System

Abstract

An object of a system according to an embodiment is to evaluate an applicant based on a unified examination standard.SOLUTION: A system includes a document examination part, an interview part, and an evaluation reference provision part. The document examination unit analyzes the applicant's document. The interview unit conducts an interview with the applicant based on the document analyzed by the document examination unit. An evaluation standard providing part evaluates the contents of the interview performed by the interview part on the basis of a unified examination standard.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, document screening and interviews in the recruitment process rely on human intuition, making it difficult to evaluate candidates using standardized screening criteria.

[0005] The system according to the embodiment aims to evaluate applicants using unified screening criteria. [Means for solving the problem]

[0006] The system according to the embodiment includes a document screening unit, an interview unit, and an evaluation criteria providing unit. The document screening unit analyzes the documents of the applicant. The interview unit conducts an interview with the applicant based on the documents analyzed by the document screening unit. The evaluation criteria providing unit evaluates the content of the interview conducted by the interview unit based on unified screening criteria. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate applicants using unified evaluation criteria. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In the recruitment process automation system according to an embodiment of the present invention, a generation AI is responsible for all steps from document screening to interviews, and evaluations are performed using unified screening criteria. This enables the recruitment process automation system to reduce labor costs on the hiring side and realize efficient recruitment activities.

[0029] The recruitment process automation system according to the embodiment includes a document screening unit, an interview unit, and an evaluation criteria providing unit. The document screening unit analyzes applicant documents. For example, the document screening unit may analyze the applicant's resume and curriculum vitae using text mining technology to evaluate the applicant's educational background, work history, and skill set. The document screening unit may also analyze the applicant's recommendation letters and score the applicant based on the recommender's evaluation. The document screening unit may also analyze the applicant's submitted portfolio to evaluate specific achievements. The interview unit conducts interviews with applicants based on the documents analyzed by the document screening unit. For example, the interview unit may use a generative AI to ask questions to the applicant and analyze the answers to evaluate their communication skills and problem-solving abilities. The interview unit may also analyze the applicant's facial expressions and gestures through online interviews to evaluate their non-verbal communication. The interview unit may also conduct group discussions to evaluate their teamwork and leadership skills. The evaluation criteria providing unit evaluates the content of the interviews conducted by the interview unit based on unified evaluation criteria. For example, the evaluation criteria providing unit evaluates applicants based on pre-set evaluation criteria to eliminate bias. The evaluation criteria providing unit can also reflect the latest industry trends and technological developments in real time and always evaluate using the latest criteria. Furthermore, the evaluation criteria providing unit can analyze past recruitment data and optimize the criteria based on successful recruitment patterns. This allows the recruitment process automation system according to the embodiment to reduce labor costs on the hiring side and realize efficient recruitment activities. For example, the output unit displays the recruitment results to applicants and recruiters via a web application or mobile application. If applicants or recruiters desire feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to applicants and recruiters.

[0030] The document review department can analyze an applicant's educational background, work history, and skill set and score the degree of suitability. For example, the document review department can analyze the educational background, work history, and skill set stated in the applicant's resume or curriculum vitae using text mining technology and score the degree of suitability. The document review department can also analyze the portfolio submitted by the applicant and evaluate specific achievements. For example, scoring can be done based on the scale and impact of the project. The document review department can also analyze the applicant's recommendation letter and score based on the recommender's evaluation. This allows for an accurate evaluation of the applicant's suitability.

[0031] The interview department can ask questions to applicants and analyze their answers to evaluate their communication skills and problem-solving skills. The interview department can, for example, use a generation AI to ask questions to applicants and analyze their answers to evaluate their communication skills and problem-solving skills. For example, the generation AI can analyze the applicant's answers using text analysis technology to evaluate their linguistic expression skills and conversational skills. The generation AI can also analyze the applicant's answers using emotion analysis technology to evaluate their emotional stability and stress tolerance. Furthermore, the generation AI can analyze the applicant's answers using logical analysis technology to evaluate their logical thinking skills and problem-solving skills. This allows for an accurate evaluation of the applicant's communication skills and problem-solving skills.

[0032] The evaluation criterion providing unit can evaluate applicants based on evaluation criteria that have been set in advance. The evaluation criterion providing unit evaluates applicants based on, for example, evaluation criteria that have been set in advance. For example, the evaluation criterion providing unit scores applicants based on their educational background, work history, and skill set. The evaluation criterion providing unit can also evaluate applicants based on their responses and non-verbal communication. Furthermore, the evaluation criterion providing unit can reflect the latest industry trends and technological developments in real time and always evaluate based on the latest criteria. This allows applicants to be evaluated fairly.

[0033] The document review department can automatically evaluate an applicant's past projects and deliverables and score specific achievements. For example, the document review department uses a generation AI to automatically analyze the projects and deliverables listed on an applicant's resume and CV, and evaluate the scale and impact of each project. For example, scoring is done based on the project's budget, team size, and achieved results. The document review department can also analyze the portfolio submitted by the applicant and evaluate specific achievements. For example, scoring is done based on the project's success rate and customer satisfaction. Furthermore, the document review department can analyze the applicant's recommendation letters and score based on the recommender's evaluations. This allows for an accurate evaluation of the applicant's specific achievements.

[0034] The document review department can also integrate external data such as the applicant's social media and published papers when making its evaluation. For example, the document review department can use AI to analyze the applicant's social media accounts and evaluate their professional activities and influence in the industry. For example, it can assign a score based on their LinkedIn profile or Twitter posts. The document review department can also analyze the applicant's published papers and evaluate their academic contributions. For example, it can assign a score based on the number of citations and impact factor of their papers. Furthermore, the document review department can also integrate external data about the applicant when making its evaluation. This allows it to integrate external data about the applicant and make an evaluation.

[0035] The document screening department can analyze applicants' video presentations to evaluate their presentation skills and expressiveness. For example, the document screening department uses a generative AI to analyze the video presentations submitted by applicants and evaluate their presentation skills (e.g., speaking style, use of visual materials). For example, it can use voice analysis technology to score the clarity and persuasiveness of the speech. The document screening department can also analyze applicants' video presentations to evaluate their expressiveness (e.g., word choice, gestures, tone of voice). Furthermore, the document screening department can analyze applicants' video presentations to evaluate their presentation skills and expressiveness. This allows for an accurate evaluation of the applicants' presentation skills and expressiveness.

[0036] The document screening department can evaluate cross-industry aptitude by referring to the standards of different industries and job types. For example, the document screening department uses generation AI to analyze an applicant's resume and job history and evaluates aptitude by referring to the standards of different industries and job types. For example, it scores the possibility of changing jobs to a different industry. The document screening department can also analyze an applicant's skill set and evaluate aptitude for different industries and job types. Furthermore, the document screening department can evaluate cross-industry aptitude by referring to the standards of different industries and job types. This makes it possible to evaluate an applicant's aptitude by referring to the standards of different industries and job types.

[0037] The interview department can refer to the applicant's past interview data and evaluate trends in growth and improvement. In the interview department, for example, a generative AI analyzes the applicant's past interview data and evaluates trends in growth and improvement. For example, the degree of growth is scored based on the answers given in past interviews and evaluation scores. The interview department can also refer to the applicant's past interview data and evaluate trends in growth and improvement. Furthermore, the interview department can analyze past interview data and evaluate trends in growth and improvement. This makes it possible to evaluate the applicant's trends in growth and improvement.

[0038] The interview section can conduct group discussions with applicants to evaluate their teamwork and leadership skills. In the interview section, for example, the generative AI conducts group discussions with applicants to evaluate their teamwork and leadership skills. For example, it scores the number of times applicants speak during the discussion and the degree of leadership demonstrated. The interview section can also conduct group discussions with applicants to evaluate their teamwork and leadership skills. The interview section can also conduct group discussions with applicants to evaluate their teamwork and leadership skills. In this way, the applicant's teamwork and leadership skills can be evaluated.

[0039] The interview section can assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. For example, the interview section uses generative AI to ask questions in multiple languages ​​and assess the international aptitude of applicants with different cultural and linguistic backgrounds. For example, questions are asked in multiple languages, such as English and Chinese. The interview section can also assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. The interview section can also assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. This makes it possible to assess the international aptitude of applicants with different cultural and linguistic backgrounds.

[0040] The evaluation criteria providing unit reflects the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. For example, the evaluation criteria providing unit uses a generation AI to collect the latest industry trends and technological trends in real time and reflect them in the screening criteria. For example, the evaluation criteria are updated based on the latest technology and market changes. The evaluation criteria providing unit can also reflect the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. Furthermore, the evaluation criteria providing unit can reflect the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. This allows applicants to be evaluated using the latest standards at all times.

[0041] The evaluation criterion providing unit can analyze past recruitment data and optimize the criteria based on successful recruitment patterns. For example, the evaluation criterion providing unit uses a generation AI to analyze past recruitment data and optimize the screening criteria based on successful recruitment patterns. For example, the evaluation criterion providing unit adjusts the evaluation criteria based on past successful recruitment cases. The evaluation criterion providing unit can also analyze past recruitment data and optimize the criteria based on successful recruitment patterns. The evaluation criterion providing unit can also analyze past recruitment data and optimize the criteria based on successful recruitment patterns. This makes it possible to optimize the evaluation criteria based on past success cases.

[0042] The evaluation criteria providing unit can integrate criteria from different industries and occupations to evaluate cross-industry aptitude. For example, the generation AI integrates criteria from different industries and occupations to evaluate cross-industry aptitude. For example, it integrates criteria for technical and managerial positions to conduct an evaluation. The evaluation criteria providing unit can also integrate criteria from different industries and occupations to evaluate cross-industry aptitude. Furthermore, the evaluation criteria providing unit can also integrate criteria from different industries and occupations to evaluate cross-industry aptitude. This makes it possible to evaluate the aptitude of applicants by integrating criteria from different industries and occupations.

[0043] The evaluation criteria providing unit reflects the company's culture and values ​​and can select the most suitable talent for the company. For example, the evaluation criteria providing unit uses a generation AI to analyze the company's culture and values ​​and reflect them in the screening criteria. For example, it scores talent that matches the company's mission and vision. The evaluation criteria providing unit can also reflect the company's culture and values ​​and select the most suitable talent for the company. Furthermore, the evaluation criteria providing unit can also reflect the company's culture and values ​​and select the most suitable talent for the company. This makes it possible to select the most suitable talent that reflects the company's culture and values.

[0044] By managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. By managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. For example, generative AI manages the entire recruitment process and automates tasks from document screening to interviews. For example, it automates applicant data analysis and interview schedule adjustment. Furthermore, by managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. Furthermore, by managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. In this way, the entire recruitment process can be automated and labor costs can be reduced.

[0045] Costs can be reduced by outsourcing part of the recruitment process and utilizing external resources. Costs can be reduced by outsourcing part of the recruitment process and utilizing external resources. For example, generative AI can reduce costs by outsourcing part of the recruitment process and utilizing external resources. For example, document screening and initial interviews can be outsourced to external specialists. Costs can also be reduced by outsourcing part of the recruitment process and utilizing external resources. Costs can also be reduced by outsourcing part of the recruitment process and utilizing external resources. This allows costs to be reduced by outsourcing part of the recruitment process.

[0046] By analyzing data from the recruitment process and eliminating unnecessary steps, it is possible to achieve efficiency gains and cost reductions. By analyzing data from the recruitment process and eliminating unnecessary steps, it is possible to achieve efficiency gains and cost reductions. For example, generative AI can analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. For example, it can reduce redundant interviews and unnecessary document screening. It is also possible to analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. It is also possible to analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. This eliminates unnecessary steps in the recruitment process, thereby achieving efficiency gains and cost reductions.

[0047] It is possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. It is possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. For example, generative AI can analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. For example, scoring can be done based on the success and contribution of past projects. It is also possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. It is also possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. This makes it possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to.

[0048] It is possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. It is possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. For example, generative AI can analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. For example, it can identify skill gaps and suggest appropriate training programs. It is also possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. It is also possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. This makes it possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development.

[0049] It is possible to analyze the project experience of internal transfer applicants and propose project-based transfers. It is possible to analyze the project experience of internal transfer applicants and propose project-based transfers. For example, the generation AI can analyze the project experience of internal transfer applicants and propose project-based transfers. For example, scoring can be done based on the success and contribution of past projects. It is also possible to analyze the project experience of internal transfer applicants and propose project-based transfers. It is also possible to analyze the project experience of internal transfer applicants and propose project-based transfers. This makes it possible to analyze the project experience of internal transfer applicants and propose project-based transfers.

[0050] It is possible to analyze the career paths of internal transfer applicants and propose long-term career plans. It is possible to analyze the career paths of internal transfer applicants and propose long-term career plans. For example, generative AI can analyze the career paths of internal transfer applicants and propose long-term career plans. For example, it can design career paths based on past performance and skill sets. It is also possible to analyze the career paths of internal transfer applicants and propose long-term career plans. It is also possible to analyze the career paths of internal transfer applicants and propose long-term career plans. This makes it possible to analyze the career paths of internal transfer applicants and propose long-term career plans.

[0051] It is possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. It is possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. For example, generative AI can manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. For example, it can automate applicant data analysis and interview schedule adjustment. It is also possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. It is also possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. This makes it possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities.

[0052] It is possible to analyze data from the recruitment process and propose the optimal recruitment strategy. It is possible to analyze data from the recruitment process and propose the optimal recruitment strategy. For example, generative AI can analyze data from the recruitment process and propose the optimal recruitment strategy. For example, it can identify effective recruitment channels based on past recruitment data. It is also possible to analyze data from the recruitment process and propose the optimal recruitment strategy. It is also possible to analyze data from the recruitment process and propose the optimal recruitment strategy. This makes it possible to analyze data from the recruitment process and propose the optimal recruitment strategy.

[0053] Efficiency can be improved by outsourcing part of the recruitment process and utilizing external resources. Efficiency can be improved by outsourcing part of the recruitment process and utilizing external resources. For example, generative AI can outsource part of the recruitment process and utilize external resources to improve efficiency. For example, document screening and initial interviews can be outsourced to external specialists. Efficiency can also be improved by outsourcing part of the recruitment process and utilizing external resources. Efficiency can also be improved by outsourcing part of the recruitment process and utilizing external resources. In this way, efficiency can be improved by outsourcing part of the recruitment process.

[0054] Efficiency can be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. Efficiency can be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. For example, generative AI can analyze the data of the recruitment process and eliminate unnecessary steps, thereby achieving efficiency. For example, it can reduce redundant interviews and unnecessary document screening. Efficiency can also be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. Efficiency can also be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. This can eliminate unnecessary steps in the recruitment process and achieve efficiency.

[0055] It is possible to analyze the recruitment processes of other companies and propose the optimal system introduction. It is possible to analyze the recruitment processes of other companies and propose the optimal system introduction. For example, generative AI can analyze the recruitment processes of other companies and propose the optimal system introduction. For example, it can design an efficient system based on a company's recruitment data. It is also possible to analyze the recruitment processes of other companies and propose the optimal system introduction. It is also possible to analyze the recruitment processes of other companies and propose the optimal system introduction. This makes it possible to analyze the recruitment processes of other companies and propose the optimal system introduction.

[0056] It is possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. It is possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. For example, generative AI can analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. For example, it can design a strategy based on success stories in a specific industry. It is also possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. It is also possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. This makes it possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry.

[0057] Efficiency can be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. Efficiency can be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. For example, generative AI could outsource part of the recruitment process to other companies and utilize external resources to improve efficiency. For example, document screening and initial interviews could be outsourced to external specialists. Efficiency can also be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. Efficiency can also be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. This allows efficiency to be improved by outsourcing part of the recruitment process to other companies.

[0058] By analyzing the recruitment data of other companies and eliminating unnecessary steps, efficiency can be achieved. By analyzing the recruitment data of other companies and eliminating unnecessary steps, efficiency can be achieved. For example, generative AI can analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. For example, it can reduce duplicate interviews and unnecessary document screening. It can also be used to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. It can also be used to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. This allows it to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The recruitment process automation system may further include a health assessment unit that evaluates the health status of applicants. For example, the health assessment unit may analyze the results of the applicant's health check and assess health risks. The health assessment unit may also collect information about the applicant's lifestyle and provide advice on maintaining health. Furthermore, the health assessment unit may monitor the applicant's stress level and make suggestions for stress management. This allows companies to select applicants with low health risks.

[0061] The recruitment process automation system can further include a cultural aptitude assessment unit that evaluates the cultural aptitude of applicants. For example, the cultural aptitude assessment unit analyzes the applicant's cultural background and values ​​to evaluate the degree of compatibility with the corporate culture. The cultural aptitude assessment unit can also evaluate the applicant's ability to coexist with multiple cultures and select personnel suitable for international projects. Furthermore, the cultural aptitude assessment unit can evaluate the applicant's cultural aptitude and contribute to promoting corporate diversity. This allows companies to select personnel who are culturally compatible.

[0062] The recruitment process automation system can further include a creativity evaluation unit that evaluates the applicant's creativity. For example, the creativity evaluation unit can analyze the applicant's past projects and ideas to evaluate their creative thinking ability. The creativity evaluation unit can also analyze the applicant's submitted work and portfolio to evaluate their originality and innovation. Furthermore, the creativity evaluation unit can evaluate the applicant's creativity and contribute to promoting innovation in the company. This allows the company to select creative personnel.

[0063] The recruitment process automation system can further include an ethics evaluation unit that evaluates the applicant's ethical standards. For example, the ethics evaluation unit can analyze the applicant's past actions and statements to evaluate their ethical judgment. The ethics evaluation unit can also analyze the applicant's submitted essays and self-promotional statements to evaluate their ethics and values. Furthermore, the ethics evaluation unit can evaluate the applicant's ethical standards and contribute to promoting corporate compliance. This allows companies to select ethical personnel.

[0064] The recruitment process automation system can further include a learning ability assessment unit that evaluates the learning ability of an applicant. For example, the learning ability assessment unit analyzes the applicant's past learning history and grades to assess their learning ability. The learning ability assessment unit can also analyze the learning plans and goals submitted by the applicant to assess their self-learning ability. Furthermore, the learning ability assessment unit can evaluate the applicant's learning ability and contribute to the growth of the company. This allows companies to select personnel with high learning ability.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The document review department analyzes the applicant's documents. For example, the document review department may use text mining technology to analyze the applicant's resume and work history and evaluate the applicant's educational background, work history, and skill set. The document review department may also analyze the applicant's recommendation letters and assign a score based on the recommender's evaluation. Furthermore, the document review department may analyze the applicant's submitted portfolio and evaluate specific achievements. Step 2: The interview department conducts interviews with applicants based on the documents analyzed by the document screening department. For example, the interview department uses generative AI to ask questions to applicants and analyzes their answers to evaluate their communication and problem-solving skills. The interview department can also analyze applicants' facial expressions and gestures through online interviews to evaluate their non-verbal communication. Furthermore, the interview department can conduct group discussions to evaluate teamwork and leadership skills. Step 3: The evaluation criteria provider evaluates the interviews conducted by the interview department based on unified screening criteria. For example, the evaluation criteria provider evaluates applicants based on pre-set evaluation criteria to eliminate bias. The evaluation criteria provider can also reflect the latest industry trends and technological developments in real time and always evaluate using the latest criteria. Furthermore, the evaluation criteria provider can analyze past hiring data and optimize criteria based on successful hiring patterns.

[0067] (Example 2) In the recruitment process automation system according to an embodiment of the present invention, a generation AI is responsible for all steps from document screening to interviews, and evaluations are performed using unified screening criteria. This enables the recruitment process automation system to reduce labor costs on the hiring side and realize efficient recruitment activities.

[0068] The recruitment process automation system according to the embodiment includes a document screening unit, an interview unit, and an evaluation criteria providing unit. The document screening unit analyzes applicant documents. For example, the document screening unit may analyze the applicant's resume and curriculum vitae using text mining technology to evaluate the applicant's educational background, work history, and skill set. The document screening unit may also analyze the applicant's recommendation letters and score the applicant based on the recommender's evaluation. The document screening unit may also analyze the applicant's submitted portfolio to evaluate specific achievements. The interview unit conducts interviews with applicants based on the documents analyzed by the document screening unit. For example, the interview unit may use a generative AI to ask questions to the applicant and analyze the answers to evaluate their communication skills and problem-solving abilities. The interview unit may also analyze the applicant's facial expressions and gestures through online interviews to evaluate their non-verbal communication. The interview unit may also conduct group discussions to evaluate their teamwork and leadership skills. The evaluation criteria providing unit evaluates the content of the interviews conducted by the interview unit based on unified evaluation criteria. For example, the evaluation criteria providing unit evaluates applicants based on pre-set evaluation criteria to eliminate bias. The evaluation criteria providing unit can also reflect the latest industry trends and technological developments in real time and always evaluate using the latest criteria. Furthermore, the evaluation criteria providing unit can analyze past recruitment data and optimize the criteria based on successful recruitment patterns. This allows the recruitment process automation system according to the embodiment to reduce labor costs on the hiring side and realize efficient recruitment activities. For example, the output unit displays the recruitment results to applicants and recruiters via a web application or mobile application. If applicants or recruiters desire feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to applicants and recruiters.

[0069] The document review department can analyze an applicant's educational background, work history, and skill set and score the degree of suitability. For example, the document review department can analyze the educational background, work history, and skill set stated in the applicant's resume or curriculum vitae using text mining technology and score the degree of suitability. The document review department can also analyze the portfolio submitted by the applicant and evaluate specific achievements. For example, scoring can be done based on the scale and impact of the project. The document review department can also analyze the applicant's recommendation letter and score based on the recommender's evaluation. This allows for an accurate evaluation of the applicant's suitability.

[0070] The interview department can ask questions to applicants and analyze their answers to evaluate their communication skills and problem-solving skills. The interview department can, for example, use a generation AI to ask questions to applicants and analyze their answers to evaluate their communication skills and problem-solving skills. For example, the generation AI can analyze the applicant's answers using text analysis technology to evaluate their linguistic expression skills and conversational skills. The generation AI can also analyze the applicant's answers using emotion analysis technology to evaluate their emotional stability and stress tolerance. Furthermore, the generation AI can analyze the applicant's answers using logical analysis technology to evaluate their logical thinking skills and problem-solving skills. This allows for an accurate evaluation of the applicant's communication skills and problem-solving skills.

[0071] The evaluation criterion providing unit can evaluate applicants based on evaluation criteria that have been set in advance. The evaluation criterion providing unit evaluates applicants based on, for example, evaluation criteria that have been set in advance. For example, the evaluation criterion providing unit scores applicants based on their educational background, work history, and skill set. The evaluation criterion providing unit can also evaluate applicants based on their responses and non-verbal communication. Furthermore, the evaluation criterion providing unit can reflect the latest industry trends and technological developments in real time and always evaluate based on the latest criteria. This allows applicants to be evaluated fairly.

[0072] The document review department can automatically evaluate an applicant's past projects and deliverables and score specific achievements. For example, the document review department uses a generation AI to automatically analyze the projects and deliverables listed on an applicant's resume and CV, and evaluate the scale and impact of each project. For example, scoring is done based on the project's budget, team size, and achieved results. The document review department can also analyze the portfolio submitted by the applicant and evaluate specific achievements. For example, scoring is done based on the project's success rate and customer satisfaction. Furthermore, the document review department can analyze the applicant's recommendation letters and score based on the recommender's evaluations. This allows for an accurate evaluation of the applicant's specific achievements.

[0073] The document review department can also integrate external data such as the applicant's social media and published papers when making its evaluation. For example, the document review department can use AI to analyze the applicant's social media accounts and evaluate their professional activities and influence in the industry. For example, it can assign a score based on their LinkedIn profile or Twitter posts. The document review department can also analyze the applicant's published papers and evaluate their academic contributions. For example, it can assign a score based on the number of citations and impact factor of their papers. Furthermore, the document review department can also integrate external data about the applicant when making its evaluation. This allows it to integrate external data about the applicant and make an evaluation.

[0074] The document screening department can use the emotion estimation function to analyze the emotional tone of an applicant's self-promotional statement and cover letter, and prioritize applicants with positive emotions in their evaluation. For example, the document screening department uses a generation AI to analyze an applicant's self-promotional statement and cover letter, and uses the emotion estimation function to score positive emotions (e.g., enthusiasm, positive attitude). For example, a sentiment analysis algorithm can be used to evaluate the frequency of positive expressions and words. The document screening department can also analyze the emotional tone of an applicant's self-promotional statement and cover letter, and prioritize applicants with positive emotions in their evaluation. Furthermore, the document screening department can also use the emotion estimation function to analyze an applicant's emotional tone, and prioritize applicants with positive emotions in their evaluation. This allows applicants with positive emotions to be prioritized in their evaluation.

[0075] The document screening department can analyze applicants' video presentations to evaluate their presentation skills and expressiveness. For example, the document screening department uses a generative AI to analyze the video presentations submitted by applicants and evaluate their presentation skills (e.g., speaking style, use of visual materials). For example, it can use voice analysis technology to score the clarity and persuasiveness of the speech. The document screening department can also analyze applicants' video presentations to evaluate their expressiveness (e.g., word choice, gestures, tone of voice). Furthermore, the document screening department can analyze applicants' video presentations to evaluate their presentation skills and expressiveness. This allows for an accurate evaluation of the applicants' presentation skills and expressiveness.

[0076] The document screening department can evaluate cross-industry aptitude by referring to the standards of different industries and job types. For example, the document screening department uses generation AI to analyze an applicant's resume and job history and evaluates aptitude by referring to the standards of different industries and job types. For example, it scores the possibility of changing jobs to a different industry. The document screening department can also analyze an applicant's skill set and evaluate aptitude for different industries and job types. Furthermore, the document screening department can evaluate cross-industry aptitude by referring to the standards of different industries and job types. This makes it possible to evaluate an applicant's aptitude by referring to the standards of different industries and job types.

[0077] The document screening unit can use the emotion estimation function to analyze the emotions of applicants when they submit their documents in real time and provide feedback to reduce stress and tension. For example, when an applicant submits their documents, the document screening unit can use the emotion estimation function to analyze the emotions of applicants in real time and provide feedback to reduce stress and tension. For example, advice on how to relax can be displayed. The document screening unit can also analyze the emotions of applicants when they submit their documents and provide feedback to reduce stress and tension. Furthermore, the document screening unit can also use the emotion estimation function to analyze the emotions of applicants when they submit their documents in real time and provide feedback to reduce stress and tension. This makes it possible to provide feedback to reduce the applicant's stress and tension.

[0078] The interview department can refer to the applicant's past interview data and evaluate trends in growth and improvement. In the interview department, for example, a generative AI analyzes the applicant's past interview data and evaluates trends in growth and improvement. For example, the degree of growth is scored based on the answers given in past interviews and evaluation scores. The interview department can also refer to the applicant's past interview data and evaluate trends in growth and improvement. Furthermore, the interview department can analyze past interview data and evaluate trends in growth and improvement. This makes it possible to evaluate the applicant's trends in growth and improvement.

[0079] The interview department can use the emotion estimation function to analyze the emotional reactions to the applicant's answers and prioritize emotionally stable applicants in the evaluation. For example, the interview department uses a generation AI to analyze the emotional reactions to the applicant's answers and prioritize emotionally stable applicants in the evaluation. For example, the interview department scores the emotional fluctuations during the answers. The interview department can also use the emotion estimation function to analyze the emotional reactions to the applicant's answers and prioritize emotionally stable applicants in the evaluation. Furthermore, the interview department can also use the emotion estimation function to analyze the emotional reactions to the applicant's answers and prioritize emotionally stable applicants in the evaluation. This allows emotionally stable applicants to be prioritized in the evaluation.

[0080] The interview section can conduct group discussions with applicants to evaluate their teamwork and leadership skills. In the interview section, for example, the generative AI conducts group discussions with applicants to evaluate their teamwork and leadership skills. For example, it scores the number of times applicants speak during the discussion and the degree of leadership demonstrated. The interview section can also conduct group discussions with applicants to evaluate their teamwork and leadership skills. The interview section can also conduct group discussions with applicants to evaluate their teamwork and leadership skills. In this way, the applicant's teamwork and leadership skills can be evaluated.

[0081] The interview section can assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. For example, the interview section uses generative AI to ask questions in multiple languages ​​and assess the international aptitude of applicants with different cultural and linguistic backgrounds. For example, questions are asked in multiple languages, such as English and Chinese. The interview section can also assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. The interview section can also assess the international aptitude of applicants with different cultural and linguistic backgrounds by asking questions in multiple languages. This makes it possible to assess the international aptitude of applicants with different cultural and linguistic backgrounds.

[0082] The interview unit can use the emotion estimation function to monitor the applicant's stress level during the interview in real time and encourage them to answer in a relaxed state. The interview unit can, for example, use the emotion estimation function to monitor the applicant's stress level during the interview in real time and encourage them to answer in a relaxed state. For example, the interview unit can provide advice on how to relax if stress increases. The interview unit can also monitor the applicant's stress level during the interview and encourage them to answer in a relaxed state. The interview unit can also use the emotion estimation function to monitor the applicant's stress level during the interview in real time and encourage them to answer in a relaxed state. This allows the applicant to attend the interview in a relaxed state.

[0083] The evaluation criteria providing unit reflects the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. For example, the evaluation criteria providing unit uses a generation AI to collect the latest industry trends and technological trends in real time and reflect them in the screening criteria. For example, the evaluation criteria are updated based on the latest technology and market changes. The evaluation criteria providing unit can also reflect the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. Furthermore, the evaluation criteria providing unit can reflect the latest industry trends and technological trends in real time, allowing evaluations to be made using the latest standards. This allows applicants to be evaluated using the latest standards at all times.

[0084] The evaluation criterion providing unit can analyze past recruitment data and optimize the criteria based on successful recruitment patterns. For example, the evaluation criterion providing unit uses a generation AI to analyze past recruitment data and optimize the screening criteria based on successful recruitment patterns. For example, the evaluation criterion providing unit adjusts the evaluation criteria based on past successful recruitment cases. The evaluation criterion providing unit can also analyze past recruitment data and optimize the criteria based on successful recruitment patterns. The evaluation criterion providing unit can also analyze past recruitment data and optimize the criteria based on successful recruitment patterns. This makes it possible to optimize the evaluation criteria based on past success cases.

[0085] The evaluation criterion providing unit can use the emotion estimation function to analyze the emotional reactions of applicants to the screening criteria and set criteria that will elicit a positive reaction. The evaluation criterion providing unit can, for example, use the emotion estimation function to analyze the emotional reactions of applicants to the screening criteria and set criteria that will elicit a positive reaction. For example, evaluation criteria that will make applicants feel positive are set. The evaluation criterion providing unit can also analyze the emotional reactions of applicants to the screening criteria and set criteria that will elicit a positive reaction. Furthermore, the evaluation criterion providing unit can also use the emotion estimation function to analyze the emotional reactions of applicants to the screening criteria and set criteria that will elicit a positive reaction. This makes it possible to set criteria that will elicit a positive reaction from applicants.

[0086] The evaluation criteria providing unit can integrate criteria from different industries and occupations to evaluate cross-industry aptitude. For example, the generation AI integrates criteria from different industries and occupations to evaluate cross-industry aptitude. For example, it integrates criteria for technical and managerial positions to conduct an evaluation. The evaluation criteria providing unit can also integrate criteria from different industries and occupations to evaluate cross-industry aptitude. Furthermore, the evaluation criteria providing unit can also integrate criteria from different industries and occupations to evaluate cross-industry aptitude. This makes it possible to evaluate the aptitude of applicants by integrating criteria from different industries and occupations.

[0087] The evaluation criteria providing unit reflects the company's culture and values ​​and can select the most suitable talent for the company. For example, the evaluation criteria providing unit uses a generation AI to analyze the company's culture and values ​​and reflect them in the screening criteria. For example, it scores talent that matches the company's mission and vision. The evaluation criteria providing unit can also reflect the company's culture and values ​​and select the most suitable talent for the company. Furthermore, the evaluation criteria providing unit can also reflect the company's culture and values ​​and select the most suitable talent for the company. This makes it possible to select the most suitable talent that reflects the company's culture and values.

[0088] The evaluation criterion providing unit can use the emotion estimation function to analyze the emotional reactions of recruiters to the screening criteria and identify areas for improvement in the hiring process. The evaluation criterion providing unit can, for example, use the emotion estimation function to analyze the emotional reactions of recruiters to the screening criteria and identify areas for improvement in the hiring process. For example, the evaluation criterion providing unit can identify causes of stress and dissatisfaction. The evaluation criterion providing unit can also analyze the emotional reactions of recruiters to the screening criteria and identify areas for improvement in the hiring process. Furthermore, the evaluation criterion providing unit can also use the emotion estimation function to analyze the emotional reactions of recruiters to the screening criteria and identify areas for improvement in the hiring process. This allows areas for improvement in the hiring process to be identified and the process to be optimized.

[0089] By managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. By managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. For example, generative AI manages the entire recruitment process and automates tasks from document screening to interviews. For example, it automates applicant data analysis and interview schedule adjustment. Furthermore, by managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. Furthermore, by managing the entire recruitment process and automating the work of recruiters, further reductions in labor costs can be achieved. In this way, the entire recruitment process can be automated and labor costs can be reduced.

[0090] The emotion estimation function can be used to monitor the stress levels of recruiters and make suggestions to reduce their workload. The emotion estimation function can be used to monitor the stress levels of recruiters in real time and make suggestions to reduce their workload. For example, recommending a break if stress increases. The emotion estimation function can also be used to monitor the stress levels of recruiters and make suggestions to reduce their workload. The emotion estimation function can also be used to monitor the stress levels of recruiters in real time and make suggestions to reduce their workload. This makes it possible to monitor the stress levels of recruiters and make suggestions to reduce their workload.

[0091] Costs can be reduced by outsourcing part of the recruitment process and utilizing external resources. Costs can be reduced by outsourcing part of the recruitment process and utilizing external resources. For example, generative AI can reduce costs by outsourcing part of the recruitment process and utilizing external resources. For example, document screening and initial interviews can be outsourced to external specialists. Costs can also be reduced by outsourcing part of the recruitment process and utilizing external resources. Costs can also be reduced by outsourcing part of the recruitment process and utilizing external resources. This allows costs to be reduced by outsourcing part of the recruitment process.

[0092] By analyzing data from the recruitment process and eliminating unnecessary steps, it is possible to achieve efficiency gains and cost reductions. By analyzing data from the recruitment process and eliminating unnecessary steps, it is possible to achieve efficiency gains and cost reductions. For example, generative AI can analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. For example, it can reduce redundant interviews and unnecessary document screening. It is also possible to analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. It is also possible to analyze data from the recruitment process and eliminate unnecessary steps, thereby achieving efficiency gains and cost reductions. This eliminates unnecessary steps in the recruitment process, thereby achieving efficiency gains and cost reductions.

[0093] The emotion estimation function can be used to analyze applicant satisfaction in the recruitment process and maintain a process with high satisfaction. The emotion estimation function can be used to analyze applicant satisfaction in the recruitment process in real time and maintain a process with high satisfaction. For example, to evaluate the progress of the interview and the quality of the feedback. The emotion estimation function can also be used to analyze applicant satisfaction in the recruitment process and maintain a process with high satisfaction. The emotion estimation function can also be used to analyze applicant satisfaction in the recruitment process in real time and maintain a process with high satisfaction. This makes it possible to analyze applicant satisfaction and maintain a process with high satisfaction.

[0094] It is possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. It is possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. For example, generative AI can analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. For example, scoring can be done based on the success and contribution of past projects. It is also possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. It is also possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to. This makes it possible to analyze the past performance data of internal transfer applicants and suggest the most suitable department for them to be assigned to.

[0095] It is possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. It is possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. For example, generative AI can analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. For example, it can identify skill gaps and suggest appropriate training programs. It is also possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. It is also possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development. This makes it possible to analyze the skill sets of internal transfer applicants and make suggestions for necessary training and skill development.

[0096] The emotion estimation function can be used to analyze the emotional satisfaction of a transfer applicant, thereby realizing a transfer with high satisfaction. The emotion estimation function can be used to analyze the emotional satisfaction of a transfer applicant in real time, thereby realizing a transfer with high satisfaction. For example, the emotional reaction to the environment and work content of the transfer destination can be evaluated. The emotion estimation function can also be used to analyze the emotional satisfaction of a transfer applicant in real time, thereby realizing a transfer with high satisfaction. The emotion estimation function can also be used to analyze the emotional satisfaction of a transfer applicant in real time, thereby realizing a transfer with high satisfaction. In this way, the emotional satisfaction of a transfer applicant can be analyzed, thereby realizing a transfer with high satisfaction.

[0097] It is possible to analyze the project experience of internal transfer applicants and propose project-based transfers. It is possible to analyze the project experience of internal transfer applicants and propose project-based transfers. For example, the generation AI can analyze the project experience of internal transfer applicants and propose project-based transfers. For example, scoring can be done based on the success and contribution of past projects. It is also possible to analyze the project experience of internal transfer applicants and propose project-based transfers. It is also possible to analyze the project experience of internal transfer applicants and propose project-based transfers. This makes it possible to analyze the project experience of internal transfer applicants and propose project-based transfers.

[0098] It is possible to analyze the career paths of internal transfer applicants and propose long-term career plans. It is possible to analyze the career paths of internal transfer applicants and propose long-term career plans. For example, generative AI can analyze the career paths of internal transfer applicants and propose long-term career plans. For example, it can design career paths based on past performance and skill sets. It is also possible to analyze the career paths of internal transfer applicants and propose long-term career plans. It is also possible to analyze the career paths of internal transfer applicants and propose long-term career plans. This makes it possible to analyze the career paths of internal transfer applicants and propose long-term career plans.

[0099] The emotion estimation function can be used to monitor the stress level of a person seeking a transfer, thereby enabling a less stressful transfer. The emotion estimation function can be used to monitor the stress level of a person seeking a transfer in real time, thereby enabling a less stressful transfer. For example, the stress response to the environment and work content of the new transfer can be evaluated. The emotion estimation function can also be used to monitor the stress level of a person seeking a transfer in real time, thereby enabling a less stressful transfer. The emotion estimation function can also be used to monitor the stress level of a person seeking a transfer in real time, thereby enabling a less stressful transfer. This makes it possible to monitor the stress level of a person seeking a transfer, thereby enabling a less stressful transfer.

[0100] It is possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. It is possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. For example, generative AI can manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. For example, it can automate applicant data analysis and interview schedule adjustment. It is also possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. It is also possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities. This makes it possible to manage the entire process for hiring new graduates and mid-career employees, thereby realizing efficient recruitment activities.

[0101] It is possible to analyze data from the recruitment process and propose the optimal recruitment strategy. It is possible to analyze data from the recruitment process and propose the optimal recruitment strategy. For example, generative AI can analyze data from the recruitment process and propose the optimal recruitment strategy. For example, it can identify effective recruitment channels based on past recruitment data. It is also possible to analyze data from the recruitment process and propose the optimal recruitment strategy. It is also possible to analyze data from the recruitment process and propose the optimal recruitment strategy. This makes it possible to analyze data from the recruitment process and propose the optimal recruitment strategy.

[0102] Efficiency can be improved by outsourcing part of the recruitment process and utilizing external resources. Efficiency can be improved by outsourcing part of the recruitment process and utilizing external resources. For example, generative AI can outsource part of the recruitment process and utilize external resources to improve efficiency. For example, document screening and initial interviews can be outsourced to external specialists. Efficiency can also be improved by outsourcing part of the recruitment process and utilizing external resources. Efficiency can also be improved by outsourcing part of the recruitment process and utilizing external resources. In this way, efficiency can be improved by outsourcing part of the recruitment process.

[0103] Efficiency can be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. Efficiency can be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. For example, generative AI can analyze the data of the recruitment process and eliminate unnecessary steps, thereby achieving efficiency. For example, it can reduce redundant interviews and unnecessary document screening. Efficiency can also be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. Efficiency can also be achieved by analyzing the data of the recruitment process and eliminating unnecessary steps. This can eliminate unnecessary steps in the recruitment process and achieve efficiency.

[0104] The emotion estimation function can be used to analyze applicant satisfaction in the hiring process and maintain a highly satisfying hiring process. The emotion estimation function can be used to analyze applicant satisfaction in the hiring process in real time and maintain a highly satisfying hiring process. For example, the progress of the interview and the quality of the feedback can be evaluated. The emotion estimation function can also be used to analyze applicant satisfaction in the hiring process and maintain a highly satisfying hiring process. The emotion estimation function can also be used to analyze applicant satisfaction in the hiring process in real time and maintain a highly satisfying hiring process. This makes it possible to analyze applicant satisfaction and maintain a highly satisfying hiring process.

[0105] It is possible to analyze the recruitment processes of other companies and propose the optimal system introduction. It is possible to analyze the recruitment processes of other companies and propose the optimal system introduction. For example, generative AI can analyze the recruitment processes of other companies and propose the optimal system introduction. For example, it can design an efficient system based on a company's recruitment data. It is also possible to analyze the recruitment processes of other companies and propose the optimal system introduction. It is also possible to analyze the recruitment processes of other companies and propose the optimal system introduction. This makes it possible to analyze the recruitment processes of other companies and propose the optimal system introduction.

[0106] It is possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. It is possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. For example, generative AI can analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. For example, it can design a strategy based on success stories in a specific industry. It is also possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. It is also possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry. This makes it possible to analyze the recruitment data of other companies and propose the optimal recruitment strategy for each industry.

[0107] The emotion estimation function can be used to provide a system that analyzes the emotional reactions of recruiters at other companies and elicits positive responses. The emotion estimation function can be used to provide a system that analyzes the emotional reactions of recruiters at other companies and elicits positive responses. For example, the usability and effectiveness of the system can be evaluated. The emotion estimation function can also be used to provide a system that analyzes the emotional reactions of recruiters at other companies and elicits positive responses. The emotion estimation function can also be used to provide a system that analyzes the emotional reactions of recruiters at other companies and elicits positive responses. This makes it possible to provide a system that analyzes the emotional reactions of recruiters at other companies and elicits positive responses.

[0108] Efficiency can be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. Efficiency can be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. For example, generative AI could outsource part of the recruitment process to other companies and utilize external resources to improve efficiency. For example, document screening and initial interviews could be outsourced to external specialists. Efficiency can also be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. Efficiency can also be improved by outsourcing part of the recruitment process to other companies and utilizing external resources. This allows efficiency to be improved by outsourcing part of the recruitment process to other companies.

[0109] By analyzing the recruitment data of other companies and eliminating unnecessary steps, efficiency can be achieved. By analyzing the recruitment data of other companies and eliminating unnecessary steps, efficiency can be achieved. For example, generative AI can analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. For example, it can reduce duplicate interviews and unnecessary document screening. It can also be used to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. It can also be used to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency. This allows it to analyze the recruitment data of other companies and eliminate unnecessary steps, resulting in efficiency.

[0110] The emotion estimation function can be used to analyze the satisfaction of applicants in the recruitment processes of other companies, and maintain a process with a high level of satisfaction. The emotion estimation function can be used to analyze the satisfaction of applicants in the recruitment processes of other companies in real time, and maintain a process with a high level of satisfaction. For example, the progress of the interview and the quality of the feedback can be evaluated. The emotion estimation function can also be used to analyze the satisfaction of applicants in the recruitment processes of other companies, and maintain a process with a high level of satisfaction. The emotion estimation function can also be used to analyze the satisfaction of applicants in the recruitment processes of other companies in real time, and maintain a process with a high level of satisfaction. This allows the satisfaction of applicants in the recruitment processes of other companies to be analyzed and maintain a process with a high level of satisfaction.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The recruitment process automation system may further include a health assessment unit that evaluates the health status of applicants. For example, the health assessment unit may analyze the results of the applicant's health check and assess health risks. The health assessment unit may also collect information about the applicant's lifestyle and provide advice on maintaining health. Furthermore, the health assessment unit may monitor the applicant's stress level and make suggestions for stress management. This allows companies to select applicants with low health risks.

[0113] The recruitment process automation system can further include a cultural aptitude assessment unit that evaluates the cultural aptitude of applicants. For example, the cultural aptitude assessment unit analyzes the applicant's cultural background and values ​​to evaluate the degree of compatibility with the corporate culture. The cultural aptitude assessment unit can also evaluate the applicant's ability to coexist with multiple cultures and select personnel suitable for international projects. Furthermore, the cultural aptitude assessment unit can evaluate the applicant's cultural aptitude and contribute to promoting corporate diversity. This allows companies to select personnel who are culturally compatible.

[0114] The recruitment process automation system can further include a creativity evaluation unit that evaluates the applicant's creativity. For example, the creativity evaluation unit can analyze the applicant's past projects and ideas to evaluate their creative thinking ability. The creativity evaluation unit can also analyze the applicant's submitted work and portfolio to evaluate their originality and innovation. Furthermore, the creativity evaluation unit can evaluate the applicant's creativity and contribute to promoting innovation in the company. This allows the company to select creative personnel.

[0115] The recruitment process automation system can further include an ethics evaluation unit that evaluates the applicant's ethical standards. For example, the ethics evaluation unit can analyze the applicant's past actions and statements to evaluate their ethical judgment. The ethics evaluation unit can also analyze the applicant's submitted essays and self-promotional statements to evaluate their ethics and values. Furthermore, the ethics evaluation unit can evaluate the applicant's ethical standards and contribute to promoting corporate compliance. This allows companies to select ethical personnel.

[0116] The recruitment process automation system can further include a learning ability assessment unit that evaluates the learning ability of an applicant. For example, the learning ability assessment unit analyzes the applicant's past learning history and grades to assess their learning ability. The learning ability assessment unit can also analyze the learning plans and goals submitted by the applicant to assess their self-learning ability. Furthermore, the learning ability assessment unit can evaluate the applicant's learning ability and contribute to the growth of the company. This allows companies to select personnel with high learning ability.

[0117] The document screening department can use the emotion estimation function to analyze the emotional tone of an applicant's self-promotional statement and cover letter, and prioritize applicants with positive emotions in their evaluation. For example, a sentiment analysis algorithm can be used to evaluate the frequency of positive expressions and words. The document screening department can also analyze the emotional tone of an applicant's self-promotional statement and cover letter, and prioritize applicants with positive emotions in their evaluation. Furthermore, the document screening department can also use the emotion estimation function to analyze the emotional tone of an applicant, and prioritize applicants with positive emotions in their evaluation. This allows applicants with positive emotions to be prioritized in their evaluation.

[0118] The interview department can use the emotion estimation function to analyze the emotional reactions to the applicant's answers and give priority to evaluating emotionally stable applicants. For example, the emotional fluctuations during the answers are scored. The interview department can also use the emotion estimation function to analyze the emotional reactions to the applicant's answers and give priority to evaluating emotionally stable applicants. The interview department can also use the emotion estimation function to analyze the emotional reactions to the applicant's answers and give priority to evaluating emotionally stable applicants. This allows priority to evaluate emotionally stable applicants.

[0119] The document screening department can use the emotion estimation function to analyze the emotions of applicants when they submit their documents in real time and provide feedback to reduce stress and tension. For example, it can display advice on how to relax. The document screening department can also analyze the emotions of applicants when they submit their documents in real time and provide feedback to reduce stress and tension. The document screening department can also use the emotion estimation function to analyze the emotions of applicants when they submit their documents in real time and provide feedback to reduce stress and tension. This makes it possible to provide feedback to reduce stress and tension for the applicant.

[0120] The interview department can use the emotion estimation function to monitor the applicant's stress level during the interview in real time and encourage them to answer in a relaxed state. For example, if stress increases, the interview department can provide advice on how to relax. The interview department can also monitor the applicant's stress level during the interview and encourage them to answer in a relaxed state. The interview department can also use the emotion estimation function to monitor the applicant's stress level during the interview in real time and encourage them to answer in a relaxed state. This allows the applicant to attend the interview in a relaxed state.

[0121] The evaluation criterion providing unit can use the emotion estimation function to analyze the applicant's emotional response to the screening criteria and set criteria that will elicit a positive response. For example, evaluation criteria that will make the applicant feel positive are set. The evaluation criterion providing unit can also analyze the applicant's emotional response to the screening criteria and set criteria that will elicit a positive response. The evaluation criterion providing unit can also use the emotion estimation function to analyze the applicant's emotional response to the screening criteria and set criteria that will elicit a positive response. This makes it possible to set criteria that will elicit a positive response from the applicant.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The document review department analyzes the applicant's documents. For example, the document review department may use text mining technology to analyze the applicant's resume and work history and evaluate the applicant's educational background, work history, and skill set. The document review department may also analyze the applicant's recommendation letters and assign a score based on the recommender's evaluation. Furthermore, the document review department may analyze the applicant's submitted portfolio and evaluate specific achievements. Step 2: The interview department conducts interviews with applicants based on the documents analyzed by the document screening department. For example, the interview department uses generative AI to ask questions to applicants and analyzes their answers to evaluate their communication and problem-solving skills. The interview department can also analyze applicants' facial expressions and gestures through online interviews to evaluate their non-verbal communication. Furthermore, the interview department can conduct group discussions to evaluate teamwork and leadership skills. Step 3: The evaluation criteria provider evaluates the interviews conducted by the interview department based on unified screening criteria. For example, the evaluation criteria provider evaluates applicants based on pre-set evaluation criteria to eliminate bias. The evaluation criteria provider can also reflect the latest industry trends and technological developments in real time and always evaluate using the latest criteria. Furthermore, the evaluation criteria provider can analyze past hiring data and optimize criteria based on successful hiring patterns.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0144] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0155] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0168] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0169] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a document review department that analyzes applicant documents; an interview department that conducts interviews with applicants based on the documents analyzed by the document screening department; and an evaluation criteria providing unit that evaluates the content of the interview conducted by the interview unit based on unified evaluation criteria. A system characterized by:

2. The document examination department Analyze the applicant's video presentation and evaluate their presentation skills and expressiveness 2. The system of claim 1.

3. The interview section is Conduct group discussions with the applicants to assess their teamwork and leadership skills 2. The system of claim 1.

4. The evaluation criterion providing unit Reflecting the latest industry trends and technological developments in real time, the evaluation is always conducted based on the latest standards.

2. The system of claim 1.

5. Monitor recruiters' stress levels and offer suggestions to reduce workload 2. The system of claim 1.

Images