusts the parameters of the matching algorithm.

[1019] Output: The analyzed emotion data and adjusted algorithm parameters are saved.

[1020] Step 4: Run the matching algorithm

[1021] 4-1. Matching condition settings:

[1022] Input: Company job information, job seeker job information.

[1023] Specific operation: The server sets matching conditions based on criteria such as skill match, work location match, and salary range match.

[1024] Output: The matching criteria that have been set.

[1025] 4-2. Scoring execution:

[1026] Input: Matching criteria, company and job seeker information.

[1027] How it works: The server scores job seekers and companies based on matching criteria, taking into account emotional data. It then quantifies the degree of compatibility for each criterion and generates a total score.

[1028] Output: The scoring results are stored in a database.

[1029] 4-3. Calculating and listing precision:

[1030] Input: Scoring results.

[1031] Specific operation: The server calculates the relevance rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of relevance rate.

[1032] Output: A generated list of candidates and job offers based on the match rate.

[1033] Step 5: Providing matching results

[1034] 5-1. Generation and distribution of matching results:

[1035] Input: A list based on precision.

[1036] Specific operation: The server generates a list of highly suitable candidates and job offers and distributes them to each company and job seeker.

[1037] Output: List of candidates and jobs provided, notified to the user via their dashboard or email.

[1038] 5-2. Check the job seeker list:

[1039] Input: The provided job seeker list.

[1040] Specific operation: The company's human resources staff uses the company terminal to check the list and select the job seekers to whom they want to send an interview request. They check the list on the dashboard and click the "Request an interview" button.

[1041] Output: An interview request is sent to the job candidate.

[1042] 5-3. Check the job listings:

[1043] Input: The provided job listing.

[1044] Specific operation: The job seeker uses the job seeker terminal to check the list and select the job they want to apply for. They check the list on the dashboard and click the "Apply" button.

[1045] Output: A job application is sent to the company.

[1046] These are the specific processing steps of the system. By using this system, it is possible to improve the accuracy of matching between companies and job seekers and achieve appropriate matching that takes emotional data into account.

[1047] (Application example 2)

[1048] 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."

[1049] Conventional systems for matching job seekers and companies do not fully utilize feedback and emotional data between the two parties, resulting in low matching accuracy and insufficient user satisfaction. Furthermore, advertisements are delivered without taking into account the user's emotional state, resulting in insufficient effectiveness. By resolving these issues, there is a need to improve the accuracy of matching job seekers and companies while also enabling advertisement delivery based on the user's emotional state.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1051] In this invention, the server includes means for a job seeker to input job search information, means for a company to input job information, means for saving the job search information and the job information, means for matching the job search information with the job information, means for providing the matching results to the job seeker and the company, means for collecting feedback from the job seeker and the company, means for improving matching accuracy based on the feedback, means for collecting user emotion data, means for personalizing advertisements based on the emotion data, and means for delivering personalized advertisements to the user.

[1052] This will not only make it possible to improve matching accuracy by utilizing feedback and emotional data between job seekers and companies, but also enable appropriate advertisement delivery based on the user's emotional state.

[1053] "Job search information" refers to information entered by job seekers, such as work experience, skills, desired job type, and work location.

[1054] "Job information" refers to information about recruitment entered by companies, such as job type, required skills, work location, and salary.

[1055] The "means for saving" is a function for saving the entered job search information and recruitment information in a database while maintaining consistency.

[1056] The "means of matching" is a function that calculates compatibility based on job search information and employment information, and extracts matching candidates.

[1057] "Means for providing matching results" refers to the function of presenting matching results to job seekers and companies.

[1058] "Means for collecting feedback" is a function for collecting opinions and feedback from job seekers and companies.

[1059] "Means for improving matching accuracy" refers to a function that analyzes collected feedback and optimizes the matching algorithm based on that.

[1060] "Means for collecting emotional data" refers to a function for collecting the user's emotional state in real time through facial expression recognition and voice analysis.

[1061] "Means for personalizing ads" refers to a function that selects the most suitable ads for users based on collected emotional data.

[1062] "Means for delivering personalized advertisements" refers to a function for delivering selected personalized advertisements to users.

[1063] The following system configuration and operation will be described as an embodiment of the present invention.

[1064] System Configuration

[1065] This system consists of the following main components:

[1066] User device: A device where job seekers and companies enter information and runs a smartphone app.

[1067] Server: Stores data, performs matching, collects feedback, and performs analytics.

[1068] Database: A database for storing job applications, job postings, feedback, and sentiment data.

[1069] Emotion Engine: An engine that collects and analyzes user emotion data in real time.

[1070] Ad management system: A system that selects and delivers advertisements appropriate for users based on emotional data.

[1071] Hardware and Software

[1072] Hardware: Smartphones, servers

[1073] Software: Emotion engines (e.g., EmotionAI SDK), database management systems (e.g., MySQL), matching algorithms, ad management systems (e.g., Google Ad API, Facebook Ads API), mobile application frameworks (e.g., React Native, Flutter)

[1074] Operational Overview

[1075] 1. User registration and information entry

[1076] The server receives information entered by job seekers and companies via a smartphone app (such as name, contact details, skills, and desired conditions) and stores it in a database.

[1077] The user device provides an intuitive interface when registering job search information or recruitment information (e.g., React Native app).

[1078] 2. Collecting Emotional Data

[1079] The emotion engine collects the user's emotional state in real time through facial expression recognition and voice analysis, and sends it to the server, where the emotional data is stored in a database.

[1080] 3. Matching and feedback collection and analysis

[1081] The server runs a matching algorithm based on the job search information and job information, calculates the degree of compatibility, and provides the results to job seekers and companies.

[1082] After the interview or after the hire, the server collects feedback from the job seeker and the company and stores it in a database.

[1083] The feedback data is analyzed to improve the accuracy of the matching algorithm.

[1084] 4. Personalizing and delivering ads

[1085] The ad management system uses the collected emotional data to select the most suitable ad for the user, and the ad is then personalized and delivered to the user.

[1086] The smartphone app displays appropriate advertisements as notifications depending on your emotional state.

[1087] Specific examples

[1088] Example 1: Recruitment advertisement example

[1089] When a job seeker requests a "software engineer" position on a user device, they enter their desired job title into a smartphone app.

[1090] The server takes into account feedback and sentiment data to display the most suitable job listings.

[1091] Example 2: Emotion-based advertising example

[1092] The emotion engine detects the user's state of relaxation.

[1093] The advertising management system selects advertisements for products with relaxing effects and sends notifications to smartphones saying, "How about some aromas that are perfect for relaxation?"

[1094] Prompt Sentence Examples

[1095] Below are some example prompts for ad selection based on emotional state:

[1096] Mechanism for collecting user relaxation data and generating appropriate advertisements

[1097] The emotion engine detects the user's state of relaxation. Based on that, it selects ads for products with the most relaxing effects and sends notifications to the user. Specifically, please explain the mechanism for sending aroma ads when the user is relaxed.

[1098] In this way, the system improves the accuracy of matching job seekers with companies and delivers personalized advertisements based on the user's emotional state.

[1099] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1100] Step 1:

[1101] User registration and information entry

[1102] Users (job seekers and companies) use a smartphone app to enter the necessary information. Job seekers enter their name, contact details, work experience, skills, desired job type, and desired work location, while companies enter their company name, location, industry, job type, required skills, work location, salary, etc. This information is sent to the server and saved in a database.

[1103] Input: Job seeker information, job information

[1104] Output: Job applicant information and job postings stored in a database

[1105] Step 2:

[1106] Collecting Emotional Data

[1107] While a user is using the smartphone app, the emotion engine collects the user's emotional data (facial expressions, voice, etc.) in real time. The emotional data is sent to a server and stored in an emotion database.

[1108] Input: Real-time user emotion data

[1109] Output: Emotion data stored in a database

[1110] Step 3:

[1111] Performing matching

[1112] The server runs a matching algorithm based on the job search information and job information stored in the database. The matching algorithm calculates the degree of compatibility by taking into account conditions such as "skill compatibility," "work location compatibility," and "salary range compatibility," and generates matching results based on this.

[1113] Input: Job search information, recruitment information

[1114] Output: Matching result (relevance score)

[1115] Step 4:

[1116] Providing matching results

[1117] The server generates a list of the most suitable job seekers and companies based on the suitability scores and provides it to the job seekers' and companies' terminals. Job seekers check the job listings, and companies check the job seeker listings.

[1118] Input: Matching result (relevance score)

[1119] Output: Job seeker list, job listing

[1120] Step 5:

[1121] Collecting feedback

[1122] Job seekers and companies enter feedback through the system after an interview or after being hired. Feedback includes impressions of the interview and level of satisfaction after being hired. The server collects this feedback and stores it in a database.

[1123] Input: Feedback from job seekers and companies

[1124] Output: Feedback stored in a database

[1125] Step 6:

[1126] Feedback analysis and algorithm optimization

[1127] The server analyzes the collected feedback data and makes necessary adjustments to improve the accuracy of the matching algorithm, which will result in better matching in the future.

[1128] Input: Feedback data

[1129] Output: Optimized matching algorithm

[1130] Step 7:

[1131] Ad personalization and delivery

[1132] The ad management system analyzes the collected emotional data and selects the advertisement that best suits the user's emotional state. The selected advertisement is then delivered to the user's smartphone as a notification.

[1133] Input: Emotion data

[1134] Output: Personalized advertising notifications

[1135] As a specific example of how it works, if the emotion engine detects that a job seeker is relaxed, the advertising management system will select an advertisement for a product with a relaxing effect and send a notification to the job seeker's smartphone saying, "Thank you for your hard work. How about an aroma with a perfect scent for relaxation?"

[1136] 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.

[1137] 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.

[1138] 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.

[1139] [Third embodiment]

[1140] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1142] 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).

[1143] 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.

[1144] 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.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] 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."

[1152] As an embodiment of the present invention, the specific configuration and operation of a system for reducing mismatches between job seekers and companies will be described.

[1153] User registration and information entry

[1154] 1. Entering company information using a company terminal

[1155] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, and then enter details such as job type, required skills, work location, salary, and work style as job information.

[1156] 2. Entering job search information using job seeker terminal

[1157] Job seekers access the system using their job seeker terminal and create a new account. They enter basic information such as their name, contact details, and career history, and then enter details such as their educational background, work experience, skills, desired job type, desired work location, and desired salary as resume information.

[1158] Information stored in a database

[1159] 3. Receipt and storage of data by the server

[1160] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases.

[1161] Executing the matching algorithm

[1162] 4. Server-based matching criteria setting and scoring

[1163] The server sets matching conditions based on the company's job information and the job seeker's job information, and then scores the candidates based on the set conditions, taking into account the degree of match in skills, work location, salary range, etc.

[1164] 5. Server calculates relevance and lists candidates

[1165] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[1166] Providing matching results

[1167] 6. Server Generation and Provision of Matching Results

[1168] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[1169] 7. Check job applicant list and request interviews via company terminal

[1170] The company's human resources personnel use the company terminal to check the provided list of job seekers and send interview requests to interested job seekers.

[1171] 8. Check job listings and apply using job seeker terminals

[1172] Job seekers use their job seeker terminals to check the job listings provided and apply to companies that interest them.

[1173] Feedback collection and post-processing

[1174] 9. Post-interview feedback input via company and job seeker terminals

[1175] After the interview, the company and job seeker each input the interview results and post-employment feedback, including whether or not they passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[1176] 10. Storage and analysis of feedback by our server

[1177] The server stores the collected feedback in a database and analyzes the data, making adjustments to improve the accuracy of the matching algorithm.

[1178] Specific examples

[1179] Example 1: Corporate Use

[1180] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1181] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1182] 3. Server: Searches the job applicant database for applicants who match the criteria and scores them.

[1183] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1184] Example 2: Job Seeker Use

[1185] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1186] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1187] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[1188] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1189] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[1190] The processing flow will be explained below.

[1191] Step 1:

[1192] Entering company information using a company terminal

[1193] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[1194] Step 2:

[1195] Job seeker entering job search information on a terminal

[1196] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[1197] Step 3:

[1198] Receiving and storing data by the server

[1199] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[1200] Step 4:

[1201] Server-based matching criteria

[1202] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[1203] Step 5:

[1204] Server-based scoring

[1205] The server scores job seekers and companies based on the matching criteria that have been set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score.

[1206] Step 6:

[1207] Server calculates relevance and lists candidates

[1208] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[1209] Step 7:

[1210] Server generates and distributes matching results

[1211] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[1212] Step 8:

[1213] Checking job seeker lists on company terminals

[1214] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[1215] Step 9:

[1216] Job seeker checking job listings on their device

[1217] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[1218] Step 10:

[1219] Feedback Input

[1220] Both companies and job seekers input the results of their interviews via their own devices, and this feedback includes whether or not the candidate passed the interview and their intention to join the company.

[1221] Step 11:

[1222] Server storage and analysis of feedback

[1223] The server receives the input feedback information, stores it in a database, analyzes the feedback data, and adjusts the algorithm to improve the matching accuracy next time.

[1224] Through these steps, the system can improve the accuracy of matching between companies and job seekers, and support efficient recruitment and job-seeking activities.

[1225] Example 1

[1226] 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."

[1227] Reducing the mismatch between job information and recruitment information that occurs between job seekers and companies has been a long-standing challenge. Conventional systems have made it difficult to efficiently match the needs of both parties, and have not adequately utilized feedback to improve matching accuracy. As a result, many job seekers are unable to find the perfect workplace for them, and companies are unable to quickly hire the right talent.

[1228] 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.

[1229] In this invention, the server includes a means for inputting and validating job search information and recruitment information, a means including a generative AI model that sets matching conditions and performs scoring, a means for providing matching results, and a means for collecting and saving feedback to improve the accuracy of the algorithm. This enables highly accurate matching of information on job seekers and companies, improving satisfaction on both sides and realizing optimal job search and recruitment activities.

[1230] "Job Seeker" means an individual who is searching for employment.

[1231] "Company" refers to a legal entity or organization that provides job information to hire job seekers.

[1232] "Job search information" refers to information provided by job seekers, such as work history, skills, and desired conditions.

[1233] "Job information" refers to information provided by companies, such as job type, required skills, work location, salary, etc.

[1234] "Input means" refers to an interface through which a user inputs job search information and recruitment information into the system.

[1235] "Storage means" refers to a database or storage device for storing input information.

[1236] "Matching conditions" refer to the criteria for evaluating the degree of match between job information and employment information.

[1237] "Scoring" refers to the process of quantifying the compatibility between a job seeker and a job offer based on matching criteria.

[1238] "Generative AI model" refers to the artificial intelligence model used in the scoring algorithm.

[1239] "Means of provision" refers to the interface for displaying matching results to job seekers and companies.

[1240] "Feedback" refers to evaluation information collected from job seekers and companies after interviews and hiring.

[1241] "Measures to improve accuracy" refers to the process of adjusting the matching algorithm based on collected feedback.

[1242] "Means for sending interview requests" refers to a function that allows a company to send interview invitations to job seekers.

[1243] "Means of applying for a job" refers to the function that allows job seekers to apply for a job posting from a company.

[1244] MODE FOR CARRYING OUT THE INVENTION

[1245] System Overview

[1246] This invention is a system for reducing mismatches between job seekers and companies and improving matching accuracy. It mainly uses a server, company terminals, and job seeker terminals to efficiently manage and match job search information and recruitment information. It also uses a generative AI model for scoring and improves the algorithm based on feedback.

[1247] Specific examples of hardware and software used

[1248] Server: Use an EC2 instance from Amazon Web Services (AWS).

[1249] Company and job seeker devices: Devices such as PCs, tablets, and smartphones.

[1250] Database: Use Amazon RDS (Relational Database Service).

[1251] Generative AI models: Use models generated using TensorFlow or PyTorch.

[1252] User Interface: Uses an interface provided through a web browser.

[1253] Program processing

[1254] This system operates in the following manner.

[1255] User registration and information entry

[1256] 1. User: A company's human resources officer accesses the system using a company terminal. A new account creation screen appears, and the employee enters information such as the company name, location, industry, job type, required skills, work location, salary, and working style.

[1257] 2. User: The job seeker accesses the system using their job seeker terminal. A new account creation screen is displayed, and they enter information such as their name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1258] 3. Server: Validates the format of the entered information and checks for invalid data.

[1259] Information stored in a database

[1260] 4. Server: Stores information received from company and job seeker terminals in a dedicated database that supports high-speed searches and filtering.

[1261] Executing the matching algorithm

[1262] 5. Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[1263] 6. Server: Uses a generative AI model to score the suitability of each job seeker with a company based on the set criteria.

[1264] Providing matching results

[1265] 7. Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to the company terminal and the job seeker terminal.

[1266] 8. User: The company's human resources staff checks the provided list and sends interview requests to interested job seekers. Job seekers check the provided job listings and apply to companies they are interested in.

[1267] Feedback collection and post-processing

[1268] 9. User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[1269] 10. Server: The collected feedback is stored in a database and analyzed. The results of this analysis are used to improve the accuracy of the matching algorithm.

[1270] Specific examples

[1271] Example 1: Corporate Use

[1272] 1. Corporate terminal: A company creates a corporate account to recruit new data scientists and enters job information. The required skills are set to "Python, machine learning," the work location to "Osaka," and the salary to "annual salary of 7 million to 9 million yen."

[1273] 2. Server: Stores company job information in a database and searches for suitable job seekers.

[1274] 3. Server: Searches for job applicants who match the criteria from the job applicant database and scores them.

[1275] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1276] Example 2: Job Seeker Use

[1277] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1278] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1279] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[1280] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1281] Example prompt for a generative AI model:

[1282] "A new job seeker has registered. They have the skills and qualifications to be a Data Scientist. Get the job listings that fit you."

[1283] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[1284] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] User: A company's HR staff member accesses the system using a company terminal. A new account creation screen is displayed.

[1287] Input: Enter information such as company name, location, industry, job type, required skills, work location, salary, and working style.

[1288] Server: Validates the format of the information entered and checks for invalid data.

[1289] Output: Verified company information data.

[1290] Step 2:

[1291] User: A job seeker accesses the system using a job seeker terminal. A new account creation screen is displayed.

[1292] Input: Enter information such as your name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1293] Server: Validates the format of the information entered and checks for invalid data.

[1294] Output: Verified job applicant information data.

[1295] Step 3:

[1296] Server: Stores information received from company terminals and job seeker terminals in a dedicated database.

[1297] Input: Verified company and job applicant information data.

[1298] Output: Company and job candidate information stored in a database.

[1299] Step 4:

[1300] Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[1301] Input: Company and job candidate information stored in a database.

[1302] Processing: Generative AI models are used to analyze company and job seeker information to generate matching criteria such as skill match, location match, and salary range.

[1303] Output: The matching conditions that have been set.

[1304] Step 5:

[1305] Server: Based on the set matching criteria, the server scores the compatibility between each job seeker and the company.

[1306] Input: The matching criteria you set and the company and job seeker information stored in the database.

[1307] Processing: A generative AI model is used to calculate a relevance score for each item.

[1308] Output: Scoring results.

[1309] Step 6:

[1310] Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to each terminal.

[1311] Input: Scoring results.

[1312] Output: Matching list (list of job seekers and job offers).

[1313] Step 7:

[1314] User (company): The company's human resources personnel checks the provided list and sends interview requests to interested job seekers.

[1315] Input: Job candidate information based on the matching list.

[1316] Action: Send interview request.

[1317] Output: Interview request status.

[1318] Step 8:

[1319] User (job seeker): Job seekers check the provided job listings and apply to companies that interest them.

[1320] Input: Job information based on the matching list.

[1321] Processing: Job applications.

[1322] Output: Application status.

[1323] Step 9:

[1324] User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[1325] Input: Interview results and post-hire feedback.

[1326] Processing: Enter and save feedback.

[1327] Output: Saved feedback data.

[1328] Step 10:

[1329] Server: Stores the collected feedback in a database and analyzes it. The results of this analysis are used to improve the accuracy of the matching algorithm.

[1330] Input: Saved feedback data.

[1331] Processing: Data analysis and algorithm tuning.

[1332] Output: Improved matching algorithm.

[1333] In this way, by performing specific operations at each processing step, validating the format of input data, saving information, setting conditions, scoring, providing results, and obtaining and analyzing feedback, highly accurate matching between job seekers and companies is possible.

[1334] (Application example 1)

[1335] 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."

[1336] Conventional matching systems often result in mismatches between job seekers and companies, resulting in high turnover rates after hiring. Furthermore, for jobs requiring specific skills, such as security work, quick on-site matching is required, but this is difficult to achieve with existing systems. In addition, there is a lack of efficient and accurate matching methods using smart devices, making it impossible to select personnel in real time.

[1337] 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.

[1338] In this invention, the server includes means for job seekers to input job search information, means for companies to input job recruitment information, means for saving the job search information and the job recruitment information, means for matching the job search information with the job recruitment information, means for providing the matching results to job seekers and companies, means for collecting feedback from the job seekers and companies, means for improving matching accuracy based on the feedback, means for scoring skill matches using cosine similarity, and means installed on a smart device. This makes it possible to quickly and accurately match personnel with appropriate skills in real time.

[1339] "Means for job seekers to enter job search information" refers to an interface that allows job seekers to enter their work history, skills, desired conditions, etc. online or on a device.

[1340] "Means for companies to enter job information" refers to an interface for companies to enter job information, such as job type, required skills, work location, salary, etc., online or on a device.

[1341] The "means for saving the job-seeking information and the recruitment information" is a function for saving the input information on job seekers and companies in a database, cloud storage, etc.

[1342] The "means for matching the job search information with the employment information" refers to an algorithm or system that evaluates whether the conditions of the two match based on the stored job search and employment information, and performs matching.

[1343] "Means for providing the matching results to job seekers and companies" refers to a mechanism for notifying the matching results to the terminals and systems of job seekers and companies.

[1344] The "means for collecting feedback from the job seekers and companies" refers to an interface or system for collecting interview results and post-employment evaluations from job seekers and companies.

[1345] The "means for improving matching accuracy based on the feedback" is a function for analyzing collected feedback data and improving the accuracy of the matching algorithm or system.

[1346] "Means for scoring skill matches using cosine similarity" refers to an algorithm or system for evaluating the similarity between the skill sets of job seekers and those required by companies using cosine similarity calculations and performing scoring.

[1347] "Means installed on a smart device" refers to applications or software that can be installed and used on smart devices such as smartphones, smart glasses, and head-mounted displays.

[1348] As an embodiment of this invention, we will explain a personnel matching system specialized for security work. This system aims to reduce mismatches between job seekers and companies, and has a function to collect feedback from job seekers and companies and improve matching accuracy.

[1349] Entering information and saving it to the database

[1350] 1. Entering company information using a company terminal

[1351] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, as well as detailed job information such as "job type," "required skills," "work location," "salary," and "work style."

[1352] 2. Entering job search information using job seeker terminal

[1353] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, and career history, as well as detailed job search information such as their educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1354] 3. Receipt and storage of data by the server

[1355] The server receives the information entered by companies and job seekers and stores it in their own dedicated databases.

[1356] Matching and Scoring

[1357] 4. Server-based matching criteria setting and scoring

[1358] The server sets matching conditions based on the company's job information and the job seeker's job information. According to the set conditions, scoring is performed taking into account the degree of match of skills, work location, salary range, etc. In particular, cosine similarity is used to evaluate the degree of match of skills and score the compatibility.

[1359] 5. Server calculates relevance and lists candidates

[1360] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[1361] Providing matching results

[1362] 6. Server Generation and Provision of Matching Results

[1363] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[1364] 7. On-site use

[1365] Through an application installed on a smart device (e.g., smart glasses, smartphone, etc.) used on-site, security personnel can receive real-time matching results, enabling them to quickly and accurately find the right personnel.

[1366] Feedback collection and post-processing

[1367] 8. Post-interview feedback input via company and job seeker terminals

[1368] After the interview, both the company and the job seeker will input the results of the interview and post-employment feedback, including whether or not the candidate passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[1369] 9. Storage and analysis of feedback by the server

[1370] The server stores the collected feedback in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[1371] Hardware and software used

[1372] The system utilizes the following hardware and software:

[1373] Server: A central server for receiving, storing and processing data, and implementing the appropriate algorithm to perform the cosine similarity calculation.

[1374] Company and Candidate Devices: Personal computers or mobile devices used to enter information, collect feedback, and review match results.

[1375] Smart devices: Applications installed on smart glasses, smartphones, head-mounted displays, etc.

[1376] Software: Skill vectorization was performed using TfidfVectorizer, and Python was used to calculate cosine similarity.

[1377] Examples of concrete examples and prompts

[1378] For example, when a security company is looking to hire new security guards, they input the job information into the system. The system scores job applicants who match the criteria and generates a list of suitable candidates. Using smart glasses on-site, they can quickly find the right candidates.

[1379] Example prompt sentence:

[1380] Describe the design of an application installed on smart glasses to quickly match personnel with the required skills in real time for security services. This application uses TfidfVectorizer to calculate the degree of skill matching using cosine similarity to generate a ranking. Please explain with a concrete example.

[1381] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1382] Step 1:

[1383] Entering company and job search information

[1384] A company's human resources staff uses a company terminal to enter job information. A job seeker uses a job seeker terminal to enter job information. The entered information includes detailed information such as the company name, location, job type, required skills, work location, and salary. The entered information is sent to a data server.

[1385] Step 2:

[1386] Data storage

[1387] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases. At this stage, company information and job seeker information are stored in the database in a structured format. The stored data includes the entered text data and numerical data.

[1388] Step 3:

[1389] Matching condition settings and scoring

[1390] The server sets matching conditions based on the saved job information of companies and job seekers. Specifically, it uses TfidfVectorizer to vectorize the skills required by companies and the skills possessed by job seekers, and scores the degree of skill match using cosine similarity. The scoring results are saved as compatibility.

[1391] Step 4:

[1392] Calculating precision and generating candidate lists

[1393] The server calculates the matching rate for each job seeker and company based on the scoring results. Based on the calculation results, it generates a list of candidates suitable for each company and job seeker in descending order of score. This list is saved on the server and formatted in an appropriate format.

[1394] Step 5:

[1395] Providing matching results

[1396] The server provides the generated candidate list to the company's human resources personnel and job seekers. The list includes matching scores and other related information. The company and job seekers receive and check the list through their own devices.

[1397] Step 6:

[1398] Enter your feedback

[1399] After the interview, both the company and the job seeker enter feedback using their respective terminals. The feedback includes the outcome of the interview, the intention to join the company, the level of satisfaction after joining the company, etc. This feedback is sent to the server.

[1400] Step 7:

[1401] Feedback storage and analysis

[1402] The server stores the received feedback in a database. The stored feedback data is analyzed and used to adjust the matching algorithm to improve its accuracy. Based on the analysis results, the system is adjusted to continuously improve matching accuracy.

[1403] Step 8:

[1404] Real-time matching on smart devices

[1405] Security personnel receive real-time matching results through an application installed on smart devices (such as smart glasses or smartphones) used on-site, enabling them to quickly find the right personnel.

[1406] 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.

[1407] As an embodiment of the present invention, the specific configuration and operation of a system that combines an emotion engine to reduce mismatches between job seekers and companies will be described.

[1408] User registration and information entry

[1409] 1. Entering company information using a company terminal

[1410] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[1411] 2. Entering job search information using job seeker terminal

[1412] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[1413] Information stored in a database

[1414] 3. Receipt and storage of data by the server

[1415] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[1416] Feedback collection and analysis using emotion engines

[1417] 4. Server-based emotion engine

[1418] The system is configured to install an emotion engine on the server and enable the engine to recognize the emotions of users (companies and job seekers), making it possible to collect user emotions when entering feedback.

[1419] 5. Feedback input via company and job seeker terminals

[1420] After the interview, the company and job seeker each input feedback on the interview results and the hiring process, including the user's emotions collected by the emotion engine.

[1421] 6. Storage and analysis of emotion data by the server

[1422] The server stores the collected feedback and emotion data in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[1423] Executing the matching algorithm

[1424] 7. Server-based matching criteria

[1425] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[1426] 8. Server-based scoring

[1427] The server scores job seekers and companies based on the matching criteria set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score. Emotional data is also taken into account.

[1428] 9. Server calculates relevance and lists candidates

[1429] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[1430] Providing matching results

[1431] 10. Server generation and distribution of matching results

[1432] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[1433] 11. Checking job applicant list via company terminal

[1434] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[1435] 12. Checking job listings on job seeker devices

[1436] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[1437] Specific examples

[1438] Example 1: Corporate Use

[1439] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1440] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1441] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1442] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1443] Example 2: Job Seeker Use

[1444] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1445] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1446] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1447] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1448] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[1449] The processing flow will be explained below.

[1450] Step 1:

[1451] Entering company information using a company terminal

[1452] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[1453] Step 2:

[1454] Job seeker entering job search information on a terminal

[1455] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience. They also enter information such as their desired job type, desired work location, desired salary, and skills.

[1456] Step 3:

[1457] Receiving and storing data by the server

[1458] The server receives all information entered by companies and job seekers, and stores the received information in dedicated databases for companies and job seekers.

[1459] Step 4:

[1460] Server-based matching criteria

[1461] The server automatically sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match in skills, work location, salary range, etc.

[1462] Step 5:

[1463] Server-based scoring

[1464] The server performs scoring between job seekers and companies using the set matching conditions. For each job seeker, the degree of suitability is quantified for each company's job information and saved as a score in the database.

[1465] Step 6:

[1466] Server calculates relevance and lists candidates

[1467] The server calculates the matching rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of matching rate, which are provided to companies and job seekers, respectively.

[1468] Step 7:

[1469] Check job applicant list and request interviews via company terminal

[1470] A company's human resources personnel uses a company terminal to check the job applicant list provided by the server, and sends interview requests to interested job applicants through the system.

[1471] Step 8:

[1472] Job seekers can check job listings and apply using their device

[1473] The job seeker uses the job seeker terminal to check the job listings provided by the server, and sends a request to apply for the jobs in the list that interest them.

[1474] Step 9:

[1475] Server-based emotion engine implementation

[1476] An emotion engine is installed on the server, which can recognize the user's emotions when inputting feedback, and emotional data is also collected.

[1477] Step 10:

[1478] Feedback input via company and job seeker terminals

[1479] After the interview, the company and job seeker use their respective devices to input the interview results and post-hire feedback, which includes emotional data collected by the emotion engine.

[1480] Step 11:

[1481] Emotion data storage and analysis by server

[1482] The server stores the input feedback and emotion data in a database, analyzes all stored data, and makes adjustments to improve the accuracy of the matching algorithm.

[1483] Specific examples

[1484] Example 1: Corporate Use

[1485] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1486] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1487] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1488] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1489] Example 2: Job Seeker Use

[1490] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1491] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1492] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1493] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1494] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[1495] Example 2

[1496] 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."

[1497] Conventional systems for matching job seekers and companies lacked sufficient matching accuracy, resulting in frequent mismatches between job seekers and companies. Furthermore, there was no way to fully utilize user feedback, making it difficult to improve matching accuracy. Furthermore, there was no mechanism for taking user emotions into consideration, making it difficult to achieve appropriate matching.

[1498] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for a job seeker to input job search information, a means for a company to input job search information, a means for saving the job search information and the job search information, a means for matching the job search information with the job search information, a means for providing the matching results to the job seeker and the company, a means for collecting feedback from the job seeker and the company, a means for improving matching accuracy based on the feedback, a means for collecting and analyzing emotional data, and a means for adjusting the matching algorithm using the collected emotional data. This enables improved matching accuracy based on user feedback and more appropriate matching that takes emotional data into consideration.

[1499] "Job search information" refers to information that job seekers enter into the system, such as name, contact information, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[1500] "Job information" refers to information that companies enter into the system, such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[1501] "Feedback" refers to information such as interview results, post-employment evaluations, and impressions that job seekers and companies enter into the system after an interview.

[1502] "Emotional Data" refers to data collected using an emotion engine that represents a user's emotional state.

[1503] A "matching algorithm" refers to a calculation method for calculating the compatibility between job search information and employment information to achieve optimal matching.

[1504] "Scoring" refers to the process of quantifying information about job seekers and companies based on set matching conditions and expressing the degree of compatibility numerically.

[1505] "Relevance rate" refers to the percentage of the score results that indicate the degree of match between job seekers and job information.

[1506] "Emotion engine" refers to a software module that analyzes and collects emotions from user input data and feedback.

[1507] "Storage means" refers to a function for storing input information in a storage device such as a database.

[1508] "Providing means" refers to the function of presenting matching results and other information to users.

[1509] The present invention relates to a system for reducing mismatches between job seekers and companies, and in particular to improving matching accuracy by using an emotion engine. This system is implemented using the following hardware and software.

[1510] User registration and information entry

[1511] First, companies and job seekers access the system using their respective terminals and create accounts.

[1512] Ordinary personal computers (PCs) and tablet devices are used as corporate terminals.

[1513] Job seekers also use PCs and smartphones as their devices.

[1514] A company's human resources staff enters information such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[1515] Job seekers enter information such as their name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[1516] Information stored in a database

[1517] The server receives the entered company and job seeker information and stores it in a database.

[1518] This database is a general relational database system (RDBMS), such as MySQL or PostgreSQL.

[1519] The stored data is validated to ensure consistency.

[1520] Feedback collection and analysis using emotion engines

[1521] The server can implement an emotion engine to collect user emotion data during feedback.

[1522] This emotion engine uses, for example, Google's emotion analysis API and IBM Watson's emotion analysis service.

[1523] After the interview, the company and job seeker enter their impressions and evaluations as feedback, which also includes emotional data collected by the emotion engine.

[1524] The server stores this feedback and emotion data in a database for later analysis.

[1525] Executing the matching algorithm

[1526] The server sets matching conditions based on the company's job information and the job seeker's job information. The conditions set include the degree of match in skills, work location, salary range, etc. The server performs scoring based on the set conditions and quantifies the suitability of each job seeker. Emotional data is also taken into account here.

[1527] Based on the scoring results, a list of candidates and job offers is generated in descending order of relevance.

[1528] Providing matching results

[1529] The server provides the generated matching results to job seekers and companies.

[1530] Corporate human resources personnel can review the list of job applicants provided and send interview requests to those who interest them.

[1531] Job seekers can also check the job listings provided and apply for the jobs that interest them.

[1532] Specific examples

[1533] Example 1: Corporate Use

[1534] 1. Company terminal: A company is looking to hire a new data scientist, so they create a company account and enter their job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1535] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1536] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1537] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1538] Example 2: Job Seeker Use

[1539] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1540] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1541] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1542] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1543] Prompt Sentence Examples

[1544] Please enter your company information.

[1545] Please enter your job search information.

[1546] "Please provide feedback and emotional state."

[1547] In this way, the system supports appropriate matching between job seekers and companies, and can achieve more satisfying matching by taking emotional data into account.

[1548] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1549] Step 1: User registration and information entry

[1550] 1-1. Entering company information using a company terminal:

[1551] Input: Company name, address, industry, contact information, job type, required skills, work location, salary, and working conditions.

[1552] Specific operation: A company's human resources officer accesses the system using a company terminal, enters the required information in the input form, and clicks the "Submit" button.

[1553] Output: Company details and job listings are sent to the server.

[1554] 1-2. Entering job search information using job seeker terminal:

[1555] Input: Name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[1556] Specific operation: The job seeker accesses the system using the job seeker terminal, enters the required information in the input form, and clicks the "Submit" button.

[1557] Output: The job seeker's basic information and job application information are sent to the server.

[1558] Step 2: Store the information in a database

[1559] 2-1. Receipt and storage of data by the server:

[1560] Input: Information submitted by companies and job seekers.

[1561] Specific operation: The server receives information sent by companies and job seekers, stores it separately in each database, and validates it to ensure data consistency.

[1562] Output: Company and job search information stored in a database.

[1563] Step 3: Collect and analyze feedback using the emotion engine

[1564] 3-1. Emotion engine settings:

[1565] Input: Configuration information for the Emotion Engine API.

[1566] Specific operation: The server calls the emotion engine's API and configures it to analyze the user's input data and feedback.

[1567] Output: The emotion engine is ready to run.

[1568] 3-2. Gathering feedback:

[1569] Input: Feedback information such as post-interview evaluations and impressions.

[1570] Specific operation: Companies and job seekers enter feedback after the interview, and the emotion engine automatically collects users' emotion data. Enter information in the feedback form and click the "Submit" button.

[1571] Output: Feedback information including emotion data is sent to the server and stored in a database.

[1572] 3-3. Emotional Data Analysis:

[1573] Input: Collected feedback and sentiment data.

[1574] Specific operation: The server analyzes the data collected through the emotion engine and adjusts the parameters of the matching algorithm.

[1575] Output: The analyzed emotion data and adjusted algorithm parameters are saved.

[1576] Step 4: Run the matching algorithm

[1577] 4-1. Matching condition settings:

[1578] Input: Company job information, job seeker job information.

[1579] Specific operation: The server sets matching conditions based on criteria such as skill match, work location match, and salary range match.

[1580] Output: The matching criteria that have been set.

[1581] 4-2. Scoring execution:

[1582] Input: Matching criteria, company and job seeker information.

[1583] How it works: The server scores job seekers and companies based on matching criteria, taking into account emotional data. It then quantifies the degree of compatibility for each criterion and generates a total score.

[1584] Output: The scoring results are stored in a database.

[1585] 4-3. Calculating and listing precision:

[1586] Input: Scoring results.

[1587] Specific operation: The server calculates the relevance rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of relevance rate.

[1588] Output: A generated list of candidates and job offers based on the match rate.

[1589] Step 5: Providing matching results

[1590] 5-1. Generation and distribution of matching results:

[1591] Input: A list based on precision.

[1592] Specific operation: The server generates a list of highly suitable candidates and job offers and distributes them to each company and job seeker.

[1593] Output: List of candidates and jobs provided, notified to the user via their dashboard or email.

[1594] 5-2. Check the job seeker list:

[1595] Input: The provided job seeker list.

[1596] Specific operation: The company's human resources staff uses the company terminal to check the list and select the job seekers to whom they want to send an interview request. They check the list on the dashboard and click the "Request an interview" button.

[1597] Output: An interview request is sent to the job candidate.

[1598] 5-3. Check the job listings:

[1599] Input: The provided job listing.

[1600] Specific operation: The job seeker uses the job seeker terminal to check the list and select the job they want to apply for. They check the list on the dashboard and click the "Apply" button.

[1601] Output: A job application is sent to the company.

[1602] These are the specific processing steps of the system. By using this system, it is possible to improve the accuracy of matching between companies and job seekers and achieve appropriate matching that takes emotional data into account.

[1603] (Application example 2)

[1604] 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."

[1605] Conventional systems for matching job seekers and companies do not fully utilize feedback and emotional data between the two parties, resulting in low matching accuracy and insufficient user satisfaction. Furthermore, advertisements are delivered without taking into account the user's emotional state, resulting in insufficient effectiveness. By resolving these issues, there is a need to improve the accuracy of matching job seekers and companies while also enabling advertisement delivery based on the user's emotional state.

[1606] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1607] In this invention, the server includes means for a job seeker to input job search information, means for a company to input job information, means for saving the job search information and the job information, means for matching the job search information with the job information, means for providing the matching results to the job seeker and the company, means for collecting feedback from the job seeker and the company, means for improving matching accuracy based on the feedback, means for collecting user emotion data, means for personalizing advertisements based on the emotion data, and means for delivering personalized advertisements to the user.

[1608] This will not only make it possible to improve matching accuracy by utilizing feedback and emotional data between job seekers and companies, but also enable appropriate advertisement delivery based on the user's emotional state.

[1609] "Job search information" refers to information entered by job seekers, such as work experience, skills, desired job type, and work location.

[1610] "Job information" refers to information about recruitment entered by companies, such as job type, required skills, work location, and salary.

[1611] The "means for saving" is a function for saving the entered job search information and recruitment information in a database while maintaining consistency.

[1612] The "means of matching" is a function that calculates compatibility based on job search information and employment information, and extracts matching candidates.

[1613] "Means for providing matching results" refers to the function of presenting matching results to job seekers and companies.

[1614] "Means for collecting feedback" is a function for collecting opinions and feedback from job seekers and companies.

[1615] "Means for improving matching accuracy" refers to a function that analyzes collected feedback and optimizes the matching algorithm based on that.

[1616] "Means for collecting emotional data" refers to a function for collecting the user's emotional state in real time through facial expression recognition and voice analysis.

[1617] "Means for personalizing ads" refers to a function that selects the most suitable ads for users based on collected emotional data.

[1618] "Means for delivering personalized advertisements" refers to a function for delivering selected personalized advertisements to users.

[1619] The following system configuration and operation will be described as an embodiment of the present invention.

[1620] System Configuration

[1621] This system consists of the following main components:

[1622] User device: A device where job seekers and companies enter information and runs a smartphone app.

[1623] Server: Stores data, performs matching, collects feedback, and performs analytics.

[1624] Database: A database for storing job applications, job postings, feedback, and sentiment data.

[1625] Emotion Engine: An engine that collects and analyzes user emotion data in real time.

[1626] Ad management system: A system that selects and delivers advertisements appropriate for users based on emotional data.

[1627] Hardware and Software

[1628] Hardware: Smartphones, servers

[1629] Software: Emotion engines (e.g., EmotionAI SDK), database management systems (e.g., MySQL), matching algorithms, ad management systems (e.g., Google Ad API, Facebook Ads API), mobile application frameworks (e.g., React Native, Flutter)

[1630] Operational Overview

[1631] 1. User registration and information entry

[1632] The server receives information entered by job seekers and companies via a smartphone app (such as name, contact details, skills, and desired conditions) and stores it in a database.

[1633] The user device provides an intuitive interface when registering job search information or recruitment information (e.g., React Native app).

[1634] 2. Collecting Emotional Data

[1635] The emotion engine collects the user's emotional state in real time through facial expression recognition and voice analysis, and sends it to the server, where the emotional data is stored in a database.

[1636] 3. Matching and feedback collection and analysis

[1637] The server runs a matching algorithm based on the job search information and job information, calculates the degree of compatibility, and provides the results to job seekers and companies.

[1638] After the interview or after the hire, the server collects feedback from the job seeker and the company and stores it in a database.

[1639] The feedback data is analyzed to improve the accuracy of the matching algorithm.

[1640] 4. Personalizing and delivering ads

[1641] The ad management system uses the collected emotional data to select the most suitable ad for the user, and the ad is then personalized and delivered to the user.

[1642] The smartphone app displays appropriate advertisements as notifications depending on your emotional state.

[1643] Specific examples

[1644] Example 1: Recruitment advertisement example

[1645] When a job seeker requests a "software engineer" position on a user device, they enter their desired job title into a smartphone app.

[1646] The server takes into account feedback and sentiment data to display the most suitable job listings.

[1647] Example 2: Emotion-based advertising example

[1648] The emotion engine detects the user's state of relaxation.

[1649] The advertising management system selects advertisements for products with relaxing effects and sends notifications to smartphones saying, "How about some aromas that are perfect for relaxation?"

[1650] Prompt Sentence Examples

[1651] Below are some example prompts for ad selection based on emotional state:

[1652] Mechanism for collecting user relaxation data and generating appropriate advertisements

[1653] The emotion engine detects the user's state of relaxation. Based on that, it selects ads for products with the most relaxing effects and sends notifications to the user. Specifically, please explain the mechanism for sending aroma ads when the user is relaxed.

[1654] In this way, the system improves the accuracy of matching job seekers with companies and delivers personalized advertisements based on the user's emotional state.

[1655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1656] Step 1:

[1657] User registration and information entry

[1658] Users (job seekers and companies) use a smartphone app to enter the necessary information. Job seekers enter their name, contact details, work experience, skills, desired job type, and desired work location, while companies enter their company name, location, industry, job type, required skills, work location, salary, etc. This information is sent to the server and saved in a database.

[1659] Input: Job seeker information, job information

[1660] Output: Job applicant information and job postings stored in a database

[1661] Step 2:

[1662] Collecting Emotional Data

[1663] While a user is using the smartphone app, the emotion engine collects the user's emotional data (facial expressions, voice, etc.) in real time. The emotional data is sent to a server and stored in an emotion database.

[1664] Input: Real-time user emotion data

[1665] Output: Emotion data stored in a database

[1666] Step 3:

[1667] Performing matching

[1668] The server runs a matching algorithm based on the job search information and job information stored in the database. The matching algorithm calculates the degree of compatibility by taking into account conditions such as "skill compatibility," "work location compatibility," and "salary range compatibility," and generates matching results based on this.

[1669] Input: Job search information, recruitment information

[1670] Output: Matching result (relevance score)

[1671] Step 4:

[1672] Providing matching results

[1673] The server generates a list of the most suitable job seekers and companies based on the suitability scores and provides it to the job seekers' and companies' terminals. Job seekers check the job listings, and companies check the job seeker listings.

[1674] Input: Matching result (relevance score)

[1675] Output: Job seeker list, job listing

[1676] Step 5:

[1677] Collecting feedback

[1678] Job seekers and companies enter feedback through the system after an interview or after being hired. Feedback includes impressions of the interview and level of satisfaction after being hired. The server collects this feedback and stores it in a database.

[1679] Input: Feedback from job seekers and companies

[1680] Output: Feedback stored in a database

[1681] Step 6:

[1682] Feedback analysis and algorithm optimization

[1683] The server analyzes the collected feedback data and makes necessary adjustments to improve the accuracy of the matching algorithm, which will result in better matching in the future.

[1684] Input: Feedback data

[1685] Output: Optimized matching algorithm

[1686] Step 7:

[1687] Ad personalization and delivery

[1688] The ad management system analyzes the collected emotional data and selects the advertisement that best suits the user's emotional state. The selected advertisement is then delivered to the user's smartphone as a notification.

[1689] Input: Emotion data

[1690] Output: Personalized advertising notifications

[1691] As a specific example of how it works, if the emotion engine detects that a job seeker is relaxed, the advertising management system will select an advertisement for a product with a relaxing effect and send a notification to the job seeker's smartphone saying, "Thank you for your hard work. How about an aroma with a perfect scent for relaxation?"

[1692] 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.

[1693] 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.

[1694] 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.

[1695] [Fourth embodiment]

[1696] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1697] 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.

[1698] 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).

[1699] 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.

[1700] 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.

[1701] 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).

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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."

[1709] As an embodiment of the present invention, the specific configuration and operation of a system for reducing mismatches between job seekers and companies will be described.

[1710] User registration and information entry

[1711] 1. Entering company information using a company terminal

[1712] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, and then enter details such as job type, required skills, work location, salary, and work style as job information.

[1713] 2. Entering job search information using job seeker terminal

[1714] Job seekers access the system using their job seeker terminal and create a new account. They enter basic information such as their name, contact details, and career history, and then enter details such as their educational background, work experience, skills, desired job type, desired work location, and desired salary as resume information.

[1715] Information stored in a database

[1716] 3. Receipt and storage of data by the server

[1717] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases.

[1718] Executing the matching algorithm

[1719] 4. Server-based matching criteria setting and scoring

[1720] The server sets matching conditions based on the company's job information and the job seeker's job information, and then scores the candidates based on the set conditions, taking into account the degree of match in skills, work location, salary range, etc.

[1721] 5. Server calculates relevance and lists candidates

[1722] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[1723] Providing matching results

[1724] 6. Server Generation and Provision of Matching Results

[1725] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[1726] 7. Check job applicant list and request interviews via company terminal

[1727] The company's human resources personnel use the company terminal to check the provided list of job seekers and send interview requests to interested job seekers.

[1728] 8. Check job listings and apply using job seeker terminals

[1729] Job seekers use their job seeker terminals to check the job listings provided and apply to companies that interest them.

[1730] Feedback collection and post-processing

[1731] 9. Post-interview feedback input via company and job seeker terminals

[1732] After the interview, the company and job seeker each input the interview results and post-employment feedback, including whether or not they passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[1733] 10. Storage and analysis of feedback by our server

[1734] The server stores the collected feedback in a database and analyzes the data, making adjustments to improve the accuracy of the matching algorithm.

[1735] Specific examples

[1736] Example 1: Corporate Use

[1737] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1738] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1739] 3. Server: Searches the job applicant database for applicants who match the criteria and scores them.

[1740] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1741] Example 2: Job Seeker Use

[1742] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1743] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1744] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[1745] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1746] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[1747] The processing flow will be explained below.

[1748] Step 1:

[1749] Entering company information using a company terminal

[1750] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[1751] Step 2:

[1752] Job seeker entering job search information on a terminal

[1753] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[1754] Step 3:

[1755] Receiving and storing data by the server

[1756] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[1757] Step 4:

[1758] Server-based matching criteria

[1759] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[1760] Step 5:

[1761] Server-based scoring

[1762] The server scores job seekers and companies based on the matching criteria that have been set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score.

[1763] Step 6:

[1764] Server calculates relevance and lists candidates

[1765] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[1766] Step 7:

[1767] Server generates and distributes matching results

[1768] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[1769] Step 8:

[1770] Checking job seeker lists on company terminals

[1771] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[1772] Step 9:

[1773] Job seeker checking job listings on their device

[1774] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[1775] Step 10:

[1776] Feedback Input

[1777] Both companies and job seekers input the results of their interviews via their own devices, and this feedback includes whether or not the candidate passed the interview and their intention to join the company.

[1778] Step 11:

[1779] Server storage and analysis of feedback

[1780] The server receives the input feedback information, stores it in a database, analyzes the feedback data, and adjusts the algorithm to improve the matching accuracy next time.

[1781] Through these steps, the system can improve the accuracy of matching between companies and job seekers, and support efficient recruitment and job-seeking activities.

[1782] Example 1

[1783] 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."

[1784] Reducing the mismatch between job information and recruitment information that occurs between job seekers and companies has been a long-standing challenge. Conventional systems have made it difficult to efficiently match the needs of both parties, and have not adequately utilized feedback to improve matching accuracy. As a result, many job seekers are unable to find the perfect workplace for them, and companies are unable to quickly hire the right talent.

[1785] 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.

[1786] In this invention, the server includes a means for inputting and validating job search information and recruitment information, a means including a generative AI model that sets matching conditions and performs scoring, a means for providing matching results, and a means for collecting and saving feedback to improve the accuracy of the algorithm. This enables highly accurate matching of information on job seekers and companies, improving satisfaction on both sides and realizing optimal job search and recruitment activities.

[1787] "Job Seeker" means an individual who is searching for employment.

[1788] "Company" refers to a legal entity or organization that provides job information to hire job seekers.

[1789] "Job search information" refers to information provided by job seekers, such as work history, skills, and desired conditions.

[1790] "Job information" refers to information provided by companies, such as job type, required skills, work location, salary, etc.

[1791] "Input means" refers to an interface through which a user inputs job search information and recruitment information into the system.

[1792] "Storage means" refers to a database or storage device for storing input information.

[1793] "Matching conditions" refer to the criteria for evaluating the degree of match between job information and employment information.

[1794] "Scoring" refers to the process of quantifying the compatibility between a job seeker and a job offer based on matching criteria.

[1795] "Generative AI model" refers to the artificial intelligence model used in the scoring algorithm.

[1796] "Means of provision" refers to the interface for displaying matching results to job seekers and companies.

[1797] "Feedback" refers to evaluation information collected from job seekers and companies after interviews and hiring.

[1798] "Measures to improve accuracy" refers to the process of adjusting the matching algorithm based on collected feedback.

[1799] "Means for sending interview requests" refers to a function that allows a company to send interview invitations to job seekers.

[1800] "Means of applying for a job" refers to the function that allows job seekers to apply for a job posting from a company.

[1801] MODE FOR CARRYING OUT THE INVENTION

[1802] System Overview

[1803] This invention is a system for reducing mismatches between job seekers and companies and improving matching accuracy. It mainly uses a server, company terminals, and job seeker terminals to efficiently manage and match job search information and recruitment information. It also uses a generative AI model for scoring and improves the algorithm based on feedback.

[1804] Specific examples of hardware and software used

[1805] Server: Use an EC2 instance from Amazon Web Services (AWS).

[1806] Company and job seeker devices: Devices such as PCs, tablets, and smartphones.

[1807] Database: Use Amazon RDS (Relational Database Service).

[1808] Generative AI models: Use models generated using TensorFlow or PyTorch.

[1809] User Interface: Uses an interface provided through a web browser.

[1810] Program processing

[1811] This system operates in the following manner.

[1812] User registration and information entry

[1813] 1. User: A company's human resources officer accesses the system using a company terminal. A new account creation screen appears, and the employee enters information such as the company name, location, industry, job type, required skills, work location, salary, and working style.

[1814] 2. User: The job seeker accesses the system using their job seeker terminal. A new account creation screen is displayed, and they enter information such as their name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1815] 3. Server: Validates the format of the entered information and checks for invalid data.

[1816] Information stored in a database

[1817] 4. Server: Stores information received from company and job seeker terminals in a dedicated database that supports high-speed searches and filtering.

[1818] Executing the matching algorithm

[1819] 5. Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[1820] 6. Server: Uses a generative AI model to score the suitability of each job seeker with a company based on the set criteria.

[1821] Providing matching results

[1822] 7. Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to the company terminal and the job seeker terminal.

[1823] 8. User: The company's human resources staff checks the provided list and sends interview requests to interested job seekers. Job seekers check the provided job listings and apply to companies they are interested in.

[1824] Feedback collection and post-processing

[1825] 9. User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[1826] 10. Server: The collected feedback is stored in a database and analyzed. The results of this analysis are used to improve the accuracy of the matching algorithm.

[1827] Specific examples

[1828] Example 1: Corporate Use

[1829] 1. Corporate terminal: A company creates a corporate account to recruit new data scientists and enters job information. The required skills are set to "Python, machine learning," the work location to "Osaka," and the salary to "annual salary of 7 million to 9 million yen."

[1830] 2. Server: Stores company job information in a database and searches for suitable job seekers.

[1831] 3. Server: Searches for job applicants who match the criteria from the job applicant database and scores them.

[1832] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[1833] Example 2: Job Seeker Use

[1834] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[1835] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[1836] 3. Server: Searches the company database for jobs that match the criteria and scores them.

[1837] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[1838] Example prompt for a generative AI model:

[1839] "A new job seeker has registered. They have the skills and qualifications to be a Data Scientist. Get the job listings that fit you."

[1840] This system aims to improve the accuracy of matching between companies and job seekers, thereby increasing satisfaction for both parties.

[1841] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1842] Step 1:

[1843] User: A company's HR staff member accesses the system using a company terminal. A new account creation screen is displayed.

[1844] Input: Enter information such as company name, location, industry, job type, required skills, work location, salary, and working style.

[1845] Server: Validates the format of the information entered and checks for invalid data.

[1846] Output: Verified company information data.

[1847] Step 2:

[1848] User: A job seeker accesses the system using a job seeker terminal. A new account creation screen is displayed.

[1849] Input: Enter information such as your name, contact details, career history, educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1850] Server: Validates the format of the information entered and checks for invalid data.

[1851] Output: Verified job applicant information data.

[1852] Step 3:

[1853] Server: Stores information received from company terminals and job seeker terminals in a dedicated database.

[1854] Input: Verified company and job applicant information data.

[1855] Output: Company and job candidate information stored in a database.

[1856] Step 4:

[1857] Server: Sets matching conditions based on the company's job information and the job seeker's job information.

[1858] Input: Company and job candidate information stored in a database.

[1859] Processing: Generative AI models are used to analyze company and job seeker information to generate matching criteria such as skill match, location match, and salary range.

[1860] Output: The matching conditions that have been set.

[1861] Step 5:

[1862] Server: Based on the set matching criteria, the server scores the compatibility between each job seeker and the company.

[1863] Input: The matching criteria you set and the company and job seeker information stored in the database.

[1864] Processing: A generative AI model is used to calculate a relevance score for each item.

[1865] Output: Scoring results.

[1866] Step 6:

[1867] Server: Based on the scoring results, a list of highly suitable job seekers and job offers is generated and provided to each terminal.

[1868] Input: Scoring results.

[1869] Output: Matching list (list of job seekers and job offers).

[1870] Step 7:

[1871] User (company): The company's human resources personnel checks the provided list and sends interview requests to interested job seekers.

[1872] Input: Job candidate information based on the matching list.

[1873] Action: Send interview request.

[1874] Output: Interview request status.

[1875] Step 8:

[1876] User (job seeker): Job seekers check the provided job listings and apply to companies that interest them.

[1877] Input: Job information based on the matching list.

[1878] Processing: Job applications.

[1879] Output: Application status.

[1880] Step 9:

[1881] User: After the interview, the company and job seeker each enter the interview results and post-hiring feedback.

[1882] Input: Interview results and post-hire feedback.

[1883] Processing: Enter and save feedback.

[1884] Output: Saved feedback data.

[1885] Step 10:

[1886] Server: Stores the collected feedback in a database and analyzes it. The results of this analysis are used to improve the accuracy of the matching algorithm.

[1887] Input: Saved feedback data.

[1888] Processing: Data analysis and algorithm tuning.

[1889] Output: Improved matching algorithm.

[1890] In this way, by performing specific operations at each processing step, validating the format of input data, saving information, setting conditions, scoring, providing results, and obtaining and analyzing feedback, highly accurate matching between job seekers and companies is possible.

[1891] (Application example 1)

[1892] 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."

[1893] Conventional matching systems often result in mismatches between job seekers and companies, resulting in high turnover rates after hiring. Furthermore, for jobs requiring specific skills, such as security work, quick on-site matching is required, but this is difficult to achieve with existing systems. In addition, there is a lack of efficient and accurate matching methods using smart devices, making it impossible to select personnel in real time.

[1894] 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.

[1895] In this invention, the server includes means for job seekers to input job search information, means for companies to input job recruitment information, means for saving the job search information and the job recruitment information, means for matching the job search information with the job recruitment information, means for providing the matching results to job seekers and companies, means for collecting feedback from the job seekers and companies, means for improving matching accuracy based on the feedback, means for scoring skill matches using cosine similarity, and means installed on a smart device. This makes it possible to quickly and accurately match personnel with appropriate skills in real time.

[1896] "Means for job seekers to enter job search information" refers to an interface that allows job seekers to enter their work history, skills, desired conditions, etc. online or on a device.

[1897] "Means for companies to enter job information" refers to an interface for companies to enter job information, such as job type, required skills, work location, salary, etc., online or on a device.

[1898] The "means for saving the job-seeking information and the recruitment information" is a function for saving the input information on job seekers and companies in a database, cloud storage, etc.

[1899] The "means for matching the job search information with the employment information" refers to an algorithm or system that evaluates whether the conditions of the two match based on the stored job search and employment information, and performs matching.

[1900] "Means for providing the matching results to job seekers and companies" refers to a mechanism for notifying the matching results to the terminals and systems of job seekers and companies.

[1901] The "means for collecting feedback from the job seekers and companies" refers to an interface or system for collecting interview results and post-employment evaluations from job seekers and companies.

[1902] The "means for improving matching accuracy based on the feedback" is a function for analyzing collected feedback data and improving the accuracy of the matching algorithm or system.

[1903] "Means for scoring skill matches using cosine similarity" refers to an algorithm or system for evaluating the similarity between the skill sets of job seekers and those required by companies using cosine similarity calculations and performing scoring.

[1904] "Means installed on a smart device" refers to applications or software that can be installed and used on smart devices such as smartphones, smart glasses, and head-mounted displays.

[1905] As an embodiment of this invention, we will explain a personnel matching system specialized for security work. This system aims to reduce mismatches between job seekers and companies, and has a function to collect feedback from job seekers and companies and improve matching accuracy.

[1906] Entering information and saving it to the database

[1907] 1. Entering company information using a company terminal

[1908] A company's human resources staff accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, and industry, as well as detailed job information such as "job type," "required skills," "work location," "salary," and "work style."

[1909] 2. Entering job search information using job seeker terminal

[1910] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, and career history, as well as detailed job search information such as their educational background, work experience, skills, desired job type, desired work location, and desired salary.

[1911] 3. Receipt and storage of data by the server

[1912] The server receives the information entered by companies and job seekers and stores it in their own dedicated databases.

[1913] Matching and Scoring

[1914] 4. Server-based matching criteria setting and scoring

[1915] The server sets matching conditions based on the company's job information and the job seeker's job information. According to the set conditions, scoring is performed taking into account the degree of match of skills, work location, salary range, etc. In particular, cosine similarity is used to evaluate the degree of match of skills and score the compatibility.

[1916] 5. Server calculates relevance and lists candidates

[1917] The server calculates the compatibility rate for each job seeker and company and lists potential matches based on that.

[1918] Providing matching results

[1919] 6. Server Generation and Provision of Matching Results

[1920] The server generates a list of highly suitable candidates and job offers for the company and job seeker based on the scores.

[1921] 7. On-site use

[1922] Through an application installed on a smart device (e.g., smart glasses, smartphone, etc.) used on-site, security personnel can receive real-time matching results, enabling them to quickly and accurately find the right personnel.

[1923] Feedback collection and post-processing

[1924] 8. Post-interview feedback input via company and job seeker terminals

[1925] After the interview, both the company and the job seeker will input the results of the interview and post-employment feedback, including whether or not the candidate passed the interview, whether they wanted to join the company, and their level of satisfaction after joining the company.

[1926] 9. Storage and analysis of feedback by the server

[1927] The server stores the collected feedback in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[1928] Hardware and software used

[1929] The system utilizes the following hardware and software:

[1930] Server: A central server for receiving, storing and processing data, and implementing the appropriate algorithm to perform the cosine similarity calculation.

[1931] Company and Candidate Devices: Personal computers or mobile devices used to enter information, collect feedback, and review match results.

[1932] Smart devices: Applications installed on smart glasses, smartphones, head-mounted displays, etc.

[1933] Software: Skill vectorization was performed using TfidfVectorizer, and Python was used to calculate cosine similarity.

[1934] Examples of concrete examples and prompts

[1935] For example, when a security company is looking to hire new security guards, they input the job information into the system. The system scores job applicants who match the criteria and generates a list of suitable candidates. Using smart glasses on-site, they can quickly find the right candidates.

[1936] Example prompt sentence:

[1937] Describe the design of an application installed on smart glasses to quickly match personnel with the required skills in real time for security services. This application uses TfidfVectorizer to calculate the degree of skill matching using cosine similarity to generate a ranking. Please explain with a concrete example.

[1938] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1939] Step 1:

[1940] Entering company and job search information

[1941] A company's human resources staff uses a company terminal to enter job information. A job seeker uses a job seeker terminal to enter job information. The entered information includes detailed information such as the company name, location, job type, required skills, work location, and salary. The entered information is sent to a data server.

[1942] Step 2:

[1943] Data storage

[1944] The server receives the information entered by companies and job seekers and stores it in their respective dedicated databases. At this stage, company information and job seeker information are stored in the database in a structured format. The stored data includes the entered text data and numerical data.

[1945] Step 3:

[1946] Matching condition settings and scoring

[1947] The server sets matching conditions based on the saved job information of companies and job seekers. Specifically, it uses TfidfVectorizer to vectorize the skills required by companies and the skills possessed by job seekers, and scores the degree of skill match using cosine similarity. The scoring results are saved as compatibility.

[1948] Step 4:

[1949] Calculating precision and generating candidate lists

[1950] The server calculates the matching rate for each job seeker and company based on the scoring results. Based on the calculation results, it generates a list of candidates suitable for each company and job seeker in descending order of score. This list is saved on the server and formatted in an appropriate format.

[1951] Step 5:

[1952] Providing matching results

[1953] The server provides the generated candidate list to the company's human resources personnel and job seekers. The list includes matching scores and other related information. The company and job seekers receive and check the list through their own devices.

[1954] Step 6:

[1955] Enter your feedback

[1956] After the interview, both the company and the job seeker enter feedback using their respective terminals. The feedback includes the outcome of the interview, the intention to join the company, the level of satisfaction after joining the company, etc. This feedback is sent to the server.

[1957] Step 7:

[1958] Feedback storage and analysis

[1959] The server stores the received feedback in a database. The stored feedback data is analyzed and used to adjust the matching algorithm to improve its accuracy. Based on the analysis results, the system is adjusted to continuously improve matching accuracy.

[1960] Step 8:

[1961] Real-time matching on smart devices

[1962] Security personnel receive real-time matching results through an application installed on smart devices (such as smart glasses or smartphones) used on-site, enabling them to quickly find the right personnel.

[1963] 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.

[1964] As an embodiment of the present invention, the specific configuration and operation of a system that combines an emotion engine to reduce mismatches between job seekers and companies will be described.

[1965] User registration and information entry

[1966] 1. Entering company information using a company terminal

[1967] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[1968] 2. Entering job search information using job seeker terminal

[1969] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience, as well as their desired job type, desired work location, desired salary, and skills.

[1970] Information stored in a database

[1971] 3. Receipt and storage of data by the server

[1972] The server receives the information entered by companies and job seekers, and stores the received information in dedicated databases to ensure data consistency.

[1973] Feedback collection and analysis using emotion engines

[1974] 4. Server-based emotion engine

[1975] The system is configured to install an emotion engine on the server and enable the engine to recognize the emotions of users (companies and job seekers), making it possible to collect user emotions when entering feedback.

[1976] 5. Feedback input via company and job seeker terminals

[1977] After the interview, the company and job seeker each input feedback on the interview results and the hiring process, including the user's emotions collected by the emotion engine.

[1978] 6. Storage and analysis of emotion data by the server

[1979] The server stores the collected feedback and emotion data in a database, analyzes the data, and makes adjustments to improve the accuracy of the matching algorithm.

[1980] Executing the matching algorithm

[1981] 7. Server-based matching criteria

[1982] The server sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match of skills, work location, salary range, etc.

[1983] 8. Server-based scoring

[1984] The server scores job seekers and companies based on the matching criteria set. For each job seeker, the degree of suitability is quantified for each job posting and saved as a score. Emotional data is also taken into account.

[1985] 9. Server calculates relevance and lists candidates

[1986] The server calculates the relevance rate of each job seeker and job information based on the scoring results, and generates a list of candidates and job offers in descending order of relevance rate.

[1987] Providing matching results

[1988] 10. Server generation and distribution of matching results

[1989] The server generates a list of candidates and job offers that are highly suitable for the job seeker and the company, and distributes them to each.

[1990] 11. Checking job applicant list via company terminal

[1991] The company's human resources personnel use their company terminal to check the job applicant list provided by the server, select the job applicants to whom they wish to send an interview request, and send the interview request through the system.

[1992] 12. Checking job listings on job seeker devices

[1993] Job seekers use their terminals to check the job listings provided by the server and apply for jobs that interest them.

[1994] Specific examples

[1995] Example 1: Corporate Use

[1996] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[1997] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[1998] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[1999] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[2000] Example 2: Job Seeker Use

[2001] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[2002] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[2003] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[2004] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[2005] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[2006] The processing flow will be explained below.

[2007] Step 1:

[2008] Entering company information using a company terminal

[2009] A company's human resources personnel accesses the system using a company terminal and creates a new account. They enter basic information such as the company name, location, industry, and contact information. They then enter detailed information about the job, such as "job type," "required skills," "work location," "salary," and "work style."

[2010] Step 2:

[2011] Job seeker entering job search information on a terminal

[2012] Job seekers access the system using their own terminal and create a new account. They enter basic information such as their name, contact details, educational background, and work experience. They also enter information such as their desired job type, desired work location, desired salary, and skills.

[2013] Step 3:

[2014] Receiving and storing data by the server

[2015] The server receives all information entered by companies and job seekers, and stores the received information in dedicated databases for companies and job seekers.

[2016] Step 4:

[2017] Server-based matching criteria

[2018] The server automatically sets matching conditions based on the job information entered by the company and the job seeker, including the degree of match in skills, work location, salary range, etc.

[2019] Step 5:

[2020] Server-based scoring

[2021] The server performs scoring between job seekers and companies using the set matching conditions. For each job seeker, the degree of suitability is quantified for each company's job information and saved as a score in the database.

[2022] Step 6:

[2023] Server calculates relevance and lists candidates

[2024] The server calculates the matching rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of matching rate, which are provided to companies and job seekers, respectively.

[2025] Step 7:

[2026] Check job applicant list and request interviews via company terminal

[2027] A company's human resources personnel uses a company terminal to check the job applicant list provided by the server, and sends interview requests to interested job applicants through the system.

[2028] Step 8:

[2029] Job seekers can check job listings and apply using their device

[2030] The job seeker uses the job seeker terminal to check the job listings provided by the server, and sends a request to apply for the jobs in the list that interest them.

[2031] Step 9:

[2032] Server-based emotion engine implementation

[2033] An emotion engine is installed on the server, which can recognize the user's emotions when inputting feedback, and emotional data is also collected.

[2034] Step 10:

[2035] Feedback input via company and job seeker terminals

[2036] After the interview, the company and job seeker use their respective devices to input the interview results and post-hire feedback, which includes emotional data collected by the emotion engine.

[2037] Step 11:

[2038] Emotion data storage and analysis by server

[2039] The server stores the input feedback and emotion data in a database, analyzes all stored data, and makes adjustments to improve the accuracy of the matching algorithm.

[2040] Specific examples

[2041] Example 1: Corporate Use

[2042] 1. Company terminal: A company wants to recruit a new data scientist, so they create a company account and enter job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[2043] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[2044] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[2045] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[2046] Example 2: Job Seeker Use

[2047] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[2048] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[2049] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[2050] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[2051] This system aims to improve the accuracy of matching between companies and job seekers, and increase satisfaction for both parties. In addition, the introduction of an emotion engine will enable optimal matching that takes into account the user's emotional state.

[2052] Example 2

[2053] 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."

[2054] Conventional systems for matching job seekers and companies lacked sufficient matching accuracy, resulting in frequent mismatches between job seekers and companies. Furthermore, there was no way to fully utilize user feedback, making it difficult to improve matching accuracy. Furthermore, there was no mechanism for taking user emotions into consideration, making it difficult to achieve appropriate matching.

[2055] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for a job seeker to input job search information, a means for a company to input job search information, a means for saving the job search information and the job search information, a means for matching the job search information with the job search information, a means for providing the matching results to the job seeker and the company, a means for collecting feedback from the job seeker and the company, a means for improving matching accuracy based on the feedback, a means for collecting and analyzing emotional data, and a means for adjusting the matching algorithm using the collected emotional data. This enables improved matching accuracy based on user feedback and more appropriate matching that takes emotional data into consideration.

[2056] "Job search information" refers to information that job seekers enter into the system, such as name, contact information, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[2057] "Job information" refers to information that companies enter into the system, such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[2058] "Feedback" refers to information such as interview results, post-employment evaluations, and impressions that job seekers and companies enter into the system after an interview.

[2059] "Emotional Data" refers to data collected using an emotion engine that represents a user's emotional state.

[2060] A "matching algorithm" refers to a calculation method for calculating the compatibility between job search information and employment information to achieve optimal matching.

[2061] "Scoring" refers to the process of quantifying information about job seekers and companies based on set matching conditions and expressing the degree of compatibility numerically.

[2062] "Relevance rate" refers to the percentage of the score results that indicate the degree of match between job seekers and job information.

[2063] "Emotion engine" refers to a software module that analyzes and collects emotions from user input data and feedback.

[2064] "Storage means" refers to a function for storing input information in a storage device such as a database.

[2065] "Providing means" refers to the function of presenting matching results and other information to users.

[2066] The present invention relates to a system for reducing mismatches between job seekers and companies, and in particular to improving matching accuracy by using an emotion engine. This system is implemented using the following hardware and software.

[2067] User registration and information entry

[2068] First, companies and job seekers access the system using their respective terminals and create accounts.

[2069] Ordinary personal computers (PCs) and tablet devices are used as corporate terminals.

[2070] Job seekers also use PCs and smartphones as their devices.

[2071] A company's human resources staff enters information such as company name, location, industry, contact information, job type, required skills, work location, salary, and working style.

[2072] Job seekers enter information such as their name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[2073] Information stored in a database

[2074] The server receives the entered company and job seeker information and stores it in a database.

[2075] This database is a general relational database system (RDBMS), such as MySQL or PostgreSQL.

[2076] The stored data is validated to ensure consistency.

[2077] Feedback collection and analysis using emotion engines

[2078] The server can implement an emotion engine to collect user emotion data during feedback.

[2079] This emotion engine uses, for example, Google's emotion analysis API and IBM Watson's emotion analysis service.

[2080] After the interview, the company and job seeker enter their impressions and evaluations as feedback, which also includes emotional data collected by the emotion engine.

[2081] The server stores this feedback and emotion data in a database for later analysis.

[2082] Executing the matching algorithm

[2083] The server sets matching conditions based on the company's job information and the job seeker's job information. The conditions set include the degree of match in skills, work location, salary range, etc. The server performs scoring based on the set conditions and quantifies the suitability of each job seeker. Emotional data is also taken into account here.

[2084] Based on the scoring results, a list of candidates and job offers is generated in descending order of relevance.

[2085] Providing matching results

[2086] The server provides the generated matching results to job seekers and companies.

[2087] Corporate human resources personnel can review the list of job applicants provided and send interview requests to those who interest them.

[2088] Job seekers can also check the job listings provided and apply for the jobs that interest them.

[2089] Specific examples

[2090] Example 1: Corporate Use

[2091] 1. Company terminal: A company is looking to hire a new data scientist, so they create a company account and enter their job information. They set the required skills as "Python, machine learning," the work location as "Osaka," and the salary as "annual salary of 7 million to 9 million yen."

[2092] 2. Server: Stores company job information in a database and searches for suitable job candidates.

[2093] 3. Server: Searches the job applicant database for candidates who match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[2094] 4. Company terminal: Review the provided list of 10 candidates and send interview requests to 5 of them.

[2095] Example 2: Job Seeker Use

[2096] 1. Job Seeker Terminal: A job seeker creates a job seeker account and enters resume information to search for a software engineer position. The skills are set to "Java, Spring," the work location is set to "Fukuoka," and the desired annual salary is set to "5.5 million to 7 million yen."

[2097] 2. Server: Stores job seeker information in a database and searches for suitable job openings at companies.

[2098] 3. Server: Searches the company database for jobs that match the criteria and scores them, taking into account the emotional data collected by the emotion engine.

[2099] 4. Job Seeker Terminal: Review the five job offers provided and select to apply for two of them.

[2100] Prompt Sentence Examples

[2101] Please enter your company information.

[2102] Please enter your job search information.

[2103] "Please provide feedback and emotional state."

[2104] In this way, the system supports appropriate matching between job seekers and companies, and can achieve more satisfying matching by taking emotional data into account.

[2105] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2106] Step 1: User registration and information entry

[2107] 1-1. Entering company information using a company terminal:

[2108] Input: Company name, address, industry, contact information, job type, required skills, work location, salary, and working conditions.

[2109] Specific operation: A company's human resources officer accesses the system using a company terminal, enters the required information in the input form, and clicks the "Submit" button.

[2110] Output: Company details and job listings are sent to the server.

[2111] 1-2. Entering job search information using job seeker terminal:

[2112] Input: Name, contact details, educational background, work experience, desired job type, desired work location, desired salary, and skills.

[2113] Specific operation: The job seeker accesses the system using the job seeker terminal, enters the required information in the input form, and clicks the "Submit" button.

[2114] Output: The job seeker's basic information and job application information are sent to the server.

[2115] Step 2: Store the information in a database

[2116] 2-1. Receipt and storage of data by the server:

[2117] Input: Information submitted by companies and job seekers.

[2118] Specific operation: The server receives information sent by companies and job seekers, stores it separately in each database, and validates it to ensure data consistency.

[2119] Output: Company and job search information stored in a database.

[2120] Step 3: Collect and analyze feedback using the emotion engine

[2121] 3-1. Emotion engine settings:

[2122] Input: Configuration information for the Emotion Engine API.

[2123] Specific operation: The server calls the emotion engine's API and configures it to analyze the user's input data and feedback.

[2124] Output: The emotion engine is ready to run.

[2125] 3-2. Gathering feedback:

[2126] Input: Feedback information such as post-interview evaluations and impressions.

[2127] Specific operation: Companies and job seekers enter feedback after the interview, and the emotion engine automatically collects users' emotion data. Enter information in the feedback form and click the "Submit" button.

[2128] Output: Feedback information including emotion data is sent to the server and stored in a database.

[2129] 3-3. Emotional Data Analysis:

[2130] Input: Collected feedback and sentiment data.

[2131] Specific operation: The server analyzes the data collected through the emotion engine and adjusts the parameters of the matching algorithm.

[2132] Output: The analyzed emotion data and adjusted algorithm parameters are saved.

[2133] Step 4: Run the matching algorithm

[2134] 4-1. Matching condition settings:

[2135] Input: Company job information, job seeker job information.

[2136] Specific operation: The server sets matching conditions based on criteria such as skill match, work location match, and salary range match.

[2137] Output: The matching criteria that have been set.

[2138] 4-2. Scoring execution:

[2139] Input: Matching criteria, company and job seeker information.

[2140] How it works: The server scores job seekers and companies based on matching criteria, taking into account emotional data. It then quantifies the degree of compatibility for each criterion and generates a total score.

[2141] Output: The scoring results are stored in a database.

[2142] 4-3. Calculating and listing precision:

[2143] Input: Scoring results.

[2144] Specific operation: The server calculates the relevance rate between job seekers and job information based on the scoring results, and generates a list of candidates and job listings in descending order of relevance rate.

[2145] Output: A generated list of candidates and job offers based on the match rate.

[2146] Step 5: Providing matching results

[2147] 5-1. Generation and distribution of matching results:

[2148] Input: A list based on precision.

[2149] Specific operation: The server generates a list of highly suitable candidates and job offers and distributes them to each company and job seeker.

[2150] Output: List of candidates and jobs provided, notified to the user via their dashboard or email.

[2151] 5-2. Check the job seeker list:

[2152] Input: The provided job seeker list.

[2153] Specific operation: The company's human resources staff uses the company terminal to check the list and select the job seekers to whom they want to send an interview request. They check the list on the dashboard and click the "Request an interview" button.

[2154] Output: An interview request is sent to the job candidate.

[2155] 5-3. Check the job listings:

[2156] Input: The provided job listing.

[2157] Specific operation: The job seeker uses the job seeker terminal to check the list and select the job they want to apply for. They check the list on the dashboard and click the "Apply" button.

[2158] Output: A job application is sent to the company.

[2159] These are the specific processing steps of the system. By using this system, it is possible to improve the accuracy of matching between companies and job seekers and achieve appropriate matching that takes emotional data into account.

[2160] (Application example 2)

[2161] 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."

[2162] Conventional systems for matching job seekers and companies do not fully utilize feedback and emotional data between the two parties, resulting in low matching accuracy and insufficient user satisfaction. Furthermore, advertisements are delivered without taking into account the user's emotional state, resulting in insufficient effectiveness. By resolving these issues, there is a need to improve the accuracy of matching job seekers and companies while also enabling advertisement delivery based on the user's emotional state.

[2163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2164] In this invention, the server includes means for a job seeker to input job search information, means for a company to input job information, means for saving the job search information and the job information, means for matching the job search information with the job information, means for providing the matching results to the job seeker and the company, means for collecting feedback from the job seeker and the company, means for improving matching accuracy based on the feedback, means for collecting user emotion data, means for personalizing advertisements based on the emotion data, and means for delivering personalized advertisements to the user.

[2165] This will not only make it possible to improve matching accuracy by utilizing feedback and emotional data between job seekers and companies, but also enable appropriate advertisement delivery based on the user's emotional state.

[2166] "Job search information" refers to information entered by job seekers, such as work experience, skills, desired job type, and work location.

[2167] "Job information" refers to information about recruitment entered by companies, such as job type, required skills, work location, and salary.

[2168] The "means for saving" is a function for saving the entered job search information and recruitment information in a database while maintaining consistency.

[2169] The "means of matching" is a function that calculates compatibility based on job search information and employment information, and extracts matching candidates.

[2170] "Means for providing matching results" refers to the function of presenting matching results to job seekers and companies.

[2171] "Means for collecting feedback" is a function for collecting opinions and feedback from job seekers and companies.

[2172] "Means for improving matching accuracy" refers to a function that analyzes collected feedback and optimizes the matching algorithm based on that.

[2173] "Means for collecting emotional data" refers to a function for collecting the user's emotional state in real time through facial expression recognition and voice analysis.

[2174] "Means for personalizing ads" refers to a function that selects the most suitable ads for users based on collected emotional data.

[2175] "Means for delivering personalized advertisements" refers to a function for delivering selected personalized advertisements to users.

[2176] The following system configuration and operation will be described as an embodiment of the present invention.

[2177] System Configuration

[2178] This system consists of the following main components:

[2179] User device: A device where job seekers and companies enter information and runs a smartphone app.

[2180] Server: Stores data, performs matching, collects feedback, and performs analytics.

[2181] Database: A database for storing job applications, job postings, feedback, and sentiment data.

[2182] Emotion Engine: An engine that collects and analyzes user emotion data in real time.

[2183] Ad management system: A system that selects and delivers advertisements appropriate for users based on emotional data.

[2184] Hardware and Software

[2185] Hardware: Smartphones, servers

[2186] Software: Emotion engines (e.g., EmotionAI SDK), database management systems (e.g., MySQL), matching algorithms, ad management systems (e.g., Google Ad API, Facebook Ads API), mobile application frameworks (e.g., React Native, Flutter)

[2187] Operational Overview

[2188] 1. User registration and information entry

[2189] The server receives information entered by job seekers and companies via a smartphone app (such as name, contact details, skills, and desired conditions) and stores it in a database.

[2190] The user device provides an intuitive interface when registering job search information or recruitment information (e.g., React Native app).

[2191] 2. Collecting Emotional Data

[2192] The emotion engine collects the user's emotional state in real time through facial expression recognition and voice analysis, and sends it to the server, where the emotional data is stored in a database.

[2193] 3. Matching and feedback collection and analysis

[2194] The server runs a matching algorithm based on the job search information and job information, calculates the degree of compatibility, and provides the results to job seekers and companies.

[2195] After the interview or after the hire, the server collects feedback from the job seeker and the company and stores it in a database.

[2196] The feedback data is analyzed to improve the accuracy of the matching algorithm.

[2197] 4. Personalizing and delivering ads

[2198] The ad management system uses the collected emotional data to select the most suitable ad for the user, and the ad is then personalized and delivered to the user.

[2199] The smartphone app displays appropriate advertisements as notifications depending on your emotional state.

[2200] Specific examples

[2201] Example 1: Recruitment advertisement example

[2202] When a job seeker requests a "software engineer" position on a user device, they enter their...

Claims

1. a means for job seekers to input job search information; A way for companies to enter job information, a means for storing the job search information and the recruitment information; a means for matching the job-seeking information with the recruitment information; A means for providing the matching results to job seekers and companies; a means for collecting feedback from said job seekers and businesses; means for improving matching accuracy based on the feedback; A system including:

2. 2. The system according to claim 1, wherein after the job-seeking information and the job-recruitment information are input, a score is generated based on the recruitment conditions of the company and the desired conditions of the job seeker.

3. means for sending an interview request based on the matching result; The system of claim 1 further comprising means for the job seeker to apply for a job.

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