System

Abstract

A system is provided.SOLUTION: A talent matching system comprising: terminals for inputting talent information; a server for storing inputted job offer information in a database; a server for analyzing the job offer information and executing an AI algorithm for generating a candidate list matching a condition; a server for automatically generating and transmitting customized scout mail to the generated candidates; terminals for receiving, analyzing and notifying reactions of the candidates; and a server for adjusting an interview schedule and notifying determined information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the medical and welfare fields, labor shortages are a serious problem, making it particularly difficult to scout for talented personnel. Traditional recruitment processes require a significant amount of time and effort for a series of tasks, from entering job information to selecting candidates, creating and sending scouting emails, collecting responses from candidates, and arranging interview dates, placing a heavy burden on corporate recruiters. This significantly reduces the speed and efficiency of recruitment, making it difficult to secure the right talent in a timely manner. To solve this issue and achieve efficient and rapid recruitment, there is a need for an optimal talent matching system that automates the scouting process and utilizes AI. [Means for solving the problem]

[0005] This invention provides a talent matching system that includes a terminal for inputting personnel information, a server that stores the input job information in a database, an AI algorithm that analyzes the job information and generates a list of candidates that meet the conditions, a server that automatically generates and sends customized scout emails to the generated candidates, a terminal that receives, analyzes, and notifies the candidates' responses, and a server that arranges interview dates and notifies the finalized information. Specifically, the system incorporates matching know-how for generating a candidate list based on job information into the AI ​​algorithm, and is equipped with a means for customizing the automatically generated scout emails for each candidate. This system enables an efficient and rapid recruitment process in the medical and welfare fields.

[0006] A "talent matching system" is a system that automates and optimizes a series of processes for matching suitable personnel with job openings based on job information and candidate information.

[0007] "Terminal" means a device that allows a user (recruiter) or candidate to access the system and input, receive, and display information.

[0008] A "server" is a computer system that stores and processes data and controls the operation of the entire system.

[0009] A "database" is a system for systematically storing and managing data such as job information and candidate information.

[0010] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and select candidates that meet the criteria.

[0011] A "scouting email" is an email sent to encourage candidates to express interest in a job opening.

[0012] "Automatic generation" refers to the process in which a system autonomously creates documents and data without human intervention.

[0013] "Interview schedule adjustment" is a process in which the candidate and the user decide on the date and time for the interview.

[0014] "Matching know-how" refers to the specialized knowledge and experience required to effectively link job information with candidate information.

[0015] "Customization" refers to tailoring and modifying content to suit a specific candidate. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The following describes an embodiment of the talent matching system: This system is mainly configured based on interactions between a server, a terminal, and a user.

[0038] 1. Enter and save job information

[0039] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, required skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server.

[0040] The server stores the received job information in a database, which is then analyzed by an AI algorithm later in the process.

[0041] 2. Generate candidate list

[0042] The server uses an AI algorithm to select suitable candidates based on the job information stored in the database. This AI algorithm has learned from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[0043] 3. Automatic generation and sending of scout emails

[0044] The server automatically generates a scouting email based on the candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. The generated scouting email is then sent from the server to the candidate's device.

[0045] 4. Receiving and notifying candidate responses

[0046] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate is interested in the email and replies, the email is sent to the server.

[0047] The server analyzes the received replies, organizes their contents, and notifies the user, allowing the user to receive the candidate's response promptly.

[0048] 5. Arranging and notifying interview dates

[0049] After the user checks the candidate's reply, the system arranges the interview date. The server notifies the candidate and the user of the confirmed interview date.

[0050] 6. Managing recruitment results

[0051] The user enters the hiring results after the interview into the system, such as recording "hired" or "rejected." The server stores these results in a database and analyzes them to improve the scouting process in the future.

[0052] Specific examples

[0053] Situation: Nurse job opening

[0054] 1. A user (a hospital recruiter) enters job information for a "nurse" through a terminal. The input information includes "3+ years of clinical experience, availability for night shifts, work location: Tokyo, annual salary: 5 million yen or more."

[0055] 2. The server stores the input information and uses an AI algorithm to analyze the candidate information in the database, generating a list of 10 candidates who match the criteria.

[0056] 3. The server automatically generates a personalized scouting email for each candidate on the list, including content tailored to the candidate's name, area of ​​expertise, and past experience.

[0057] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[0058] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[0059] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. The server uses this information to improve the scouting process for the next time.

[0060] This will enable efficient and rapid recruitment activities, helping to solve the problem of labor shortages in the medical and welfare fields.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user logs in to the terminal, enters their user ID and password on the login screen, and is authenticated.

[0064] Step 2:

[0065] The user enters the required information into the job information entry form, including job type, skills, years of experience, location, salary, etc. Once all required fields are entered, the user presses the "Save" button.

[0066] Step 3:

[0067] The server receives the job information sent from the terminal and stores it in a database, while simultaneously checking the consistency of the entered information to detect any defects or inconsistencies.

[0068] Step 4:

[0069] The server uses an AI algorithm to analyze the candidate information in the database based on the saved job information, and creates a list of candidates who match the criteria. This list is generated based on skills, experience, location, salary expectations, etc.

[0070] Step 5:

[0071] The server automatically generates scouting emails for the generated candidate list, which are customized for each candidate and include the candidate's name, past experience, and a summary of the job information.

[0072] Step 6:

[0073] The server sends automatically generated scouting emails to candidates on the list, and the sending history is recorded in a database.

[0074] Step 7:

[0075] The terminal (the candidate's email receiving terminal) receives the scout email. The candidate checks the contents of the email and, if interested, sends a reply email.

[0076] Step 8:

[0077] The server receives reply emails from candidates, analyzes their contents, organizes the reply contents, and generates information to notify the user.

[0078] Step 9:

[0079] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen within the system to schedule an interview.

[0080] Step 10:

[0081] The user checks the available schedule, arranges an interview date with the candidate, and enters the confirmed date into the system.

[0082] Step 11:

[0083] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[0084] Step 12:

[0085] After the interview, the user enters the hiring result into the system, for example, the result is recorded as "hired" or "rejected."

[0086] Step 13:

[0087] The server stores the recruitment results in a database and analyzes past successes and failures, which helps improve the accuracy of the next scouting process.

[0088] Example 1

[0089] 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."

[0090] In conventional talent matching systems, processes such as entering and managing job information, selecting candidates, creating and sending scouting emails, managing candidate replies, and arranging interview dates were often done manually, resulting in problems of time and effort. Furthermore, manually creating customized messages for each candidate was a significant burden, making efficient talent matching difficult. For this reason, a system that can quickly and efficiently match talent is needed.

[0091] 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.

[0092] In this invention, the server includes a user terminal for inputting personnel information, a recording device for saving the input job information in a database, a processing device that executes an artificial intelligence model that analyzes the job information and generates a list of candidates that meet the conditions, a device for automatically generating and sending customized information emails to the generated candidates, a notification device that receives, analyzes, and notifies the candidates of their responses, and a management device that arranges interview dates and notifies the confirmed information. This automates the entire process from entering job information to arranging interview dates, enabling efficient and rapid personnel matching.

[0093] A "user terminal" is an electronic device that a user uses to input personnel information.

[0094] The "recording device" is a device for storing input job information in a database.

[0095] An "artificial intelligence model" is an algorithm or program used to generate a list of qualified candidates based on a job posting.

[0096] "Processing Device" means a computing device for executing artificial intelligence models and analyzing job listings.

[0097] An "invitation email" is a customized email sent to the generated candidate.

[0098] A "notification device" is a device that receives and analyzes the responses of candidates and notifies the user.

[0099] The "management device" is a device for arranging interview schedules and notifying confirmed information.

[0100] "Matching know-how" is a method based on specialized knowledge and experience for generating a candidate list based on job information.

[0101] This talent matching system is primarily comprised of the interaction between user terminals, a server, and candidate terminals. The system automates the entire process, from entering job information to arranging interview dates, achieving efficient and rapid talent matching.

[0102] Hardware and software used

[0103] 1. User device: An electronic device such as a computer or smartphone that allows users to enter job information.

[0104] 2. Recording device: A database system (e.g. MySQL) to store the entered job information.

[0105] 3. Processing unit: A server that analyzes job information and generates a list of candidates that meet the requirements. This server is equipped with artificial intelligence models such as TensorFlow and PyTorch.

[0106] 4. Information Email Generator: A system for generating and sending customized information emails to candidates. It uses natural language processing algorithms such as OpenAI's GPT-3 for generation. An SMTP server is used to send emails.

[0107] 5. Notification device: A real-time chat system or push notification system that receives and analyzes candidate responses and notifies users.

[0108] 6. Management device: A system for arranging interview dates and notifying candidates and users of confirmed information. It uses Google Calendar API, etc.

[0109] Specific example of system operation

[0110] 1. Enter and save job information

[0111] A user uses a device to input job information. The input form contains detailed information such as "desired job type," "required skills," "years of experience," "work location," and "salary." When this information is entered and the "Submit" button is clicked, the device sends the information to the server. The server receives this information and stores it in a recording device (MySQL database).

[0112] 2. Generate candidate list

[0113] The server runs a pre-trained artificial intelligence model using TensorFlow and PyTorch on the stored job information, analyzes the candidate information in the database based on the job requirements, and generates a list of candidates who match the requirements.

[0114] 3. Automatic generation and sending of scout emails

[0115] The server automatically generates customized invitation emails for the generated candidate list using OpenAI's GPT-3. For example, it might say, "Mr. Fujita, your three or more years of clinical experience will be extremely beneficial to our hospital." The generated emails are sent to the candidate's device via the SMTP server.

[0116] 4. Receiving and notifying candidate responses

[0117] The candidate's device receives the information email, and interested candidates reply. This reply is sent to the server, which analyzes the content and notifies the user via the notification device. Natural language processing technology is used for the analysis, and important keywords (e.g., "interview request" and "condition confirmation") are extracted from the reply content.

[0118] 5. Arranging and notifying interview dates

[0119] After checking the candidate's reply, the user can schedule an interview within the system. The server manages the interview schedule via the Google Calendar API, and the confirmed date is automatically notified to the candidate and the user.

[0120] Prompt Sentence Examples

[0121] "Please explain the specific operation of a system that generates a list of suitable candidates based on nursing job postings and automatically generates customized invitation emails."

[0122] In this way, the operation of the entire system is made more efficient, and human resource matching in the medical and welfare fields is carried out more quickly.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The user enters job information using their device. The input form contains detailed items such as "desired job type," "required skills," "years of experience," "work location," and "salary." After entering the information, the user clicks the "Submit" button, and the device sends the information to the server via an HTTP POST request. The entered job information is sent to the server in JSON format or form data format.

[0126] Step 2:

[0127] The server saves the received job information in a MySQL database. First, the server parses the received data and breaks it down into the necessary data items. Then, it generates an SQL query to save the job information and inserts it into the database. If the save is successful, it returns a confirmation message to the user.

[0128] Step 3:

[0129] The server runs an artificial intelligence model (such as TensorFlow or PyTorch) based on the saved job information. The input is the saved job information, and the model uses this data to filter the candidate information in the database. Specifically, it extracts candidate data based on the job criteria (for example, "nurse" and "work location: Tokyo") and generates a list of candidates that match the criteria. The output is a list of candidates that match the criteria.

[0130] Step 4:

[0131] The server automatically generates customized invitation emails using OpenAI's GPT-3 based on the generated candidate list. The input is the candidate list, and a message is generated based on individual information for each candidate. Specifically, the message is personalized based on the candidate's name, skills, and past experience. The output is an individually customized invitation email.

[0132] Step 5:

[0133] The server sends the generated invitation email to the candidate's email address via the SMTP server. The input is the customized email, which is passed to the email sending server according to the SMTP protocol. The SMTP server delivers the email to the candidate's email account. The output is confirmation that the email has been sent.

[0134] Step 6:

[0135] The candidate terminal receives the invitation email. If the candidate is interested in the email and replies, the reply is sent to the server as an email. The input is the reply email from the candidate, and the server receives this information.

[0136] Step 7:

[0137] The server analyzes the received reply email and organizes its contents. First, it analyzes the contents of the reply email using NLP technology and extracts important keywords (e.g., "interview request" and "condition confirmation"). The analysis results are displayed on the user's management screen. The output is the organized reply content.

[0138] Step 8:

[0139] After checking the candidate's reply, the user arranges the interview schedule within the system. The input is the candidate's reply, and the user enters the schedule on the management screen. The server creates the interview schedule using the Google Calendar API and notifies the participants. The output is a notification of the interview schedule sent to the candidate and the user.

[0140] Step 9:

[0141] The user inputs the hiring results after the interview into the system. The input is "hired" or "not hired," and the server stores this result in a database. The stored data is fed back into the next candidate selection process. The output is the saved hiring results.

[0142] These steps automate the entire process, from entering job information to scheduling interviews and managing recruitment results, enabling efficient talent matching.

[0143] (Application example 1)

[0144] 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."

[0145] In the logistics industry, inappropriate recruitment and allocation of workers often leads to reduced work efficiency and problems. Even though securing personnel is crucial, especially during busy periods, the difficulty of quickly allocating the right personnel is a challenge. Traditional manual methods of recruiting and allocating workers take time and effort, and do not allow for efficient human resource management.

[0146] 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.

[0147] In this invention, the server includes a terminal for inputting job information, a means for saving the input job information in a database, a means for analyzing the job information and running an AI algorithm for generating a list of candidates that meet the conditions, a means for automatically generating and sending customized scout messages to the generated candidates, a means for receiving, analyzing, and notifying the candidates' responses, a means for arranging interview dates and notifying the confirmed information, a means including an AI algorithm for inputting and saving worker information for logistics operations and generating a list of suitable workers, a means for automatically generating and sending customized messages to workers at the logistics center, and a means for receiving, analyzing, and notifying the workers' responses, thereby enabling efficient recruitment and appropriate allocation of workers.

[0148] "Job information" refers to information such as the type of job being recruited, required skills, working hours, work location, and compensation.

[0149] A "terminal" is an electronic device that allows a user to input information.

[0150] A "database" is a system for storing and managing input information.

[0151] A "server" is a computer system that processes and stores data and communicates with other devices.

[0152] An "AI algorithm" is a calculation method that uses artificial intelligence to analyze data and make predictions and selections.

[0153] A "scouting message" is a customized message used to notify candidates about job openings.

[0154] A "response" is a candidate's response or opinion to a scouting message.

[0155] An "interview date" is the date and time when the candidate and the recruiter will conduct an interview.

[0156] "Logistics operations" include the transportation, storage, and delivery of goods.

[0157] "Worker information" is information about the skills, experience, available working hours, etc. of workers who perform logistics work.

[0158] The "worker list" is a list of workers who meet the conditions.

[0159] "Workers" are workers who perform tasks such as picking, packing, and shipping goods at logistics centers.

[0160] An embodiment for implementing this worker matching system will be described below. This system is mainly configured based on interactions between a server, a terminal, and a user.

[0161] 1. Enter and save job information

[0162] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. This job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. The entered job information is sent from the device to a server. The server stores the received job information in a database. The database is structured using a relational database management system such as MySQL.

[0163] 2. Generate candidate list

[0164] The server uses an AI algorithm to select suitable candidates based on the stored job information. This AI algorithm uses a machine learning model written in Python. The algorithm learns from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[0165] 3. Automatic generation and sending of scouting messages

[0166] The server automatically generates a customized scouting message based on the candidate list. This scouting message includes a summary of the work, the candidate's name, and a customized message tailored to their past experience. The generated scouting message is sent to the candidate's device using Firebase Cloud Messaging.

[0167] 4. Receiving and notifying candidate responses

[0168] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase. The server analyzes the received response, organizes its contents, and notifies the administrator. This allows the administrator to receive the candidate's response quickly.

[0169] 5. Arranging and notifying interview dates

[0170] After checking the candidate's response, the user (administrator) schedules the interview within the system. The server notifies the candidate and administrator of the confirmed interview date. This notification also uses Firebase Cloud Messaging.

[0171] 6. Managing recruitment results

[0172] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[0173] Specific examples

[0174] Consider a distribution center looking to hire pickers for peak season. The manager types the following:

[0175] Job: Picker

[0176] Required skills: Quick and accurate product selection

[0177] Years of experience: No experience necessary

[0178] Location: Nagoya

[0179] Remuneration: 1,200 yen per hour

[0180] Operating hours: 9:00-18:00

[0181] Once this information is entered, the system automatically generates a list of responding workers and sends a scout message, enabling the right workers to be deployed quickly and efficiently.

[0182] Keywords: generative AI model, prompt sentence

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. The entered job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. This information is sent from the device to the server.

[0186] Input: Job information (work content, skills, working hours, work location, compensation, etc.)

[0187] Output: The job post sent to the server

[0188] Step 2:

[0189] The server stores the received job information in a database, where the information is managed appropriately using a database management system (e.g., MySQL).

[0190] Input: Job information sent to the server

[0191] Output: Job listings stored in a database

[0192] Step 3:

[0193] The server selects suitable candidates based on the saved job information using an AI algorithm, a Python machine learning model that has learned from past successful matching know-how, and generates a list of candidates that meet the job requirements.

[0194] Input: Job listings stored in a database

[0195] Output: A list of candidates generated by the AI ​​algorithm

[0196] Step 4:

[0197] The server automatically generates a customized scouting message based on the candidate list, including a message tailored to the candidate's name, past experience, and skill set, and sends the generated scouting message to the candidate's device using Firebase Cloud Messaging.

[0198] Input: Candidate list generated by AI algorithm

[0199] Output: Scout message sent

[0200] Step 5:

[0201] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase.

[0202] Input: Scouting message sent to candidate

[0203] Output: Candidate response sent to server

[0204] Step 6:

[0205] The server analyzes the responses received from the candidates, organizes the content, and notifies the administrator, allowing the administrator to quickly check the candidates' reactions.

[0206] Input: Candidate response sent to server

[0207] Output: Candidate's response notified to administrator

[0208] Step 7:

[0209] After checking the candidate's response, the user (administrator) schedules the interview within the system. This schedule is confirmed by the server and notified to the candidate and administrator. This notification also uses Firebase Cloud Messaging.

[0210] Input: Interview date arranged in the system

[0211] Output: Interview dates notified to candidates and managers

[0212] Step 8:

[0213] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[0214] Input: Hiring result after interview

[0215] Output: Recruitment results stored in a database and used for next process improvement

[0216] 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.

[0217] This talent matching system manages talent information, selects candidates, automatically generates and sends scouting emails, collects responses, schedules interviews, and combines an emotion engine to create an efficient and human-like recruitment process. Below, we will explain the detailed processing and specific operation of this system.

[0218] 1. Enter and save job information

[0219] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server, which then stores the information in a database.

[0220] 2. Operation of the Emotion Engine

[0221] When a user inputs job information, the device captures the user's facial expressions and voice and analyzes their emotional state using an emotion engine. The emotion engine organizes the user's emotional data at the time of input and associates it with the job information.

[0222] 3. Generate candidate list

[0223] The server uses AI algorithms to analyze the job information and sentiment data stored in the database. This analysis, including sentiment data, helps generate a more suitable candidate list. The generated candidate list is optimized based on skills, experience, location, salary expectations, etc.

[0224] 4. Automatic generation and sending of scout emails

[0225] The server automatically generates a scouting email based on the optimized candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. Furthermore, an emotion engine uses a communication style that reflects the user's emotional state. The generated scouting email is sent from the server to the candidate's device.

[0226] 5. Receiving and notifying candidate responses

[0227] When the terminal (the candidate's email receiving terminal) receives the scout email and the candidate is interested in the email and replies, the email is sent to the server. The server analyzes the received reply, organizes the content, and notifies the user.

[0228] 6. Interview schedule arrangement and notification

[0229] Once the user receives the notification from the server and the candidate's interest is confirmed, the user can schedule an interview within the system. The server will notify the candidate and the user of the confirmed interview date.

[0230] 7. Managing recruitment results

[0231] The user enters the hiring results after the interview into the system. The results are recorded as "hired" or "not hired," for example. The server stores these results in a database and analyzes past successes and failures. This analysis, based on emotional data, helps improve the accuracy of the next scouting process.

[0232] Specific examples

[0233] Situation: Nurse job opening

[0234] 1. A user (hospital recruiter) enters job information for a "nurse" through a terminal. The input items include "3+ years of clinical experience, available to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." At this time, the terminal captures the user's facial expressions and voice, and an emotion engine analyzes the user's emotional state.

[0235] 2. The server stores the input information and emotional data, analyzes the candidate information in the database using AI algorithms, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[0236] 3. The server automatically generates a personalized scouting email for each candidate on the list, including mentioning the candidate's name, area of ​​expertise, and past experience, and using a writing style that reflects the user's emotional state.

[0237] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[0238] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[0239] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. Analysis based on the sentiment data is also performed to help improve the scouting process for the next time.

[0240] This system will enable efficient and humane recruitment activities, making it possible to more effectively solve the problem of labor shortages in the medical and welfare fields.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user logs in to the terminal and enters their user ID and password on the login screen to receive authentication for access to the system.

[0244] Step 2:

[0245] The user enters the necessary information such as job type, skills, years of experience, work location, salary, etc. into the job information input form. Once the input is complete, the terminal sends this information to the server.

[0246] Step 3:

[0247] The device captures the user's facial expressions and voice and sends them to the emotion engine, which analyzes this data and determines the user's emotional state.

[0248] Step 4:

[0249] The server stores the received job information in a database, along with the emotion data provided by the emotion engine.

[0250] Step 5:

[0251] The server uses AI algorithms to analyze the candidate information in the database based on the stored job information and sentiment data, and generates a list of candidates that match the criteria.

[0252] Step 6:

[0253] The server automatically generates a scouting email based on the candidate list, including the results of the emotion engine. The scouting email includes the candidate's name, past experience, a summary of the job information, and a customized message according to the user's emotional state.

[0254] Step 7:

[0255] The server generates a scouting email and sends it to the candidate's email address. This sending history is recorded in the database.

[0256] Step 8:

[0257] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate reads the email and is interested, they will send a reply email.

[0258] Step 9:

[0259] The server receives reply emails from candidates, analyzes their contents, organizes the analysis results, generates notification data, and sends it to the user.

[0260] Step 10:

[0261] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen on their device to schedule an interview.

[0262] Step 11:

[0263] The user checks available schedules and arranges interview dates with the candidates. The arranged interview dates are then entered into the system.

[0264] Step 12:

[0265] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[0266] Step 13:

[0267] After the interview, the user enters the hiring results into the system, which are then sent to the server and stored in the database.

[0268] Step 14:

[0269] The server analyzes the recruitment results and links them with emotional data to help improve the next scouting process. Past successes and failures are statistically processed and used as data to improve the accuracy of the AI ​​algorithm.

[0270] Example 2

[0271] 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."

[0272] While existing talent matching systems provide basic functions such as inputting and saving job information, generating candidate lists, and sending scouting emails, they are insufficient to provide efficient and human-like support throughout the entire recruitment process. Furthermore, they lack the ability to communicate and select candidates while taking into account the user's emotional state, resulting in inaccurate matching.

[0273] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input device for inputting job information, a data storage device for saving the input job information in a database, an information processing device that executes an artificial intelligence algorithm that analyzes the job information and generates a list of candidates who meet the conditions, a communication device for automatically generating and sending customized scout messages to the generated candidates, an information acquisition device for receiving, analyzing, and notifying the responses of the candidates, a schedule adjustment device for arranging interview dates and notifying the confirmed information, a sentiment analysis device for analyzing the emotional state of the user when entering the job information, and a data association device for storing the emotional data acquired by the sentiment analysis device in association with the job information. This enables high-precision matching that takes the user's emotional state into consideration and a human-like hiring process.

[0274] The "information input device" is a device for inputting job information, and has the function of inputting data such as the type of job, skills, years of experience, work location, and salary desired by the user.

[0275] The "data storage device" is a device for storing input job information in a database, and is a device with a data management function built into the server.

[0276] The "artificial intelligence algorithm" is an algorithm that analyzes job information and generates a list of candidates that meet the conditions, and has the ability to automatically analyze information and select the most suitable candidates.

[0277] An "information processing device" is a device for executing artificial intelligence algorithms and has the function of analyzing data and generating candidate lists.

[0278] A "communication device" is a device for automatically generating and sending customized scouting messages to generated candidates, and has the functionality to send messages using email or other communication means.

[0279] An "information acquisition device" is a device that receives, analyzes, and notifies candidates of their responses, and has the function of collecting reply data from candidates and providing the analysis results to the user.

[0280] The "schedule adjustment device" is a device for adjusting interview dates and notifying the confirmed information, and has the function of adjusting and notifying the interview date based on the free time of both parties.

[0281] An "emotion analysis device" is a device for analyzing the emotional state of a user when entering job information, and has the function of analyzing facial expressions and voice data to infer the user's emotions.

[0282] The "data association device" is a device for storing emotion data acquired by the emotion analysis device in association with job information, and has a database management function for linking emotion data with job information.

[0283] This invention provides a system configuration for a talent matching system that takes into account user emotional information to realize an advanced and human-like recruitment process. The system of the present invention is composed of the following main components:

[0284] 1. Enter and save job information

[0285] Users use an information input device (terminal) to input job information such as the desired job type, skills, years of experience, work location, and salary. The information input device verifies this data in real time to confirm its accuracy. The input job information is sent from the information input device to a server, and a data storage device stores it in a database.

[0286] 2. Operation of the Emotion Engine

[0287] When a user enters job information, the device uses a built-in camera and microphone to capture the user's facial expressions and voice, and the emotion analyzer analyzes this data. The emotion analyzer infers the user's emotional state and records it as emotion data. The emotion data is stored in association with the job information in a data storage device.

[0288] 3. Generate candidate list

[0289] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database to generate a candidate list. The algorithm calculates the suitability of candidates by taking into account various factors (skills, experience, work location, desired salary, etc.), and also evaluates the suitability of candidates based on emotion data. The final candidate list is recorded in a data storage device.

[0290] 4. Automatic generation and sending of scout emails

[0291] The server automatically generates a scouting email on a communication device based on the generated candidate list. The scouting email includes a summary of the job information, the name of each candidate, and a customized message based on their past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scouting email is sent to the candidates via the communication device.

[0292] 5. Receiving and notifying candidate responses

[0293] The terminal (the candidate's email receiving terminal) receives the scout email and the candidate replies. The candidate's reply is sent to the server, where the information acquisition device analyzes it and notifies the user of the contents. The analyzed contents include the candidate's level of interest.

[0294] 6. Interview schedule arrangement and notification

[0295] After the user confirms the candidate's interest, the system uses a schedule adjustment device to arrange an interview date. The server proposes the optimal date for the interview based on the user's and candidate's calendar information. After confirmation, the schedule adjustment device notifies both parties of the confirmed interview date.

[0296] 7. Managing recruitment results

[0297] The user inputs the hiring results (e.g., hired or not hired) after the interview into the system. The hiring results are stored in the server's data storage device. The server then analyzes these results and emotion data to select the next candidate and improve the accuracy of the scouting process.

[0298] In this way, the talent matching system utilizes emotional data to realize an advanced and human-like recruitment process. The specific names of the main hardware and software used are an emotional analysis device, an artificial intelligence algorithm, an information input device, a data storage device, an information processing device, a communication device, an information acquisition device, and a schedule adjustment device.

[0299] Specific examples

[0300] Situation: Nurse job opening

[0301] 1. A user (a hospital recruiter) enters job information for a "nurse" through an information input device. The input information includes "three or more years of clinical experience, availability to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." The device captures the user's facial expressions and voice, and an emotion analyzer analyzes the user's emotional state.

[0302] 2. The server records the input information and emotional data in a data storage device, analyzes the candidate information in the database using an artificial intelligence algorithm in the information processing device, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[0303] 3. The server automatically generates a scouting email for each candidate on the list, using a communication device, that is individually customized, mentioning the candidate's name, field of expertise, and past experience, and employing a writing style that reflects the user's emotional state.

[0304] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server, where the information acquisition device analyzes it and notifies the user.

[0305] 5. The user arranges an interview date with the candidate using the schedule adjustment device within the system, and the server notifies both parties of the confirmed date.

[0306] 6. After the interview, the user enters the hiring results into the system, and the server stores the results along with the emotional data in a data storage device. This is also analyzed for the next candidate selection.

[0307] Prompt Sentence Examples

[0308] As a recruiter at a medical and welfare facility, you input job information for a nurse (three or more years of clinical experience, night shift availability, work location: Tokyo, annual salary: 5 million yen or more). Explain the program that generates a customized email that reflects the employee's emotional state and creates a list of candidates.

[0309] Through this system, it will be possible to realize efficient and humane recruitment activities in the medical and welfare fields, and more effectively solve the problem of labor shortages.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1: Enter and save your job information

[0312] The user inputs job information using an information input device. Specifically, the user inputs information such as the desired job type, skills, years of experience, work location, and salary into the input screen. After the input data is validated and its accuracy is confirmed, the terminal sends the job information to the server. The server stores the received job information in a data storage device.

[0313] Input: User-entered data for job postings

[0314] Data processing / calculation: Real-time validation by terminal

[0315] Output: The job post sent to the server

[0316] Specific behavior:

[0317] The user enters "Nurse, 3+ years of clinical experience, available for night shifts, work location: Tokyo, annual salary of 5 million yen or more" into the input screen of the terminal.

[0318] The terminal validates the data format and required fields.

[0319] After validation, the device sends the job information to the server.

[0320] The server stores the job information in a data storage device.

[0321] Step 2: Emotion Engine in Action

[0322] The device uses a built-in camera and microphone to capture the user's facial expressions and voice as they enter job information. An emotion analyzer analyzes this data in real time to infer the user's emotional state. The acquired emotion data is associated with the job information, transmitted to a server, and stored in a data storage device.

[0323] Input: User's facial expression data and voice data

[0324] Data processing / calculation: Emotional state estimation using emotion analyzer

[0325] Output: Emotion data sent to the server

[0326] Specific behavior:

[0327] The device captures the user's face and voice while they enter information.

[0328] The emotion analyzer infers emotions such as "excitement" or "alertness" in real time from the captured data.

[0329] The estimated emotion data is transmitted to a server and stored in a data storage device.

[0330] Step 3: Generate a candidate list

[0331] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database. The algorithm calculates the suitability of skills, experience, work location, and salary, and evaluates the suitability of candidates using emotion data. Finally, it generates a list of around 10 highly suitable candidates, which is stored in a data storage device.

[0332] Input: Job posting data and sentiment data

[0333] Data processing / calculation: Calculation of fitness and suitability evaluation using artificial intelligence algorithms

[0334] Output: Generated candidate list

[0335] Specific behavior:

[0336] The server extracts job information and sentiment data.

[0337] An artificial intelligence algorithm calculates suitability based on skills and experience.

[0338] Evaluate candidate suitability based on emotional data.

[0339] An optimized list of 10 candidates is generated and stored in a data storage device.

[0340] Step 4: Automatically generate and send scout emails

[0341] The server automatically generates a scout email using a communication device based on the candidate list. This scout email includes a summary of the job information, the candidate's name, and a customized message tailored to the candidate's past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scout email is sent to the candidate's terminal via the communication device.

[0342] Input: Candidate list data

[0343] Data processing / calculation: Generate customized messages and adjust tones

[0344] Output: Scout email sent

[0345] Specific behavior:

[0346] The server extracts the candidate list and job information.

[0347] An AI model generates a customized scouting email for each candidate.

[0348] Adjust the tone of your email based on emotional data (e.g., a brighter tone if the emotion is excited).

[0349] The generated scout mail is sent to the candidate via a communication device.

[0350] Step 5: Receiving and notifying candidate responses

[0351] The candidate's reply to the scout email received by the terminal (the candidate's email receiving terminal) is sent to the server. The server analyzes this reply using an information acquisition device and notifies the user of its contents. The analyzed contents include the candidate's level of interest and requests.

[0352] Input: Candidate response data

[0353] Data processing / calculation: Analysis of reply emails

[0354] Output: Analysis results reported to the user

[0355] Specific behavior:

[0356] The candidate responds to the scouting email.

[0357] The server analyzes the received email and classifies it into categories such as "interested" or "request more details."

[0358] Send users notifications like "Candidate A has expressed interest" or "Candidate B wants to confirm details."

[0359] Step 6: Scheduling and notifying interview schedule

[0360] After the user confirms the candidate's interest, the server uses the schedule adjustment device to arrange an interview date. The server proposes an optimal interview date based on the calendar information of the user and the candidate, and notifies both parties of the confirmed date from the adjustment device.

[0361] Input: User and candidate calendar information

[0362] Data processing / calculation: Optimizing interview schedules

[0363] Output: Notified interview date

[0364] Specific behavior:

[0365] The user opens the interview scheduling screen.

[0366] The server proposes the best date based on the availability of the user and the candidates.

[0367] The user confirms the schedule, and the server notifies the user of the confirmed interview schedule.

[0368] Step 7: Manage recruitment results

[0369] The user inputs the hiring results after the interview into the system. The server stores these results in a data storage device and uses past emotion data and hiring results to improve the accuracy of the next candidate selection and scouting process.

[0370] Input: Interview result data

[0371] Data processing / calculation: saving and analyzing result data

[0372] Output: Saved recruitment results and analysis results

[0373] Specific behavior:

[0374] The user enters the interview results into the system (e.g., "hired" or "rejected").

[0375] The server stores the results in a data storage device.

[0376] The server analyzes the emotional data and identifies areas for improvement in the next scouting process.

[0377] (Application example 2)

[0378] 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."

[0379] In conventional talent matching systems, the input and analysis of job information, the creation of candidate lists, and the sending of scouting emails are often done manually, resulting in inefficiency and a lack of communication that takes human emotions into consideration. This makes it difficult to find suitable candidates quickly and in a human-like manner. Furthermore, the lack of systems that take into account usage scenarios while on the move makes it difficult to achieve integrated talent matching that utilizes multiple devices.

[0380] 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.

[0381] In this invention, the server includes a means for displaying job information using a smart display or smart glasses, a means for capturing passengers' facial expressions and voices and analyzing their emotional state using an emotion engine, a means for reflecting the emotion data in generated scout emails, and a means for arranging and notifying interview dates within the system. This enables efficient talent matching even in an on-the-go environment and a more human-like recruitment process using emotion data.

[0382] A "terminal for inputting personnel information" is an electronic device such as a personal computer, smartphone, or tablet that is used by a job seeker or recruiter to input job information or job search information.

[0383] A "server that stores information in a database" is an information system that stores input job information and job search information and allows for searching and updating as needed.

[0384] The "server that executes the algorithm" is a computer system that performs specific calculations to analyze the job information input and generate a list of candidates that meet the criteria.

[0385] A "server that automatically generates and sends scouting emails" is a computer system that has the function of automatically creating customized scouting emails for candidates and sending them by email.

[0386] The "terminal that receives, analyzes, and notifies responses" is an electronic device that receives the responses from candidates to scouting emails, analyzes the content, and notifies the hiring manager.

[0387] The "server that notifies confirmed information" is a computer system that notifies the target user of confirmed information such as interview dates and hiring results.

[0388] "Means for displaying job information using a smart display or smart glasses" refers to a function that visually provides job information using a smart display or smart glasses that are installed on an autonomous bus or worn by passengers.

[0389] "Means of capturing facial expressions and voice and analyzing emotional state using an emotion engine" is a function that uses the passenger's facial expressions and words to analyze their emotional state using an emotion engine, and reflects this in job information and scouting emails.

[0390] "Means for reflecting emotional data" refers to a function that adjusts the content and expression of scouting emails sent to candidates based on emotional data analyzed by the emotion engine.

[0391] "Means of arranging and notifying interview dates within the system" is a function that determines interview dates based on the convenience of both the hiring manager and the candidate, and automatically notifies them of those dates.

[0392] As an embodiment of the present invention, a talent matching system is configured as follows.

[0393] 1. Enter and save personnel information

[0394] Users input job information using electronic devices (personal computers, smartphones, tablets, etc.). The information entered includes job type, skills, years of experience, work location, salary, etc. This information is sent from the device to a server, which stores it in a database. This accumulates data necessary for subsequent analysis and matching.

[0395] 2. Operation of the Emotion Engine

[0396] When a user enters a job posting, the device or smart glasses capture facial expressions and speech. This captured data is sent to an emotion engine on the server, which analyzes the user's emotional state. The emotion data is then associated with the job posting.

[0397] 3. Generate candidate list

[0398] The server runs an algorithm based on the job information and sentiment data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and sentiment data, resulting in the selection of the most suitable candidates.

[0399] 4. Displaying job information using smart displays or smart glasses

[0400] The server displays job information using smart displays installed inside the autonomous bus and smart glasses worn by passengers, allowing users to check job information in real time even while on the move.

[0401] 5. Automatic generation and sending of scouting emails

[0402] The server automatically generates a scouting email customized for each candidate based on the generated candidate list. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. Furthermore, the user's emotional state, analyzed by an emotion engine, is reflected in the email, enabling communication with a more human touch. The generated scouting email is then sent to the candidate's device.

[0403] 6. Receiving and notifying candidate responses

[0404] When a candidate replies to a scouting email, the reply is sent to the server, which analyzes the received email, organizes the content, and notifies the user, allowing the user to quickly understand the candidate's intentions.

[0405] 7. Interview schedule arrangement and notification

[0406] The user receives the notification from the server and arranges an interview date with the candidate. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[0407] 8. Managing recruitment results

[0408] The user enters the hiring results after the interview into the system. The results are saved on the server as either "hired" or "not hired." The server then stores these results in a database for later analysis. Based on past successes and failures, emotional data is used to improve the accuracy of the next scouting process.

[0409] Specific examples

[0410] As a concrete example of how this system can be implemented, the following example shows the use of smart glasses inside an autonomous bus.

[0411] example:

[0412] The user puts on the smart glasses and inputs the job information for a "nurse." The smart glasses capture facial expressions and voice and analyze the user's emotional state. The server generates a list of candidates based on this information and emotional data, and displays the job information on the smart glasses. In addition, customized scouting emails are generated and sent to suitable candidates. The following is an example of a prompt.

[0413] Example prompt sentence:

[0414] Please provide suitable job listings for nurses based on the following information:

[0415] Candidate's name: Taro Yamada

[0416] Skills: Python, Data Analysis

[0417] Years of experience: 5 years

[0418] Preferred location: Tokyo

[0419] Hardware and software used

[0420] Specific hardware used includes smart glasses (e.g., Google Glass, Vuzix) and smart displays, and software uses Python, a Flask server, and Emotion Analysis libraries.

[0421] As a result, this system can achieve efficient talent matching even in an on-the-go environment and provide a more human-like recruitment process by utilizing emotional data.

[0422] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0423] Step 1:

[0424] Users use electronic terminals to input job information. This information covers a wide range of topics, including job type, skills, years of experience, work location, and salary. The input data is sent from the terminal to a server and saved in a database. This allows the accumulation of job information.

[0425] Input: Job information (job type, skills, years of experience, work location, salary)

[0426] Output: Job listings stored in a database

[0427] Step 2:

[0428] The device captures facial expressions and voice as the user enters job information. This facial and voice data is sent to a server where it is analyzed by an emotion engine. This analysis generates user emotion data, which is stored in a database along with the associated job information.

[0429] Input: facial expression data, voice data

[0430] Output: Emotion data

[0431] Step 3:

[0432] The server runs an algorithm using the job information and emotion data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and emotion data. The optimized list is then prepared for further processing.

[0433] Input: Job information, emotion data

[0434] Output: Optimized candidate list

[0435] Step 4:

[0436] The server displays job information on smart displays or smart glasses installed inside the autonomous bus, allowing users to check job information in real time even while on the move. The displayed information is based on the optimized candidate list generated in the previous step.

[0437] Input: Optimized candidate list

[0438] Output: Job listings displayed on a smart display or smart glasses

[0439] Step 5:

[0440] The server automatically generates a scouting email customized for each candidate based on the candidate list generated in the previous step. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. The writing style also reflects the emotional data. The generated scouting email is sent to the candidate's device.

[0441] Input: Optimized candidate list, sentiment data

[0442] Output: Scout email sent to candidate's device

[0443] Step 6:

[0444] When a candidate replies to a scouting email, the reply is sent to the server. The server analyzes the received email, organizes the content, and notifies the user. This analysis allows the candidate's intentions to be quickly understood.

[0445] Input: Reply email from candidate

[0446] Output: Parsed reply, notification to user

[0447] Step 7:

[0448] The user receives the notification from the server and arranges an interview date with the candidate on the server. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[0449] Input: Candidate's reply, user input

[0450] Output: Confirmed interview date, notification to candidate and user

[0451] Step 8:

[0452] Once the interview is over, the user enters the hiring result on the server. The result is saved on the server as either "hired" or "not hired." The server stores these results in a database and uses them for subsequent analysis. Based on past successes and failures, the accuracy of the next scouting process can be improved.

[0453] Input: Interview result (hired or not hired)

[0454] Output: Interview results stored in a database, feedback for the next scouting process

[0455] The above are the specific processing steps for carrying out the present invention, through which efficient and human resource matching is achieved.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] The following describes an embodiment of the talent matching system: This system is mainly configured based on interactions between a server, a terminal, and a user.

[0473] 1. Enter and save job information

[0474] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, required skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server.

[0475] The server stores the received job information in a database, which is then analyzed by an AI algorithm later in the process.

[0476] 2. Generate candidate list

[0477] The server uses an AI algorithm to select suitable candidates based on the job information stored in the database. This AI algorithm has learned from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[0478] 3. Automatic generation and sending of scout emails

[0479] The server automatically generates a scouting email based on the candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. The generated scouting email is then sent from the server to the candidate's device.

[0480] 4. Receiving and notifying candidate responses

[0481] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate is interested in the email and replies, the email is sent to the server.

[0482] The server analyzes the received replies, organizes their contents, and notifies the user, allowing the user to receive the candidate's response promptly.

[0483] 5. Arranging and notifying interview dates

[0484] After the user checks the candidate's reply, the system arranges the interview date. The server notifies the candidate and the user of the confirmed interview date.

[0485] 6. Managing recruitment results

[0486] The user enters the hiring results after the interview into the system, such as recording "hired" or "rejected." The server stores these results in a database and analyzes them to improve the scouting process in the future.

[0487] Specific examples

[0488] Situation: Nurse job opening

[0489] 1. A user (a hospital recruiter) enters job information for a "nurse" through a terminal. The input information includes "3+ years of clinical experience, availability for night shifts, work location: Tokyo, annual salary: 5 million yen or more."

[0490] 2. The server stores the input information and uses an AI algorithm to analyze the candidate information in the database, generating a list of 10 candidates who match the criteria.

[0491] 3. The server automatically generates a personalized scouting email for each candidate on the list, including content tailored to the candidate's name, area of ​​expertise, and past experience.

[0492] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[0493] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[0494] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. The server uses this information to improve the scouting process for the next time.

[0495] This will enable efficient and rapid recruitment activities, helping to solve the problem of labor shortages in the medical and welfare fields.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The user logs in to the terminal, enters their user ID and password on the login screen, and is authenticated.

[0499] Step 2:

[0500] The user enters the required information into the job information entry form, including job type, skills, years of experience, location, salary, etc. Once all required fields are entered, the user presses the "Save" button.

[0501] Step 3:

[0502] The server receives the job information sent from the terminal and stores it in a database, while simultaneously checking the consistency of the entered information to detect any defects or inconsistencies.

[0503] Step 4:

[0504] The server uses an AI algorithm to analyze the candidate information in the database based on the saved job information, and creates a list of candidates who match the criteria. This list is generated based on skills, experience, location, salary expectations, etc.

[0505] Step 5:

[0506] The server automatically generates scouting emails for the generated candidate list, which are customized for each candidate and include the candidate's name, past experience, and a summary of the job information.

[0507] Step 6:

[0508] The server sends automatically generated scouting emails to candidates on the list, and the sending history is recorded in a database.

[0509] Step 7:

[0510] The terminal (the candidate's email receiving terminal) receives the scout email. The candidate checks the contents of the email and, if interested, sends a reply email.

[0511] Step 8:

[0512] The server receives reply emails from candidates, analyzes their contents, organizes the reply contents, and generates information to notify the user.

[0513] Step 9:

[0514] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen within the system to schedule an interview.

[0515] Step 10:

[0516] The user checks the available schedule, arranges an interview date with the candidate, and enters the confirmed date into the system.

[0517] Step 11:

[0518] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[0519] Step 12:

[0520] After the interview, the user enters the hiring result into the system, for example, the result is recorded as "hired" or "rejected."

[0521] Step 13:

[0522] The server stores the recruitment results in a database and analyzes past successes and failures, which helps improve the accuracy of the next scouting process.

[0523] Example 1

[0524] 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."

[0525] In conventional talent matching systems, processes such as entering and managing job information, selecting candidates, creating and sending scouting emails, managing candidate replies, and arranging interview dates were often done manually, resulting in problems of time and effort. Furthermore, manually creating customized messages for each candidate was a significant burden, making efficient talent matching difficult. For this reason, a system that can quickly and efficiently match talent is needed.

[0526] 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.

[0527] In this invention, the server includes a user terminal for inputting personnel information, a recording device for saving the input job information in a database, a processing device that executes an artificial intelligence model that analyzes the job information and generates a list of candidates that meet the conditions, a device for automatically generating and sending customized information emails to the generated candidates, a notification device that receives, analyzes, and notifies the candidates of their responses, and a management device that arranges interview dates and notifies the confirmed information. This automates the entire process from entering job information to arranging interview dates, enabling efficient and rapid personnel matching.

[0528] A "user terminal" is an electronic device that a user uses to input personnel information.

[0529] The "recording device" is a device for storing input job information in a database.

[0530] An "artificial intelligence model" is an algorithm or program used to generate a list of qualified candidates based on a job posting.

[0531] "Processing Device" means a computing device for executing artificial intelligence models and analyzing job listings.

[0532] An "invitation email" is a customized email sent to the generated candidate.

[0533] A "notification device" is a device that receives and analyzes the responses of candidates and notifies the user.

[0534] The "management device" is a device for arranging interview schedules and notifying confirmed information.

[0535] "Matching know-how" is a method based on specialized knowledge and experience for generating a candidate list based on job information.

[0536] This talent matching system is primarily comprised of the interaction between user terminals, a server, and candidate terminals. The system automates the entire process, from entering job information to arranging interview dates, achieving efficient and rapid talent matching.

[0537] Hardware and software used

[0538] 1. User device: An electronic device such as a computer or smartphone that allows users to enter job information.

[0539] 2. Recording device: A database system (e.g. MySQL) to store the entered job information.

[0540] 3. Processing unit: A server that analyzes job information and generates a list of candidates that meet the requirements. This server is equipped with artificial intelligence models such as TensorFlow and PyTorch.

[0541] 4. Information Email Generator: A system for generating and sending customized information emails to candidates. It uses natural language processing algorithms such as OpenAI's GPT-3 for generation. An SMTP server is used to send emails.

[0542] 5. Notification device: A real-time chat system or push notification system that receives and analyzes candidate responses and notifies users.

[0543] 6. Management device: A system for arranging interview dates and notifying candidates and users of confirmed information. It uses Google Calendar API, etc.

[0544] Specific example of system operation

[0545] 1. Enter and save job information

[0546] A user uses a device to input job information. The input form contains detailed information such as "desired job type," "required skills," "years of experience," "work location," and "salary." When this information is entered and the "Submit" button is clicked, the device sends the information to the server. The server receives this information and stores it in a recording device (MySQL database).

[0547] 2. Generate candidate list

[0548] The server runs a pre-trained artificial intelligence model using TensorFlow and PyTorch on the stored job information, analyzes the candidate information in the database based on the job requirements, and generates a list of candidates who match the requirements.

[0549] 3. Automatic generation and sending of scout emails

[0550] The server automatically generates customized invitation emails for the generated candidate list using OpenAI's GPT-3. For example, it might say, "Mr. Fujita, your three or more years of clinical experience will be extremely beneficial to our hospital." The generated emails are sent to the candidate's device via the SMTP server.

[0551] 4. Receiving and notifying candidate responses

[0552] The candidate's device receives the information email, and interested candidates reply. This reply is sent to the server, which analyzes the content and notifies the user via the notification device. Natural language processing technology is used for the analysis, and important keywords (e.g., "interview request" and "condition confirmation") are extracted from the reply content.

[0553] 5. Arranging and notifying interview dates

[0554] After checking the candidate's reply, the user can schedule an interview within the system. The server manages the interview schedule via the Google Calendar API, and the confirmed date is automatically notified to the candidate and the user.

[0555] Prompt Sentence Examples

[0556] "Please explain the specific operation of a system that generates a list of suitable candidates based on nursing job postings and automatically generates customized invitation emails."

[0557] In this way, the operation of the entire system is made more efficient, and human resource matching in the medical and welfare fields is carried out more quickly.

[0558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0559] Step 1:

[0560] The user enters job information using their device. The input form contains detailed items such as "desired job type," "required skills," "years of experience," "work location," and "salary." After entering the information, the user clicks the "Submit" button, and the device sends the information to the server via an HTTP POST request. The entered job information is sent to the server in JSON format or form data format.

[0561] Step 2:

[0562] The server saves the received job information in a MySQL database. First, the server parses the received data and breaks it down into the necessary data items. Then, it generates an SQL query to save the job information and inserts it into the database. If the save is successful, it returns a confirmation message to the user.

[0563] Step 3:

[0564] The server runs an artificial intelligence model (such as TensorFlow or PyTorch) based on the saved job information. The input is the saved job information, and the model uses this data to filter the candidate information in the database. Specifically, it extracts candidate data based on the job criteria (for example, "nurse" and "work location: Tokyo") and generates a list of candidates that match the criteria. The output is a list of candidates that match the criteria.

[0565] Step 4:

[0566] The server automatically generates customized invitation emails using OpenAI's GPT-3 based on the generated candidate list. The input is the candidate list, and a message is generated based on individual information for each candidate. Specifically, the message is personalized based on the candidate's name, skills, and past experience. The output is an individually customized invitation email.

[0567] Step 5:

[0568] The server sends the generated invitation email to the candidate's email address via the SMTP server. The input is the customized email, which is passed to the email sending server according to the SMTP protocol. The SMTP server delivers the email to the candidate's email account. The output is confirmation that the email has been sent.

[0569] Step 6:

[0570] The candidate terminal receives the invitation email. If the candidate is interested in the email and replies, the reply is sent to the server as an email. The input is the reply email from the candidate, and the server receives this information.

[0571] Step 7:

[0572] The server analyzes the received reply email and organizes its contents. First, it analyzes the contents of the reply email using NLP technology and extracts important keywords (e.g., "interview request" and "condition confirmation"). The analysis results are displayed on the user's management screen. The output is the organized reply content.

[0573] Step 8:

[0574] After checking the candidate's reply, the user arranges the interview schedule within the system. The input is the candidate's reply, and the user enters the schedule on the management screen. The server creates the interview schedule using the Google Calendar API and notifies the participants. The output is a notification of the interview schedule sent to the candidate and the user.

[0575] Step 9:

[0576] The user inputs the hiring results after the interview into the system. The input is "hired" or "not hired," and the server stores this result in a database. The stored data is fed back into the next candidate selection process. The output is the saved hiring results.

[0577] These steps automate the entire process, from entering job information to scheduling interviews and managing recruitment results, enabling efficient talent matching.

[0578] (Application example 1)

[0579] 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."

[0580] In the logistics industry, inappropriate recruitment and allocation of workers often leads to reduced work efficiency and problems. Even though securing personnel is crucial, especially during busy periods, the difficulty of quickly allocating the right personnel is a challenge. Traditional manual methods of recruiting and allocating workers take time and effort, and do not allow for efficient human resource management.

[0581] 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.

[0582] In this invention, the server includes a terminal for inputting job information, a means for saving the input job information in a database, a means for analyzing the job information and running an AI algorithm for generating a list of candidates that meet the conditions, a means for automatically generating and sending customized scout messages to the generated candidates, a means for receiving, analyzing, and notifying the candidates' responses, a means for arranging interview dates and notifying the confirmed information, a means including an AI algorithm for inputting and saving worker information for logistics operations and generating a list of suitable workers, a means for automatically generating and sending customized messages to workers at the logistics center, and a means for receiving, analyzing, and notifying the workers' responses, thereby enabling efficient recruitment and appropriate allocation of workers.

[0583] "Job information" refers to information such as the type of job being recruited, required skills, working hours, work location, and compensation.

[0584] A "terminal" is an electronic device that allows a user to input information.

[0585] A "database" is a system for storing and managing input information.

[0586] A "server" is a computer system that processes and stores data and communicates with other devices.

[0587] An "AI algorithm" is a calculation method that uses artificial intelligence to analyze data and make predictions and selections.

[0588] A "scouting message" is a customized message used to notify candidates about job openings.

[0589] A "response" is a candidate's response or opinion to a scouting message.

[0590] An "interview date" is the date and time when the candidate and the recruiter will conduct an interview.

[0591] "Logistics operations" include the transportation, storage, and delivery of goods.

[0592] "Worker information" is information about the skills, experience, available working hours, etc. of workers who perform logistics work.

[0593] The "worker list" is a list of workers who meet the conditions.

[0594] "Workers" are workers who perform tasks such as picking, packing, and shipping goods at logistics centers.

[0595] An embodiment for implementing this worker matching system will be described below. This system is mainly configured based on interactions between a server, a terminal, and a user.

[0596] 1. Enter and save job information

[0597] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. This job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. The entered job information is sent from the device to a server. The server stores the received job information in a database. The database is structured using a relational database management system such as MySQL.

[0598] 2. Generate candidate list

[0599] The server uses an AI algorithm to select suitable candidates based on the stored job information. This AI algorithm uses a machine learning model written in Python. The algorithm learns from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[0600] 3. Automatic generation and sending of scouting messages

[0601] The server automatically generates a customized scouting message based on the candidate list. This scouting message includes a summary of the work, the candidate's name, and a customized message tailored to their past experience. The generated scouting message is sent to the candidate's device using Firebase Cloud Messaging.

[0602] 4. Receiving and notifying candidate responses

[0603] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase. The server analyzes the received response, organizes its contents, and notifies the administrator. This allows the administrator to receive the candidate's response quickly.

[0604] 5. Arranging and notifying interview dates

[0605] After checking the candidate's response, the user (administrator) schedules the interview within the system. The server notifies the candidate and administrator of the confirmed interview date. This notification also uses Firebase Cloud Messaging.

[0606] 6. Managing recruitment results

[0607] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[0608] Specific examples

[0609] Consider a distribution center looking to hire pickers for peak season. The manager types the following:

[0610] Job: Picker

[0611] Required skills: Quick and accurate product selection

[0612] Years of experience: No experience necessary

[0613] Location: Nagoya

[0614] Remuneration: 1,200 yen per hour

[0615] Operating hours: 9:00-18:00

[0616] Once this information is entered, the system automatically generates a list of responding workers and sends a scout message, enabling the right workers to be deployed quickly and efficiently.

[0617] Keywords: generative AI model, prompt sentence

[0618] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0619] Step 1:

[0620] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. The entered job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. This information is sent from the device to the server.

[0621] Input: Job information (work content, skills, working hours, work location, compensation, etc.)

[0622] Output: The job post sent to the server

[0623] Step 2:

[0624] The server stores the received job information in a database, where the information is managed appropriately using a database management system (e.g., MySQL).

[0625] Input: Job information sent to the server

[0626] Output: Job listings stored in a database

[0627] Step 3:

[0628] The server selects suitable candidates based on the saved job information using an AI algorithm, a Python machine learning model that has learned from past successful matching know-how, and generates a list of candidates that meet the job requirements.

[0629] Input: Job listings stored in a database

[0630] Output: A list of candidates generated by the AI ​​algorithm

[0631] Step 4:

[0632] The server automatically generates a customized scouting message based on the candidate list, including a message tailored to the candidate's name, past experience, and skill set, and sends the generated scouting message to the candidate's device using Firebase Cloud Messaging.

[0633] Input: Candidate list generated by AI algorithm

[0634] Output: Scout message sent

[0635] Step 5:

[0636] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase.

[0637] Input: Scouting message sent to candidate

[0638] Output: Candidate response sent to server

[0639] Step 6:

[0640] The server analyzes the responses received from the candidates, organizes the content, and notifies the administrator, allowing the administrator to quickly check the candidates' reactions.

[0641] Input: Candidate response sent to server

[0642] Output: Candidate's response notified to administrator

[0643] Step 7:

[0644] After checking the candidate's response, the user (administrator) schedules the interview within the system. This schedule is confirmed by the server and notified to the candidate and administrator. This notification also uses Firebase Cloud Messaging.

[0645] Input: Interview date arranged in the system

[0646] Output: Interview dates notified to candidates and managers

[0647] Step 8:

[0648] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[0649] Input: Hiring result after interview

[0650] Output: Recruitment results stored in a database and used for next process improvement

[0651] 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.

[0652] This talent matching system manages talent information, selects candidates, automatically generates and sends scouting emails, collects responses, schedules interviews, and combines an emotion engine to create an efficient and human-like recruitment process. Below, we will explain the detailed processing and specific operation of this system.

[0653] 1. Enter and save job information

[0654] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server, which then stores the information in a database.

[0655] 2. Operation of the Emotion Engine

[0656] When a user inputs job information, the device captures the user's facial expressions and voice and analyzes their emotional state using an emotion engine. The emotion engine organizes the user's emotional data at the time of input and associates it with the job information.

[0657] 3. Generate candidate list

[0658] The server uses AI algorithms to analyze the job information and sentiment data stored in the database. This analysis, including sentiment data, helps generate a more suitable candidate list. The generated candidate list is optimized based on skills, experience, location, salary expectations, etc.

[0659] 4. Automatic generation and sending of scout emails

[0660] The server automatically generates a scouting email based on the optimized candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. Furthermore, an emotion engine uses a communication style that reflects the user's emotional state. The generated scouting email is sent from the server to the candidate's device.

[0661] 5. Receiving and notifying candidate responses

[0662] When the terminal (the candidate's email receiving terminal) receives the scout email and the candidate is interested in the email and replies, the email is sent to the server. The server analyzes the received reply, organizes the content, and notifies the user.

[0663] 6. Interview schedule arrangement and notification

[0664] Once the user receives the notification from the server and the candidate's interest is confirmed, the user can schedule an interview within the system. The server will notify the candidate and the user of the confirmed interview date.

[0665] 7. Managing recruitment results

[0666] The user enters the hiring results after the interview into the system. The results are recorded as "hired" or "not hired," for example. The server stores these results in a database and analyzes past successes and failures. This analysis, based on emotional data, helps improve the accuracy of the next scouting process.

[0667] Specific examples

[0668] Situation: Nurse job opening

[0669] 1. A user (hospital recruiter) enters job information for a "nurse" through a terminal. The input items include "3+ years of clinical experience, available to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." At this time, the terminal captures the user's facial expressions and voice, and an emotion engine analyzes the user's emotional state.

[0670] 2. The server stores the input information and emotional data, analyzes the candidate information in the database using AI algorithms, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[0671] 3. The server automatically generates a personalized scouting email for each candidate on the list, including mentioning the candidate's name, area of ​​expertise, and past experience, and using a writing style that reflects the user's emotional state.

[0672] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[0673] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[0674] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. Analysis based on the sentiment data is also performed to help improve the scouting process for the next time.

[0675] This system will enable efficient and humane recruitment activities, making it possible to more effectively solve the problem of labor shortages in the medical and welfare fields.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] The user logs in to the terminal and enters their user ID and password on the login screen to receive authentication for access to the system.

[0679] Step 2:

[0680] The user enters the necessary information such as job type, skills, years of experience, work location, salary, etc. into the job information input form. Once the input is complete, the terminal sends this information to the server.

[0681] Step 3:

[0682] The device captures the user's facial expressions and voice and sends them to the emotion engine, which analyzes this data and determines the user's emotional state.

[0683] Step 4:

[0684] The server stores the received job information in a database, along with the emotion data provided by the emotion engine.

[0685] Step 5:

[0686] The server uses AI algorithms to analyze the candidate information in the database based on the stored job information and sentiment data, and generates a list of candidates that match the criteria.

[0687] Step 6:

[0688] The server automatically generates a scouting email based on the candidate list, including the results of the emotion engine. The scouting email includes the candidate's name, past experience, a summary of the job information, and a customized message according to the user's emotional state.

[0689] Step 7:

[0690] The server generates a scouting email and sends it to the candidate's email address. This sending history is recorded in the database.

[0691] Step 8:

[0692] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate reads the email and is interested, they will send a reply email.

[0693] Step 9:

[0694] The server receives reply emails from candidates, analyzes their contents, organizes the analysis results, generates notification data, and sends it to the user.

[0695] Step 10:

[0696] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen on their device to schedule an interview.

[0697] Step 11:

[0698] The user checks available schedules and arranges interview dates with the candidates. The arranged interview dates are then entered into the system.

[0699] Step 12:

[0700] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[0701] Step 13:

[0702] After the interview, the user enters the hiring results into the system, which are then sent to the server and stored in the database.

[0703] Step 14:

[0704] The server analyzes the recruitment results and links them with emotional data to help improve the next scouting process. Past successes and failures are statistically processed and used as data to improve the accuracy of the AI ​​algorithm.

[0705] Example 2

[0706] 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."

[0707] While existing talent matching systems provide basic functions such as inputting and saving job information, generating candidate lists, and sending scouting emails, they are insufficient to provide efficient and human-like support throughout the entire recruitment process. Furthermore, they lack the ability to communicate and select candidates while taking into account the user's emotional state, resulting in inaccurate matching.

[0708] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input device for inputting job information, a data storage device for saving the input job information in a database, an information processing device that executes an artificial intelligence algorithm that analyzes the job information and generates a list of candidates who meet the conditions, a communication device for automatically generating and sending customized scout messages to the generated candidates, an information acquisition device for receiving, analyzing, and notifying the responses of the candidates, a schedule adjustment device for arranging interview dates and notifying the confirmed information, a sentiment analysis device for analyzing the emotional state of the user when entering the job information, and a data association device for storing the emotional data acquired by the sentiment analysis device in association with the job information. This enables high-precision matching that takes the user's emotional state into consideration and a human-like hiring process.

[0709] The "information input device" is a device for inputting job information, and has the function of inputting data such as the type of job, skills, years of experience, work location, and salary desired by the user.

[0710] The "data storage device" is a device for storing input job information in a database, and is a device with a data management function built into the server.

[0711] The "artificial intelligence algorithm" is an algorithm that analyzes job information and generates a list of candidates that meet the conditions, and has the ability to automatically analyze information and select the most suitable candidates.

[0712] An "information processing device" is a device for executing artificial intelligence algorithms and has the function of analyzing data and generating candidate lists.

[0713] A "communication device" is a device for automatically generating and sending customized scouting messages to generated candidates, and has the functionality to send messages using email or other communication means.

[0714] An "information acquisition device" is a device that receives, analyzes, and notifies candidates of their responses, and has the function of collecting reply data from candidates and providing the analysis results to the user.

[0715] The "schedule adjustment device" is a device for adjusting interview dates and notifying the confirmed information, and has the function of adjusting and notifying the interview date based on the free time of both parties.

[0716] An "emotion analysis device" is a device for analyzing the emotional state of a user when entering job information, and has the function of analyzing facial expressions and voice data to infer the user's emotions.

[0717] The "data association device" is a device for storing emotion data acquired by the emotion analysis device in association with job information, and has a database management function for linking emotion data with job information.

[0718] This invention provides a system configuration for a talent matching system that takes into account user emotional information to realize an advanced and human-like recruitment process. The system of the present invention is composed of the following main components:

[0719] 1. Enter and save job information

[0720] Users use an information input device (terminal) to input job information such as the desired job type, skills, years of experience, work location, and salary. The information input device verifies this data in real time to confirm its accuracy. The input job information is sent from the information input device to a server, and a data storage device stores it in a database.

[0721] 2. Operation of the Emotion Engine

[0722] When a user enters job information, the device uses a built-in camera and microphone to capture the user's facial expressions and voice, and the emotion analyzer analyzes this data. The emotion analyzer infers the user's emotional state and records it as emotion data. The emotion data is stored in association with the job information in a data storage device.

[0723] 3. Generate candidate list

[0724] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database to generate a candidate list. The algorithm calculates the suitability of candidates by taking into account various factors (skills, experience, work location, desired salary, etc.), and also evaluates the suitability of candidates based on emotion data. The final candidate list is recorded in a data storage device.

[0725] 4. Automatic generation and sending of scout emails

[0726] The server automatically generates a scouting email on a communication device based on the generated candidate list. The scouting email includes a summary of the job information, the name of each candidate, and a customized message based on their past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scouting email is sent to the candidates via the communication device.

[0727] 5. Receiving and notifying candidate responses

[0728] The terminal (the candidate's email receiving terminal) receives the scout email and the candidate replies. The candidate's reply is sent to the server, where the information acquisition device analyzes it and notifies the user of the contents. The analyzed contents include the candidate's level of interest.

[0729] 6. Interview schedule arrangement and notification

[0730] After the user confirms the candidate's interest, the system uses a schedule adjustment device to arrange an interview date. The server proposes the optimal date for the interview based on the user's and candidate's calendar information. After confirmation, the schedule adjustment device notifies both parties of the confirmed interview date.

[0731] 7. Managing recruitment results

[0732] The user inputs the hiring results (e.g., hired or not hired) after the interview into the system. The hiring results are stored in the server's data storage device. The server then analyzes these results and emotion data to select the next candidate and improve the accuracy of the scouting process.

[0733] In this way, the talent matching system utilizes emotional data to realize an advanced and human-like recruitment process. The specific names of the main hardware and software used are an emotional analysis device, an artificial intelligence algorithm, an information input device, a data storage device, an information processing device, a communication device, an information acquisition device, and a schedule adjustment device.

[0734] Specific examples

[0735] Situation: Nurse job opening

[0736] 1. A user (a hospital recruiter) enters job information for a "nurse" through an information input device. The input information includes "three or more years of clinical experience, availability to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." The device captures the user's facial expressions and voice, and an emotion analyzer analyzes the user's emotional state.

[0737] 2. The server records the input information and emotional data in a data storage device, analyzes the candidate information in the database using an artificial intelligence algorithm in the information processing device, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[0738] 3. The server automatically generates a scouting email for each candidate on the list, using a communication device, that is individually customized, mentioning the candidate's name, field of expertise, and past experience, and employing a writing style that reflects the user's emotional state.

[0739] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server, where the information acquisition device analyzes it and notifies the user.

[0740] 5. The user arranges an interview date with the candidate using the schedule adjustment device within the system, and the server notifies both parties of the confirmed date.

[0741] 6. After the interview, the user enters the hiring results into the system, and the server stores the results along with the emotional data in a data storage device. This is also analyzed for the next candidate selection.

[0742] Prompt Sentence Examples

[0743] As a recruiter at a medical and welfare facility, you input job information for a nurse (three or more years of clinical experience, night shift availability, work location: Tokyo, annual salary: 5 million yen or more). Explain the program that generates a customized email that reflects the employee's emotional state and creates a list of candidates.

[0744] Through this system, it will be possible to realize efficient and humane recruitment activities in the medical and welfare fields, and more effectively solve the problem of labor shortages.

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Step 1: Enter and save your job information

[0747] The user inputs job information using an information input device. Specifically, the user inputs information such as the desired job type, skills, years of experience, work location, and salary into the input screen. After the input data is validated and its accuracy is confirmed, the terminal sends the job information to the server. The server stores the received job information in a data storage device.

[0748] Input: User-entered data for job postings

[0749] Data processing / calculation: Real-time validation by terminal

[0750] Output: The job post sent to the server

[0751] Specific behavior:

[0752] The user enters "Nurse, 3+ years of clinical experience, available for night shifts, work location: Tokyo, annual salary of 5 million yen or more" into the input screen of the terminal.

[0753] The terminal validates the data format and required fields.

[0754] After validation, the device sends the job information to the server.

[0755] The server stores the job information in a data storage device.

[0756] Step 2: Emotion Engine in Action

[0757] The device uses a built-in camera and microphone to capture the user's facial expressions and voice as they enter job information. An emotion analyzer analyzes this data in real time to infer the user's emotional state. The acquired emotion data is associated with the job information, transmitted to a server, and stored in a data storage device.

[0758] Input: User's facial expression data and voice data

[0759] Data processing / calculation: Emotional state estimation using emotion analyzer

[0760] Output: Emotion data sent to the server

[0761] Specific behavior:

[0762] The device captures the user's face and voice while they enter information.

[0763] The emotion analyzer infers emotions such as "excitement" or "alertness" in real time from the captured data.

[0764] The estimated emotion data is transmitted to a server and stored in a data storage device.

[0765] Step 3: Generate a candidate list

[0766] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database. The algorithm calculates the suitability of skills, experience, work location, and salary, and evaluates the suitability of candidates using emotion data. Finally, it generates a list of around 10 highly suitable candidates, which is stored in a data storage device.

[0767] Input: Job posting data and sentiment data

[0768] Data processing / calculation: Calculation of fitness and suitability evaluation using artificial intelligence algorithms

[0769] Output: Generated candidate list

[0770] Specific behavior:

[0771] The server extracts job information and sentiment data.

[0772] An artificial intelligence algorithm calculates suitability based on skills and experience.

[0773] Evaluate candidate suitability based on emotional data.

[0774] An optimized list of 10 candidates is generated and stored in a data storage device.

[0775] Step 4: Automatically generate and send scout emails

[0776] The server automatically generates a scout email using a communication device based on the candidate list. This scout email includes a summary of the job information, the candidate's name, and a customized message tailored to the candidate's past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scout email is sent to the candidate's terminal via the communication device.

[0777] Input: Candidate list data

[0778] Data processing / calculation: Generate customized messages and adjust tones

[0779] Output: Scout email sent

[0780] Specific behavior:

[0781] The server extracts the candidate list and job information.

[0782] An AI model generates a customized scouting email for each candidate.

[0783] Adjust the tone of your email based on emotional data (e.g., a brighter tone if the emotion is excited).

[0784] The generated scout mail is sent to the candidate via a communication device.

[0785] Step 5: Receiving and notifying candidate responses

[0786] The candidate's reply to the scout email received by the terminal (the candidate's email receiving terminal) is sent to the server. The server analyzes this reply using an information acquisition device and notifies the user of its contents. The analyzed contents include the candidate's level of interest and requests.

[0787] Input: Candidate response data

[0788] Data processing / calculation: Analysis of reply emails

[0789] Output: Analysis results reported to the user

[0790] Specific behavior:

[0791] The candidate responds to the scouting email.

[0792] The server analyzes the received email and classifies it into categories such as "interested" or "request more details."

[0793] Send users notifications like "Candidate A has expressed interest" or "Candidate B wants to confirm details."

[0794] Step 6: Scheduling and notifying interview schedule

[0795] After the user confirms the candidate's interest, the server uses the schedule adjustment device to arrange an interview date. The server proposes an optimal interview date based on the calendar information of the user and the candidate, and notifies both parties of the confirmed date from the adjustment device.

[0796] Input: User and candidate calendar information

[0797] Data processing / calculation: Optimizing interview schedules

[0798] Output: Notified interview date

[0799] Specific behavior:

[0800] The user opens the interview scheduling screen.

[0801] The server proposes the best date based on the availability of the user and the candidates.

[0802] The user confirms the schedule, and the server notifies the user of the confirmed interview schedule.

[0803] Step 7: Manage recruitment results

[0804] The user inputs the hiring results after the interview into the system. The server stores these results in a data storage device and uses past emotion data and hiring results to improve the accuracy of the next candidate selection and scouting process.

[0805] Input: Interview result data

[0806] Data processing / calculation: saving and analyzing result data

[0807] Output: Saved recruitment results and analysis results

[0808] Specific behavior:

[0809] The user enters the interview results into the system (e.g., "hired" or "rejected").

[0810] The server stores the results in a data storage device.

[0811] The server analyzes the emotional data and identifies areas for improvement in the next scouting process.

[0812] (Application example 2)

[0813] 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."

[0814] In conventional talent matching systems, the input and analysis of job information, the creation of candidate lists, and the sending of scouting emails are often done manually, resulting in inefficiency and a lack of communication that takes human emotions into consideration. This makes it difficult to find suitable candidates quickly and in a human-like manner. Furthermore, the lack of systems that take into account usage scenarios while on the move makes it difficult to achieve integrated talent matching that utilizes multiple devices.

[0815] 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.

[0816] In this invention, the server includes a means for displaying job information using a smart display or smart glasses, a means for capturing passengers' facial expressions and voices and analyzing their emotional state using an emotion engine, a means for reflecting the emotion data in generated scout emails, and a means for arranging and notifying interview dates within the system. This enables efficient talent matching even in an on-the-go environment and a more human-like recruitment process using emotion data.

[0817] A "terminal for inputting personnel information" is an electronic device such as a personal computer, smartphone, or tablet that is used by a job seeker or recruiter to input job information or job search information.

[0818] A "server that stores information in a database" is an information system that stores input job information and job search information and allows for searching and updating as needed.

[0819] The "server that executes the algorithm" is a computer system that performs specific calculations to analyze the job information input and generate a list of candidates that meet the criteria.

[0820] A "server that automatically generates and sends scouting emails" is a computer system that has the function of automatically creating customized scouting emails for candidates and sending them by email.

[0821] The "terminal that receives, analyzes, and notifies responses" is an electronic device that receives the responses from candidates to scouting emails, analyzes the content, and notifies the hiring manager.

[0822] The "server that notifies confirmed information" is a computer system that notifies the target user of confirmed information such as interview dates and hiring results.

[0823] "Means for displaying job information using a smart display or smart glasses" refers to a function that visually provides job information using a smart display or smart glasses that are installed on an autonomous bus or worn by passengers.

[0824] "Means of capturing facial expressions and voice and analyzing emotional state using an emotion engine" is a function that uses the passenger's facial expressions and words to analyze their emotional state using an emotion engine, and reflects this in job information and scouting emails.

[0825] "Means for reflecting emotional data" refers to a function that adjusts the content and expression of scouting emails sent to candidates based on emotional data analyzed by the emotion engine.

[0826] "Means of arranging and notifying interview dates within the system" is a function that determines interview dates based on the convenience of both the hiring manager and the candidate, and automatically notifies them of those dates.

[0827] As an embodiment of the present invention, a talent matching system is configured as follows.

[0828] 1. Enter and save personnel information

[0829] Users input job information using electronic devices (personal computers, smartphones, tablets, etc.). The information entered includes job type, skills, years of experience, work location, salary, etc. This information is sent from the device to a server, which stores it in a database. This accumulates data necessary for subsequent analysis and matching.

[0830] 2. Operation of the Emotion Engine

[0831] When a user enters a job posting, the device or smart glasses capture facial expressions and speech. This captured data is sent to an emotion engine on the server, which analyzes the user's emotional state. The emotion data is then associated with the job posting.

[0832] 3. Generate candidate list

[0833] The server runs an algorithm based on the job information and sentiment data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and sentiment data, resulting in the selection of the most suitable candidates.

[0834] 4. Displaying job information using smart displays or smart glasses

[0835] The server displays job information using smart displays installed inside the autonomous bus and smart glasses worn by passengers, allowing users to check job information in real time even while on the move.

[0836] 5. Automatic generation and sending of scouting emails

[0837] The server automatically generates a scouting email customized for each candidate based on the generated candidate list. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. Furthermore, the user's emotional state, analyzed by an emotion engine, is reflected in the email, enabling communication with a more human touch. The generated scouting email is then sent to the candidate's device.

[0838] 6. Receiving and notifying candidate responses

[0839] When a candidate replies to a scouting email, the reply is sent to the server, which analyzes the received email, organizes the content, and notifies the user, allowing the user to quickly understand the candidate's intentions.

[0840] 7. Interview schedule arrangement and notification

[0841] The user receives the notification from the server and arranges an interview date with the candidate. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[0842] 8. Managing recruitment results

[0843] The user enters the hiring results after the interview into the system. The results are saved on the server as either "hired" or "not hired." The server then stores these results in a database for later analysis. Based on past successes and failures, emotional data is used to improve the accuracy of the next scouting process.

[0844] Specific examples

[0845] As a concrete example of how this system can be implemented, the following example shows the use of smart glasses inside an autonomous bus.

[0846] example:

[0847] The user puts on the smart glasses and inputs the job information for a "nurse." The smart glasses capture facial expressions and voice and analyze the user's emotional state. The server generates a list of candidates based on this information and emotional data, and displays the job information on the smart glasses. In addition, customized scouting emails are generated and sent to suitable candidates. The following is an example of a prompt.

[0848] Example prompt sentence:

[0849] Please provide suitable job listings for nurses based on the following information:

[0850] Candidate's name: Taro Yamada

[0851] Skills: Python, Data Analysis

[0852] Years of experience: 5 years

[0853] Preferred location: Tokyo

[0854] Hardware and software used

[0855] Specific hardware used includes smart glasses (e.g., Google Glass, Vuzix) and smart displays, and software uses Python, a Flask server, and Emotion Analysis libraries.

[0856] As a result, this system can achieve efficient talent matching even in an on-the-go environment and provide a more human-like recruitment process by utilizing emotional data.

[0857] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0858] Step 1:

[0859] Users use electronic terminals to input job information. This information covers a wide range of topics, including job type, skills, years of experience, work location, and salary. The input data is sent from the terminal to a server and saved in a database. This allows the accumulation of job information.

[0860] Input: Job information (job type, skills, years of experience, work location, salary)

[0861] Output: Job listings stored in a database

[0862] Step 2:

[0863] The device captures facial expressions and voice as the user enters job information. This facial and voice data is sent to a server where it is analyzed by an emotion engine. This analysis generates user emotion data, which is stored in a database along with the associated job information.

[0864] Input: facial expression data, voice data

[0865] Output: Emotion data

[0866] Step 3:

[0867] The server runs an algorithm using the job information and emotion data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and emotion data. The optimized list is then prepared for further processing.

[0868] Input: Job information, emotion data

[0869] Output: Optimized candidate list

[0870] Step 4:

[0871] The server displays job information on smart displays or smart glasses installed inside the autonomous bus, allowing users to check job information in real time even while on the move. The displayed information is based on the optimized candidate list generated in the previous step.

[0872] Input: Optimized candidate list

[0873] Output: Job listings displayed on a smart display or smart glasses

[0874] Step 5:

[0875] The server automatically generates a scouting email customized for each candidate based on the candidate list generated in the previous step. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. The writing style also reflects the emotional data. The generated scouting email is sent to the candidate's device.

[0876] Input: Optimized candidate list, sentiment data

[0877] Output: Scout email sent to candidate's device

[0878] Step 6:

[0879] When a candidate replies to a scouting email, the reply is sent to the server. The server analyzes the received email, organizes the content, and notifies the user. This analysis allows the candidate's intentions to be quickly understood.

[0880] Input: Reply email from candidate

[0881] Output: Parsed reply, notification to user

[0882] Step 7:

[0883] The user receives the notification from the server and arranges an interview date with the candidate on the server. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[0884] Input: Candidate's reply, user input

[0885] Output: Confirmed interview date, notification to candidate and user

[0886] Step 8:

[0887] Once the interview is over, the user enters the hiring result on the server. The result is saved on the server as either "hired" or "not hired." The server stores these results in a database and uses them for subsequent analysis. Based on past successes and failures, the accuracy of the next scouting process can be improved.

[0888] Input: Interview result (hired or not hired)

[0889] Output: Interview results stored in a database, feedback for the next scouting process

[0890] The above are the specific processing steps for carrying out the present invention, through which efficient and human resource matching is achieved.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] [Third embodiment]

[0895] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0896] 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.

[0897] 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).

[0898] 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.

[0899] 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.

[0900] 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).

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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."

[0907] The following describes an embodiment of the talent matching system: This system is mainly configured based on interactions between a server, a terminal, and a user.

[0908] 1. Enter and save job information

[0909] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, required skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server.

[0910] The server stores the received job information in a database, which is then analyzed by an AI algorithm later in the process.

[0911] 2. Generate candidate list

[0912] The server uses an AI algorithm to select suitable candidates based on the job information stored in the database. This AI algorithm has learned from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[0913] 3. Automatic generation and sending of scout emails

[0914] The server automatically generates a scouting email based on the candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. The generated scouting email is then sent from the server to the candidate's device.

[0915] 4. Receiving and notifying candidate responses

[0916] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate is interested in the email and replies, the email is sent to the server.

[0917] The server analyzes the received replies, organizes their contents, and notifies the user, allowing the user to receive the candidate's response promptly.

[0918] 5. Arranging and notifying interview dates

[0919] After the user checks the candidate's reply, the system arranges the interview date. The server notifies the candidate and the user of the confirmed interview date.

[0920] 6. Managing recruitment results

[0921] The user enters the hiring results after the interview into the system, such as recording "hired" or "rejected." The server stores these results in a database and analyzes them to improve the scouting process in the future.

[0922] Specific examples

[0923] Situation: Nurse job opening

[0924] 1. A user (a hospital recruiter) enters job information for a "nurse" through a terminal. The input information includes "3+ years of clinical experience, availability for night shifts, work location: Tokyo, annual salary: 5 million yen or more."

[0925] 2. The server stores the input information and uses an AI algorithm to analyze the candidate information in the database, generating a list of 10 candidates who match the criteria.

[0926] 3. The server automatically generates a personalized scouting email for each candidate on the list, including content tailored to the candidate's name, area of ​​expertise, and past experience.

[0927] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[0928] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[0929] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. The server uses this information to improve the scouting process for the next time.

[0930] This will enable efficient and rapid recruitment activities, helping to solve the problem of labor shortages in the medical and welfare fields.

[0931] The processing flow will be explained below.

[0932] Step 1:

[0933] The user logs in to the terminal, enters their user ID and password on the login screen, and is authenticated.

[0934] Step 2:

[0935] The user enters the required information into the job information entry form, including job type, skills, years of experience, location, salary, etc. Once all required fields are entered, the user presses the "Save" button.

[0936] Step 3:

[0937] The server receives the job information sent from the terminal and stores it in a database, while simultaneously checking the consistency of the entered information to detect any defects or inconsistencies.

[0938] Step 4:

[0939] The server uses an AI algorithm to analyze the candidate information in the database based on the saved job information, and creates a list of candidates who match the criteria. This list is generated based on skills, experience, location, salary expectations, etc.

[0940] Step 5:

[0941] The server automatically generates scouting emails for the generated candidate list, which are customized for each candidate and include the candidate's name, past experience, and a summary of the job information.

[0942] Step 6:

[0943] The server sends automatically generated scouting emails to candidates on the list, and the sending history is recorded in a database.

[0944] Step 7:

[0945] The terminal (the candidate's email receiving terminal) receives the scout email. The candidate checks the contents of the email and, if interested, sends a reply email.

[0946] Step 8:

[0947] The server receives reply emails from candidates, analyzes their contents, organizes the reply contents, and generates information to notify the user.

[0948] Step 9:

[0949] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen within the system to schedule an interview.

[0950] Step 10:

[0951] The user checks the available schedule, arranges an interview date with the candidate, and enters the confirmed date into the system.

[0952] Step 11:

[0953] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[0954] Step 12:

[0955] After the interview, the user enters the hiring result into the system, for example, the result is recorded as "hired" or "rejected."

[0956] Step 13:

[0957] The server stores the recruitment results in a database and analyzes past successes and failures, which helps improve the accuracy of the next scouting process.

[0958] Example 1

[0959] 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."

[0960] In conventional talent matching systems, processes such as entering and managing job information, selecting candidates, creating and sending scouting emails, managing candidate replies, and arranging interview dates were often done manually, resulting in problems of time and effort. Furthermore, manually creating customized messages for each candidate was a significant burden, making efficient talent matching difficult. For this reason, a system that can quickly and efficiently match talent is needed.

[0961] 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.

[0962] In this invention, the server includes a user terminal for inputting personnel information, a recording device for saving the input job information in a database, a processing device that executes an artificial intelligence model that analyzes the job information and generates a list of candidates that meet the conditions, a device for automatically generating and sending customized information emails to the generated candidates, a notification device that receives, analyzes, and notifies the candidates of their responses, and a management device that arranges interview dates and notifies the confirmed information. This automates the entire process from entering job information to arranging interview dates, enabling efficient and rapid personnel matching.

[0963] A "user terminal" is an electronic device that a user uses to input personnel information.

[0964] The "recording device" is a device for storing input job information in a database.

[0965] An "artificial intelligence model" is an algorithm or program used to generate a list of qualified candidates based on a job posting.

[0966] "Processing Device" means a computing device for executing artificial intelligence models and analyzing job listings.

[0967] An "invitation email" is a customized email sent to the generated candidate.

[0968] A "notification device" is a device that receives and analyzes the responses of candidates and notifies the user.

[0969] The "management device" is a device for arranging interview schedules and notifying confirmed information.

[0970] "Matching know-how" is a method based on specialized knowledge and experience for generating a candidate list based on job information.

[0971] This talent matching system is primarily comprised of the interaction between user terminals, a server, and candidate terminals. The system automates the entire process, from entering job information to arranging interview dates, achieving efficient and rapid talent matching.

[0972] Hardware and software used

[0973] 1. User device: An electronic device such as a computer or smartphone that allows users to enter job information.

[0974] 2. Recording device: A database system (e.g. MySQL) to store the entered job information.

[0975] 3. Processing unit: A server that analyzes job information and generates a list of candidates that meet the requirements. This server is equipped with artificial intelligence models such as TensorFlow and PyTorch.

[0976] 4. Information Email Generator: A system for generating and sending customized information emails to candidates. It uses natural language processing algorithms such as OpenAI's GPT-3 for generation. An SMTP server is used to send emails.

[0977] 5. Notification device: A real-time chat system or push notification system that receives and analyzes candidate responses and notifies users.

[0978] 6. Management device: A system for arranging interview dates and notifying candidates and users of confirmed information. It uses Google Calendar API, etc.

[0979] Specific example of system operation

[0980] 1. Enter and save job information

[0981] A user uses a device to input job information. The input form contains detailed information such as "desired job type," "required skills," "years of experience," "work location," and "salary." When this information is entered and the "Submit" button is clicked, the device sends the information to the server. The server receives this information and stores it in a recording device (MySQL database).

[0982] 2. Generate candidate list

[0983] The server runs a pre-trained artificial intelligence model using TensorFlow and PyTorch on the stored job information, analyzes the candidate information in the database based on the job requirements, and generates a list of candidates who match the requirements.

[0984] 3. Automatic generation and sending of scout emails

[0985] The server automatically generates customized invitation emails for the generated candidate list using OpenAI's GPT-3. For example, it might say, "Mr. Fujita, your three or more years of clinical experience will be extremely beneficial to our hospital." The generated emails are sent to the candidate's device via the SMTP server.

[0986] 4. Receiving and notifying candidate responses

[0987] The candidate's device receives the information email, and interested candidates reply. This reply is sent to the server, which analyzes the content and notifies the user via the notification device. Natural language processing technology is used for the analysis, and important keywords (e.g., "interview request" and "condition confirmation") are extracted from the reply content.

[0988] 5. Arranging and notifying interview dates

[0989] After checking the candidate's reply, the user can schedule an interview within the system. The server manages the interview schedule via the Google Calendar API, and the confirmed date is automatically notified to the candidate and the user.

[0990] Prompt Sentence Examples

[0991] "Please explain the specific operation of a system that generates a list of suitable candidates based on nursing job postings and automatically generates customized invitation emails."

[0992] In this way, the operation of the entire system is made more efficient, and human resource matching in the medical and welfare fields is carried out more quickly.

[0993] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0994] Step 1:

[0995] The user enters job information using their device. The input form contains detailed items such as "desired job type," "required skills," "years of experience," "work location," and "salary." After entering the information, the user clicks the "Submit" button, and the device sends the information to the server via an HTTP POST request. The entered job information is sent to the server in JSON format or form data format.

[0996] Step 2:

[0997] The server saves the received job information in a MySQL database. First, the server parses the received data and breaks it down into the necessary data items. Then, it generates an SQL query to save the job information and inserts it into the database. If the save is successful, it returns a confirmation message to the user.

[0998] Step 3:

[0999] The server runs an artificial intelligence model (such as TensorFlow or PyTorch) based on the saved job information. The input is the saved job information, and the model uses this data to filter the candidate information in the database. Specifically, it extracts candidate data based on the job criteria (for example, "nurse" and "work location: Tokyo") and generates a list of candidates that match the criteria. The output is a list of candidates that match the criteria.

[1000] Step 4:

[1001] The server automatically generates customized invitation emails using OpenAI's GPT-3 based on the generated candidate list. The input is the candidate list, and a message is generated based on individual information for each candidate. Specifically, the message is personalized based on the candidate's name, skills, and past experience. The output is an individually customized invitation email.

[1002] Step 5:

[1003] The server sends the generated invitation email to the candidate's email address via the SMTP server. The input is the customized email, which is passed to the email sending server according to the SMTP protocol. The SMTP server delivers the email to the candidate's email account. The output is confirmation that the email has been sent.

[1004] Step 6:

[1005] The candidate terminal receives the invitation email. If the candidate is interested in the email and replies, the reply is sent to the server as an email. The input is the reply email from the candidate, and the server receives this information.

[1006] Step 7:

[1007] The server analyzes the received reply email and organizes its contents. First, it analyzes the contents of the reply email using NLP technology and extracts important keywords (e.g., "interview request" and "condition confirmation"). The analysis results are displayed on the user's management screen. The output is the organized reply content.

[1008] Step 8:

[1009] After checking the candidate's reply, the user arranges the interview schedule within the system. The input is the candidate's reply, and the user enters the schedule on the management screen. The server creates the interview schedule using the Google Calendar API and notifies the participants. The output is a notification of the interview schedule sent to the candidate and the user.

[1010] Step 9:

[1011] The user inputs the hiring results after the interview into the system. The input is "hired" or "not hired," and the server stores this result in a database. The stored data is fed back into the next candidate selection process. The output is the saved hiring results.

[1012] These steps automate the entire process, from entering job information to scheduling interviews and managing recruitment results, enabling efficient talent matching.

[1013] (Application example 1)

[1014] 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."

[1015] In the logistics industry, inappropriate recruitment and allocation of workers often leads to reduced work efficiency and problems. Even though securing personnel is crucial, especially during busy periods, the difficulty of quickly allocating the right personnel is a challenge. Traditional manual methods of recruiting and allocating workers take time and effort, and do not allow for efficient human resource management.

[1016] 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.

[1017] In this invention, the server includes a terminal for inputting job information, a means for saving the input job information in a database, a means for analyzing the job information and running an AI algorithm for generating a list of candidates that meet the conditions, a means for automatically generating and sending customized scout messages to the generated candidates, a means for receiving, analyzing, and notifying the candidates' responses, a means for arranging interview dates and notifying the confirmed information, a means including an AI algorithm for inputting and saving worker information for logistics operations and generating a list of suitable workers, a means for automatically generating and sending customized messages to workers at the logistics center, and a means for receiving, analyzing, and notifying the workers' responses, thereby enabling efficient recruitment and appropriate allocation of workers.

[1018] "Job information" refers to information such as the type of job being recruited, required skills, working hours, work location, and compensation.

[1019] A "terminal" is an electronic device that allows a user to input information.

[1020] A "database" is a system for storing and managing input information.

[1021] A "server" is a computer system that processes and stores data and communicates with other devices.

[1022] An "AI algorithm" is a calculation method that uses artificial intelligence to analyze data and make predictions and selections.

[1023] A "scouting message" is a customized message used to notify candidates about job openings.

[1024] A "response" is a candidate's response or opinion to a scouting message.

[1025] An "interview date" is the date and time when the candidate and the recruiter will conduct an interview.

[1026] "Logistics operations" include the transportation, storage, and delivery of goods.

[1027] "Worker information" is information about the skills, experience, available working hours, etc. of workers who perform logistics work.

[1028] The "worker list" is a list of workers who meet the conditions.

[1029] "Workers" are workers who perform tasks such as picking, packing, and shipping goods at logistics centers.

[1030] An embodiment for implementing this worker matching system will be described below. This system is mainly configured based on interactions between a server, a terminal, and a user.

[1031] 1. Enter and save job information

[1032] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. This job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. The entered job information is sent from the device to a server. The server stores the received job information in a database. The database is structured using a relational database management system such as MySQL.

[1033] 2. Generate candidate list

[1034] The server uses an AI algorithm to select suitable candidates based on the stored job information. This AI algorithm uses a machine learning model written in Python. The algorithm learns from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[1035] 3. Automatic generation and sending of scouting messages

[1036] The server automatically generates a customized scouting message based on the candidate list. This scouting message includes a summary of the work, the candidate's name, and a customized message tailored to their past experience. The generated scouting message is sent to the candidate's device using Firebase Cloud Messaging.

[1037] 4. Receiving and notifying candidate responses

[1038] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase. The server analyzes the received response, organizes its contents, and notifies the administrator. This allows the administrator to receive the candidate's response quickly.

[1039] 5. Arranging and notifying interview dates

[1040] After checking the candidate's response, the user (administrator) schedules the interview within the system. The server notifies the candidate and administrator of the confirmed interview date. This notification also uses Firebase Cloud Messaging.

[1041] 6. Managing recruitment results

[1042] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[1043] Specific examples

[1044] Consider a distribution center looking to hire pickers for peak season. The manager types the following:

[1045] Job: Picker

[1046] Required skills: Quick and accurate product selection

[1047] Years of experience: No experience necessary

[1048] Location: Nagoya

[1049] Remuneration: 1,200 yen per hour

[1050] Operating hours: 9:00-18:00

[1051] Once this information is entered, the system automatically generates a list of responding workers and sends a scout message, enabling the right workers to be deployed quickly and efficiently.

[1052] Keywords: generative AI model, prompt sentence

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. The entered job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. This information is sent from the device to the server.

[1056] Input: Job information (work content, skills, working hours, work location, compensation, etc.)

[1057] Output: The job post sent to the server

[1058] Step 2:

[1059] The server stores the received job information in a database, where the information is managed appropriately using a database management system (e.g., MySQL).

[1060] Input: Job information sent to the server

[1061] Output: Job listings stored in a database

[1062] Step 3:

[1063] The server selects suitable candidates based on the saved job information using an AI algorithm, a Python machine learning model that has learned from past successful matching know-how, and generates a list of candidates that meet the job requirements.

[1064] Input: Job listings stored in a database

[1065] Output: A list of candidates generated by the AI ​​algorithm

[1066] Step 4:

[1067] The server automatically generates a customized scouting message based on the candidate list, including a message tailored to the candidate's name, past experience, and skill set, and sends the generated scouting message to the candidate's device using Firebase Cloud Messaging.

[1068] Input: Candidate list generated by AI algorithm

[1069] Output: Scout message sent

[1070] Step 5:

[1071] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase.

[1072] Input: Scouting message sent to candidate

[1073] Output: Candidate response sent to server

[1074] Step 6:

[1075] The server analyzes the responses received from the candidates, organizes the content, and notifies the administrator, allowing the administrator to quickly check the candidates' reactions.

[1076] Input: Candidate response sent to server

[1077] Output: Candidate's response notified to administrator

[1078] Step 7:

[1079] After checking the candidate's response, the user (administrator) schedules the interview within the system. This schedule is confirmed by the server and notified to the candidate and administrator. This notification also uses Firebase Cloud Messaging.

[1080] Input: Interview date arranged in the system

[1081] Output: Interview dates notified to candidates and managers

[1082] Step 8:

[1083] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[1084] Input: Hiring result after interview

[1085] Output: Recruitment results stored in a database and used for next process improvement

[1086] 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.

[1087] This talent matching system manages talent information, selects candidates, automatically generates and sends scouting emails, collects responses, schedules interviews, and combines an emotion engine to create an efficient and human-like recruitment process. Below, we will explain the detailed processing and specific operation of this system.

[1088] 1. Enter and save job information

[1089] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server, which then stores the information in a database.

[1090] 2. Operation of the Emotion Engine

[1091] When a user inputs job information, the device captures the user's facial expressions and voice and analyzes their emotional state using an emotion engine. The emotion engine organizes the user's emotional data at the time of input and associates it with the job information.

[1092] 3. Generate candidate list

[1093] The server uses AI algorithms to analyze the job information and sentiment data stored in the database. This analysis, including sentiment data, helps generate a more suitable candidate list. The generated candidate list is optimized based on skills, experience, location, salary expectations, etc.

[1094] 4. Automatic generation and sending of scout emails

[1095] The server automatically generates a scouting email based on the optimized candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. Furthermore, an emotion engine uses a communication style that reflects the user's emotional state. The generated scouting email is sent from the server to the candidate's device.

[1096] 5. Receiving and notifying candidate responses

[1097] When the terminal (the candidate's email receiving terminal) receives the scout email and the candidate is interested in the email and replies, the email is sent to the server. The server analyzes the received reply, organizes the content, and notifies the user.

[1098] 6. Interview schedule arrangement and notification

[1099] Once the user receives the notification from the server and the candidate's interest is confirmed, the user can schedule an interview within the system. The server will notify the candidate and the user of the confirmed interview date.

[1100] 7. Managing recruitment results

[1101] The user enters the hiring results after the interview into the system. The results are recorded as "hired" or "not hired," for example. The server stores these results in a database and analyzes past successes and failures. This analysis, based on emotional data, helps improve the accuracy of the next scouting process.

[1102] Specific examples

[1103] Situation: Nurse job opening

[1104] 1. A user (hospital recruiter) enters job information for a "nurse" through a terminal. The input items include "3+ years of clinical experience, available to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." At this time, the terminal captures the user's facial expressions and voice, and an emotion engine analyzes the user's emotional state.

[1105] 2. The server stores the input information and emotional data, analyzes the candidate information in the database using AI algorithms, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[1106] 3. The server automatically generates a personalized scouting email for each candidate on the list, including mentioning the candidate's name, area of ​​expertise, and past experience, and using a writing style that reflects the user's emotional state.

[1107] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[1108] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[1109] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. Analysis based on the sentiment data is also performed to help improve the scouting process for the next time.

[1110] This system will enable efficient and humane recruitment activities, making it possible to more effectively solve the problem of labor shortages in the medical and welfare fields.

[1111] The processing flow will be explained below.

[1112] Step 1:

[1113] The user logs in to the terminal and enters their user ID and password on the login screen to receive authentication for access to the system.

[1114] Step 2:

[1115] The user enters the necessary information such as job type, skills, years of experience, work location, salary, etc. into the job information input form. Once the input is complete, the terminal sends this information to the server.

[1116] Step 3:

[1117] The device captures the user's facial expressions and voice and sends them to the emotion engine, which analyzes this data and determines the user's emotional state.

[1118] Step 4:

[1119] The server stores the received job information in a database, along with the emotion data provided by the emotion engine.

[1120] Step 5:

[1121] The server uses AI algorithms to analyze the candidate information in the database based on the stored job information and sentiment data, and generates a list of candidates that match the criteria.

[1122] Step 6:

[1123] The server automatically generates a scouting email based on the candidate list, including the results of the emotion engine. The scouting email includes the candidate's name, past experience, a summary of the job information, and a customized message according to the user's emotional state.

[1124] Step 7:

[1125] The server generates a scouting email and sends it to the candidate's email address. This sending history is recorded in the database.

[1126] Step 8:

[1127] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate reads the email and is interested, they will send a reply email.

[1128] Step 9:

[1129] The server receives reply emails from candidates, analyzes their contents, organizes the analysis results, generates notification data, and sends it to the user.

[1130] Step 10:

[1131] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen on their device to schedule an interview.

[1132] Step 11:

[1133] The user checks available schedules and arranges interview dates with the candidates. The arranged interview dates are then entered into the system.

[1134] Step 12:

[1135] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[1136] Step 13:

[1137] After the interview, the user enters the hiring results into the system, which are then sent to the server and stored in the database.

[1138] Step 14:

[1139] The server analyzes the recruitment results and links them with emotional data to help improve the next scouting process. Past successes and failures are statistically processed and used as data to improve the accuracy of the AI ​​algorithm.

[1140] Example 2

[1141] 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."

[1142] While existing talent matching systems provide basic functions such as inputting and saving job information, generating candidate lists, and sending scouting emails, they are insufficient to provide efficient and human-like support throughout the entire recruitment process. Furthermore, they lack the ability to communicate and select candidates while taking into account the user's emotional state, resulting in inaccurate matching.

[1143] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input device for inputting job information, a data storage device for saving the input job information in a database, an information processing device that executes an artificial intelligence algorithm that analyzes the job information and generates a list of candidates who meet the conditions, a communication device for automatically generating and sending customized scout messages to the generated candidates, an information acquisition device for receiving, analyzing, and notifying the responses of the candidates, a schedule adjustment device for arranging interview dates and notifying the confirmed information, a sentiment analysis device for analyzing the emotional state of the user when entering the job information, and a data association device for storing the emotional data acquired by the sentiment analysis device in association with the job information. This enables high-precision matching that takes the user's emotional state into consideration and a human-like hiring process.

[1144] The "information input device" is a device for inputting job information, and has the function of inputting data such as the type of job, skills, years of experience, work location, and salary desired by the user.

[1145] The "data storage device" is a device for storing input job information in a database, and is a device with a data management function built into the server.

[1146] The "artificial intelligence algorithm" is an algorithm that analyzes job information and generates a list of candidates that meet the conditions, and has the ability to automatically analyze information and select the most suitable candidates.

[1147] An "information processing device" is a device for executing artificial intelligence algorithms and has the function of analyzing data and generating candidate lists.

[1148] A "communication device" is a device for automatically generating and sending customized scouting messages to generated candidates, and has the functionality to send messages using email or other communication means.

[1149] An "information acquisition device" is a device that receives, analyzes, and notifies candidates of their responses, and has the function of collecting reply data from candidates and providing the analysis results to the user.

[1150] The "schedule adjustment device" is a device for adjusting interview dates and notifying the confirmed information, and has the function of adjusting and notifying the interview date based on the free time of both parties.

[1151] An "emotion analysis device" is a device for analyzing the emotional state of a user when entering job information, and has the function of analyzing facial expressions and voice data to infer the user's emotions.

[1152] The "data association device" is a device for storing emotion data acquired by the emotion analysis device in association with job information, and has a database management function for linking emotion data with job information.

[1153] This invention provides a system configuration for a talent matching system that takes into account user emotional information to realize an advanced and human-like recruitment process. The system of the present invention is composed of the following main components:

[1154] 1. Enter and save job information

[1155] Users use an information input device (terminal) to input job information such as the desired job type, skills, years of experience, work location, and salary. The information input device verifies this data in real time to confirm its accuracy. The input job information is sent from the information input device to a server, and a data storage device stores it in a database.

[1156] 2. Operation of the Emotion Engine

[1157] When a user enters job information, the device uses a built-in camera and microphone to capture the user's facial expressions and voice, and the emotion analyzer analyzes this data. The emotion analyzer infers the user's emotional state and records it as emotion data. The emotion data is stored in association with the job information in a data storage device.

[1158] 3. Generate candidate list

[1159] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database to generate a candidate list. The algorithm calculates the suitability of candidates by taking into account various factors (skills, experience, work location, desired salary, etc.), and also evaluates the suitability of candidates based on emotion data. The final candidate list is recorded in a data storage device.

[1160] 4. Automatic generation and sending of scout emails

[1161] The server automatically generates a scouting email on a communication device based on the generated candidate list. The scouting email includes a summary of the job information, the name of each candidate, and a customized message based on their past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scouting email is sent to the candidates via the communication device.

[1162] 5. Receiving and notifying candidate responses

[1163] The terminal (the candidate's email receiving terminal) receives the scout email and the candidate replies. The candidate's reply is sent to the server, where the information acquisition device analyzes it and notifies the user of the contents. The analyzed contents include the candidate's level of interest.

[1164] 6. Interview schedule arrangement and notification

[1165] After the user confirms the candidate's interest, the system uses a schedule adjustment device to arrange an interview date. The server proposes the optimal date for the interview based on the user's and candidate's calendar information. After confirmation, the schedule adjustment device notifies both parties of the confirmed interview date.

[1166] 7. Managing recruitment results

[1167] The user inputs the hiring results (e.g., hired or not hired) after the interview into the system. The hiring results are stored in the server's data storage device. The server then analyzes these results and emotion data to select the next candidate and improve the accuracy of the scouting process.

[1168] In this way, the talent matching system utilizes emotional data to realize an advanced and human-like recruitment process. The specific names of the main hardware and software used are an emotional analysis device, an artificial intelligence algorithm, an information input device, a data storage device, an information processing device, a communication device, an information acquisition device, and a schedule adjustment device.

[1169] Specific examples

[1170] Situation: Nurse job opening

[1171] 1. A user (a hospital recruiter) enters job information for a "nurse" through an information input device. The input information includes "three or more years of clinical experience, availability to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." The device captures the user's facial expressions and voice, and an emotion analyzer analyzes the user's emotional state.

[1172] 2. The server records the input information and emotional data in a data storage device, analyzes the candidate information in the database using an artificial intelligence algorithm in the information processing device, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[1173] 3. The server automatically generates a scouting email for each candidate on the list, using a communication device, that is individually customized, mentioning the candidate's name, field of expertise, and past experience, and employing a writing style that reflects the user's emotional state.

[1174] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server, where the information acquisition device analyzes it and notifies the user.

[1175] 5. The user arranges an interview date with the candidate using the schedule adjustment device within the system, and the server notifies both parties of the confirmed date.

[1176] 6. After the interview, the user enters the hiring results into the system, and the server stores the results along with the emotional data in a data storage device. This is also analyzed for the next candidate selection.

[1177] Prompt Sentence Examples

[1178] As a recruiter at a medical and welfare facility, you input job information for a nurse (three or more years of clinical experience, night shift availability, work location: Tokyo, annual salary: 5 million yen or more). Explain the program that generates a customized email that reflects the employee's emotional state and creates a list of candidates.

[1179] Through this system, it will be possible to realize efficient and humane recruitment activities in the medical and welfare fields, and more effectively solve the problem of labor shortages.

[1180] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1181] Step 1: Enter and save your job information

[1182] The user inputs job information using an information input device. Specifically, the user inputs information such as the desired job type, skills, years of experience, work location, and salary into the input screen. After the input data is validated and its accuracy is confirmed, the terminal sends the job information to the server. The server stores the received job information in a data storage device.

[1183] Input: User-entered data for job postings

[1184] Data processing / calculation: Real-time validation by terminal

[1185] Output: The job post sent to the server

[1186] Specific behavior:

[1187] The user enters "Nurse, 3+ years of clinical experience, available for night shifts, work location: Tokyo, annual salary of 5 million yen or more" into the input screen of the terminal.

[1188] The terminal validates the data format and required fields.

[1189] After validation, the device sends the job information to the server.

[1190] The server stores the job information in a data storage device.

[1191] Step 2: Emotion Engine in Action

[1192] The device uses a built-in camera and microphone to capture the user's facial expressions and voice as they enter job information. An emotion analyzer analyzes this data in real time to infer the user's emotional state. The acquired emotion data is associated with the job information, transmitted to a server, and stored in a data storage device.

[1193] Input: User's facial expression data and voice data

[1194] Data processing / calculation: Emotional state estimation using emotion analyzer

[1195] Output: Emotion data sent to the server

[1196] Specific behavior:

[1197] The device captures the user's face and voice while they enter information.

[1198] The emotion analyzer infers emotions such as "excitement" or "alertness" in real time from the captured data.

[1199] The estimated emotion data is transmitted to a server and stored in a data storage device.

[1200] Step 3: Generate a candidate list

[1201] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database. The algorithm calculates the suitability of skills, experience, work location, and salary, and evaluates the suitability of candidates using emotion data. Finally, it generates a list of around 10 highly suitable candidates, which is stored in a data storage device.

[1202] Input: Job posting data and sentiment data

[1203] Data processing / calculation: Calculation of fitness and suitability evaluation using artificial intelligence algorithms

[1204] Output: Generated candidate list

[1205] Specific behavior:

[1206] The server extracts job information and sentiment data.

[1207] An artificial intelligence algorithm calculates suitability based on skills and experience.

[1208] Evaluate candidate suitability based on emotional data.

[1209] An optimized list of 10 candidates is generated and stored in a data storage device.

[1210] Step 4: Automatically generate and send scout emails

[1211] The server automatically generates a scout email using a communication device based on the candidate list. This scout email includes a summary of the job information, the candidate's name, and a customized message tailored to the candidate's past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scout email is sent to the candidate's terminal via the communication device.

[1212] Input: Candidate list data

[1213] Data processing / calculation: Generate customized messages and adjust tones

[1214] Output: Scout email sent

[1215] Specific behavior:

[1216] The server extracts the candidate list and job information.

[1217] An AI model generates a customized scouting email for each candidate.

[1218] Adjust the tone of your email based on emotional data (e.g., a brighter tone if the emotion is excited).

[1219] The generated scout mail is sent to the candidate via a communication device.

[1220] Step 5: Receiving and notifying candidate responses

[1221] The candidate's reply to the scout email received by the terminal (the candidate's email receiving terminal) is sent to the server. The server analyzes this reply using an information acquisition device and notifies the user of its contents. The analyzed contents include the candidate's level of interest and requests.

[1222] Input: Candidate response data

[1223] Data processing / calculation: Analysis of reply emails

[1224] Output: Analysis results reported to the user

[1225] Specific behavior:

[1226] The candidate responds to the scouting email.

[1227] The server analyzes the received email and classifies it into categories such as "interested" or "request more details."

[1228] Send users notifications like "Candidate A has expressed interest" or "Candidate B wants to confirm details."

[1229] Step 6: Scheduling and notifying interview schedule

[1230] After the user confirms the candidate's interest, the server uses the schedule adjustment device to arrange an interview date. The server proposes an optimal interview date based on the calendar information of the user and the candidate, and notifies both parties of the confirmed date from the adjustment device.

[1231] Input: User and candidate calendar information

[1232] Data processing / calculation: Optimizing interview schedules

[1233] Output: Notified interview date

[1234] Specific behavior:

[1235] The user opens the interview scheduling screen.

[1236] The server proposes the best date based on the availability of the user and the candidates.

[1237] The user confirms the schedule, and the server notifies the user of the confirmed interview schedule.

[1238] Step 7: Manage recruitment results

[1239] The user inputs the hiring results after the interview into the system. The server stores these results in a data storage device and uses past emotion data and hiring results to improve the accuracy of the next candidate selection and scouting process.

[1240] Input: Interview result data

[1241] Data processing / calculation: saving and analyzing result data

[1242] Output: Saved recruitment results and analysis results

[1243] Specific behavior:

[1244] The user enters the interview results into the system (e.g., "hired" or "rejected").

[1245] The server stores the results in a data storage device.

[1246] The server analyzes the emotional data and identifies areas for improvement in the next scouting process.

[1247] (Application example 2)

[1248] 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."

[1249] In conventional talent matching systems, the input and analysis of job information, the creation of candidate lists, and the sending of scouting emails are often done manually, resulting in inefficiency and a lack of communication that takes human emotions into consideration. This makes it difficult to find suitable candidates quickly and in a human-like manner. Furthermore, the lack of systems that take into account usage scenarios while on the move makes it difficult to achieve integrated talent matching that utilizes multiple devices.

[1250] 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.

[1251] In this invention, the server includes a means for displaying job information using a smart display or smart glasses, a means for capturing passengers' facial expressions and voices and analyzing their emotional state using an emotion engine, a means for reflecting the emotion data in generated scout emails, and a means for arranging and notifying interview dates within the system. This enables efficient talent matching even in an on-the-go environment and a more human-like recruitment process using emotion data.

[1252] A "terminal for inputting personnel information" is an electronic device such as a personal computer, smartphone, or tablet that is used by a job seeker or recruiter to input job information or job search information.

[1253] A "server that stores information in a database" is an information system that stores input job information and job search information and allows for searching and updating as needed.

[1254] The "server that executes the algorithm" is a computer system that performs specific calculations to analyze the job information input and generate a list of candidates that meet the criteria.

[1255] A "server that automatically generates and sends scouting emails" is a computer system that has the function of automatically creating customized scouting emails for candidates and sending them by email.

[1256] The "terminal that receives, analyzes, and notifies responses" is an electronic device that receives the responses from candidates to scouting emails, analyzes the content, and notifies the hiring manager.

[1257] The "server that notifies confirmed information" is a computer system that notifies the target user of confirmed information such as interview dates and hiring results.

[1258] "Means for displaying job information using a smart display or smart glasses" refers to a function that visually provides job information using a smart display or smart glasses that are installed on an autonomous bus or worn by passengers.

[1259] "Means of capturing facial expressions and voice and analyzing emotional state using an emotion engine" is a function that uses the passenger's facial expressions and words to analyze their emotional state using an emotion engine, and reflects this in job information and scouting emails.

[1260] "Means for reflecting emotional data" refers to a function that adjusts the content and expression of scouting emails sent to candidates based on emotional data analyzed by the emotion engine.

[1261] "Means of arranging and notifying interview dates within the system" is a function that determines interview dates based on the convenience of both the hiring manager and the candidate, and automatically notifies them of those dates.

[1262] As an embodiment of the present invention, a talent matching system is configured as follows.

[1263] 1. Enter and save personnel information

[1264] Users input job information using electronic devices (personal computers, smartphones, tablets, etc.). The information entered includes job type, skills, years of experience, work location, salary, etc. This information is sent from the device to a server, which stores it in a database. This accumulates data necessary for subsequent analysis and matching.

[1265] 2. Operation of the Emotion Engine

[1266] When a user enters a job posting, the device or smart glasses capture facial expressions and speech. This captured data is sent to an emotion engine on the server, which analyzes the user's emotional state. The emotion data is then associated with the job posting.

[1267] 3. Generate candidate list

[1268] The server runs an algorithm based on the job information and sentiment data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and sentiment data, resulting in the selection of the most suitable candidates.

[1269] 4. Displaying job information using smart displays or smart glasses

[1270] The server displays job information using smart displays installed inside the autonomous bus and smart glasses worn by passengers, allowing users to check job information in real time even while on the move.

[1271] 5. Automatic generation and sending of scouting emails

[1272] The server automatically generates a scouting email customized for each candidate based on the generated candidate list. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. Furthermore, the user's emotional state, analyzed by an emotion engine, is reflected in the email, enabling communication with a more human touch. The generated scouting email is then sent to the candidate's device.

[1273] 6. Receiving and notifying candidate responses

[1274] When a candidate replies to a scouting email, the reply is sent to the server, which analyzes the received email, organizes the content, and notifies the user, allowing the user to quickly understand the candidate's intentions.

[1275] 7. Interview schedule arrangement and notification

[1276] The user receives the notification from the server and arranges an interview date with the candidate. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[1277] 8. Managing recruitment results

[1278] The user enters the hiring results after the interview into the system. The results are saved on the server as either "hired" or "not hired." The server then stores these results in a database for later analysis. Based on past successes and failures, emotional data is used to improve the accuracy of the next scouting process.

[1279] Specific examples

[1280] As a concrete example of how this system can be implemented, the following example shows the use of smart glasses inside an autonomous bus.

[1281] example:

[1282] The user puts on the smart glasses and inputs the job information for a "nurse." The smart glasses capture facial expressions and voice and analyze the user's emotional state. The server generates a list of candidates based on this information and emotional data, and displays the job information on the smart glasses. In addition, customized scouting emails are generated and sent to suitable candidates. The following is an example of a prompt.

[1283] Example prompt sentence:

[1284] Please provide suitable job listings for nurses based on the following information:

[1285] Candidate's name: Taro Yamada

[1286] Skills: Python, Data Analysis

[1287] Years of experience: 5 years

[1288] Preferred location: Tokyo

[1289] Hardware and software used

[1290] Specific hardware used includes smart glasses (e.g., Google Glass, Vuzix) and smart displays, and software uses Python, a Flask server, and Emotion Analysis libraries.

[1291] As a result, this system can achieve efficient talent matching even in an on-the-go environment and provide a more human-like recruitment process by utilizing emotional data.

[1292] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1293] Step 1:

[1294] Users use electronic terminals to input job information. This information covers a wide range of topics, including job type, skills, years of experience, work location, and salary. The input data is sent from the terminal to a server and saved in a database. This allows the accumulation of job information.

[1295] Input: Job information (job type, skills, years of experience, work location, salary)

[1296] Output: Job listings stored in a database

[1297] Step 2:

[1298] The device captures facial expressions and voice as the user enters job information. This facial and voice data is sent to a server where it is analyzed by an emotion engine. This analysis generates user emotion data, which is stored in a database along with the associated job information.

[1299] Input: facial expression data, voice data

[1300] Output: Emotion data

[1301] Step 3:

[1302] The server runs an algorithm using the job information and emotion data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and emotion data. The optimized list is then prepared for further processing.

[1303] Input: Job information, emotion data

[1304] Output: Optimized candidate list

[1305] Step 4:

[1306] The server displays job information on smart displays or smart glasses installed inside the autonomous bus, allowing users to check job information in real time even while on the move. The displayed information is based on the optimized candidate list generated in the previous step.

[1307] Input: Optimized candidate list

[1308] Output: Job listings displayed on a smart display or smart glasses

[1309] Step 5:

[1310] The server automatically generates a scouting email customized for each candidate based on the candidate list generated in the previous step. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. The writing style also reflects the emotional data. The generated scouting email is sent to the candidate's device.

[1311] Input: Optimized candidate list, sentiment data

[1312] Output: Scout email sent to candidate's device

[1313] Step 6:

[1314] When a candidate replies to a scouting email, the reply is sent to the server. The server analyzes the received email, organizes the content, and notifies the user. This analysis allows the candidate's intentions to be quickly understood.

[1315] Input: Reply email from candidate

[1316] Output: Parsed reply, notification to user

[1317] Step 7:

[1318] The user receives the notification from the server and arranges an interview date with the candidate on the server. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[1319] Input: Candidate's reply, user input

[1320] Output: Confirmed interview date, notification to candidate and user

[1321] Step 8:

[1322] Once the interview is over, the user enters the hiring result on the server. The result is saved on the server as either "hired" or "not hired." The server stores these results in a database and uses them for subsequent analysis. Based on past successes and failures, the accuracy of the next scouting process can be improved.

[1323] Input: Interview result (hired or not hired)

[1324] Output: Interview results stored in a database, feedback for the next scouting process

[1325] The above are the specific processing steps for carrying out the present invention, through which efficient and human resource matching is achieved.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] [Fourth embodiment]

[1330] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1331] 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.

[1332] 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).

[1333] 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.

[1334] 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.

[1335] 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).

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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."

[1343] The following describes an embodiment of the talent matching system: This system is mainly configured based on interactions between a server, a terminal, and a user.

[1344] 1. Enter and save job information

[1345] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, required skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server.

[1346] The server stores the received job information in a database, which is then analyzed by an AI algorithm later in the process.

[1347] 2. Generate candidate list

[1348] The server uses an AI algorithm to select suitable candidates based on the job information stored in the database. This AI algorithm has learned from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[1349] 3. Automatic generation and sending of scout emails

[1350] The server automatically generates a scouting email based on the candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. The generated scouting email is then sent from the server to the candidate's device.

[1351] 4. Receiving and notifying candidate responses

[1352] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate is interested in the email and replies, the email is sent to the server.

[1353] The server analyzes the received replies, organizes their contents, and notifies the user, allowing the user to receive the candidate's response promptly.

[1354] 5. Arranging and notifying interview dates

[1355] After the user checks the candidate's reply, the system arranges the interview date. The server notifies the candidate and the user of the confirmed interview date.

[1356] 6. Managing recruitment results

[1357] The user enters the hiring results after the interview into the system, such as recording "hired" or "rejected." The server stores these results in a database and analyzes them to improve the scouting process in the future.

[1358] Specific examples

[1359] Situation: Nurse job opening

[1360] 1. A user (a hospital recruiter) enters job information for a "nurse" through a terminal. The input information includes "3+ years of clinical experience, availability for night shifts, work location: Tokyo, annual salary: 5 million yen or more."

[1361] 2. The server stores the input information and uses an AI algorithm to analyze the candidate information in the database, generating a list of 10 candidates who match the criteria.

[1362] 3. The server automatically generates a personalized scouting email for each candidate on the list, including content tailored to the candidate's name, area of ​​expertise, and past experience.

[1363] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[1364] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[1365] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. The server uses this information to improve the scouting process for the next time.

[1366] This will enable efficient and rapid recruitment activities, helping to solve the problem of labor shortages in the medical and welfare fields.

[1367] The processing flow will be explained below.

[1368] Step 1:

[1369] The user logs in to the terminal, enters their user ID and password on the login screen, and is authenticated.

[1370] Step 2:

[1371] The user enters the required information into the job information entry form, including job type, skills, years of experience, location, salary, etc. Once all required fields are entered, the user presses the "Save" button.

[1372] Step 3:

[1373] The server receives the job information sent from the terminal and stores it in a database, while simultaneously checking the consistency of the entered information to detect any defects or inconsistencies.

[1374] Step 4:

[1375] The server uses an AI algorithm to analyze the candidate information in the database based on the saved job information, and creates a list of candidates who match the criteria. This list is generated based on skills, experience, location, salary expectations, etc.

[1376] Step 5:

[1377] The server automatically generates scouting emails for the generated candidate list, which are customized for each candidate and include the candidate's name, past experience, and a summary of the job information.

[1378] Step 6:

[1379] The server sends automatically generated scouting emails to candidates on the list, and the sending history is recorded in a database.

[1380] Step 7:

[1381] The terminal (the candidate's email receiving terminal) receives the scout email. The candidate checks the contents of the email and, if interested, sends a reply email.

[1382] Step 8:

[1383] The server receives reply emails from candidates, analyzes their contents, organizes the reply contents, and generates information to notify the user.

[1384] Step 9:

[1385] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen within the system to schedule an interview.

[1386] Step 10:

[1387] The user checks the available schedule, arranges an interview date with the candidate, and enters the confirmed date into the system.

[1388] Step 11:

[1389] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[1390] Step 12:

[1391] After the interview, the user enters the hiring result into the system, for example, the result is recorded as "hired" or "rejected."

[1392] Step 13:

[1393] The server stores the recruitment results in a database and analyzes past successes and failures, which helps improve the accuracy of the next scouting process.

[1394] Example 1

[1395] 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."

[1396] In conventional talent matching systems, processes such as entering and managing job information, selecting candidates, creating and sending scouting emails, managing candidate replies, and arranging interview dates were often done manually, resulting in problems of time and effort. Furthermore, manually creating customized messages for each candidate was a significant burden, making efficient talent matching difficult. For this reason, a system that can quickly and efficiently match talent is needed.

[1397] 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.

[1398] In this invention, the server includes a user terminal for inputting personnel information, a recording device for saving the input job information in a database, a processing device that executes an artificial intelligence model that analyzes the job information and generates a list of candidates that meet the conditions, a device for automatically generating and sending customized information emails to the generated candidates, a notification device that receives, analyzes, and notifies the candidates of their responses, and a management device that arranges interview dates and notifies the confirmed information. This automates the entire process from entering job information to arranging interview dates, enabling efficient and rapid personnel matching.

[1399] A "user terminal" is an electronic device that a user uses to input personnel information.

[1400] The "recording device" is a device for storing input job information in a database.

[1401] An "artificial intelligence model" is an algorithm or program used to generate a list of qualified candidates based on a job posting.

[1402] "Processing Device" means a computing device for executing artificial intelligence models and analyzing job listings.

[1403] An "invitation email" is a customized email sent to the generated candidate.

[1404] A "notification device" is a device that receives and analyzes the responses of candidates and notifies the user.

[1405] The "management device" is a device for arranging interview schedules and notifying confirmed information.

[1406] "Matching know-how" is a method based on specialized knowledge and experience for generating a candidate list based on job information.

[1407] This talent matching system is primarily comprised of the interaction between user terminals, a server, and candidate terminals. The system automates the entire process, from entering job information to arranging interview dates, achieving efficient and rapid talent matching.

[1408] Hardware and software used

[1409] 1. User device: An electronic device such as a computer or smartphone that allows users to enter job information.

[1410] 2. Recording device: A database system (e.g. MySQL) to store the entered job information.

[1411] 3. Processing unit: A server that analyzes job information and generates a list of candidates that meet the requirements. This server is equipped with artificial intelligence models such as TensorFlow and PyTorch.

[1412] 4. Information Email Generator: A system for generating and sending customized information emails to candidates. It uses natural language processing algorithms such as OpenAI's GPT-3 for generation. An SMTP server is used to send emails.

[1413] 5. Notification device: A real-time chat system or push notification system that receives and analyzes candidate responses and notifies users.

[1414] 6. Management device: A system for arranging interview dates and notifying candidates and users of confirmed information. It uses Google Calendar API, etc.

[1415] Specific example of system operation

[1416] 1. Enter and save job information

[1417] A user uses a device to input job information. The input form contains detailed information such as "desired job type," "required skills," "years of experience," "work location," and "salary." When this information is entered and the "Submit" button is clicked, the device sends the information to the server. The server receives this information and stores it in a recording device (MySQL database).

[1418] 2. Generate candidate list

[1419] The server runs a pre-trained artificial intelligence model using TensorFlow and PyTorch on the stored job information, analyzes the candidate information in the database based on the job requirements, and generates a list of candidates who match the requirements.

[1420] 3. Automatic generation and sending of scout emails

[1421] The server automatically generates customized invitation emails for the generated candidate list using OpenAI's GPT-3. For example, it might say, "Mr. Fujita, your three or more years of clinical experience will be extremely beneficial to our hospital." The generated emails are sent to the candidate's device via the SMTP server.

[1422] 4. Receiving and notifying candidate responses

[1423] The candidate's device receives the information email, and interested candidates reply. This reply is sent to the server, which analyzes the content and notifies the user via the notification device. Natural language processing technology is used for the analysis, and important keywords (e.g., "interview request" and "condition confirmation") are extracted from the reply content.

[1424] 5. Arranging and notifying interview dates

[1425] After checking the candidate's reply, the user can schedule an interview within the system. The server manages the interview schedule via the Google Calendar API, and the confirmed date is automatically notified to the candidate and the user.

[1426] Prompt Sentence Examples

[1427] "Please explain the specific operation of a system that generates a list of suitable candidates based on nursing job postings and automatically generates customized invitation emails."

[1428] In this way, the operation of the entire system is made more efficient, and human resource matching in the medical and welfare fields is carried out more quickly.

[1429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1430] Step 1:

[1431] The user enters job information using their device. The input form contains detailed items such as "desired job type," "required skills," "years of experience," "work location," and "salary." After entering the information, the user clicks the "Submit" button, and the device sends the information to the server via an HTTP POST request. The entered job information is sent to the server in JSON format or form data format.

[1432] Step 2:

[1433] The server saves the received job information in a MySQL database. First, the server parses the received data and breaks it down into the necessary data items. Then, it generates an SQL query to save the job information and inserts it into the database. If the save is successful, it returns a confirmation message to the user.

[1434] Step 3:

[1435] The server runs an artificial intelligence model (such as TensorFlow or PyTorch) based on the saved job information. The input is the saved job information, and the model uses this data to filter the candidate information in the database. Specifically, it extracts candidate data based on the job criteria (for example, "nurse" and "work location: Tokyo") and generates a list of candidates that match the criteria. The output is a list of candidates that match the criteria.

[1436] Step 4:

[1437] The server automatically generates customized invitation emails using OpenAI's GPT-3 based on the generated candidate list. The input is the candidate list, and a message is generated based on individual information for each candidate. Specifically, the message is personalized based on the candidate's name, skills, and past experience. The output is an individually customized invitation email.

[1438] Step 5:

[1439] The server sends the generated invitation email to the candidate's email address via the SMTP server. The input is the customized email, which is passed to the email sending server according to the SMTP protocol. The SMTP server delivers the email to the candidate's email account. The output is confirmation that the email has been sent.

[1440] Step 6:

[1441] The candidate terminal receives the invitation email. If the candidate is interested in the email and replies, the reply is sent to the server as an email. The input is the reply email from the candidate, and the server receives this information.

[1442] Step 7:

[1443] The server analyzes the received reply email and organizes its contents. First, it analyzes the contents of the reply email using NLP technology and extracts important keywords (e.g., "interview request" and "condition confirmation"). The analysis results are displayed on the user's management screen. The output is the organized reply content.

[1444] Step 8:

[1445] After checking the candidate's reply, the user arranges the interview schedule within the system. The input is the candidate's reply, and the user enters the schedule on the management screen. The server creates the interview schedule using the Google Calendar API and notifies the participants. The output is a notification of the interview schedule sent to the candidate and the user.

[1446] Step 9:

[1447] The user inputs the hiring results after the interview into the system. The input is "hired" or "not hired," and the server stores this result in a database. The stored data is fed back into the next candidate selection process. The output is the saved hiring results.

[1448] These steps automate the entire process, from entering job information to scheduling interviews and managing recruitment results, enabling efficient talent matching.

[1449] (Application example 1)

[1450] 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."

[1451] In the logistics industry, inappropriate recruitment and allocation of workers often leads to reduced work efficiency and problems. Even though securing personnel is crucial, especially during busy periods, the difficulty of quickly allocating the right personnel is a challenge. Traditional manual methods of recruiting and allocating workers take time and effort, and do not allow for efficient human resource management.

[1452] 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.

[1453] In this invention, the server includes a terminal for inputting job information, a means for saving the input job information in a database, a means for analyzing the job information and running an AI algorithm for generating a list of candidates that meet the conditions, a means for automatically generating and sending customized scout messages to the generated candidates, a means for receiving, analyzing, and notifying the candidates' responses, a means for arranging interview dates and notifying the confirmed information, a means including an AI algorithm for inputting and saving worker information for logistics operations and generating a list of suitable workers, a means for automatically generating and sending customized messages to workers at the logistics center, and a means for receiving, analyzing, and notifying the workers' responses, thereby enabling efficient recruitment and appropriate allocation of workers.

[1454] "Job information" refers to information such as the type of job being recruited, required skills, working hours, work location, and compensation.

[1455] A "terminal" is an electronic device that allows a user to input information.

[1456] A "database" is a system for storing and managing input information.

[1457] A "server" is a computer system that processes and stores data and communicates with other devices.

[1458] An "AI algorithm" is a calculation method that uses artificial intelligence to analyze data and make predictions and selections.

[1459] A "scouting message" is a customized message used to notify candidates about job openings.

[1460] A "response" is a candidate's response or opinion to a scouting message.

[1461] An "interview date" is the date and time when the candidate and the recruiter will conduct an interview.

[1462] "Logistics operations" include the transportation, storage, and delivery of goods.

[1463] "Worker information" is information about the skills, experience, available working hours, etc. of workers who perform logistics work.

[1464] The "worker list" is a list of workers who meet the conditions.

[1465] "Workers" are workers who perform tasks such as picking, packing, and shipping goods at logistics centers.

[1466] An embodiment for implementing this worker matching system will be described below. This system is mainly configured based on interactions between a server, a terminal, and a user.

[1467] 1. Enter and save job information

[1468] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. This job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. The entered job information is sent from the device to a server. The server stores the received job information in a database. The database is structured using a relational database management system such as MySQL.

[1469] 2. Generate candidate list

[1470] The server uses an AI algorithm to select suitable candidates based on the stored job information. This AI algorithm uses a machine learning model written in Python. The algorithm learns from past successful matching know-how and generates an optimal candidate list based on the job requirements.

[1471] 3. Automatic generation and sending of scouting messages

[1472] The server automatically generates a customized scouting message based on the candidate list. This scouting message includes a summary of the work, the candidate's name, and a customized message tailored to their past experience. The generated scouting message is sent to the candidate's device using Firebase Cloud Messaging.

[1473] 4. Receiving and notifying candidate responses

[1474] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase. The server analyzes the received response, organizes its contents, and notifies the administrator. This allows the administrator to receive the candidate's response quickly.

[1475] 5. Arranging and notifying interview dates

[1476] After checking the candidate's response, the user (administrator) schedules the interview within the system. The server notifies the candidate and administrator of the confirmed interview date. This notification also uses Firebase Cloud Messaging.

[1477] 6. Managing recruitment results

[1478] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[1479] Specific examples

[1480] Consider a distribution center looking to hire pickers for peak season. The manager types the following:

[1481] Job: Picker

[1482] Required skills: Quick and accurate product selection

[1483] Years of experience: No experience necessary

[1484] Location: Nagoya

[1485] Remuneration: 1,200 yen per hour

[1486] Operating hours: 9:00-18:00

[1487] Once this information is entered, the system automatically generates a list of responding workers and sends a scout message, enabling the right workers to be deployed quickly and efficiently.

[1488] Keywords: generative AI model, prompt sentence

[1489] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1490] Step 1:

[1491] The user (logistics center manager) uses a device such as a smartphone or tablet to enter job information. The entered job information includes items such as the desired work content, necessary skills, working hours, work location, and compensation. This information is sent from the device to the server.

[1492] Input: Job information (work content, skills, working hours, work location, compensation, etc.)

[1493] Output: The job post sent to the server

[1494] Step 2:

[1495] The server stores the received job information in a database, where the information is managed appropriately using a database management system (e.g., MySQL).

[1496] Input: Job information sent to the server

[1497] Output: Job listings stored in a database

[1498] Step 3:

[1499] The server selects suitable candidates based on the saved job information using an AI algorithm, a Python machine learning model that has learned from past successful matching know-how, and generates a list of candidates that meet the job requirements.

[1500] Input: Job listings stored in a database

[1501] Output: A list of candidates generated by the AI ​​algorithm

[1502] Step 4:

[1503] The server automatically generates a customized scouting message based on the candidate list, including a message tailored to the candidate's name, past experience, and skill set, and sends the generated scouting message to the candidate's device using Firebase Cloud Messaging.

[1504] Input: Candidate list generated by AI algorithm

[1505] Output: Scout message sent

[1506] Step 5:

[1507] The device (candidate's smartphone or head-mounted display) receives the scout message. If the candidate is interested in the message and responds, the response is sent to the server via Firebase.

[1508] Input: Scouting message sent to candidate

[1509] Output: Candidate response sent to server

[1510] Step 6:

[1511] The server analyzes the responses received from the candidates, organizes the content, and notifies the administrator, allowing the administrator to quickly check the candidates' reactions.

[1512] Input: Candidate response sent to server

[1513] Output: Candidate's response notified to administrator

[1514] Step 7:

[1515] After checking the candidate's response, the user (administrator) schedules the interview within the system. This schedule is confirmed by the server and notified to the candidate and administrator. This notification also uses Firebase Cloud Messaging.

[1516] Input: Interview date arranged in the system

[1517] Output: Interview dates notified to candidates and managers

[1518] Step 8:

[1519] The user enters the hiring result after the interview into the system, for example, recording "hired" or "rejected." The server stores this result in a database and uses it to improve the matching process next time.

[1520] Input: Hiring result after interview

[1521] Output: Recruitment results stored in a database and used for next process improvement

[1522] 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.

[1523] This talent matching system manages talent information, selects candidates, automatically generates and sends scouting emails, collects responses, schedules interviews, and combines an emotion engine to create an efficient and human-like recruitment process. Below, we will explain the detailed processing and specific operation of this system.

[1524] 1. Enter and save job information

[1525] The user (a recruiter at a medical or welfare facility) uses a terminal to input job information. This information includes the desired job type, skills, years of experience, work location, salary, etc. The input job information is sent from the terminal to the server, which then stores the information in a database.

[1526] 2. Operation of the Emotion Engine

[1527] When a user inputs job information, the device captures the user's facial expressions and voice and analyzes their emotional state using an emotion engine. The emotion engine organizes the user's emotional data at the time of input and associates it with the job information.

[1528] 3. Generate candidate list

[1529] The server uses AI algorithms to analyze the job information and sentiment data stored in the database. This analysis, including sentiment data, helps generate a more suitable candidate list. The generated candidate list is optimized based on skills, experience, location, salary expectations, etc.

[1530] 4. Automatic generation and sending of scout emails

[1531] The server automatically generates a scouting email based on the optimized candidate list. This scouting email includes a summary of the job information, the candidate's name, and a customized message based on the candidate's past experience. Furthermore, an emotion engine uses a communication style that reflects the user's emotional state. The generated scouting email is sent from the server to the candidate's device.

[1532] 5. Receiving and notifying candidate responses

[1533] When the terminal (the candidate's email receiving terminal) receives the scout email and the candidate is interested in the email and replies, the email is sent to the server. The server analyzes the received reply, organizes the content, and notifies the user.

[1534] 6. Interview schedule arrangement and notification

[1535] Once the user receives the notification from the server and the candidate's interest is confirmed, the user can schedule an interview within the system. The server will notify the candidate and the user of the confirmed interview date.

[1536] 7. Managing recruitment results

[1537] The user enters the hiring results after the interview into the system. The results are recorded as "hired" or "not hired," for example. The server stores these results in a database and analyzes past successes and failures. This analysis, based on emotional data, helps improve the accuracy of the next scouting process.

[1538] Specific examples

[1539] Situation: Nurse job opening

[1540] 1. A user (hospital recruiter) enters job information for a "nurse" through a terminal. The input items include "3+ years of clinical experience, available to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." At this time, the terminal captures the user's facial expressions and voice, and an emotion engine analyzes the user's emotional state.

[1541] 2. The server stores the input information and emotional data, analyzes the candidate information in the database using AI algorithms, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[1542] 3. The server automatically generates a personalized scouting email for each candidate on the list, including mentioning the candidate's name, area of ​​expertise, and past experience, and using a writing style that reflects the user's emotional state.

[1543] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server.

[1544] 5. The server analyzes the received reply and notifies the user. The user arranges an interview date with the candidate within the system, and the server notifies both parties of the date.

[1545] 6. After the interview, the user enters the hiring results into the system, and the server stores the results in a database. Analysis based on the sentiment data is also performed to help improve the scouting process for the next time.

[1546] This system will enable efficient and humane recruitment activities, making it possible to more effectively solve the problem of labor shortages in the medical and welfare fields.

[1547] The processing flow will be explained below.

[1548] Step 1:

[1549] The user logs in to the terminal and enters their user ID and password on the login screen to receive authentication for access to the system.

[1550] Step 2:

[1551] The user enters the necessary information such as job type, skills, years of experience, work location, salary, etc. into the job information input form. Once the input is complete, the terminal sends this information to the server.

[1552] Step 3:

[1553] The device captures the user's facial expressions and voice and sends them to the emotion engine, which analyzes this data and determines the user's emotional state.

[1554] Step 4:

[1555] The server stores the received job information in a database, along with the emotion data provided by the emotion engine.

[1556] Step 5:

[1557] The server uses AI algorithms to analyze the candidate information in the database based on the stored job information and sentiment data, and generates a list of candidates that match the criteria.

[1558] Step 6:

[1559] The server automatically generates a scouting email based on the candidate list, including the results of the emotion engine. The scouting email includes the candidate's name, past experience, a summary of the job information, and a customized message according to the user's emotional state.

[1560] Step 7:

[1561] The server generates a scouting email and sends it to the candidate's email address. This sending history is recorded in the database.

[1562] Step 8:

[1563] The terminal (the candidate's email receiving terminal) receives the scout email. If the candidate reads the email and is interested, they will send a reply email.

[1564] Step 9:

[1565] The server receives reply emails from candidates, analyzes their contents, organizes the analysis results, generates notification data, and sends it to the user.

[1566] Step 10:

[1567] The user receives a notification from the server, and once the candidate's interest is confirmed, the user is taken to a screen on their device to schedule an interview.

[1568] Step 11:

[1569] The user checks available schedules and arranges interview dates with the candidates. The arranged interview dates are then entered into the system.

[1570] Step 12:

[1571] The server notifies both the candidate and the user of the confirmed interview schedule, including the interview date, time, location, and information about the person in charge.

[1572] Step 13:

[1573] After the interview, the user enters the hiring results into the system, which are then sent to the server and stored in the database.

[1574] Step 14:

[1575] The server analyzes the recruitment results and links them with emotional data to help improve the next scouting process. Past successes and failures are statistically processed and used as data to improve the accuracy of the AI ​​algorithm.

[1576] Example 2

[1577] 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."

[1578] While existing talent matching systems provide basic functions such as inputting and saving job information, generating candidate lists, and sending scouting emails, they are insufficient to provide efficient and human-like support throughout the entire recruitment process. Furthermore, they lack the ability to communicate and select candidates while taking into account the user's emotional state, resulting in inaccurate matching.

[1579] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input device for inputting job information, a data storage device for saving the input job information in a database, an information processing device that executes an artificial intelligence algorithm that analyzes the job information and generates a list of candidates who meet the conditions, a communication device for automatically generating and sending customized scout messages to the generated candidates, an information acquisition device for receiving, analyzing, and notifying the responses of the candidates, a schedule adjustment device for arranging interview dates and notifying the confirmed information, a sentiment analysis device for analyzing the emotional state of the user when entering the job information, and a data association device for storing the emotional data acquired by the sentiment analysis device in association with the job information. This enables high-precision matching that takes the user's emotional state into consideration and a human-like hiring process.

[1580] The "information input device" is a device for inputting job information, and has the function of inputting data such as the type of job, skills, years of experience, work location, and salary desired by the user.

[1581] The "data storage device" is a device for storing input job information in a database, and is a device with a data management function built into the server.

[1582] The "artificial intelligence algorithm" is an algorithm that analyzes job information and generates a list of candidates that meet the conditions, and has the ability to automatically analyze information and select the most suitable candidates.

[1583] An "information processing device" is a device for executing artificial intelligence algorithms and has the function of analyzing data and generating candidate lists.

[1584] A "communication device" is a device for automatically generating and sending customized scouting messages to generated candidates, and has the functionality to send messages using email or other communication means.

[1585] An "information acquisition device" is a device that receives, analyzes, and notifies candidates of their responses, and has the function of collecting reply data from candidates and providing the analysis results to the user.

[1586] The "schedule adjustment device" is a device for adjusting interview dates and notifying the confirmed information, and has the function of adjusting and notifying the interview date based on the free time of both parties.

[1587] An "emotion analysis device" is a device for analyzing the emotional state of a user when entering job information, and has the function of analyzing facial expressions and voice data to infer the user's emotions.

[1588] The "data association device" is a device for storing emotion data acquired by the emotion analysis device in association with job information, and has a database management function for linking emotion data with job information.

[1589] This invention provides a system configuration for a talent matching system that takes into account user emotional information to realize an advanced and human-like recruitment process. The system of the present invention is composed of the following main components:

[1590] 1. Enter and save job information

[1591] Users use an information input device (terminal) to input job information such as the desired job type, skills, years of experience, work location, and salary. The information input device verifies this data in real time to confirm its accuracy. The input job information is sent from the information input device to a server, and a data storage device stores it in a database.

[1592] 2. Operation of the Emotion Engine

[1593] When a user enters job information, the device uses a built-in camera and microphone to capture the user's facial expressions and voice, and the emotion analyzer analyzes this data. The emotion analyzer infers the user's emotional state and records it as emotion data. The emotion data is stored in association with the job information in a data storage device.

[1594] 3. Generate candidate list

[1595] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database to generate a candidate list. The algorithm calculates the suitability of candidates by taking into account various factors (skills, experience, work location, desired salary, etc.), and also evaluates the suitability of candidates based on emotion data. The final candidate list is recorded in a data storage device.

[1596] 4. Automatic generation and sending of scout emails

[1597] The server automatically generates a scouting email on a communication device based on the generated candidate list. The scouting email includes a summary of the job information, the name of each candidate, and a customized message based on their past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scouting email is sent to the candidates via the communication device.

[1598] 5. Receiving and notifying candidate responses

[1599] The terminal (the candidate's email receiving terminal) receives the scout email and the candidate replies. The candidate's reply is sent to the server, where the information acquisition device analyzes it and notifies the user of the contents. The analyzed contents include the candidate's level of interest.

[1600] 6. Interview schedule arrangement and notification

[1601] After the user confirms the candidate's interest, the system uses a schedule adjustment device to arrange an interview date. The server proposes the optimal date for the interview based on the user's and candidate's calendar information. After confirmation, the schedule adjustment device notifies both parties of the confirmed interview date.

[1602] 7. Managing recruitment results

[1603] The user inputs the hiring results (e.g., hired or not hired) after the interview into the system. The hiring results are stored in the server's data storage device. The server then analyzes these results and emotion data to select the next candidate and improve the accuracy of the scouting process.

[1604] In this way, the talent matching system utilizes emotional data to realize an advanced and human-like recruitment process. The specific names of the main hardware and software used are an emotional analysis device, an artificial intelligence algorithm, an information input device, a data storage device, an information processing device, a communication device, an information acquisition device, and a schedule adjustment device.

[1605] Specific examples

[1606] Situation: Nurse job opening

[1607] 1. A user (a hospital recruiter) enters job information for a "nurse" through an information input device. The input information includes "three or more years of clinical experience, availability to work night shifts, work location: Tokyo, annual salary: 5 million yen or more." The device captures the user's facial expressions and voice, and an emotion analyzer analyzes the user's emotional state.

[1608] 2. The server records the input information and emotional data in a data storage device, analyzes the candidate information in the database using an artificial intelligence algorithm in the information processing device, generates a list of 10 candidates who meet the criteria, and further optimizes it based on the emotional data.

[1609] 3. The server automatically generates a scouting email for each candidate on the list, using a communication device, that is individually customized, mentioning the candidate's name, field of expertise, and past experience, and employing a writing style that reflects the user's emotional state.

[1610] 4. The terminal (the candidate's email receiving terminal) receives the scout email, and interested candidates reply. This reply is sent to the server, where the information acquisition device analyzes it and notifies the user.

[1611] 5. The user arranges an interview date with the candidate using the schedule adjustment device within the system, and the server notifies both parties of the confirmed date.

[1612] 6. After the interview, the user enters the hiring results into the system, and the server stores the results along with the emotional data in a data storage device. This is also analyzed for the next candidate selection.

[1613] Prompt Sentence Examples

[1614] As a recruiter at a medical and welfare facility, you input job information for a nurse (three or more years of clinical experience, night shift availability, work location: Tokyo, annual salary: 5 million yen or more). Explain the program that generates a customized email that reflects the employee's emotional state and creates a list of candidates.

[1615] Through this system, it will be possible to realize efficient and humane recruitment activities in the medical and welfare fields, and more effectively solve the problem of labor shortages.

[1616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1617] Step 1: Enter and save your job information

[1618] The user inputs job information using an information input device. Specifically, the user inputs information such as the desired job type, skills, years of experience, work location, and salary into the input screen. After the input data is validated and its accuracy is confirmed, the terminal sends the job information to the server. The server stores the received job information in a data storage device.

[1619] Input: User-entered data for job postings

[1620] Data processing / calculation: Real-time validation by terminal

[1621] Output: The job post sent to the server

[1622] Specific behavior:

[1623] The user enters "Nurse, 3+ years of clinical experience, available for night shifts, work location: Tokyo, annual salary of 5 million yen or more" into the input screen of the terminal.

[1624] The terminal validates the data format and required fields.

[1625] After validation, the device sends the job information to the server.

[1626] The server stores the job information in a data storage device.

[1627] Step 2: Emotion Engine in Action

[1628] The device uses a built-in camera and microphone to capture the user's facial expressions and voice as they enter job information. An emotion analyzer analyzes this data in real time to infer the user's emotional state. The acquired emotion data is associated with the job information, transmitted to a server, and stored in a data storage device.

[1629] Input: User's facial expression data and voice data

[1630] Data processing / calculation: Emotional state estimation using emotion analyzer

[1631] Output: Emotion data sent to the server

[1632] Specific behavior:

[1633] The device captures the user's face and voice while they enter information.

[1634] The emotion analyzer infers emotions such as "excitement" or "alertness" in real time from the captured data.

[1635] The estimated emotion data is transmitted to a server and stored in a data storage device.

[1636] Step 3: Generate a candidate list

[1637] The server runs an artificial intelligence algorithm in its information processing device based on the job information and emotion data stored in the database. The algorithm calculates the suitability of skills, experience, work location, and salary, and evaluates the suitability of candidates using emotion data. Finally, it generates a list of around 10 highly suitable candidates, which is stored in a data storage device.

[1638] Input: Job posting data and sentiment data

[1639] Data processing / calculation: Calculation of fitness and suitability evaluation using artificial intelligence algorithms

[1640] Output: Generated candidate list

[1641] Specific behavior:

[1642] The server extracts job information and sentiment data.

[1643] An artificial intelligence algorithm calculates suitability based on skills and experience.

[1644] Evaluate candidate suitability based on emotional data.

[1645] An optimized list of 10 candidates is generated and stored in a data storage device.

[1646] Step 4: Automatically generate and send scout emails

[1647] The server automatically generates a scout email using a communication device based on the candidate list. This scout email includes a summary of the job information, the candidate's name, and a customized message tailored to the candidate's past experience. Furthermore, the tone of the email is adjusted to reflect the emotional data. The generated scout email is sent to the candidate's terminal via the communication device.

[1648] Input: Candidate list data

[1649] Data processing / calculation: Generate customized messages and adjust tones

[1650] Output: Scout email sent

[1651] Specific behavior:

[1652] The server extracts the candidate list and job information.

[1653] An AI model generates a customized scouting email for each candidate.

[1654] Adjust the tone of your email based on emotional data (e.g., a brighter tone if the emotion is excited).

[1655] The generated scout mail is sent to the candidate via a communication device.

[1656] Step 5: Receiving and notifying candidate responses

[1657] The candidate's reply to the scout email received by the terminal (the candidate's email receiving terminal) is sent to the server. The server analyzes this reply using an information acquisition device and notifies the user of its contents. The analyzed contents include the candidate's level of interest and requests.

[1658] Input: Candidate response data

[1659] Data processing / calculation: Analysis of reply emails

[1660] Output: Analysis results reported to the user

[1661] Specific behavior:

[1662] The candidate responds to the scouting email.

[1663] The server analyzes the received email and classifies it into categories such as "interested" or "request more details."

[1664] Send users notifications like "Candidate A has expressed interest" or "Candidate B wants to confirm details."

[1665] Step 6: Scheduling and notifying interview schedule

[1666] After the user confirms the candidate's interest, the server uses the schedule adjustment device to arrange an interview date. The server proposes an optimal interview date based on the calendar information of the user and the candidate, and notifies both parties of the confirmed date from the adjustment device.

[1667] Input: User and candidate calendar information

[1668] Data processing / calculation: Optimizing interview schedules

[1669] Output: Notified interview date

[1670] Specific behavior:

[1671] The user opens the interview scheduling screen.

[1672] The server proposes the best date based on the availability of the user and the candidates.

[1673] The user confirms the schedule, and the server notifies the user of the confirmed interview schedule.

[1674] Step 7: Manage recruitment results

[1675] The user inputs the hiring results after the interview into the system. The server stores these results in a data storage device and uses past emotion data and hiring results to improve the accuracy of the next candidate selection and scouting process.

[1676] Input: Interview result data

[1677] Data processing / calculation: saving and analyzing result data

[1678] Output: Saved recruitment results and analysis results

[1679] Specific behavior:

[1680] The user enters the interview results into the system (e.g., "hired" or "rejected").

[1681] The server stores the results in a data storage device.

[1682] The server analyzes the emotional data and identifies areas for improvement in the next scouting process.

[1683] (Application example 2)

[1684] 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."

[1685] In conventional talent matching systems, the input and analysis of job information, the creation of candidate lists, and the sending of scouting emails are often done manually, resulting in inefficiency and a lack of communication that takes human emotions into consideration. This makes it difficult to find suitable candidates quickly and in a human-like manner. Furthermore, the lack of systems that take into account usage scenarios while on the move makes it difficult to achieve integrated talent matching that utilizes multiple devices.

[1686] 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.

[1687] In this invention, the server includes a means for displaying job information using a smart display or smart glasses, a means for capturing passengers' facial expressions and voices and analyzing their emotional state using an emotion engine, a means for reflecting the emotion data in generated scout emails, and a means for arranging and notifying interview dates within the system. This enables efficient talent matching even in an on-the-go environment and a more human-like recruitment process using emotion data.

[1688] A "terminal for inputting personnel information" is an electronic device such as a personal computer, smartphone, or tablet that is used by a job seeker or recruiter to input job information or job search information.

[1689] A "server that stores information in a database" is an information system that stores input job information and job search information and allows for searching and updating as needed.

[1690] The "server that executes the algorithm" is a computer system that performs specific calculations to analyze the job information input and generate a list of candidates that meet the criteria.

[1691] A "server that automatically generates and sends scouting emails" is a computer system that has the function of automatically creating customized scouting emails for candidates and sending them by email.

[1692] The "terminal that receives, analyzes, and notifies responses" is an electronic device that receives the responses from candidates to scouting emails, analyzes the content, and notifies the hiring manager.

[1693] The "server that notifies confirmed information" is a computer system that notifies the target user of confirmed information such as interview dates and hiring results.

[1694] "Means for displaying job information using a smart display or smart glasses" refers to a function that visually provides job information using a smart display or smart glasses that are installed on an autonomous bus or worn by passengers.

[1695] "Means of capturing facial expressions and voice and analyzing emotional state using an emotion engine" is a function that uses the passenger's facial expressions and words to analyze their emotional state using an emotion engine, and reflects this in job information and scouting emails.

[1696] "Means for reflecting emotional data" refers to a function that adjusts the content and expression of scouting emails sent to candidates based on emotional data analyzed by the emotion engine.

[1697] "Means of arranging and notifying interview dates within the system" is a function that determines interview dates based on the convenience of both the hiring manager and the candidate, and automatically notifies them of those dates.

[1698] As an embodiment of the present invention, a talent matching system is configured as follows.

[1699] 1. Enter and save personnel information

[1700] Users input job information using electronic devices (personal computers, smartphones, tablets, etc.). The information entered includes job type, skills, years of experience, work location, salary, etc. This information is sent from the device to a server, which stores it in a database. This accumulates data necessary for subsequent analysis and matching.

[1701] 2. Operation of the Emotion Engine

[1702] When a user enters a job posting, the device or smart glasses capture facial expressions and speech. This captured data is sent to an emotion engine on the server, which analyzes the user's emotional state. The emotion data is then associated with the job posting.

[1703] 3. Generate candidate list

[1704] The server runs an algorithm based on the job information and sentiment data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and sentiment data, resulting in the selection of the most suitable candidates.

[1705] 4. Displaying job information using smart displays or smart glasses

[1706] The server displays job information using smart displays installed inside the autonomous bus and smart glasses worn by passengers, allowing users to check job information in real time even while on the move.

[1707] 5. Automatic generation and sending of scouting emails

[1708] The server automatically generates a scouting email customized for each candidate based on the generated candidate list. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. Furthermore, the user's emotional state, analyzed by an emotion engine, is reflected in the email, enabling communication with a more human touch. The generated scouting email is then sent to the candidate's device.

[1709] 6. Receiving and notifying candidate responses

[1710] When a candidate replies to a scouting email, the reply is sent to the server, which analyzes the received email, organizes the content, and notifies the user, allowing the user to quickly understand the candidate's intentions.

[1711] 7. Interview schedule arrangement and notification

[1712] The user receives the notification from the server and arranges an interview date with the candidate. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[1713] 8. Managing recruitment results

[1714] The user enters the hiring results after the interview into the system. The results are saved on the server as either "hired" or "not hired." The server then stores these results in a database for later analysis. Based on past successes and failures, emotional data is used to improve the accuracy of the next scouting process.

[1715] Specific examples

[1716] As a concrete example of how this system can be implemented, the following example shows the use of smart glasses inside an autonomous bus.

[1717] example:

[1718] The user puts on the smart glasses and inputs the job information for a "nurse." The smart glasses capture facial expressions and voice and analyze the user's emotional state. The server generates a list of candidates based on this information and emotional data, and displays the job information on the smart glasses. In addition, customized scouting emails are generated and sent to suitable candidates. The following is an example of a prompt.

[1719] Example prompt sentence:

[1720] Please provide suitable job listings for nurses based on the following information:

[1721] Candidate's name: Taro Yamada

[1722] Skills: Python, Data Analysis

[1723] Years of experience: 5 years

[1724] Preferred location: Tokyo

[1725] Hardware and software used

[1726] Specific hardware used includes smart glasses (e.g., Google Glass, Vuzix) and smart displays, and software uses Python, a Flask server, and Emotion Analysis libraries.

[1727] As a result, this system can achieve efficient talent matching even in an on-the-go environment and provide a more human-like recruitment process by utilizing emotional data.

[1728] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1729] Step 1:

[1730] Users use electronic terminals to input job information. This information covers a wide range of topics, including job type, skills, years of experience, work location, and salary. The input data is sent from the terminal to a server and saved in a database. This allows the accumulation of job information.

[1731] Input: Job information (job type, skills, years of experience, work location, salary)

[1732] Output: Job listings stored in a database

[1733] Step 2:

[1734] The device captures facial expressions and voice as the user enters job information. This facial and voice data is sent to a server where it is analyzed by an emotion engine. This analysis generates user emotion data, which is stored in a database along with the associated job information.

[1735] Input: facial expression data, voice data

[1736] Output: Emotion data

[1737] Step 3:

[1738] The server runs an algorithm using the job information and emotion data stored in the database to generate a list of suitable candidates. This list is optimized based on skills, experience, location, salary expectations, and emotion data. The optimized list is then prepared for further processing.

[1739] Input: Job information, emotion data

[1740] Output: Optimized candidate list

[1741] Step 4:

[1742] The server displays job information on smart displays or smart glasses installed inside the autonomous bus, allowing users to check job information in real time even while on the move. The displayed information is based on the optimized candidate list generated in the previous step.

[1743] Input: Optimized candidate list

[1744] Output: Job listings displayed on a smart display or smart glasses

[1745] Step 5:

[1746] The server automatically generates a scouting email customized for each candidate based on the candidate list generated in the previous step. The scouting email includes a message tailored to the candidate's name, skills, experience, and past work history. The writing style also reflects the emotional data. The generated scouting email is sent to the candidate's device.

[1747] Input: Optimized candidate list, sentiment data

[1748] Output: Scout email sent to candidate's device

[1749] Step 6:

[1750] When a candidate replies to a scouting email, the reply is sent to the server. The server analyzes the received email, organizes the content, and notifies the user. This analysis allows the candidate's intentions to be quickly understood.

[1751] Input: Reply email from candidate

[1752] Output: Parsed reply, notification to user

[1753] Step 7:

[1754] The user receives the notification from the server and arranges an interview date with the candidate on the server. The server then notifies the candidate and the user of the confirmed interview date. This allows for efficient scheduling of interview dates.

[1755] Input: Candidate's reply, user input

[1756] Output: Confirmed interview date, notification to candidate and user

[1757] Step 8:

[1758] Once the interview is over, the user enters the hiring result on the server. The result is saved on the server as either "hired" or "not hired." The server stores these results in a database and uses them for subsequent analysis. Based on past successes and failures, the accuracy of the next scouting process can be improved.

[1759] Input: Interview result (hired or not hired)

[1760] Output: Interview results stored in a database, feedback for the next scouting process

[1761] The above are the specific processing steps for carrying out the present invention, through which efficient and human resource matching is achieved.

[1762] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1763] 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.

[1764] 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 robot 414.

[1765] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1766] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1767] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1768] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1769] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1770] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1771] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1772] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1773] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1774] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1775] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1776] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1777] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1778] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1779] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1780] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1781] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the im...

Claims

1. a terminal for inputting personnel information; A server that stores the entered job information in a database; A server that runs an AI algorithm that analyzes job listings and generates a list of candidates that meet the criteria; a server that automatically generates and sends customized scouting emails to the generated candidates; A terminal that receives, analyzes, and notifies the candidate's responses; A human resources matching system including a server that arranges interview dates and notifies confirmed information.

2. The human resources matching system according to claim 1, further comprising means for incorporating matching know-how into an AI algorithm for generating a candidate list based on job information.

3. 2. The talent matching system according to claim 1, further comprising a means for customizing the automatically generated scouting email for each candidate.

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