system

Abstract

The system according to this embodiment aims to reduce the burden associated with job hunting. [Solution] The system according to the embodiment comprises a reception unit, a receiving unit, a creation unit, a coordination unit, and a progress unit. The reception unit registers profiles. The receiving unit receives scouts based on the profile information registered by the reception unit. The creation unit creates application documents based on the scouts received by the receiving unit. The coordination unit schedules interviews based on the application documents created by the creation unit. The progress unit conducts interviews based on the schedules arranged by the coordination unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can reduce the burden associated with job hunting. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The job change support system according to an embodiment of the present invention is a system for significantly reducing the burden of job hunting. In this job change support system, when a user registers a profile, an AI agent receives scouting offers, automatically creates and submits application documents for job openings that match the user's preferences. Next, the AI ​​agent schedules interviews and conducts interviews on behalf of the user. The personal AI responds in a manner that matches the user's tone of voice and character, and conducts the interview. Furthermore, since the AI ​​agent can handle interactions with multiple companies in parallel, the user only needs to check the application details and selection results. This mechanism significantly reduces the burden on the user in job hunting and allows them to efficiently proceed with activities to find their ideal job. In addition, since the user can personally handle important parts such as the final interview, the success rate of job hunting is also improved. For example, the job change support system allows the user to register a profile. For example, the job change support system can scan a handwritten profile and convert it into digital data, or the user can directly input it in digital format. This data is converted into a format that is easy for the generating AI to analyze. Next, the job change support system uses the generating AI to analyze the user's profile and summarize its contents. The input to the generative AI is the user's profile itself, and the generative AI generates a summary based on its content. For example, if the generative AI receives a prompt such as "Please summarize the key points of this profile," it will extract the key points from the profile and create a summary. Next, the job placement system calculates the similarity between the summary created by the generative AI and pre-prepared job postings. Natural language processing techniques are used to calculate the similarity. For example, the generative AI analyzes the degree of word matching and sentence structure similarity between the summary and the job posting to calculate a similarity score. Next, the job placement system calculates the suitability of the job posting based on the similarity score. For example, it may be set so that a higher similarity score corresponds to a higher suitability. The final suitability is fed back to the user. This allows the user to know how well their profile matches the job posting. The job placement system also applies the same process to multiple job postings.The job placement system reads the user's profile, a generating AI summarizes it, and calculates its similarity to job postings. For example, the system summarizes the user's skills and experience related to the job posting's theme and compares it to the job posting to calculate the degree of suitability. This allows the job placement system to significantly reduce the burden of job searching and enable it to proceed more efficiently. In this way, the job placement system can efficiently support the user's job search and reduce their burden.

[0029] The job change support system according to this embodiment comprises a reception unit, a receiving unit, a creation unit, a coordination unit, and a progress unit. The reception unit receives user profiles. User profiles include, but are not limited to, name, work history, skill set, and career goals. The reception unit, for example, scans handwritten profiles and converts them into digital data. The reception unit can also directly read profiles submitted in digital format. Furthermore, the reception unit can read printed profiles using OCR technology. For example, the reception unit scans handwritten profiles with a high-resolution scanner and converts them into text information using OCR technology. Digital profiles can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The receiving unit receives scouts based on the profile information registered by the reception unit. Scouts include, but are not limited to, offers and interview invitations from companies. The receiving unit automatically receives offers from companies and notifies the user. The receiving unit can also receive interview invitations and notify users. Furthermore, the receiving unit can analyze the content of scouts and determine whether they match the user's preferences. For example, the receiving unit can analyze offers from companies and determine if they match the user's desired job type and work location. The creation unit creates application documents based on the scouts received by the receiving unit. Application documents include, but are not limited to, resumes, CVs, and cover letters. For example, the creation unit can automatically create a resume based on the user's profile information. The creation unit can also analyze the user's work history and skill set to create a CV. Furthermore, the creation unit can customize the content of a cover letter based on the user's career goals and desired job type. For example, the creation unit can create a resume based on the user's work history and highlight their skill set. The scheduling unit schedules interviews based on the application documents created by the creation unit.The scheduling of interviews includes, but is not limited to, the presentation of candidate dates and scheduling methods. The scheduling unit adjusts interview dates considering the convenience of both the company and the user. The scheduling unit can also analyze the user's schedule to automatically adjust interview dates. Furthermore, the scheduling unit can also consider the company's schedule when scheduling interview dates. For example, the scheduling unit adjusts interview dates based on the user's schedule to accommodate the company's needs. The progress unit conducts the interview based on the schedule adjusted by the scheduling unit. The progress unit provides responses that are tailored to the user's tone of voice and character. The progress unit can use personal AI to provide responses that are tailored to the user's tone of voice and character. For example, the progress unit customizes the interview process based on the user's tone of voice and character. As a result, the job change support system according to this embodiment can efficiently support the user's job change activities and reduce their burden.

[0030] The reception desk allows users to register their profiles. These profiles may include, but are not limited to, name, work history, skill set, and career goals. The reception desk can, for example, scan handwritten profiles and convert them into digital data. It can also directly read profiles submitted in digital format. Furthermore, the reception desk can read printed profiles using OCR technology. For example, it can scan handwritten profiles with a high-resolution scanner and convert them into text information using OCR technology. Digital profiles submitted in specific file formats can be directly read. OCR technology accurately recognizes printed characters and converts them into digital text. This allows users to easily register their profiles, and the system can efficiently process profiles in various formats. Additionally, the reception desk is responsive to user profile updates, ensuring that the information is always up-to-date. For example, if a user acquires new skills or adds to their work history, the reception desk can quickly reflect this information. This ensures that user profiles are always current, providing appropriate information to companies seeking their services. Furthermore, the reception desk has also considered the ease of use for users registering their profiles, providing an intuitive interface. For example, features such as the ability to easily upload files using drag-and-drop functionality have been implemented to improve user convenience. As a result, users can register their profiles without stress, promoting the use of the system.

[0031] The receiving unit receives scouts based on profile information registered by the reception unit. Scouts include, but are not limited to, offers and interview invitations from companies. For example, the receiving unit automatically receives offers from companies and notifies the user. The receiving unit can also receive interview invitations and notify the user. Furthermore, the receiving unit can analyze the content of scouts and determine whether they match the user's preferences. For example, the receiving unit analyzes offers from companies and determines whether they match the user's desired job type and work location. This allows the user to efficiently receive scouts that are suitable for them. The receiving unit uses AI to analyze the content of scouts and compare them with the user's desired conditions. For example, the AI ​​analyzes the job type, work location, salary conditions etc. listed in the offer from the company and compares them with the user's registered information. This allows the user to be notified only of scouts that match their preferences, and the user can proceed with their job search efficiently without being bothered by irrelevant offers. Furthermore, the receiving section includes a function to set the priority of scouts, allowing it to prioritize notifications of offers that are highly important to the user. For example, it can support the user's job search by prioritizing offers with high salary conditions or offers for job types that the user particularly desires. In addition, the receiving section also has a function to manage the history of scouts, allowing users to view a list of offers and interview invitations received in the past. This allows users to review past scouts and keep track of the progress of their job search.

[0032] The creation unit creates application documents based on recruitment offers received by the receiving unit. These documents include, but are not limited to, resumes, CVs, and cover letters. For example, the creation unit can automatically create a resume based on the user's profile information. It can also analyze the user's work history and skill set to create a CV. Furthermore, it can customize the content of a cover letter based on the user's career goals and desired position. For example, the creation unit creates a resume based on the user's work history and highlights their skill set. This allows the user to create high-quality application documents with minimal effort. The creation unit uses AI to analyze the user's profile information and generate optimal application documents. For example, the AI ​​analyzes the user's work history and skill set in detail and customizes resumes and CVs to match the requirements of the company. In creating cover letters, it generates content that highlights the user's strengths to the company based on their career goals and desired position. This allows the user to effectively promote themselves to companies. Furthermore, the creation function also considers the format and design of application documents, allowing users to create documents that meet the specific requirements of each company. For example, by creating a resume according to a format specified by a particular company, it provides documents that meet the company's requirements. The creation function also provides an interface for users to review and revise their application documents, allowing them to check the generated documents and make corrections as needed. This enables users to create application documents that reflect their intentions, increasing their chances of success in their job search.

[0033] The scheduling unit schedules interviews based on application documents created by the creation unit. Interview scheduling includes, but is not limited to, suggesting candidate dates and scheduling methods. For example, the scheduling unit considers the convenience of both the company and the user when scheduling interviews. The scheduling unit can also analyze the user's schedule to automatically schedule interviews. Furthermore, the scheduling unit can consider the company's schedule when scheduling interviews. For example, the scheduling unit adjusts the interview schedule based on the user's schedule to accommodate the company's needs. This ensures that interviews are conducted on dates convenient for both the user and the company. The scheduling unit uses AI to analyze the schedules of both the user and the company and proposes the optimal interview date. For example, the AI ​​analyzes the user's calendar information and the company's available interview dates to automatically extract dates that are convenient for both parties. The scheduling unit also provides multiple candidate dates for the user and company to choose from, enabling flexible scheduling. Furthermore, after the interview date is confirmed, the scheduling unit provides a reminder function, sending notifications to both the user and the company the day before the interview. This ensures that interviews are conducted smoothly without forgetting scheduled appointments. Furthermore, the scheduling department can handle interview date changes, enabling quick responses even in the event of sudden schedule changes. For example, if an interview date needs to be changed due to the user's or company's circumstances, the scheduling department will propose new candidate dates and reschedule. This allows for flexible responses for both users and companies, increasing the interview completion rate.

[0034] The interview management team conducts the interview based on the schedule set by the coordination team. The management team, for example, provides responses tailored to the user's tone of voice and character. The management team can use personal AI to provide responses tailored to the user's tone of voice and character. For example, the management team customizes the interview process based on the user's tone of voice and character. This allows the user to respond in a way that is true to themselves, increasing the chances of a successful interview. The management team uses AI to analyze the user's past interview data and profile information and proposes optimal responses. For example, the AI ​​suggests areas for improvement and points to emphasize based on the user's past interview responses and evaluations. The management team also simulates responses tailored to the user's character, supporting the user in approaching the interview with confidence. Furthermore, the management team has the function to monitor the progress of the interview in real time and provide advice as needed. For example, if the user encounters a difficult situation during the interview, the management team provides appropriate advice to help the user continue responding smoothly. The management team also collects feedback after the interview and provides advice to help the user improve in future interviews. This allows users to grow with each interview, increasing their chances of success in their job search. Furthermore, the system manages interview results and notifies users of those results. This allows users to quickly understand the results of their interviews and move on to the next step.

[0035] The interviewer can conduct the interview using a personal AI. For example, the interviewer can provide responses tailored to the user's tone of voice and character. The interviewer can customize the interview process based on the user's tone of voice and character using the personal AI. For example, the interviewer can adjust the interview process based on the user's tone of voice and character. Furthermore, the interviewer can optimize the interview process based on the user's tone of voice and character using the personal AI. For example, the interviewer can adjust the interview process in real time based on the user's tone of voice and character. This allows the interviewer to provide responses tailored to the user's tone of voice and character. The personal AI is implemented using, for example, natural language processing techniques and machine learning algorithms. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. Machine learning algorithms include, for example, deep learning, support vector machines, and random forests. This allows the interviewer to optimize the interview process based on the user's tone of voice and character.

[0036] The job placement system has the functionality to handle interactions with multiple companies in parallel. For example, the job placement system can simultaneously receive and notify the user of recruitment offers from multiple companies. To handle interactions with multiple companies in parallel, the job placement system uses methods for parallel processing and prioritization. For example, the job placement system can simultaneously receive and notify the user of recruitment offers from multiple companies. The job placement system can also use algorithms to determine priorities in order to handle interactions with multiple companies in parallel. For example, the job placement system can determine the priority of recruitment offers based on the user's desired job type and work location. This allows the user to simply check the application details and selection results. Some or all of the above processes in the job placement system may be performed using AI, or not. For example, the job placement system can receive recruitment offers from multiple companies, input the details of the offers into AI, and the AI ​​can determine the priority of the recruitment offers.

[0037] The creation unit can create application documents for job postings that match the user's preferences. For example, the creation unit can create application documents based on the user's desired job type and work location. The creation unit can customize the content of the application documents based on the user's preferences. For example, the creation unit can create a resume and work history based on the user's desired job type. The creation unit can also create a cover letter based on the user's desired work location. This allows the creation of application documents for job postings that match the user's preferences. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's desired job type and work location into AI, and the AI ​​can create the application documents.

[0038] The scheduling unit can schedule interviews. For example, the scheduling unit will schedule interviews considering the convenience of both the company and the user. The scheduling unit can analyze the user's schedule in order to automatically schedule interviews. For example, the scheduling unit will adjust the interview schedule based on the user's schedule to suit the company's convenience. The scheduling unit can also consider the company's schedule when scheduling interviews. This allows the scheduling unit to schedule interviews. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's schedule and the company's schedule into the AI, which can then adjust the optimal interview schedule.

[0039] The facilitator can respond in a way that suits the user's tone of voice and character. For example, the facilitator can customize the interview process based on the user's tone of voice and character. The facilitator can use a personal AI to optimize the interview process based on the user's tone of voice and character. For example, the facilitator can adjust the interview process in real time based on the user's tone of voice and character. The facilitator can also use a personal AI to optimize the interview process based on the user's tone of voice and character. This allows the facilitator to respond in a way that suits the user's tone of voice and character. Some or all of the above processes in the facilitator may be performed using AI, for example, or not using AI. For example, the facilitator can input the user's tone of voice and character into the AI, which can then adjust the interview process.

[0040] The reception desk can analyze a user's past work experience and skill set to select the optimal profile registration method. For example, the reception desk can analyze a user's past work experience and automatically input relevant skill sets. Based on the user's skill set, the reception desk can suggest the most suitable job type and industry. The reception desk can also combine the user's work experience and skill set to customize the optimal profile registration method. This allows the reception desk to select the most suitable profile registration method based on the user's past work experience and skill set. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's work experience and skill set into an AI, which can then select the most suitable profile registration method.

[0041] The reception desk can filter users based on their current career goals and areas of interest during profile registration. For example, the reception desk can prioritize displaying relevant job titles and industries based on the user's career goals. The reception desk can highlight relevant skills and experience based on the user's areas of interest. The reception desk can also combine the user's career goals and areas of interest to suggest the most suitable profile registration content. This allows the profile registration content to be filtered based on the user's career goals and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's career goals and areas of interest into an AI, which can then filter the profile registration content.

[0042] The reception desk can prioritize registering highly relevant information when a user registers their profile, taking into account their geographical location. For example, the reception desk can prioritize displaying nearby job postings based on the user's current location. The reception desk can also provide information on commute time and transportation methods, taking into account the user's geographical location. Furthermore, the reception desk can highlight region-specific skills and experience based on the user's geographical location. This allows for the priority registration of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into an AI, which can then prioritize registering highly relevant information.

[0043] The reception desk can analyze a user's social media activity and register relevant information during profile registration. For example, the reception desk can automatically import a user's social media profile information to simplify profile registration. The reception desk can analyze a user's social media activity and highlight relevant skills and experience. It can also leverage a user's social media network to automatically add letters of recommendation and referrals. This allows for the registration of relevant information based on the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input a user's social media activity into an AI, which can then register relevant information.

[0044] The receiving unit can analyze the user's past application history to select the most suitable scout when it receives a scout offer. For example, the receiving unit analyzes the user's past application history and displays relevant scout offers with priority. The receiving unit can automatically select the most suitable scout based on the user's application history. The receiving unit can also determine the priority of scout offers by referring to the user's application history. This allows the receiving unit to select the most suitable scout based on the user's past application history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's application history into AI, which can then select the most suitable scout.

[0045] The receiving unit can filter scouts based on the user's current career goals when a scout is received. For example, the receiving unit can prioritize displaying relevant scouts based on the user's career goals. The receiving unit can automatically filter scouts that align with the user's career goals. The receiving unit can also suggest the most suitable scouts, taking the user's career goals into consideration. This allows for filtering scouts based on the user's career goals. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's career goals into AI, which can then filter scouts.

[0046] The receiving unit can prioritize receiving highly relevant scouts when it receives a scout, taking into account the user's geographical location information. For example, the receiving unit can prioritize displaying nearby job postings based on the user's current location. The receiving unit can also provide information on commute time and transportation methods, taking into account the user's geographical location information. Furthermore, the receiving unit can prioritize receiving region-specific scouts based on the user's geographical location information. This allows the user to prioritize receiving highly relevant scouts based on their geographical location information. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's geographical location information into AI, which can then prioritize receiving highly relevant scouts.

[0047] The receiving unit can analyze the user's social media activity when a scout is received and receive relevant scouts. For example, the receiving unit can prioritize displaying relevant scouts based on the user's social media profile information. The receiving unit can analyze the user's social media activity and receive relevant scouts. The receiving unit can also utilize the user's social media network to receive scouts that include letters of recommendation and referrals. This allows the user to receive relevant scouts based on their social media activity. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not using AI. For example, the receiving unit can input the user's social media activity into AI, which can then receive relevant scouts.

[0048] The creation unit can analyze the user's past work experience and skill set to create the most suitable application documents. For example, the creation unit can analyze the user's past work experience and create application documents that highlight relevant skill sets. Based on the user's skill set, the creation unit can create application documents tailored to the most suitable job and industry. The creation unit can also combine the user's work experience and skill set to customize the most suitable application documents. This allows the creation of the most suitable application documents based on the user's past work experience and skill set. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's work experience and skill set into AI, which can then create the most suitable application documents.

[0049] The creation unit can customize the content of application documents based on the user's current career goals when creating them. For example, the creation unit can create application documents tailored to relevant job types and industries based on the user's career goals. The creation unit can create application documents that highlight skills and experience aligned with the user's career goals. Furthermore, the creation unit can customize the application documents to be optimal, taking the user's career goals into consideration. This allows the content of application documents to be customized based on the user's career goals. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's career goals into AI, which can then customize the content of the application documents.

[0050] The creation function can prioritize highly relevant information when creating application documents, taking into account the user's geographical location. For example, it can prioritize nearby job postings based on the user's current location. It can also include information about commute time and transportation methods, taking into account the user's geographical location. Furthermore, it can highlight region-specific skills and experience based on the user's geographical location. This allows for the prioritization of highly relevant information based on the user's geographical location. Some or all of the above processing in the creation function may be performed using AI, for example, or not. For example, the creation function can input the user's geographical location into AI, which can then prioritize the inclusion of highly relevant information.

[0051] The creation unit can analyze the user's social media activity and include relevant information when creating application documents. For example, the creation unit can include relevant skills and experience based on the user's social media profile information. The creation unit can analyze the user's social media activity and include relevant information. The creation unit can also utilize the user's social media network to include letters of recommendation and introductions. This allows for the inclusion of relevant information based on the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's social media activity into AI, and the AI ​​can include relevant information.

[0052] The scheduling unit can analyze the user's past interview history to select the optimal date when scheduling an interview. For example, the scheduling unit can analyze the user's past interview history and propose the optimal date. Based on the user's interview history, the scheduling unit can select time slots with a high success rate for interviews. The scheduling unit can also determine the priority of interviews by referring to the user's interview history. This allows the system to select the optimal date based on the user's past interview history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's interview history into AI, which can then select the optimal date.

[0053] The scheduling unit can customize interview dates based on the user's current schedule. For example, the scheduling unit can check the user's schedule and set up interviews during available time slots. The scheduling unit can suggest the optimal interview dates based on the user's schedule. The scheduling unit can also prioritize interviews, taking the user's schedule into consideration. This allows for the customization of interview dates based on the user's current schedule. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's schedule into the AI, which can then customize the interview dates.

[0054] The scheduling unit can select the optimal date for an interview by considering the user's geographical location. For example, the scheduling unit can prioritize nearby interview locations based on the user's current location. The scheduling unit can also provide information on commuting time and transportation methods, taking the user's geographical location into consideration. Furthermore, the scheduling unit can prioritize region-specific interview dates based on the user's geographical location. This allows for the selection of the optimal date based on the user's geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the user's geographical location information into AI, which can then select the optimal date.

[0055] The scheduling unit can analyze the user's social media activity and consider relevant information when scheduling interviews. For example, the scheduling unit can set relevant interview dates based on the user's social media profile information. The scheduling unit can analyze the user's social media activity and set interview dates considering relevant information. The scheduling unit can also utilize the user's social media network to schedule interview dates that include letters of recommendation or referrals. This allows the scheduling unit to consider relevant information based on the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the user's social media activity into AI, which can then consider relevant information.

[0056] The interview management unit can analyze the user's past interview history and select the optimal management method during the interview process. For example, the unit can analyze the user's past interview history and propose the optimal management method. Based on the user's interview history, the unit can select a management method with a high success rate. The unit can also determine the priority of the process by referring to the user's interview history. This allows the unit to select the optimal management method based on the user's past interview history. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's interview history into AI, which can then select the optimal management method.

[0057] The interview management unit can customize the interview process based on the user's current career goals. For example, the unit can provide an interview process tailored to the relevant job type and industry based on the user's career goals. The unit can also provide an interview process that emphasizes skills and experience aligned with the user's career goals. Furthermore, the unit can customize the optimal interview process considering the user's career goals. This allows the interview process to be customized based on the user's career goals. Some or all of the above processes in the interview management unit may be performed using AI, for example, or not. For example, the interview management unit can input the user's career goals into the AI, which can then customize the interview process.

[0058] The interview management unit can select the optimal method of conducting the interview, taking into account the user's geographical location. For example, the unit can prioritize nearby interview locations based on the user's current location. The unit can also provide information on commuting time and transportation methods, taking into account the user's geographical location. Furthermore, the unit can prioritize region-specific methods of conducting the interview based on the user's geographical location. This allows the unit to select the optimal method of conducting the interview based on the user's geographical location. Some or all of the above processing in the interview management unit may be performed using AI, for example, or without AI. For example, the interview management unit can input the user's geographical location into AI, which can then select the optimal method of conducting the interview.

[0059] The facilitator can analyze the user's social media activity and consider relevant information during the interview process. For example, the facilitator can provide relevant interview methods based on the user's social media profile information. The facilitator can analyze the user's social media activity and provide interview methods that take relevant information into consideration. The facilitator can also leverage the user's social media network to provide interview methods that include letters of recommendation and introductions. This allows the facilitator to consider relevant information based on the user's social media activity. Some or all of the above processing in the facilitator may be performed using AI, for example, or not. For example, the facilitator can input the user's social media activity into AI, which can then consider relevant information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past work history and skill set to select the optimal profile registration method. For example, it can analyze a user's past work history and automatically input relevant skill sets. It can also suggest the most suitable job type and industry based on the user's skill set. Furthermore, it can combine the user's work history and skill set to customize the optimal profile registration method. This allows the reception desk to select the most suitable profile registration method based on the user's past work history and skill set. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work history and skill set into an AI, which can then select the optimal profile registration method.

[0062] The receiving unit can analyze the user's past application history to select the most suitable scout when it receives a scout offer. For example, it can analyze the user's past application history and prioritize displaying relevant scout offers. It can also automatically select the most suitable scout based on the user's application history. Furthermore, it can determine the priority of scout offers by referring to the user's application history. This allows for the selection of the most suitable scout based on the user's past application history. Some or all of the above processing in the receiving unit may be performed using AI, or it may be performed without AI. For example, the receiving unit can input the user's application history into AI, which can then select the most suitable scout.

[0063] The creation unit can analyze the user's past work experience and skill set to create the most suitable application documents. For example, it can analyze the user's past work experience and create application documents that highlight relevant skill sets. It can also create application documents tailored to the most suitable job and industry based on the user's skill set. Furthermore, it can combine the user's work experience and skill set to customize the most suitable application documents. This allows for the creation of optimal application documents based on the user's past work experience and skill set. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the user's work experience and skill set into an AI, which can then create the most suitable application documents.

[0064] The scheduling unit can analyze the user's past interview history to select the optimal date when scheduling an interview. For example, it can analyze the user's past interview history and suggest the best date. It can also select time slots with a high success rate for interviews based on the user's interview history. Furthermore, it can determine the priority of interviews by referring to the user's interview history. This allows for the selection of the optimal date based on the user's past interview history. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's interview history into the AI, which can then select the optimal date.

[0065] The interview management unit can analyze the user's past interview history to select the optimal management method during the interview process. For example, it can analyze the user's past interview history and propose the optimal management method. It can also select a management method with a high success rate based on the user's interview history. Furthermore, it can determine the priority of the process by referring to the user's interview history. This allows for the selection of the optimal management method based on the user's past interview history. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the user's interview history into the AI, which can then select the optimal management method.

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

[0067] Step 1: The reception desk registers the user's profile. The user's profile includes name, work history, skill set, career goals, etc. The reception desk can scan handwritten profiles and convert them into digital data, or directly read profiles submitted in digital format. It can also read printed profiles using OCR technology. Step 2: The receiving unit receives scouts based on the profile information registered by the reception unit. Scouts include offers from companies and invitations to interviews. The receiving unit automatically receives offers from companies and notifies the user. It can also receive and notify the user of interview invitations. Furthermore, it can analyze the content of the scouts and determine whether they match the user's preferences. Step 3: The creation unit creates application documents based on the recruitment offers received by the receiving unit. These documents include a resume, work history, and cover letter. The creation unit automatically generates a resume based on the user's profile information. It can also analyze the user's work history and skill set to create a work history. Furthermore, it can customize the content of the cover letter based on the user's career goals and desired job type. Step 4: The scheduling unit schedules interviews based on the application documents created by the creation unit. This includes suggesting possible dates and providing scheduling instructions. The scheduling unit adjusts interview dates considering the availability of both the company and the user. It can also analyze the user's schedule to automatically schedule interviews. Furthermore, it can also consider the company's schedule. Step 5: The facilitator conducts the interview based on the schedule set by the coordinator. The facilitator responds in a manner that matches the user's tone and character. Personal AI can be used to provide responses that match the user's tone and character.

[0068] (Example of form 2) The job change support system according to an embodiment of the present invention is a system for significantly reducing the burden of job hunting. In this job change support system, when a user registers a profile, an AI agent receives scouting offers, automatically creates and submits application documents for job openings that match the user's preferences. Next, the AI ​​agent schedules interviews and conducts interviews on behalf of the user. The personal AI responds in a manner that matches the user's tone of voice and character, and conducts the interview. Furthermore, since the AI ​​agent can handle interactions with multiple companies in parallel, the user only needs to check the application details and selection results. This mechanism significantly reduces the burden on the user in job hunting and allows them to efficiently proceed with activities to find their ideal job. In addition, since the user can personally handle important parts such as the final interview, the success rate of job hunting is also improved. For example, the job change support system allows the user to register a profile. For example, the job change support system can scan a handwritten profile and convert it into digital data, or the user can directly input it in digital format. This data is converted into a format that is easy for the generating AI to analyze. Next, the job change support system uses the generating AI to analyze the user's profile and summarize its contents. The input to the generative AI is the user's profile itself, and the generative AI generates a summary based on its content. For example, if the generative AI receives a prompt such as "Please summarize the key points of this profile," it will extract the key points from the profile and create a summary. Next, the job placement system calculates the similarity between the summary created by the generative AI and pre-prepared job postings. Natural language processing techniques are used to calculate the similarity. For example, the generative AI analyzes the degree of word matching and sentence structure similarity between the summary and the job posting to calculate a similarity score. Next, the job placement system calculates the suitability of the job posting based on the similarity score. For example, it may be set so that a higher similarity score corresponds to a higher suitability. The final suitability is fed back to the user. This allows the user to know how well their profile matches the job posting. The job placement system also applies the same process to multiple job postings.The job placement system reads the user's profile, a generating AI summarizes it, and calculates its similarity to job postings. For example, the system summarizes the user's skills and experience related to the job posting's theme and compares it to the job posting to calculate the degree of suitability. This allows the job placement system to significantly reduce the burden of job searching and enable it to proceed more efficiently. In this way, the job placement system can efficiently support the user's job search and reduce their burden.

[0069] The job change support system according to this embodiment comprises a reception unit, a receiving unit, a creation unit, a coordination unit, and a progress unit. The reception unit receives user profiles. User profiles include, but are not limited to, name, work history, skill set, and career goals. The reception unit, for example, scans handwritten profiles and converts them into digital data. The reception unit can also directly read profiles submitted in digital format. Furthermore, the reception unit can read printed profiles using OCR technology. For example, the reception unit scans handwritten profiles with a high-resolution scanner and converts them into text information using OCR technology. Digital profiles can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The receiving unit receives scouts based on the profile information registered by the reception unit. Scouts include, but are not limited to, offers and interview invitations from companies. The receiving unit automatically receives offers from companies and notifies the user. The receiving unit can also receive interview invitations and notify users. Furthermore, the receiving unit can analyze the content of scouts and determine whether they match the user's preferences. For example, the receiving unit can analyze offers from companies and determine if they match the user's desired job type and work location. The creation unit creates application documents based on the scouts received by the receiving unit. Application documents include, but are not limited to, resumes, CVs, and cover letters. For example, the creation unit can automatically create a resume based on the user's profile information. The creation unit can also analyze the user's work history and skill set to create a CV. Furthermore, the creation unit can customize the content of a cover letter based on the user's career goals and desired job type. For example, the creation unit can create a resume based on the user's work history and highlight their skill set. The scheduling unit schedules interviews based on the application documents created by the creation unit.The scheduling of interviews includes, but is not limited to, the presentation of candidate dates and scheduling methods. The scheduling unit adjusts interview dates considering the convenience of both the company and the user. The scheduling unit can also analyze the user's schedule to automatically adjust interview dates. Furthermore, the scheduling unit can also consider the company's schedule when scheduling interview dates. For example, the scheduling unit adjusts interview dates based on the user's schedule to accommodate the company's needs. The progress unit conducts the interview based on the schedule adjusted by the scheduling unit. The progress unit provides responses that are tailored to the user's tone of voice and character. The progress unit can use personal AI to provide responses that are tailored to the user's tone of voice and character. For example, the progress unit customizes the interview process based on the user's tone of voice and character. As a result, the job change support system according to this embodiment can efficiently support the user's job change activities and reduce their burden.

[0070] The reception desk allows users to register their profiles. These profiles may include, but are not limited to, name, work history, skill set, and career goals. The reception desk can, for example, scan handwritten profiles and convert them into digital data. It can also directly read profiles submitted in digital format. Furthermore, the reception desk can read printed profiles using OCR technology. For example, it can scan handwritten profiles with a high-resolution scanner and convert them into text information using OCR technology. Digital profiles submitted in specific file formats can be directly read. OCR technology accurately recognizes printed characters and converts them into digital text. This allows users to easily register their profiles, and the system can efficiently process profiles in various formats. Additionally, the reception desk is responsive to user profile updates, ensuring that the information is always up-to-date. For example, if a user acquires new skills or adds to their work history, the reception desk can quickly reflect this information. This ensures that user profiles are always current, providing appropriate information to companies seeking their services. Furthermore, the reception desk has also considered the ease of use for users registering their profiles, providing an intuitive interface. For example, features such as the ability to easily upload files using drag-and-drop functionality have been implemented to improve user convenience. As a result, users can register their profiles without stress, promoting the use of the system.

[0071] The receiving unit receives scouts based on profile information registered by the reception unit. Scouts include, but are not limited to, offers and interview invitations from companies. For example, the receiving unit automatically receives offers from companies and notifies the user. The receiving unit can also receive interview invitations and notify the user. Furthermore, the receiving unit can analyze the content of scouts and determine whether they match the user's preferences. For example, the receiving unit analyzes offers from companies and determines whether they match the user's desired job type and work location. This allows the user to efficiently receive scouts that are suitable for them. The receiving unit uses AI to analyze the content of scouts and compare them with the user's desired conditions. For example, the AI ​​analyzes the job type, work location, salary conditions etc. listed in the offer from the company and compares them with the user's registered information. This allows the user to be notified only of scouts that match their preferences, and the user can proceed with their job search efficiently without being bothered by irrelevant offers. Furthermore, the receiving section includes a function to set the priority of scouts, allowing it to prioritize notifications of offers that are highly important to the user. For example, it can support the user's job search by prioritizing offers with high salary conditions or offers for job types that the user particularly desires. In addition, the receiving section also has a function to manage the history of scouts, allowing users to view a list of offers and interview invitations received in the past. This allows users to review past scouts and keep track of the progress of their job search.

[0072] The creation unit creates application documents based on recruitment offers received by the receiving unit. These documents include, but are not limited to, resumes, CVs, and cover letters. For example, the creation unit can automatically create a resume based on the user's profile information. It can also analyze the user's work history and skill set to create a CV. Furthermore, it can customize the content of a cover letter based on the user's career goals and desired position. For example, the creation unit creates a resume based on the user's work history and highlights their skill set. This allows the user to create high-quality application documents with minimal effort. The creation unit uses AI to analyze the user's profile information and generate optimal application documents. For example, the AI ​​analyzes the user's work history and skill set in detail and customizes resumes and CVs to match the requirements of the company. In creating cover letters, it generates content that highlights the user's strengths to the company based on their career goals and desired position. This allows the user to effectively promote themselves to companies. Furthermore, the creation function also considers the format and design of application documents, allowing users to create documents that meet the specific requirements of each company. For example, by creating a resume according to a format specified by a particular company, it provides documents that meet the company's requirements. The creation function also provides an interface for users to review and revise their application documents, allowing them to check the generated documents and make corrections as needed. This enables users to create application documents that reflect their intentions, increasing their chances of success in their job search.

[0073] The scheduling unit schedules interviews based on application documents created by the creation unit. Interview scheduling includes, but is not limited to, suggesting candidate dates and scheduling methods. For example, the scheduling unit considers the convenience of both the company and the user when scheduling interviews. The scheduling unit can also analyze the user's schedule to automatically schedule interviews. Furthermore, the scheduling unit can consider the company's schedule when scheduling interviews. For example, the scheduling unit adjusts the interview schedule based on the user's schedule to accommodate the company's needs. This ensures that interviews are conducted on dates convenient for both the user and the company. The scheduling unit uses AI to analyze the schedules of both the user and the company and proposes the optimal interview date. For example, the AI ​​analyzes the user's calendar information and the company's available interview dates to automatically extract dates that are convenient for both parties. The scheduling unit also provides multiple candidate dates for the user and company to choose from, enabling flexible scheduling. Furthermore, after the interview date is confirmed, the scheduling unit provides a reminder function, sending notifications to both the user and the company the day before the interview. This ensures that interviews are conducted smoothly without forgetting scheduled appointments. Furthermore, the scheduling department can handle interview date changes, enabling quick responses even in the event of sudden schedule changes. For example, if an interview date needs to be changed due to the user's or company's circumstances, the scheduling department will propose new candidate dates and reschedule. This allows for flexible responses for both users and companies, increasing the interview completion rate.

[0074] The interview management team conducts the interview based on the schedule set by the coordination team. The management team, for example, provides responses tailored to the user's tone of voice and character. The management team can use personal AI to provide responses tailored to the user's tone of voice and character. For example, the management team customizes the interview process based on the user's tone of voice and character. This allows the user to respond in a way that is true to themselves, increasing the chances of a successful interview. The management team uses AI to analyze the user's past interview data and profile information and proposes optimal responses. For example, the AI ​​suggests areas for improvement and points to emphasize based on the user's past interview responses and evaluations. The management team also simulates responses tailored to the user's character, supporting the user in approaching the interview with confidence. Furthermore, the management team has the function to monitor the progress of the interview in real time and provide advice as needed. For example, if the user encounters a difficult situation during the interview, the management team provides appropriate advice to help the user continue responding smoothly. The management team also collects feedback after the interview and provides advice to help the user improve in future interviews. This allows users to grow with each interview, increasing their chances of success in their job search. Furthermore, the system manages interview results and notifies users of those results. This allows users to quickly understand the results of their interviews and move on to the next step.

[0075] The interviewer can conduct the interview using a personal AI. For example, the interviewer can provide responses tailored to the user's tone of voice and character. The interviewer can customize the interview process based on the user's tone of voice and character using the personal AI. For example, the interviewer can adjust the interview process based on the user's tone of voice and character. Furthermore, the interviewer can optimize the interview process based on the user's tone of voice and character using the personal AI. For example, the interviewer can adjust the interview process in real time based on the user's tone of voice and character. This allows the interviewer to provide responses tailored to the user's tone of voice and character. The personal AI is implemented using, for example, natural language processing techniques and machine learning algorithms. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. Machine learning algorithms include, for example, deep learning, support vector machines, and random forests. This allows the interviewer to optimize the interview process based on the user's tone of voice and character.

[0076] The job placement system has the functionality to handle interactions with multiple companies in parallel. For example, the job placement system can simultaneously receive and notify the user of recruitment offers from multiple companies. To handle interactions with multiple companies in parallel, the job placement system uses methods for parallel processing and prioritization. For example, the job placement system can simultaneously receive and notify the user of recruitment offers from multiple companies. The job placement system can also use algorithms to determine priorities in order to handle interactions with multiple companies in parallel. For example, the job placement system can determine the priority of recruitment offers based on the user's desired job type and work location. This allows the user to simply check the application details and selection results. Some or all of the above processes in the job placement system may be performed using AI, or not. For example, the job placement system can receive recruitment offers from multiple companies, input the details of the offers into AI, and the AI ​​can determine the priority of the recruitment offers.

[0077] The creation unit can create application documents for job postings that match the user's preferences. For example, the creation unit can create application documents based on the user's desired job type and work location. The creation unit can customize the content of the application documents based on the user's preferences. For example, the creation unit can create a resume and work history based on the user's desired job type. The creation unit can also create a cover letter based on the user's desired work location. This allows the creation of application documents for job postings that match the user's preferences. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's desired job type and work location into AI, and the AI ​​can create the application documents.

[0078] The scheduling unit can schedule interviews. For example, the scheduling unit will schedule interviews considering the convenience of both the company and the user. The scheduling unit can analyze the user's schedule in order to automatically schedule interviews. For example, the scheduling unit will adjust the interview schedule based on the user's schedule to suit the company's convenience. The scheduling unit can also consider the company's schedule when scheduling interviews. This allows the scheduling unit to schedule interviews. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's schedule and the company's schedule into the AI, which can then adjust the optimal interview schedule.

[0079] The facilitator can respond in a way that suits the user's tone of voice and character. For example, the facilitator can customize the interview process based on the user's tone of voice and character. The facilitator can use a personal AI to optimize the interview process based on the user's tone of voice and character. For example, the facilitator can adjust the interview process in real time based on the user's tone of voice and character. The facilitator can also use a personal AI to optimize the interview process based on the user's tone of voice and character. This allows the facilitator to respond in a way that suits the user's tone of voice and character. Some or all of the above processes in the facilitator may be performed using AI, for example, or not using AI. For example, the facilitator can input the user's tone of voice and character into the AI, which can then adjust the interview process.

[0080] The reception desk can estimate the user's emotions and adjust the timing of profile registration based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may encourage profile registration during a time when the user can relax. If the user is highly motivated, the reception desk may encourage profile registration immediately. Furthermore, if the user is tired, the reception desk may suggest that they rest before registering their profile. This allows the timing of profile registration to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into an AI, which can then adjust the timing of profile registration.

[0081] The reception desk can analyze a user's past work experience and skill set to select the optimal profile registration method. For example, the reception desk can analyze a user's past work experience and automatically input relevant skill sets. Based on the user's skill set, the reception desk can suggest the most suitable job type and industry. The reception desk can also combine the user's work experience and skill set to customize the optimal profile registration method. This allows the reception desk to select the most suitable profile registration method based on the user's past work experience and skill set. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's work experience and skill set into an AI, which can then select the most suitable profile registration method.

[0082] The reception desk can filter users based on their current career goals and areas of interest during profile registration. For example, the reception desk can prioritize displaying relevant job titles and industries based on the user's career goals. The reception desk can highlight relevant skills and experience based on the user's areas of interest. The reception desk can also combine the user's career goals and areas of interest to suggest the most suitable profile registration content. This allows the profile registration content to be filtered based on the user's career goals and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's career goals and areas of interest into an AI, which can then filter the profile registration content.

[0083] The reception desk can estimate the user's emotions and determine the priority of profile registration based on the estimated emotions. For example, if the user is anxious, the reception desk may encourage them to prioritize profile registration. If the user is relaxed, the reception desk may suggest that they register their profile in parallel with other tasks. The reception desk can also support the user in registering their profile in a comfortable environment if they are feeling anxious. This allows the priority of profile registration to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into an AI, which can then determine the priority of profile registration.

[0084] The reception desk can prioritize registering highly relevant information when a user registers their profile, taking into account their geographical location. For example, the reception desk can prioritize displaying nearby job postings based on the user's current location. The reception desk can also provide information on commute time and transportation methods, taking into account the user's geographical location. Furthermore, the reception desk can highlight region-specific skills and experience based on the user's geographical location. This allows for the priority registration of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into an AI, which can then prioritize registering highly relevant information.

[0085] The reception desk can analyze a user's social media activity and register relevant information during profile registration. For example, the reception desk can automatically import a user's social media profile information to simplify profile registration. The reception desk can analyze a user's social media activity and highlight relevant skills and experience. It can also leverage a user's social media network to automatically add letters of recommendation and referrals. This allows for the registration of relevant information based on the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input a user's social media activity into an AI, which can then register relevant information.

[0086] The receiving unit can estimate the user's emotions and adjust the timing of receiving recruitment offers based on the estimated emotions. For example, if the user is feeling stressed, the receiving unit will receive recruitment offers during a time when the user can relax. If the user is highly motivated, the receiving unit can receive recruitment offers immediately. Also, if the user is tired, the receiving unit can receive recruitment offers after the user has rested. In this way, the timing of receiving recruitment offers can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the receiving unit may be performed using AI, or not using AI. For example, the receiving unit can input user emotion data into an AI, and the AI ​​can adjust the timing of receiving recruitment offers.

[0087] The receiving unit can analyze the user's past application history to select the most suitable scout when it receives a scout offer. For example, the receiving unit analyzes the user's past application history and displays relevant scout offers with priority. The receiving unit can automatically select the most suitable scout based on the user's application history. The receiving unit can also determine the priority of scout offers by referring to the user's application history. This allows the receiving unit to select the most suitable scout based on the user's past application history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's application history into AI, which can then select the most suitable scout.

[0088] The receiving unit can filter scouts based on the user's current career goals when a scout is received. For example, the receiving unit can prioritize displaying relevant scouts based on the user's career goals. The receiving unit can automatically filter scouts that align with the user's career goals. The receiving unit can also suggest the most suitable scouts, taking the user's career goals into consideration. This allows for filtering scouts based on the user's career goals. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's career goals into AI, which can then filter scouts.

[0089] The receiving unit can estimate the user's emotions and determine the priority of recruitment offers based on the estimated emotions. For example, if the user is anxious, the receiving unit will display important recruitment offers with the highest priority. If the user is relaxed, the receiving unit can display recruitment offers in parallel with other tasks. Furthermore, if the user is feeling anxious, the receiving unit can display recruitment offers in a reassuring environment. This allows the system to determine the priority of recruitment offers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the receiving unit may be performed using AI, or not using AI. For example, the receiving unit can input user emotion data into an AI, which can then determine the priority of recruitment offers.

[0090] The receiving unit can prioritize receiving highly relevant scouts when it receives a scout, taking into account the user's geographical location information. For example, the receiving unit can prioritize displaying nearby job postings based on the user's current location. The receiving unit can also provide information on commute time and transportation methods, taking into account the user's geographical location information. Furthermore, the receiving unit can prioritize receiving region-specific scouts based on the user's geographical location information. This allows the user to prioritize receiving highly relevant scouts based on their geographical location information. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's geographical location information into AI, which can then prioritize receiving highly relevant scouts.

[0091] The receiving unit can analyze the user's social media activity when a scout is received and receive relevant scouts. For example, the receiving unit can prioritize displaying relevant scouts based on the user's social media profile information. The receiving unit can analyze the user's social media activity and receive relevant scouts. The receiving unit can also utilize the user's social media network to receive scouts that include letters of recommendation and referrals. This allows the user to receive relevant scouts based on their social media activity. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not using AI. For example, the receiving unit can input the user's social media activity into AI, which can then receive relevant scouts.

[0092] The creation unit can estimate the user's emotions and adjust how the application documents are created based on the estimated emotions. For example, if the user is relaxed, the creation unit can create an application document that includes detailed information. If the user is in a hurry, the creation unit can create a concise and to-the-point application document. Furthermore, if the user is nervous, the creation unit can create a simple and highly visual application document. This allows the application document creation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into an AI, which can then adjust how the application documents are created.

[0093] The creation unit can analyze the user's past work experience and skill set to create the most suitable application documents. For example, the creation unit can analyze the user's past work experience and create application documents that highlight relevant skill sets. Based on the user's skill set, the creation unit can create application documents tailored to the most suitable job and industry. The creation unit can also combine the user's work experience and skill set to customize the most suitable application documents. This allows the creation of the most suitable application documents based on the user's past work experience and skill set. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's work experience and skill set into AI, which can then create the most suitable application documents.

[0094] The creation unit can customize the content of application documents based on the user's current career goals when creating them. For example, the creation unit can create application documents tailored to relevant job types and industries based on the user's career goals. The creation unit can create application documents that highlight skills and experience aligned with the user's career goals. Furthermore, the creation unit can customize the application documents to be optimal, taking the user's career goals into consideration. This allows the content of application documents to be customized based on the user's career goals. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's career goals into AI, which can then customize the content of the application documents.

[0095] The creation unit can estimate the user's emotions and prioritize application documents based on those emotions. For example, if the user is anxious, the creation unit will prioritize important application documents. If the user is relaxed, the creation unit can create application documents in parallel with other tasks. Furthermore, if the user is feeling anxious, the creation unit can create application documents in a reassuring environment. This allows the system to prioritize application documents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into an AI, which can then determine the priority of application documents.

[0096] The creation function can prioritize highly relevant information when creating application documents, taking into account the user's geographical location. For example, it can prioritize nearby job postings based on the user's current location. It can also include information about commute time and transportation methods, taking into account the user's geographical location. Furthermore, it can highlight region-specific skills and experience based on the user's geographical location. This allows for the prioritization of highly relevant information based on the user's geographical location. Some or all of the above processing in the creation function may be performed using AI, for example, or not. For example, the creation function can input the user's geographical location into AI, which can then prioritize the inclusion of highly relevant information.

[0097] The creation unit can analyze the user's social media activity and include relevant information when creating application documents. For example, the creation unit can include relevant skills and experience based on the user's social media profile information. The creation unit can analyze the user's social media activity and include relevant information. The creation unit can also utilize the user's social media network to include letters of recommendation and introductions. This allows for the inclusion of relevant information based on the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the user's social media activity into AI, and the AI ​​can include relevant information.

[0098] The scheduling unit can estimate the user's emotions and schedule interviews based on those emotions. For example, if the user is nervous, the scheduling unit can schedule the interview at a time when the user can relax. If the user is highly motivated, the scheduling unit can schedule the interview immediately. Furthermore, if the user is tired, the scheduling unit can schedule the interview after the user has rested. This allows for scheduling interviews according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input user emotion data into an AI, which can then schedule the interview.

[0099] The scheduling unit can analyze the user's past interview history to select the optimal date when scheduling an interview. For example, the scheduling unit can analyze the user's past interview history and propose the optimal date. Based on the user's interview history, the scheduling unit can select time slots with a high success rate for interviews. The scheduling unit can also determine the priority of interviews by referring to the user's interview history. This allows the system to select the optimal date based on the user's past interview history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's interview history into AI, which can then select the optimal date.

[0100] The scheduling unit can customize interview dates based on the user's current schedule. For example, the scheduling unit can check the user's schedule and set up interviews during available time slots. The scheduling unit can suggest the optimal interview dates based on the user's schedule. The scheduling unit can also prioritize interviews, taking the user's schedule into consideration. This allows for the customization of interview dates based on the user's current schedule. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the user's schedule into the AI, which can then customize the interview dates.

[0101] The scheduling unit can estimate the user's emotions and prioritize interview dates based on those emotions. For example, if the user is anxious, the scheduling unit will prioritize important interviews. If the user is relaxed, the scheduling unit can schedule interviews in parallel with other tasks. Furthermore, if the user is feeling anxious, the scheduling unit can schedule interviews in a comfortable environment. This allows the scheduling unit to prioritize interview dates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into an AI, which can then determine the priority of interview dates.

[0102] The scheduling unit can select the optimal date for an interview by considering the user's geographical location. For example, the scheduling unit can prioritize nearby interview locations based on the user's current location. The scheduling unit can also provide information on commuting time and transportation methods, taking the user's geographical location into consideration. Furthermore, the scheduling unit can prioritize region-specific interview dates based on the user's geographical location. This allows for the selection of the optimal date based on the user's geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the user's geographical location information into AI, which can then select the optimal date.

[0103] The scheduling unit can analyze the user's social media activity and consider relevant information when scheduling interviews. For example, the scheduling unit can set relevant interview dates based on the user's social media profile information. The scheduling unit can analyze the user's social media activity and set interview dates considering relevant information. The scheduling unit can also utilize the user's social media network to schedule interview dates that include letters of recommendation or referrals. This allows the scheduling unit to consider relevant information based on the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the user's social media activity into AI, which can then consider relevant information.

[0104] The interviewer can estimate the user's emotions and adjust the interview process based on those emotions. For example, if the user is nervous, the interviewer can provide a relaxing approach. If the user is highly motivated, the interviewer can provide a more proactive approach. Furthermore, if the user is tired, the interviewer can provide a method that allows for rest. This allows the interview process to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the interviewer may be performed using AI, or not. For example, the interviewer can input user emotion data into an AI, which can then adjust the interview process.

[0105] The interview management unit can analyze the user's past interview history and select the optimal management method during the interview process. For example, the unit can analyze the user's past interview history and propose the optimal management method. Based on the user's interview history, the unit can select a management method with a high success rate. The unit can also determine the priority of the process by referring to the user's interview history. This allows the unit to select the optimal management method based on the user's past interview history. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's interview history into AI, which can then select the optimal management method.

[0106] The interview management unit can customize the interview process based on the user's current career goals. For example, the unit can provide an interview process tailored to the relevant job type and industry based on the user's career goals. The unit can also provide an interview process that emphasizes skills and experience aligned with the user's career goals. Furthermore, the unit can customize the optimal interview process considering the user's career goals. This allows the interview process to be customized based on the user's career goals. Some or all of the above processes in the interview management unit may be performed using AI, for example, or not. For example, the interview management unit can input the user's career goals into the AI, which can then customize the interview process.

[0107] The interviewer can estimate the user's emotions and determine the priority of the interview based on the estimated emotions. For example, if the user is anxious, the interviewer will prioritize important interviews. If the user is relaxed, the interviewer can conduct interviews in parallel with other tasks. Furthermore, if the user is feeling anxious, the interviewer can conduct the interview in a reassuring environment. This allows the interviewer to determine the priority of the interview according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using AI or not. For example, the interviewer can input user emotion data into an AI, which can then determine the priority of the interview.

[0108] The interview management unit can select the optimal method of conducting the interview, taking into account the user's geographical location. For example, the unit can prioritize nearby interview locations based on the user's current location. The unit can also provide information on commuting time and transportation methods, taking into account the user's geographical location. Furthermore, the unit can prioritize region-specific methods of conducting the interview based on the user's geographical location. This allows the unit to select the optimal method of conducting the interview based on the user's geographical location. Some or all of the above processing in the interview management unit may be performed using AI, for example, or without AI. For example, the interview management unit can input the user's geographical location into AI, which can then select the optimal method of conducting the interview.

[0109] The facilitator can analyze the user's social media activity and consider relevant information during the interview process. For example, the facilitator can provide relevant interview methods based on the user's social media profile information. The facilitator can analyze the user's social media activity and provide interview methods that take relevant information into consideration. The facilitator can also leverage the user's social media network to provide interview methods that include letters of recommendation and introductions. This allows the facilitator to consider relevant information based on the user's social media activity. Some or all of the above processing in the facilitator may be performed using AI, for example, or not. For example, the facilitator can input the user's social media activity into AI, which can then consider relevant information.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the timing of profile registration based on the estimated emotions. For example, if the user is feeling stressed, it can encourage profile registration during a time when they can relax. If the user is highly motivated, it can encourage immediate profile registration. Furthermore, if the user is tired, it can suggest that they register after taking a rest. This allows the timing of profile registration to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can then adjust the timing of profile registration.

[0112] The receiving unit can estimate the user's emotions and adjust the timing of receiving recruitment offers based on the estimated emotions. For example, if the user is feeling stressed, they can receive recruitment offers during a time when they can relax. If the user is highly motivated, they can receive recruitment offers immediately. Furthermore, if the user is tired, they can receive recruitment offers after resting. This allows the timing of recruitment offers to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the receiving unit may be performed using AI or not. For example, the receiving unit can input user emotion data into an AI, which can then adjust the timing of recruitment offers.

[0113] The creation unit can estimate the user's emotions and adjust the application document creation process based on the estimated emotions. For example, if the user is relaxed, it can create an application document containing detailed information. If the user is in a hurry, it can create a concise and to-the-point application document. Furthermore, if the user is nervous, it can create a simple and highly visual application document. This allows the application document creation process to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into an AI, which can then adjust the application document creation process.

[0114] The scheduling unit can estimate the user's emotions and schedule interviews based on those estimated emotions. For example, if the user is nervous, the interview can be scheduled for a time when they can relax. If the user is highly motivated, the interview can be scheduled immediately. Furthermore, if the user is tired, the interview can be scheduled after they have rested. This allows for scheduling interviews according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into an AI, which can then schedule interviews.

[0115] The interviewer can estimate the user's emotions and adjust the interview process based on those emotions. For example, if the user is nervous, a relaxing approach can be provided. If the user is highly motivated, an active approach can be provided. Furthermore, if the user is tired, a method that allows for rest can be provided. This allows the interview process to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using AI or not. For example, the interviewer can input user emotion data into an AI, which can then adjust the interview process.

[0116] The reception desk can analyze a user's past work history and skill set to select the optimal profile registration method. For example, it can analyze a user's past work history and automatically input relevant skill sets. It can also suggest the most suitable job type and industry based on the user's skill set. Furthermore, it can combine the user's work history and skill set to customize the optimal profile registration method. This allows the reception desk to select the most suitable profile registration method based on the user's past work history and skill set. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work history and skill set into an AI, which can then select the optimal profile registration method.

[0117] The receiving unit can analyze the user's past application history to select the most suitable scout when it receives a scout offer. For example, it can analyze the user's past application history and prioritize displaying relevant scout offers. It can also automatically select the most suitable scout based on the user's application history. Furthermore, it can determine the priority of scout offers by referring to the user's application history. This allows for the selection of the most suitable scout based on the user's past application history. Some or all of the above processing in the receiving unit may be performed using AI, or it may be performed without AI. For example, the receiving unit can input the user's application history into AI, which can then select the most suitable scout.

[0118] The creation unit can analyze the user's past work experience and skill set to create the most suitable application documents. For example, it can analyze the user's past work experience and create application documents that highlight relevant skill sets. It can also create application documents tailored to the most suitable job and industry based on the user's skill set. Furthermore, it can combine the user's work experience and skill set to customize the most suitable application documents. This allows for the creation of optimal application documents based on the user's past work experience and skill set. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the user's work experience and skill set into an AI, which can then create the most suitable application documents.

[0119] The scheduling unit can analyze the user's past interview history to select the optimal date when scheduling an interview. For example, it can analyze the user's past interview history and suggest the best date. It can also select time slots with a high success rate for interviews based on the user's interview history. Furthermore, it can determine the priority of interviews by referring to the user's interview history. This allows for the selection of the optimal date based on the user's past interview history. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's interview history into the AI, which can then select the optimal date.

[0120] The interview management unit can analyze the user's past interview history to select the optimal management method during the interview process. For example, it can analyze the user's past interview history and propose the optimal management method. It can also select a management method with a high success rate based on the user's interview history. Furthermore, it can determine the priority of the process by referring to the user's interview history. This allows for the selection of the optimal management method based on the user's past interview history. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the user's interview history into the AI, which can then select the optimal management method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk registers the user's profile. The user's profile includes name, work history, skill set, career goals, etc. The reception desk can scan handwritten profiles and convert them into digital data, or directly read profiles submitted in digital format. It can also read printed profiles using OCR technology. Step 2: The receiving unit receives scouts based on the profile information registered by the reception unit. Scouts include offers from companies and invitations to interviews. The receiving unit automatically receives offers from companies and notifies the user. It can also receive and notify the user of interview invitations. Furthermore, it can analyze the content of the scouts and determine whether they match the user's preferences. Step 3: The creation unit creates application documents based on the recruitment offers received by the receiving unit. These documents include a resume, work history, and cover letter. The creation unit automatically generates a resume based on the user's profile information. It can also analyze the user's work history and skill set to create a work history. Furthermore, it can customize the content of the cover letter based on the user's career goals and desired job type. Step 4: The scheduling unit schedules interviews based on the application documents created by the creation unit. This includes suggesting possible dates and providing scheduling instructions. The scheduling unit adjusts interview dates considering the availability of both the company and the user. It can also analyze the user's schedule to automatically schedule interviews. Furthermore, it can also consider the company's schedule. Step 5: The facilitator conducts the interview based on the schedule set by the coordinator. The facilitator responds in a manner that matches the user's tone and character. Personal AI can be used to provide responses that match the user's tone and character.

[0123] 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, receiving unit, creation unit, adjustment unit, and progress unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user registers their profile. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where the scouting offer is received. The creation unit is implemented by, for example, the control unit 46A of the smart device 14, where application documents are created. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where interview scheduling is performed. The progress unit is implemented by, for example, the control unit 46A of the smart device 14, where the interview is conducted. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, receiving unit, creation unit, adjustment unit, and progress unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user registers their profile. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where the scouting offer is received. The creation unit is implemented by, for example, the control unit 46A of the smart glasses 214, where application documents are created. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where interview scheduling is performed. The progress unit is implemented by, for example, the control unit 46A of the smart glasses 214, where the interview is conducted. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, receiving unit, creation unit, adjustment unit, and progress unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user registers their profile. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the scouting offer is received. The creation unit is implemented by, for example, the control unit 46A of the headset terminal 314, where application documents are created. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the interview schedule is arranged. The progress unit is implemented by, for example, the control unit 46A of the headset terminal 314, where the interview is conducted. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, receiving unit, creation unit, adjustment unit, and progress unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user registers their profile. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the scouting offer is received. The creation unit is implemented by, for example, the control unit 46A of the robot 414, where application documents are created. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where interview scheduling is performed. The progress unit is implemented by, for example, the control unit 46A of the robot 414, where the interview is conducted. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] 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.

[0179] 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.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception desk where you register your profile, A receiving unit that receives scouts based on profile information registered by the aforementioned reception unit, A creation unit that creates application documents based on the scout received by the aforementioned receiving unit, The coordination unit schedules interviews based on the application documents prepared by the aforementioned preparation unit, The system includes a progress unit that conducts interviews based on the schedule adjusted by the adjustment unit. A system characterized by the following features. (Note 2) Equipped with a system that uses personal AI to conduct interviews. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a function that allows you to communicate with multiple companies simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, Create application documents for job postings that match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Schedule an interview. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned progress section is, The system will respond in a manner that matches the user's tone and personality. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of profile registration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past work experience and skill set to select the optimal profile registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When registering a profile, filtering is performed based on the user's current career goals and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of profile registration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When registering a profile, the system prioritizes registering highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a user registers their profile, the system analyzes their social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The receiving unit is It estimates the user's emotions and adjusts the timing of receiving recruitment messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The receiving unit is When a user receives a recruitment offer, the system analyzes their past application history to select the most suitable offer. The system described in Appendix 1, characterized by the features described herein. (Note 15) The receiving unit is When a recruitment offer is received, the offer is filtered based on the user's current career goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The receiving unit is It estimates the user's emotions and determines the priority of scouting based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The receiving unit is When receiving a scout offer, the system prioritizes receiving highly relevant offers by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The receiving unit is When a user receives a recruitment offer, the system analyzes their social media activity and selects relevant offers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, We estimate the user's emotions and adjust the application process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating application documents, we analyze the user's past work experience and skill set to create the most suitable documents. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating application documents, customize the document content based on the user's current career goals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, The system estimates user sentiment and prioritizes application documents based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating application documents, prioritize including highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When preparing your application documents, analyze the user's social media activity and include relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, The system estimates the user's emotions and schedules interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, When scheduling interviews, the system analyzes the user's past interview history to select the optimal date. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When scheduling interviews, customize the schedule based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, The system estimates the user's emotions and prioritizes interview scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, When scheduling interviews, the system will take the user's geographical location into consideration to select the most suitable date. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When scheduling interviews, we analyze the user's social media activity and consider relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned progress section is, The system estimates the user's emotions and adjusts the interview process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned progress section is, During the interview process, the system analyzes the user's past interview history to select the most suitable approach. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned progress section is, During the interview process, the method of conducting the interview is customized based on the user's current career goals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned progress section is, The system estimates the user's emotions and prioritizes the interview process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned progress section is, During the interview process, the optimal method of conducting the interview will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned progress section is, During the interview process, we will analyze the user's social media activity and consider relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you register your profile, A receiving unit that receives scouts based on profile information registered by the aforementioned reception unit, A creation unit that creates application documents based on the scout received by the aforementioned receiving unit, The coordination unit schedules interviews based on the application documents prepared by the aforementioned preparation unit, The system includes a progress unit that conducts interviews based on the schedule adjusted by the adjustment unit. A system characterized by the following features.

2. It features a system that uses personal AI to conduct interviews. The system according to feature 1.

3. It has a function that allows you to communicate with multiple companies simultaneously. The system according to feature 1.

4. The aforementioned creation unit, Create application documents for job postings that match the user's preferences. The system according to feature 1.

5. The adjustment unit is, Schedule an interview. The system according to feature 1.

6. The aforementioned progress section is, The system will respond in a manner that matches the user's tone and personality. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of profile registration based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past work experience and skill set to select the optimal profile registration method. The system according to feature 1.

9. The aforementioned reception unit is When registering a profile, filtering is performed based on the user's current career goals and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of profile registration based on the estimated emotions. The system according to feature 1.

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