System

Abstract

An object of a system according to an embodiment is to analyze information on an applicant and propose an occupation that meets the needs of a company.SOLUTION: A system includes a collection unit, an analysis unit, a collation unit, and a proposal unit. The collection part collects information on an applicant. The analysis unit analyzes the information collected by the collection unit. The collation unit collates the information analyzed by the analysis unit with the needs of the company. The proposal unit proposes an appropriate job type based on the information collated by the collation unit.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 technologies do not efficiently analyze applicant information and suggest job types that meet the needs of companies, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze information about applicants and propose job types that meet the needs of companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a proposal unit. The collection unit collects information about applicants. The analysis unit analyzes the information collected by the collection unit. The matching unit matches the information analyzed by the analysis unit with the needs of the company. The proposal unit proposes appropriate job types based on the information matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about applicants and propose job types that match the needs of companies. [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) An AI tool according to an embodiment of the present invention is a system that collects applicant information, analyzes it with a generation AI, and compares it with the company's needs to suggest the most suitable job. This AI tool collects applicants' skills, experience, and values, and then the generation AI analyzes this information and compares it with the company's needs to suggest the most suitable job. For example, an applicant inputs their skills, experience, and values. The generation AI then analyzes this information and identifies the job that best suits the applicant. Furthermore, the generation AI matches the applicant's information with the company's desired talent to find qualified candidates. This improves workplace diversity and inclusion and overall corporate performance. For example, the generation AI determines which job the applicant's skill set and experience are most suitable for. The generation AI then compares the applicant's information with the company's job description and desired skill set to find qualified candidates. This allows companies to hire diverse talent, improves the workplace atmosphere, and increases employee satisfaction. This allows the AI ​​tool to achieve optimal matches for both applicants and companies, improves workplace diversity and inclusion, and improves overall corporate performance. For example, bringing together diverse perspectives and ideas promotes innovation and improves business performance. It also makes it easier for applicants to identify with a company's values, making it easier to build long-term employment relationships.

[0029] The AI ​​tool according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a proposal unit. The collection unit collects information about applicants. The applicant information includes, but is not limited to, skills, experience, values, and work history. The collection unit stores the information entered by the applicant in a database. The collection unit can also collect the applicant's social media activities and past work history. For example, the collection unit analyzes the applicant's social media activities and collects related information. The analysis unit uses a generative AI to analyze the information collected by the collection unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can perform a detailed analysis of the applicant's skills and experience to determine which job the applicant is best suited for. The analysis unit can also identify a job that is likely to fit the applicant's culture, taking into account the applicant's values ​​and cultural background. The matching unit matches the information analyzed by the analysis unit with the company's needs. The matching can be performed using, but is not limited to, methods such as skill matching, keyword matching, and scoring. For example, the matching unit compares the applicant's information with the job description and required skill set offered by the company to find qualified personnel. The suggestion unit suggests an appropriate job type based on the information matched by the matching unit. The suggestion may be made, for example, by identifying the job type that is best suited to the applicant and recommending it to the company, but is not limited to such an example. For example, the suggestion unit considers the applicant's values ​​and cultural background and suggests a job type that is likely to fit the company's culture. This enables the AI ​​tool according to the embodiment to efficiently collect, analyze, match, and suggest applicant information. Some or all of the above-described processing by the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may use a generation AI to analyze the applicant's information and identify the best job type.

[0030] The collection unit can collect the skills, experience, and values ​​of applicants. For example, the collection unit stores the skills, experience, and values ​​entered by applicants in a database. For example, the collection unit collects the qualifications and work experience obtained by applicants in the past. The collection unit can also collect the self-assessment and career goals of applicants. For example, the collection unit collects information filled out by applicants in self-assessment sheets. The collection unit can also analyze the social media activities of applicants and collect related information. For example, the collection unit collects information based on the work experience shared by applicants on social media. This allows for the collection of detailed information about applicants, making it possible to suggest more appropriate job positions. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the social media activities of applicants and collect related information.

[0031] The analysis unit can perform a detailed analysis of the applicant's skills, experience, and values. The analysis unit can perform a detailed analysis of the applicant's skills, experience, and values, for example, using a generative AI. For example, the analysis unit can analyze the applicant's skill set and determine which job the applicant is best suited for. The analysis unit can also analyze the applicant's work history and evaluate how useful their past experience is. For example, the analysis unit can evaluate the applicant's skill development based on their past work history. The analysis unit can also identify jobs that are likely to fit the applicant's culture by taking into account the applicant's values ​​and cultural background. For example, the analysis unit can evaluate whether the applicant's values ​​are consistent with the company's values. This enables more accurate job suggestions by analyzing the applicant's information in detail. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform a detailed analysis of the applicant's skills, experience, and values ​​using a generative AI.

[0032] The matching unit can compare applicant information with the job description or desired skill set offered by the company. For example, the matching unit compares applicant information with the job description or desired skill set offered by the company. For example, the matching unit can identify applicants with specific skills and experience sought by the company and recommend the applicants to the company. The matching unit can also match the company's needs with the applicant's skill set to find qualified talent. For example, the matching unit can evaluate whether the applicant's skills match the company's desired skill set. The matching unit can also evaluate whether the applicant's values ​​match the company's culture and values. For example, the matching unit can identify applicants who are likely to fit into the company's culture. This makes it possible to find qualified talent by comparing the company's needs with the applicant's information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can use a generation AI to compare the applicant information with the job description or desired skill set offered by the company.

[0033] The proposal unit can identify the job type that best suits the applicant and recommend it to the company. The proposal unit can, for example, use a generation AI to identify the job type that best suits the applicant and recommend it to the company. For example, the proposal unit can determine which job type best suits the applicant's skill set and experience. The proposal unit can also identify the job type that best fits the company's culture by taking into account the applicant's values ​​and cultural background. For example, the proposal unit can evaluate whether the applicant's values ​​match those of the company. The proposal unit can also identify applicants with specific skills and experience that the company is looking for and recommend the applicant to the company. For example, the proposal unit can evaluate whether the applicant's skills match the skill set that the company is looking for. This allows the proposal unit to identify the job type that best suits the applicant and recommend it to the company, thereby achieving appropriate matching. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to identify the job type that best suits the applicant and recommend it to the company.

[0034] The proposal unit can consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture. The proposal unit can, for example, use a generative AI to consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture. For example, the proposal unit can evaluate whether the applicant's values ​​are consistent with the company's values. The proposal unit can also evaluate whether the applicant's cultural background fits into the company's culture. For example, the proposal unit can evaluate whether the applicant's cultural background is likely to fit into the company's culture. This makes it easier for the applicant to fit into the company's culture, improving the workplace atmosphere and increasing employee satisfaction. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generative AI, or can be performed without using a generative AI. For example, the proposal unit can use a generative AI to consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture.

[0035] The collection unit can analyze the applicant's past work history and select the optimal information collection method. The collection unit can, for example, use a generation AI to analyze the applicant's past work history and select the optimal information collection method. For example, if the applicant has a lot of experience in technical positions, the collection unit can collect information mainly through technical questions. Also, if the applicant has a lot of experience in managerial positions, the collection unit can focus on collecting information about leadership. For example, if the applicant has diverse work experience, the collection unit can collect information using a wide range of questions. This enables more effective information collection by analyzing the applicant's past work history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the applicant's past work history and select the optimal information collection method.

[0036] The collection unit can filter information based on the applicant's current career goals and areas of interest when collecting information. The collection unit can use, for example, a generation AI to filter information based on the applicant's current career goals and areas of interest when collecting information. For example, if the applicant is seeking a technical position, the collection unit can prioritize collecting technology-related information. Also, if the applicant is seeking a managerial position, the collection unit can prioritize collecting information about leadership. For example, if the applicant is interested in a particular industry, the collection unit can collect information related to that industry. In this way, more relevant information can be collected by filtering information based on the applicant's career goals and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to filter based on the applicant's career goals and areas of interest.

[0037] The collection unit can select an appropriate collection means depending on the applicant's input method when collecting information. The collection unit can use, for example, a generation AI to select an appropriate collection means depending on the applicant's input method (voice, text, image, etc.) when collecting information. For example, if the applicant prefers voice input, the collection unit can collect information via voice. Also, if the applicant prefers text input, the collection unit can collect information via text. For example, if the applicant provides information using images, the collection unit can collect information using image analysis. This improves the efficiency of information collection by selecting the optimal collection means depending on the applicant's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to select an appropriate collection means depending on the applicant's input method.

[0038] When collecting information, the collection unit can prioritize collecting relevant information by taking into account the applicant's geographic location information. The collection unit, for example, uses a generation AI to prioritize collecting relevant information by taking into account the applicant's geographic location information. For example, if the applicant lives in a specific area, the collection unit can prioritize collecting job information related to that area. Also, if the applicant wants to work in a specific area, the collection unit can prioritize collecting company information in that area. For example, if the applicant is interested in a specific area, the collection unit can prioritize collecting industry information in that area. This allows for more relevant information to be collected by taking into account the applicant's geographic location information. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can use a generation AI to prioritize collecting relevant information by taking into account the applicant's geographic location information.

[0039] The collection unit can analyze the applicant's social media activity and collect relevant information when collecting information. The collection unit can use, for example, a generation AI to analyze the applicant's social media activity and collect relevant information when collecting information. For example, the collection unit collects information based on the work experience the applicant has shared on social media. The collection unit can also collect information related to the companies and industries that the applicant follows on social media. For example, the collection unit collects information related to groups and communities that the applicant participates in on social media. This allows for more relevant information to be collected by analyzing the applicant's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the applicant's social media activity and collect relevant information.

[0040] The collection unit can customize the collection method by reflecting the applicant's past feedback when collecting information. The collection unit, for example, uses a generation AI to customize the collection method by reflecting the applicant's past feedback when collecting information. For example, the collection unit adjusts information collection questions based on feedback provided by the applicant in the past. The collection unit can also preferentially use information collection methods that the applicant has previously preferred. For example, the collection unit avoids information collection methods that the applicant has previously been dissatisfied with. This enables more effective information collection by reflecting the applicant's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can use a generation AI to customize the collection method by reflecting the applicant's past feedback.

[0041] The analysis unit can adjust the specificity of the analysis based on the importance of the applicant's skills and experience during analysis. The analysis unit can adjust the specificity of the analysis based on the importance of the applicant's skills and experience during analysis, for example, using a generation AI. For example, the analysis unit performs a detailed analysis if the applicant has high skills. Furthermore, the analysis unit can also perform a detailed analysis based on the experience if the applicant has a lot of experience. For example, the analysis unit performs a basic analysis if the applicant has little skills or experience. This allows for adjusting the level of detail of the analysis based on the importance of the applicant's skills and experience, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to adjust the specificity of the analysis based on the importance of the applicant's skills and experience.

[0042] The analysis unit can apply different analysis algorithms depending on the job category of the applicant during analysis. The analysis unit can apply different analysis algorithms depending on the job category of the applicant during analysis, for example, using a generation AI. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to applicants for technical positions. The analysis unit can also apply an analysis algorithm specialized in leadership skills to applicants for managerial positions. For example, the analysis unit can apply an analysis algorithm specialized in creativity to applicants for creative positions. In this way, by applying different analysis algorithms depending on the job category of the applicant, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to apply different analysis algorithms depending on the job category of the applicant.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the applicant's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by, for example, using a generation AI. For example, the analysis unit can evaluate the growth of skills based on the applicant's past analysis results. The analysis unit can also evaluate the depth of experience based on the applicant's past analysis results. For example, the analysis unit can evaluate changes in values ​​based on the applicant's past analysis results. In this way, the accuracy of the analysis is improved by referring to the applicant's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the applicant's past analysis results using a generation AI.

[0044] The analysis unit can determine the order of analysis based on the time of applicant information submission during analysis. The analysis unit can determine the order of analysis based on the time of applicant information submission during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis if an applicant submits information early. The analysis unit can also quickly analyze if an applicant submits information just before the deadline. For example, if an applicant submits information within a specific period, the analysis unit can prioritize analysis of applicants within that period. This enables faster analysis by determining the analysis priority based on the time of applicant information submission. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can use a generation AI to determine the order of analysis based on the time of applicant information submission.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the applicant information during analysis. The analysis unit can adjust the order of analysis based on the relevance of the applicant information during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis if the applicant information is highly relevant to the company's needs. The analysis unit can also prioritize analysis if the applicant information is only partially relevant to the company's needs. For example, the analysis unit can postpone analysis if the applicant information is not very relevant to the company's needs. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the applicant information. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the applicant information using a generation AI.

[0046] The analysis unit can adjust the use of technical terminology in the analysis according to the applicant's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the applicant's level of expertise during analysis, for example, using a generation AI. For example, if the applicant has high expertise, the analysis unit can perform an analysis that uses a lot of technical terminology. Alternatively, if the applicant has medium expertise, the analysis unit can perform an analysis that uses a moderate amount of technical terminology. For example, if the applicant has low expertise, the analysis unit can perform an analysis that avoids technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the applicant's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to adjust the use of technical terminology in the analysis according to the applicant's level of expertise.

[0047] The matching unit can improve the accuracy of matching by taking into account the interrelationship between the company's needs and the applicant's skills during matching. The matching unit can improve the accuracy of matching by using, for example, a generation AI. For example, the matching unit increases the accuracy of matching when the skills required by the company match the applicant's skills. The matching unit can also adjust the accuracy of matching when the skills required by the company partially match the applicant's skills. For example, the matching unit decreases the accuracy of matching when the skills required by the company do not match the applicant's skills. This enables more accurate matching by taking into account the interrelationship between the company's needs and the applicant's skills. Some or all of the above-described processing in the matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can improve the accuracy of matching by using a generation AI to take into account the interrelationship between the company's needs and the applicant's skills.

[0048] The matching unit can emphasize the degree of match between the skill set desired by the company and the applicant's skills when matching. The matching unit, for example, uses a generation AI to emphasize the degree of match between the skill set desired by the company and the applicant's skills when matching. For example, the matching unit prioritizes matching when the skill set desired by the company completely matches the applicant's skills. The matching unit can also prioritize matching when the skill set desired by the company only partially matches the applicant's skills. For example, the matching unit postpones matching when the skill set desired by the company does not match the applicant's skills. This makes it possible to find more suitable personnel by emphasizing the degree of match between the skill set desired by the company and the applicant's skills. Some or all of the above-described processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can use a generation AI to emphasize the degree of match between the skill set desired by the company and the applicant's skills.

[0049] The matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​when matching. The matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​when matching, for example, using a generation AI. For example, the matching unit can prioritize matching if the company's culture and values ​​and the applicant's values ​​match. In addition, the matching unit can also prioritize matching if the company's culture and values ​​and the applicant's values ​​partially match. For example, the matching unit can postpone matching if the company's culture and values ​​and the applicant's values ​​do not match. This makes it possible to find more suitable candidates by considering the degree of match between the company's culture and values ​​and the applicant's values. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​using a generation AI.

[0050] The matching unit can perform matching taking into account the geographical conditions of the company when matching. The matching unit, for example, uses a generation AI to perform matching taking into account the geographical conditions of the company when matching. For example, the matching unit prioritizes matching when the company's location and the applicant's place of residence are close. The matching unit can also perform matching as a second priority when the company's location and the applicant's place of residence are far apart. For example, the matching unit postpones matching when the company's location and the applicant's place of residence are very far apart. This makes it possible to find more suitable candidates by taking into account the geographical conditions of the company. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can perform matching taking into account the geographical conditions of the company using a generation AI.

[0051] The matching unit can improve the accuracy of matching by referring to the company's related projects and work content during matching. The matching unit can improve the accuracy of matching by, for example, using a generation AI. For example, the matching unit prioritizes matching when the company's related projects and the applicant's skills match. The matching unit can also prioritize matching when the company's work content and the applicant's experience match. For example, the matching unit can postpone matching when the company's related projects and work content do not match the applicant's skills or experience. In this way, by referring to the company's related projects and work content, more accurate matching is possible. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can improve the accuracy of matching by referring to the company's related projects and work content using a generation AI.

[0052] The matching unit can perform matching taking into account the market value and growth potential of the company when matching. The matching unit can perform matching taking into account the market value and growth potential of the company when matching, for example, using a generation AI. For example, the matching unit can perform matching as a priority when a company has a high market value and growth potential. The matching unit can also perform matching as a second priority when a company has a medium market value and growth potential. For example, the matching unit can perform matching as a second priority when a company has a low market value and no growth potential. In this way, by taking into account the market value and growth potential of the company, more suitable personnel can be found. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can perform matching taking into account the market value and growth potential of the company using a generation AI.

[0053] The proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience when making a proposal. The proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience when making a proposal, for example, using a generation AI. For example, if the applicant has high skills, the proposal unit can make a detailed proposal. Also, if the applicant has a lot of experience, the proposal unit can make a detailed proposal based on the experience. For example, if the applicant has little skills or experience, the proposal unit can make a basic proposal. In this way, by adjusting the level of detail of the proposal based on the importance of the applicant's skills and experience, a more appropriate proposal can be made. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience using a generation AI.

[0054] The proposal unit can apply different proposal algorithms depending on the job category of the applicant when making a proposal. The proposal unit can use, for example, a generation AI to apply different proposal algorithms depending on the job category of the applicant when making a proposal. For example, the proposal unit can apply a proposal algorithm specialized in technical skills to applicants for technical positions. The proposal unit can also apply a proposal algorithm specialized in leadership skills to applicants for managerial positions. For example, the proposal unit can apply a proposal algorithm specialized in creativity to applicants for creative positions. In this way, by applying different proposal algorithms depending on the job category of the applicant, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to apply different proposal algorithms depending on the job category of the applicant.

[0055] The proposal unit can improve the accuracy of the proposal when making a proposal by referring to the applicant's past proposal results. The proposal unit can improve the accuracy of the proposal when making a proposal by using, for example, a generation AI. For example, the proposal unit can evaluate the growth of skills based on the applicant's past proposal results. The proposal unit can also evaluate the depth of experience based on the applicant's past proposal results. For example, the proposal unit can evaluate changes in values ​​based on the applicant's past proposal results. In this way, the accuracy of the proposal is improved by referring to the applicant's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can improve the accuracy of the proposal by using a generation AI by referring to the applicant's past proposal results.

[0056] The proposal unit can determine the order of proposals based on the time when applicants submit their information when making proposals. The proposal unit can use, for example, a generation AI to determine the order of proposals based on the time when applicants submit their information when making proposals. For example, the proposal unit can prioritize proposals if applicants submit their information early. The proposal unit can also quickly make proposals if applicants submit their information just before the deadline. For example, if applicants submit their information within a specific period, the proposal unit can prioritize proposals from applicants within that period. This enables faster proposals by determining the priority of proposals based on the time when applicants submit their information. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to determine the order of proposals based on the time when applicants submit their information.

[0057] The proposal unit can adjust the order of proposals based on the relevance of the applicant's information when making a proposal. The proposal unit can adjust the order of proposals based on the relevance of the applicant's information when making a proposal, for example, using a generation AI. For example, the proposal unit can prioritize a proposal when the applicant's information is highly relevant to the company's needs. The proposal unit can also prioritize a proposal when the applicant's information is only partially relevant to the company's needs. For example, the proposal unit can postpone a proposal when the applicant's information is not very relevant to the company's needs. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the applicant's information. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can adjust the order of proposals based on the relevance of the applicant's information using a generation AI.

[0058] The proposal unit can adjust the use of technical terminology in the proposal according to the applicant's level of expertise when making a proposal. The proposal unit can, for example, use a generation AI to adjust the use of technical terminology in the proposal according to the applicant's level of expertise when making a proposal. For example, if the applicant has high expertise, the proposal unit can make a proposal that uses a lot of technical terminology. Alternatively, if the applicant has medium expertise, the proposal unit can make a proposal that uses moderate technical terminology. For example, if the applicant has low expertise, the proposal unit can make a proposal that avoids technical terminology. This allows for a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the applicant's level of expertise. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to adjust the use of technical terminology in the proposal according to the applicant's level of expertise.

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

[0060] When collecting information about applicants, the collection department can analyze in detail the applicant's past projects and work content to evaluate the applicant's skills and depth of experience. For example, the collection department can evaluate the scale and results of projects in which the applicant has been involved in the past to determine the applicant's skill level. The collection department can also analyze in detail the work content in which the applicant has been responsible in the past to evaluate the applicant's depth of experience. For example, the collection department can identify projects in which the applicant has demonstrated leadership in the past and evaluate those leadership skills. This enables more appropriate job suggestions to be made by analyzing the applicant's past projects and work content in detail.

[0061] The analysis unit can take into account the applicant's learning style and learning speed when conducting a detailed analysis of the applicant's skills, experience, and values. For example, the analysis unit can analyze how the applicant acquired skills in the past and identify the applicant's learning style. The analysis unit can also evaluate the speed at which the applicant acquires new skills and determine the applicant's learning ability. For example, if the applicant has acquired many skills in a short period of time, the analysis unit can rate the applicant's learning speed highly. This makes it possible to propose more appropriate jobs by taking into account the applicant's learning style and learning speed.

[0062] The matching department can take into account the company's future growth strategy and vision when comparing applicant information with the job descriptions and desired skill sets offered by the company. For example, if a company plans to introduce new technology as part of its future growth strategy, the matching department will prioritize matching applicants with skills related to that technology. The matching department can also identify applicants who empathize with the company's vision and recommend those applicants to the company. For example, the matching department can identify applicants whose values ​​align with the company's vision and recommend those applicants to the company. This makes it possible to find more suitable talent by taking into account the company's future growth strategy and vision.

[0063] The proposal department can take into consideration the applicant's career path and future goals when identifying the job type that is best suited to the applicant and recommending it to a company. For example, if the applicant is aiming for a managerial position as their future career path, the proposal department can suggest a job type that is suitable for a managerial position. In addition, if the applicant wants to build a career in a specific industry, the proposal department can also suggest a job type related to that industry. For example, if the applicant wants to move from a technical position to a managerial position, the proposal department can suggest a job type that is suitable for that career path. This makes it possible to suggest more appropriate job types by taking into consideration the applicant's career path and future goals.

[0064] The proposal department can take into account the company's team dynamics and communication style when considering applicants' values ​​and cultural backgrounds and ensuring they fit into the company's culture. For example, if the company's teams have a collaborative communication style, the proposal department can suggest applicants who are suited to that style. Also, if the company's teams value working independently, the proposal department can suggest applicants who are suited to that style. For example, if the company's teams hold frequent meetings, the proposal department can suggest applicants who are suited to that meeting style. This makes it possible to propose more appropriate job positions by taking into account the company's team dynamics and communication style.

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

[0066] Step 1: The collection department collects information about applicants. Applicant information includes skills, experience, values, and work history. The collection department stores the information entered by the applicant in a database. The collection department can also collect applicants' social media activities and past work history. For example, the collection department analyzes applicants' social media activities and collects related information. Step 2: The analysis unit uses the generative AI to analyze the information collected by the collection unit. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit conducts a detailed analysis of the applicant's skills and experience to determine which job they are best suited for. The analysis unit can also take into account the applicant's values ​​and cultural background to identify jobs that are a good fit for the company's culture. Step 3: The matching unit matches the information analyzed by the analysis unit with the company's needs. Matching is done using methods such as skill matching, keyword matching, and scoring. For example, the matching unit compares the applicant's information with the job description and required skill set offered by the company to find qualified personnel. Step 4: The proposal unit proposes suitable job types based on the information collated by the collation unit. The proposal is made by identifying the job type that best suits the applicant and recommending it to the company. For example, the proposal unit may consider the applicant's values ​​and cultural background and propose a job type that is likely to fit well with the company's culture. The processing in the proposal unit may also be performed using generative AI.

[0067] (Example 2) An AI tool according to an embodiment of the present invention is a system that collects applicant information, analyzes it with a generation AI, and compares it with the company's needs to suggest the most suitable job. This AI tool collects applicants' skills, experience, and values, and then the generation AI analyzes this information and compares it with the company's needs to suggest the most suitable job. For example, an applicant inputs their skills, experience, and values. The generation AI then analyzes this information and identifies the job that best suits the applicant. Furthermore, the generation AI matches the applicant's information with the company's desired talent to find qualified candidates. This improves workplace diversity and inclusion and overall corporate performance. For example, the generation AI determines which job the applicant's skill set and experience are most suitable for. The generation AI then compares the applicant's information with the company's job description and desired skill set to find qualified candidates. This allows companies to hire diverse talent, improves the workplace atmosphere, and increases employee satisfaction. This allows the AI ​​tool to achieve optimal matches for both applicants and companies, improves workplace diversity and inclusion, and improves overall corporate performance. For example, bringing together diverse perspectives and ideas promotes innovation and improves business performance. It also makes it easier for applicants to identify with a company's values, making it easier to build long-term employment relationships.

[0068] The AI ​​tool according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a proposal unit. The collection unit collects information about applicants. The applicant information includes, but is not limited to, skills, experience, values, and work history. The collection unit stores the information entered by the applicant in a database. The collection unit can also collect the applicant's social media activities and past work history. For example, the collection unit analyzes the applicant's social media activities and collects related information. The analysis unit uses a generative AI to analyze the information collected by the collection unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can perform a detailed analysis of the applicant's skills and experience to determine which job the applicant is best suited for. The analysis unit can also identify a job that is likely to fit the applicant's culture, taking into account the applicant's values ​​and cultural background. The matching unit matches the information analyzed by the analysis unit with the company's needs. The matching can be performed using, but is not limited to, methods such as skill matching, keyword matching, and scoring. For example, the matching unit compares the applicant's information with the job description and required skill set offered by the company to find qualified personnel. The suggestion unit suggests an appropriate job type based on the information matched by the matching unit. The suggestion may be made, for example, by identifying the job type that is best suited to the applicant and recommending it to the company, but is not limited to such an example. For example, the suggestion unit considers the applicant's values ​​and cultural background and suggests a job type that is likely to fit the company's culture. This enables the AI ​​tool according to the embodiment to efficiently collect, analyze, match, and suggest applicant information. Some or all of the above-described processing by the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may use a generation AI to analyze the applicant's information and identify the best job type.

[0069] The collection unit can collect the skills, experience, and values ​​of applicants. For example, the collection unit stores the skills, experience, and values ​​entered by applicants in a database. For example, the collection unit collects the qualifications and work experience obtained by applicants in the past. The collection unit can also collect the self-assessment and career goals of applicants. For example, the collection unit collects information filled out by applicants in self-assessment sheets. The collection unit can also analyze the social media activities of applicants and collect related information. For example, the collection unit collects information based on the work experience shared by applicants on social media. This allows for the collection of detailed information about applicants, making it possible to suggest more appropriate job positions. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the social media activities of applicants and collect related information.

[0070] The analysis unit can perform a detailed analysis of the applicant's skills, experience, and values. The analysis unit can perform a detailed analysis of the applicant's skills, experience, and values, for example, using a generative AI. For example, the analysis unit can analyze the applicant's skill set and determine which job the applicant is best suited for. The analysis unit can also analyze the applicant's work history and evaluate how useful their past experience is. For example, the analysis unit can evaluate the applicant's skill development based on their past work history. The analysis unit can also identify jobs that are likely to fit the applicant's culture by taking into account the applicant's values ​​and cultural background. For example, the analysis unit can evaluate whether the applicant's values ​​are consistent with the company's values. This enables more accurate job suggestions by analyzing the applicant's information in detail. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform a detailed analysis of the applicant's skills, experience, and values ​​using a generative AI.

[0071] The matching unit can compare applicant information with the job description or desired skill set offered by the company. For example, the matching unit compares applicant information with the job description or desired skill set offered by the company. For example, the matching unit can identify applicants with specific skills and experience sought by the company and recommend the applicants to the company. The matching unit can also match the company's needs with the applicant's skill set to find qualified talent. For example, the matching unit can evaluate whether the applicant's skills match the company's desired skill set. The matching unit can also evaluate whether the applicant's values ​​match the company's culture and values. For example, the matching unit can identify applicants who are likely to fit into the company's culture. This makes it possible to find qualified talent by comparing the company's needs with the applicant's information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can use a generation AI to compare the applicant information with the job description or desired skill set offered by the company.

[0072] The proposal unit can identify the job type that best suits the applicant and recommend it to the company. The proposal unit can, for example, use a generation AI to identify the job type that best suits the applicant and recommend it to the company. For example, the proposal unit can determine which job type best suits the applicant's skill set and experience. The proposal unit can also identify the job type that best fits the company's culture by taking into account the applicant's values ​​and cultural background. For example, the proposal unit can evaluate whether the applicant's values ​​match those of the company. The proposal unit can also identify applicants with specific skills and experience that the company is looking for and recommend the applicant to the company. For example, the proposal unit can evaluate whether the applicant's skills match the skill set that the company is looking for. This allows the proposal unit to identify the job type that best suits the applicant and recommend it to the company, thereby achieving appropriate matching. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to identify the job type that best suits the applicant and recommend it to the company.

[0073] The proposal unit can consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture. The proposal unit can, for example, use a generative AI to consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture. For example, the proposal unit can evaluate whether the applicant's values ​​are consistent with the company's values. The proposal unit can also evaluate whether the applicant's cultural background fits into the company's culture. For example, the proposal unit can evaluate whether the applicant's cultural background is likely to fit into the company's culture. This makes it easier for the applicant to fit into the company's culture, improving the workplace atmosphere and increasing employee satisfaction. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generative AI, or can be performed without using a generative AI. For example, the proposal unit can use a generative AI to consider the applicant's values ​​and cultural background to ensure that the applicant fits into the company's culture.

[0074] The collection unit can estimate the applicant's emotions and adjust the timing of information collection based on the estimated applicant's emotions. The collection unit can estimate the applicant's emotions using, for example, a generation AI and adjust the timing of information collection based on the estimated applicant's emotions. For example, the collection unit can immediately start information collection if the applicant is relaxed. The collection unit can also temporarily delay information collection if the applicant is stressed. For example, the collection unit can quickly collect information if the applicant is concentrating. This enables more appropriate information collection by adjusting the timing of information collection according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can estimate the applicant's emotions using a generation AI and adjust the timing of information collection based on the estimated applicant's emotions.

[0075] The collection unit can analyze the applicant's past work history and select the optimal information collection method. The collection unit can, for example, use a generation AI to analyze the applicant's past work history and select the optimal information collection method. For example, if the applicant has a lot of experience in technical positions, the collection unit can collect information mainly through technical questions. Also, if the applicant has a lot of experience in managerial positions, the collection unit can focus on collecting information about leadership. For example, if the applicant has diverse work experience, the collection unit can collect information using a wide range of questions. This enables more effective information collection by analyzing the applicant's past work history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the applicant's past work history and select the optimal information collection method.

[0076] The collection unit can filter information based on the applicant's current career goals and areas of interest when collecting information. The collection unit can use, for example, a generation AI to filter information based on the applicant's current career goals and areas of interest when collecting information. For example, if the applicant is seeking a technical position, the collection unit can prioritize collecting technology-related information. Also, if the applicant is seeking a managerial position, the collection unit can prioritize collecting information about leadership. For example, if the applicant is interested in a particular industry, the collection unit can collect information related to that industry. In this way, more relevant information can be collected by filtering information based on the applicant's career goals and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to filter based on the applicant's career goals and areas of interest.

[0077] The collection unit can select an appropriate collection means depending on the applicant's input method when collecting information. The collection unit can use, for example, a generation AI to select an appropriate collection means depending on the applicant's input method (voice, text, image, etc.) when collecting information. For example, if the applicant prefers voice input, the collection unit can collect information via voice. Also, if the applicant prefers text input, the collection unit can collect information via text. For example, if the applicant provides information using images, the collection unit can collect information using image analysis. This improves the efficiency of information collection by selecting the optimal collection means depending on the applicant's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to select an appropriate collection means depending on the applicant's input method.

[0078] The collection unit can estimate the applicant's emotions and determine the priority of information to be collected based on the estimated applicant's emotions. The collection unit can estimate the applicant's emotions using, for example, a generation AI and determine the priority of information to be collected based on the estimated applicant's emotions. For example, if the applicant is relaxed, the collection unit can prioritize collecting detailed information. The collection unit can also prioritize collecting basic information if the applicant is stressed. For example, if the applicant is concentrating, the collection unit can prioritize collecting important information. This enables more effective information collection by determining the priority of information according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can estimate the applicant's emotions using a generation AI and determine the priority of information to be collected based on the estimated applicant's emotions.

[0079] When collecting information, the collection unit can prioritize collecting relevant information by taking into account the applicant's geographic location information. The collection unit, for example, uses a generation AI to prioritize collecting relevant information by taking into account the applicant's geographic location information. For example, if the applicant lives in a specific area, the collection unit can prioritize collecting job information related to that area. Also, if the applicant wants to work in a specific area, the collection unit can prioritize collecting company information in that area. For example, if the applicant is interested in a specific area, the collection unit can prioritize collecting industry information in that area. This allows for more relevant information to be collected by taking into account the applicant's geographic location information. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can use a generation AI to prioritize collecting relevant information by taking into account the applicant's geographic location information.

[0080] The collection unit can analyze the applicant's social media activity and collect relevant information when collecting information. The collection unit can use, for example, a generation AI to analyze the applicant's social media activity and collect relevant information when collecting information. For example, the collection unit collects information based on the work experience the applicant has shared on social media. The collection unit can also collect information related to the companies and industries that the applicant follows on social media. For example, the collection unit collects information related to groups and communities that the applicant participates in on social media. This allows for more relevant information to be collected by analyzing the applicant's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can use a generation AI to analyze the applicant's social media activity and collect relevant information.

[0081] The collection unit can customize the collection method by reflecting the applicant's past feedback when collecting information. The collection unit, for example, uses a generation AI to customize the collection method by reflecting the applicant's past feedback when collecting information. For example, the collection unit adjusts information collection questions based on feedback provided by the applicant in the past. The collection unit can also preferentially use information collection methods that the applicant has previously preferred. For example, the collection unit avoids information collection methods that the applicant has previously been dissatisfied with. This enables more effective information collection by reflecting the applicant's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can use a generation AI to customize the collection method by reflecting the applicant's past feedback.

[0082] The analysis unit can estimate the applicant's emotions and adjust the presentation method of the analysis based on the estimated emotions of the applicant. The analysis unit can estimate the applicant's emotions using, for example, a generation AI and adjust the presentation method of the analysis based on the estimated emotions of the applicant. For example, the analysis unit can provide detailed analysis results when the applicant is relaxed. The analysis unit can also provide concise analysis results when the applicant is stressed. For example, the analysis unit can provide analysis results that emphasize important points when the applicant is concentrating. This allows for adjusting the presentation method of the analysis according to the applicant's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without a generation AI. For example, the analysis unit can estimate the applicant's emotions using a generation AI and adjust the presentation method of the analysis based on the estimated emotions of the applicant.

[0083] The analysis unit can adjust the specificity of the analysis based on the importance of the applicant's skills and experience during analysis. The analysis unit can adjust the specificity of the analysis based on the importance of the applicant's skills and experience during analysis, for example, using a generation AI. For example, the analysis unit performs a detailed analysis if the applicant has high skills. Furthermore, the analysis unit can also perform a detailed analysis based on the experience if the applicant has a lot of experience. For example, the analysis unit performs a basic analysis if the applicant has little skills or experience. This allows for adjusting the level of detail of the analysis based on the importance of the applicant's skills and experience, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to adjust the specificity of the analysis based on the importance of the applicant's skills and experience.

[0084] The analysis unit can apply different analysis algorithms depending on the job category of the applicant during analysis. The analysis unit can apply different analysis algorithms depending on the job category of the applicant during analysis, for example, using a generation AI. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to applicants for technical positions. The analysis unit can also apply an analysis algorithm specialized in leadership skills to applicants for managerial positions. For example, the analysis unit can apply an analysis algorithm specialized in creativity to applicants for creative positions. In this way, by applying different analysis algorithms depending on the job category of the applicant, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to apply different analysis algorithms depending on the job category of the applicant.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the applicant's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by, for example, using a generation AI. For example, the analysis unit can evaluate the growth of skills based on the applicant's past analysis results. The analysis unit can also evaluate the depth of experience based on the applicant's past analysis results. For example, the analysis unit can evaluate changes in values ​​based on the applicant's past analysis results. In this way, the accuracy of the analysis is improved by referring to the applicant's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the applicant's past analysis results using a generation AI.

[0086] The analysis unit can estimate the applicant's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the applicant's emotions using, for example, a generation AI, and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can perform a detailed analysis when the applicant is relaxed. The analysis unit can also perform a concise analysis when the applicant is stressed. For example, the analysis unit can perform an analysis that emphasizes important points when the applicant is concentrating. This allows for adjusting the length of the analysis based on the applicant's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, 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 analysis unit can be performed using, for example, a generation AI, or can be performed without a generation AI. For example, the analysis unit can estimate the applicant's emotions using a generation AI and adjust the length of the analysis based on the estimated emotions.

[0087] The analysis unit can determine the order of analysis based on the time of applicant information submission during analysis. The analysis unit can determine the order of analysis based on the time of applicant information submission during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis if an applicant submits information early. The analysis unit can also quickly analyze if an applicant submits information just before the deadline. For example, if an applicant submits information within a specific period, the analysis unit can prioritize analysis of applicants within that period. This enables faster analysis by determining the analysis priority based on the time of applicant information submission. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can use a generation AI to determine the order of analysis based on the time of applicant information submission.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the applicant information during analysis. The analysis unit can adjust the order of analysis based on the relevance of the applicant information during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis if the applicant information is highly relevant to the company's needs. The analysis unit can also prioritize analysis if the applicant information is only partially relevant to the company's needs. For example, the analysis unit can postpone analysis if the applicant information is not very relevant to the company's needs. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the applicant information. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the applicant information using a generation AI.

[0089] The analysis unit can adjust the use of technical terminology in the analysis according to the applicant's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the applicant's level of expertise during analysis, for example, using a generation AI. For example, if the applicant has high expertise, the analysis unit can perform an analysis that uses a lot of technical terminology. Alternatively, if the applicant has medium expertise, the analysis unit can perform an analysis that uses a moderate amount of technical terminology. For example, if the applicant has low expertise, the analysis unit can perform an analysis that avoids technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the applicant's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to adjust the use of technical terminology in the analysis according to the applicant's level of expertise.

[0090] The matching unit can estimate the applicant's emotions and adjust the matching criteria based on the estimated applicant's emotions. The matching unit can estimate the applicant's emotions using, for example, a generation AI and adjust the matching criteria based on the estimated applicant's emotions. For example, the matching unit can use detailed matching criteria when the applicant is relaxed. The matching unit can also use concise matching criteria when the applicant is stressed. For example, the matching unit can use matching criteria that emphasize important points when the applicant is concentrating. This allows the matching criteria to be adjusted according to the applicant's emotions, thereby providing more appropriate matching results. Emotion estimation is achieved using an emotion estimation function, for example, 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 matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can estimate the applicant's emotions using a generation AI and adjust the matching criteria based on the estimated applicant's emotions.

[0091] The matching unit can improve the accuracy of matching by taking into account the interrelationship between the company's needs and the applicant's skills during matching. The matching unit can improve the accuracy of matching by using, for example, a generation AI. For example, the matching unit increases the accuracy of matching when the skills required by the company match the applicant's skills. The matching unit can also adjust the accuracy of matching when the skills required by the company partially match the applicant's skills. For example, the matching unit decreases the accuracy of matching when the skills required by the company do not match the applicant's skills. This enables more accurate matching by taking into account the interrelationship between the company's needs and the applicant's skills. Some or all of the above-described processing in the matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can improve the accuracy of matching by using a generation AI to take into account the interrelationship between the company's needs and the applicant's skills.

[0092] The matching unit can emphasize the degree of match between the skill set desired by the company and the applicant's skills when matching. The matching unit, for example, uses a generation AI to emphasize the degree of match between the skill set desired by the company and the applicant's skills when matching. For example, the matching unit prioritizes matching when the skill set desired by the company completely matches the applicant's skills. The matching unit can also prioritize matching when the skill set desired by the company only partially matches the applicant's skills. For example, the matching unit postpones matching when the skill set desired by the company does not match the applicant's skills. This makes it possible to find more suitable personnel by emphasizing the degree of match between the skill set desired by the company and the applicant's skills. Some or all of the above-described processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can use a generation AI to emphasize the degree of match between the skill set desired by the company and the applicant's skills.

[0093] The matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​when matching. The matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​when matching, for example, using a generation AI. For example, the matching unit can prioritize matching if the company's culture and values ​​and the applicant's values ​​match. In addition, the matching unit can also prioritize matching if the company's culture and values ​​and the applicant's values ​​partially match. For example, the matching unit can postpone matching if the company's culture and values ​​and the applicant's values ​​do not match. This makes it possible to find more suitable candidates by considering the degree of match between the company's culture and values ​​and the applicant's values. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can consider the degree of match between the company's culture and values ​​and the applicant's values ​​using a generation AI.

[0094] The matching unit can estimate the applicant's emotions and adjust the display order of the matching results based on the estimated applicant's emotions. The matching unit can estimate the applicant's emotions using, for example, a generation AI and adjust the display order of the matching results based on the estimated applicant's emotions. For example, if the applicant is relaxed, the matching unit can prioritize displaying detailed matching results. The matching unit can also prioritize displaying concise matching results if the applicant is stressed. For example, if the applicant is concentrating, the matching unit can prioritize displaying matching results that highlight important points. This allows for adjusting the display order of the matching results according to the applicant's emotions, thereby providing more appropriate matching results. Emotion estimation is achieved using an emotion estimation function, for example, 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 matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can estimate the applicant's emotions using a generation AI and adjust the display order of the matching results based on the estimated applicant's emotions.

[0095] The matching unit can perform matching taking into account the geographical conditions of the company when matching. The matching unit, for example, uses a generation AI to perform matching taking into account the geographical conditions of the company when matching. For example, the matching unit prioritizes matching when the company's location and the applicant's place of residence are close. The matching unit can also perform matching as a second priority when the company's location and the applicant's place of residence are far apart. For example, the matching unit postpones matching when the company's location and the applicant's place of residence are very far apart. This makes it possible to find more suitable candidates by taking into account the geographical conditions of the company. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can perform matching taking into account the geographical conditions of the company using a generation AI.

[0096] The matching unit can improve the accuracy of matching by referring to the company's related projects and work content during matching. The matching unit can improve the accuracy of matching by, for example, using a generation AI. For example, the matching unit prioritizes matching when the company's related projects and the applicant's skills match. The matching unit can also prioritize matching when the company's work content and the applicant's experience match. For example, the matching unit can postpone matching when the company's related projects and work content do not match the applicant's skills or experience. In this way, by referring to the company's related projects and work content, more accurate matching is possible. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can improve the accuracy of matching by referring to the company's related projects and work content using a generation AI.

[0097] The matching unit can perform matching taking into account the market value and growth potential of the company when matching. The matching unit can perform matching taking into account the market value and growth potential of the company when matching, for example, using a generation AI. For example, the matching unit can perform matching as a priority when a company has a high market value and growth potential. The matching unit can also perform matching as a second priority when a company has a medium market value and growth potential. For example, the matching unit can perform matching as a second priority when a company has a low market value and no growth potential. In this way, by taking into account the market value and growth potential of the company, more suitable personnel can be found. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the matching unit can perform matching taking into account the market value and growth potential of the company using a generation AI.

[0098] The proposal unit can estimate the applicant's emotions and adjust the way the proposal is presented based on the estimated emotions. The proposal unit can estimate the applicant's emotions using, for example, a generation AI, and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can make a detailed proposal when the applicant is relaxed. The proposal unit can also make a concise proposal when the applicant is stressed. For example, the proposal unit can make a proposal that emphasizes important points when the applicant is concentrating. This allows for a more appropriate proposal by adjusting the way the proposal is presented based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the proposal unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the proposal unit can estimate the applicant's emotions using a generation AI and adjust the way the proposal is presented based on the estimated emotions.

[0099] The proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience when making a proposal. The proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience when making a proposal, for example, using a generation AI. For example, if the applicant has high skills, the proposal unit can make a detailed proposal. Also, if the applicant has a lot of experience, the proposal unit can make a detailed proposal based on the experience. For example, if the applicant has little skills or experience, the proposal unit can make a basic proposal. In this way, by adjusting the level of detail of the proposal based on the importance of the applicant's skills and experience, a more appropriate proposal can be made. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can adjust the specificity of the proposal based on the importance of the applicant's skills and experience using a generation AI.

[0100] The proposal unit can apply different proposal algorithms depending on the job category of the applicant when making a proposal. The proposal unit can use, for example, a generation AI to apply different proposal algorithms depending on the job category of the applicant when making a proposal. For example, the proposal unit can apply a proposal algorithm specialized in technical skills to applicants for technical positions. The proposal unit can also apply a proposal algorithm specialized in leadership skills to applicants for managerial positions. For example, the proposal unit can apply a proposal algorithm specialized in creativity to applicants for creative positions. In this way, by applying different proposal algorithms depending on the job category of the applicant, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to apply different proposal algorithms depending on the job category of the applicant.

[0101] The proposal unit can improve the accuracy of the proposal when making a proposal by referring to the applicant's past proposal results. The proposal unit can improve the accuracy of the proposal when making a proposal by using, for example, a generation AI. For example, the proposal unit can evaluate the growth of skills based on the applicant's past proposal results. The proposal unit can also evaluate the depth of experience based on the applicant's past proposal results. For example, the proposal unit can evaluate changes in values ​​based on the applicant's past proposal results. In this way, the accuracy of the proposal is improved by referring to the applicant's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can improve the accuracy of the proposal by using a generation AI by referring to the applicant's past proposal results.

[0102] The suggestion unit can estimate the applicant's emotions and adjust the length of the proposal based on the estimated emotions. The suggestion unit can estimate the applicant's emotions using, for example, a generation AI and adjust the length of the proposal based on the estimated emotions. For example, the suggestion unit can make a detailed proposal when the applicant is relaxed. The suggestion unit can also make a concise proposal when the applicant is stressed. For example, the suggestion unit can make a proposal that emphasizes important points when the applicant is concentrating. This allows for adjusting the length of the proposal according to the applicant's emotions, thereby enabling more appropriate proposals. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can estimate the applicant's emotions using a generation AI and adjust the length of the proposal based on the estimated emotions.

[0103] The proposal unit can determine the order of proposals based on the time when applicants submit their information when making proposals. The proposal unit can use, for example, a generation AI to determine the order of proposals based on the time when applicants submit their information when making proposals. For example, the proposal unit can prioritize proposals if applicants submit their information early. The proposal unit can also quickly make proposals if applicants submit their information just before the deadline. For example, if applicants submit their information within a specific period, the proposal unit can prioritize proposals from applicants within that period. This enables faster proposals by determining the priority of proposals based on the time when applicants submit their information. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to determine the order of proposals based on the time when applicants submit their information.

[0104] The proposal unit can adjust the order of proposals based on the relevance of the applicant's information when making a proposal. The proposal unit can adjust the order of proposals based on the relevance of the applicant's information when making a proposal, for example, using a generation AI. For example, the proposal unit can prioritize a proposal when the applicant's information is highly relevant to the company's needs. The proposal unit can also prioritize a proposal when the applicant's information is only partially relevant to the company's needs. For example, the proposal unit can postpone a proposal when the applicant's information is not very relevant to the company's needs. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the applicant's information. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can adjust the order of proposals based on the relevance of the applicant's information using a generation AI.

[0105] The proposal unit can adjust the use of technical terminology in the proposal according to the applicant's level of expertise when making a proposal. The proposal unit can, for example, use a generation AI to adjust the use of technical terminology in the proposal according to the applicant's level of expertise when making a proposal. For example, if the applicant has high expertise, the proposal unit can make a proposal that uses a lot of technical terminology. Alternatively, if the applicant has medium expertise, the proposal unit can make a proposal that uses moderate technical terminology. For example, if the applicant has low expertise, the proposal unit can make a proposal that avoids technical terminology. This allows for a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the applicant's level of expertise. Some or all of the above-described processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can use a generation AI to adjust the use of technical terminology in the proposal according to the applicant's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, an applicant inputs information using the smart device 14, and the control unit 46A stores the information in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the applicant's information using a generation AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the applicant's information with the company's needs. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and suggests the most suitable job for the applicant. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, an applicant inputs information using the smart glasses 214, and the control unit 46A stores the information in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the applicant's information using a generative AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the applicant's information with the company's needs. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and suggests the most suitable job for the applicant. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, an applicant inputs information using the headset-type terminal 314, and the control unit 46A stores the information in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the applicant's information using a generation AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the applicant's information with the company's needs. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and suggests the most suitable job for the applicant. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, an applicant inputs information using the robot 414, and the control unit 46A stores the information in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the applicant's information using a generative AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the applicant's information with the company's needs. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests the most suitable job type for the applicant.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When collecting information about applicants, the collection department can analyze in detail the applicant's past projects and work content to evaluate the applicant's skills and depth of experience. For example, the collection department can evaluate the scale and results of projects in which the applicant has been involved in the past to determine the applicant's skill level. The collection department can also analyze in detail the work content in which the applicant has been responsible in the past to evaluate the applicant's depth of experience. For example, the collection department can identify projects in which the applicant has demonstrated leadership in the past and evaluate those leadership skills. This enables more appropriate job suggestions to be made by analyzing the applicant's past projects and work content in detail.

[0108] The analysis unit can take into account the applicant's learning style and learning speed when conducting a detailed analysis of the applicant's skills, experience, and values. For example, the analysis unit can analyze how the applicant acquired skills in the past and identify the applicant's learning style. The analysis unit can also evaluate the speed at which the applicant acquires new skills and determine the applicant's learning ability. For example, if the applicant has acquired many skills in a short period of time, the analysis unit can rate the applicant's learning speed highly. This makes it possible to propose more appropriate jobs by taking into account the applicant's learning style and learning speed.

[0109] The matching department can take into account the company's future growth strategy and vision when comparing applicant information with the job descriptions and desired skill sets offered by the company. For example, if a company plans to introduce new technology as part of its future growth strategy, the matching department will prioritize matching applicants with skills related to that technology. The matching department can also identify applicants who empathize with the company's vision and recommend those applicants to the company. For example, the matching department can identify applicants whose values ​​align with the company's vision and recommend those applicants to the company. This makes it possible to find more suitable talent by taking into account the company's future growth strategy and vision.

[0110] The proposal department can take into consideration the applicant's career path and future goals when identifying the job type that is best suited to the applicant and recommending it to a company. For example, if the applicant is aiming for a managerial position as their future career path, the proposal department can suggest a job type that is suitable for a managerial position. In addition, if the applicant wants to build a career in a specific industry, the proposal department can also suggest a job type related to that industry. For example, if the applicant wants to move from a technical position to a managerial position, the proposal department can suggest a job type that is suitable for that career path. This makes it possible to suggest more appropriate job types by taking into consideration the applicant's career path and future goals.

[0111] The proposal department can take into account the company's team dynamics and communication style when considering applicants' values ​​and cultural backgrounds and ensuring they fit into the company's culture. For example, if the company's teams have a collaborative communication style, the proposal department can suggest applicants who are suited to that style. Also, if the company's teams value working independently, the proposal department can suggest applicants who are suited to that style. For example, if the company's teams hold frequent meetings, the proposal department can suggest applicants who are suited to that meeting style. This makes it possible to propose more appropriate job positions by taking into account the company's team dynamics and communication style.

[0112] The collection unit can estimate the emotions of the applicant and customize the method of collecting information based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the collection unit can collect information in a dialogue format. Also, if the applicant is feeling stressed, the collection unit can collect information in a simple question format. For example, if the applicant is concentrating, the collection unit can ask detailed questions. In this way, by customizing the method of collecting information according to the emotions of the applicant, more effective information collection is possible.

[0113] The analysis unit can estimate the applicant's emotions and adjust the timing of the analysis based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the analysis unit will immediately start the analysis. The analysis unit can also temporarily delay the analysis if the applicant is feeling stressed. For example, if the applicant is concentrating, the analysis unit will quickly perform the analysis. This allows the timing of the analysis to be adjusted according to the applicant's emotions, making it possible to provide more appropriate analysis results.

[0114] The matching unit can estimate the applicant's emotions and customize the matching method based on the estimated applicant's emotions. For example, if the applicant is relaxed, the matching unit performs detailed matching. Alternatively, if the applicant is stressed, the matching unit can perform concise matching. For example, if the applicant is concentrating, the matching unit performs matching that emphasizes important points. In this way, by customizing the matching method according to the applicant's emotions, more appropriate matching results can be provided.

[0115] The suggestion unit can estimate the emotions of the applicant and adjust the timing of the suggestion based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the suggestion unit makes a suggestion immediately. Also, if the applicant is feeling stressed, the suggestion unit can temporarily delay the suggestion. For example, if the applicant is concentrating, the suggestion unit makes a suggestion quickly. In this way, by adjusting the timing of the suggestion according to the emotions of the applicant, more appropriate suggestions can be made.

[0116] The suggestion unit can estimate the emotions of the applicant and customize the content of the suggestion based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the suggestion unit can provide detailed content of the suggestion. In addition, if the applicant is stressed, the suggestion unit can provide concise content of the suggestion. For example, if the applicant is concentrating, the suggestion unit can provide content of the suggestion that emphasizes important points. In this way, by customizing the content of the suggestion according to the emotions of the applicant, more appropriate suggestions can be made.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects information about applicants. Applicant information includes skills, experience, values, and work history. The collection department stores the information entered by the applicant in a database. The collection department can also collect applicants' social media activities and past work history. For example, the collection department analyzes applicants' social media activities and collects related information. Step 2: The analysis unit uses the generative AI to analyze the information collected by the collection unit. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit conducts a detailed analysis of the applicant's skills and experience to determine which job they are best suited for. The analysis unit can also take into account the applicant's values ​​and cultural background to identify jobs that are a good fit for the company's culture. Step 3: The matching unit matches the information analyzed by the analysis unit with the company's needs. Matching is done using methods such as skill matching, keyword matching, and scoring. For example, the matching unit compares the applicant's information with the job description and required skill set offered by the company to find qualified personnel. Step 4: The proposal unit proposes suitable job types based on the information collated by the collation unit. The proposal is made by identifying the job type that best suits the applicant and recommending it to the company. For example, the proposal unit may consider the applicant's values ​​and cultural background and propose a job type that is likely to fit well with the company's culture. The processing in the proposal unit may also be performed using generative AI.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, to avoid confusion and 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.

[0189] 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.

[0190] [Explanation of symbols]

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

Claims

1. A collection department that collects applicant information; an analysis unit that analyzes the information collected by the collection unit; a collation unit that compares the information analyzed by the analysis unit with the needs of the company; a suggestion unit that suggests an appropriate job type based on the information collated by the collation unit; Equipped with A system characterized by:

2. The collecting unit Gathering applicants' skills, experiences, and values 2. The system of claim 1.

3. The analysis unit Conduct a detailed analysis of applicants' skills, experience, and values 2. The system of claim 1.

4. The collation unit Compare the applicant's information with the job description or skill set the company is looking for 2. The system of claim 1.

5. The proposal unit Identify and recommend suitable jobs for applicants 2. The system of claim 1.

6. The proposal unit Consider the applicant's values ​​and cultural background to ensure they fit into the company's culture 2. The system of claim 1.

7. The collecting unit Estimate the applicant's feelings and adjust the timing of information collection based on the estimated applicant's feelings 2. The system of claim 1.

8. The collecting unit Analyze applicants' past work history and select the most appropriate method of collecting information 2. The system of claim 1.

Images