system

Abstract

The system according to this embodiment aims to use AI to analyze the potential profile of applicants and to secure personnel that are a good match for the company and to improve the rate of early employee turnover. [Solution] The system according to the embodiment comprises a reception unit, a recognition unit, an evaluation unit, a proposal unit, and a processing unit. The reception unit receives information from applicants. The recognition unit performs emotion and voice recognition based on the information received by the reception unit. The evaluation unit performs an evaluation based on the results obtained by the recognition unit. The proposal unit proposes a placement based on the evaluation results obtained by the evaluation unit. The processing unit performs administrative processing based on the placement proposed by the proposal unit.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can use AI to analyze the potential profile of applicants, thereby enabling companies to secure personnel that are a good match for them and to improve the rate of early retirement. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The recruitment support system according to an embodiment of the present invention is a system that uses AI to streamline a company's recruitment process, aiming to secure excellent talent and improve early employee retention. In the recruitment support system, applicants select an AI interviewer from a list and conduct an AI interview. Next, an emotion and voice recognition AI reads the applicant's emotions from their facial expressions, tone of voice, and language use, and evaluates their personality. The evaluation results are compared with past hire data, and the degree of match to the job and a score are analyzed. In addition, it is linked with the HR data of current employees to suggest references for post-hire assignments. This allows for analysis of placements that match departmental and individual skills, and aims to quickly develop employees who can contribute immediately. Furthermore, after the recruitment interview, the AI ​​also handles the transmission of acceptance / rejection results and administrative processing related to onboarding procedures according to a predetermined schedule, leading to a reduction in the workload of HR personnel. As a result, the recruitment support system can streamline a company's recruitment process, aim to secure excellent talent, and improve early employee retention.

[0029] The recruitment support system according to this embodiment comprises a reception unit, a recognition unit, an evaluation unit, a proposal unit, and a processing unit. The reception unit receives information from applicants. For example, the reception unit stores the information entered by the applicant in a database. The reception unit can also receive information from the AI ​​interviewer selected by the applicant. The recognition unit performs emotion and voice recognition based on the information received by the reception unit. For example, the recognition unit analyzes the applicant's facial expressions to read their emotions. The recognition unit can also analyze the tone of voice and word choice to read their emotions. The recognition unit quantifies and analyzes the emotions. The evaluation unit performs an evaluation based on the results obtained by the recognition unit. For example, the evaluation unit compares the results with past recruit data to analyze the degree of match to the job and the score. The evaluation unit can also store the evaluation results in a database. The proposal unit proposes a placement based on the evaluation results obtained by the evaluation unit. For example, the proposal unit analyzes trends such as the applicant's commute time and the residential area of ​​current employees to propose the optimal placement. The proposal unit can also save the results of the assignment proposals to a database. The processing unit performs administrative processing based on the assignments proposed by the proposal unit. The processing unit performs administrative processing such as sending acceptance / rejection results and procedures related to onboarding. The processing unit can also save the results of the administrative processing to a database. As a result, the recruitment support system according to this embodiment can efficiently receive, recognize, evaluate, propose, and process applicant information.

[0030] The reception department receives applicant information. For example, it stores the information entered by applicants in a database. Specifically, it receives detailed information such as name, address, contact information, educational background, work history, skills, and qualifications entered by applicants through online forms. This information is assigned a unique ID to each applicant and stored in the database. The reception department can also receive information on AI interviewers selected by applicants. AI interviewers are selected according to the applicant's preferred interview date, time, and format (video interview, audio interview, etc.). Based on this information, the reception department automatically adjusts the interview schedule and sends a confirmation email to the applicant. Furthermore, the reception department uses encryption technology to protect data in order to securely manage applicant information. This protects applicants' personal information from unauthorized access and leakage. The reception department can also update applicant information in real time and coordinate with other departments as needed. For example, if an applicant provides additional information or changes the interview date and time, the reception department immediately updates the database and notifies the relevant departments. This allows the reception department to efficiently and accurately receive applicant information, supporting the smooth operation of the entire system.

[0031] The recognition unit performs emotion and voice recognition based on information received by the reception unit. For example, the recognition unit analyzes the applicant's facial expressions to read their emotions. Specifically, it uses a high-precision camera and image analysis algorithm to detect subtle changes in the applicant's facial expressions during the interview and identify emotions such as joy, surprise, anger, and sadness. The recognition unit can also analyze the tone and word choice of the voice to read emotions. Using voice recognition technology, it analyzes the pitch, speed, intonation, and volume of the applicant's voice and quantifies and analyzes emotions such as tension, confidence, and sincerity. This allows the recognition unit to evaluate the applicant's emotional state from multiple perspectives. Furthermore, the recognition unit quantifies and analyzes emotions. For example, it calculates an emotion score based on data obtained from the applicant's facial expressions and voice and stores it in a database. The recognition unit can also process this data in real time using AI and provide feedback to the interviewer. This allows the interviewer to understand the applicant's emotional state and ask more appropriate questions and respond accordingly. The recognition unit can also analyze changes and trends in the applicant's emotions by comparing them with past data. This allows the recognition unit to accurately recognize the applicant's emotions and provide useful information to the evaluation and proposal units.

[0032] The evaluation department conducts evaluations based on the results obtained by the recognition department. For example, the evaluation department compares applicant data with past hire data to analyze the degree of suitability for the job and assign scores. Specifically, it refers to a database of past applicants and compares applicants' skill sets, experience, and emotional scores. This quantifies how well an applicant is suited to a particular job and calculates an evaluation score. The evaluation department can also save the evaluation results in a database. The evaluation results are generated as detailed reports for each applicant, making them available for interviewers and HR personnel to refer to. Furthermore, the evaluation department uses AI to automate the evaluation process and conduct rapid and accurate evaluations. For example, it uses machine learning algorithms to analyze applicant data and build predictive models based on past successes and failures. This allows the evaluation department to predict applicants' future performance and retention rates, supporting more appropriate hiring decisions. In addition, the evaluation department can flexibly set evaluation criteria and customize them according to the job content and company needs. This allows the evaluation department to consider the diverse characteristics and backgrounds of applicants and conduct more fair and comprehensive evaluations.

[0033] The Proposal Department proposes placements based on evaluation results obtained by the Evaluation Department. For example, the Proposal Department analyzes trends such as applicants' commute times and the residential areas of existing employees to propose the most suitable placements. Specifically, it calculates commute times based on applicants' address information and selects placements with minimal commuting burden. It also analyzes the residential areas of existing employees to assess how well applicants can adapt to existing teams. The Proposal Department can also save the placement proposal results in a database. Detailed reports are generated for each applicant, making them accessible to HR personnel. Furthermore, the Proposal Department uses AI to optimize the placement proposal process, resulting in more accurate proposals. For example, it uses machine learning algorithms to analyze past placement data and build predictive models based on success and failure cases. This allows the Proposal Department to identify and propose placements best suited to each applicant's characteristics and job responsibilities. Additionally, the Proposal Department considers the job responsibilities, team structure, and project progress of each placement to provide an environment where applicants can perform at their best. This allows the Proposal Department to improve applicant satisfaction and work efficiency.

[0034] The processing unit performs administrative tasks based on the assignments proposed by the proposal department. For example, the processing unit handles tasks related to sending acceptance / rejection results and onboarding procedures. Specifically, it notifies applicants of acceptance / rejection results via email or SMS, and provides successful applicants with the necessary documents and information for onboarding. The processing unit can also save the results of administrative tasks to a database. This allows for centralized management of applicant status and the progress of procedures. Furthermore, the processing unit uses AI to improve the efficiency of administrative tasks. For example, it introduces a chatbot that automatically responds to applicant inquiries using natural language processing technology, enabling quick responses. The processing unit also simplifies and expedites procedures through the automatic generation of necessary documents and the introduction of electronic signatures. As a result, the processing unit can provide applicants with a smooth onboarding process and streamline the company's recruitment activities. In addition, the processing unit can provide post-onboarding support, such as follow-up and scheduling of training. As a result, the processing unit can support the entire process from applicant onboarding to retention, strengthening the company's human resource management.

[0035] The evaluation unit can compare the data with past hire data to analyze the degree of suitability for the job and the score. The evaluation unit can also save the evaluation results to a database. This improves the accuracy of the analysis of the degree of suitability for the job and the score by comparing it with past hire data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0036] The proposal department can analyze trends such as applicants' commuting times and the residential areas of current employees to propose the most suitable placement. For example, the proposal department analyzes trends such as applicants' commuting times and the residential areas of current employees to propose the most suitable placement. The proposal department can also save the placement proposal results in a database. This allows for the proposal of the most suitable placement by analyzing trends in commuting times and residential areas. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0037] The processing unit can perform administrative tasks related to sending acceptance / rejection results and onboarding procedures. For example, the processing unit performs administrative tasks related to sending acceptance / rejection results and onboarding procedures. The processing unit can also save the results of the administrative tasks to a database. This reduces the workload of HR personnel by performing administrative tasks related to sending acceptance / rejection results and onboarding procedures. Some or all of the above-mentioned processes in the processing unit may be performed using AI, for example, or without using AI.

[0038] The reception department can analyze an applicant's past application history and select the most appropriate application method. For example, the reception department can prioritize relevant questions based on the job titles and companies the applicant has applied to in the past. The reception department can also focus questions on specific skills and experience based on the applicant's past application history. The reception department can also adjust the content and format of questions based on feedback from companies the applicant has applied to in the past. In this way, the reception department can select the most appropriate application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, for example, or not.

[0039] The reception desk can filter applicants' current occupations and areas of interest when receiving information. For example, the reception desk can prioritize questions related to the applicant's current occupation. The reception desk can also ask questions on relevant topics based on the applicant's areas of interest. The reception desk can also automatically select appropriate interviewers based on the applicant's occupations and areas of interest. This allows the reception desk to receive highly relevant information by filtering based on current occupations and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0040] The reception department can prioritize receiving highly relevant information by considering the applicant's geographical location when receiving information. For example, the reception department can prioritize receiving information about nearby companies and job types based on the applicant's place of residence. The reception department can also prioritize asking questions about commute time and transportation based on the applicant's geographical location. The reception department can also prioritize asking questions about region-specific skills and experience based on the applicant's place of residence. By prioritizing the receipt of highly relevant information based on geographical location, more appropriate information can be received. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI.

[0041] The reception department can analyze the applicant's social media activity when receiving information and receive relevant information. For example, the reception department can prioritize questions on topics of interest based on the applicant's social media activity. The reception department can also ask questions on relevant skills and experience based on the applicant's statements and posts on social media. The reception department can also analyze the applicant's social media activity and automatically select appropriate interviewers. This allows the reception department to receive relevant information by analyzing social media activity. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI.

[0042] The recognition unit can optimize its recognition algorithm by referring to the applicant's past interview data during recognition. For example, the recognition unit can learn specific facial expressions and voice patterns based on the applicant's past interview data to improve recognition accuracy. The recognition unit can also analyze responses to specific questions from the applicant's past interview data and adjust its recognition algorithm. The recognition unit can also refer to the applicant's past interview data to make it easier to identify specific emotional patterns. This optimizes the recognition algorithm and improves recognition accuracy by referring to past interview data. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0043] The recognition unit can apply different recognition methods depending on the applicant's occupation and skills during recognition. For example, if the applicant is in a technical position, the recognition unit may focus on analyzing responses to technical questions. If the applicant is in a sales position, the recognition unit may focus on analyzing responses related to communication skills. If the applicant is in a management position, the recognition unit may focus on analyzing responses related to leadership. This improves recognition accuracy by applying different recognition methods according to occupation and skills. Some or all of the processing described above in the recognition unit may be performed using AI, for example, or without AI.

[0044] The recognition unit can determine the recognition priority based on the applicant's submission date during recognition. For example, if an applicant submits early, the recognition unit will prioritize recognition and provide results quickly. The recognition unit can also increase the recognition priority and process quickly if the applicant is close to the submission deadline. The recognition unit can also dynamically adjust the recognition priority according to the applicant's submission date. This enables rapid recognition by determining the recognition priority based on the submission date. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0045] The recognition unit can improve the accuracy of its recognition by referring to the applicant's relevant literature during the recognition process. For example, the recognition unit can improve the accuracy of its recognition regarding specific skills and knowledge based on the relevant literature submitted by the applicant. The recognition unit can also improve the accuracy of its recognition regarding specific industries or job types by referring to the applicant's relevant literature. The recognition unit can also improve the accuracy of its recognition regarding specific topics by analyzing the applicant's relevant literature. In this way, the accuracy of recognition is improved by referring to relevant literature. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0046] The evaluation unit can optimize its evaluation algorithm by referring to past hire data during the evaluation process. For example, the evaluation unit can optimize evaluation criteria for specific skills and experience based on past hire data. The evaluation unit can also optimize evaluation criteria for specific job types or industries from past hire data. The evaluation unit can also optimize evaluation criteria for specific evaluation items by referring to past hire data. This optimizes the evaluation algorithm and improves evaluation accuracy by referring to past hire data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0047] The evaluation department can apply different evaluation methods depending on the applicant's occupation and skills during the evaluation process. For example, if the applicant is in a technical position, the evaluation department will apply evaluation methods related to technical skills. If the applicant is in a sales position, the evaluation department may also apply evaluation methods related to communication skills. If the applicant is in a management position, the evaluation department may also apply evaluation methods related to leadership. By applying different evaluation methods according to occupation and skills, the accuracy of the evaluation is improved. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI.

[0048] The evaluation unit can determine the priority of evaluations based on the timing of applicant submissions. For example, if an applicant submits early, the evaluation unit will prioritize their evaluation and provide results quickly. The evaluation unit can also increase the priority of evaluations and process them quickly if an applicant is close to the submission deadline. The evaluation unit can also dynamically adjust the evaluation priority according to the timing of applicant submissions. This enables rapid evaluation by determining the evaluation priority based on the submission timing. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI.

[0049] The evaluation department can improve the accuracy of its evaluations by referring to the applicant's relevant literature during the evaluation process. For example, the evaluation department can improve the accuracy of its evaluations regarding specific skills and knowledge based on the relevant literature submitted by the applicant. The evaluation department can also improve the accuracy of its evaluations regarding specific industries or job types by referring to the applicant's relevant literature. The evaluation department can also improve the accuracy of its evaluations regarding specific topics by analyzing the applicant's relevant literature. In this way, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI.

[0050] The proposal department can select the most suitable placement by referring to the applicant's past work history when making a proposal. For example, the proposal department can propose placements that utilize the applicant's relevant skills and experience based on their past work history. The proposal department can also select placements suitable for specific industries or job types based on the applicant's past work history. The proposal department can also propose placements that align with the applicant's career path by referring to their past work history. In this way, the most suitable placement can be selected by referring to past work history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0051] The proposal department can customize the proposed placements based on the applicant's current living situation. For example, the proposal department can suggest placements that are convenient for commuting, taking into account the applicant's commute time. The proposal department can also suggest placements that allow for flexible working hours, taking into account the applicant's family situation. The proposal department can also suggest placements that are less stressful, taking into account the applicant's health condition. By customizing the proposed placements based on the applicant's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0052] The proposal department can select the most suitable placement by considering the applicant's geographical location when making a proposal. For example, the proposal department may prioritize suggesting placements close to the applicant's place of residence. The proposal department may also suggest placements that reduce the applicant's commute time. Based on the applicant's geographical location, the proposal department may also suggest placements that allow the applicant to utilize their region-specific skills and experience. In this way, the most suitable placement can be selected by considering geographical location. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0053] The proposal department can analyze an applicant's social media activity when making a proposal and suggest suitable placements. For example, the proposal department can suggest placements related to industries and job types of interest based on the applicant's social media activity. The proposal department can also suggest placements that utilize relevant skills and experience based on the applicant's statements and posts on social media. The proposal department can also analyze an applicant's social media activity and automatically select appropriate placements. This allows for more appropriate placement suggestions by analyzing social media activity. Some or all of the above processes in the proposal department may be performed using AI, for example, or not.

[0054] The processing unit can optimize processing algorithms by referring to past processing data during business processing. For example, the processing unit can optimize algorithms for specific business processes based on past processing data. The processing unit can also optimize processing algorithms for specific tasks from past processing data. The processing unit can also optimize algorithms for specific processing items by referring to past processing data. This optimizes processing algorithms and improves processing accuracy by referring to past processing data. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0055] The processing unit can apply different processing methods to applicants depending on their occupation and skills during the administrative processing. For example, if the applicant is in a technical position, the processing unit will apply administrative processing methods related to technical skills. If the applicant is in a sales position, the processing unit may also apply administrative processing methods related to communication skills. If the applicant is in a management position, the processing unit may also apply administrative processing methods related to leadership. By applying different processing methods according to occupation and skills, the processing accuracy is improved. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without using AI.

[0056] The processing unit can determine the priority of processing based on the applicant's submission timing during administrative processing. For example, if an applicant submits early, the processing unit will prioritize processing and provide results quickly. The processing unit can also increase the priority of processing and expedite processing if the applicant is close to the submission deadline. The processing unit can also dynamically adjust the priority of processing according to the applicant's submission timing. This enables rapid administrative processing by determining the priority of processing based on the submission timing. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without using AI.

[0057] The processing unit can improve the accuracy of its processing by referring to the applicant's relevant literature during the administrative processing. For example, the processing unit can improve the accuracy of a specific administrative process based on the relevant literature submitted by the applicant. The processing unit can also improve the accuracy of administrative processing related to a specific task by referring to the applicant's relevant literature. The processing unit can also analyze the applicant's relevant literature and improve the accuracy of a specific processing item. As a result, the accuracy of processing is improved by referring to relevant literature. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception department can analyze an applicant's past application history and select the most appropriate application method. For example, the reception department can prioritize relevant questions based on the job titles and companies the applicant has applied to in the past. The reception department can also focus questions on specific skills and experience based on the applicant's past application history. The reception department can also adjust the content and format of questions based on feedback from companies the applicant has applied to in the past. In this way, the reception department can select the most appropriate application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, for example, or not.

[0060] The evaluation unit can optimize its evaluation algorithm by referring to past hire data during the evaluation process. For example, the evaluation unit can optimize evaluation criteria for specific skills and experience based on past hire data. The evaluation unit can also optimize evaluation criteria for specific job types or industries from past hire data. The evaluation unit can also optimize evaluation criteria for specific evaluation items by referring to past hire data. This optimizes the evaluation algorithm and improves evaluation accuracy by referring to past hire data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0061] The proposal department can select the most suitable placement by referring to the applicant's past work history when making a proposal. For example, the proposal department can propose placements that utilize the applicant's relevant skills and experience based on their past work history. The proposal department can also select placements suitable for specific industries or job types based on the applicant's past work history. The proposal department can also propose placements that align with the applicant's career path by referring to their past work history. In this way, the most suitable placement can be selected by referring to past work history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0062] The reception desk can filter applicants' current occupations and areas of interest when receiving information. For example, the reception desk can prioritize questions related to the applicant's current occupation. The reception desk can also ask questions on relevant topics based on the applicant's areas of interest. The reception desk can also automatically select appropriate interviewers based on the applicant's occupations and areas of interest. This allows the reception desk to receive highly relevant information by filtering based on current occupations and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0063] The recognition unit can apply different recognition methods depending on the applicant's occupation and skills during recognition. For example, if the applicant is in a technical position, the recognition unit may focus on analyzing responses to technical questions. If the applicant is in a sales position, the recognition unit may focus on analyzing responses related to communication skills. If the applicant is in a management position, the recognition unit may focus on analyzing responses related to leadership. This improves recognition accuracy by applying different recognition methods according to occupation and skills. Some or all of the processing described above in the recognition unit may be performed using AI, for example, or without AI.

[0064] The proposal department can select the most suitable placement by considering the applicant's geographical location when making a proposal. For example, the proposal department may prioritize suggesting placements close to the applicant's place of residence. The proposal department may also suggest placements that reduce the applicant's commute time. Based on the applicant's geographical location, the proposal department may also suggest placements that allow the applicant to utilize their region-specific skills and experience. In this way, the most suitable placement can be selected by considering geographical location. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0065] The processing unit can improve the accuracy of its processing by referring to the applicant's relevant literature during the administrative processing. For example, the processing unit can improve the accuracy of a specific administrative process based on the relevant literature submitted by the applicant. The processing unit can also improve the accuracy of administrative processing related to a specific task by referring to the applicant's relevant literature. The processing unit can also analyze the applicant's relevant literature and improve the accuracy of a specific processing item. As a result, the accuracy of processing is improved by referring to relevant literature. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives applicant information. For example, it stores the information entered by the applicant in a database. It can also receive information about the AI ​​interviewer selected by the applicant. Step 2: The recognition unit performs emotion and voice recognition based on the information received by the reception unit. For example, it analyzes the applicant's facial expressions to read their emotions. It can also analyze the tone of voice and word choice to read their emotions. The recognition unit quantifies and analyzes the emotions. Step 3: The evaluation unit performs an evaluation based on the results obtained by the recognition unit. For example, it compares the results with past hire data and analyzes the degree of suitability for the job and assigns a score. The evaluation results can also be saved to a database. Step 4: The proposal department proposes placements based on the evaluation results obtained by the evaluation department. For example, it analyzes trends such as applicants' commuting time and the residential areas of current employees to propose the most suitable placement. The placement proposal results can also be saved in a database. Step 5: The processing unit performs administrative tasks based on the assignments proposed by the proposal department. For example, it handles tasks such as sending acceptance / rejection results and onboarding procedures. The results of the administrative tasks can also be saved to a database.

[0068] (Example of form 2) The recruitment support system according to an embodiment of the present invention is a system that uses AI to streamline a company's recruitment process, aiming to secure excellent talent and improve early employee retention. In the recruitment support system, applicants select an AI interviewer from a list and conduct an AI interview. Next, an emotion and voice recognition AI reads the applicant's emotions from their facial expressions, tone of voice, and language use, and evaluates their personality. The evaluation results are compared with past hire data, and the degree of match to the job and a score are analyzed. In addition, it is linked with the HR data of current employees to suggest references for post-hire assignments. This allows for analysis of placements that match departmental and individual skills, and aims to quickly develop employees who can contribute immediately. Furthermore, after the recruitment interview, the AI ​​also handles the transmission of acceptance / rejection results and administrative processing related to onboarding procedures according to a predetermined schedule, leading to a reduction in the workload of HR personnel. As a result, the recruitment support system can streamline a company's recruitment process, aim to secure excellent talent, and improve early employee retention.

[0069] The recruitment support system according to this embodiment comprises a reception unit, a recognition unit, an evaluation unit, a proposal unit, and a processing unit. The reception unit receives information from applicants. For example, the reception unit stores the information entered by the applicant in a database. The reception unit can also receive information from the AI ​​interviewer selected by the applicant. The recognition unit performs emotion and voice recognition based on the information received by the reception unit. For example, the recognition unit analyzes the applicant's facial expressions to read their emotions. The recognition unit can also analyze the tone of voice and word choice to read their emotions. The recognition unit quantifies and analyzes the emotions. The evaluation unit performs an evaluation based on the results obtained by the recognition unit. For example, the evaluation unit compares the results with past recruit data to analyze the degree of match to the job and the score. The evaluation unit can also store the evaluation results in a database. The proposal unit proposes a placement based on the evaluation results obtained by the evaluation unit. For example, the proposal unit analyzes trends such as the applicant's commute time and the residential area of ​​current employees to propose the optimal placement. The proposal unit can also save the results of the assignment proposals to a database. The processing unit performs administrative processing based on the assignments proposed by the proposal unit. The processing unit performs administrative processing such as sending acceptance / rejection results and procedures related to onboarding. The processing unit can also save the results of the administrative processing to a database. As a result, the recruitment support system according to this embodiment can efficiently receive, recognize, evaluate, propose, and process applicant information.

[0070] The reception department receives applicant information. For example, it stores the information entered by applicants in a database. Specifically, it receives detailed information such as name, address, contact information, educational background, work history, skills, and qualifications entered by applicants through online forms. This information is assigned a unique ID to each applicant and stored in the database. The reception department can also receive information on AI interviewers selected by applicants. AI interviewers are selected according to the applicant's preferred interview date, time, and format (video interview, audio interview, etc.). Based on this information, the reception department automatically adjusts the interview schedule and sends a confirmation email to the applicant. Furthermore, the reception department uses encryption technology to protect data in order to securely manage applicant information. This protects applicants' personal information from unauthorized access and leakage. The reception department can also update applicant information in real time and coordinate with other departments as needed. For example, if an applicant provides additional information or changes the interview date and time, the reception department immediately updates the database and notifies the relevant departments. This allows the reception department to efficiently and accurately receive applicant information, supporting the smooth operation of the entire system.

[0071] The recognition unit performs emotion and voice recognition based on information received by the reception unit. For example, the recognition unit analyzes the applicant's facial expressions to read their emotions. Specifically, it uses a high-precision camera and image analysis algorithm to detect subtle changes in the applicant's facial expressions during the interview and identify emotions such as joy, surprise, anger, and sadness. The recognition unit can also analyze the tone and word choice of the voice to read emotions. Using voice recognition technology, it analyzes the pitch, speed, intonation, and volume of the applicant's voice and quantifies and analyzes emotions such as tension, confidence, and sincerity. This allows the recognition unit to evaluate the applicant's emotional state from multiple perspectives. Furthermore, the recognition unit quantifies and analyzes emotions. For example, it calculates an emotion score based on data obtained from the applicant's facial expressions and voice and stores it in a database. The recognition unit can also process this data in real time using AI and provide feedback to the interviewer. This allows the interviewer to understand the applicant's emotional state and ask more appropriate questions and respond accordingly. The recognition unit can also analyze changes and trends in the applicant's emotions by comparing them with past data. This allows the recognition unit to accurately recognize the applicant's emotions and provide useful information to the evaluation and proposal units.

[0072] The evaluation department conducts evaluations based on the results obtained by the recognition department. For example, the evaluation department compares applicant data with past hire data to analyze the degree of suitability for the job and assign scores. Specifically, it refers to a database of past applicants and compares applicants' skill sets, experience, and emotional scores. This quantifies how well an applicant is suited to a particular job and calculates an evaluation score. The evaluation department can also save the evaluation results in a database. The evaluation results are generated as detailed reports for each applicant, making them available for interviewers and HR personnel to refer to. Furthermore, the evaluation department uses AI to automate the evaluation process and conduct rapid and accurate evaluations. For example, it uses machine learning algorithms to analyze applicant data and build predictive models based on past successes and failures. This allows the evaluation department to predict applicants' future performance and retention rates, supporting more appropriate hiring decisions. In addition, the evaluation department can flexibly set evaluation criteria and customize them according to the job content and company needs. This allows the evaluation department to consider the diverse characteristics and backgrounds of applicants and conduct more fair and comprehensive evaluations.

[0073] The Proposal Department proposes placements based on evaluation results obtained by the Evaluation Department. For example, the Proposal Department analyzes trends such as applicants' commute times and the residential areas of existing employees to propose the most suitable placements. Specifically, it calculates commute times based on applicants' address information and selects placements with minimal commuting burden. It also analyzes the residential areas of existing employees to assess how well applicants can adapt to existing teams. The Proposal Department can also save the placement proposal results in a database. Detailed reports are generated for each applicant, making them accessible to HR personnel. Furthermore, the Proposal Department uses AI to optimize the placement proposal process, resulting in more accurate proposals. For example, it uses machine learning algorithms to analyze past placement data and build predictive models based on success and failure cases. This allows the Proposal Department to identify and propose placements best suited to each applicant's characteristics and job responsibilities. Additionally, the Proposal Department considers the job responsibilities, team structure, and project progress of each placement to provide an environment where applicants can perform at their best. This allows the Proposal Department to improve applicant satisfaction and work efficiency.

[0074] The processing unit performs administrative tasks based on the assignments proposed by the proposal department. For example, the processing unit handles tasks related to sending acceptance / rejection results and onboarding procedures. Specifically, it notifies applicants of acceptance / rejection results via email or SMS, and provides successful applicants with the necessary documents and information for onboarding. The processing unit can also save the results of administrative tasks to a database. This allows for centralized management of applicant status and the progress of procedures. Furthermore, the processing unit uses AI to improve the efficiency of administrative tasks. For example, it introduces a chatbot that automatically responds to applicant inquiries using natural language processing technology, enabling quick responses. The processing unit also simplifies and expedites procedures through the automatic generation of necessary documents and the introduction of electronic signatures. As a result, the processing unit can provide applicants with a smooth onboarding process and streamline the company's recruitment activities. In addition, the processing unit can provide post-onboarding support, such as follow-up and scheduling of training. As a result, the processing unit can support the entire process from applicant onboarding to retention, strengthening the company's human resource management.

[0075] The recognition unit can read and quantify emotions from the applicant's facial expressions, tone of voice, and word choice, and analyze them. For example, the recognition unit can analyze the applicant's facial expressions to read their emotions. The recognition unit can also analyze the tone of voice and word choice to read their emotions. The recognition unit quantifies and analyzes the emotions. This quantification and analysis of the applicant's emotions improves recognition accuracy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The evaluation unit can compare the data with past hire data to analyze the degree of suitability for the job and the score. The evaluation unit can also save the evaluation results to a database. This improves the accuracy of the analysis of the degree of suitability for the job and the score by comparing it with past hire data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0077] The proposal department can analyze trends such as applicants' commuting times and the residential areas of current employees to propose the most suitable placement. For example, the proposal department analyzes trends such as applicants' commuting times and the residential areas of current employees to propose the most suitable placement. The proposal department can also save the placement proposal results in a database. This allows for the proposal of the most suitable placement by analyzing trends in commuting times and residential areas. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0078] The processing unit can perform administrative tasks related to sending acceptance / rejection results and onboarding procedures. For example, the processing unit performs administrative tasks related to sending acceptance / rejection results and onboarding procedures. The processing unit can also save the results of the administrative tasks to a database. This reduces the workload of HR personnel by performing administrative tasks related to sending acceptance / rejection results and onboarding procedures. Some or all of the above-mentioned processes in the processing unit may be performed using AI, for example, or without using AI.

[0079] The reception desk can estimate the applicant's emotions and adjust how information is received based on the estimated emotions. For example, if the applicant is nervous, the reception desk can provide a relaxing interface and adjust the order of questions. If the applicant is relaxed, the reception desk can ask more detailed questions to gather deeper information. If the applicant is in a hurry, the reception desk can prioritize concise questions to receive information quickly. This allows for the collection of more relevant information by adjusting how information is received based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception department can analyze an applicant's past application history and select the most appropriate application method. For example, the reception department can prioritize relevant questions based on the job titles and companies the applicant has applied to in the past. The reception department can also focus questions on specific skills and experience based on the applicant's past application history. The reception department can also adjust the content and format of questions based on feedback from companies the applicant has applied to in the past. In this way, the reception department can select the most appropriate application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, for example, or not.

[0081] The reception desk can filter applicants' current occupations and areas of interest when receiving information. For example, the reception desk can prioritize questions related to the applicant's current occupation. The reception desk can also ask questions on relevant topics based on the applicant's areas of interest. The reception desk can also automatically select appropriate interviewers based on the applicant's occupations and areas of interest. This allows the reception desk to receive highly relevant information by filtering based on current occupations and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0082] The reception desk can estimate the applicant's emotions and prioritize the information to be received based on those emotions. For example, if the applicant is nervous, the reception desk will prioritize questions that help them relax. If the applicant is relaxed, the reception desk may also prioritize questions that request more detailed information. If the applicant is in a hurry, the reception desk may also prioritize concise questions. By prioritizing information based on the applicant's emotions, more relevant information can be received preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception department can prioritize receiving highly relevant information by considering the applicant's geographical location when receiving information. For example, the reception department can prioritize receiving information about nearby companies and job types based on the applicant's place of residence. The reception department can also prioritize asking questions about commute time and transportation based on the applicant's geographical location. The reception department can also prioritize asking questions about region-specific skills and experience based on the applicant's place of residence. By prioritizing the receipt of highly relevant information based on geographical location, more appropriate information can be received. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI.

[0084] The reception department can analyze the applicant's social media activity when receiving information and receive relevant information. For example, the reception department can prioritize questions on topics of interest based on the applicant's social media activity. The reception department can also ask questions on relevant skills and experience based on the applicant's statements and posts on social media. The reception department can also analyze the applicant's social media activity and automatically select appropriate interviewers. This allows the reception department to receive relevant information by analyzing social media activity. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI.

[0085] The recognition unit can estimate the applicant's emotions and adjust the accuracy of recognition based on the estimated emotions. For example, if the applicant is nervous, the recognition unit will focus on analyzing subtle changes in facial expressions to improve the accuracy of emotion recognition. If the applicant is relaxed, the recognition unit can also focus on recognizing emotions by paying attention to tone of voice and word choice. If the applicant is in a hurry, the recognition unit can analyze both voice and facial expressions simultaneously to accurately recognize emotions in a short amount of time. This improves recognition accuracy by adjusting the accuracy of recognition based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The recognition unit can optimize its recognition algorithm by referring to the applicant's past interview data during recognition. For example, the recognition unit can learn specific facial expressions and voice patterns based on the applicant's past interview data to improve recognition accuracy. The recognition unit can also analyze responses to specific questions from the applicant's past interview data and adjust its recognition algorithm. The recognition unit can also refer to the applicant's past interview data to make it easier to identify specific emotional patterns. This optimizes the recognition algorithm and improves recognition accuracy by referring to past interview data. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0087] The recognition unit can apply different recognition methods depending on the applicant's occupation and skills during recognition. For example, if the applicant is in a technical position, the recognition unit may focus on analyzing responses to technical questions. If the applicant is in a sales position, the recognition unit may focus on analyzing responses related to communication skills. If the applicant is in a management position, the recognition unit may focus on analyzing responses related to leadership. This improves recognition accuracy by applying different recognition methods according to occupation and skills. Some or all of the processing described above in the recognition unit may be performed using AI, for example, or without AI.

[0088] The recognition unit can estimate the applicant's emotions and adjust the display method of the recognition results based on the estimated emotions. For example, if the applicant is nervous, the recognition unit provides a simple and highly visible display method. If the applicant is relaxed, the recognition unit can also provide a display method that includes detailed information. If the applicant is in a hurry, the recognition unit can also provide a display method that gets straight to the point. By adjusting the display method of the recognition results based on the applicant's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The recognition unit can determine the recognition priority based on the applicant's submission date during recognition. For example, if an applicant submits early, the recognition unit will prioritize recognition and provide results quickly. The recognition unit can also increase the recognition priority and process quickly if the applicant is close to the submission deadline. The recognition unit can also dynamically adjust the recognition priority according to the applicant's submission date. This enables rapid recognition by determining the recognition priority based on the submission date. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0090] The recognition unit can improve the accuracy of its recognition by referring to the applicant's relevant literature during the recognition process. For example, the recognition unit can improve the accuracy of its recognition regarding specific skills and knowledge based on the relevant literature submitted by the applicant. The recognition unit can also improve the accuracy of its recognition regarding specific industries or job types by referring to the applicant's relevant literature. The recognition unit can also improve the accuracy of its recognition regarding specific topics by analyzing the applicant's relevant literature. In this way, the accuracy of recognition is improved by referring to relevant literature. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI.

[0091] The evaluation unit can estimate the applicant's emotions and adjust the evaluation criteria based on those estimated emotions. For example, if the applicant is nervous, the evaluation unit will relax the evaluation criteria to take their emotions into consideration. If the applicant is relaxed, the evaluation unit can also apply detailed evaluation criteria. If the applicant is in a hurry, the evaluation unit can also simplify the evaluation criteria to conduct a quick evaluation. This allows for a more appropriate evaluation by adjusting the evaluation criteria based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The evaluation unit can optimize its evaluation algorithm by referring to past hire data during the evaluation process. For example, the evaluation unit can optimize evaluation criteria for specific skills and experience based on past hire data. The evaluation unit can also optimize evaluation criteria for specific job types or industries from past hire data. The evaluation unit can also optimize evaluation criteria for specific evaluation items by referring to past hire data. This optimizes the evaluation algorithm and improves evaluation accuracy by referring to past hire data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0093] The evaluation department can apply different evaluation methods depending on the applicant's occupation and skills during the evaluation process. For example, if the applicant is in a technical position, the evaluation department will apply evaluation methods related to technical skills. If the applicant is in a sales position, the evaluation department may also apply evaluation methods related to communication skills. If the applicant is in a management position, the evaluation department may also apply evaluation methods related to leadership. By applying different evaluation methods according to occupation and skills, the accuracy of the evaluation is improved. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI.

[0094] The evaluation unit can estimate the applicant's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the applicant is nervous, the evaluation unit can provide a simple and highly visible display method. If the applicant is relaxed, the evaluation unit can also provide a display method that includes detailed information. If the applicant is in a hurry, the evaluation unit can also provide a display method that gets straight to the point. By adjusting the display method of the evaluation results based on the applicant's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The evaluation unit can determine the priority of evaluations based on the timing of applicant submissions. For example, if an applicant submits early, the evaluation unit will prioritize their evaluation and provide results quickly. The evaluation unit can also increase the priority of evaluations and process them quickly if an applicant is close to the submission deadline. The evaluation unit can also dynamically adjust the evaluation priority according to the timing of applicant submissions. This enables rapid evaluation by determining the evaluation priority based on the submission timing. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI.

[0096] The evaluation department can improve the accuracy of its evaluations by referring to the applicant's relevant literature during the evaluation process. For example, the evaluation department can improve the accuracy of its evaluations regarding specific skills and knowledge based on the relevant literature submitted by the applicant. The evaluation department can also improve the accuracy of its evaluations regarding specific industries or job types by referring to the applicant's relevant literature. The evaluation department can also improve the accuracy of its evaluations regarding specific topics by analyzing the applicant's relevant literature. In this way, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI.

[0097] The proposal department can estimate the applicant's emotions and adjust the placement proposal method based on the estimated emotions. For example, if the applicant is nervous, the proposal department can provide a placement proposal method that helps them relax. If the applicant is relaxed, the proposal department can also provide a placement proposal method that includes detailed information. If the applicant is in a hurry, the proposal department can also provide a concise and quick placement proposal method. This allows for more appropriate proposals by adjusting the placement proposal method based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The proposal department can select the most suitable placement by referring to the applicant's past work history when making a proposal. For example, the proposal department can propose placements that utilize the applicant's relevant skills and experience based on their past work history. The proposal department can also select placements suitable for specific industries or job types based on the applicant's past work history. The proposal department can also propose placements that align with the applicant's career path by referring to their past work history. In this way, the most suitable placement can be selected by referring to past work history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0099] The proposal department can customize the proposed placements based on the applicant's current living situation. For example, the proposal department can suggest placements that are convenient for commuting, taking into account the applicant's commute time. The proposal department can also suggest placements that allow for flexible working hours, taking into account the applicant's family situation. The proposal department can also suggest placements that are less stressful, taking into account the applicant's health condition. By customizing the proposed placements based on the applicant's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0100] The proposal department can estimate the applicant's emotions and prioritize placements based on those emotions. For example, if an applicant is nervous, the proposal department will prioritize suggesting placements that allow them to relax. If an applicant is relaxed, the proposal department may also prioritize suggesting challenging placements. If an applicant is in a hurry, the proposal department may also prioritize suggesting placements that allow for quick placement. This allows for more appropriate placements by prioritizing placements based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The proposal department can select the most suitable placement by considering the applicant's geographical location when making a proposal. For example, the proposal department may prioritize suggesting placements close to the applicant's place of residence. The proposal department may also suggest placements that reduce the applicant's commute time. Based on the applicant's geographical location, the proposal department may also suggest placements that allow the applicant to utilize their region-specific skills and experience. In this way, the most suitable placement can be selected by considering geographical location. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0102] The proposal department can analyze an applicant's social media activity when making a proposal and suggest suitable placements. For example, the proposal department can suggest placements related to industries and job types of interest based on the applicant's social media activity. The proposal department can also suggest placements that utilize relevant skills and experience based on the applicant's statements and posts on social media. The proposal department can also analyze an applicant's social media activity and automatically select appropriate placements. This allows for more appropriate placement suggestions by analyzing social media activity. Some or all of the above processes in the proposal department may be performed using AI, for example, or not.

[0103] The processing unit can estimate the applicant's emotions and adjust the processing method based on the estimated emotions. For example, if the applicant is nervous, the processing unit can provide a relaxing processing method. If the applicant is relaxed, the processing unit can also provide a processing method that includes detailed information. If the applicant is in a hurry, the processing unit can also provide a concise and quick processing method. This allows for more appropriate processing by adjusting the processing method based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The processing unit can optimize processing algorithms by referring to past processing data during business processing. For example, the processing unit can optimize algorithms for specific business processes based on past processing data. The processing unit can also optimize processing algorithms for specific tasks from past processing data. The processing unit can also optimize algorithms for specific processing items by referring to past processing data. This optimizes processing algorithms and improves processing accuracy by referring to past processing data. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0105] The processing unit can apply different processing methods to applicants depending on their occupation and skills during the administrative processing. For example, if the applicant is in a technical position, the processing unit will apply administrative processing methods related to technical skills. If the applicant is in a sales position, the processing unit may also apply administrative processing methods related to communication skills. If the applicant is in a management position, the processing unit may also apply administrative processing methods related to leadership. By applying different processing methods according to occupation and skills, the processing accuracy is improved. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without using AI.

[0106] The processing unit can estimate the applicant's emotions and determine the priority of administrative tasks based on the estimated emotions. For example, if the applicant is nervous, the processing unit will prioritize tasks that help them relax. If the applicant is relaxed, the processing unit may also prioritize detailed tasks. If the applicant is in a hurry, the processing unit may also prioritize quick tasks. This allows for more appropriate administrative processing by prioritizing tasks based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The processing unit can determine the priority of processing based on the applicant's submission timing during administrative processing. For example, if an applicant submits early, the processing unit will prioritize processing and provide results quickly. The processing unit can also increase the priority of processing and expedite processing if the applicant is close to the submission deadline. The processing unit can also dynamically adjust the priority of processing according to the applicant's submission timing. This enables rapid administrative processing by determining the priority of processing based on the submission timing. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without using AI.

[0108] The processing unit can improve the accuracy of its processing by referring to the applicant's relevant literature during the administrative processing. For example, the processing unit can improve the accuracy of a specific administrative process based on the relevant literature submitted by the applicant. The processing unit can also improve the accuracy of administrative processing related to a specific task by referring to the applicant's relevant literature. The processing unit can also analyze the applicant's relevant literature and improve the accuracy of a specific processing item. As a result, the accuracy of processing is improved by referring to relevant literature. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception department can analyze an applicant's past application history and select the most appropriate application method. For example, the reception department can prioritize relevant questions based on the job titles and companies the applicant has applied to in the past. The reception department can also focus questions on specific skills and experience based on the applicant's past application history. The reception department can also adjust the content and format of questions based on feedback from companies the applicant has applied to in the past. In this way, the reception department can select the most appropriate application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, for example, or not.

[0111] The recognition unit can estimate the applicant's emotions and adjust the accuracy of recognition based on the estimated emotions. For example, if the applicant is nervous, the recognition unit will focus on analyzing subtle changes in facial expressions to improve the accuracy of emotion recognition. If the applicant is relaxed, the recognition unit can also focus on recognizing emotions by paying attention to tone of voice and word choice. If the applicant is in a hurry, the recognition unit can analyze both voice and facial expressions simultaneously to accurately recognize emotions in a short amount of time. This improves recognition accuracy by adjusting the accuracy of recognition based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The evaluation unit can optimize its evaluation algorithm by referring to past hire data during the evaluation process. For example, the evaluation unit can optimize evaluation criteria for specific skills and experience based on past hire data. The evaluation unit can also optimize evaluation criteria for specific job types or industries from past hire data. The evaluation unit can also optimize evaluation criteria for specific evaluation items by referring to past hire data. This optimizes the evaluation algorithm and improves evaluation accuracy by referring to past hire data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0113] The proposal department can select the most suitable placement by referring to the applicant's past work history when making a proposal. For example, the proposal department can propose placements that utilize the applicant's relevant skills and experience based on their past work history. The proposal department can also select placements suitable for specific industries or job types based on the applicant's past work history. The proposal department can also propose placements that align with the applicant's career path by referring to their past work history. In this way, the most suitable placement can be selected by referring to past work history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0114] The processing unit can estimate the applicant's emotions and adjust the processing method based on the estimated emotions. For example, if the applicant is nervous, the processing unit can provide a relaxing processing method. If the applicant is relaxed, the processing unit can also provide a processing method that includes detailed information. If the applicant is in a hurry, the processing unit can also provide a concise and quick processing method. This allows for more appropriate processing by adjusting the processing method based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The reception desk can filter applicants' current occupations and areas of interest when receiving information. For example, the reception desk can prioritize questions related to the applicant's current occupation. The reception desk can also ask questions on relevant topics based on the applicant's areas of interest. The reception desk can also automatically select appropriate interviewers based on the applicant's occupations and areas of interest. This allows the reception desk to receive highly relevant information by filtering based on current occupations and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0116] The recognition unit can apply different recognition methods depending on the applicant's occupation and skills during recognition. For example, if the applicant is in a technical position, the recognition unit may focus on analyzing responses to technical questions. If the applicant is in a sales position, the recognition unit may focus on analyzing responses related to communication skills. If the applicant is in a management position, the recognition unit may focus on analyzing responses related to leadership. This improves recognition accuracy by applying different recognition methods according to occupation and skills. Some or all of the processing described above in the recognition unit may be performed using AI, for example, or without AI.

[0117] The evaluation unit can estimate the applicant's emotions and adjust the evaluation criteria based on those estimated emotions. For example, if the applicant is nervous, the evaluation unit will relax the evaluation criteria to take their emotions into consideration. If the applicant is relaxed, the evaluation unit can also apply detailed evaluation criteria. If the applicant is in a hurry, the evaluation unit can also simplify the evaluation criteria to conduct a quick evaluation. This allows for a more appropriate evaluation by adjusting the evaluation criteria based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The proposal department can select the most suitable placement by considering the applicant's geographical location when making a proposal. For example, the proposal department may prioritize suggesting placements close to the applicant's place of residence. The proposal department may also suggest placements that reduce the applicant's commute time. Based on the applicant's geographical location, the proposal department may also suggest placements that allow the applicant to utilize their region-specific skills and experience. In this way, the most suitable placement can be selected by considering geographical location. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0119] The processing unit can improve the accuracy of its processing by referring to the applicant's relevant literature during the administrative processing. For example, the processing unit can improve the accuracy of a specific administrative process based on the relevant literature submitted by the applicant. The processing unit can also improve the accuracy of administrative processing related to a specific task by referring to the applicant's relevant literature. The processing unit can also analyze the applicant's relevant literature and improve the accuracy of a specific processing item. As a result, the accuracy of processing is improved by referring to relevant literature. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk receives applicant information. For example, it stores the information entered by the applicant in a database. It can also receive information about the AI ​​interviewer selected by the applicant. Step 2: The recognition unit performs emotion and voice recognition based on the information received by the reception unit. For example, it analyzes the applicant's facial expressions to read their emotions. It can also analyze the tone of voice and word choice to read their emotions. The recognition unit quantifies and analyzes the emotions. Step 3: The evaluation unit performs an evaluation based on the results obtained by the recognition unit. For example, it compares the results with past hire data and analyzes the degree of suitability for the job and assigns a score. The evaluation results can also be saved to a database. Step 4: The proposal department proposes placements based on the evaluation results obtained by the evaluation department. For example, it analyzes trends such as applicants' commuting time and the residential areas of current employees to propose the most suitable placement. The placement proposal results can also be saved in a database. Step 5: The processing unit performs administrative tasks based on the assignments proposed by the proposal department. For example, it handles tasks such as sending acceptance / rejection results and onboarding procedures. The results of the administrative tasks can also be saved to a database.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, recognition unit, evaluation unit, proposal unit, and processing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives applicant information. The recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reads emotions by analyzing the applicant's facial expressions and voice tone. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the degree of suitability for the job and scores by comparing it with past hire data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a department to be assigned to. The processing unit is implemented by, for example, the control unit 46A of the smart device 14 and performs administrative processing related to sending acceptance / rejection results and onboarding procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, recognition unit, evaluation unit, proposal unit, and processing unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives applicant information. The recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reads emotions by analyzing the applicant's facial expressions and voice tone. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the degree of suitability for the job and scores by comparing it with past hire data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a department to be assigned to. The processing unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs administrative processing related to sending acceptance / rejection results and onboarding procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, recognition unit, evaluation unit, proposal unit, and processing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives applicant information. The recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reads emotions by analyzing the applicant's facial expressions and voice tone. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the degree of suitability for the job and scores by comparing it with past hire data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a department to be assigned to. The processing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs administrative processing related to sending acceptance / rejection results and onboarding procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, recognition unit, evaluation unit, proposal unit, and processing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information about applicants. The recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reads emotions by analyzing the applicant's facial expressions and tone of voice. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the degree of suitability for the job and scores by comparing it with past hire data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a department to be assigned to. The processing unit is implemented by, for example, the control unit 46A of the robot 414 and performs administrative processing related to sending acceptance / rejection results and onboarding procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) The reception department receives applicant information, A recognition unit that performs emotion and voice recognition based on the information received by the aforementioned reception unit, An evaluation unit that performs an evaluation based on the results obtained by the recognition unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the proposal unit proposes a placement, The system includes a processing unit that performs administrative tasks based on the assignment location proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned recognition unit, We analyze applicants' emotions by reading their facial expressions, tone of voice, and choice of words, and then quantifying and analyzing them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, We analyze the degree of suitability for the job and assign scores by comparing them with past hiring data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze trends such as applicants' commute times and the residential areas of current employees to propose the most suitable placement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned processing unit, This involves sending out acceptance / rejection results and handling administrative tasks related to onboarding procedures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We estimate the applicant's emotions and adjust the information processing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze applicants' past application history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving information, the system filters applicants based on their current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the applicant's emotions and prioritizes the information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, we prioritize receiving information that is highly relevant based on the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system analyzes the applicant's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, The system estimates the applicant's emotions and adjusts the accuracy of the recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, During recognition, the recognition algorithm is optimized by referring to the applicant's past interview data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recognition unit, During recognition, different recognition methods are applied depending on the applicant's occupation and skills. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recognition unit, The system estimates the applicant's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recognition unit, When recognizing an applicant, priority is determined based on the submission date of the applicant. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recognition unit, During the recognition process, the accuracy of the recognition is improved by referring to the applicant's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, The system estimates the applicant's emotions and adjusts the evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to past hire data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, different evaluation methods will be applied depending on the applicant's occupation and skills. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, The system estimates the applicant's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, priority will be determined based on the submission timing of applicants. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, we refer to the applicant's relevant literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, The system estimates the applicant's emotions and adjusts the placement proposal method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, the most suitable placement will be selected by referring to the applicant's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we customize the suggested placement based on the applicant's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, The system estimates the applicant's emotions and determines the priority of placement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, the most suitable placement will be selected considering the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we analyze the applicant's social media activity and suggest suitable placements. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned processing unit, The system estimates the applicant's emotions and adjusts the processing methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned processing unit, During administrative processing, the processing algorithm is optimized by referring to past processing data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned processing unit, When processing applications, different processing methods are applied depending on the applicant's occupation and skills. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned processing unit, The system estimates the applicant's emotions and prioritizes administrative tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned processing unit, During the administrative process, the priority of processing is determined based on when the applicants submitted their applications. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned processing unit, During administrative processing, we improve the accuracy of processing by referring to the applicant's relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception department receives applicant information, A recognition unit that performs emotion and voice recognition based on the information received by the aforementioned reception unit, An evaluation unit that performs an evaluation based on the results obtained by the recognition unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the proposal unit proposes a placement, The system includes a processing unit that performs administrative tasks based on the assignment location proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned recognition unit, We analyze applicants' emotions by reading their facial expressions, tone of voice, and choice of words, and then quantifying and analyzing them. The system according to feature 1.

3. The evaluation unit, We analyze the degree of suitability for the job and assign scores by comparing them with past hiring data. The system according to feature 1.

4. The aforementioned proposal section is, We analyze trends such as applicants' commute times and the residential areas of current employees to propose the most suitable placement. The system according to feature 1.

5. The aforementioned processing unit, This involves sending out acceptance / rejection results and handling administrative tasks related to onboarding procedures. The system according to feature 1.

6. The aforementioned reception unit is We estimate the applicant's emotions and adjust the information processing method based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze applicants' past application history and select the most suitable application method. The system according to feature 1.

8. The aforementioned reception unit is When receiving information, the system filters applicants based on their current occupation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the applicant's emotions and prioritizes the information to be received based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving information, we prioritize receiving information that is highly relevant based on the applicant's geographical location. The system according to feature 1.

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