System

Abstract

An object of the system according to the embodiment is to quickly extract a suitable person in consideration of the skills and work experience of employees.SOLUTION: A system includes a skill database creation unit, a candidate extraction unit, a belonging information extraction unit, and a display unit. The skill database creation unit creates a skill database. When a matter to be executed is inputted to the generation AI, a candidate extraction part extracts candidates from the skill database in consideration of skill attributes and work experience. A belonging information extraction part extracts belonging information from the smile information and displays the name of the candidate. The display unit displays the name of the candidate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to quickly identify the most suitable candidate while taking into account the skills and work experience of employees.

[0005] The system according to the embodiment aims to quickly extract suitable candidates by taking into consideration the skills and work experience of employees. [Means for solving the problem]

[0006] The system according to the embodiment includes a skill database creation unit, a candidate extraction unit, an affiliation information extraction unit, and a display unit. The skill database creation unit creates a skill database. When the candidate extraction unit inputs the task to be performed into the generation AI, it extracts candidates from the skill database, taking into consideration skill attributes and work experience. The affiliation information extraction unit extracts affiliation information from Smile information and displays the candidate's name. The display unit displays the candidate's name. [Effects of the Invention]

[0007] The system according to the embodiment can quickly extract suitable candidates by taking into consideration the skills and work experience of employees. [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) The skill database system according to an embodiment of the present invention quantifies the skills of all employees within SB and creates a skills database. This system uses generative AI to automatically extract candidates suited to specific tasks. This enables the skill database system to quickly and accurately extract candidates suited to specific tasks, thereby achieving efficient personnel allocation.

[0029] A skill database system according to an embodiment includes a skill database creation unit, a candidate extraction unit, an affiliation information extraction unit, and a display unit. The skill database creation unit quantifies the skills of all employees at SB and creates a skill database. For example, each employee's programming skills, project management skills, marketing skills, etc. are quantified and registered in the database. The candidate extraction unit inputs a desired task into the generation AI, and extracts candidates from the skill database, taking into account their skill attributes and work experience. For example, if a user wants to hold a "web seminar," the generation AI searches the database for employees with skills related to the web seminar and extracts suitable candidates. The affiliation information extraction unit extracts affiliation information (department name and job title) from Smile information and displays the names of suitable candidates. For example, the extracted candidates' departments and job titles are displayed so that users can check detailed information about the candidates. The display unit displays the names of the candidates. For example, the display unit displays a list of the names of suitable candidates so that users can easily check them. This enables the skill database system according to an embodiment to quickly and accurately extract candidates suitable for specific tasks and achieve efficient personnel allocation.

[0030] The skill database creation unit links employees' past project history and deliverables to the skill database, and can perform skill evaluations based on performance. The skill database creation unit, for example, links employees' past project history to the skill database and performs skill evaluations based on performance. For example, it reflects the project success rate and details of the work assigned. It also links employees' deliverables to the skill database and performs skill evaluations based on performance. For example, it includes submitted reports and the quality of developed software as evaluation criteria. It also integrates past project history and deliverables into the skill database and performs skill evaluations based on performance. For example, it adjusts skill values ​​taking into account the scale and difficulty of the project. This makes it possible to perform skill evaluations based on performance.

[0031] The skill database creation unit adds a function to the skill database that automatically updates employees' learning history and acquired qualifications, thereby enabling the latest skill information to be maintained at all times. The skill database creation unit, for example, adds a function to automatically update employees' learning history to the skill database, thereby enabling the latest skill information to be maintained at all times. For example, online course completion information is automatically reflected. A function is also added to automatically update acquired qualifications to the skill database, thereby enabling the latest skill information to be maintained at all times. For example, newly acquired qualification information is reflected in the database in real time. A function is also added to the skill database that automatically updates learning history and acquired qualifications, thereby enabling the latest skill information to be maintained at all times. For example, participation history in in-house training is automatically recorded. This enables the latest skill information to be maintained at all times.

[0032] The skill database creation unit can also register employees' hobbies and areas of interest in the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. The skill database creation unit, for example, registers employees' hobbies and areas of interest in the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. For example, it reflects the skills of employees who do programming as a hobby. In addition, hobbies and areas of interest are registered in the skill database, and candidate extraction that takes into account skills and knowledge outside of work is performed. For example, employees with skills related to music or art are identified. In addition, employees' hobbies and areas of interest are added to the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. For example, employees with DIY or cooking skills are extracted. This makes it possible to extract candidates that take into account skills and knowledge outside of work.

[0033] The skill database creation unit can link with skill databases from different companies and industries, allowing external experts and freelancers to be extracted as candidates. The skill database creation unit can, for example, link with skill databases from different companies and industries, allowing external experts and freelancers to be extracted as candidates. For example, it can connect with other companies' databases via API. It can also link with skill databases of external experts and freelancers to expand the range of candidate extraction. For example, it can integrate data from freelance platforms. It can also link with skill databases from different industries to build a system that extracts external experts and freelancers as candidates. For example, it can achieve cross-industry skill matching. This makes it possible to extract external experts and freelancers as candidates.

[0034] When selecting candidates, the candidate extraction unit can refer to performance data from past projects and perform an evaluation based on their track record. When selecting candidates, the candidate extraction unit, for example, refers to performance data from past projects and performs an evaluation based on their track record. For example, the project success rate and details of the tasks assigned are included in the evaluation criteria. In addition, a system is constructed that refers to past project performance data and performs an evaluation based on their track record. For example, candidates are selected taking into consideration the scale and difficulty of the project. In addition, the candidate's past project performance data is analyzed and an evaluation based on their track record is performed. For example, the project deliverables and role within the team are included in the evaluation criteria. This makes it possible to extract candidates based on their track record.

[0035] The candidate extraction unit can select the most suitable personnel from a long-term perspective by considering employees' career paths and growth goals in the candidate extraction algorithm. The candidate extraction unit, for example, considers employees' career paths and growth goals in the candidate extraction algorithm to select the most suitable personnel from a long-term perspective. For example, it preferentially extracts candidates who match future career goals. It also develops a candidate extraction algorithm that considers employees' career paths and growth goals to select the most suitable personnel from a long-term perspective. For example, it evaluates candidates based on their career plans. It also builds a system that considers employees' career paths and growth goals when extracting candidates and selects the most suitable personnel from a long-term perspective. For example, it selects candidates who have skills that match growth goals. This makes it possible to select the most suitable personnel from a long-term perspective.

[0036] The candidate extraction unit can support cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, when extracting candidates, the candidate extraction unit supports cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, it extracts employees with experience in different industries. In addition, a candidate extraction algorithm is developed that takes into consideration employees with skill sets from different industries and fields, and supports cross-functional team formation. For example, it selects employees with knowledge in different industries. In addition, when extracting candidates, a system is built that supports cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, it prioritizes the extraction of employees with skills from different industries. This makes it possible to support cross-functional team formation.

[0037] The candidate extraction unit can display the results of candidate extraction on a visual dashboard, allowing the user to intuitively understand. The candidate extraction unit, for example, displays the results of candidate extraction on a visual dashboard, allowing the user to intuitively understand. For example, the skills and experience of candidates are visualized using graphs and charts. A system is also constructed that uses the visual dashboard to intuitively display the results of candidate extraction. For example, the candidate's skill map and years of experience are visualized. The results of candidate extraction are also displayed on a visual dashboard, allowing the user to intuitively understand. For example, interactive graphs and filter functions are provided. This allows the results of candidate extraction to be intuitively understood.

[0038] The affiliation information extraction unit can include an employee's past transfer history and job title changes in the display of affiliation information, allowing an overall picture of a career to be grasped. The affiliation information extraction unit can, for example, include an employee's past transfer history and job title changes in the display of affiliation information, allowing an overall picture of a career to be grasped. For example, the transfer history can be displayed in timeline format. Also, a system can be constructed that displays an employee's past transfer history and job title changes, allowing an overall picture of a career to be grasped. For example, the job title changes can be visualized in a graph. Also, the affiliation information can include transfer history and job title changes in the display, allowing an overall picture of a career to be grasped. For example, the transfer history can be displayed on a map. This allows an overall picture of an employee's career to be grasped.

[0039] The affiliation information extraction unit can provide more detailed information by including an employee's internal evaluations and feedback when displaying the affiliation information. The affiliation information extraction unit, for example, provides more detailed information by including an employee's internal evaluations and feedback when displaying the affiliation information. For example, it displays evaluation comments from superiors and colleagues. It also builds a system that displays internal evaluations and feedback and provides more detailed information. For example, it visualizes evaluation scores and feedback content. It also provides more detailed information by including internal evaluations and feedback when displaying the affiliation information. For example, it displays evaluation history in a timeline format. This makes it possible to provide more detailed information about employees.

[0040] The affiliation information extraction unit can evaluate teamwork aptitude by including an employee's internal network and collaboration history in the display of affiliation information. The affiliation information extraction unit can evaluate teamwork aptitude by, for example, including an employee's internal network and collaboration history in the display of affiliation information. For example, it can display the history of joint projects. A system can also be constructed that displays internal networks and collaboration history to evaluate teamwork aptitude. For example, it can visualize the frequency and results of collaboration. A system can also evaluate teamwork aptitude by including the network and collaboration history in the display of affiliation information. For example, it can display a network diagram within the company. This makes it possible to evaluate teamwork aptitude.

[0041] The affiliation information extraction unit can evaluate the degree of social contribution by including the employee's outside activities and volunteer experience in the display of affiliation information. The affiliation information extraction unit, for example, evaluates the degree of social contribution by including the employee's outside activities and volunteer experience in the display of affiliation information. For example, it displays the history of volunteer activities. Also, a system is constructed that displays outside activities and volunteer experience and evaluates the degree of social contribution. For example, it visualizes the frequency and content of social contribution activities. Also, it evaluates the degree of social contribution by including outside activities and volunteer experience in the display of affiliation information. For example, it displays the results of volunteer activities. This makes it possible to evaluate the degree of social contribution of an employee.

[0042] The candidate extraction unit can refer to performance data of employees in past projects when extracting candidates within the organization and evaluate them based on their achievements. The candidate extraction unit, for example, refers to performance data of employees in past projects when extracting candidates within the organization and evaluates them based on their achievements. For example, the project success rate and details of the work assigned are included in the evaluation criteria. In addition, a system is constructed that refers to past project performance data and evaluates them based on their achievements. For example, candidates are selected taking into consideration the scale and difficulty of the project. In addition, past project performance data of candidates within the organization is analyzed and evaluations are made based on their achievements. For example, project deliverables and roles within the team are included in the evaluation criteria. This makes it possible to extract candidates within the organization based on their achievements.

[0043] The candidate extraction unit can select the most suitable human resources from a long-term perspective by taking into account the career paths and growth goals of employees when extracting candidates within the organization. The candidate extraction unit, for example, takes into account the career paths and growth goals of employees when extracting candidates within the organization and selects the most suitable human resources from a long-term perspective. For example, it prioritizes the extraction of candidates who match future career goals. It also develops a candidate extraction algorithm that takes into account the career paths and growth goals of employees and selects the most suitable human resources from a long-term perspective. For example, it evaluates candidates based on their career plans. It also builds a system that takes into account the career paths and growth goals of employees when extracting candidates within the organization and selects the most suitable human resources from a long-term perspective. For example, it selects candidates who have skills that match growth goals. This makes it possible to select the most suitable human resources within the organization from a long-term perspective.

[0044] The candidate extraction unit can support cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. The candidate extraction unit, for example, supports cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. For example, it extracts employees with experience in different industries. It also develops a candidate extraction algorithm that takes into consideration employees with skill sets from different departments and teams, and supports cross-functional team formation. For example, it selects employees with knowledge in different industries. It also builds a system that supports cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. For example, it prioritizes the extraction of employees with skills from different industries. This makes it possible to support cross-functional team formation within the organization.

[0045] The candidate extraction unit can display the results of candidate extraction within its own organization on a visual dashboard, allowing the user to intuitively understand. The candidate extraction unit, for example, displays the results of candidate extraction within its own organization on a visual dashboard, allowing the user to intuitively understand. For example, the skills and experience of candidates are visualized using graphs and charts. A system is also constructed that uses the visual dashboard to intuitively display the results of candidate extraction within its own organization. For example, the candidate's skill map and years of experience are visualized. The results of candidate extraction within its own organization are also displayed on a visual dashboard, allowing the user to intuitively understand. For example, interactive graphs and filter functions are provided. This allows the results of candidate extraction within its own organization to be intuitively understood.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The skill database creation department can also perform skill evaluations that take into account employees' health data. For example, it can increase the skill evaluation of employees in good health based on their stress levels and sleep data. It can also link health data to the skill database to evaluate the impact of health on work performance. For example, it can reflect the results of regular health checkups. It can also build a system that performs skill evaluations that take into account employees' health status based on health data. For example, it can prioritize employees in good health. This makes it possible to perform skill evaluations that take health status into account.

[0048] The skills database creation department can also evaluate skills by taking into account employees' internal communication history. For example, it can analyze internal chats and email exchanges to increase the skill ratings of employees with strong communication skills. It can also link communication history to the skills database to evaluate internal communication skills. For example, it can increase the skill ratings of employees who frequently collaborate with other departments. It can also build a system that evaluates skills taking into account employees' communication skills based on communication history. For example, it can prioritize employees with strong communication skills. This makes it possible to evaluate skills by taking communication skills into account.

[0049] The skills database creation department can also evaluate skills by taking into account employees' activities outside the company and volunteer experience. For example, volunteer activity history can be linked to the skills database to evaluate the employee's level of social contribution. In addition, skill evaluation can be performed taking into account the employee's level of social contribution based on the employee's activity history outside the company. For example, the frequency and content of volunteer activities can be included in the evaluation criteria. In addition, the activity history outside the company can be integrated into the skills database to build a system for skill evaluation that takes into account the employee's level of social contribution. For example, employees who have engaged in many social contribution activities can be preferentially extracted. This makes it possible to evaluate skills by taking into account the employee's level of social contribution.

[0050] The skills database creation department can also evaluate skills by taking into account employees' learning style and speed. For example, the speed at which online courses are completed and learning style can be linked to the skills database to evaluate learning ability. Skill evaluation can also be performed based on learning history, taking into account employees' learning style and speed. For example, the skill evaluation can be increased for employees who complete many courses in a short period of time. Learning history can also be integrated into the skills database, creating a system for skill evaluation that takes learning ability into account. For example, employees with a fast learning speed can be preferentially extracted. This makes it possible to evaluate skills by taking learning ability into account.

[0051] The skills database creation department can also evaluate employees' skills by taking into account their creativity and innovation capabilities. For example, the history of creative ideas and innovations in past projects can be linked to the skills database to evaluate creativity. Employee skills can also be evaluated based on creativity and innovation capabilities. For example, the skill evaluation of employees involved in patent applications or new product development can be increased. Creativity and innovation capabilities can also be integrated into the skills database, creating a system for skill evaluation that takes creativity into account. For example, employees who have come up with many creative ideas can be prioritized. This makes it possible to evaluate skills that take creativity into account.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The Skills Database Creation Department quantifies the skills of all employees within SB and creates a skills database. For example, each employee's programming skills, project management skills, marketing skills, etc. are quantified and registered in the database. Step 2: The candidate extraction unit inputs the desired task into the generation AI, which then extracts candidates from the skills database, taking into account their skill attributes and work experience. For example, if you want to hold a "web seminar," the generation AI will search the database for employees with skills related to the web seminar and extract the most suitable candidate. Step 3: The affiliation information extraction unit extracts affiliation information (department name and job title) from the Smile information and displays the names of suitable candidates. For example, by displaying the extracted candidate's department and job title, the user can check the candidate's detailed information. Step 4: The display unit displays the names of the candidates. For example, it displays a list of the names of the suitable candidates so that the user can easily check them.

[0054] (Example 2) The skill database system according to an embodiment of the present invention quantifies the skills of all employees within SB and creates a skills database. This system uses generative AI to automatically extract candidates suited to specific tasks. This enables the skill database system to quickly and accurately extract candidates suited to specific tasks, thereby achieving efficient personnel allocation.

[0055] A skill database system according to an embodiment includes a skill database creation unit, a candidate extraction unit, an affiliation information extraction unit, and a display unit. The skill database creation unit quantifies the skills of all employees at SB and creates a skill database. For example, each employee's programming skills, project management skills, marketing skills, etc. are quantified and registered in the database. The candidate extraction unit inputs a desired task into the generation AI, and extracts candidates from the skill database, taking into account their skill attributes and work experience. For example, if a user wants to hold a "web seminar," the generation AI searches the database for employees with skills related to the web seminar and extracts suitable candidates. The affiliation information extraction unit extracts affiliation information (department name and job title) from Smile information and displays the names of suitable candidates. For example, the extracted candidates' departments and job titles are displayed so that users can check detailed information about the candidates. The display unit displays the names of the candidates. For example, the display unit displays a list of the names of suitable candidates so that users can easily check them. This enables the skill database system according to an embodiment to quickly and accurately extract candidates suitable for specific tasks and achieve efficient personnel allocation.

[0056] When quantifying each employee's skills, the skill database creation unit uses generative AI to perform sentiment analysis and adjust the skill value taking into account the employee's self-assessment and peer evaluations. For example, when quantifying each employee's skills, the skill database creation unit uses generative AI to perform sentiment analysis and adjust the skill value taking into account the employee's self-assessment and peer evaluations. For example, if an employee's self-assessment is high but their peer evaluation is low, the skill value is averaged. In addition, sentiment analysis is used to correct the emotional bias between the employee's self-assessment and peer evaluations and calculate a more objective skill value. For example, emphasis is placed on ratings with strong positive emotions. In addition, sentiment analysis is used to analyze the employee's self-assessment and peer evaluations and adjust the skill value based on the intensity and type of emotion. For example, ratings with strong emotions of joy or surprise are reflected as a high score. In this way, sentiment analysis enables more objective skill evaluation.

[0057] The skill database creation unit links employees' past project history and deliverables to the skill database, and can perform skill evaluations based on performance. The skill database creation unit, for example, links employees' past project history to the skill database and performs skill evaluations based on performance. For example, it reflects the project success rate and details of the work assigned. It also links employees' deliverables to the skill database and performs skill evaluations based on performance. For example, it includes submitted reports and the quality of developed software as evaluation criteria. It also integrates past project history and deliverables into the skill database and performs skill evaluations based on performance. For example, it adjusts skill values ​​taking into account the scale and difficulty of the project. This makes it possible to perform skill evaluations based on performance.

[0058] The skill database creation unit adds a function to the skill database that automatically updates employees' learning history and acquired qualifications, thereby enabling the latest skill information to be maintained at all times. The skill database creation unit, for example, adds a function to automatically update employees' learning history to the skill database, thereby enabling the latest skill information to be maintained at all times. For example, online course completion information is automatically reflected. A function is also added to automatically update acquired qualifications to the skill database, thereby enabling the latest skill information to be maintained at all times. For example, newly acquired qualification information is reflected in the database in real time. A function is also added to the skill database that automatically updates learning history and acquired qualifications, thereby enabling the latest skill information to be maintained at all times. For example, participation history in in-house training is automatically recorded. This enables the latest skill information to be maintained at all times.

[0059] The skill database creation unit can also register employees' hobbies and areas of interest in the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. The skill database creation unit, for example, registers employees' hobbies and areas of interest in the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. For example, it reflects the skills of employees who do programming as a hobby. In addition, hobbies and areas of interest are registered in the skill database, and candidate extraction that takes into account skills and knowledge outside of work is performed. For example, employees with skills related to music or art are identified. In addition, employees' hobbies and areas of interest are added to the skill database, enabling candidate extraction that takes into account skills and knowledge outside of work. For example, employees with DIY or cooking skills are extracted. This makes it possible to extract candidates that take into account skills and knowledge outside of work.

[0060] The skill database creation unit can link with skill databases from different companies and industries, allowing external experts and freelancers to be extracted as candidates. The skill database creation unit can, for example, link with skill databases from different companies and industries, allowing external experts and freelancers to be extracted as candidates. For example, it can connect with other companies' databases via API. It can also link with skill databases of external experts and freelancers to expand the range of candidate extraction. For example, it can integrate data from freelance platforms. It can also link with skill databases from different industries to build a system that extracts external experts and freelancers as candidates. For example, it can achieve cross-industry skill matching. This makes it possible to extract external experts and freelancers as candidates.

[0061] The skill database creation unit can use the emotion estimation function to identify the skills and tasks that employees find most motivating and reflect that information in the skill database. The skill database creation unit can, for example, use the emotion estimation function to identify the skills and tasks that employees find most motivating and reflect that information in the skill database. For example, it can analyze employees' facial expressions and voices to measure their motivation. It can also analyze employees' emotional responses and build a system to identify the skills and tasks that employees find most motivating. For example, it can register tasks that evoke strong positive emotions in the skill database. It can also identify the skills and tasks that employees find most motivating based on the emotion estimation data and reflect that information in the skill database. For example, it can register skills with high emotion scores preferentially. This makes it possible to evaluate skills while taking employee motivation into consideration.

[0062] The candidate extraction unit can use the emotion estimation function to consider how positively the candidate feels about the job. For example, when the generation AI extracts candidates, the candidate extraction unit uses the emotion estimation function to consider how positively the candidate feels about the job. For example, it can prioritize the extraction of candidates with high emotion scores. It can also use the emotion estimation function to evaluate whether the candidate feels positively about the job and reflect this in the extraction results. For example, it can select candidates with strong positive emotions. It can also analyze the emotional responses of candidates and build a system to extract candidates who have positive emotions about the job. For example, it can filter candidates based on their emotion scores. This makes it possible to extract candidates while taking their emotions into consideration.

[0063] When selecting candidates, the candidate extraction unit can refer to performance data from past projects and perform an evaluation based on their track record. When selecting candidates, the candidate extraction unit, for example, refers to performance data from past projects and performs an evaluation based on their track record. For example, the project success rate and details of the tasks assigned are included in the evaluation criteria. In addition, a system is constructed that refers to past project performance data and performs an evaluation based on their track record. For example, candidates are selected taking into consideration the scale and difficulty of the project. In addition, the candidate's past project performance data is analyzed and an evaluation based on their track record is performed. For example, the project deliverables and role within the team are included in the evaluation criteria. This makes it possible to extract candidates based on their track record.

[0064] The candidate extraction unit can select the most suitable personnel from a long-term perspective by considering employees' career paths and growth goals in the candidate extraction algorithm. The candidate extraction unit, for example, considers employees' career paths and growth goals in the candidate extraction algorithm to select the most suitable personnel from a long-term perspective. For example, it preferentially extracts candidates who match future career goals. It also develops a candidate extraction algorithm that considers employees' career paths and growth goals to select the most suitable personnel from a long-term perspective. For example, it evaluates candidates based on their career plans. It also builds a system that considers employees' career paths and growth goals when extracting candidates and selects the most suitable personnel from a long-term perspective. For example, it selects candidates who have skills that match growth goals. This makes it possible to select the most suitable personnel from a long-term perspective.

[0065] The candidate extraction unit can support cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, when extracting candidates, the candidate extraction unit supports cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, it extracts employees with experience in different industries. In addition, a candidate extraction algorithm is developed that takes into consideration employees with skill sets from different industries and fields, and supports cross-functional team formation. For example, it selects employees with knowledge in different industries. In addition, when extracting candidates, a system is built that supports cross-functional team formation by taking into consideration employees with skill sets from different industries and fields. For example, it prioritizes the extraction of employees with skills from different industries. This makes it possible to support cross-functional team formation.

[0066] The candidate extraction unit can display the results of candidate extraction on a visual dashboard, allowing the user to intuitively understand. The candidate extraction unit, for example, displays the results of candidate extraction on a visual dashboard, allowing the user to intuitively understand. For example, the skills and experience of candidates are visualized using graphs and charts. A system is also constructed that uses the visual dashboard to intuitively display the results of candidate extraction. For example, the candidate's skill map and years of experience are visualized. The results of candidate extraction are also displayed on a visual dashboard, allowing the user to intuitively understand. For example, interactive graphs and filter functions are provided. This allows the results of candidate extraction to be intuitively understood.

[0067] The candidate extraction unit can use the emotion estimation function to evaluate how passionate candidates are about the work and preferentially extract the most passionate candidates. The candidate extraction unit, for example, uses the emotion estimation function to evaluate how passionate candidates are about the work and preferentially extract the most passionate candidates. For example, it selects candidates with high emotion scores. It also analyzes the emotional responses of candidates and builds a system to evaluate their passion for the work. For example, it preferentially extracts candidates with strong positive emotions. It also evaluates how passionate candidates are about the work based on the emotion estimation data and selects the most passionate candidates. For example, it filters candidates with emphasis on emotion scores. This makes it possible to preferentially extract passionate candidates.

[0068] The affiliation information extraction unit can use the emotion estimation function to display the candidate's level of satisfaction with their current work environment. For example, when extracting affiliation information, the affiliation information extraction unit uses the emotion estimation function to display the candidate's level of satisfaction with their current work environment. For example, the satisfaction with the work environment is quantified based on an emotion score. Furthermore, a system is constructed that analyzes the candidate's emotional response to the work environment and displays the satisfaction level. For example, a work environment with strong positive emotions is displayed as highly rated. Furthermore, the candidate's level of satisfaction with the work environment is displayed based on the emotion estimation data. For example, the satisfaction with the work environment is visualized using the emotion score. This makes it possible to display the candidate's level of satisfaction with the work environment.

[0069] The affiliation information extraction unit can include an employee's past transfer history and job title changes in the display of affiliation information, allowing an overall picture of a career to be grasped. The affiliation information extraction unit can, for example, include an employee's past transfer history and job title changes in the display of affiliation information, allowing an overall picture of a career to be grasped. For example, the transfer history can be displayed in timeline format. Also, a system can be constructed that displays an employee's past transfer history and job title changes, allowing an overall picture of a career to be grasped. For example, the job title changes can be visualized in a graph. Also, the affiliation information can include transfer history and job title changes in the display, allowing an overall picture of a career to be grasped. For example, the transfer history can be displayed on a map. This allows an overall picture of an employee's career to be grasped.

[0070] The affiliation information extraction unit can provide more detailed information by including an employee's internal evaluations and feedback when displaying the affiliation information. The affiliation information extraction unit, for example, provides more detailed information by including an employee's internal evaluations and feedback when displaying the affiliation information. For example, it displays evaluation comments from superiors and colleagues. It also builds a system that displays internal evaluations and feedback and provides more detailed information. For example, it visualizes evaluation scores and feedback content. It also provides more detailed information by including internal evaluations and feedback when displaying the affiliation information. For example, it displays evaluation history in a timeline format. This makes it possible to provide more detailed information about employees.

[0071] The affiliation information extraction unit can evaluate teamwork aptitude by including an employee's internal network and collaboration history in the display of affiliation information. The affiliation information extraction unit can evaluate teamwork aptitude by, for example, including an employee's internal network and collaboration history in the display of affiliation information. For example, it can display the history of joint projects. A system can also be constructed that displays internal networks and collaboration history to evaluate teamwork aptitude. For example, it can visualize the frequency and results of collaboration. A system can also evaluate teamwork aptitude by including the network and collaboration history in the display of affiliation information. For example, it can display a network diagram within the company. This makes it possible to evaluate teamwork aptitude.

[0072] The affiliation information extraction unit can evaluate the degree of social contribution by including the employee's outside activities and volunteer experience in the display of affiliation information. The affiliation information extraction unit, for example, evaluates the degree of social contribution by including the employee's outside activities and volunteer experience in the display of affiliation information. For example, it displays the history of volunteer activities. Also, a system is constructed that displays outside activities and volunteer experience and evaluates the degree of social contribution. For example, it visualizes the frequency and content of social contribution activities. Also, it evaluates the degree of social contribution by including outside activities and volunteer experience in the display of affiliation information. For example, it displays the results of volunteer activities. This makes it possible to evaluate the degree of social contribution of an employee.

[0073] The affiliation information extraction unit can use the emotion estimation function to display the candidate's emotion regarding the current work environment and prioritize employees who work in a positive environment. The affiliation information extraction unit, for example, uses the emotion estimation function to display the candidate's emotion regarding the current work environment and prioritize employees who work in a positive environment. For example, employees with a high emotion score are selected. Furthermore, a system is constructed that analyzes the candidate's emotional response to the work environment and displays employees who work in a positive environment. For example, employees with strong positive emotions are displayed preferentially. Furthermore, based on the emotion estimation data, the candidate's emotion regarding the work environment is displayed and employees who work in a positive environment are selected. For example, employees are filtered with an emphasis on the emotion score. This makes it possible to prioritize employees who work in a positive environment.

[0074] When extracting candidates from only the organization, the candidate extraction unit can use the emotion estimation function to consider how satisfied a candidate is within the organization. For example, when extracting candidates from only the organization, the candidate extraction unit uses the emotion estimation function to consider how satisfied a candidate is within the organization. For example, candidates with high emotion scores are preferentially extracted. Furthermore, the emotion estimation function evaluates the candidate's level of satisfaction within the organization and reflects this in the extraction results. For example, candidates with strong positive emotions are selected. Furthermore, based on the emotion estimation data, the candidate's level of satisfaction within the organization is evaluated and the candidate with the highest satisfaction is selected. For example, candidates are filtered with an emphasis on the emotion score. This makes it possible to extract candidates while taking satisfaction within the organization into consideration.

[0075] The candidate extraction unit can refer to performance data of employees in past projects when extracting candidates within the organization and evaluate them based on their achievements. The candidate extraction unit, for example, refers to performance data of employees in past projects when extracting candidates within the organization and evaluates them based on their achievements. For example, the project success rate and details of the work assigned are included in the evaluation criteria. In addition, a system is constructed that refers to past project performance data and evaluates them based on their achievements. For example, candidates are selected taking into consideration the scale and difficulty of the project. In addition, past project performance data of candidates within the organization is analyzed and evaluations are made based on their achievements. For example, project deliverables and roles within the team are included in the evaluation criteria. This makes it possible to extract candidates within the organization based on their achievements.

[0076] The candidate extraction unit can select the most suitable human resources from a long-term perspective by taking into account the career paths and growth goals of employees when extracting candidates within the organization. The candidate extraction unit, for example, takes into account the career paths and growth goals of employees when extracting candidates within the organization and selects the most suitable human resources from a long-term perspective. For example, it prioritizes the extraction of candidates who match future career goals. It also develops a candidate extraction algorithm that takes into account the career paths and growth goals of employees and selects the most suitable human resources from a long-term perspective. For example, it evaluates candidates based on their career plans. It also builds a system that takes into account the career paths and growth goals of employees when extracting candidates within the organization and selects the most suitable human resources from a long-term perspective. For example, it selects candidates who have skills that match growth goals. This makes it possible to select the most suitable human resources within the organization from a long-term perspective.

[0077] The candidate extraction unit can support cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. The candidate extraction unit, for example, supports cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. For example, it extracts employees with experience in different industries. It also develops a candidate extraction algorithm that takes into consideration employees with skill sets from different departments and teams, and supports cross-functional team formation. For example, it selects employees with knowledge in different industries. It also builds a system that supports cross-functional team formation by taking into consideration employees with skill sets from different departments and teams when extracting candidates from within the organization. For example, it prioritizes the extraction of employees with skills from different industries. This makes it possible to support cross-functional team formation within the organization.

[0078] The candidate extraction unit can display the results of candidate extraction within its own organization on a visual dashboard, allowing the user to intuitively understand. The candidate extraction unit, for example, displays the results of candidate extraction within its own organization on a visual dashboard, allowing the user to intuitively understand. For example, the skills and experience of candidates are visualized using graphs and charts. A system is also constructed that uses the visual dashboard to intuitively display the results of candidate extraction within its own organization. For example, the candidate's skill map and years of experience are visualized. The results of candidate extraction within its own organization are also displayed on a visual dashboard, allowing the user to intuitively understand. For example, interactive graphs and filter functions are provided. This allows the results of candidate extraction within its own organization to be intuitively understood.

[0079] The candidate extraction unit can use the emotion estimation function to evaluate how passionate candidates within the organization are about their work and preferentially extract the most passionate candidates. The candidate extraction unit, for example, uses the emotion estimation function to evaluate how passionate candidates within the organization are about their work and preferentially extract the most passionate candidates. For example, it selects candidates with high emotion scores. It also analyzes the emotional responses of candidates and builds a system to evaluate their passion for their work. For example, it preferentially extracts candidates with strong positive emotions. It also evaluates how passionate candidates are about their work based on the emotion estimation data and selects the most passionate candidate. For example, it filters candidates with an emphasis on emotion scores. This makes it possible to preferentially extract passionate candidates within the organization.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The skill database creation department can also perform skill evaluations that take into account employees' health data. For example, it can increase the skill evaluation of employees in good health based on their stress levels and sleep data. It can also link health data to the skill database to evaluate the impact of health on work performance. For example, it can reflect the results of regular health checkups. It can also build a system that performs skill evaluations that take into account employees' health status based on health data. For example, it can prioritize employees in good health. This makes it possible to perform skill evaluations that take health status into account.

[0082] The skills database creation department can also evaluate skills by taking into account employees' internal communication history. For example, it can analyze internal chats and email exchanges to increase the skill ratings of employees with strong communication skills. It can also link communication history to the skills database to evaluate internal communication skills. For example, it can increase the skill ratings of employees who frequently collaborate with other departments. It can also build a system that evaluates skills taking into account employees' communication skills based on communication history. For example, it can prioritize employees with strong communication skills. This makes it possible to evaluate skills by taking communication skills into account.

[0083] The skills database creation department can also evaluate skills by taking into account employees' activities outside the company and volunteer experience. For example, volunteer activity history can be linked to the skills database to evaluate the employee's level of social contribution. In addition, skill evaluation can be performed taking into account the employee's level of social contribution based on the employee's activity history outside the company. For example, the frequency and content of volunteer activities can be included in the evaluation criteria. In addition, the activity history outside the company can be integrated into the skills database to build a system for skill evaluation that takes into account the employee's level of social contribution. For example, employees who have engaged in many social contribution activities can be preferentially extracted. This makes it possible to evaluate skills by taking into account the employee's level of social contribution.

[0084] The skills database creation department can also evaluate skills by taking into account employees' learning style and speed. For example, the speed at which online courses are completed and learning style can be linked to the skills database to evaluate learning ability. Skill evaluation can also be performed based on learning history, taking into account employees' learning style and speed. For example, the skill evaluation can be increased for employees who complete many courses in a short period of time. Learning history can also be integrated into the skills database, creating a system for skill evaluation that takes learning ability into account. For example, employees with a fast learning speed can be preferentially extracted. This makes it possible to evaluate skills by taking learning ability into account.

[0085] The skills database creation department can also evaluate employees' skills by taking into account their creativity and innovation capabilities. For example, the history of creative ideas and innovations in past projects can be linked to the skills database to evaluate creativity. Employee skills can also be evaluated based on creativity and innovation capabilities. For example, the skill evaluation of employees involved in patent applications or new product development can be increased. Creativity and innovation capabilities can also be integrated into the skills database, creating a system for skill evaluation that takes creativity into account. For example, employees who have come up with many creative ideas can be prioritized. This makes it possible to evaluate skills that take creativity into account.

[0086] The skill database creation unit can use the emotion estimation function to identify the tasks and skills that employees find most stressful and reflect that information in the skill database. For example, it can analyze employees' facial expressions and voices to measure their stress levels. It can also build a system that analyzes employees' emotional responses and identifies the tasks and skills that employees find most stressful. For example, it can register tasks that evoke strong negative emotions in the skill database. It can also identify the tasks and skills that employees find most stressful based on the emotion estimation data and reflect those in the skill database. For example, it can register skills with high stress scores as a warning. This makes it possible to evaluate skills while taking employee stress into account.

[0087] The candidate extraction unit can use the emotion estimation function to consider how anxious a candidate feels about the job. For example, it can prioritize the extraction of candidates with low emotion scores. It can also use the emotion estimation function to evaluate whether a candidate feels anxious about the job and reflect this in the extraction results. For example, it can exclude candidates with strong negative emotions. It can also analyze the emotional responses of candidates and build a system to extract candidates who are anxious about the job. For example, it can filter candidates based on their emotion scores. This makes it possible to extract candidates with consideration for their anxiety.

[0088] The candidate extraction unit uses the emotion estimation function to evaluate how interested candidates are in the job and can prioritize the extraction of the most interested candidates. For example, it selects candidates with high emotion scores. It also analyzes the emotional responses of candidates and builds a system to evaluate their interest in the job. For example, it prioritizes the extraction of candidates with strong positive emotions. It also evaluates how interested candidates are in the job based on the emotion estimation data and selects the most interested candidates. For example, it filters candidates with emphasis on emotion scores. This allows it to prioritize the extraction of interested candidates.

[0089] The candidate extraction unit uses the emotion estimation function to evaluate how satisfied candidates are with their work, and can prioritize the extraction of the most satisfied candidates. For example, it selects candidates with high emotion scores. It also analyzes the emotional responses of candidates and builds a system to evaluate their satisfaction with their work. For example, it prioritizes the extraction of candidates with strong positive emotions. It also evaluates how satisfied candidates are with their work based on the emotion estimation data, and selects the most satisfied candidate. For example, it filters candidates with an emphasis on emotion scores. This makes it possible to prioritize the extraction of satisfied candidates.

[0090] The candidate extraction unit uses the emotion estimation function to evaluate how confident candidates are in their work and can prioritize the extraction of the most confident candidates. For example, it selects candidates with high emotion scores. It also builds a system that analyzes candidates' emotional responses and evaluates their confidence in their work. For example, it prioritizes the extraction of candidates with strong positive emotions. It also evaluates how confident candidates are in their work based on the emotion estimation data and selects the most confident candidate. For example, it filters candidates with an emphasis on emotion scores. This allows it to prioritize the extraction of confident candidates.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The Skills Database Creation Department quantifies the skills of all employees within SB and creates a skills database. For example, each employee's programming skills, project management skills, marketing skills, etc. are quantified and registered in the database. Step 2: The candidate extraction unit inputs the desired task into the generation AI, which then extracts candidates from the skills database, taking into account their skill attributes and work experience. For example, if you want to hold a "web seminar," the generation AI will search the database for employees with skills related to the web seminar and extract the most suitable candidate. Step 3: The affiliation information extraction unit extracts affiliation information (department name and job title) from the Smile information and displays the names of suitable candidates. For example, by displaying the extracted candidate's department and job title, the user can check the candidate's detailed information. Step 4: The display unit displays the names of the candidates. For example, it displays a list of the names of the suitable candidates so that the user can easily check them.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

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

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

[0137] In the robot 414, 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 robot 414 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.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0141] The data processing system 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 skill database creation unit that creates a skill database; a candidate extraction unit that extracts candidates from the skill database by inputting the task to be performed into the generation AI, taking into consideration skill attributes and work experience; an affiliation information extraction unit that extracts affiliation information from Smile information and displays the name of the candidate; a display unit that displays the name of the candidate; A system characterized by:

2. The skill database creation unit When quantifying each employee's skills, the generative AI is used to perform sentiment analysis and adjust the skill value taking into account the employee's self-evaluation and evaluations from colleagues.

2. The system of claim 1.

3. The skill database creation unit Link the employee's past project history and deliverables to the skills database to perform performance-based skill evaluations.

2. The system of claim 1.

4. The skill database creation unit Add a function to automatically update the employee's learning history and acquired qualifications to the skills database, thereby always maintaining the latest skill information.

2. The system of claim 1.

5. The skill database creation unit The employee's hobbies and areas of interest are also registered in the skills database, making it possible to select candidates taking into account skills and knowledge outside of work.

2. The system of claim 1.

Images