system

Abstract

We provide the system. [Solution] A data storage means for storing candidate information, An analytical means for analyzing candidate information, A candidate selection method for selecting applicable candidates based on the analysis results, A scheduling method for coordinating interview dates with selected candidates, A system that includes this.

Description

【Technical Field】 【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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the adoption process, it is difficult to quickly select appropriate candidates from a huge number of resumes, and a large amount of time and labor are also required to adjust the interview schedule with the selected candidates. As a result, there is a problem that the burden on human resources increases and the efficiency of the adoption activity decreases. 【Means for Solving the Problems】 【0005】 This invention provides a system equipped with data storage means, analysis means, candidate selection means, and schedule adjustment means, thereby automating resume analysis and candidate selection, and proposing an optimized interview schedule. In particular, by analyzing candidate information using natural language processing technology and extracting keywords, rapid and highly accurate candidate selection is achieved. Furthermore, by automatically adjusting interview schedules using the calendar information of candidates and interviewers, the burden on human resources is reduced, and the efficiency of recruitment activities is improved. 【0006】 "Candidate information" refers to data including personal information and work history of job candidates. 【0007】 "Data storage means" refers to devices or systems that have the function of storing and retaining candidate information for later use. 【0008】 "Analysis means" refers to the processes and techniques used to analyze collected candidate information and extract useful information. 【0009】 "Natural language processing technology" is a technology that uses computers to analyze and understand human language and extract specific information from it. 【0010】 "Keywords" are words or phrases from candidate information that are deemed to be of high importance or relevance. 【0011】 A "candidate selection method" refers to a device or system that has the function of selecting candidates who meet specific criteria based on analyzed information. 【0012】 A "scheduling method" refers to a method or system for determining the optimal date and time based on the availability of multiple stakeholders. 【0013】 "Calendar information" refers to digital data containing the appointments of candidates and interviewers, used for scheduling. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【MODE FOR CARRYING OUT THE INVENTION】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 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. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 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. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 The 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. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention relates to an AI system for streamlining the recruitment process, and a specific embodiment thereof is shown below. 【0036】 This system functions through the cooperation of servers, terminals, and users. 【0037】 First, the server receives resumes from candidates via the internet. This is done using dedicated upload forms or email servers. The received resumes are stored in a database on the server, which functions as a data storage system. 【0038】 Next, the server analyzes the received resume file as an analytical tool. In this process, OCR is used to digitize the paper-based information, and natural language processing technology is used to extract keywords and important information. At the same time, it evaluates whether the candidate's skills and experience meet the pre-defined requirements. 【0039】 Once the analysis is complete, the server uses a candidate selection method to filter for suitable candidates and generate a candidate list. The selected candidates are evaluated and prioritized through a detailed scoring system. This information is displayed on the terminal for the user (recruiter) to review. 【0040】 The server then functions as a scheduling tool, automatically suggesting the optimal interview date and time based on the calendar information of the selected candidates and interviewers. It uses a calendar API to retrieve both parties' schedules and find overlapping time slots. The suggested date and time are notified to both the candidate and the interviewer via email, prompting them to confirm and finalize the schedule. 【0041】 For example, when a user is implementing a process to hire top engineers, the system automatically selects the top 10 candidates from over 100 based on their skills and experience. Interview dates with these candidates are then quickly scheduled, and both parties are immediately notified to minimize any delays in communication. 【0042】 In this way, this system automates and streamlines recruitment operations, optimizing resources and improving recruitment accuracy. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The user initiates the hiring process by uploading the candidate's resume to the system via email or a dedicated application form. 【0046】 Step 2: 【0047】 The server stores the received resume files in a database and begins managing resumes as a data storage method. It detects duplicate files or files in inappropriate formats and notifies the user. 【0048】 Step 3: 【0049】 The server converts saved resumes into text data using OCR technology. This makes it possible to extract information from handwritten or scanned documents. 【0050】 Step 4: 【0051】 The server uses natural language processing technology to analyze keywords and important information from resumes. Specifically, it identifies and categorizes the candidate's work experience, skills, educational background, etc. 【0052】 Step 5: 【0053】 Based on the analysis, the server scores candidates who meet the set criteria. Candidates with higher scores are then prioritized and filtered using the candidate selection method. 【0054】 Step 6: 【0055】 The terminal displays a list of selected candidates to the user. The user can view detailed information about each candidate on the screen and decide who to interview. 【0056】 Step 7: 【0057】 The server retrieves the interviewer's and candidate's calendar data from an API and automatically selects the optimal interview date and time using a scheduling tool. 【0058】 Step 8: 【0059】 The server notifies both the candidate and the interviewer via email of the scheduled interview date and time. The notification includes a confirmation button and an access link for the candidate to finalize the schedule. 【0060】 Step 9: 【0061】 Once the interview is complete, the user (interviewer) is provided with a form to enter the results. This information is sent to the server, recorded, and used to help with the final hiring decision. 【0062】 (Example 1) 【0063】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0064】 In recent years, processing large amounts of candidate information and efficiently selecting suitable personnel has become a major challenge in corporate recruitment activities. Traditional manual processes are time-consuming and often inefficient in terms of resource utilization. Furthermore, scheduling interviews with candidates also requires considerable time and effort for coordinating schedules between recruiters and candidates. A new system is needed to solve these problems. 【0065】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0066】 In this invention, the server includes an information holding means for collecting and storing candidate information, a conversion means that uses optical character recognition technology to digitize the candidate information, and an analysis means that uses natural language processing technology to extract keywords and important information from the digital information. This enables efficient processing of candidate information and rapid selection of suitable personnel. 【0067】 "Information retention means" refers to a function for collecting candidate data, storing it in a database, and managing it. 【0068】 "Conversion means" refers to a function that uses optical character recognition technology to convert paper-based information obtained from candidates into digital data. 【0069】 "Analysis means" refers to a function that uses natural language processing technology to extract keywords and important information from digitized candidate information. 【0070】 "Selection method" refers to a function that selects candidates who meet pre-set criteria based on analyzed candidate information. 【0071】 "Evaluation method" refers to a function that uses a scoring algorithm to prioritize selected candidates. 【0072】 "Time scheduling" refers to a function that obtains calendar information from both candidates and recruiters and automatically suggests the most suitable interview date and time based on that information. 【0073】 In order to implement this invention, the server, terminal, and user must cooperate and fulfill specific roles. Here, we will specifically describe the role of the server. 【0074】 The server receives candidate resumes via the internet and stores the information in a database as a means of data retention. When candidates submit their resumes via web forms or email, the server receives them via a secure connection, organizes them, and stores them. A commonly used open-source relational database management system can be used for the database management system. 【0075】 Next, the server uses OCR (Optical Character Recognition) technology to convert paper-based and image-based resumes into text data. Open-source OCR libraries are a possible software choice for this process. The converted digital information is then stored back into the database. 【0076】 Subsequently, the server uses natural language processing techniques as an analysis tool to extract specific keywords and important information from the resume. For this purpose, it employs a machine learning-based natural language processing library. For example, open-source natural language processing libraries can be useful tools. 【0077】 Furthermore, the server evaluates candidates based on the analyzed data, acting as both a selection and evaluation tool. It applies a scoring algorithm according to established criteria and assigns priorities to candidates based on the scoring results. 【0078】 Finally, as a means of scheduling, the server retrieves the calendar information of both the candidate and the interviewer via a calendar API and suggests the optimal date and time for the interview. This suggestion is notified to both parties via email, enabling quick scheduling. 【0079】 For example, when a user is hiring a top-tier engineer, this system allows them to select the 10 most suitable candidates from over 100 and quickly schedule interviews. 【0080】 An example of a prompt to input into the generating AI model is, "Please tell me the steps to streamline the engineer recruitment process." In this way, the entire recruitment process becomes automated and operates efficiently. 【0081】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0082】 Step 1: 【0083】 The server receives resumes from candidates. This input is a digital file received via web form or email. The server stores this file in a database, which functions as a means of information retention. The database stores and indexes the file along with the resume's metadata. 【0084】 Step 2: 【0085】 The server converts received resume files into digital text using OCR technology. This conversion method takes paper-based or image-based resumes as input and generates readable text data as output. The OCR software recognizes the characters within the text and stores them in a database in text format. 【0086】 Step 3: 【0087】 The server uses natural language processing technology to analyze information contained in digitized resumes. It takes digital text as input and uses analysis tools to extract keywords and important information. The extracted data is output in the form of skills, experience, and career history, and is supplied to the next step for evaluation and selection. 【0088】 Step 4: 【0089】 The server uses the analysis results to select candidates according to established criteria. Here, it determines whether a candidate's skill set and experience meet pre-defined requirements. The analysis results are used as input, and a list of candidates who meet the criteria is generated as output. The selection process is performed automatically using an algorithm. 【0090】 Step 5: 【0091】 The server evaluates the selected candidates and applies a scoring algorithm. Using the selected candidate information as input, it outputs a list of candidates with scores according to priority. The evaluation method calculates a specific score for each candidate. 【0092】 Step 6: 【0093】 The server retrieves candidate and interviewer calendar information via a calendar API for scheduling. It functions as a time scheduling tool, receiving calendar information as input and outputting optimal interview dates and times based on that information. Suggested dates and times are notified via email for quick confirmation and adjustment. 【0094】 (Application Example 1) 【0095】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0096】 Traditional recruitment processes involved the manual management and analysis of large amounts of candidate information, which was time-consuming and labor-intensive. Furthermore, scheduling interviews was cumbersome, making smooth communication with candidates difficult. Therefore, there is a need for increased efficiency and accuracy in recruitment operations. 【0097】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0098】 In this invention, the server includes data storage means for storing candidate information, analysis means for analyzing candidate information, candidate selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, scoring means for evaluating candidates based on the analysis results, display means for presenting and confirming candidate information through a user interface, and information distribution means for notifying candidates and interviewers of the schedule via a communication network. This enables the automation and efficiency of the recruitment process. 【0099】 "Candidate information" refers to personal data and skills information included in the resume and work history of a person being considered during the recruitment process. 【0100】 A "data storage means" is a device for storing entered candidate information and making it accessible as needed. 【0101】 "Analysis means" refers to a device or system that digitizes candidate information and analyzes the data using natural language processing technology or the like. 【0102】 A "candidate selection method" is a device that selects appropriate candidates based on information and criteria obtained through analysis. 【0103】 A "scheduling adjustment tool" is a device that coordinates the availability of candidates and interviewers to determine the optimal interview date and time. 【0104】 A "scoring means" is a device or method that quantifies and makes comparable the evaluation results of candidates. 【0105】 A "display means" is a device that visually displays analysis results and candidate information, enabling the user to confirm and make a judgment. 【0106】 An "information distribution device" is a device that notifies relevant parties of the decided interview date and time, candidate information, etc., via a communication network. 【0107】 The system for implementing this invention is configured in which a server, terminals, and users work together. The server first functions as a platform for receiving resumes submitted by candidates via the internet. The resumes are stored on the server through data storage means. The stored data is analyzed using OCR technology "Tesseract OCR" and natural language processing software "Google® NLP API". This extracts important information and skills from the candidate information. 【0108】 After analysis, the server uses a candidate selection tool to filter out suitable candidates based on the analysis results. This selection process employs a scoring tool to evaluate and score candidates, and the results are then prioritized. Next, to schedule interviews with the selected candidates, the server uses the "Google Calendar API" as a scheduling tool to obtain calendar information from both candidates and interviewers and find common availability. 【0109】 Users can use their devices to view candidate information through the user interface and easily understand the displayed priority and evaluation scores. The server also serves as an information distribution method, notifying both candidates and interviewers of the scheduled interview date and time via email or other means. 【0110】 A concrete example is a company hiring engineers where the system automatically selects the top 10 candidates from 100 applicants based on scoring, and then automatically schedules and notifies them of their interview dates. This system makes it possible to streamline and improve the accuracy of the hiring process. 【0111】 An example of a prompt to input into a generating AI model is: "Please describe the detailed steps for a system that selects the best candidate from engineering resumes and automatically determines possible interview dates and times." 【0112】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0113】 Step 1: 【0114】 The server receives resumes from candidates via a dedicated upload form or email system over the internet. This input data is stored on the server in electronic file format and then stored in a database using data storage means. This completes the initial input of resume information. 【0115】 Step 2: 【0116】 The server processes the received resume data using OCR technology, converting paper-based information into digital text. Specifically, it uses "Tesseract OCR" to convert image data into text data. This converted data becomes input and is analyzed using natural language processing technology, "Google NLP API." As a result of the analysis, the candidate's skills, experience, and keywords are extracted and output as structured data of candidate information. 【0117】 Step 3: 【0118】 The server selects suitable candidates by comparing structured candidate information against pre-defined requirements. This selection process is performed using a scoring system. It receives analyzed candidate information as input and assigns an evaluation score to each candidate based on that information. The scoring results are output, and a prioritized candidate list is generated. 【0119】 Step 4: 【0120】 Users use their devices to view candidate lists and evaluation scores provided by the server through a user interface. Here, users can review detailed candidate information and make decisions based on the scored results. This process expedites candidate selection. 【0121】 Step 5: 【0122】 The server uses the Google Calendar API as a scheduling tool to coordinate interview dates with selected candidates. It reads the calendar data of both the candidate and the interviewer as input and detects overlapping free time slots. This generates optimal interview date and time candidates, and the interview schedule is automatically adjusted. 【0123】 Step 6: 【0124】 The server notifies candidates and interviewers of the scheduled interview date and time via information distribution. It also sends notifications via email or messaging services to prompt confirmation and finalization of the interview date and time. This allows for immediate scheduling sharing among all parties involved. 【0125】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0126】 This invention incorporates an emotion engine into an AI system for streamlining the recruitment process, and specific embodiments thereof are shown below. 【0127】 This system has a configuration that combines a server, terminals, users, and an emotion engine. 【0128】 First, the server receives resumes from candidates via the internet and stores them in a database. This allows candidate information, such as their work history and skill sets, to be managed by data storage. 【0129】 Next, the server uses OCR technology to convert the resume into text data, which is then analyzed using natural language processing technology. The analysis tool extracts keywords and generates a candidate profile. 【0130】 During this process, the emotion engine obtains real-time emotional data by analyzing the user's facial expressions and tone of voice. This allows the system to recognize the user's current emotional state. For example, if the user is relaxed, it will perform normal processing, but if they are stressed, it will suggest filtering to focus only on important candidates. 【0131】 Furthermore, the server uses a candidate selection mechanism to select the most suitable candidate based on the analysis results and sentiment data. The selection is performed using a scoring system that reflects the user's emotional state, thereby appropriately customizing the information the user receives to match their emotions. 【0132】 After selection, the device displays a list of candidates to the user, allowing them to quickly review emotionally preferred options as needed. 【0133】 Subsequently, the scheduling mechanism takes into account the analysis results from the emotion engine to adjust the interview date. For example, if the user is busy, it prioritizes suggesting interview times when they are expected to be more relaxed. 【0134】 For example, when users are running the hiring process at a busy end of the month, the emotion engine can detect their stress levels and handle candidate selection and interview scheduling with minimal burden on the user. 【0135】 In this way, by incorporating an emotion engine, the recruitment process becomes more personalized, efficient, and user-friendly. 【0136】 The following describes the processing flow. 【0137】 Step 1: 【0138】 Users submit candidate resumes to the system via an online application form or email. The application data is sent to the server and securely stored in the database. 【0139】 Step 2: 【0140】 The server converts the received resumes into text data using OCR technology. This text format allows for further data analysis. 【0141】 Step 3: 【0142】 The server uses natural language processing (NLP) techniques to analyze the transcribed resume and extract keywords such as skill sets, work experience, and educational background. Based on this, it profiles the candidate's characteristics. 【0143】 Step 4: 【0144】 The emotion engine monitors the user's facial expressions and tone of voice while they are reviewing their resume, and analyzes their emotional state in real time. 【0145】 Step 5: 【0146】 The server receives data from the emotion engine and adjusts the candidate selection criteria based on the user's emotional state. Specifically, if the user is feeling tired, optimizations are made such as displaying only the top-scoring candidates. 【0147】 Step 6: 【0148】 The user is provided with a refined list of candidates on their device. Through an intuitive interface, the user can review candidate details and select candidates for interviews. 【0149】 Step 7: 【0150】 The server checks the calendar information of both the interviewer and the candidate for the selected candidates and schedules the interview. Taking into account the emotion engine data, it prioritizes suggesting times that are easy to relax during. 【0151】 Step 8: 【0152】 The server will notify both the candidate and the interviewer of the scheduled interview date and time. This notification will include a confirmation button and a link to the interview. 【0153】 Step 9: 【0154】 After completing an interview, the user (interviewer) uses the feedback function provided by the emotion engine to input an evaluation of the candidate. The evaluation is sent to the server and used to inform future processes. 【0155】 (Example 2) 【0156】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0157】 In the recruitment process, it is necessary to efficiently manage candidate information and select the most suitable candidates while considering the emotional state of the users. However, conventional systems have difficulty reflecting user emotional data in their selection process, and scheduling interviews has not always taken into account the user's stress level. As a result, this can sometimes be burdensome for the user. 【0158】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0159】 In this invention, the server includes an information storage means for storing candidate information, an information analysis means for analyzing candidate information, and an emotion acquisition means for acquiring emotion data by analyzing the user's facial expressions and tone of voice. This makes it possible to select candidates and adjust interview schedules while taking into account the user's emotional state. 【0160】 "Information storage means" refers to a means of storing and managing information, including a candidate's work history and skill set, in a database or similar system. 【0161】 "Information analysis means" refers to methods for processing text data using OCR technology and natural language processing technology to extract necessary keywords and profiles. 【0162】 "Candidate selection methods" refer to the means used to select suitable candidates based on the results of the analysis. 【0163】 An "emotion acquisition method" is a means of acquiring emotional data in real time by analyzing the user's facial expressions and tone of voice. 【0164】 "Scheduling methods" refer to techniques for optimizing interview schedules between users and candidates, while taking into account acquired emotional data. 【0165】 This invention provides a recruitment support system that incorporates user emotional data to streamline the recruitment process. The system consists of a server, terminals, and an emotional engine. 【0166】 First, the server receives resumes submitted by candidates via the internet and stores the information in a database. The server uses OCR technology to convert the resumes into text data and performs analysis using natural language processing (NLP) technology. This extracts keywords from the candidate's work history and skill set, and builds a profile. 【0167】 Next, the emotion engine uses the user's camera and microphone to analyze their facial expressions and voice tone in real time, acquiring emotional data. This data is used to identify the user's current emotional state. If the user is relaxed, normal processing is performed; if they are stressed, filtering is suggested to narrow down the candidates to those who are most important. 【0168】 The server combines analytical income data and sentiment data, and uses a selection algorithm to choose the most suitable candidate. This selection is scored based on sentiment data, and the information received by the user is individually customized. 【0169】 The terminal then displays a list of the most suitable candidates to the user. As a specific example, consider a scenario where the user is using the system during a busy period at the end of the month. The emotion engine senses the user's stress level and takes steps to minimize the user's burden when suggesting interview dates and displaying candidates. 【0170】 Examples of prompt statements include the following: 【0171】 "Analyze resumes and generate candidate profiles. Also, analyze the user's current emotional state to suggest the most suitable candidate list." 【0172】 In this way, by incorporating an emotion engine, the system personalizes the recruitment process, resulting in an efficient and user-friendly procedure. 【0173】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0174】 Step 1: 【0175】 The server receives resume files submitted by candidates via the internet. It takes resume data in PDF or image format from candidates as input. The server stores these resumes in a database, preparing them for managing work history and skill sets. The output is the candidate information stored in the database. Specifically, it saves received files to a designated folder and stores their metadata in the database. 【0176】 Step 2: 【0177】 The server uses OCR technology to convert the received resume into text data. The input is the candidate's resume file saved in step 1. The OCR process extracts text information from the image. The output is the text data of the resume. Specifically, the OCR engine analyzes the image file and generates text information. 【0178】 Step 3: 【0179】 The server analyzes text data using natural language processing (NLP) techniques. The input is the text data obtained in step 2. Noun phrases and verb phrases are extracted from this data to generate candidate profiles. The output is the analyzed candidate profile data and related keywords. Specifically, the NLP algorithm extracts key keywords and phrases to construct the profile. 【0180】 Step 4: 【0181】 The emotion engine analyzes the user's facial expressions and voice tone in real time using the user's camera and microphone. The input is audio and video data from the user. Emotional data is acquired here. The output is data representing the user's current emotional state. Specifically, emotion scoring is performed using facial recognition and voice analysis technologies. 【0182】 Step 5: 【0183】 The server integrates the analysis results and sentiment data and selects the most suitable candidate using a candidate selection algorithm. The inputs are the profile data from step 3 and the sentiment data from step 4. It generates selection results that reflect the user's sentiment. The output is a scored list of candidates. Specifically, the selection algorithm weights the sentiment data and evaluates suitability. 【0184】 Step 6: 【0185】 The terminal displays a list of the most suitable candidates for the user. The input is the candidate selection result from step 5. The user can view the list, displayed in order of score, and check the details of each candidate. The output is the displayed candidate list. Specifically, the GUI is updated, and the profiles of the selected candidates are displayed side by side. 【0186】 Step 7: 【0187】 The scheduling mechanism adjusts interview dates while considering the user's stress level based on emotional data. Inputs include a candidate list and emotional data. It provides scheduling suggestions aimed at reducing stress. The output is a suggestion of the optimal interview date. Specifically, it compares the user's calendar with available time slots and notifies them of the best date and time. 【0188】 (Application Example 2) 【0189】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0190】 In the recruitment process, it is necessary not only to analyze candidate information and select suitable candidates, but also to provide a personalized experience that takes into account the user's emotional state. Similarly, in household task management, optimization based on the user's emotions is required. 【0191】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0192】 In this invention, the server includes information recording means for storing candidate information, information analysis means for analyzing candidate information, selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, sentiment analysis means for analyzing the user's emotional state, and household adjustment means for optimizing household tasks based on the user's emotional state. This enables the user to comfortably and more personally manage the recruitment process and household tasks. 【0193】 "Information recording means" refers to devices or systems for centrally storing candidate and user information and maintaining it in a state where it can be quickly accessed as needed. 【0194】 "Information analysis methods" refer to the techniques and processes used to analyze collected information and extract useful data from it. 【0195】 A "selection method" is a system that has criteria or algorithms for selecting the most suitable candidate based on the analysis results. 【0196】 "Scheduling methods" refer to methods or devices for proposing and determining the most suitable interview date and time, taking into account the schedules of all parties involved. 【0197】 "Emotional analysis techniques" are technologies used to analyze a user's emotional state based on their facial expressions, tone of voice, and other factors. 【0198】 A "domestic adjustment system" is a system for optimizing household tasks and environmental settings according to the user's emotional state. 【0199】 This invention aims to optimize a personalized recruitment process and household tasks while taking into account the user's emotional state. The main components of the system are a server, a terminal, and the user. 【0200】 The server receives candidate information and stores it in a database through an information recording device. Candidate resumes are converted into text data using OCR technology and analyzed using natural language processing technology. This allows the analysis device to extract keywords and generate a candidate profile. 【0201】 Furthermore, the server uses sentiment analysis tools to analyze the user's emotions. This is achieved by analyzing the user's facial expressions and tone of voice. Based on the results of the sentiment analysis, appropriate responses and information are provided. 【0202】 The device displays information about candidates selected by the user and adjusts household tasks based on their emotions. For example, if the user is feeling stressed, relaxing environment settings are suggested through household adjustment tools. 【0203】 Users can review this information at their own pace and proceed through the hiring process. Furthermore, task management tailored to their emotional state enhances user convenience. 【0204】 For example, if a user wants to relax after a long workday, the system can analyze their emotions and recommend relaxing music. A concrete example of a prompt would be: "Come up with ideas for a robot assistant that analyzes the user's facial expressions and tone of voice to recognize their emotional state and optimize household tasks." 【0205】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0206】 Step 1: 【0207】 The server receives candidate resumes via the internet. Since the resumes are sent in formats such as PDF, they are stored in a database through an information recording system. The input is the candidate's resume data, and the output is the resume information stored in the database. 【0208】 Step 2: 【0209】 The server uses OCR technology to convert stored resume data into text data. At this stage, it analyzes information in image formats such as PDFs and converts it into text information. The input is resume data in image format, and the output is the converted text data. 【0210】 Step 3: 【0211】 The server analyzes text data using natural language processing techniques and extracts important keywords. This process utilizes a generative AI model to identify meaningful information within the text. The input is text data, and the output is the extracted keywords. 【0212】 Step 4: 【0213】 The emotion analysis system is activated, analyzing the user's facial expressions and voice tone in real time. Based on the data collected using the camera and microphone on the device, the user's emotional state is estimated. The input is real-time collected audio and video data, and the output is the analyzed emotional state. 【0214】 Step 5: 【0215】 The server selects the most suitable candidates based on the analyzed keywords and the user's emotional state. The suggested candidates change according to the emotional state. The input is keyword and emotional state data, and the output is a list of selected candidates. 【0216】 Step 6: 【0217】 The device presents the user with a list of selected candidates. The user can then decide on their next action based on this list. The input is the candidate list, and the output is the user's selection or feedback. 【0218】 Step 7: 【0219】 The device uses in-home adjustment tools to suggest task optimizations tailored to the user's emotions. This process utilizes in-home IoT devices to create an environment that promotes user relaxation, such as adjusting lighting. Inputs are the user's choices and emotional state, while outputs are optimized household tasks and settings. 【0220】 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. 【0221】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0222】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0227】 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. 【0228】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0229】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0230】 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. 【0231】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0232】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0233】 The 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. 【0234】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0235】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0236】 This invention relates to an AI system for streamlining the recruitment process, and a specific embodiment thereof is shown below. 【0237】 This system functions through the cooperation of servers, terminals, and users. 【0238】 First, the server receives resumes from candidates via the internet. This is done using dedicated upload forms or email servers. The received resumes are stored in a database on the server, which functions as a data storage system. 【0239】 Next, the server analyzes the received resume file as an analytical tool. In this process, OCR is used to digitize the paper-based information, and natural language processing technology is used to extract keywords and important information. At the same time, it evaluates whether the candidate's skills and experience meet the pre-defined requirements. 【0240】 Once the analysis is complete, the server uses a candidate selection method to filter for suitable candidates and generate a candidate list. The selected candidates are evaluated and prioritized through a detailed scoring system. This information is displayed on the terminal for the user (recruiter) to review. 【0241】 The server then functions as a scheduling tool, automatically suggesting the optimal interview date and time based on the calendar information of the selected candidates and interviewers. It uses a calendar API to retrieve both parties' schedules and find overlapping time slots. The suggested date and time are notified to both the candidate and the interviewer via email, prompting them to confirm and finalize the schedule. 【0242】 For example, when a user is implementing a process to hire top engineers, the system automatically selects the top 10 candidates from over 100 based on their skills and experience. Interview dates with these candidates are then quickly scheduled, and both parties are immediately notified to minimize any delays in communication. 【0243】 In this way, this system automates and streamlines recruitment operations, optimizing resources and improving recruitment accuracy. 【0244】 The following describes the processing flow. 【0245】 Step 1: 【0246】 The user initiates the hiring process by uploading the candidate's resume to the system via email or a dedicated application form. 【0247】 Step 2: 【0248】 The server stores the received resume files in a database and begins managing resumes as a data storage method. It detects duplicate files or files in inappropriate formats and notifies the user. 【0249】 Step 3: 【0250】 The server converts saved resumes into text data using OCR technology. This makes it possible to extract information from handwritten or scanned documents. 【0251】 Step 4: 【0252】 The server uses natural language processing technology to analyze keywords and important information from resumes. Specifically, it identifies and categorizes the candidate's work experience, skills, educational background, etc. 【0253】 Step 5: 【0254】 Based on the analysis, the server scores candidates who meet the set criteria. Candidates with higher scores are then prioritized and filtered using the candidate selection method. 【0255】 Step 6: 【0256】 The terminal displays a list of selected candidates to the user. The user can view detailed information about each candidate on the screen and decide who to interview. 【0257】 Step 7: 【0258】 The server retrieves the interviewer's and candidate's calendar data from an API and automatically selects the optimal interview date and time using a scheduling tool. 【0259】 Step 8: 【0260】 The server notifies both the candidate and the interviewer via email of the scheduled interview date and time. The notification includes a confirmation button and an access link for the candidate to finalize the schedule. 【0261】 Step 9: 【0262】 Once the interview is complete, the user (interviewer) is provided with a form to enter the results. This information is sent to the server, recorded, and used to help with the final hiring decision. 【0263】 (Example 1) 【0264】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0265】 In recent years, processing large amounts of candidate information and efficiently selecting suitable personnel has become a major challenge in corporate recruitment activities. Traditional manual processes are time-consuming and often inefficient in terms of resource utilization. Furthermore, scheduling interviews with candidates also requires considerable time and effort for coordinating schedules between recruiters and candidates. A new system is needed to solve these problems. 【0266】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0267】 In this invention, the server includes an information holding means for collecting and storing candidate information, a conversion means that uses optical character recognition technology to digitize the candidate information, and an analysis means that uses natural language processing technology to extract keywords and important information from the digital information. This enables efficient processing of candidate information and rapid selection of suitable personnel. 【0268】 "Information retention means" refers to a function for collecting candidate data, storing it in a database, and managing it. 【0269】 "Conversion means" refers to a function that uses optical character recognition technology to convert paper-based information obtained from candidates into digital data. 【0270】 "Analysis means" refers to a function that uses natural language processing technology to extract keywords and important information from digitized candidate information. 【0271】 "Selection method" refers to a function that selects candidates who meet pre-set criteria based on analyzed candidate information. 【0272】 "Evaluation method" refers to a function that uses a scoring algorithm to prioritize selected candidates. 【0273】 "Time scheduling" refers to a function that obtains calendar information from both candidates and recruiters and automatically suggests the most suitable interview date and time based on that information. 【0274】 In order to implement this invention, the server, terminal, and user must cooperate and fulfill specific roles. Here, we will specifically describe the role of the server. 【0275】 The server receives candidate resumes via the internet and stores the information in a database as a means of data retention. When candidates submit their resumes via web forms or email, the server receives them via a secure connection, organizes them, and stores them. A commonly used open-source relational database management system can be used for the database management system. 【0276】 Next, the server uses OCR (Optical Character Recognition) technology to convert paper-based and image-based resumes into text data. Open-source OCR libraries are a possible software choice for this process. The converted digital information is then stored back into the database. 【0277】 After that, as an analysis means, the server uses natural language processing technology to extract specific keywords and important information from the resume. For this purpose, a natural language processing library utilizing machine learning is used. For example, an open-source natural language processing library becomes a useful tool. 【0278】 Furthermore, as a screening means and an evaluation means, the server evaluates candidates based on the analyzed data. According to the set criteria, a scoring algorithm is applied, and a priority is assigned to the candidates based on the scoring results. 【0279】 Finally, as a time adjustment means, the server obtains the calendar information of the candidates and the interviewers via the calendar API and proposes the optimal interview date and time. This proposal is notified to both parties by email, enabling rapid schedule adjustment. 【0280】 As a specific example, when a user hires excellent engineers, through this system, it is possible to select the most suitable 10 candidates from over 100 candidates and quickly arrange the interview schedule. 【0281】 As an example of the prompt sentence input to the generative AI model, "Please teach me the steps to streamline the engineer recruitment process" can be cited. In this way, the entire recruitment process is automated and operates efficiently. 【0282】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0283】 Step 1: 【0284】 The server receives resumes from candidates. This input is a digital file received through a web form or email. The server stores this file in the database and functions as an information retention means. The file is saved and indexed in the database together with the resume metadata. 【0285】 Step 2: 【0286】 The server converts the received resume file into digital text using OCR technology. In this conversion means, a paper-based or image-formatted resume is taken as input, and readable text data is generated as output. The OCR software recognizes the characters therein and stores them in the database in text format. 【0287】 Step 3: 【0288】 The server analyzes the information contained in the digitized resume by utilizing natural language processing technology. Taking digital text as input, it extracts keywords and important information using the analysis means. The extracted data is output in the form of skills, experience, work history, etc., and is supplied to the next step for evaluation and screening. 【0289】 Step 4: 【0290】 The server screens candidates according to criteria based on the analysis results. Here, it determines whether the candidate's skill set and experience meet the pre-set requirements. Using the analysis results as input, it generates a list of candidates who meet the criteria as output. The screening is automatically performed using an algorithm. 【0291】 Step 5: 【0292】 The server evaluates the selected candidates and applies a scoring algorithm. Using the selected candidate information as input, it outputs a ranked list of candidates with scores. The evaluation means calculates specific scores for each candidate. 【0293】 Step 6: 【0294】 The server retrieves candidate and interviewer calendar information via a calendar API for scheduling. It functions as a time scheduling tool, receiving calendar information as input and outputting optimal interview dates and times based on that information. Suggested dates and times are notified via email for quick confirmation and adjustment. 【0295】 (Application Example 1) 【0296】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0297】 Traditional recruitment processes involved the manual management and analysis of large amounts of candidate information, which was time-consuming and labor-intensive. Furthermore, scheduling interviews was cumbersome, making smooth communication with candidates difficult. Therefore, there is a need for increased efficiency and accuracy in recruitment operations. 【0298】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0299】 In this invention, the server includes data storage means for storing candidate information, analysis means for analyzing candidate information, candidate selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, scoring means for evaluating candidates based on the analysis results, display means for presenting and confirming candidate information through a user interface, and information distribution means for notifying candidates and interviewers of the schedule via a communication network. This enables the automation and efficiency of the recruitment process. 【0300】 "Candidate information" refers to personal data and skills information included in the resume and work history of a person being considered during the recruitment process. 【0301】 The "data storage means" is a device for storing the input candidate information and making it accessible as needed. 【0302】 The "analysis means" is a device or system that digitizes candidate information and analyzes the data using natural language processing technology and the like. 【0303】 The "candidate selection means" is a device that selects appropriate candidates based on the information obtained by analysis and the criteria. 【0304】 The "schedule adjustment means" is a device that adjusts the available times of candidates and interviewers to determine the optimal interview date and time. 【0305】 The "scoring means" is a device or method that quantifies the evaluation results of candidates to make them comparable. 【0306】 The "display means" is a device that visually shows the analysis results and candidate information so that the user can confirm and judge. 【0307】 The "information distribution means" is a device that notifies relevant parties of the determined interview date and time, candidate information, etc. via a communication network. 【0308】 The system for implementing this invention is configured such that the server, terminal, and user cooperate to function. First, the server functions as a platform for receiving resumes transmitted from candidates via the Internet. The resume is stored in the server through the data storage means. The stored data is analyzed using the OCR technology "Tesseract OCR" and the natural language processing software "Google NLP API". As a result, important information and skills are extracted from the candidate information. 【0309】 After analysis, the server uses a candidate selection tool to filter out suitable candidates based on the analysis results. This selection process employs a scoring tool to evaluate and score candidates, and the results are then prioritized. Next, to schedule interviews with the selected candidates, the server uses the "Google Calendar API" as a scheduling tool to obtain calendar information from both candidates and interviewers and find common availability. 【0310】 Users can use their devices to view candidate information through the user interface and easily understand the displayed priority and evaluation scores. The server also serves as an information distribution method, notifying both candidates and interviewers of the scheduled interview date and time via email or other means. 【0311】 A concrete example is a company hiring engineers where the system automatically selects the top 10 candidates from 100 applicants based on scoring, and then automatically schedules and notifies them of their interview dates. This system makes it possible to streamline and improve the accuracy of the hiring process. 【0312】 An example of a prompt to input into a generating AI model is: "Please describe the detailed steps for a system that selects the best candidate from engineering resumes and automatically determines possible interview dates and times." 【0313】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0314】 Step 1: 【0315】 The server receives resumes from candidates via a dedicated upload form or email system over the internet. This input data is stored on the server in electronic file format and then stored in a database using data storage means. This completes the initial input of resume information. 【0316】 Step 2: 【0317】 The server processes the received resume data using OCR technology, converting paper-based information into digital text. Specifically, it uses "Tesseract OCR" to convert image data into text data. This converted data becomes input and is analyzed using natural language processing technology, "Google NLP API." As a result of the analysis, the candidate's skills, experience, and keywords are extracted and output as structured data of candidate information. 【0318】 Step 3: 【0319】 The server selects suitable candidates by comparing structured candidate information against pre-defined requirements. This selection process is performed using a scoring system. It receives analyzed candidate information as input and assigns an evaluation score to each candidate based on that information. The scoring results are output, and a prioritized candidate list is generated. 【0320】 Step 4: 【0321】 Users use their devices to view candidate lists and evaluation scores provided by the server through a user interface. Here, users can review detailed candidate information and make decisions based on the scored results. This process expedites candidate selection. 【0322】 Step 5: 【0323】 The server uses the Google Calendar API as a scheduling tool to coordinate interview dates with selected candidates. It reads the calendar data of both the candidate and the interviewer as input and detects overlapping free time slots. This generates optimal interview date and time candidates, and the interview schedule is automatically adjusted. 【0324】 Step 6: 【0325】 The server notifies candidates and interviewers of the scheduled interview date and time via information distribution. It also sends notifications via email or messaging services to prompt confirmation and finalization of the interview date and time. This allows for immediate scheduling sharing among all parties involved. 【0326】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0327】 This invention incorporates an emotion engine into an AI system for streamlining the recruitment process, and specific embodiments thereof are shown below. 【0328】 This system has a configuration that combines a server, terminals, users, and an emotion engine. 【0329】 First, the server receives resumes from candidates via the internet and stores them in a database. This allows candidate information, such as their work history and skill sets, to be managed by data storage. 【0330】 Next, the server uses OCR technology to convert the resume into text data, which is then analyzed using natural language processing technology. The analysis tool extracts keywords and generates a candidate profile. 【0331】 During this process, the emotion engine obtains real-time emotional data by analyzing the user's facial expressions and tone of voice. This allows the system to recognize the user's current emotional state. For example, if the user is relaxed, it will perform normal processing, but if they are stressed, it will suggest filtering to focus only on important candidates. 【0332】 Furthermore, the server uses a candidate selection mechanism to select the most suitable candidate based on the analysis results and sentiment data. The selection is performed using a scoring system that reflects the user's emotional state, thereby appropriately customizing the information the user receives to match their emotions. 【0333】 After selection, the device displays a list of candidates to the user, allowing them to quickly review emotionally preferred options as needed. 【0334】 Subsequently, the scheduling mechanism takes into account the analysis results from the emotion engine to adjust the interview date. For example, if the user is busy, it prioritizes suggesting interview times when they are expected to be more relaxed. 【0335】 For example, when users are running the hiring process at a busy end of the month, the emotion engine can detect their stress levels and handle candidate selection and interview scheduling with minimal burden on the user. 【0336】 In this way, by incorporating an emotion engine, the recruitment process becomes more personalized, efficient, and user-friendly. 【0337】 The following describes the processing flow. 【0338】 Step 1: 【0339】 Users submit candidate resumes to the system via an online application form or email. The application data is sent to the server and securely stored in the database. 【0340】 Step 2: 【0341】 The server converts the received resumes into text data using OCR technology. This text format allows for further data analysis. 【0342】 Step 3: 【0343】 The server uses natural language processing (NLP) techniques to analyze the transcribed resume and extract keywords such as skill sets, work experience, and educational background. Based on this, it profiles the candidate's characteristics. 【0344】 Step 4: 【0345】 The emotion engine monitors the user's facial expressions and tone of voice while they are reviewing their resume, and analyzes their emotional state in real time. 【0346】 Step 5: 【0347】 The server receives data from the emotion engine and adjusts the candidate selection criteria based on the user's emotional state. Specifically, if the user is feeling tired, optimizations are made such as displaying only the top-scoring candidates. 【0348】 Step 6: 【0349】 The user is provided with a refined list of candidates on their device. Through an intuitive interface, the user can review candidate details and select candidates for interviews. 【0350】 Step 7: 【0351】 The server checks the calendar information of both the interviewer and the candidate for the selected candidates and schedules the interview. Taking into account the emotion engine data, it prioritizes suggesting times that are easy to relax during. 【0352】 Step 8: 【0353】 The server will notify both the candidate and the interviewer of the scheduled interview date and time. This notification will include a confirmation button and a link to the interview. 【0354】 Step 9: 【0355】 After completing an interview, the user (interviewer) uses the feedback function provided by the emotion engine to input an evaluation of the candidate. The evaluation is sent to the server and used to inform future processes. 【0356】 (Example 2) 【0357】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0358】 In the recruitment process, it is necessary to efficiently manage candidate information and select the most suitable candidates while considering the emotional state of the users. However, conventional systems have difficulty reflecting user emotional data in their selection process, and scheduling interviews has not always taken into account the user's stress level. As a result, this can sometimes be burdensome for the user. 【0359】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0360】 In this invention, the server includes an information storage means for storing candidate information, an information analysis means for analyzing candidate information, and an emotion acquisition means for acquiring emotion data by analyzing the user's facial expressions and tone of voice. This makes it possible to select candidates and adjust interview schedules while taking into account the user's emotional state. 【0361】 "Information storage means" refers to a means of storing and managing information, including a candidate's work history and skill set, in a database or similar system. 【0362】 "Information analysis means" refers to methods for processing text data using OCR technology and natural language processing technology to extract necessary keywords and profiles. 【0363】 "Candidate selection methods" refer to the means used to select suitable candidates based on the results of the analysis. 【0364】 An "emotion acquisition method" is a means of acquiring emotional data in real time by analyzing the user's facial expressions and tone of voice. 【0365】 "Scheduling methods" refer to techniques for optimizing interview schedules between users and candidates, while taking into account acquired emotional data. 【0366】 This invention provides a recruitment support system that incorporates user emotional data to streamline the recruitment process. The system consists of a server, terminals, and an emotional engine. 【0367】 First, the server receives resumes submitted by candidates via the internet and stores the information in a database. The server uses OCR technology to convert the resumes into text data and performs analysis using natural language processing (NLP) technology. This extracts keywords from the candidate's work history and skill set, and builds a profile. 【0368】 Next, the emotion engine uses the user's camera and microphone to analyze their facial expressions and voice tone in real time, acquiring emotional data. This data is used to identify the user's current emotional state. If the user is relaxed, normal processing is performed; if they are stressed, filtering is suggested to narrow down the candidates to those who are most important. 【0369】 The server combines analytical income data and sentiment data, and uses a selection algorithm to choose the most suitable candidate. This selection is scored based on sentiment data, and the information received by the user is individually customized. 【0370】 The terminal then displays a list of the most suitable candidates to the user. As a specific example, consider a scenario where the user is using the system during a busy period at the end of the month. The emotion engine senses the user's stress level and takes steps to minimize the user's burden when suggesting interview dates and displaying candidates. 【0371】 Examples of prompt statements include the following: 【0372】 "Analyze resumes and generate candidate profiles. Also, analyze the user's current emotional state to suggest the most suitable candidate list." 【0373】 In this way, by incorporating an emotion engine, the system personalizes the recruitment process, resulting in an efficient and user-friendly procedure. 【0374】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0375】 Step 1: 【0376】 The server receives resume files submitted by candidates via the internet. It takes resume data in PDF or image format from candidates as input. The server stores these resumes in a database, preparing them for managing work history and skill sets. The output is the candidate information stored in the database. Specifically, it saves received files to a designated folder and stores their metadata in the database. 【0377】 Step 2: 【0378】 The server uses OCR technology to convert the received resume into text data. The input is the candidate's resume file saved in step 1. The OCR process extracts text information from the image. The output is the text data of the resume. Specifically, the OCR engine analyzes the image file and generates text information. 【0379】 Step 3: 【0380】 The server analyzes text data using natural language processing (NLP) techniques. The input is the text data obtained in step 2. Noun phrases and verb phrases are extracted from this data to generate candidate profiles. The output is the analyzed candidate profile data and related keywords. Specifically, the NLP algorithm extracts key keywords and phrases to construct the profile. 【0381】 Step 4: 【0382】 The emotion engine analyzes the user's facial expressions and voice tone in real time using the user's camera and microphone. The input is audio and video data from the user. Emotional data is acquired here. The output is data representing the user's current emotional state. Specifically, emotion scoring is performed using facial recognition and voice analysis technologies. 【0383】 Step 5: 【0384】 The server integrates the analysis results and sentiment data and selects the most suitable candidate using a candidate selection algorithm. The inputs are the profile data from step 3 and the sentiment data from step 4. It generates selection results that reflect the user's sentiment. The output is a scored list of candidates. Specifically, the selection algorithm weights the sentiment data and evaluates suitability. 【0385】 Step 6: 【0386】 The terminal displays a list of the most suitable candidates for the user. The input is the candidate selection result from step 5. The user can view the list, displayed in order of score, and check the details of each candidate. The output is the displayed candidate list. Specifically, the GUI is updated, and the profiles of the selected candidates are displayed side by side. 【0387】 Step 7: 【0388】 The scheduling mechanism adjusts interview dates while considering the user's stress level based on emotional data. Inputs include a candidate list and emotional data. It provides scheduling suggestions aimed at reducing stress. The output is a suggestion of the optimal interview date. Specifically, it compares the user's calendar with available time slots and notifies them of the best date and time. 【0389】 (Application Example 2) 【0390】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0391】 In the recruitment process, it is necessary not only to analyze candidate information and select suitable candidates, but also to provide a personalized experience that takes into account the user's emotional state. Similarly, in household task management, optimization based on the user's emotions is required. 【0392】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0393】 In this invention, the server includes information recording means for storing candidate information, information analysis means for analyzing candidate information, selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, sentiment analysis means for analyzing the user's emotional state, and household adjustment means for optimizing household tasks based on the user's emotional state. This enables the user to comfortably and more personally manage the recruitment process and household tasks. 【0394】 "Information recording means" refers to devices or systems for centrally storing candidate and user information and maintaining it in a state where it can be quickly accessed as needed. 【0395】 "Information analysis methods" refer to the techniques and processes used to analyze collected information and extract useful data from it. 【0396】 A "selection method" is a system that has criteria or algorithms for selecting the most suitable candidate based on the analysis results. 【0397】 "Scheduling methods" refer to methods or devices for proposing and determining the most suitable interview date and time, taking into account the schedules of all parties involved. 【0398】 "Emotional analysis techniques" are technologies used to analyze a user's emotional state based on their facial expressions, tone of voice, and other factors. 【0399】 A "domestic adjustment system" is a system for optimizing household tasks and environmental settings according to the user's emotional state. 【0400】 This invention aims to optimize a personalized recruitment process and household tasks while taking into account the user's emotional state. The main components of the system are a server, a terminal, and the user. 【0401】 The server receives candidate information and stores it in a database through an information recording device. Candidate resumes are converted into text data using OCR technology and analyzed using natural language processing technology. This allows the analysis device to extract keywords and generate a candidate profile. 【0402】 Furthermore, the server uses sentiment analysis tools to analyze the user's emotions. This is achieved by analyzing the user's facial expressions and tone of voice. Based on the results of the sentiment analysis, appropriate responses and information are provided. 【0403】 The device displays information about candidates selected by the user and adjusts household tasks based on their emotions. For example, if the user is feeling stressed, relaxing environment settings are suggested through household adjustment tools. 【0404】 Users can review this information at their own pace and proceed through the hiring process. Furthermore, task management tailored to their emotional state enhances user convenience. 【0405】 For example, if a user wants to relax after a long workday, the system can analyze their emotions and recommend relaxing music. A concrete example of a prompt would be: "Come up with ideas for a robot assistant that analyzes the user's facial expressions and tone of voice to recognize their emotional state and optimize household tasks." 【0406】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0407】 Step 1: 【0408】 The server receives candidate resumes via the internet. Since the resumes are sent in formats such as PDF, they are stored in a database through an information recording system. The input is the candidate's resume data, and the output is the resume information stored in the database. 【0409】 Step 2: 【0410】 The server uses OCR technology to convert stored resume data into text data. At this stage, it analyzes information in image formats such as PDFs and converts it into text information. The input is resume data in image format, and the output is the converted text data. 【0411】 Step 3: 【0412】 The server analyzes text data using natural language processing techniques and extracts important keywords. This process utilizes a generative AI model to identify meaningful information within the text. The input is text data, and the output is the extracted keywords. 【0413】 Step 4: 【0414】 The emotion analysis system is activated, analyzing the user's facial expressions and voice tone in real time. Based on the data collected using the camera and microphone on the device, the user's emotional state is estimated. The input is real-time collected audio and video data, and the output is the analyzed emotional state. 【0415】 Step 5: 【0416】 The server selects the most suitable candidates based on the analyzed keywords and the user's emotional state. The suggested candidates change according to the emotional state. The input is keyword and emotional state data, and the output is a list of selected candidates. 【0417】 Step 6: 【0418】 The device presents the user with a list of selected candidates. The user can then decide on their next action based on this list. The input is the candidate list, and the output is the user's selection or feedback. 【0419】 Step 7: 【0420】 The device uses in-home adjustment tools to suggest task optimizations tailored to the user's emotions. This process utilizes in-home IoT devices to create an environment that promotes user relaxation, such as adjusting lighting. Inputs are the user's choices and emotional state, while outputs are optimized household tasks and settings. 【0421】 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. 【0422】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0423】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0424】 [Third Embodiment] 【0425】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0426】 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. 【0427】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0428】 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. 【0429】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0430】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0431】 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. 【0432】 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. 【0433】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0434】 The 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. 【0435】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0436】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0437】 This invention relates to an AI system for streamlining the recruitment process, and a specific embodiment thereof is shown below. 【0438】 This system functions through the cooperation of servers, terminals, and users. 【0439】 First, the server receives resumes from candidates via the internet. This is done using dedicated upload forms or email servers. The received resumes are stored in a database on the server, which functions as a data storage system. 【0440】 Next, the server analyzes the received resume file as an analytical tool. In this process, OCR is used to digitize the paper-based information, and natural language processing technology is used to extract keywords and important information. At the same time, it evaluates whether the candidate's skills and experience meet the pre-defined requirements. 【0441】 Once the analysis is complete, the server uses a candidate selection method to filter for suitable candidates and generate a candidate list. The selected candidates are evaluated and prioritized through a detailed scoring system. This information is displayed on the terminal for the user (recruiter) to review. 【0442】 The server then functions as a scheduling tool, automatically suggesting the optimal interview date and time based on the calendar information of the selected candidates and interviewers. It uses a calendar API to retrieve both parties' schedules and find overlapping time slots. The suggested date and time are notified to both the candidate and the interviewer via email, prompting them to confirm and finalize the schedule. 【0443】 For example, when a user is implementing a process to hire top engineers, the system automatically selects the top 10 candidates from over 100 based on their skills and experience. Interview dates with these candidates are then quickly scheduled, and both parties are immediately notified to minimize any delays in communication. 【0444】 In this way, this system automates and streamlines recruitment operations, optimizing resources and improving recruitment accuracy. 【0445】 The following describes the processing flow. 【0446】 Step 1: 【0447】 The user initiates the hiring process by uploading the candidate's resume to the system via email or a dedicated application form. 【0448】 Step 2: 【0449】 The server stores the received resume files in a database and begins managing resumes as a data storage method. It detects duplicate files or files in inappropriate formats and notifies the user. 【0450】 Step 3: 【0451】 The server converts saved resumes into text data using OCR technology. This makes it possible to extract information from handwritten or scanned documents. 【0452】 Step 4: 【0453】 The server uses natural language processing technology to analyze keywords and important information from resumes. Specifically, it identifies and categorizes the candidate's work experience, skills, educational background, etc. 【0454】 Step 5: 【0455】 Based on the analysis, the server scores candidates who meet the set criteria. Candidates with higher scores are then prioritized and filtered using the candidate selection method. 【0456】 Step 6: 【0457】 The terminal displays a list of selected candidates to the user. The user can view detailed information about each candidate on the screen and decide who to interview. 【0458】 Step 7: 【0459】 The server retrieves the interviewer's and candidate's calendar data from an API and automatically selects the optimal interview date and time using a scheduling tool. 【0460】 Step 8: 【0461】 The server notifies both the candidate and the interviewer via email of the scheduled interview date and time. The notification includes a confirmation button and an access link for the candidate to finalize the schedule. 【0462】 Step 9: 【0463】 Once the interview is complete, the user (interviewer) is provided with a form to enter the results. This information is sent to the server, recorded, and used to help with the final hiring decision. 【0464】 (Example 1) 【0465】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0466】 In recent years, processing large amounts of candidate information and efficiently selecting suitable personnel has become a major challenge in corporate recruitment activities. Traditional manual processes are time-consuming and often inefficient in terms of resource utilization. Furthermore, scheduling interviews with candidates also requires considerable time and effort for coordinating schedules between recruiters and candidates. A new system is needed to solve these problems. 【0467】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0468】 In this invention, the server includes an information holding means for collecting and storing candidate information, a conversion means that uses optical character recognition technology to digitize the candidate information, and an analysis means that uses natural language processing technology to extract keywords and important information from the digital information. This enables efficient processing of candidate information and rapid selection of suitable personnel. 【0469】 "Information retention means" refers to a function for collecting candidate data, storing it in a database, and managing it. 【0470】 "Conversion means" refers to a function that uses optical character recognition technology to convert paper-based information obtained from candidates into digital data. 【0471】 "Analysis means" refers to a function that uses natural language processing technology to extract keywords and important information from digitized candidate information. 【0472】 "Selection method" refers to a function that selects candidates who meet pre-set criteria based on analyzed candidate information. 【0473】 "Evaluation method" refers to a function that uses a scoring algorithm to prioritize selected candidates. 【0474】 "Time scheduling" refers to a function that obtains calendar information from both candidates and recruiters and automatically suggests the most suitable interview date and time based on that information. 【0475】 In order to implement this invention, the server, terminal, and user must cooperate and fulfill specific roles. Here, we will specifically describe the role of the server. 【0476】 The server receives candidate resumes via the internet and stores the information in a database as a means of data retention. When candidates submit their resumes via web forms or email, the server receives them via a secure connection, organizes them, and stores them. A commonly used open-source relational database management system can be used for the database management system. 【0477】 Next, the server uses OCR (Optical Character Recognition) technology to convert paper-based and image-based resumes into text data. Open-source OCR libraries are a possible software choice for this process. The converted digital information is then stored back into the database. 【0478】 Subsequently, the server uses natural language processing techniques as an analysis tool to extract specific keywords and important information from the resume. For this purpose, it employs a machine learning-based natural language processing library. For example, open-source natural language processing libraries can be useful tools. 【0479】 Furthermore, the server evaluates candidates based on the analyzed data, acting as both a selection and evaluation tool. It applies a scoring algorithm according to established criteria and assigns priorities to candidates based on the scoring results. 【0480】 Finally, as a means of scheduling, the server retrieves the calendar information of both the candidate and the interviewer via a calendar API and suggests the optimal date and time for the interview. This suggestion is notified to both parties via email, enabling quick scheduling. 【0481】 For example, when a user is hiring a top-tier engineer, this system allows them to select the 10 most suitable candidates from over 100 and quickly schedule interviews. 【0482】 An example of a prompt to input into the generating AI model is, "Please tell me the steps to streamline the engineer recruitment process." In this way, the entire recruitment process becomes automated and operates efficiently. 【0483】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0484】 Step 1: 【0485】 The server receives resumes from candidates. This input is a digital file received via web form or email. The server stores this file in a database, which functions as a means of information retention. The database stores and indexes the file along with the resume's metadata. 【0486】 Step 2: 【0487】 The server converts received resume files into digital text using OCR technology. This conversion method takes paper-based or image-based resumes as input and generates readable text data as output. The OCR software recognizes the characters within the text and stores them in a database in text format. 【0488】 Step 3: 【0489】 The server uses natural language processing technology to analyze information contained in digitized resumes. It takes digital text as input and uses analysis tools to extract keywords and important information. The extracted data is output in the form of skills, experience, and career history, and is supplied to the next step for evaluation and selection. 【0490】 Step 4: 【0491】 The server uses the analysis results to select candidates according to established criteria. Here, it determines whether a candidate's skill set and experience meet pre-defined requirements. The analysis results are used as input, and a list of candidates who meet the criteria is generated as output. The selection process is performed automatically using an algorithm. 【0492】 Step 5: 【0493】 The server evaluates the selected candidates and applies a scoring algorithm. Using the selected candidate information as input, it outputs a list of candidates with scores according to priority. The evaluation method calculates a specific score for each candidate. 【0494】 Step 6: 【0495】 The server retrieves candidate and interviewer calendar information via a calendar API for scheduling. It functions as a time scheduling tool, receiving calendar information as input and outputting optimal interview dates and times based on that information. Suggested dates and times are notified via email for quick confirmation and adjustment. 【0496】 (Application Example 1) 【0497】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0498】 Traditional recruitment processes involved the manual management and analysis of large amounts of candidate information, which was time-consuming and labor-intensive. Furthermore, scheduling interviews was cumbersome, making smooth communication with candidates difficult. Therefore, there is a need for increased efficiency and accuracy in recruitment operations. 【0499】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0500】 In this invention, the server includes data storage means for storing candidate information, analysis means for analyzing candidate information, candidate selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, scoring means for evaluating candidates based on the analysis results, display means for presenting and confirming candidate information through a user interface, and information distribution means for notifying candidates and interviewers of the schedule via a communication network. This enables the automation and efficiency of the recruitment process. 【0501】 "Candidate information" refers to personal data and skills information included in the resume and work history of a person being considered during the recruitment process. 【0502】 A "data storage means" is a device for storing entered candidate information and making it accessible as needed. 【0503】 "Analysis means" refers to a device or system that digitizes candidate information and analyzes the data using natural language processing technology or the like. 【0504】 A "candidate selection method" is a device that selects appropriate candidates based on information and criteria obtained through analysis. 【0505】 A "scheduling adjustment tool" is a device that coordinates the availability of candidates and interviewers to determine the optimal interview date and time. 【0506】 A "scoring means" is a device or method that quantifies and makes comparable the evaluation results of candidates. 【0507】 A "display means" is a device that visually displays analysis results and candidate information, enabling the user to confirm and make a judgment. 【0508】 An "information distribution device" is a device that notifies relevant parties of the decided interview date and time, candidate information, etc., via a communication network. 【0509】 The system for implementing this invention is configured in which a server, terminals, and users work together. The server first functions as a platform for receiving resumes submitted by candidates via the internet. The resumes are stored on the server through data storage means. The stored data is analyzed using OCR technology "Tesseract OCR" and natural language processing software "Google NLP API". This extracts important information and skills from the candidate information. 【0510】 After analysis, the server uses a candidate selection tool to filter out suitable candidates based on the analysis results. This selection process employs a scoring tool to evaluate and score candidates, and the results are then prioritized. Next, to schedule interviews with the selected candidates, the server uses the "Google Calendar API" as a scheduling tool to obtain calendar information from both candidates and interviewers and find common availability. 【0511】 Users can use their devices to view candidate information through the user interface and easily understand the displayed priority and evaluation scores. The server also serves as an information distribution method, notifying both candidates and interviewers of the scheduled interview date and time via email or other means. 【0512】 A concrete example is a company hiring engineers where the system automatically selects the top 10 candidates from 100 applicants based on scoring, and then automatically schedules and notifies them of their interview dates. This system makes it possible to streamline and improve the accuracy of the hiring process. 【0513】 An example of a prompt to input into a generating AI model is: "Please describe the detailed steps for a system that selects the best candidate from engineering resumes and automatically determines possible interview dates and times." 【0514】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0515】 Step 1: 【0516】 The server receives resumes from candidates via a dedicated upload form or email system over the internet. This input data is stored on the server in electronic file format and then stored in a database using data storage means. This completes the initial input of resume information. 【0517】 Step 2: 【0518】 The server processes the received resume data using OCR technology, converting paper-based information into digital text. Specifically, it uses "Tesseract OCR" to convert image data into text data. This converted data becomes input and is analyzed using natural language processing technology, "Google NLP API." As a result of the analysis, the candidate's skills, experience, and keywords are extracted and output as structured data of candidate information. 【0519】 Step 3: 【0520】 The server selects suitable candidates by comparing structured candidate information against pre-defined requirements. This selection process is performed using a scoring system. It receives analyzed candidate information as input and assigns an evaluation score to each candidate based on that information. The scoring results are output, and a prioritized candidate list is generated. 【0521】 Step 4: 【0522】 Users use their devices to view candidate lists and evaluation scores provided by the server through a user interface. Here, users can review detailed candidate information and make decisions based on the scored results. This process expedites candidate selection. 【0523】 Step 5: 【0524】 The server uses the Google Calendar API as a scheduling tool to coordinate interview dates with selected candidates. It reads the calendar data of both the candidate and the interviewer as input and detects overlapping free time slots. This generates optimal interview date and time candidates, and the interview schedule is automatically adjusted. 【0525】 Step 6: 【0526】 The server notifies candidates and interviewers of the scheduled interview date and time via information distribution. It also sends notifications via email or messaging services to prompt confirmation and finalization of the interview date and time. This allows for immediate scheduling sharing among all parties involved. 【0527】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0528】 This invention incorporates an emotion engine into an AI system for streamlining the recruitment process, and specific embodiments thereof are shown below. 【0529】 This system has a configuration that combines a server, terminals, users, and an emotion engine. 【0530】 First, the server receives resumes from candidates via the internet and stores them in a database. This allows candidate information, such as their work history and skill sets, to be managed by data storage. 【0531】 Next, the server uses OCR technology to convert the resume into text data, which is then analyzed using natural language processing technology. The analysis tool extracts keywords and generates a candidate profile. 【0532】 During this process, the emotion engine obtains real-time emotional data by analyzing the user's facial expressions and tone of voice. This allows the system to recognize the user's current emotional state. For example, if the user is relaxed, it will perform normal processing, but if they are stressed, it will suggest filtering to focus only on important candidates. 【0533】 Furthermore, the server uses a candidate selection mechanism to select the most suitable candidate based on the analysis results and sentiment data. The selection is performed using a scoring system that reflects the user's emotional state, thereby appropriately customizing the information the user receives to match their emotions. 【0534】 After selection, the device displays a list of candidates to the user, allowing them to quickly review emotionally preferred options as needed. 【0535】 Subsequently, the scheduling mechanism takes into account the analysis results from the emotion engine to adjust the interview date. For example, if the user is busy, it prioritizes suggesting interview times when they are expected to be more relaxed. 【0536】 For example, when users are running the hiring process at a busy end of the month, the emotion engine can detect their stress levels and handle candidate selection and interview scheduling with minimal burden on the user. 【0537】 In this way, by incorporating an emotion engine, the recruitment process becomes more personalized, efficient, and user-friendly. 【0538】 The following describes the processing flow. 【0539】 Step 1: 【0540】 Users submit candidate resumes to the system via an online application form or email. The application data is sent to the server and securely stored in the database. 【0541】 Step 2: 【0542】 The server converts the received resumes into text data using OCR technology. This text format allows for further data analysis. 【0543】 Step 3: 【0544】 The server uses natural language processing (NLP) techniques to analyze the transcribed resume and extract keywords such as skill sets, work experience, and educational background. Based on this, it profiles the candidate's characteristics. 【0545】 Step 4: 【0546】 The emotion engine monitors the user's facial expressions and tone of voice while they are reviewing their resume, and analyzes their emotional state in real time. 【0547】 Step 5: 【0548】 The server receives data from the emotion engine and adjusts the candidate selection criteria based on the user's emotional state. Specifically, if the user is feeling tired, optimizations are made such as displaying only the top-scoring candidates. 【0549】 Step 6: 【0550】 The user is provided with a refined list of candidates on their device. Through an intuitive interface, the user can review candidate details and select candidates for interviews. 【0551】 Step 7: 【0552】 The server checks the calendar information of both the interviewer and the candidate for the selected candidates and schedules the interview. Taking into account the emotion engine data, it prioritizes suggesting times that are easy to relax during. 【0553】 Step 8: 【0554】 The server will notify both the candidate and the interviewer of the scheduled interview date and time. This notification will include a confirmation button and a link to the interview. 【0555】 Step 9: 【0556】 After completing an interview, the user (interviewer) uses the feedback function provided by the emotion engine to input an evaluation of the candidate. The evaluation is sent to the server and used to inform future processes. 【0557】 (Example 2) 【0558】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0559】 In the recruitment process, it is necessary to efficiently manage candidate information and select the most suitable candidates while considering the emotional state of the users. However, conventional systems have difficulty reflecting user emotional data in their selection process, and scheduling interviews has not always taken into account the user's stress level. As a result, this can sometimes be burdensome for the user. 【0560】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0561】 In this invention, the server includes an information storage means for storing candidate information, an information analysis means for analyzing candidate information, and an emotion acquisition means for acquiring emotion data by analyzing the user's facial expressions and tone of voice. This makes it possible to select candidates and adjust interview schedules while taking into account the user's emotional state. 【0562】 "Information storage means" refers to a means of storing and managing information, including a candidate's work history and skill set, in a database or similar system. 【0563】 "Information analysis means" refers to methods for processing text data using OCR technology and natural language processing technology to extract necessary keywords and profiles. 【0564】 "Candidate selection methods" refer to the means used to select suitable candidates based on the results of the analysis. 【0565】 An "emotion acquisition method" is a means of acquiring emotional data in real time by analyzing the user's facial expressions and tone of voice. 【0566】 "Scheduling methods" refer to techniques for optimizing interview schedules between users and candidates, while taking into account acquired emotional data. 【0567】 This invention provides a recruitment support system that incorporates user emotional data to streamline the recruitment process. The system consists of a server, terminals, and an emotional engine. 【0568】 First, the server receives resumes submitted by candidates via the internet and stores the information in a database. The server uses OCR technology to convert the resumes into text data and performs analysis using natural language processing (NLP) technology. This extracts keywords from the candidate's work history and skill set, and builds a profile. 【0569】 Next, the emotion engine uses the user's camera and microphone to analyze their facial expressions and voice tone in real time, acquiring emotional data. This data is used to identify the user's current emotional state. If the user is relaxed, normal processing is performed; if they are stressed, filtering is suggested to narrow down the candidates to those who are most important. 【0570】 The server combines analytical income data and sentiment data, and uses a selection algorithm to choose the most suitable candidate. This selection is scored based on sentiment data, and the information received by the user is individually customized. 【0571】 The terminal then displays a list of the most suitable candidates to the user. As a specific example, consider a scenario where the user is using the system during a busy period at the end of the month. The emotion engine senses the user's stress level and takes steps to minimize the user's burden when suggesting interview dates and displaying candidates. 【0572】 Examples of prompt statements include the following: 【0573】 "Analyze resumes and generate candidate profiles. Also, analyze the user's current emotional state to suggest the most suitable candidate list." 【0574】 In this way, by incorporating an emotion engine, the system personalizes the recruitment process, resulting in an efficient and user-friendly procedure. 【0575】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0576】 Step 1: 【0577】 The server receives resume files submitted by candidates via the internet. It takes resume data in PDF or image format from candidates as input. The server stores these resumes in a database, preparing them for managing work history and skill sets. The output is the candidate information stored in the database. Specifically, it saves received files to a designated folder and stores their metadata in the database. 【0578】 Step 2: 【0579】 The server uses OCR technology to convert the received resume into text data. The input is the candidate's resume file saved in step 1. The OCR process extracts text information from the image. The output is the text data of the resume. Specifically, the OCR engine analyzes the image file and generates text information. 【0580】 Step 3: 【0581】 The server analyzes text data using natural language processing (NLP) techniques. The input is the text data obtained in step 2. Noun phrases and verb phrases are extracted from this data to generate candidate profiles. The output is the analyzed candidate profile data and related keywords. Specifically, the NLP algorithm extracts key keywords and phrases to construct the profile. 【0582】 Step 4: 【0583】 The emotion engine analyzes the user's facial expressions and voice tone in real time using the user's camera and microphone. The input is audio and video data from the user. Emotional data is acquired here. The output is data representing the user's current emotional state. Specifically, emotion scoring is performed using facial recognition and voice analysis technologies. 【0584】 Step 5: 【0585】 The server integrates the analysis results and sentiment data and selects the most suitable candidate using a candidate selection algorithm. The inputs are the profile data from step 3 and the sentiment data from step 4. It generates selection results that reflect the user's sentiment. The output is a scored list of candidates. Specifically, the selection algorithm weights the sentiment data and evaluates suitability. 【0586】 Step 6: 【0587】 The terminal displays a list of the most suitable candidates for the user. The input is the candidate selection result from step 5. The user can view the list, displayed in order of score, and check the details of each candidate. The output is the displayed candidate list. Specifically, the GUI is updated, and the profiles of the selected candidates are displayed side by side. 【0588】 Step 7: 【0589】 The scheduling mechanism adjusts interview dates while considering the user's stress level based on emotional data. Inputs include a candidate list and emotional data. It provides scheduling suggestions aimed at reducing stress. The output is a suggestion of the optimal interview date. Specifically, it compares the user's calendar with available time slots and notifies them of the best date and time. 【0590】 (Application Example 2) 【0591】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0592】 In the recruitment process, it is necessary not only to analyze candidate information and select suitable candidates, but also to provide a personalized experience that takes into account the user's emotional state. Similarly, in household task management, optimization based on the user's emotions is required. 【0593】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0594】 In this invention, the server includes information recording means for storing candidate information, information analysis means for analyzing candidate information, selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, sentiment analysis means for analyzing the user's emotional state, and household adjustment means for optimizing household tasks based on the user's emotional state. This enables the user to comfortably and more personally manage the recruitment process and household tasks. 【0595】 "Information recording means" refers to devices or systems for centrally storing candidate and user information and maintaining it in a state where it can be quickly accessed as needed. 【0596】 "Information analysis methods" refer to the techniques and processes used to analyze collected information and extract useful data from it. 【0597】 A "selection method" is a system that has criteria or algorithms for selecting the most suitable candidate based on the analysis results. 【0598】 "Scheduling methods" refer to methods or devices for proposing and determining the most suitable interview date and time, taking into account the schedules of all parties involved. 【0599】 "Emotional analysis techniques" are technologies used to analyze a user's emotional state based on their facial expressions, tone of voice, and other factors. 【0600】 A "domestic adjustment system" is a system for optimizing household tasks and environmental settings according to the user's emotional state. 【0601】 This invention aims to optimize a personalized recruitment process and household tasks while taking into account the user's emotional state. The main components of the system are a server, a terminal, and the user. 【0602】 The server receives candidate information and stores it in a database through an information recording device. Candidate resumes are converted into text data using OCR technology and analyzed using natural language processing technology. This allows the analysis device to extract keywords and generate a candidate profile. 【0603】 Furthermore, the server uses sentiment analysis tools to analyze the user's emotions. This is achieved by analyzing the user's facial expressions and tone of voice. Based on the results of the sentiment analysis, appropriate responses and information are provided. 【0604】 The device displays information about candidates selected by the user and adjusts household tasks based on their emotions. For example, if the user is feeling stressed, relaxing environment settings are suggested through household adjustment tools. 【0605】 Users can review this information at their own pace and proceed through the hiring process. Furthermore, task management tailored to their emotional state enhances user convenience. 【0606】 For example, if a user wants to relax after a long workday, the system can analyze their emotions and recommend relaxing music. A concrete example of a prompt would be: "Come up with ideas for a robot assistant that analyzes the user's facial expressions and tone of voice to recognize their emotional state and optimize household tasks." 【0607】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0608】 Step 1: 【0609】 The server receives candidate resumes via the internet. Since the resumes are sent in formats such as PDF, they are stored in a database through an information recording system. The input is the candidate's resume data, and the output is the resume information stored in the database. 【0610】 Step 2: 【0611】 The server uses OCR technology to convert stored resume data into text data. At this stage, it analyzes information in image formats such as PDFs and converts it into text information. The input is resume data in image format, and the output is the converted text data. 【0612】 Step 3: 【0613】 The server analyzes text data using natural language processing techniques and extracts important keywords. This process utilizes a generative AI model to identify meaningful information within the text. The input is text data, and the output is the extracted keywords. 【0614】 Step 4: 【0615】 The emotion analysis system is activated, analyzing the user's facial expressions and voice tone in real time. Based on the data collected using the camera and microphone on the device, the user's emotional state is estimated. The input is real-time collected audio and video data, and the output is the analyzed emotional state. 【0616】 Step 5: 【0617】 The server selects the most suitable candidates based on the analyzed keywords and the user's emotional state. The suggested candidates change according to the emotional state. The input is keyword and emotional state data, and the output is a list of selected candidates. 【0618】 Step 6: 【0619】 The device presents the user with a list of selected candidates. The user can then decide on their next action based on this list. The input is the candidate list, and the output is the user's selection or feedback. 【0620】 Step 7: 【0621】 The device uses in-home adjustment tools to suggest task optimizations tailored to the user's emotions. This process utilizes in-home IoT devices to create an environment that promotes user relaxation, such as adjusting lighting. Inputs are the user's choices and emotional state, while outputs are optimized household tasks and settings. 【0622】 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. 【0623】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0624】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0625】 [Fourth Embodiment] 【0626】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0627】 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. 【0628】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0629】 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. 【0630】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0631】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0632】 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. 【0633】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0634】 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. 【0635】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0636】 The 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. 【0637】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0638】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0639】 This invention relates to an AI system for streamlining the recruitment process, and a specific embodiment thereof is shown below. 【0640】 This system functions through the cooperation of servers, terminals, and users. 【0641】 First, the server receives resumes from candidates via the internet. This is done using dedicated upload forms or email servers. The received resumes are stored in a database on the server, which functions as a data storage system. 【0642】 Next, the server analyzes the received resume file as an analytical tool. In this process, OCR is used to digitize the paper-based information, and natural language processing technology is used to extract keywords and important information. At the same time, it evaluates whether the candidate's skills and experience meet the pre-defined requirements. 【0643】 Once the analysis is complete, the server uses a candidate selection method to filter for suitable candidates and generate a candidate list. The selected candidates are evaluated and prioritized through a detailed scoring system. This information is displayed on the terminal for the user (recruiter) to review. 【0644】 The server then functions as a scheduling tool, automatically suggesting the optimal interview date and time based on the calendar information of the selected candidates and interviewers. It uses a calendar API to retrieve both parties' schedules and find overlapping time slots. The suggested date and time are notified to both the candidate and the interviewer via email, prompting them to confirm and finalize the schedule. 【0645】 For example, when a user is implementing a process to hire top engineers, the system automatically selects the top 10 candidates from over 100 based on their skills and experience. Interview dates with these candidates are then quickly scheduled, and both parties are immediately notified to minimize any delays in communication. 【0646】 In this way, this system automates and streamlines recruitment operations, optimizing resources and improving recruitment accuracy. 【0647】 The following describes the processing flow. 【0648】 Step 1: 【0649】 The user initiates the hiring process by uploading the candidate's resume to the system via email or a dedicated application form. 【0650】 Step 2: 【0651】 The server stores the received resume files in a database and begins managing resumes as a data storage method. It detects duplicate files or files in inappropriate formats and notifies the user. 【0652】 Step 3: 【0653】 The server converts saved resumes into text data using OCR technology. This makes it possible to extract information from handwritten or scanned documents. 【0654】 Step 4: 【0655】 The server uses natural language processing technology to analyze keywords and important information from resumes. Specifically, it identifies and categorizes the candidate's work experience, skills, educational background, etc. 【0656】 Step 5: 【0657】 Based on the analysis, the server scores candidates who meet the set criteria. Candidates with higher scores are then prioritized and filtered using the candidate selection method. 【0658】 Step 6: 【0659】 The terminal displays a list of selected candidates to the user. The user can view detailed information about each candidate on the screen and decide who to interview. 【0660】 Step 7: 【0661】 The server retrieves the interviewer's and candidate's calendar data from an API and automatically selects the optimal interview date and time using a scheduling tool. 【0662】 Step 8: 【0663】 The server notifies both the candidate and the interviewer via email of the scheduled interview date and time. The notification includes a confirmation button and an access link for the candidate to finalize the schedule. 【0664】 Step 9: 【0665】 Once the interview is complete, the user (interviewer) is provided with a form to enter the results. This information is sent to the server, recorded, and used to help with the final hiring decision. 【0666】 (Example 1) 【0667】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0668】 In recent years, processing large amounts of candidate information and efficiently selecting suitable personnel has become a major challenge in corporate recruitment activities. Traditional manual processes are time-consuming and often inefficient in terms of resource utilization. Furthermore, scheduling interviews with candidates also requires considerable time and effort for coordinating schedules between recruiters and candidates. A new system is needed to solve these problems. 【0669】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0670】 In this invention, the server includes an information holding means for collecting and storing candidate information, a conversion means that uses optical character recognition technology to digitize the candidate information, and an analysis means that uses natural language processing technology to extract keywords and important information from the digital information. This enables efficient processing of candidate information and rapid selection of suitable personnel. 【0671】 "Information retention means" refers to a function for collecting candidate data, storing it in a database, and managing it. 【0672】 "Conversion means" refers to a function that uses optical character recognition technology to convert paper-based information obtained from candidates into digital data. 【0673】 "Analysis means" refers to a function that uses natural language processing technology to extract keywords and important information from digitized candidate information. 【0674】 "Selection method" refers to a function that selects candidates who meet pre-set criteria based on analyzed candidate information. 【0675】 "Evaluation method" refers to a function that uses a scoring algorithm to prioritize selected candidates. 【0676】 "Time scheduling" refers to a function that obtains calendar information from both candidates and recruiters and automatically suggests the most suitable interview date and time based on that information. 【0677】 In order to implement this invention, the server, terminal, and user must cooperate and fulfill specific roles. Here, we will specifically describe the role of the server. 【0678】 The server receives candidate resumes via the internet and stores the information in a database as a means of data retention. When candidates submit their resumes via web forms or email, the server receives them via a secure connection, organizes them, and stores them. A commonly used open-source relational database management system can be used for the database management system. 【0679】 Next, the server uses OCR (Optical Character Recognition) technology to convert paper-based and image-based resumes into text data. Open-source OCR libraries are a possible software choice for this process. The converted digital information is then stored back into the database. 【0680】 Subsequently, the server uses natural language processing techniques as an analysis tool to extract specific keywords and important information from the resume. For this purpose, it employs a machine learning-based natural language processing library. For example, open-source natural language processing libraries can be useful tools. 【0681】 Furthermore, the server evaluates candidates based on the analyzed data, acting as both a selection and evaluation tool. It applies a scoring algorithm according to established criteria and assigns priorities to candidates based on the scoring results. 【0682】 Finally, as a means of scheduling, the server retrieves the calendar information of both the candidate and the interviewer via a calendar API and suggests the optimal date and time for the interview. This suggestion is notified to both parties via email, enabling quick scheduling. 【0683】 For example, when a user is hiring a top-tier engineer, this system allows them to select the 10 most suitable candidates from over 100 and quickly schedule interviews. 【0684】 An example of a prompt to input into the generating AI model is, "Please tell me the steps to streamline the engineer recruitment process." In this way, the entire recruitment process becomes automated and operates efficiently. 【0685】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0686】 Step 1: 【0687】 The server receives resumes from candidates. This input is a digital file received via web form or email. The server stores this file in a database, which functions as a means of information retention. The database stores and indexes the file along with the resume's metadata. 【0688】 Step 2: 【0689】 The server converts received resume files into digital text using OCR technology. This conversion method takes paper-based or image-based resumes as input and generates readable text data as output. The OCR software recognizes the characters within the text and stores them in a database in text format. 【0690】 Step 3: 【0691】 The server uses natural language processing technology to analyze information contained in digitized resumes. It takes digital text as input and uses analysis tools to extract keywords and important information. The extracted data is output in the form of skills, experience, and career history, and is supplied to the next step for evaluation and selection. 【0692】 Step 4: 【0693】 The server uses the analysis results to select candidates according to established criteria. Here, it determines whether a candidate's skill set and experience meet pre-defined requirements. The analysis results are used as input, and a list of candidates who meet the criteria is generated as output. The selection process is performed automatically using an algorithm. 【0694】 Step 5: 【0695】 The server evaluates the selected candidates and applies a scoring algorithm. Using the selected candidate information as input, it outputs a list of candidates with scores according to priority. The evaluation method calculates a specific score for each candidate. 【0696】 Step 6: 【0697】 The server retrieves candidate and interviewer calendar information via a calendar API for scheduling. It functions as a time scheduling tool, receiving calendar information as input and outputting optimal interview dates and times based on that information. Suggested dates and times are notified via email for quick confirmation and adjustment. 【0698】 (Application Example 1) 【0699】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0700】 Traditional recruitment processes involved the manual management and analysis of large amounts of candidate information, which was time-consuming and labor-intensive. Furthermore, scheduling interviews was cumbersome, making smooth communication with candidates difficult. Therefore, there is a need for increased efficiency and accuracy in recruitment operations. 【0701】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0702】 In this invention, the server includes data storage means for storing candidate information, analysis means for analyzing candidate information, candidate selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, scoring means for evaluating candidates based on the analysis results, display means for presenting and confirming candidate information through a user interface, and information distribution means for notifying candidates and interviewers of the schedule via a communication network. This enables the automation and efficiency of the recruitment process. 【0703】 "Candidate information" refers to personal data and skills information included in the resume and work history of a person being considered during the recruitment process. 【0704】 A "data storage means" is a device for storing entered candidate information and making it accessible as needed. 【0705】 "Analysis means" refers to a device or system that digitizes candidate information and analyzes the data using natural language processing technology or the like. 【0706】 A "candidate selection method" is a device that selects appropriate candidates based on information and criteria obtained through analysis. 【0707】 A "scheduling adjustment tool" is a device that coordinates the availability of candidates and interviewers to determine the optimal interview date and time. 【0708】 A "scoring means" is a device or method that quantifies and makes comparable the evaluation results of candidates. 【0709】 A "display means" is a device that visually displays analysis results and candidate information, enabling the user to confirm and make a judgment. 【0710】 An "information distribution device" is a device that notifies relevant parties of the decided interview date and time, candidate information, etc., via a communication network. 【0711】 The system for implementing this invention is configured in which a server, terminals, and users work together. The server first functions as a platform for receiving resumes submitted by candidates via the internet. The resumes are stored on the server through data storage means. The stored data is analyzed using OCR technology "Tesseract OCR" and natural language processing software "Google NLP API". This extracts important information and skills from the candidate information. 【0712】 After analysis, the server uses a candidate selection tool to filter out suitable candidates based on the analysis results. This selection process employs a scoring tool to evaluate and score candidates, and the results are then prioritized. Next, to schedule interviews with the selected candidates, the server uses the "Google Calendar API" as a scheduling tool to obtain calendar information from both candidates and interviewers and find common availability. 【0713】 Users can use their devices to view candidate information through the user interface and easily understand the displayed priority and evaluation scores. The server also serves as an information distribution method, notifying both candidates and interviewers of the scheduled interview date and time via email or other means. 【0714】 A concrete example is a company hiring engineers where the system automatically selects the top 10 candidates from 100 applicants based on scoring, and then automatically schedules and notifies them of their interview dates. This system makes it possible to streamline and improve the accuracy of the hiring process. 【0715】 An example of a prompt to input into a generating AI model is: "Please describe the detailed steps for a system that selects the best candidate from engineering resumes and automatically determines possible interview dates and times." 【0716】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0717】 Step 1: 【0718】 The server receives resumes from candidates via a dedicated upload form or email system over the internet. This input data is stored on the server in electronic file format and then stored in a database using data storage means. This completes the initial input of resume information. 【0719】 Step 2: 【0720】 The server processes the received resume data using OCR technology, converting paper-based information into digital text. Specifically, it uses "Tesseract OCR" to convert image data into text data. This converted data becomes input and is analyzed using natural language processing technology, "Google NLP API." As a result of the analysis, the candidate's skills, experience, and keywords are extracted and output as structured data of candidate information. 【0721】 Step 3: 【0722】 The server selects suitable candidates by comparing structured candidate information against pre-defined requirements. This selection process is performed using a scoring system. It receives analyzed candidate information as input and assigns an evaluation score to each candidate based on that information. The scoring results are output, and a prioritized candidate list is generated. 【0723】 Step 4: 【0724】 Users use their devices to view candidate lists and evaluation scores provided by the server through a user interface. Here, users can review detailed candidate information and make decisions based on the scored results. This process expedites candidate selection. 【0725】 Step 5: 【0726】 The server uses the Google Calendar API as a scheduling tool to coordinate interview dates with selected candidates. It reads the calendar data of both the candidate and the interviewer as input and detects overlapping free time slots. This generates optimal interview date and time candidates, and the interview schedule is automatically adjusted. 【0727】 Step 6: 【0728】 The server notifies candidates and interviewers of the scheduled interview date and time via information distribution. It also sends notifications via email or messaging services to prompt confirmation and finalization of the interview date and time. This allows for immediate scheduling sharing among all parties involved. 【0729】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0730】 This invention incorporates an emotion engine into an AI system for streamlining the recruitment process, and specific embodiments thereof are shown below. 【0731】 This system has a configuration that combines a server, terminals, users, and an emotion engine. 【0732】 First, the server receives resumes from candidates via the internet and stores them in a database. This allows candidate information, such as their work history and skill sets, to be managed by data storage. 【0733】 Next, the server uses OCR technology to convert the resume into text data, which is then analyzed using natural language processing technology. The analysis tool extracts keywords and generates a candidate profile. 【0734】 During this process, the emotion engine obtains real-time emotional data by analyzing the user's facial expressions and tone of voice. This allows the system to recognize the user's current emotional state. For example, if the user is relaxed, it will perform normal processing, but if they are stressed, it will suggest filtering to focus only on important candidates. 【0735】 Furthermore, the server uses a candidate selection mechanism to select the most suitable candidate based on the analysis results and sentiment data. The selection is performed using a scoring system that reflects the user's emotional state, thereby appropriately customizing the information the user receives to match their emotions. 【0736】 After selection, the device displays a list of candidates to the user, allowing them to quickly review emotionally preferred options as needed. 【0737】 Subsequently, the scheduling mechanism takes into account the analysis results from the emotion engine to adjust the interview date. For example, if the user is busy, it prioritizes suggesting interview times when they are expected to be more relaxed. 【0738】 For example, when users are running the hiring process at a busy end of the month, the emotion engine can detect their stress levels and handle candidate selection and interview scheduling with minimal burden on the user. 【0739】 In this way, by incorporating an emotion engine, the recruitment process becomes more personalized, efficient, and user-friendly. 【0740】 The following describes the processing flow. 【0741】 Step 1: 【0742】 Users submit candidate resumes to the system via an online application form or email. The application data is sent to the server and securely stored in the database. 【0743】 Step 2: 【0744】 The server converts the received resumes into text data using OCR technology. This text format allows for further data analysis. 【0745】 Step 3: 【0746】 The server uses natural language processing (NLP) techniques to analyze the transcribed resume and extract keywords such as skill sets, work experience, and educational background. Based on this, it profiles the candidate's characteristics. 【0747】 Step 4: 【0748】 The emotion engine monitors the user's facial expressions and tone of voice while they are reviewing their resume, and analyzes their emotional state in real time. 【0749】 Step 5: 【0750】 The server receives data from the emotion engine and adjusts the candidate selection criteria based on the user's emotional state. Specifically, if the user is feeling tired, optimizations are made such as displaying only the top-scoring candidates. 【0751】 Step 6: 【0752】 The user is provided with a refined list of candidates on their device. Through an intuitive interface, the user can review candidate details and select candidates for interviews. 【0753】 Step 7: 【0754】 The server checks the calendar information of both the interviewer and the candidate for the selected candidates and schedules the interview. Taking into account the emotion engine data, it prioritizes suggesting times that are easy to relax during. 【0755】 Step 8: 【0756】 The server will notify both the candidate and the interviewer of the scheduled interview date and time. This notification will include a confirmation button and a link to the interview. 【0757】 Step 9: 【0758】 After completing an interview, the user (interviewer) uses the feedback function provided by the emotion engine to input an evaluation of the candidate. The evaluation is sent to the server and used to inform future processes. 【0759】 (Example 2) 【0760】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0761】 In the recruitment process, it is necessary to efficiently manage candidate information and select the most suitable candidates while considering the emotional state of the users. However, conventional systems have difficulty reflecting user emotional data in their selection process, and scheduling interviews has not always taken into account the user's stress level. As a result, this can sometimes be burdensome for the user. 【0762】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0763】 In this invention, the server includes an information storage means for storing candidate information, an information analysis means for analyzing candidate information, and an emotion acquisition means for acquiring emotion data by analyzing the user's facial expressions and tone of voice. This makes it possible to select candidates and adjust interview schedules while taking into account the user's emotional state. 【0764】 "Information storage means" refers to a means of storing and managing information, including a candidate's work history and skill set, in a database or similar system. 【0765】 "Information analysis means" refers to methods for processing text data using OCR technology and natural language processing technology to extract necessary keywords and profiles. 【0766】 "Candidate selection methods" refer to the means used to select suitable candidates based on the results of the analysis. 【0767】 An "emotion acquisition method" is a means of acquiring emotional data in real time by analyzing the user's facial expressions and tone of voice. 【0768】 "Scheduling methods" refer to techniques for optimizing interview schedules between users and candidates, while taking into account acquired emotional data. 【0769】 This invention provides a recruitment support system that incorporates user emotional data to streamline the recruitment process. The system consists of a server, terminals, and an emotional engine. 【0770】 First, the server receives resumes submitted by candidates via the internet and stores the information in a database. The server uses OCR technology to convert the resumes into text data and performs analysis using natural language processing (NLP) technology. This extracts keywords from the candidate's work history and skill set, and builds a profile. 【0771】 Next, the emotion engine uses the user's camera and microphone to analyze their facial expressions and voice tone in real time, acquiring emotional data. This data is used to identify the user's current emotional state. If the user is relaxed, normal processing is performed; if they are stressed, filtering is suggested to narrow down the candidates to those who are most important. 【0772】 The server combines analytical income data and sentiment data, and uses a selection algorithm to choose the most suitable candidate. This selection is scored based on sentiment data, and the information received by the user is individually customized. 【0773】 The terminal then displays a list of the most suitable candidates to the user. As a specific example, consider a scenario where the user is using the system during a busy period at the end of the month. The emotion engine senses the user's stress level and takes steps to minimize the user's burden when suggesting interview dates and displaying candidates. 【0774】 Examples of prompt statements include the following: 【0775】 "Analyze resumes and generate candidate profiles. Also, analyze the user's current emotional state to suggest the most suitable candidate list." 【0776】 In this way, by incorporating an emotion engine, the system personalizes the recruitment process, resulting in an efficient and user-friendly procedure. 【0777】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0778】 Step 1: 【0779】 The server receives resume files submitted by candidates via the internet. It takes resume data in PDF or image format from candidates as input. The server stores these resumes in a database, preparing them for managing work history and skill sets. The output is the candidate information stored in the database. Specifically, it saves received files to a designated folder and stores their metadata in the database. 【0780】 Step 2: 【0781】 The server uses OCR technology to convert the received resume into text data. The input is the candidate's resume file saved in step 1. The OCR process extracts text information from the image. The output is the text data of the resume. Specifically, the OCR engine analyzes the image file and generates text information. 【0782】 Step 3: 【0783】 The server analyzes text data using natural language processing (NLP) techniques. The input is the text data obtained in step 2. Noun phrases and verb phrases are extracted from this data to generate candidate profiles. The output is the analyzed candidate profile data and related keywords. Specifically, the NLP algorithm extracts key keywords and phrases to construct the profile. 【0784】 Step 4: 【0785】 The emotion engine analyzes the user's facial expressions and voice tone in real time using the user's camera and microphone. The input is audio and video data from the user. Emotional data is acquired here. The output is data representing the user's current emotional state. Specifically, emotion scoring is performed using facial recognition and voice analysis technologies. 【0786】 Step 5: 【0787】 The server integrates the analysis results and sentiment data and selects the most suitable candidate using a candidate selection algorithm. The inputs are the profile data from step 3 and the sentiment data from step 4. It generates selection results that reflect the user's sentiment. The output is a scored list of candidates. Specifically, the selection algorithm weights the sentiment data and evaluates suitability. 【0788】 Step 6: 【0789】 The terminal displays a list of the most suitable candidates for the user. The input is the candidate selection result from step 5. The user can view the list, displayed in order of score, and check the details of each candidate. The output is the displayed candidate list. Specifically, the GUI is updated, and the profiles of the selected candidates are displayed side by side. 【0790】 Step 7: 【0791】 The scheduling mechanism adjusts interview dates while considering the user's stress level based on emotional data. Inputs include a candidate list and emotional data. It provides scheduling suggestions aimed at reducing stress. The output is a suggestion of the optimal interview date. Specifically, it compares the user's calendar with available time slots and notifies them of the best date and time. 【0792】 (Application Example 2) 【0793】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0794】 In the recruitment process, it is necessary not only to analyze candidate information and select suitable candidates, but also to provide a personalized experience that takes into account the user's emotional state. Similarly, in household task management, optimization based on the user's emotions is required. 【0795】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0796】 In this invention, the server includes information recording means for storing candidate information, information analysis means for analyzing candidate information, selection means for selecting applicable candidates based on the analysis results, scheduling means for coordinating interview dates with selected candidates, sentiment analysis means for analyzing the user's emotional state, and household adjustment means for optimizing household tasks based on the user's emotional state. This enables the user to comfortably and more personally manage the recruitment process and household tasks. 【0797】 "Information recording means" refers to devices or systems for centrally storing candidate and user information and maintaining it in a state where it can be quickly accessed as needed. 【0798】 "Information analysis methods" refer to the techniques and processes used to analyze collected information and extract useful data from it. 【0799】 A "selection method" is a system that has criteria or algorithms for selecting the most suitable candidate based on the analysis results. 【0800】 "Scheduling methods" refer to methods or devices for proposing and determining the most suitable interview date and time, taking into account the schedules of all parties involved. 【0801】 "Emotional analysis techniques" are technologies used to analyze a user's emotional state based on their facial expressions, tone of voice, and other factors. 【0802】 A "domestic adjustment system" is a system for optimizing household tasks and environmental settings according to the user's emotional state. 【0803】 This invention aims to optimize a personalized recruitment process and household tasks while taking into account the user's emotional state. The main components of the system are a server, a terminal, and the user. 【0804】 The server receives candidate information and stores it in a database through an information recording device. Candidate resumes are converted into text data using OCR technology and analyzed using natural language processing technology. This allows the analysis device to extract keywords and generate a candidate profile. 【0805】 Furthermore, the server uses sentiment analysis tools to analyze the user's emotions. This is achieved by analyzing the user's facial expressions and tone of voice. Based on the results of the sentiment analysis, appropriate responses and information are provided. 【0806】 The device displays information about candidates selected by the user and adjusts household tasks based on their emotions. For example, if the user is feeling stressed, relaxing environment settings are suggested through household adjustment tools. 【0807】 Users can review this information at their own pace and proceed through the hiring process. Furthermore, task management tailored to their emotional state enhances user convenience. 【0808】 For example, if a user wants to relax after a long workday, the system can analyze their emotions and recommend relaxing music. A concrete example of a prompt would be: "Come up with ideas for a robot assistant that analyzes the user's facial expressions and tone of voice to recognize their emotional state and optimize household tasks." 【0809】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0810】 Step 1: 【0811】 The server receives candidate resumes via the internet. Since the resumes are sent in formats such as PDF, they are stored in a database through an information recording system. The input is the candidate's resume data, and the output is the resume information stored in the database. 【0812】 Step 2: 【0813】 The server uses OCR technology to convert stored resume data into text data. At this stage, it analyzes information in image formats such as PDFs and converts it into text information. The input is resume data in image format, and the output is the converted text data. 【0814】 Step 3: 【0815】 The server analyzes text data using natural language processing techniques and extracts important keywords. This process utilizes a generative AI model to identify meaningful information within the text. The input is text data, and the output is the extracted keywords. 【0816】 Step 4: 【0817】 The emotion analysis system is activated, analyzing the user's facial expressions and voice tone in real time. Based on the data collected using the camera and microphone on the device, the user's emotional state is estimated. The input is real-time collected audio and video data, and the output is the analyzed emotional state. 【0818】 Step 5: 【0819】 The server selects the most suitable candidates based on the analyzed keywords and the user's emotional state. The suggested candidates change according to the emotional state. The input is keyword and emotional state data, and the output is a list of selected candidates. 【0820】 Step 6: 【0821】 The device presents the user with a list of selected candidates. The user can then decide on their next action based on this list. The input is the candidate list, and the output is the user's selection or feedback. 【0822】 Step 7: 【0823】 The device uses in-home adjustment tools to suggest task optimizations tailored to the user's emotions. This process utilizes in-home IoT devices to create an environment that promotes user relaxation, such as adjusting lighting. Inputs are the user's choices and emotional state, while outputs are optimized household tasks and settings. 【0824】 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. 【0825】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0826】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0827】 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. 【0828】 Figure 9 shows an 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. 【0829】 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. 【0830】 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. 【0831】 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, motorcycles, etc., 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, for example, based 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. 【0832】 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." 【0833】 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. 【0834】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0835】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0836】 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. 【0837】 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. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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 the like 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. 【0844】 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 as being incorporated by reference. 【0845】 The following is further disclosed regarding the embodiments described above. 【0846】 (Claim 1) 【0847】 A data storage means for storing candidate information, 【0848】 An analytical means for analyzing candidate information, 【0849】 A candidate selection method for selecting applicable candidates based on the analysis results, 【0850】 A scheduling method for coordinating interview dates with selected candidates, 【0851】 A system that includes this. 【0852】 (Claim 2) 【0853】 The system according to claim 1, comprising means for extracting keywords using natural language processing techniques in the analysis of candidate information. 【0854】 (Claim 3) 【0855】 The system according to claim 1, which includes means for selecting the optimal date and time when scheduling interviews with candidates, taking into account the calendar information of both parties. 【0856】 "Example 1" 【0857】 (Claim 1) 【0858】 Information storage means for collecting and storing candidate information, 【0859】 A conversion means that uses optical character recognition technology to digitize candidate information, 【0860】 An analysis means for extracting keywords and important information from digital information using natural language processing technology, 【0861】 A selection method for selecting candidates who meet the criteria based on the analysis results, 【0862】 Evaluation methods for prioritizing selected candidates, 【0863】 A time scheduling method that automatically proposes and adjusts interview dates with candidates by acquiring calendar information and setting the optimal date and time, 【0864】 A system that includes this. 【0865】 (Claim 2) 【0866】 The system according to claim 1, comprising means for extracting keywords and important information from candidate information using natural language processing technology. 【0867】 (Claim 3) 【0868】 The system according to claim 1, comprising means for automatically suggesting an optimal interview date and time, taking into account the calendar information of selected candidates and interviewers. 【0869】 "Application Example 1" 【0870】 (Claim 1) 【0871】 A data storage means for storing candidate information, 【0872】 An analytical means for analyzing candidate information, 【0873】 A candidate selection method for selecting applicable candidates based on the analysis results, 【0874】 A scheduling method for coordinating interview dates with selected candidates, 【0875】 A scoring method for evaluating candidates based on the analysis results, 【0876】 A display means that presents and allows confirmation of candidate information through a user interface, 【0877】 An information distribution method that notifies candidates and interviewers of the schedule via a communication network, 【0878】 A system that includes this. 【0879】 (Claim 2) 【0880】 The system according to claim 1, comprising means for extracting important information using natural language processing technology in the analysis of candidate information. 【0881】 (Claim 3) 【0882】 The system according to claim 1, which includes means for selecting the optimal date and time by extracting common free time from the schedule information of both parties when coordinating interview dates for candidates. 【0883】 "Example 2 of combining an emotion engine" 【0884】 (Claim 1) 【0885】 Information storage means for storing candidate information, 【0886】 Information analysis means for analyzing candidate information, 【0887】 A candidate selection method for selecting applicable candidates based on the analysis results, 【0888】 An emotion acquisition method that obtains emotional data by analyzing the user's facial expressions and voice tone, 【0889】 A scheduling method for coordinating interview dates with candidates selected based on analysis results and emotional data, 【0890】 A system that includes this. 【0891】 (Claim 2) 【0892】 The system according to claim 1, comprising means for extracting keywords using natural language processing techniques in the analysis of candidate information. 【0893】 (Claim 3) 【0894】 The system according to claim 1, which includes means for selecting the optimal date and time for scheduling candidate interviews, taking into account the user's emotional state. 【0895】 "Application example 2 when combining with an emotional engine" 【0896】 (Claim 1) 【0897】 Information recording means for storing candidate information, 【0898】 Information analysis means for analyzing candidate information, 【0899】 A selection method for selecting applicable candidates based on the analysis results, 【0900】 A scheduling method for coordinating interview dates with selected candidates, 【0901】 A means of analyzing the emotional state of a user, 【0902】 A home adjustment mechanism that optimizes household tasks based on the user's emotional state, 【0903】 A system that includes this. 【0904】 (Claim 2) 【0905】 The system according to claim 1, comprising means for extracting keywords using natural language processing techniques in the analysis of candidate information. 【0906】 (Claim 3) 【0907】 The system according to claim 1, which includes means for selecting the optimal date and time when scheduling interviews for candidates, taking into account the schedule information of both parties. [Explanation of Symbols] 【0908】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data storage means for storing candidate information, An analytical means for analyzing candidate information, A candidate selection method for selecting applicable candidates based on the analysis results, A scheduling method for coordinating interview dates with selected candidates, A system that includes this. [Claim 2] The system according to claim 1, comprising means for extracting keywords using natural language processing technology in the analysis of candidate information. [Claim 3] The system according to claim 1, which includes means for selecting the optimal date and time when scheduling interviews with candidates, taking into account the calendar information of both parties.

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