System

Abstract

An object of a system according to an embodiment is to reduce the time required for adoption by data-based graduate recruitment criteria.SOLUTION: A system includes a reception unit, an analysis unit, a collection unit, a collation unit, and a provision unit. The reception unit inputs a graduate recruitment criterion required by a company. The analysis unit analyzes the reference input by the reception unit. The collection unit collects information on a candidate. The collation unit collates the information collected by the collection unit with a reference. The providing unit provides a final evaluation result.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] Conventional technology does not adequately digitize new graduate recruitment criteria to efficiently advance the recruitment process, and there is room for improvement.

[0005] The system according to the embodiment aims to digitize the criteria for recruiting new graduates and reduce the time required for recruitment. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a matching unit, and a provision unit. The reception unit inputs the new graduate recruitment criteria required by the company. The analysis unit analyzes the criteria input by the reception unit. The collection unit collects information about the candidate. The matching unit matches the information collected by the collection unit with the criteria. The provision unit provides the final evaluation results. [Effects of the Invention]

[0007] The system according to the embodiment digitizes the criteria for recruiting new graduates, thereby reducing the time required for recruitment. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A recruitment support system according to an embodiment of the present invention digitizes a company's desired new graduate recruitment criteria, reducing the time required for recruitment. The recruitment support system inputs the company's desired new graduate recruitment criteria, and AI analyzes the data and compares it with candidate information. AI analyzes information such as the candidate's resume and interview results to determine whether the candidate meets the company's recruitment criteria. This allows recruiters to quickly evaluate candidate information and significantly reduce the time required for recruitment. For example, a recruitment support system inputs a company's desired new graduate recruitment criteria as data. This input includes detailed criteria such as educational background, skills, experience, and personality traits. For example, if a candidate prioritizes graduates of a specific university or candidates with specific skills, the system inputs that information. Next, the recruitment support system uses AI to analyze the input recruitment criteria. The AI ​​understands the company's desired criteria and evaluates the candidate's information based on those criteria. For example, the AI ​​analyzes a candidate's resume and determines whether their educational background and skills meet the company's criteria. Furthermore, the recruitment support system also analyzes information such as interview results. For example, the AI ​​can analyze interview audio data to evaluate the candidate's communication ability and personality traits. This allows candidates who meet a company's hiring criteria to be identified quickly. This allows the hiring support system to digitize a company's new graduate hiring criteria, reducing the time it takes to hire. This allows the hiring support system to digitize a company's new graduate hiring criteria, reducing the time it takes to hire. For example, while a traditional manual evaluation process often takes several weeks, using AI makes it possible to complete the evaluation within a few days. Additionally, because AI objectively evaluates candidate information, the fairness of the hiring process is improved.

[0029] The recruitment support system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a matching unit, and a provision unit. The reception unit inputs the new graduate recruitment criteria required by a company. The new graduate recruitment criteria required by a company include, but are not limited to, educational background, skills, experience, and personality traits. The reception unit may input criteria, for example, prioritizing graduates of a specific university or candidates with specific skills. The analysis unit uses AI to analyze the criteria input by the reception unit. The analysis unit may analyze the criteria using, for example, natural language processing technology. The analysis unit may also analyze the criteria using voice recognition technology. For example, the analysis unit may understand the criteria required by a company and evaluate candidate information based on those criteria. The collection unit collects candidate information. The collection unit may collect information such as candidate resumes and interview results. The collection unit may also analyze candidate social media activities and online portfolios. For example, the collection unit may analyze candidate social media posts and follower counts to collect candidate information. The matching unit compares the information collected by the collection unit with the criteria. The matching unit, for example, uses an algorithm to match the candidate information with criteria. For example, the matching unit uses a matching algorithm to match the candidate information with criteria. The providing unit provides the final evaluation results. For example, the providing unit provides the evaluation results to the hiring manager. For example, the providing unit provides the evaluation results in report format. The providing unit can also provide the evaluation results in a dashboard display. As a result, the recruitment support system according to the embodiment can digitize a company's new graduate recruitment criteria, reducing the time required for recruitment.

[0030] The analysis unit can analyze the criteria using natural language processing or speech recognition technology. The analysis unit analyzes the criteria using, for example, natural language processing technology. For example, the analysis unit analyzes the criteria using morphological analysis. The analysis unit can also analyze the criteria using grammatical analysis. The analysis unit can also analyze the criteria using semantic analysis. For example, the analysis unit divides the words of the criteria using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the criteria using semantic analysis. The analysis unit can also analyze the criteria using speech recognition technology. For example, the analysis unit collects speech samples and analyzes the criteria using a speech analysis algorithm. In this way, the use of natural language processing or speech recognition technology improves the accuracy of the analysis of the criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the criteria using an AI model that analyzes the criteria using natural language processing technology.

[0031] The collection unit can analyze the candidate's social media activity or online portfolio. The collection unit, for example, analyzes the candidate's social media activity. For example, the collection unit can analyze the content of the candidate's posts to collect information about the candidate. The collection unit can also analyze the number of the candidate's followers to collect information about the candidate. The collection unit can also analyze the candidate's engagement rate to collect information about the candidate. For example, the collection unit collects information about the candidate by analyzing the content of the candidate's posts, the number of followers, and the engagement rate. The collection unit can also analyze the candidate's online portfolio. For example, the collection unit can analyze the content of the candidate's projects to collect information about the candidate. The collection unit can also analyze the candidate's skill set to collect information about the candidate. The collection unit can also analyze the candidate's evaluations to collect information about the candidate. For example, the collection unit collects information about the candidate by analyzing the content of the candidate's projects, the skill set, and the evaluations. This makes it possible to collect more detailed information by analyzing the candidate's social media activity or online portfolio. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect candidate information using an AI model that analyzes the candidate's social media activity.

[0032] The matching unit can match the candidate information with the standard using an algorithm. The matching unit, for example, matches the candidate information with the standard using an algorithm. For example, the matching unit matches the candidate information with the standard using a matching algorithm. The matching unit can also match the candidate information with the standard using a machine learning algorithm. The matching unit can also match the candidate information with the standard using a statistical analysis algorithm. For example, the matching unit matches the candidate information with the standard using a matching algorithm, matches the candidate information with the standard using a machine learning algorithm, and matches the candidate information with the standard using a statistical analysis algorithm. In this way, the use of algorithms improves the accuracy of matching the candidate information with the standard. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can match the candidate information with the standard using an AI model that matches the candidate information with the standard.

[0033] The providing unit can provide the evaluation results to the hiring manager. The providing unit, for example, provides the evaluation results to the hiring manager. For example, the providing unit provides the evaluation results in report format. The providing unit can also provide the evaluation results in dashboard display. The providing unit can also provide the evaluation results by a notification method. For example, the providing unit provides the evaluation results in report format, dashboard display, and notification method. In this way, by providing the evaluation results to the hiring manager, the efficiency of the hiring process is improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the evaluation results using an AI model that provides the evaluation results.

[0034] The reception unit can analyze the company's past hiring criteria data and propose the optimal input method. The reception unit, for example, analyzes the company's past hiring criteria data and proposes the optimal input method. For example, the reception unit automatically displays hiring criteria that were frequently used in the past as candidates. The reception unit can also propose an input method suitable for a specific industry or job type based on the past hiring criteria data. The reception unit can also analyze the past hiring criteria data and propose the most effective input method. For example, the reception unit automatically displays hiring criteria that were frequently used in the past as candidates, proposes an input method suitable for a specific industry or job type based on the past hiring criteria data, analyzes the past hiring criteria data, and proposes the most effective input method. In this way, the optimal input method can be proposed by analyzing the past hiring criteria data. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose the optimal input method using an AI model that analyzes the company's past hiring criteria data.

[0035] The reception unit can provide input fields customized according to the industry and size of the company when entering the hiring criteria. For example, the reception unit can provide input fields customized according to the industry and size of the company when entering the hiring criteria. For example, the reception unit can provide fields for entering detailed skill sets and years of experience for large companies. The reception unit can also provide simplified input fields for small and medium-sized companies, enabling quick input. The reception unit can also provide input fields specialized for a specific industry, allowing input of industry-specific skills and experience. For example, the reception unit can provide fields for entering detailed skill sets and years of experience for large companies, and simplified input fields for small and medium-sized companies, enabling quick input, and input fields specialized for a specific industry, allowing input of industry-specific skills and experience. By providing input fields customized according to the industry and size of the company, the accuracy of input is improved. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can provide input fields using an AI model that provides input fields customized according to the industry and size of the company.

[0036] The reception unit can adjust the priority of input items based on the company's current hiring situation when inputting hiring criteria. For example, when inputting hiring criteria, the reception unit adjusts the priority of input items based on the company's current hiring situation. For example, the reception unit prompts the user to input the most important criteria first based on the current hiring situation. Furthermore, when the hiring situation is urgent, the reception unit can simplify the input items to allow for quick input. Furthermore, when the hiring situation is stable, the reception unit can provide detailed input items to allow for highly accurate criteria to be set. For example, the reception unit prompts the user to input the most important criteria first based on the current hiring situation, and when the hiring situation is urgent, the reception unit simplifies the input items to allow for quick input, while when the hiring situation is stable, the reception unit provides detailed input items to allow for highly accurate criteria to be set. This allows for efficient input by adjusting the priority of input items based on the company's current hiring situation. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can adjust the priority of input items using an AI model that adjusts the priority of input items based on the company's current hiring situation.

[0037] When inputting hiring criteria, the reception unit can prioritize inputting highly relevant criteria by taking into account the geographical location information of companies. For example, when inputting hiring criteria, the reception unit prioritizes inputting highly relevant criteria by taking into account the geographical location information of companies. For example, the reception unit inputs criteria that prioritize graduates of geographically nearby universities. The reception unit can also input criteria that prioritize candidates with experience working at geographically nearby companies. The reception unit can also input criteria that prioritize candidates with experience working in geographically nearby areas. For example, the reception unit inputs criteria that prioritize graduates of geographically nearby universities, inputs criteria that prioritize candidates with experience working at geographically nearby companies, and inputs criteria that prioritize candidates with experience working in geographically nearby areas. In this way, highly relevant criteria can be prioritized by taking geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input criteria using an AI model that prioritizes inputting highly relevant criteria by taking into account the geographical location information of companies.

[0038] The reception unit can analyze the company's social media activity and propose related criteria when the hiring criteria are input. For example, the reception unit can analyze the company's social media activity and propose related criteria when the hiring criteria are input. For example, the reception unit can propose criteria that prioritize candidates with relevant skills and experience based on the company's social media activity. The reception unit can also analyze the company's social media posts and propose related criteria. The reception unit can also propose related criteria by referring to the activity of the company's followers on social media. For example, the reception unit can propose criteria that prioritize candidates with relevant skills and experience based on the company's social media activity, analyze the company's social media posts and propose related criteria, and propose related criteria by referring to the activity of the company's followers on social media. In this way, related criteria can be proposed by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose related criteria using an AI model that analyzes the company's social media activity.

[0039] The reception unit can customize the input method by reflecting the company's past feedback when entering the hiring criteria. For example, the reception unit customizes the input method by reflecting the company's past feedback when entering the hiring criteria. For example, the reception unit suggests an optimal input method based on feedback obtained in past hiring processes. The reception unit can also adjust the order and content of input fields by reflecting the past feedback. The reception unit can also improve the design of the input interface based on the past feedback. For example, the reception unit suggests an optimal input method based on feedback obtained in past hiring processes, adjusts the order and content of input fields by reflecting the past feedback, and improves the design of the input interface based on the past feedback. In this way, the optimal input method can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that customizes the input method by reflecting the company's past feedback.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the adopted criteria during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the adopted criteria during analysis. For example, the analysis unit performs a detailed analysis for criteria with high importance. The analysis unit can also perform a simplified analysis for criteria with low importance. The analysis unit can also adjust the display method of the analysis results according to the importance. For example, the analysis unit performs a detailed analysis for criteria with high importance and a simplified analysis for criteria with low importance, and adjusts the display method of the analysis results according to the importance. In this way, adjusting the level of detail of the analysis based on the importance of the adopted criteria enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the adopted criteria.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the hiring criteria during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the hiring criteria during analysis. For example, the analysis unit applies an educational background analysis algorithm to criteria related to educational background. The analysis unit can also apply a skill analysis algorithm to criteria related to skills. The analysis unit can also apply a personality analysis algorithm to criteria related to personality traits. For example, the analysis unit applies an educational background analysis algorithm to criteria related to educational background, a skill analysis algorithm to criteria related to skills, and a personality analysis algorithm to criteria related to personality traits. In this way, by applying different analysis algorithms depending on the category of the hiring criteria, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply the analysis algorithm using an AI model that applies different analysis algorithms depending on the category of the hiring criteria.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. The analysis unit can also improve the analysis method based on the past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results, improves the accuracy of the analysis by referring to the past analysis results, and improves the analysis method based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that improves the accuracy of the analysis by referring to the company's past analysis results.

[0043] The analysis unit can determine the priority of analysis based on the submission time of the adopted criteria during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the adopted criteria during analysis. For example, the analysis unit prioritizes analysis of criteria submitted earlier. The analysis unit can also postpone analysis of criteria submitted later. The analysis unit can also adjust the order of analysis based on the submission time. For example, the analysis unit prioritizes analysis of criteria submitted earlier and postpones analysis of criteria submitted later, and adjusts the order of analysis based on the submission time. In this way, determining the priority of analysis based on the submission time enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of analysis using an AI model that determines the priority of analysis based on the submission time of the adopted criteria.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the adopted criteria during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the adopted criteria during analysis. For example, the analysis unit prioritizes analysis of highly relevant criteria. The analysis unit can also postpone analysis of less relevant criteria. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit prioritizes analysis of highly relevant criteria and postpones analysis of less relevant criteria, and adjusts the order of analysis based on relevance. In this way, adjusting the order of analysis based on relevance enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that adjusts the order of analysis based on the relevance of the adopted criteria.

[0045] The analysis unit can adjust the use of technical terms in the analysis results according to the company's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the company's level of expertise during analysis. For example, if the expertise level is high, the analysis unit displays analysis results that use a lot of technical terms. Furthermore, if the expertise level is low, the analysis unit can display analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the expertise level. For example, if the expertise level is high, the analysis unit displays analysis results that use a lot of technical terms, and if the expertise level is low, the analysis unit displays analysis results that are concise and easy to understand, thereby adjusting the way the analysis results are presented according to the expertise level. In this way, adjusting the use of technical terms in the analysis results according to the expertise level improves understanding of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the use of technical terms in the analysis results using an AI model that adjusts the use of technical terms in the analysis results according to the company's level of expertise.

[0046] The collection unit can analyze the candidate's past activity history and select the optimal collection method. The collection unit, for example, analyzes the candidate's past activity history and selects the optimal collection method. For example, the collection unit selects the most effective information collection method from the past activity history. The collection unit can also select an information collection method according to the candidate's interests and concerns based on the past activity history. The collection unit can also analyze the past activity history and select the optimal collection means. For example, the collection unit selects the most effective information collection method from the past activity history, selects an information collection method according to the candidate's interests and concerns based on the past activity history, analyzes the past activity history, and selects the optimal collection means. In this way, the optimal information collection method can be selected by analyzing the past activity history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal collection method using an AI model that analyzes the candidate's past activity history.

[0047] The collection unit can perform filtering based on the candidate's current situation and areas of interest when collecting information. For example, the collection unit performs filtering based on the candidate's current situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the candidate's current situation. The collection unit can also filter and collect related information based on the candidate's areas of interest. The collection unit can also collect optimal information taking into account the candidate's current situation and areas of interest. For example, the collection unit prioritizes collecting highly relevant information based on the candidate's current situation, filters and collects related information based on the candidate's areas of interest, and collects optimal information taking into account the candidate's current situation and areas of interest. In this way, highly relevant information can be collected by filtering based on the candidate's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that performs filtering based on the candidate's current situation and areas of interest.

[0048] The collection unit can select the optimal collection means depending on the candidate's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the candidate's input method when collecting information. For example, if the candidate uses voice input, the collection unit can prioritize collecting voice data. Also, if the candidate uses text input, the collection unit can prioritize collecting text data. Also, if the candidate uses image input, the collection unit can prioritize collecting image data. For example, if the candidate uses voice input, the collection unit prioritizes collecting voice data; if the candidate uses text input, the collection unit prioritizes collecting text data; and if the candidate uses image input, the collection unit prioritizes collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the candidate's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that selects the optimal collection means depending on the candidate's input method.

[0049] The collection unit can prioritize collecting highly relevant information by taking into account the candidate's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the candidate's geographical location information when collecting information. For example, the collection unit prioritizes collecting information about nearby companies and workplaces based on the candidate's geographical location information. The collection unit can also prioritize collecting information about region-specific skills and experience by taking into account the candidate's geographical location information. The collection unit can also filter and collect highly relevant information based on the candidate's geographical location information. For example, the collection unit prioritizes collecting information about nearby companies and workplaces based on the candidate's geographical location information, prioritizes collecting information about region-specific skills and experience by taking into account the candidate's geographical location information, and filters and collects highly relevant information based on the candidate's geographical location information. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that prioritizes collecting highly relevant information by taking into account the candidate's geographical location information.

[0050] The collection unit may analyze the candidate's social media activities and collect relevant information when collecting information. For example, the collection unit may analyze the candidate's social media activities and collect relevant information when collecting information. For example, the collection unit may analyze the candidate's social media activities and collect information on related skills and experience. The collection unit may also collect relevant information based on the candidate's social media posts. The collection unit may also collect relevant information by referring to the activities of the candidate's friends on social media. For example, the collection unit may analyze the candidate's social media activities and collect information on related skills and experience, collect relevant information based on the candidate's social media posts, and collect relevant information by referring to the activities of the candidate's friends on social media. In this way, relevant information can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model that analyzes the candidate's social media activities.

[0051] The collection unit can customize the collection method by reflecting the candidate's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the candidate's past feedback when collecting information. For example, the collection unit proposes an optimal information collection method based on the past feedback. The collection unit can also adjust the information collection procedure and content by reflecting the past feedback. The collection unit can also improve the information collection interface based on the past feedback. For example, the collection unit proposes an optimal information collection method based on the past feedback, adjusts the information collection procedure and content by reflecting the past feedback, and improves the information collection interface based on the past feedback. In this way, the optimal information collection method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can customize the collection method using an AI model that customizes the collection method by reflecting the candidate's past feedback.

[0052] The matching unit can adjust the level of detail of the matching based on the importance of the accepted criteria during matching. For example, the matching unit adjusts the level of detail of the matching based on the importance of the accepted criteria during matching. For example, the matching unit performs detailed matching for criteria with high importance. The matching unit can also perform simplified matching for criteria with low importance. The matching unit can also adjust the display method of the matching results according to the importance. For example, the matching unit performs detailed matching for criteria with high importance and simplified matching for criteria with low importance, and adjusts the display method of the matching results according to the importance. In this way, adjusting the level of detail of the matching based on the importance of the accepted criteria enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the level of detail of the matching using an AI model that adjusts the level of detail of the matching based on the importance of the accepted criteria.

[0053] The matching unit can apply different matching algorithms depending on the category of the hiring criteria during matching. For example, the matching unit applies different matching algorithms depending on the category of the hiring criteria during matching. For example, the matching unit applies an education matching algorithm to criteria related to educational background. The matching unit can also apply a skill matching algorithm to criteria related to skills. The matching unit can also apply a personality matching algorithm to criteria related to personality traits. For example, the matching unit applies an education matching algorithm to criteria related to educational background, a skill matching algorithm to criteria related to skills, and a personality matching algorithm to criteria related to personality traits. In this way, by applying different matching algorithms depending on the category of the hiring criteria, matching accuracy is improved. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can apply the matching algorithm using an AI model that applies different matching algorithms depending on the category of the hiring criteria.

[0054] The matching unit can improve the accuracy of matching by referring to the company's past matching results during matching. For example, the matching unit can improve the accuracy of matching by referring to the company's past matching results during matching. For example, the matching unit adjusts the matching algorithm based on the past matching results. The matching unit can also improve the accuracy of matching by referring to the past matching results. The matching unit can also improve the matching method based on the past matching results. For example, the matching unit adjusts the matching algorithm based on the past matching results, improves the accuracy of matching by referring to the past matching results, and improves the matching method based on the past matching results. In this way, the accuracy of matching is improved by referring to the past matching results. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can improve the accuracy of matching by using an AI model that improves the accuracy of matching by referring to the company's past matching results.

[0055] The matching unit can determine the matching priority based on the submission time of the adopted criteria during matching. The matching unit, for example, determines the matching priority based on the submission time of the adopted criteria during matching. For example, the matching unit prioritizes matching of criteria that were submitted earlier. The matching unit can also postpone matching of criteria that were submitted later. The matching unit can also adjust the matching order based on the submission time. For example, the matching unit prioritizes matching of criteria that were submitted earlier and postpones matching of criteria that were submitted later, and adjusts the matching order based on the submission time. In this way, determining the matching priority based on the submission time enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can determine the matching priority using an AI model that determines the matching priority based on the submission time of the adopted criteria.

[0056] The matching unit can adjust the order of matching based on the relevance of the adoption criteria during matching. For example, the matching unit adjusts the order of matching based on the relevance of the adoption criteria during matching. For example, the matching unit prioritizes matching of highly relevant criteria. The matching unit can also postpone matching of less relevant criteria. The matching unit can also adjust the order of matching based on relevance. For example, the matching unit prioritizes matching of highly relevant criteria and postpones matching of less relevant criteria, and adjusts the order of matching based on relevance. In this way, adjusting the order of matching based on relevance enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the order of matching using an AI model that adjusts the order of matching based on the relevance of the adoption criteria.

[0057] The matching unit can adjust the use of technical terms in the matching results during matching depending on the company's level of expertise. For example, the matching unit adjusts the use of technical terms in the matching results depending on the company's level of expertise during matching. For example, if the level of expertise is high, the matching unit displays a matching result that uses a lot of technical terms. Furthermore, if the level of expertise is low, the matching unit can display a matching result that is concise and easy to understand. Furthermore, the matching unit can adjust the way the matching results are presented depending on the level of expertise. For example, if the level of expertise is high, the matching unit displays a matching result that uses a lot of technical terms, and if the level of expertise is low, the matching unit displays a matching result that is concise and easy to understand, thereby adjusting the way the matching results are presented depending on the level of expertise. In this way, adjusting the use of technical terms in the matching results depending on the level of expertise improves the understandability of the matching results. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the use of technical terms in the matching results using an AI model that adjusts the use of technical terms in the matching results depending on the company's level of expertise.

[0058] The providing unit can adjust the level of detail of the display based on the importance of the adoption criteria when providing the evaluation results. For example, the providing unit adjusts the level of detail of the display based on the importance of the adoption criteria when providing the evaluation results. For example, the providing unit displays detailed evaluation results for criteria with high importance. The providing unit can also display simplified evaluation results for criteria with low importance. The providing unit can also adjust the display method of the evaluation results according to the importance. For example, the providing unit displays detailed evaluation results for criteria with high importance and simplified evaluation results for criteria with low importance, and adjusts the display method of the evaluation results according to the importance. This enables efficient provision of evaluation results by adjusting the level of detail of the display based on the importance of the adoption criteria. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail of the display using an AI model that adjusts the level of detail of the display based on the importance of the adoption criteria.

[0059] The providing unit can apply different display algorithms depending on the category of the hiring criteria when providing the evaluation results. For example, the providing unit applies different display algorithms depending on the category of the hiring criteria when providing the evaluation results. For example, the providing unit applies an educational background evaluation algorithm to criteria related to educational background. The providing unit can also apply a skill evaluation algorithm to criteria related to skills. The providing unit can also apply a personality evaluation algorithm to criteria related to personality traits. For example, the providing unit applies an educational background evaluation algorithm to criteria related to educational background, a skill evaluation algorithm to criteria related to skills, and a personality evaluation algorithm to criteria related to personality traits. In this way, by applying different display algorithms depending on the category of the hiring criteria, the accuracy of the evaluation results is improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply the display algorithms using an AI model that applies different display algorithms depending on the category of the hiring criteria.

[0060] When providing the evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, when providing the evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results. The providing unit can also improve the accuracy of the display by referring to the past evaluation results. The providing unit can also improve the display method based on the past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results, improves the accuracy of the display by referring to the past evaluation results, and improves the display method based on the past evaluation results. In this way, the accuracy of the display is improved by referring to the past evaluation results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve the accuracy of the display by using an AI model that improves the accuracy of the display by referring to the company's past evaluation results.

[0061] The providing unit can determine the display priority based on the submission time of the adopted criteria when providing the evaluation results. For example, when providing the evaluation results, the providing unit determines the display priority based on the submission time of the adopted criteria. For example, the providing unit prioritizes displaying criteria that were submitted earlier. The providing unit can also display criteria that were submitted later later. The providing unit can also adjust the display order based on the submission time. For example, the providing unit prioritizes displaying criteria that were submitted earlier and later criteria and displays the display order based on the submission time. This enables efficient provision of evaluation results by determining the display priority based on the submission time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the display priority using an AI model that determines the display priority based on the submission time of the adopted criteria.

[0062] The providing unit can adjust the display order based on the relevance of the adoption criteria when providing the evaluation results. For example, the providing unit adjusts the display order based on the relevance of the adoption criteria when providing the evaluation results. For example, the providing unit prioritizes displaying highly relevant criteria. The providing unit can also postpone displaying less relevant criteria. The providing unit can also adjust the display order based on relevance. For example, the providing unit prioritizes displaying highly relevant criteria and postpones displaying less relevant criteria, and adjusts the display order based on relevance. This makes it possible to provide efficient evaluation results by adjusting the display order based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the display order using an AI model that adjusts the display order based on the relevance of the adoption criteria.

[0063] When providing the evaluation results, the providing unit can adjust the use of technical terms in the displayed content according to the company's level of expertise. For example, when providing the evaluation results, the providing unit adjusts the use of technical terms in the displayed content according to the company's level of expertise. For example, when the expertise level is high, the providing unit displays evaluation results that use a lot of technical terms. Furthermore, when the expertise level is low, the providing unit can display evaluation results that are concise and easy to understand. Furthermore, the providing unit can adjust the way the evaluation results are presented according to the level of expertise. For example, when the expertise level is high, the providing unit displays evaluation results that use a lot of technical terms, and when the expertise level is low, displays evaluation results that are concise and easy to understand, and adjusts the way the evaluation results are presented according to the level of expertise. In this way, adjusting the use of technical terms in the displayed content according to the level of expertise improves understanding of the evaluation results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the use of technical terms in the displayed content using an AI model that adjusts the use of technical terms in the displayed content according to the company's level of expertise.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The analysis unit can analyze a company's past hiring criteria data and propose the optimal analysis method. For example, the analysis unit automatically applies analysis methods that have been frequently used in the past. The analysis unit can also propose an analysis method suitable for a specific industry or job type based on past hiring criteria data. The analysis unit can also analyze past hiring criteria data and propose the most effective analysis method. In this way, the optimal analysis method can be proposed by analyzing past hiring criteria data.

[0066] The matching unit can improve the accuracy of matching by referring to the company's past matching results. For example, the matching unit adjusts the matching algorithm based on the past matching results. The matching unit can also improve the accuracy of matching by referring to the past matching results. The matching unit can also improve the matching method based on the past matching results. In this way, the accuracy of matching is improved by referring to the past matching results.

[0067] The reception department can input highly relevant criteria with priority given to the geographical location information of the companies. For example, the reception department can input criteria that give priority to graduates of geographically nearby universities. The reception department can also input criteria that give priority to candidates who have experience working at geographically nearby companies. The reception department can also input criteria that give priority to candidates who have experience working in geographically nearby areas. In this way, highly relevant criteria can be input with priority given to the geographical location information.

[0068] The collection unit can analyze the candidate's past activity history and select the optimal collection method. For example, the collection unit selects the most effective information collection method from the past activity history. The collection unit can also select an information collection method that suits the candidate's interests and concerns based on the past activity history. The collection unit can also analyze the past activity history and select the optimal collection means. In this way, the optimal information collection method can be selected by analyzing the past activity history.

[0069] When providing evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results. The providing unit can also improve the accuracy of the display by referring to the past evaluation results. The providing unit can also improve the display method based on the past evaluation results. In this way, the accuracy of the display is improved by referring to the past evaluation results.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The receptionist inputs the company's desired recruiting criteria for new graduates. These criteria include educational background, skills, experience, and personality traits. For example, the receptionist can input criteria to prioritize graduates of a specific university or candidates with specific skills. Step 2: The analysis unit uses AI to analyze the criteria entered by the reception unit. The analysis unit uses natural language processing and voice recognition technology to analyze the criteria, understand the criteria required by the company, and evaluate the candidate information based on those criteria. Step 3: The collection department collects candidate information. The collection department collects information such as the candidate's resume, interview results, social media activity, and online portfolio. For example, they analyze the candidate's social media posts and number of followers to collect candidate information. Step 4: The matching unit matches the information collected by the collection unit with the criteria. The matching unit uses an algorithm to match the candidate's information with the criteria, for example, using a matching algorithm. Step 5: The reporting department provides the final evaluation results. The reporting department provides the evaluation results to the hiring manager in the form of a report or dashboard display.

[0072] (Example 2) A recruitment support system according to an embodiment of the present invention digitizes a company's desired new graduate recruitment criteria, reducing the time required for recruitment. The recruitment support system inputs the company's desired new graduate recruitment criteria, and AI analyzes the data and compares it with candidate information. AI analyzes information such as the candidate's resume and interview results to determine whether the candidate meets the company's recruitment criteria. This allows recruiters to quickly evaluate candidate information and significantly reduce the time required for recruitment. For example, a recruitment support system inputs a company's desired new graduate recruitment criteria as data. This input includes detailed criteria such as educational background, skills, experience, and personality traits. For example, if a candidate prioritizes graduates of a specific university or candidates with specific skills, the system inputs that information. Next, the recruitment support system uses AI to analyze the input recruitment criteria. The AI ​​understands the company's desired criteria and evaluates the candidate's information based on those criteria. For example, the AI ​​analyzes a candidate's resume and determines whether their educational background and skills meet the company's criteria. Furthermore, the recruitment support system also analyzes information such as interview results. For example, the AI ​​can analyze interview audio data to evaluate the candidate's communication ability and personality traits. This allows candidates who meet a company's hiring criteria to be identified quickly. This allows the hiring support system to digitize a company's new graduate hiring criteria, reducing the time it takes to hire. This allows the hiring support system to digitize a company's new graduate hiring criteria, reducing the time it takes to hire. For example, while a traditional manual evaluation process often takes several weeks, using AI makes it possible to complete the evaluation within a few days. Additionally, because AI objectively evaluates candidate information, the fairness of the hiring process is improved.

[0073] The recruitment support system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a matching unit, and a provision unit. The reception unit inputs the new graduate recruitment criteria required by a company. The new graduate recruitment criteria required by a company include, but are not limited to, educational background, skills, experience, and personality traits. The reception unit may input criteria, for example, prioritizing graduates of a specific university or candidates with specific skills. The analysis unit uses AI to analyze the criteria input by the reception unit. The analysis unit may analyze the criteria using, for example, natural language processing technology. The analysis unit may also analyze the criteria using voice recognition technology. For example, the analysis unit may understand the criteria required by a company and evaluate candidate information based on those criteria. The collection unit collects candidate information. The collection unit may collect information such as candidate resumes and interview results. The collection unit may also analyze candidate social media activities and online portfolios. For example, the collection unit may analyze candidate social media posts and follower counts to collect candidate information. The matching unit compares the information collected by the collection unit with the criteria. The matching unit, for example, uses an algorithm to match the candidate information with criteria. For example, the matching unit uses a matching algorithm to match the candidate information with criteria. The providing unit provides the final evaluation results. For example, the providing unit provides the evaluation results to the hiring manager. For example, the providing unit provides the evaluation results in report format. The providing unit can also provide the evaluation results in a dashboard display. As a result, the recruitment support system according to the embodiment can digitize a company's new graduate recruitment criteria, reducing the time required for recruitment.

[0074] The analysis unit can analyze the criteria using natural language processing or speech recognition technology. The analysis unit analyzes the criteria using, for example, natural language processing technology. For example, the analysis unit analyzes the criteria using morphological analysis. The analysis unit can also analyze the criteria using grammatical analysis. The analysis unit can also analyze the criteria using semantic analysis. For example, the analysis unit divides the words of the criteria using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the criteria using semantic analysis. The analysis unit can also analyze the criteria using speech recognition technology. For example, the analysis unit collects speech samples and analyzes the criteria using a speech analysis algorithm. In this way, the use of natural language processing or speech recognition technology improves the accuracy of the analysis of the criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the criteria using an AI model that analyzes the criteria using natural language processing technology.

[0075] The collection unit can analyze the candidate's social media activity or online portfolio. The collection unit, for example, analyzes the candidate's social media activity. For example, the collection unit can analyze the content of the candidate's posts to collect information about the candidate. The collection unit can also analyze the number of the candidate's followers to collect information about the candidate. The collection unit can also analyze the candidate's engagement rate to collect information about the candidate. For example, the collection unit collects information about the candidate by analyzing the content of the candidate's posts, the number of followers, and the engagement rate. The collection unit can also analyze the candidate's online portfolio. For example, the collection unit can analyze the content of the candidate's projects to collect information about the candidate. The collection unit can also analyze the candidate's skill set to collect information about the candidate. The collection unit can also analyze the candidate's evaluations to collect information about the candidate. For example, the collection unit collects information about the candidate by analyzing the content of the candidate's projects, the skill set, and the evaluations. This makes it possible to collect more detailed information by analyzing the candidate's social media activity or online portfolio. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect candidate information using an AI model that analyzes the candidate's social media activity.

[0076] The matching unit can match the candidate information with the standard using an algorithm. The matching unit, for example, matches the candidate information with the standard using an algorithm. For example, the matching unit matches the candidate information with the standard using a matching algorithm. The matching unit can also match the candidate information with the standard using a machine learning algorithm. The matching unit can also match the candidate information with the standard using a statistical analysis algorithm. For example, the matching unit matches the candidate information with the standard using a matching algorithm, matches the candidate information with the standard using a machine learning algorithm, and matches the candidate information with the standard using a statistical analysis algorithm. In this way, the use of algorithms improves the accuracy of matching the candidate information with the standard. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can match the candidate information with the standard using an AI model that matches the candidate information with the standard.

[0077] The providing unit can provide the evaluation results to the hiring manager. The providing unit, for example, provides the evaluation results to the hiring manager. For example, the providing unit provides the evaluation results in report format. The providing unit can also provide the evaluation results in dashboard display. The providing unit can also provide the evaluation results by a notification method. For example, the providing unit provides the evaluation results in report format, dashboard display, and notification method. In this way, by providing the evaluation results to the hiring manager, the efficiency of the hiring process is improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the evaluation results using an AI model that provides the evaluation results.

[0078] The reception unit can estimate the emotions of a company's recruiters and adjust the display method of the input interface based on the emotions. For example, the reception unit estimates the emotions of a company's recruiters and adjusts the display method of the input interface based on the emotions. For example, if the recruiters are stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. If the recruiters are relaxed, the reception unit can provide detailed input options and suggest customizable input methods. If the recruiters are in a hurry, the reception unit can prioritize voice input to enable them to quickly enter the hiring criteria. For example, if the recruiters are stressed, the reception unit can provide a simple and intuitive interface and minimize input steps, and if the recruiters are relaxed, the reception unit can provide detailed input options and suggest customizable input methods, and if the recruiters are in a hurry, the reception unit can prioritize voice input to enable them to quickly enter the hiring criteria. This improves input efficiency by adjusting the input interface according to the recruiters' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model that estimates the emotions of a company's recruiting staff to estimate the emotions and adjust the display method of the input interface based on the emotions.

[0079] The reception unit can analyze the company's past hiring criteria data and propose the optimal input method. The reception unit, for example, analyzes the company's past hiring criteria data and proposes the optimal input method. For example, the reception unit automatically displays hiring criteria that were frequently used in the past as candidates. The reception unit can also propose an input method suitable for a specific industry or job type based on the past hiring criteria data. The reception unit can also analyze the past hiring criteria data and propose the most effective input method. For example, the reception unit automatically displays hiring criteria that were frequently used in the past as candidates, proposes an input method suitable for a specific industry or job type based on the past hiring criteria data, analyzes the past hiring criteria data, and proposes the most effective input method. In this way, the optimal input method can be proposed by analyzing the past hiring criteria data. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose the optimal input method using an AI model that analyzes the company's past hiring criteria data.

[0080] The reception unit can provide input fields customized according to the industry and size of the company when entering the hiring criteria. For example, the reception unit can provide input fields customized according to the industry and size of the company when entering the hiring criteria. For example, the reception unit can provide fields for entering detailed skill sets and years of experience for large companies. The reception unit can also provide simplified input fields for small and medium-sized companies, enabling quick input. The reception unit can also provide input fields specialized for a specific industry, allowing input of industry-specific skills and experience. For example, the reception unit can provide fields for entering detailed skill sets and years of experience for large companies, and simplified input fields for small and medium-sized companies, enabling quick input, and input fields specialized for a specific industry, allowing input of industry-specific skills and experience. By providing input fields customized according to the industry and size of the company, the accuracy of input is improved. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can provide input fields using an AI model that provides input fields customized according to the industry and size of the company.

[0081] The reception unit can adjust the priority of input items based on the company's current hiring situation when inputting hiring criteria. For example, when inputting hiring criteria, the reception unit adjusts the priority of input items based on the company's current hiring situation. For example, the reception unit prompts the user to input the most important criteria first based on the current hiring situation. Furthermore, when the hiring situation is urgent, the reception unit can simplify the input items to allow for quick input. Furthermore, when the hiring situation is stable, the reception unit can provide detailed input items to allow for highly accurate criteria to be set. For example, the reception unit prompts the user to input the most important criteria first based on the current hiring situation, and when the hiring situation is urgent, the reception unit simplifies the input items to allow for quick input, while when the hiring situation is stable, the reception unit provides detailed input items to allow for highly accurate criteria to be set. This allows for efficient input by adjusting the priority of input items based on the company's current hiring situation. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can adjust the priority of input items using an AI model that adjusts the priority of input items based on the company's current hiring situation.

[0082] The reception unit can estimate the emotions of the company's recruiters and determine the priority of criteria to be input based on the emotions. The reception unit, for example, estimates the emotions of the company's recruiters and determines the priority of criteria to be input based on the emotions. For example, if the recruiters are stressed, the reception unit may prompt them to input the most important criteria first. Furthermore, if the recruiters are relaxed, the reception unit may prompt them to input detailed criteria. Furthermore, if the recruiters are in a hurry, the reception unit may prioritize displaying criteria that can be input quickly. For example, if the recruiters are stressed, the reception unit may prompt them to input the most important criteria first; if the recruiters are relaxed, the reception unit may prompt them to input detailed criteria; and if the recruiters are in a hurry, the reception unit may prioritize displaying criteria that can be input quickly. This allows efficient input by determining the priority of criteria according to the recruiters' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model that estimates the emotions of company recruiters to estimate their emotions and determine the priority of the criteria to be input based on the emotions.

[0083] When inputting hiring criteria, the reception unit can prioritize inputting highly relevant criteria by taking into account the geographical location information of companies. For example, when inputting hiring criteria, the reception unit prioritizes inputting highly relevant criteria by taking into account the geographical location information of companies. For example, the reception unit inputs criteria that prioritize graduates of geographically nearby universities. The reception unit can also input criteria that prioritize candidates with experience working at geographically nearby companies. The reception unit can also input criteria that prioritize candidates with experience working in geographically nearby areas. For example, the reception unit inputs criteria that prioritize graduates of geographically nearby universities, inputs criteria that prioritize candidates with experience working at geographically nearby companies, and inputs criteria that prioritize candidates with experience working in geographically nearby areas. In this way, highly relevant criteria can be prioritized by taking geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input criteria using an AI model that prioritizes inputting highly relevant criteria by taking into account the geographical location information of companies.

[0084] The reception unit can analyze the company's social media activity and propose related criteria when the hiring criteria are input. For example, the reception unit can analyze the company's social media activity and propose related criteria when the hiring criteria are input. For example, the reception unit can propose criteria that prioritize candidates with relevant skills and experience based on the company's social media activity. The reception unit can also analyze the company's social media posts and propose related criteria. The reception unit can also propose related criteria by referring to the activity of the company's followers on social media. For example, the reception unit can propose criteria that prioritize candidates with relevant skills and experience based on the company's social media activity, analyze the company's social media posts and propose related criteria, and propose related criteria by referring to the activity of the company's followers on social media. In this way, related criteria can be proposed by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose related criteria using an AI model that analyzes the company's social media activity.

[0085] The reception unit can customize the input method by reflecting the company's past feedback when entering the hiring criteria. For example, the reception unit customizes the input method by reflecting the company's past feedback when entering the hiring criteria. For example, the reception unit suggests an optimal input method based on feedback obtained in past hiring processes. The reception unit can also adjust the order and content of input fields by reflecting the past feedback. The reception unit can also improve the design of the input interface based on the past feedback. For example, the reception unit suggests an optimal input method based on feedback obtained in past hiring processes, adjusts the order and content of input fields by reflecting the past feedback, and improves the design of the input interface based on the past feedback. In this way, the optimal input method can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that customizes the input method by reflecting the company's past feedback.

[0086] The analysis unit can estimate the emotions of the company's recruiters and adjust the display method of the analysis results based on the emotions. For example, the analysis unit estimates the emotions of the company's recruiters and adjusts the display method of the analysis results based on the emotions. For example, if the recruiters are stressed, the analysis unit displays simple, highly visible analysis results. Furthermore, if the recruiters are relaxed, the analysis unit can display detailed analysis results. Furthermore, if the recruiters are in a hurry, the analysis unit can display analysis results that focus on the main points. For example, if the recruiters are stressed, the analysis unit displays simple, highly visible analysis results; if the recruiters are relaxed, the analysis unit displays detailed analysis results; and if the recruiters are in a hurry, the analysis unit displays analysis results that focus on the main points. In this way, adjusting the display method of the analysis results according to the recruiters' emotions improves understanding of the analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use an AI model that estimates the emotions of company recruiters to estimate their emotions and adjust the display method of the analysis results based on the emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the adopted criteria during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the adopted criteria during analysis. For example, the analysis unit performs a detailed analysis for criteria with high importance. The analysis unit can also perform a simplified analysis for criteria with low importance. The analysis unit can also adjust the display method of the analysis results according to the importance. For example, the analysis unit performs a detailed analysis for criteria with high importance and a simplified analysis for criteria with low importance, and adjusts the display method of the analysis results according to the importance. In this way, adjusting the level of detail of the analysis based on the importance of the adopted criteria enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the adopted criteria.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the hiring criteria during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the hiring criteria during analysis. For example, the analysis unit applies an educational background analysis algorithm to criteria related to educational background. The analysis unit can also apply a skill analysis algorithm to criteria related to skills. The analysis unit can also apply a personality analysis algorithm to criteria related to personality traits. For example, the analysis unit applies an educational background analysis algorithm to criteria related to educational background, a skill analysis algorithm to criteria related to skills, and a personality analysis algorithm to criteria related to personality traits. In this way, by applying different analysis algorithms depending on the category of the hiring criteria, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply the analysis algorithm using an AI model that applies different analysis algorithms depending on the category of the hiring criteria.

[0089] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. The analysis unit can also improve the analysis method based on the past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results, improves the accuracy of the analysis by referring to the past analysis results, and improves the analysis method based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that improves the accuracy of the analysis by referring to the company's past analysis results.

[0090] The analysis unit can estimate the emotions of the company's recruiters and adjust the length of the analysis results based on the emotions. For example, the analysis unit estimates the emotions of the company's recruiters and adjusts the length of the analysis results based on the emotions. For example, if the recruiters are stressed, the analysis unit displays short and concise analysis results. Furthermore, if the recruiters are relaxed, the analysis unit can display detailed analysis results. Furthermore, if the recruiters are in a hurry, the analysis unit can display concise analysis results. For example, if the recruiters are stressed, the analysis unit displays short and concise analysis results, if the recruiters are relaxed, the analysis unit displays detailed analysis results, and if the recruiters are in a hurry, the analysis unit displays concise analysis results. In this way, adjusting the length of the analysis results according to the recruiters' emotions improves comprehension of the analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use an AI model that estimates the emotions of corporate recruiters to estimate their emotions and adjust the length of the analysis result based on the emotions.

[0091] The analysis unit can determine the priority of analysis based on the submission time of the adopted criteria during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the adopted criteria during analysis. For example, the analysis unit prioritizes analysis of criteria submitted earlier. The analysis unit can also postpone analysis of criteria submitted later. The analysis unit can also adjust the order of analysis based on the submission time. For example, the analysis unit prioritizes analysis of criteria submitted earlier and postpones analysis of criteria submitted later, and adjusts the order of analysis based on the submission time. In this way, determining the priority of analysis based on the submission time enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of analysis using an AI model that determines the priority of analysis based on the submission time of the adopted criteria.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the adopted criteria during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the adopted criteria during analysis. For example, the analysis unit prioritizes analysis of highly relevant criteria. The analysis unit can also postpone analysis of less relevant criteria. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit prioritizes analysis of highly relevant criteria and postpones analysis of less relevant criteria, and adjusts the order of analysis based on relevance. In this way, adjusting the order of analysis based on relevance enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that adjusts the order of analysis based on the relevance of the adopted criteria.

[0093] The analysis unit can adjust the use of technical terms in the analysis results according to the company's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the company's level of expertise during analysis. For example, if the expertise level is high, the analysis unit displays analysis results that use a lot of technical terms. Furthermore, if the expertise level is low, the analysis unit can display analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the expertise level. For example, if the expertise level is high, the analysis unit displays analysis results that use a lot of technical terms, and if the expertise level is low, the analysis unit displays analysis results that are concise and easy to understand, thereby adjusting the way the analysis results are presented according to the expertise level. In this way, adjusting the use of technical terms in the analysis results according to the expertise level improves understanding of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the use of technical terms in the analysis results using an AI model that adjusts the use of technical terms in the analysis results according to the company's level of expertise.

[0094] The collection unit can estimate the candidate's emotions and adjust the timing of information collection based on the emotions. For example, the collection unit estimates the candidate's emotions and adjusts the timing of information collection based on the emotions. For example, if the candidate is relaxed, the collection unit selects the timing to collect detailed information. Also, if the candidate is nervous, the collection unit can select the timing to collect concise information. Also, if the candidate is in a hurry, the collection unit can select the timing to collect information quickly. For example, if the candidate is relaxed, the collection unit selects the timing to collect detailed information; if the candidate is nervous, the collection unit selects the timing to collect concise information; and if the candidate is in a hurry, the collection unit selects the timing to collect information quickly. This enables efficient information collection by adjusting the timing of information collection according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection department can use an AI model that estimates a candidate's emotions to estimate their emotions and adjust the timing of information collection based on those emotions.

[0095] The collection unit can analyze the candidate's past activity history and select the optimal collection method. The collection unit, for example, analyzes the candidate's past activity history and selects the optimal collection method. For example, the collection unit selects the most effective information collection method from the past activity history. The collection unit can also select an information collection method according to the candidate's interests and concerns based on the past activity history. The collection unit can also analyze the past activity history and select the optimal collection means. For example, the collection unit selects the most effective information collection method from the past activity history, selects an information collection method according to the candidate's interests and concerns based on the past activity history, analyzes the past activity history, and selects the optimal collection means. In this way, the optimal information collection method can be selected by analyzing the past activity history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal collection method using an AI model that analyzes the candidate's past activity history.

[0096] The collection unit can perform filtering based on the candidate's current situation and areas of interest when collecting information. For example, the collection unit performs filtering based on the candidate's current situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the candidate's current situation. The collection unit can also filter and collect related information based on the candidate's areas of interest. The collection unit can also collect optimal information taking into account the candidate's current situation and areas of interest. For example, the collection unit prioritizes collecting highly relevant information based on the candidate's current situation, filters and collects related information based on the candidate's areas of interest, and collects optimal information taking into account the candidate's current situation and areas of interest. In this way, highly relevant information can be collected by filtering based on the candidate's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that performs filtering based on the candidate's current situation and areas of interest.

[0097] The collection unit can select the optimal collection means depending on the candidate's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the candidate's input method when collecting information. For example, if the candidate uses voice input, the collection unit can prioritize collecting voice data. Also, if the candidate uses text input, the collection unit can prioritize collecting text data. Also, if the candidate uses image input, the collection unit can prioritize collecting image data. For example, if the candidate uses voice input, the collection unit prioritizes collecting voice data; if the candidate uses text input, the collection unit prioritizes collecting text data; and if the candidate uses image input, the collection unit prioritizes collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the candidate's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that selects the optimal collection means depending on the candidate's input method.

[0098] The collection unit can estimate the candidate's emotions and prioritize the information to be collected based on those emotions. For example, the collection unit estimates the candidate's emotions and prioritizes the information to be collected based on those emotions. For example, if the candidate is relaxed, the collection unit prioritizes collecting detailed information. Furthermore, if the candidate is nervous, the collection unit can prioritize collecting concise information. Furthermore, if the candidate is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. For example, if the candidate is relaxed, the collection unit prioritizes collecting detailed information; if the candidate is nervous, the collection unit prioritizes collecting concise information; and if the candidate is in a hurry, the collection unit prioritizes collecting information that can be collected quickly. This enables efficient information collection by prioritizing information according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection department can use an AI model that estimates a candidate's emotions to estimate their emotions and prioritize the information to collect based on those emotions.

[0099] The collection unit can prioritize collecting highly relevant information by taking into account the candidate's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the candidate's geographical location information when collecting information. For example, the collection unit prioritizes collecting information about nearby companies and workplaces based on the candidate's geographical location information. The collection unit can also prioritize collecting information about region-specific skills and experience by taking into account the candidate's geographical location information. The collection unit can also filter and collect highly relevant information based on the candidate's geographical location information. For example, the collection unit prioritizes collecting information about nearby companies and workplaces based on the candidate's geographical location information, prioritizes collecting information about region-specific skills and experience by taking into account the candidate's geographical location information, and filters and collects highly relevant information based on the candidate's geographical location information. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that prioritizes collecting highly relevant information by taking into account the candidate's geographical location information.

[0100] The collection unit may analyze the candidate's social media activities and collect relevant information when collecting information. For example, the collection unit may analyze the candidate's social media activities and collect relevant information when collecting information. For example, the collection unit may analyze the candidate's social media activities and collect information on related skills and experience. The collection unit may also collect relevant information based on the candidate's social media posts. The collection unit may also collect relevant information by referring to the activities of the candidate's friends on social media. For example, the collection unit may analyze the candidate's social media activities and collect information on related skills and experience, collect relevant information based on the candidate's social media posts, and collect relevant information by referring to the activities of the candidate's friends on social media. In this way, relevant information can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model that analyzes the candidate's social media activities.

[0101] The collection unit can customize the collection method by reflecting the candidate's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the candidate's past feedback when collecting information. For example, the collection unit proposes an optimal information collection method based on the past feedback. The collection unit can also adjust the information collection procedure and content by reflecting the past feedback. The collection unit can also improve the information collection interface based on the past feedback. For example, the collection unit proposes an optimal information collection method based on the past feedback, adjusts the information collection procedure and content by reflecting the past feedback, and improves the information collection interface based on the past feedback. In this way, the optimal information collection method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can customize the collection method using an AI model that customizes the collection method by reflecting the candidate's past feedback.

[0102] The matching unit can estimate the candidate's emotions and adjust the display method of the matching results based on the emotions. For example, the matching unit estimates the candidate's emotions and adjusts the display method of the matching results based on the emotions. For example, if the candidate is relaxed, the matching unit displays detailed matching results. Furthermore, if the candidate is nervous, the matching unit can display concise and highly visible matching results. Furthermore, if the candidate is in a hurry, the matching unit can display matching results that focus on the main points. For example, if the candidate is relaxed, the matching unit displays detailed matching results; if the candidate is nervous, the matching unit displays concise and highly visible matching results; and if the candidate is in a hurry, the matching unit displays matching results that focus on the main points. In this way, adjusting the display method of the matching results according to the candidate's emotions improves understanding of the matching results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may use an AI model that estimates the candidate's emotions to estimate the emotions and adjust the display method of the matching results based on the emotions.

[0103] The matching unit can adjust the level of detail of the matching based on the importance of the accepted criteria during matching. For example, the matching unit adjusts the level of detail of the matching based on the importance of the accepted criteria during matching. For example, the matching unit performs detailed matching for criteria with high importance. The matching unit can also perform simplified matching for criteria with low importance. The matching unit can also adjust the display method of the matching results according to the importance. For example, the matching unit performs detailed matching for criteria with high importance and simplified matching for criteria with low importance, and adjusts the display method of the matching results according to the importance. In this way, adjusting the level of detail of the matching based on the importance of the accepted criteria enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the level of detail of the matching using an AI model that adjusts the level of detail of the matching based on the importance of the accepted criteria.

[0104] The matching unit can apply different matching algorithms depending on the category of the hiring criteria during matching. For example, the matching unit applies different matching algorithms depending on the category of the hiring criteria during matching. For example, the matching unit applies an education matching algorithm to criteria related to educational background. The matching unit can also apply a skill matching algorithm to criteria related to skills. The matching unit can also apply a personality matching algorithm to criteria related to personality traits. For example, the matching unit applies an education matching algorithm to criteria related to educational background, a skill matching algorithm to criteria related to skills, and a personality matching algorithm to criteria related to personality traits. In this way, by applying different matching algorithms depending on the category of the hiring criteria, matching accuracy is improved. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can apply the matching algorithm using an AI model that applies different matching algorithms depending on the category of the hiring criteria.

[0105] The matching unit can improve the accuracy of matching by referring to the company's past matching results during matching. For example, the matching unit can improve the accuracy of matching by referring to the company's past matching results during matching. For example, the matching unit adjusts the matching algorithm based on the past matching results. The matching unit can also improve the accuracy of matching by referring to the past matching results. The matching unit can also improve the matching method based on the past matching results. For example, the matching unit adjusts the matching algorithm based on the past matching results, improves the accuracy of matching by referring to the past matching results, and improves the matching method based on the past matching results. In this way, the accuracy of matching is improved by referring to the past matching results. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can improve the accuracy of matching by using an AI model that improves the accuracy of matching by referring to the company's past matching results.

[0106] The matching unit can estimate the candidate's emotions and adjust the length of the matching result based on the emotions. For example, the matching unit estimates the candidate's emotions and adjusts the length of the matching result based on the emotions. For example, if the candidate is relaxed, the matching unit displays a detailed matching result. Furthermore, if the candidate is nervous, the matching unit can display a concise, highly visible matching result. Furthermore, if the candidate is in a hurry, the matching unit can display a matching result that focuses on the main points. For example, if the candidate is relaxed, the matching unit displays a detailed matching result; if the candidate is nervous, the matching unit displays a concise, highly visible matching result; and if the candidate is in a hurry, the matching unit displays a matching result that focuses on the main points. This adjusts the length of the matching result according to the candidate's emotions, thereby improving comprehension of the matching result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can use an AI model that estimates the candidate's emotions to estimate their emotions and adjust the length of the matching result based on those emotions.

[0107] The matching unit can determine the matching priority based on the submission time of the adopted criteria during matching. The matching unit, for example, determines the matching priority based on the submission time of the adopted criteria during matching. For example, the matching unit prioritizes matching of criteria that were submitted earlier. The matching unit can also postpone matching of criteria that were submitted later. The matching unit can also adjust the matching order based on the submission time. For example, the matching unit prioritizes matching of criteria that were submitted earlier and postpones matching of criteria that were submitted later, and adjusts the matching order based on the submission time. In this way, determining the matching priority based on the submission time enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can determine the matching priority using an AI model that determines the matching priority based on the submission time of the adopted criteria.

[0108] The matching unit can adjust the order of matching based on the relevance of the adoption criteria during matching. For example, the matching unit adjusts the order of matching based on the relevance of the adoption criteria during matching. For example, the matching unit prioritizes matching of highly relevant criteria. The matching unit can also postpone matching of less relevant criteria. The matching unit can also adjust the order of matching based on relevance. For example, the matching unit prioritizes matching of highly relevant criteria and postpones matching of less relevant criteria, and adjusts the order of matching based on relevance. In this way, adjusting the order of matching based on relevance enables efficient matching. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the order of matching using an AI model that adjusts the order of matching based on the relevance of the adoption criteria.

[0109] The matching unit can adjust the use of technical terms in the matching results during matching depending on the company's level of expertise. For example, the matching unit adjusts the use of technical terms in the matching results depending on the company's level of expertise during matching. For example, if the level of expertise is high, the matching unit displays a matching result that uses a lot of technical terms. Furthermore, if the level of expertise is low, the matching unit can display a matching result that is concise and easy to understand. Furthermore, the matching unit can adjust the way the matching results are presented depending on the level of expertise. For example, if the level of expertise is high, the matching unit displays a matching result that uses a lot of technical terms, and if the level of expertise is low, the matching unit displays a matching result that is concise and easy to understand, thereby adjusting the way the matching results are presented depending on the level of expertise. In this way, adjusting the use of technical terms in the matching results depending on the level of expertise improves the understandability of the matching results. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the use of technical terms in the matching results using an AI model that adjusts the use of technical terms in the matching results depending on the company's level of expertise.

[0110] The providing unit can estimate the emotions of the recruiter and adjust the display method of the evaluation results based on the emotions. For example, the providing unit estimates the emotions of the recruiter and adjusts the display method of the evaluation results based on the emotions. For example, if the recruiter is stressed, the providing unit displays simple, highly visible evaluation results. Furthermore, if the recruiter is relaxed, the providing unit can display detailed evaluation results. Furthermore, if the recruiter is in a hurry, the providing unit can display evaluation results that focus on the main points. For example, if the recruiter is stressed, the providing unit displays simple, highly visible evaluation results; if the recruiter is relaxed, the providing unit displays detailed evaluation results; and if the recruiter is in a hurry, the providing unit displays evaluation results that focus on the main points. This improves understanding of the evaluation results by adjusting the display method of the evaluation results according to the emotions of the recruiter. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model that estimates the emotions of hiring managers to estimate their emotions and adjust the display method of the evaluation results based on the emotions.

[0111] The providing unit can adjust the level of detail of the display based on the importance of the adoption criteria when providing the evaluation results. For example, the providing unit adjusts the level of detail of the display based on the importance of the adoption criteria when providing the evaluation results. For example, the providing unit displays detailed evaluation results for criteria with high importance. The providing unit can also display simplified evaluation results for criteria with low importance. The providing unit can also adjust the display method of the evaluation results according to the importance. For example, the providing unit displays detailed evaluation results for criteria with high importance and simplified evaluation results for criteria with low importance, and adjusts the display method of the evaluation results according to the importance. This enables efficient provision of evaluation results by adjusting the level of detail of the display based on the importance of the adoption criteria. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail of the display using an AI model that adjusts the level of detail of the display based on the importance of the adoption criteria.

[0112] The providing unit can apply different display algorithms depending on the category of the hiring criteria when providing the evaluation results. For example, the providing unit applies different display algorithms depending on the category of the hiring criteria when providing the evaluation results. For example, the providing unit applies an educational background evaluation algorithm to criteria related to educational background. The providing unit can also apply a skill evaluation algorithm to criteria related to skills. The providing unit can also apply a personality evaluation algorithm to criteria related to personality traits. For example, the providing unit applies an educational background evaluation algorithm to criteria related to educational background, a skill evaluation algorithm to criteria related to skills, and a personality evaluation algorithm to criteria related to personality traits. In this way, by applying different display algorithms depending on the category of the hiring criteria, the accuracy of the evaluation results is improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply the display algorithms using an AI model that applies different display algorithms depending on the category of the hiring criteria.

[0113] When providing the evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, when providing the evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results. The providing unit can also improve the accuracy of the display by referring to the past evaluation results. The providing unit can also improve the display method based on the past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results, improves the accuracy of the display by referring to the past evaluation results, and improves the display method based on the past evaluation results. In this way, the accuracy of the display is improved by referring to the past evaluation results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve the accuracy of the display by using an AI model that improves the accuracy of the display by referring to the company's past evaluation results.

[0114] The providing unit can estimate the emotions of the recruiter and adjust the length of the evaluation result based on the emotions. For example, the providing unit estimates the emotions of the recruiter and adjusts the length of the evaluation result based on the emotions. For example, if the recruiter is stressed, the providing unit displays a short and concise evaluation result. Furthermore, if the recruiter is relaxed, the providing unit can display a detailed evaluation result. Furthermore, if the recruiter is in a hurry, the providing unit can display a concise evaluation result. For example, if the recruiter is stressed, the providing unit displays a short and concise evaluation result, if the recruiter is relaxed, the providing unit displays a detailed evaluation result, and if the recruiter is in a hurry, the providing unit displays a concise evaluation result. In this way, adjusting the length of the evaluation result according to the emotions of the recruiter improves comprehension of the evaluation result. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model that estimates the emotions of recruiters to estimate the emotions and adjust the length of the evaluation results based on the emotions.

[0115] The providing unit can determine the display priority based on the submission time of the adopted criteria when providing the evaluation results. For example, when providing the evaluation results, the providing unit determines the display priority based on the submission time of the adopted criteria. For example, the providing unit prioritizes displaying criteria that were submitted earlier. The providing unit can also display criteria that were submitted later later. The providing unit can also adjust the display order based on the submission time. For example, the providing unit prioritizes displaying criteria that were submitted earlier and later criteria and displays the display order based on the submission time. This enables efficient provision of evaluation results by determining the display priority based on the submission time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the display priority using an AI model that determines the display priority based on the submission time of the adopted criteria.

[0116] The providing unit can adjust the display order based on the relevance of the adoption criteria when providing the evaluation results. For example, the providing unit adjusts the display order based on the relevance of the adoption criteria when providing the evaluation results. For example, the providing unit prioritizes displaying highly relevant criteria. The providing unit can also postpone displaying less relevant criteria. The providing unit can also adjust the display order based on relevance. For example, the providing unit prioritizes displaying highly relevant criteria and postpones displaying less relevant criteria, and adjusts the display order based on relevance. This makes it possible to provide efficient evaluation results by adjusting the display order based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the display order using an AI model that adjusts the display order based on the relevance of the adoption criteria.

[0117] When providing the evaluation results, the providing unit can adjust the use of technical terms in the displayed content according to the company's level of expertise. For example, when providing the evaluation results, the providing unit adjusts the use of technical terms in the displayed content according to the company's level of expertise. For example, when the expertise level is high, the providing unit displays evaluation results that use a lot of technical terms. Furthermore, when the expertise level is low, the providing unit can display evaluation results that are concise and easy to understand. Furthermore, the providing unit can adjust the way the evaluation results are presented according to the level of expertise. For example, when the expertise level is high, the providing unit displays evaluation results that use a lot of technical terms, and when the expertise level is low, displays evaluation results that are concise and easy to understand, and adjusts the way the evaluation results are presented according to the level of expertise. In this way, adjusting the use of technical terms in the displayed content according to the level of expertise improves understanding of the evaluation results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the use of technical terms in the displayed content using an AI model that adjusts the use of technical terms in the displayed content according to the company's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, matching unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the company's new graduate recruitment criteria. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the criteria using AI. The collection unit collects information about candidates using the camera 42 and microphone 38B of the smart device 14. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the collected information with the criteria. The provision unit is realized by the control unit 46A of the smart device 14 and provides the evaluation results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, collection unit, matching unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the company's new graduate recruitment criteria. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the criteria using AI. The collection unit collects information about candidates using the camera 42 and microphone 238 of the smart glasses 214. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the collected information with the criteria. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the evaluation results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, matching unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the new graduate recruitment criteria required by the company. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the criteria using AI. The collection unit collects information about candidates using the camera 42 and microphone 238 of the headset type terminal 314. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the collected information with the criteria. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the evaluation results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, matching unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the new graduate recruitment criteria required by the company. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the criteria using AI. The collection unit collects information about candidates using the camera 42 and microphone 238 of the robot 414. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the collected information with the criteria. The provision unit is realized by the control unit 46A of the robot 414 and provides the evaluation results.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit can estimate the emotions of a company's recruiters and adjust the display method of the input interface based on the emotions. For example, if the recruiter is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Alternatively, if the recruiter is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the recruiter is in a hurry, the reception unit can prioritize voice input to enable the recruiter to quickly enter hiring criteria. In this way, input efficiency is improved by adjusting the input interface according to the recruiter's emotions.

[0120] The analysis unit can analyze a company's past hiring criteria data and propose the optimal analysis method. For example, the analysis unit automatically applies analysis methods that have been frequently used in the past. The analysis unit can also propose an analysis method suitable for a specific industry or job type based on past hiring criteria data. The analysis unit can also analyze past hiring criteria data and propose the most effective analysis method. In this way, the optimal analysis method can be proposed by analyzing past hiring criteria data.

[0121] The collection unit can estimate the candidate's emotions and adjust the timing of information collection based on those emotions. For example, if the candidate is relaxed, the collection unit can select the timing to collect detailed information. If the candidate is nervous, the collection unit can also select the timing to collect concise information. If the candidate is in a hurry, the collection unit can also select the timing to collect information quickly. This allows for efficient information collection by adjusting the timing of information collection according to the candidate's emotions.

[0122] The matching unit can improve the accuracy of matching by referring to the company's past matching results. For example, the matching unit adjusts the matching algorithm based on the past matching results. The matching unit can also improve the accuracy of matching by referring to the past matching results. The matching unit can also improve the matching method based on the past matching results. In this way, the accuracy of matching is improved by referring to the past matching results.

[0123] The providing unit can estimate the emotions of the recruiter and adjust the display method of the evaluation results based on the emotions. For example, if the recruiter is feeling stressed, the providing unit can display simple, highly visible evaluation results. If the recruiter is relaxed, the providing unit can also display detailed evaluation results. If the recruiter is in a hurry, the providing unit can also display evaluation results that focus on the main points. In this way, adjusting the display method of the evaluation results according to the emotions of the recruiter improves understanding of the evaluation results.

[0124] The reception department can input highly relevant criteria with priority given to the geographical location information of the companies. For example, the reception department can input criteria that give priority to graduates of geographically nearby universities. The reception department can also input criteria that give priority to candidates who have experience working at geographically nearby companies. The reception department can also input criteria that give priority to candidates who have experience working in geographically nearby areas. In this way, highly relevant criteria can be input with priority given to the geographical location information.

[0125] The analysis unit can estimate the emotions of a company's recruiters and adjust the way the analysis results are displayed based on those emotions. For example, if the recruiter is feeling stressed, the analysis unit can display simple, highly visible analysis results. If the recruiter is relaxed, the analysis unit can also display detailed analysis results. If the recruiter is in a hurry, the analysis unit can also display analysis results that focus on the main points. In this way, adjusting the way the analysis results are displayed according to the recruiter's emotions improves understanding of the analysis results.

[0126] The collection unit can analyze the candidate's past activity history and select the optimal collection method. For example, the collection unit selects the most effective information collection method from the past activity history. The collection unit can also select an information collection method that suits the candidate's interests and concerns based on the past activity history. The collection unit can also analyze the past activity history and select the optimal collection means. In this way, the optimal information collection method can be selected by analyzing the past activity history.

[0127] The matching unit can estimate the candidate's emotions and adjust the display method of the matching results based on those emotions. For example, if the candidate is relaxed, the matching unit can display detailed matching results. If the candidate is nervous, the matching unit can also display concise, highly visible matching results. If the candidate is in a hurry, the matching unit can also display matching results that focus on the main points. In this way, adjusting the display method of the matching results according to the candidate's emotions improves the candidate's understanding of the matching results.

[0128] When providing evaluation results, the providing unit can improve the accuracy of the display by referring to the company's past evaluation results. For example, the providing unit adjusts the display algorithm based on the past evaluation results. The providing unit can also improve the accuracy of the display by referring to the past evaluation results. The providing unit can also improve the display method based on the past evaluation results. In this way, the accuracy of the display is improved by referring to the past evaluation results.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The receptionist inputs the company's desired recruiting criteria for new graduates. These criteria include educational background, skills, experience, and personality traits. For example, the receptionist can input criteria to prioritize graduates of a specific university or candidates with specific skills. Step 2: The analysis unit uses AI to analyze the criteria entered by the reception unit. The analysis unit uses natural language processing and voice recognition technology to analyze the criteria, understand the criteria required by the company, and evaluate the candidate information based on those criteria. Step 3: The collection department collects candidate information. The collection department collects information such as the candidate's resume, interview results, social media activity, and online portfolio. For example, they analyze the candidate's social media posts and number of followers to collect candidate information. Step 4: The matching unit matches the information collected by the collection unit with the criteria. The matching unit uses an algorithm to match the candidate's information with the criteria, for example, using a matching algorithm. Step 5: The reporting department provides the final evaluation results. The reporting department provides the evaluation results to the hiring manager in the form of a report or dashboard display.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0159] 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.

[0160] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 AI 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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 AI 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 reception desk where companies input their recruitment criteria for new graduates, an analysis unit that analyzes the criteria input by the reception unit; a collection department that collects candidate information; a collation unit that compares the information collected by the collection unit with a standard; a providing unit that provides a final evaluation result. A system characterized by:

2. The analysis unit Parsing the criteria using natural language processing or speech recognition technology 2. The system of claim 1.

3. The collecting unit Analyzing candidates' social media activity or online portfolios 2. The system of claim 1.

4. The collation unit Algorithms are used to match candidate information with criteria 2. The system of claim 1.

5. The providing unit Provide the assessment results to the recruiter 2. The system of claim 1.

6. The reception unit Estimate the emotions of corporate recruiters and adjust the input interface display based on those emotions.

2. The system of claim 1.

7. The reception unit Analyzes a company's past hiring criteria data and suggests the best way to enter it 2. The system of claim 1.

8. The reception unit When entering recruitment criteria, provide input fields customized according to the industry and size of the company.

2. The system of claim 1.

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